On the road - RIO Principal
Transcription
On the road - RIO Principal
La portada es una versión editada de una fotografía tomada por Dr. Nuno Negrões en el Camino del Control, Reserva Biológica de Doñana (España). Ad Angela e Melo, per la terza volta On the road: los distintos impactos del tráfico motorizado sobre poblaciones animales Marcello D’Amico Tesis Doctoral Sevilla, 2015 Estación Biológica de Doñana, CSIC Departamento de Biología de la Conservación Universidad Pablo de Olavide Facultad de Ciencias Experimentales Departamento de Biología Molecular e Ingeniería Bioquímica Doctorado en Estudios Medioambientales On the road: los distintos impactos del tráfico motorizado sobre poblaciones animales Memoria presentada por el Licenciado en Ciencias Naturales Marcello D’Amico para optar al título de Doctor por la Universidad de Pablo de Olavide Fdo. Marcello D’Amico Dr. Eloy Revilla y Dr. Jacinto Román, Estación Biológica de Doñana (CSIC) CERTIFICAN Que los trabajos de investigación desarrollados en la Memoria de Tesis Doctoral “On the road: los distintos impactos del tráfico motorizado sobre poblaciones animales” son aptos para ser presentados por el Ldo. Marcello D’Amico ante el Tribunal que en su día se designe, para aspirar al Grado de Doctor por la Universidad Pablo de Olavide. Y para que así conste, y en cumplimiento de las disposiciones legales vigentes, extendemos el presente certificado a 18 de Diciembre de 2014 Directores: Fdo. Eloy Revilla Tutor: Fdo. Francisco Eduardo Narbona Fernández Universidad Pablo de Olavide Fdo. Jacinto Román “And this was really the way that my whole road experience began, and the things that were to come are too fantastic not to tell.” Jack Kerouac, On the Road Índice Resumen..........................................................................................................................12 Introducción general........................................................................................................13 Capítulo 1, Atropellos: Patrones de atropello en hábitats mediterráneos: quién, cuándo y dónde.......................24 Capítulo 2, Efecto barrera: ¿Cuánto tráfico es demasiado? El impacto de redes viarias heterogéneas en áreas protegidas.........................................................................................................................86 Capítulo 3, Medidas de mitigación: Comparación de muestreos genéricos y específicos: estimando la eficiencia de diferentes pasos de fauna para micromamíferos............................................................116 Capítulo 4, Reproducción y comportamiento: El tráfico motorizado como elemento de estrés que estimula los cuidados parentales en aves..........................................................................................................140 Capítulo 5, Invasiones Biológicas: Invasores en ruta: aves sinantrópicas alimentándose en las carreteras..........................168 Discusión general y Conclusiones.................................................................................188 Agradecimientos............................................................................................................197 11 Resumen Resumen: Esta tesis doctoral describe patrones y mecanismos de impacto viario sobre poblaciones animales, acerca de los temas principales de la Ecología de carreteras: atropello, efecto barrera, medidas de mitigación, impacto sobre comportamiento y reproducción, invasiones biológicas. En el primer capítulo vimos que los ectotermos comunes y abundantes son los vertebrados mediterráneos con más probabilidad de ser atropellados en Doñana (España), los anfibios durante las lluvias y los reptiles (serpientes y lagartijas) coincidiendo con altas temperaturas. Las lagartijas (y también los micromamíferos) son atropelladas sobre todo en grandes vías muy transitadas. En el segundo capítulo observamos que la red viaria heterogénea (i.e. con variación de tráfico y superficie viaria) de Doñana afecta la probabilidad de presencia de ciervos y jabalíes, y especialmente la distancia al camino más cercano, aunque no tenga tráfico ni pavimentación. La consecuencia a nivel de paisaje es una reducción de probabilidad de presencia del 32% y del 45%, con una grande reducción de calidad de hábitat en un enclave protegido. En el tercer capítulo demostramos que los muestreos genéricos de eficacia de pasos de fauna para micromamíferos están sesgados hacía especies generalistas de hábitat en detrimento de las especialistas, pero ambas parecen preferir los ecoductos a los pasos subterráneos. En el cuarto capítulo vimos que los abejarucos que crían en las áreas laterales de los caminos perciben el tráfico como un elemento de estrés que estimula alarmas y estampidas. Sin embargo, las tasas de ceba son mayores en nidos situados en carreteras de tránsito elevado, aumentando durante los días laborales y con picos en las horas punta. En el quinto capítulo observamos que las aves sinantrópicas invasoras en Nueva Zelanda usan las carreteras intermunicipales para la búsqueda de comida, los que podría favorecer que estas actuaran como corredores de invasión. Todos los resultados de esta tesis nos ayudan a comprender mejor el impacto de carreteras y tráfico sobre la biodiversidad y como mitigarlo. 12 Introducción general Introducción general Road ecology (en español Ecología de carreteras) es una disciplina de la Biología de la Conservación que estudia las interacciones entre organismos y ambientes relacionados con redes viarias y vehículos (Forman et al., 2003). Este concepto fue desarrollado por primera vez por Ellenberg et al. (1981) usando el término alemán Straßenökologie; traducido posteriormente al inglés por Richard T.T. Forman, uno de los padres de esta disciplina (Forman, 1998). En esta publicación, Forman irónicamente se preguntaba: “¿Qué es enorme, conspicuo, y eludido por los ecólogos?”. La respuesta era el sistema viario, que definía como “el gigante que nos abraza” (the giant embracing us) y que de alguna forma nos une y amenaza al mismo tiempo. Como veremos más adelante, se puede considerar un ámbito de investigación relativamente reciente, que hunde sus raíces mayoritariamente en la Ecología, pero también en la Geografía y en la Ingeniería civil (Forman et al., 2003; Coffin, 2007). Importancia de la Ecología de carreteras, aspectos ecológicos: La metáfora del gigante de Forman resulta especialmente acertada en los países industrializados. En los Estados Unidos de América, por ejemplo, el 1% de la superficie total del país está ocupado por carreteras (lo que equivale a un área del tamaño de Austria; Forman, 1998; Forman and Alexander, 1998). Es probable que en otros países industrializados más pequeños y densamente poblados (como los europeos, por ejemplo) este porcentaje sea incluso mayor. Pero las redes viarias se están rápidamente convirtiendo en gigantes también en el resto del mundo, y especialmente en Europa oriental, India, China y América latina (van der Ree et al., 2011). 13 Introducción general Los efectos de las redes viarias sobre el entorno no se limitan a la mera destrucción de hábitat por la presencia de caminos y carreteras o a los atropellos de fauna que ocurren a lo largo de estas infra-estructuras, sino que se extienden considerablemente en los ambientes colindantes, principalmente a causa de las emisiones asociadas al tráfico motorizado (e.g. contaminación química, acústica y lumínica; Forman and Alexander, 1998; Forman et al., 2003). Los mismos autores que estimaban que el 1% de la superficie de los Estados Unidos de América estaba ocupado por carreteras, también consideraban que hasta el 20% del país podría sufrir los efectos de estas emisiones (Forman, 1998; Forman and Alexander, 1998), en las que se han denominado road-effect zones (Forman, 2000; Forman and Deblinger, 2000). Con el tiempo, nuevos estudios resaltaron que los efectos de las infra-estructuras lineares actúan de forma sinérgica, aumentando la extensión de estas zonas de impacto. Este concepto es la base de la Teoría ecológica de las redes viarias (Ecological road-network theory) que postula que los efectos acumulativos de distintas infra-estructuras lineares pueden llegar incluso a saturar un paisaje con baja densidad de carreteras, no quedando áreas libres de su efecto (Forman et al., 2003; Coffin, 2007). Finalmente, hay que considerar que los impactos asociados a la red viaria pueden afectar a distintos hábitats de forma diferente, componiendo zonas de impacto heterogéneas que, por otro lado, siguen actuando de forma sinérgica, causando la que se ha denominado hiperfragmentación (Trombulak and Frissell, 2000). Importancia de la Ecología de carreteras, aspectos sociales Las redes viarias conectan gente con gente, sociedades con sociedades, ciudades con ciudades, haciendo posible el movimiento de personas y bienes. Sin ellas, la calidad 14 Introducción general de la vida bajaría dramáticamente (Forman et al., 2003). Los humanos las utilizamos intensamente; solo hace falta pensar que un habitante de un país industrializado pasa, de media, una hora diaria viajando a lo largo de infra-estructuras lineares (Forman and Alexander, 1998). Esta intensidad de uso aumenta inexorablemente la probabilidad de accidentes, con lo que esto conlleva en términos económicos y de vidas humanas. Una parte de estos accidentes hoy en día es debida a las colisiones entre vehículos y fauna, cuya principal causa radica en el desarrollo extraurbano de las redes viarias (Forman et al., 2003). Un ejemplo de la importancia de las colisiones entre vehículos y fauna en el cómputo total de accidentes nos lo proporciona el caso de dos especies de cérvidos de los Estados Unidos de América (Odocoileus hemionus y Odocoileus virginianus). En este país, solo estas dos especies causan cada año aproximadamente 720000 accidentes, que suelen comportar el fallecimiento de los animales atropellados, la implicación traumática de unas 29000 personas (incluidas más de 200 muertes) e ingentes daños económicos (Conover et al., 1995). Si consideramos que a lo largo de las redes viarias estadounidenses se registran en total unas 33000 víctimas humanas al año, veremos que solo estas dos especies causan un 0.6% de estas muertes (International Traffic Safety Data and Analysis Group, 2014). El gasto económico que supondría acondicionar adecuadamente las infra-estructuras lineares sería considerablemente inferior a los perjuicios monetarios resultantes de las colisiones entre vehículos y fauna (Conover et al., 1995; Forman et al., 2003). Resulta evidente, entonces, que la resolución de los conflictos viarios entre humanos y fauna no aporta únicamente beneficios para las poblaciones animales y, precisamente por esta razón, debería ser un objetivo explícito de las políticas viarias (Forman, 1998). Todos los gobiernos implicados deberían considerar, por lo tanto, la Ecología de carreteras como la principal herramienta para el cumplimiento de este propósito (Forman et al., 2003). 15 Introducción general Breve historia de la Ecología de carreteras Los primeros trabajos de esta disciplina trataban problemas de erosión y drenaje en pistas de tierra de Norteamérica a principio del siglo XX (Lay, 1992). La relativamente temprana implementación de superficies pavimentadas significó la resolución de estos problemas y la momentánea perdida de interés en este campo de investigación (Forman et al., 2003). Por otro lado, la pavimentación de pistas permitió el aumento de la velocidad de circulación de los vehículos, incrementándose las colisiones entre vehículos y fauna, dando impulso a un tema que ha sido desde siempre central en Ecología de carreteras (Forman et al., 2003). Los atropellos de fauna representaban un impacto evidente y llamativo sobre las poblaciones animales y, como hemos visto, tenían y siguen teniendo una relevancia económica (sobre todo en Canadá, USA y Escandinavia, donde vehículos motorizados y grandes mamíferos abundaban; Forman et al., 2003). Por lo tanto, no es de extrañar que pronto algunos ecólogos y gestores ambientales empezaran a interesarse en este tema. En realidad hasta los años ‘70 solo se llevaron a cabo una decena de estudios (e.g. Barnes, 1936; Dickerson, 1939; Davis, 1940; Pickles, 1942; Finnis, 1960; Hodson, 1962; Dunthorn and Errington, 1964) motivados por un interés puntual de científicos y gestores (Forman et al., 2003). Incluso entonces, a pesar de que todavía la Ecología de carreteras no se había desarrollado de una forma orgánica, la importancia de esta disciplina resultaba evidente para algunos investigadores (“Few forces have been more influential in modifying the earth than transportation”; Ullman, 1956). Entre los años ‘70 y los ‘80 los estudios sobre atropellos de fauna se intensificaron (e.g. McCaffery, 1973; Case, 1978; Coulson, 1982). La construcción en 16 Introducción general Francia de los primeros pasos de fauna inspiró estudios preliminares sobre la eficacia de esta medida de mitigación (Camby and Maizeret, 1985; Ballon, 1985), y empezaron a publicarse trabajos sobre contaminación (Sherwood and Bowers, 1970; Hickman and Colwill, 1982) y efecto barrera debido a infra-estructuras lineares (Oxley et al., 1974; Mader, 1984). A partir de 1990 y gracias al desarrollo de la Ecología del paisaje (Coffin, 2007), el interés de científicos y gestores hacía la Ecología de carreteras aumentó considerablemente. Fue entonces cuando realmente se convirtió en una disciplina, como demuestran las abundantes revisiones bibliográficas que se empezaron a publicar entre el final de esa década y el principio de la siguiente (Forman, 1998, 2000, 2004; Forman and Alexander, 1998; Underhill and Angold, 1999; Trombulak and Frissell, 2000; Seiler, 2001; Zong et al., 2002; Li et al., 2003). Aun así, en 1998 Richard T.T. Forman y Lauren E. Alexander todavía consideraban que la Ecología de carreteras era una disciplina que tenía las potencialidades para aportar soluciones a los problemas viarios pero que aún no había cumplido adecuadamente con su cometido (Forman and Alexander, 1998). Precisamente por esta razón los mayores expertos de Ecología de carreteras se reunieron para llevar a cabo la publicación del libro “Road ecology: science and solutions”. Por primera vez se revisaban de forma orgánica todos los aspectos de esta disciplina, sugiriendo un cambio conceptual y apuntando a un enfoque más científico e interdisciplinar (Forman et al., 2003). 17 Introducción general Objetivos generales y particulares de esta tesis doctoral El hilo conductor a lo largo de la bibliografía existente en Ecología de carreteras es que la mayoría de trabajos realizados hasta el momento expone casos de estudio con aproximaciones más o menos descriptivas, y por lo tanto con un rango amplio de posibilidades de inferencia. En este contexto, probablemente, la necesidad más urgente en Ecología de carreteras es fomentar la realización de estudios que no se limiten solo a una descripción de patrones sino que investiguen los mecanismos que los generan. El objetivo general de esta tesis doctoral es proporcionar unos ejemplos de esta clase de estudios, aportando unas aproximaciones lo suficientemente generales como para contribuir al desarrollo de la Ecología de carreteras pero que al mismo tiempo puedan sugerir también como resolver problemáticas reales y mitigar amenazas concretas relacionadas con el impacto de la presencia de infra-estructuras lineares y tráfico motorizado. Más concretamente, esta tesis doctoral se centra en el impacto de carreteras y trafico sobre poblaciones animales, desarrollándose a lo largo de cinco capítulos, cada uno relativo a uno de los principales temas de Ecología de carreteras, que hemos mencionado anteriormente: atropellos, efecto barrera, medidas de mitigación, impacto sobre reproducción y comportamiento, e invasiones biológicas. La mortalidad por atropello es uno de los impactos más llamativos sobre poblaciones animales y conlleva un no desdeñable coste económico. Probablemente por esta razón se lleva estudiando desde hace varias décadas (Conover et al., 1995; Forman et al., 2003). Hasta la fecha se han recolectado a lo largo del planeta una cantidad considerable de datos (Forman et al., 2003; Coffin, 2007), y un considerable número de publicaciones están contribuyendo a salvar muchas poblaciones animales de la extinción 18 Introducción general local o generalizada (Jones, 2000; Downs et al., 2014). A pesar de esto, muchos estudios de mortalidad por colisión con vehículos se limitan a meros listados de las especies atropelladas en lugares concretos, investigando raramente los mecanismos (tanto temporales como espaciales o de características específicas intrínsecas) que se asocian directamente al fenómeno. Entender estos mecanismos debería ser el principal objetivo futuro de los estudios sobre atropellos, permitiendo el diseño e implementación de medidas de mitigación más eficaces. Lo mismo se puede decir de los trabajos relativos al efecto barrera, que en la última década se han incrementado sobre todo en los países industrializados (Forman et al., 2003; van der Ree et al., 2011). A pesar de que en buena parte sean estudios descriptivos, han aportado indicaciones sobre los mecanismos que pueden causar la evitación de infra-estructuras lineares en varias especies (e.g. McGregor et al., 2008; Brehme et al., 2013). Esta clase de estudios necesitará en el futuro ubicar los fenómenos descritos en el marco teórico establecido por Jaeger et al. (2005), que diferencia entre evitación de superficie viaria, emisiones asociadas y el efecto de la presencia de vehículos, y que resulta esencial para elegir la medida de mitigación más oportuna. Con respecto a la eficacia de las medidas de mitigación, este resulta ser uno de los temas principales en Ecología de carreteras, y en las últimas décadas ha aportado grandes mejoras a la conectividad del paisaje (Forman et al., 2003). El siguiente paso debería ser averiguar los mecanismos que determinan la eficacia de estas medidas de mitigación, con el fin último de aumentarla para el rango más amplio posible de especies. Muchos trabajos futuros probablemente serán aproximaciones más generales a este tema, con el propósito de poder extrapolar las conclusiones de estos estudios concretos a estructuras, especies y entornos distintos. 19 Introducción general También hemos mencionado la existencia de estudios que tratan la relación entre infra-estructuras lineares y tráfico a motor con modificaciones del comportamiento o variaciones en el éxito reproductivo. Las cuestiones comportamentales asociadas al tráfico que se han investigado hasta ahora en Ecología de carreteras son aquellas relativas a colisiones y efecto barrera, y en menor medida a tolerancia y habituación (Dean and Milton, 2003; Laurian et al., 2008; Malo et al., 2011). Apenas existen trabajos acerca de capacidad cognitiva, personalidad, aprendizaje y transmisión cultural, comunicación intra e inter-especifica, conducta territorial, social o sexual; cuando todas estas aproximaciones en realidad serían seguramente muy valiosas a la hora de diseñar medidas de mitigación. Análogamente, podemos observar que los estudios de biología reproductiva realizados hasta ahora suelen limitarse a la descripción de un impacto viario sobre el éxito reproductivo de una especie, normalmente a corto plazo (e.g. Reijnen et al., 1997; Flaspohler et al., 2001). Todavía no se están llevando a cabo trabajos sobre el impacto del tráfico en la calidad de los reproductores y la prole, con lo que eso conlleva en términos de éxito reproductivo a largo plazo. Finalmente, el estudio de invasiones biológicas se ha integrado con la Ecología de carreteras especialmente en el caso de las plantas invasoras, mientras que apenas existen estudios relativos a especies animales y mucho menos con extrapolaciones a nivel de población y paisaje, que serían seguramente importantes para la contención de este impacto. De todas formas, con respecto a la relación entre redes viarias e invasiones biológicas, comportamiento animal o éxito reproductivo todavía quedan muchas preguntas básicas por contestar. En concreto, los objetivos particulares de cada capítulo son: 20 Introducción general 1) Atropellos: investigar en un mismo estudio que factores específicos intrínsecos, temporales y espaciales afectan la probabilidad de atropello en vertebrados. 2) Efecto barrera: investigar en una red viaria heterogénea (con variación en intensidad de tráfico y superficie viaria), que factores determinan el efecto barrera en ungulados (evitación de infra-estructuras lineares, de tráfico y emisiones asociadas o de vehículos concretos), y su impacto en la calidad del hábitat. 3) Medidas de mitigación: investigar en un mismo estudio y en micromamíferos la eficacia de unas medidas de mitigación y de los métodos que normalmente se usan para evaluarlas. 4) Reproducción y comportamiento: investigar algunos de los efectos potencialmente negativos del tráfico sobre reproducción de aves que aparentemente se benefician de la presencia de carreteras. 5) Invasiones biológicas: investigar la asunción previa a la existencia de un patrón de dispersión de aves sinantrópicas invasoras a lo largo de carreteras. 21 Introducción general Bibliografía Balkenhol, N. and Waits, L.P. 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Road-kill patterns in Mediterranean habitats: who, when and where. a Estación Biológica de Doñana CSIC, Seville (Spain). b Área de Conservación, Espacio Natural de Doñana, Almonte (Spain). Road-kill patterns in Mediterranean habitats: who, when and where Resumen: La mortalidad por atropello es el impacto más estudiado del tráfico y una amenaza para la biodiversidad. Sin embargo, gran parte de la investigación se enfoca en determinadas especies o medidas de mitigación, describiendo las causas próximas de atropello y raramente investigando los mecanismos. Este estudio es una aproximación más general, que analiza factores de historia natural, temporales y espaciales que afecten el atropello de vertebrados en paisajes mediterráneos, que son unos puntos calientes de biodiversidad con relevantes y poco estudiados impactos de carreteras. Durante un año recolectamos atropellos en carreteras pavimentadas del Parque Natural de Doñana. Encontramos 2368 atropellos de 66 especies (32% de la lista de referencia), teniendo los ectotermos abundantes más probabilidad de ser atropellados. Además, investigamos los factores temporales y espaciales que afectaran los patrones de atropello de diferentes grupos de especies. Las lluvias coincidieron con los picos de atropello de anfibios, mientras las altas temperaturas se asociaban a un aumento de atropellos en serpientes, lagartijas y aves nidificantes. Lagartijas y micromamíferos fueron atropellados sobre todo a lo largo de grandes vías con mucho tráfico y altos límites de velocidad. Exhortamos priorizar medidas de mitigación capaces de reducir permanentemente la mortalidad de ectotermos (anfibios y reptiles), como pasos de fauna específicos y cercados impermeables en correspondencia de sus puntos calientes de atropello. Al mismo tiempo, las medidas temporales de mitigación pueden aplicarse durante los picos estacionales de atropellos. El presente trabajo proporciona algunas recomendaciones para reducir el impacto de los atropellos en ambientes mediterráneos, pero paralelamente intenta contribuir a un desarrollo más general de la ecología de carreteras, sugiriendo algunas directrices generales para realizar estudios sobre la mortalidad debida al tráfico motorizado. 25 Capítulo 1: Atropellos Abstract: Road mortality is the most recognized traffic impact and a threat for biodiversity. Nevertheless, most research on this topic deals with particular species or with mitigation measures, describing road-kill proximate correlates but rarely investigating the mechanisms. Here we provide an example of a more general approximation, describing life history, temporal and spatial factors affecting vertebrate road-kills in Mediterranean landscapes, which are a biodiversity hotspot with relevant and little studied road impacts. During one year we recorded road casualties found in paved roads within Doñana Natural Park. We found 2368 road-kills belonging to 66 species (32% of the reference list), with more abundant ectotherm species more likely to be found as road-kills. We also investigated temporal and spatial factors affecting the road-kill patterns of different taxonomic and functional groups. Rainfall events coincided with road-kill peaks for amphibians, while high temperatures were associated with increases in the number of casualties for snakes, lizards and nesting birds. Lizards and small mammals were mainly road-killed along wider roads, with more traffic volume and higher allowed speed. We suggest prioritizing the mitigation measures which can permanently decrease the road mortality of ectotherm species (mostly amphibians and reptiles), such as specific road-crossing structures and drift fences on road-kill hotspots. Concurrently, group-specific temporal mitigation measures can be applied during the road-kill seasonal peaks. The present work provides recommendations to decrease road-kill impact in Mediterranean environments, but simultaneously tries to contribute to a more general development of road ecology research, suggesting several useful guidelines to perform road mortality studies. 26 Road-kill patterns in Mediterranean habitats: who, when and where Introduction Road-kill mortality is the most well-known impact of traffic (Forman and Alexander, 1998; Trombulak and Frissell, 2000; Forman et al., 2003), but, in spite of mitigation efforts, a very large number of vertebrates die worldwide on paved and unpaved roads (Coffin, 2007; van der Grift et al., 2013). Road-kills are a considerable threat for populations of several species (Fahrig et al., 1995; Mumme et al., 2000; Taylor et al., 2002) and a relevant issue for human road safety, involving also high economic costs (Conover et al., 1995; Forman et al., 2003; Huijser et al., 2009). As a consequence, in the last decades the number of road-related studies has increased considerably, improving our knowledge on what has been defined as road ecology (Forman et al., 2003; Coffin, 2007). Most research has focused on a few emblematic species or on particular mitigation measures (Forman et al., 2003; van der Grift et al., 2013), and therefore at present there is still a need for more general approximations. For example, most studies describing road-kill patterns investigate only the role of local variables or proximate causes, thus their recommendations to limit road mortality are difficult to translate to other localities or to other species. Road-kill patterns could probably be better understood by additionally considering the role of more general predictors. An example of those more general descriptors is the study of species-specific life history traits affecting the susceptibility to collision with vehicles. This is an overlooked issue in road ecology with few published studies (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010; Cook and Blumstein, 2013), although the available 27 Capítulo 1: Atropellos bibliography demonstrates the existence of strong patterns and relevant differences among species (e.g. Ashley and Robinson, 1996; Erritzoe et al., 2003; Orlowski and Nowak, 2006; Glista et al., 2008). An additional step should be testing the relevance of other predictors which may be potentially correlating with both road-kill probability and life-history traits, as is the case for example of species abundance, which has been proposed in the past to be the underlying factor affecting road-kill probability (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010; Møller et al., 2011). Non-road-killed species should be also included in the analysis, because they can provide valuable information on key species-specific traits. Life history research can improve road mortality knowledge, but at the same time it can also provide direct recommendations that can be applied to the conservation of vulnerable groups of species for which data is not yet available. Road mortality research focused on life history traits can also improve our understanding of temporal and spatial road-kill patterns, especially if the target species are aggregated at relevant taxonomic or functional groups (e.g. amphibians or migrant birds) that might be differentially affected by specific climatic or environmental conditions. Previous studies have found that temporal variations in road-kill patterns are associated with seasonal behaviors such as dispersal or migration, thus helping to predict outbreaks in the number of casualties (Bonnet et al., 1999; Gryz and Krauze, 2008; Smith-Patten and Patten, 2008). The drivers inducing these behaviors (e.g. temperature or rainfall) have been suggested to be directly linked to the road-kill peaks (Puky, 2005; Andrews et al., 2006; Glista et al., 2008), but usually without statistical 28 Road-kill patterns in Mediterranean habitats: who, when and where analyses. Conversely, understanding which additional factors affect seasonal behaviors and the consequent temporal variability in road-kill patterns would potentially improve mitigation measures, especially those that can be differentially managed in time (e.g. traffic intensity). Finally, a central issue in road mortality research is the spatial distribution of casualties, which is probably the most investigated topic in road ecology. Most studies have investigated the correlations between the presence of road-kills and some spatial variables describing road features or traffic volume (Forman and Alexander, 1998; Trombulak and Frissell, 2000; Forman et al., 2003). Nevertheless, the spatial distribution of road-kills can be affected by a large amount of landscape and road features, and some of them can act simultaneously (Clevenger et al., 2001a; 2003; Forman et al., 2003). It is probably for this reason that several spatial variables (e.g. traffic volume or surrounding habitat) have shown contradictory effects in different studies (Forman et al., 2003). A simultaneous evaluation of the potentially relevant predictors, obviously controlling for correlated factors, is the way to proceed. In this study we aimed to comprehensively describe the life history, temporal and spatial factors potentially affecting road-kill patterns, performing a detailed selection of candidate predictors previously found or suggested as affecting road mortality. A priori, we expect that some species are more prone to be found as road-kills than others, species with particular life history traits directly or indirectly related to their abundance or movement behavior. We also expect that temporal road-kill patterns should be determined by predictors affecting temporal variations in species abundance 29 Capítulo 1: Atropellos or activity, and/or by traffic variations along time. Finally, we expect that road-kill spatial patterns should be related with predictors describing local variations in abundance, road properties and traffic. We tested these hypotheses focusing on terrestrial vertebrates in a typical Mediterranean landscape (Doñana Natural Park, south-western Spain). The Mediterranean basin is a biodiversity hotspot (Mittermeier et al., 1998; Myers et al., 2000) with an ancient and widespread road-network development, affecting even protected areas and involving potential threats for wildlife (Ferreras et al., 1992; Gomes et al., 2009; Grilo et al., 2009). Nevertheless, road-kill studies have been mainly performed in temperate landscapes (Trombulak and Frissell, 2000; Forman et al., 2003), with few contributions from Mediterranean areas (Malo et al., 2004; Carvalho and Mira, 2011; Garriga et al., 2012). This study should provide some general management and conservation recommendations for an emblematic protected area and for other Mediterranean environments, while providing scientists and conservation biologists with some guidelines to better understand the factors behind road-kill patterns. 30 Road-kill patterns in Mediterranean habitats: who, when and where Materials and Methods Study area Doñana Natural Area (36º59’ N, 6º26’ W; Figure 1) is a Biosphere Reserve with a Mediterranean climate, characterized by a mosaic of urban, rural and natural environments in which, depending on the level of protection (National and Natural parks), some human activities are allowed. The local road-network is widespread, with different types of linear infra-structures and traffic intensities. We surveyed all paved roads within the Natural Park: one regional road (A494 Matalascañas-Mazagón, 23 km) and three paved forestry/agricultural roads (Cabezudos, 5 km; A483-Hinojos, 11 km; A483-Villamanrique de la Condesa, 16 km; Figure 1). All the surveyed roads were located in the same ecosystem, Mediterranean forest and scrubland. Local terrestrial vertebrate communities are composed by typically Mediterranean species. 31 Capítulo 1: Atropellos Figure 1: Study area. Black lines represent the paved road-network. Bold lines are the surveyed road sections: regional road A494 Matalascañas-Mazagón, forestry/agricultural road of Cabezudos, forestry/agricultural road A483-Hinojos, and forestry/agricultural road A483-Villamanrique de la Condesa. Data collection From March 2006 to February 2007 (July 2007 excluded) we surveyed driving at 15 km/h the four roads twice per week (one day in the morning and one day in the afternoon, randomly determined), on both directions. The observer (always the same person, LdlR) searched for road-killed animals on road surface and along the adjacent verges and ditches. We georeferenced, identified (when possible) and removed all the road-killed animals. 32 Road-kill patterns in Mediterranean habitats: who, when and where We used bibliography and data from the Natural Processes Monitoring Team (ESPN) of EBD-CSIC to compile a checklist of the species present in the study area in association with the vegetation and land-uses adjacent to the roads (Appendix 1). A priori we considered that all the species included in the checklist could be found as road-kills. For those species we compiled a set of life history variables previously found or suggested as affecting road-kill probability (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010; Cook and Blumstein, 2013; Appendix 1 for more details). The candidate predictors directly or indirectly described species as a function of their abundance or movement. In the first case the candidate predictors were abundance (rare, common, or abundant), size (body mass), breeding (average number of offspring per female-year), activity (nocturnal or diurnal), habitat preferences (urban, farmlands, grasslands, scrubland/woodland and freshwater), food habits (carnivorous, insectivorous or herbivorous) and territoriality (territorial or not). In the second case the candidate predictors were movement (terrestrial or flying), speed (fast or slow movers) and their metabolic strategy (ectotherms or endotherms; see Appendix 1 for more details). In the analysis of the temporal patterns of road-kill mortality we considered species as belonging to taxonomic or functional groups (temporal groups hereafter) with similar seasonal responses in abundance, behavior and activity: amphibians, snakes, lizards, resident birds, migrant nesting birds, migrant wintering birds, transient migrant birds and small mammals (Appendix 2 for more details). For this set of analyses we selected three candidate predictors suggested by previous studies as affecting seasonal variations in species abundance and consequently 33 Capítulo 1: Atropellos in road-kill mortality (Puky, 2005; Andrews et al., 2006; Glista et al., 2008; Appendix 2 for more details): photoperiod variation (average of the daily variation for each month), rainfall and temperature (both measured as the daily average measured in the week prior to each survey). We also selected another candidate predictor related with seasonal traffic variation (average of daily traffic per season). Finally, considering that carcasses suffer temporal degradation (Antworth et al., 2005; Santos et al., 2011), we also selected as candidate predictor the time elapsed since the previous survey. We obtained photoperiod variation data from the U.S. Naval Oceanography Portal (http://www.usno.navy.mil/USNO/astronomical-applications). Rainfall and temperature were calculated using data from the meteorological station of Palacio de Doñana (ESPN). Traffic in the forestry/agricultural roads (Cabezudos, A483-Hinojos, and A483-Villamanrique de la Condesa) was obtained using a magnetometer (TRAFx Vehicle Counter Generation III; sampling during one week per season), while the traffic of the regional road (A494 Matalascañas-Mazagón) was obtained from the Andalusian Regional Government (sampling during one weekend and two weekdays per season at an official counter station). In the analyses of spatial patterns we classified the road-killed species with enough observations into five taxonomic or functional groups with a priori similar spatial behaviors (spatial groups hereafter): amphibians, snakes, lizards, small passerines and small mammals (Appendix 3 for more details). We divided the surveyed roads (55 km in total) into 1100 sections. These sections were 50-m long with the aim to avoid spatial auto-correlation and simultaneously describe changes in spatial predictors. Each section was characterized 34 Road-kill patterns in Mediterranean habitats: who, when and where by a set of candidate predictors previously found or suggested as affecting spatial variations in the distribution of road-kills: species presence/abundance, the probability of road-crossing at the specific site and traffic (Clevenger et al., 2001a; 2003; Forman et al., 2003). The predictors potentially affecting the spatial variation in species abundance were related to the adjacent landscape: macrohabitat (grassland, scrubland or woodland) and distance to the nearest water body. The predictors potentially affecting spatial variation in wildlife road-crossing were related to road verges: road verge habitat (grass, grass with trees, grass with shrubs and trees, shrubs), presence/absence of roadfences, drainage culverts and wildlife road-crossing structures. The predictors potentially affecting spatial variations in traffic and its speed were related to the road itself, they were road width, annual average of daily traffic, maximum allowed speed, and the presence/absence of road-shoulders, curves, slope changes in the road, and vertical or horizontal road signs. We characterized all road sections in the field and using a geographic information system (landscape variables). Data analysis First, we calculated the correlation between predictors in order to consider the potential effect of collinearity in the life history analysis (Appendix 1). Abundance strongly correlated with size, food habits, territoriality and movement. Therefore, we fitted an initial generalized linear model (GLM) testing the relationship between a dependent variable describing all the species initially considered in the checklist with a value of 1 for those found road killed at least once in any of the roads and 0 for those that were not found (i.e. species road-kill probability), and, as a predictor, the 35 Capítulo 1: Atropellos abundance (model with a binomial error distribution and logit link function; GLIMMIX procedure of SAS version 9.3; SAS Institute Inc., 2012). As the probability of rare species to be detected as road-killed was much lower, all subsequent life-history analyses were done on a reduced species checklist in which we removed rare species (both the road-killed and not road-killed ones). For every lifehistory predictor we fitted a separate GLM (again with species road-kill probability as response variable and with binomial error distribution and logit link function). We considered as plausible those models at ΔAIC < 2 from the best model (Burnham et al. 2011). Finally, we calculated model support using Akaike weights (wAIC, ranging from 0 to 1, with larger numbers indicating greater support; Burnham and Anderson 2002). In the temporal analyses we followed a similar approach to evaluate the collinearity between predictors (Appendix 2). In this case, as the correlation was low, we run a single model for each temporal group of species to evaluate the impact of the selected predictors on the number of road-killed individuals per survey, using generalized linear mixed models (GLMM) in which the surveyed road was included as a random factor (negative binomial error distribution and log link function using procedure GLIMMIX in SAS version 9.3). In the spatial analyses, we found that several candidate predictors were strongly correlated, especially road width, traffic and speed (Appendix 3). As a consequence, we replaced these with a new explanatory variable named road magnitude, obtained from a principal component analysis of those variables (PCA; FACTOR procedure in SAS version 9.3). We also excluded from further analyses all the variables that correlated 36 Road-kill patterns in Mediterranean habitats: who, when and where with road width, traffic or speed. The implemented spatial predictors then were road magnitude, presence/absence of wildlife road-crossing structures and distance to the nearest water body. Finally, for every spatial group we evaluated the impact of the selected predictors on the number of road-killed individuals accumulated during the study in each of the 50-m road sections using GLMMs in which the surveyed road was included as a random factor (negative binomial error distribution and log link function using procedure GLIMMIX in SAS version 9.3). Results We detected 2368 road-killed vertebrates of which the majority (67%) were amphibians (Table 1). They included both anurans and urodeles (1568 and 20 casualties, respectively). We could identify only a small fraction of the road-killed amphibians, mostly corresponding to natterjack toads Epidalea calamita (381 casualties) and western spadefoot toads Pelobates cultripes (142 casualties). We found 299 reptile casualties (12.6% of the total), including snakes (mainly Malpolon monspessulanus and Rhinechis scalaris) and lizards (mostly Psammodromus manuelae). Birds accounted for 12.2% of the casualties (290), covering a large range of orders (Erithacus rubecula was the most common). Finally, we found 191 (8.1%) roadkilled mammals (133 small mammals, mainly rodents). 37 Capítulo 1: Atropellos Table 1: Road-killed species and groups. Number of road-killed individuals found and their assignation to taxonomic or functional groups used in the different analyses. Species Number of road-kills Amphibians Discoglossus galganoi 1 Epidalea calamita 381 Pelobates cultripes 142 Pelophylax perezi 7 Pleurodeles waltl 20 Unidentified Amphibians 1037 Total Amphibians 1588 Reptiles Coronella girondica 6 Hemorrhois hippocrepis 1 Macroprotodon brevis 4 Malpolon monspessulanus 39 Natrix maura 10 Rhinechis scalaris 39 Vipera latasti 8 Unidentified Snakes 22 Total Snakes 129 Acanthodactylus erythrurus 20 Blanus cinereus 1 Chalcides bedriagai 2 Chalcides striatus 1 Chamaeleo chamaeleon 2 Podarcis carbonelli 19 Podarcis vaucheri 1 Psammodromus manuelae 79 Tarentola mauritanica 15 Timon lepidus 3 Unidentified Lizards 33 Total Lizards 170 Birds Alectoris rufa 2 Apus pallidus 1 Bubo bubo 1 Buteo buteo 2 Caprimulgus ruficollis 13 Columba livia domestica 6 Cyanistes caeruleus 1 Cyanopica cooki 4 Emberiza calandra 4 Erithacus rubecula 27 Ficedula hypoleuca 7 Fringilla coelebs 2 Hirundo rustica 1 38 Temporal group Spatial group Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Amphibians Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Snakes Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Lizards Resident Nesting Resident Resident Nesting Resident Resident Resident Resident Wintering Migrant Resident Nesting Small passerines Small passerines Small passerines Small passerines Small passerines - Road-kill patterns in Mediterranean habitats: who, when and where Lanius senator Lophophanes cristatus Lullula arborea Muscicapa striata Oceanodroma leucorhoa Parus major Passer domesticus Passer montanus Phylloscopus collybita/ibericus Saxicola rubetra Saxicola rubicola Serinus serinus Strix aluco Sylvia atricapilla Sylvia cantillans Sylvia melanocephala Sylvia undata Troglodytes troglodytes Turdus philomelos Unidentified Small passerines Total Birds Mammals Apodemus sylvaticus Canis lupus familiaris Crocidura russula Eliomys quercinus Erinaceus europaeus Felis silvestris catus Herpestes ichneumon Lepus granatensis Mus spretus Oryctolagus cuniculus Pipistrellus pipistrellus Rattus rattus Vulpes vulpes Unidentified Mammals Unidentified Small mammals Total Mammals 5 1 1 1 1 1 4 1 10 2 1 4 2 3 2 6 8 1 2 163 290 Nesting Resident Resident Migrant Resident Resident Resident Migrant Migrant Resident Resident Resident Migrant Migrant Resident Resident Resident Wintering - Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines Small passerines 5 2 1 2 17 2 1 4 4 17 1 2 5 9 119 191 Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals Small mammals 39 Capítulo 1: Atropellos We were able to identify 991 casualties as belonging to 66 different species (Table 1), representing 32% of our species checklist (Appendix 1). The results of the first GLM showed that abundance affected the species road-kill probability, with lower probability for rare species (F = 9.13; p = 0.0002; Figure 2). The reduced dataset included only the 148 abundant and common species, of which 60 were found as roadkills. The rest of the life history models were run with this reduced subset. The most supported model was the one including metabolic strategy (Table 2). Overall, ectotherm species had a higher probability to be found as road-kills than endotherms (Figure 2). Table 2: Factors affecting species road-kill probability, model ranks by AIC weights (wAIC). ΔAIC is the difference of a given AIC value compared to the smallest AIC value. The only supported model (ΔAIC < 2) is marked with an asterisk. AIC weights indicate the relative support of every model. Evidence ratio (ER) is the ratio of wAIC, comparing the best supported model with every competing one. Model Body weight Offsprings Habitat preference Circadian activity Feeding strategy Territoriality Movement type Speed Thermal strategy * 40 AIC ΔAIC wAIC Rank 199.8 198.3 196.1 200.3 202.7 202.5 192.8 203.4 189.8 10.0 8.6 6.3 10.5 12.9 12.7 3.1 13.7 0.0 0.00 0.01 0.03 0.00 0.00 0.00 0.17 0.00 0.78 5 4 3 6 8 7 2 9 1 ER 154.4 72.2 23.7 194.4 648.7 587.0 4.7 939.2 1.0 Road-kill patterns in Mediterranean habitats: who, when and where Figure 2: Life history factors and species road-kill probability. Probability (± standard error) of species present to be found as road kills as predicted by the initial model and the best supported model, respectively. Left: species categorized by three coarse abundance categories (parameter estimates: β = 0.8 ± 0.4; rare = -1.8 ± 0.6, common = 0.5 ± 0.4, abundant = 0). Right: species included within the abundant and common categories by their metabolic strategy (β = 1.1 ± 0.5; endotherms = -1.8 ± 0.5, ectotherms = 0). In the analysis of temporal patterns we considered 2156 road-kills (Table 1; Appendix 2 for more information on the temporal distribution of each group). High road-kill frequencies for lizards and resident birds occurred when the duration of the photoperiod was increasing, while in the case of snakes, small mammals and migrant birds they occurred when the photoperiod decreased (Table 3). Peaks in amphibian road-kill mortality were associated with rainfall events, while in the case of lizards they were associated with the lack of rain (Figure 3). Months with high temperatures associated with increases in the number of casualties of snakes, lizards and nesting birds, while months with low temperatures did so for amphibians and wintering birds (Figure 3). Seasonal traffic intensity was associated with the number of road-killed 41 Capítulo 1: Atropellos amphibians and snakes, with more casualties corresponding to periods with less traffic (Table 3). Table 3: Factors affecting temporal distribution of road-kills. In the table header we show the name of the explanatory variable and its average value (range). Each row represents the results of the model for a temporal group. For every explanatory variable with we give the parameter estimate (with standard error) and, below, the p-value when p < 0.05. Temporal variables Temporal groups Intercept Photoperiod variation (minutes) 1.3 (-70, 71) Temperature (ºC) 2.0 (0, 15.3) 0.21 + 0.04 p < 0.0001 -0.14 + 0.06 p = 0.01 17.0 (6.2, 27.8) -0.09 + 0.03 p = 0.003 0.17 + 0.03 p < 0.0001 0.26 + 0.03 p < 0.0001 -0.04 + 0.06 0.02 + 0.03 Amphibians 3.5 + 1.2 Snakes -2.7 + 1.0 Lizards -5.3 + 1.0 Resident birds -2.3 + 0.7 Nesting birds -7.1 + 2.0 0.006 + 0.006 -0.21 + 0.21 Wintering birds -0.5 + 0.6 -0.018 + 0.005 0.06 + 0.05 Migrant birds -2.7 + 0.9 Small mammals -0.9 + 0.8 42 -0.005 + 0.003 Rainfall (mm/day) -0.005 + 0.002 p = 0.005 0.011 + 0.002 p < 0.0001 0.008 + 0.003 p = 0.01 -0.022 + 0.008 p = 0.007 -0.005 + 0.002 p = 0.003 0.004 + 0.027 0.22 + 0.09 p = 0.01 -0.15 + 0.04 p = 0.0005 Seasonal traffic (cars/day) 712 (98.3, 3217) -0.002 + 0.001 p = 0.03 -0.0011 + 0.0003 p = 0.0005 Time since previous survey (days) 8 (0, 47) -0.0004 + 0.0003 0.004 + 0.007 0.0004 + 0.0004 0.01 + 0.02 0.0002 + 0.0004 0.006 + 0.030 0.0002 + 0.0003 0.01 + 0.02 -0.008 + 0.017 -0.005 + 0.010 -0.01 + 0.06 -0.03 + 0.05 -0.0001 + 0.0004 0.02 + 0.02 -0.01 + 0.03 -0.01 + 0.02 0.0001 + 0.0001 0.016 + 0.009 Road-kill patterns in Mediterranean habitats: who, when and where Figure 3: Temporal road-kill patterns. Relationship between the number of road-kills found per survey and temporal predictors: in the upper two graphs we show the effect of rainfall; in the lower five graphs the effect of temperature. The solid line represents the estimated effect of the predictor, dotted lines are the upper and lower 95% CI. For correspondent plots with raw data see Appendix 2. 43 Capítulo 1: Atropellos Finally, for the spatial analyses we considered 1617 road-kills which could be assigned to the spatial groups (Table 1; Appendix 3 for more information on the spatial distribution of each group). Road magnitude was the most relevant factor affecting road-kill patterns, with more lizard and small mammal casualties found in wider road sections with more traffic and higher maximum speeds (Table 4; Figure 4). Road sections near water had higher numbers of amphibian road-kills while those away from water had more lizard casualties (Table 4). Overall, the presence of road-crossing structures was not associated with the number of road-kills found for any group. Table 4: Factors affecting the spatial distribution of road-kills. In the table header we show the name of the explanatory variables, with its average value (range) in the case of continuous factors or its levels in the case of categorical factors. Each row represents the results of the model for a spatial group. For every explanatory variable with we give the parameter estimate (with standard error) and, below, the pvalue when p < 0.05. Road magnitude range (*) corresponds with 5-10 m of Road width (average = 8.79 m), 171-2499 cars/day of Traffic (average = 1547 cars/day) and 30-100 Km/h of Speed (average = 78.69 Km/h). Spatial variables Spatial groups Intercept 44 Road magnitude 0 (-2, 2.3) * Crossing structures Absent Present Distance from water (m) 1873 (0, 4632) -0.0001 + 0.0001 p = 0.02 Amphibians 0.5 + 0.3 0.02 + 0.09 -0.06 + 0.14 0 Snakes -2.1 + 0.5 -0.009 + 0.010 -0.06 + 0.46 0 -0.0001 + 0.0001 Lizards -2.6 + 0.6 0.4 + 0.1 p = 0.001 0.1 + 0.5 0 -0.0001 + 0.0001 p = 0.002 Small passerines -1.8 + 0.4 0.1 + 0.1 -0.1 + 0.3 0 -0.0001 + 0.0001 Small mammals -2.0 + 0.8 0.7 + 0.3 p = 0.02 -0.2 + 0.4 0 -0.0001 + 0.0001 Road-kill patterns in Mediterranean habitats: who, when and where Figure 4: Spatial road-kill patterns and road magnitude. Relationship between the number of roadkills accumulated in all surveys and road magnitude. Road magnitude range corresponds with 5-10 m of Road width (average = 8.79 m), 171-2499 car/day of Traffic (average = 1547 car/day) and 30-100 Km/h of Speed (average = 78.69 Km/h). The solid line represents the estimated effect of the road magnitude dotted lines are the upper and lower 95% CI. For correspondent plots with raw data see Appendix 3. Discussion In the present study we found that several life history, temporal and spatial factors had a clear association with road-kill patterns. Life history analysis indicated that abundant and ectotherm species had more probability to be road-killed, temporal analysis highlighted the general factors influencing group-specific road-kill seasonality (i.e. photoperiod variation, rainfall and temperature), and spatial analysis showed the relevance of road features related with road magnitude (i.e. road width, traffic and speed). These results improve our understanding on some of the underlying mechanisms causing group-specific collisions in Mediterranean habitats. 45 Capítulo 1: Atropellos The first interesting results on life history traits and road mortality were the correlations among the candidate predictors. Previous studies already suggested that several of these factors might be correlated, such as abundance with body size and food habits (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010). In our case, we found that a coarse descriptor of species abundance correlated with other predictors, making difficult its inclusion in further analysis. An initial analysis confirmed that rare species were less likely to be found road-killed, as previously suggested both directly (Møller et al., 2011) and indirectly (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010). The remaining analyses focusing only on abundant and common species showed that ectotherm species (amphibians and reptiles) had a higher probability to be roadkilled. This is most probably because their metabolism causes slowness in amphibians (Hels and Buchwald, 2001; Puky, 2005) and basking behavior in reptiles (Ashley and Robinson, 1996; Tanner and Perry, 2007), but other reasons can be their relatively lower environmental awareness and behavioral freezing responses to threats (Andrews et al., 2006; Lima et al., 2014). Numbers and results make clear that mitigation measures should focus on amphibians and reptiles. The establishment of drift fences should be extensively implemented to permanently reduce their road-kill numbers (Aresco, 2005; Puky, 2005), including roads with low traffic where basking behavior is more frequent (Aresco, 2005; Jones et al., 2008). The number of casualties showed strong variations along the year for the different taxonomic and functional groups, as previously found (Ashley and Robinson, 1996; Erritzoe et al., 2003; Orlowski and Nowak, 2006), in association with periods of 46 Road-kill patterns in Mediterranean habitats: who, when and where the year in which there are increases in activity or abundance, such as during seasonal mate search, juvenile dispersal or migration (Bonnet et al., 1999; Clevenger et al., 2001b; Gryz and Krauze, 2008; Smith-Patten and Patten, 2008). Additionally, we found a clear association among road-kill numbers and weather predictors, which usually contribute by triggering the above mentioned seasonal behaviors (Fryxell and Sinclair, 1988; Lloyd, 1999; Gwinner and Helm, 2003). It is important to note that in Mediterranean environments those variables can be quite unpredictable, in spite of which they can be used by managers to determine when to enforce temporal mitigation measures. We found that photoperiod variation was the only temporal factor having a significant association with seasonal peaks in road-kill mortality of small mammals and both resident and migrant birds, most probably in correspondence with variations in their presence and abundance (García et al., 2000; Mumme et al., 2000; Todd, 2001; Moreno and Rouco, 2013). In these cases it would be therefore relatively easy to reduce the group-specific casualties, through the implementation of temporary traffic-calmed zones in the relevant spatial road-kill hotspots (Jaarsma and Willems, 2002; Jaarsma et al., 2013). The temporary limitation of maximum speed can be alternatively applied when traffic reduction is not possible (Sullivan et al., 2004; Hardy et al., 2006). The positive association between temperature and both snake and lizard road-kills depends on their increase in activity during the warm season (Koenig et al., 2002; Meek, 2009) and the basking behavior often carried out on the road surface (Ashley and Robinson, 1996; Tanner and Perry, 2007). On the other hand, amphibian road-kill peaks occur during events of high rainfall events, which in Mediterranean landscapes generate breeding habitats and the seasonal migrations of toads and newts (Díaz-Paniagua et al., 47 Capítulo 1: Atropellos 2005; Semlitsch, 2008). The same pattern has been previously suggested in boreal and temperate landscapes (Clevenger et al., 2003; Glista et al., 2008), in which water is a less limiting factor. The unpredictability of Mediterranean precipitations would make difficult the planning of traffic-calmed zones, and therefore with the aim to limit amphibian road-kill peaks we recommend the implementation of both temporary traffic signs and drift fences in amphibian road-kill hotspots (Aresco, 2005; Puky, 2005), and, if needed, crossing rescue campaigns (Puky, 2005; Beebee, 2013). The implementation of temporary mitigation measures can be applied with more accuracy for groups with more than one temporal factor affecting road-kill seasonality. For example, snakes were more road-killed during the decrease of photoperiod length, but especially in association with a quantifiable threshold of high temperatures, which usually coincides with the early autumn, just after the hatching of most Mediterranean snakes (Bonnet et al., 1999; Pleguezuelos and Feriche, 2006; Feriche et al., 2008). Reaching the temperature threshold during other time of the year will not mark a similar temporal road-kill peak, and could be therefore ignored by managers. In the spatial analysis we found that several potentially relevant descriptors of road characteristics were strongly correlated, as previously suggested for road width, traffic volume and maximum allowed speed (Forman et al., 2002; Jaeger et al., 2005). The composed road magnitude was the most relevant factor affecting the spatial distribution of road-kills, with more lizard and small mammal casualties found in wider road sections, which hold more and faster traffic. This pattern has been previously described for a large amount of species (Inbar and Mayer, 1999; Coelho et al., 2008), 48 Road-kill patterns in Mediterranean habitats: who, when and where and implies a tangible threat for populations of several species (Rosen and Lowe, 1994; Jones, 2000). We did not find an effect of the presence of wildlife road-crossing structures on road-kill spatial distribution, probably because they are actually correcting the threat and because in our study area these structures are mainly focused on medium-sized mammals just as carnivores of which we detected a relatively low number of casualties. In our study area they are built for rare species with proved road-kill susceptibility, such as the critically endangered Iberian lynx Lynx pardinus (Ferreras et al., 1992). Nevertheless, it is clear that managers should also consider the implementation of appropriate road-crossing structures for smaller vertebrates (e.g. with effective drift fences; Glista et al., 2009; Pagnucco et al., 2012) in correspondence to the groupspecific road-kill risk areas, and the improvement of the structures already present. Our results also show that mitigation measures should be prioritized along major roads. Unfortunately, we could not discern the single effects of road width, traffic volume and maximum allowed speed; and we could not even determine the potential relevance of the correlated spatial variables that we needed to remove from the analyses (for example the presence of shoulders, curves, traffic signals, etc.). The effects of all of these factors on the road-kill spatial distribution of the different taxonomic and functional groups should be explored in the future. We believe that our results have relevant implications for the establishment of measures to mitigate road-kill patterns regarding life history, temporal and spatial causes in our study area and also in other Mediterranean environments. Our spatial analysis suggests how to permanently decrease wildlife-vehicle collisions through the 49 Capítulo 1: Atropellos implementation of drift fences and road-crossing structures in group-specific road-kill risk areas. When these mitigation measures cannot be permanently applied, we also suggest temporal actions which can reduce road-related mortality during the temporal road-kill peaks. It is also very clear that managers should focus on abundant and ectotherm species, many of them with conservation problems, as is the case of amphibians. Road ecology is an expanding discipline, and road mortality is its most investigated issue. Currently many researchers, managers and also nonprofessional environmental organizations are collecting data that should be used to improve our general understanding of the problem. We hope that this study contributes to the development of this discipline and to the conservation of threatened ecosystems. Acknowledgements The road-kill survey was realized by Doñana Conservation Area, while the explanatory variable surveys were funded by the project 2008X0963 (Andalusian Ministry of Environment; Andalusian Regional Government). The ESPN (EBD-CSIC) provided unpublished data for life history variable survey, and Carmen Díaz-Paniagua, Margarita Florencio and Carlos Ibañez suggested bibliographic references. The ESPN also provided data for temporal variable survey. The Huelva Provincial District of Public Works and Transportation (Andalusian Regional Government) provided traffic data for the A494 Matalascañas-Mazagón regional road. Land-Rover España S.A. kindly lent two vehicles for spatial variable survey, while Gemma Calvo, Clara Grilo, Pablo Lucas and especially Andrea Barón helped with fieldwork. MD was supported by 50 Road-kill patterns in Mediterranean habitats: who, when and where JAE-PRE fellowship from CSIC; ER was supported by projects funded by the Spanish Ministry of Science and Innovation co-funded by FEDER (CGL2009-07301/BOS, CGL2012-35931/BOS). Sofía Conradi-Fernández and Angela Genovese provided logistic support. 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Dispersal patterns and post-fledging mortality of juvenile burrowing owls in Saskatchewan. Journal of Raptor Research. 35: 282-287. Trombulak, S.C. and Frissell, C.A. (2000). Review of ecological effects of roads on terrestrial and aquatic communities. Conservation Biology. 14: 18-30. van der Grift, E.A., van der Ree, R., Fahrig, L., Findlay, S., Houlahan, J., Jaeger, J.A.G., Klar, N., Madriñan, L.F. and Olson, L. (2013). Evaluating the effectiveness of road mitigation measures. Biodiversity and Conservation. 22: 425-448. 54 Road-kill patterns in Mediterranean habitats: who, when and where Appendix 1 – Life history patterns Table 1: Species checklist and life history analysis data set. Species column indicates the check-list classified by taxonomic categories (Amphibians, Reptiles, Birds and Mammals). In the second column the meaning of the acronym Rk is road-killed species, indicating if for the given species was found at least once as a casualty (1) during the study period. The other columns are the ten predictors of the Life Body mass (g) Breeding (offsprings/female/year) Activity Habitat preference Food habits Territoriality Movement Speed Metabolic strategy Amphibians Alytes cisternasii Bufo bufo Discoglossus galganoi Epidalea calamita Hyla meridionalis Lissotriton boscai Pelobates cultripes Pelodytes ibericus Pelophylax perezi Pleurodeles waltl Triturus pygmaeus Reptiles Acanthodactylus erythrurus Blanus mariae Chalcides bedriagai Chalcides striatus Chamaeleo chamaeleon Coronella girondica Emys orbicularis Hemorrhois hippocrepis Macroprotodon brevis Malpolon monspessulanus Mauremys leprosa Natrix maura Natrix natrix Podarcis carbonelli Podarcis vaucheri Abundance Species Road-killed history analysis. Below the table there is a legend for the acronyms used. 0 0 1 1 0 0 1 0 1 1 0 1 1 1 3 3 2 3 1 2 2 3 6.4 149.3 9.3 12.5 2.3 0.5 17.8 2.5 15.4 25.2 2.5 73.0 6920.0 671.0 2247.0 316.0 175.0 2500.0 600.0 9000.0 750.0 250.0 Noct Noct Noct Noct Noct Noct Noct Noct Noct Noct Noct S/W S/W Fw S/W S/W Fw S/W Fw Fw Fw Fw Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins No No No No No No No No No No No Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Slow Slow Slow Slow Slow Slow Slow Slow Slow Slow Slow Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 6.2 4.1 3.1 36.6 53.6 30.0 596.0 125.8 26.1 165.0 658.2 50.0 92.2 1.8 3.1 9.0 1.0 3.5 8.0 20.0 7.0 10.5 7.5 2.0 9.0 7.0 7.0 30.0 6.7 3.0 Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu S/W S/W S/W S/W S/W S/W Fw S/W S/W S/W Fw Fw Fw S/W Ua Ins Ins Ins Ins Ins Carn Ins Carn Carn Carn Ins Carn Carn Ins Ins No Yes Yes No Yes Yes Yes Yes Yes Yes Yes No No Yes Yes Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Fast Slow Fast Fast Slow Fast Slow Fast Fast Fast Slow Fast Fast Fast Fast Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto Ecto 55 Capítulo 1: Atropellos Psammodromus hispanicus Psammodromus manuelae Rhinechis scalaris Tarentola mauritanica Timon lepidus Vipera latasti Birds Accipiter gentilis Accipiter nisus Acrocephalus arundinaceus Acrocephalus scirpaceus Aegithalos caudatus Alauda arvensis Alcedo atthis Alectoris rufa Anas platyrhynchos Anthus campestris Anthus pratensis Anthus spinoletta Anthus trivialis Apus apus Apus pallidus Aquila adalberti Asio flammeus Asio otus Athene noctua Bubo bubo Bubulcus ibis Burhinus oedicnemus Buteo buteo Calandrella brachydactyla Caprimulgus europaeus Caprimulgus ruficollis Carduelis cannabina Carduelis carduelis Carduelis chloris Carduelis spinus Cecropis daurica Certhia brachydactyla Cettia cetti Ciconia ciconia Circaetus gallicus Circus aeruginosus Circus cyaneus Circus pygargus Cisticola juncidis Clamator glandarius Coccothraustes coccothraustes Columba livia domestica Columba oenas Columba palumbus Coracias garrulus Corvus corax Corvus monedula Coturnix coturnix Cuculus canorus 56 0 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 2 2 2 2 2 1 1 2 3 1 3 2 2 3 2 3 2 2 3 3 1 1 1 2 2 3 2 2 2 1 2 1 2 3 2 2 2 2 3 1 2 1 1 2 1 1 3 1 2 1 2 2 2 2 1.7 3.8 128.4 8.0 100.0 40.0 8.0 16.2 9.5 3.7 13.5 3.2 Diu Diu Diu Noct Diu Noct S/W S/W S/W Ua S/W S/W Ins Ins Carn Ins Ins Carn No No No Yes Yes Yes Terr Terr Terr Terr Terr Terr Fast Fast Fast Fast Fast Fast Ecto Ecto Ecto Ecto Ecto Ecto 792.5 204.0 28.3 12.7 7.8 33.0 31.6 497.5 1077.1 23.7 18.2 21.8 24.0 44.9 41.3 3000.0 320.0 265.0 157.0 1980.0 345.0 443.5 848.3 22.2 65.0 75.0 17.9 15.7 25.9 12.6 22.2 8.2 13.1 3471.7 1699.5 584.5 436.5 310.2 8.4 146.2 54.0 267.5 299.8 499.2 130.8 1131.2 237.5 101.5 109.0 3.5 5.0 9.0 10.0 10.0 16.0 9.7 11.2 11.0 0.0 0.0 0.0 0.0 2.5 2.4 2.5 0.0 4.1 3.0 3.0 4.5 2.0 3.0 3.0 3.0 4.0 10.0 10.0 10.0 8.0 11.2 13.0 10.0 4.0 1.0 5.5 0.0 4.5 12.5 18.0 3.0 10.0 0.0 1.5 4.0 5.0 5.2 10.5 9.2 Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Noct Noct Noct Diu Noct Diu Diu Noct Noct Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu S/W S/W Fw Fw S/W S/W Fw S/W Fw Gr S/W Fw S/W Ua S/W S/W Gr S/W S/W S/W S/W Gr S/W Gr S/W S/W S/W S/W S/W S/W S/W S/W Fw S/W S/W Fw Fa Fa Fw S/W S/W Ua S/W S/W S/W S/W S/W S/W S/W Carn Carn Ins Ins Ins Herb Carn Herb Herb Ins Ins Ins Ins Ins Ins Carn Carn Carn Ins Carn Ins Ins Carn Ins Ins Ins Herb Herb Herb Herb Ins Ins Ins Carn Carn Carn Carn Carn Ins Ins Herb Herb Herb Herb Ins Carn Ins Herb Ins Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes No Yes Yes No Yes Yes Yes Yes No No Yes Yes Yes No Yes Yes Fly Fly Fly Fly Fly Fly Fly Terr Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Terr Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Terr Fly Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Road-kill patterns in Mediterranean habitats: who, when and where Cyanistes caeruleus Cyanopica cooki Delichon urbicum Dendrocopos major Elanus caeruleus Emberiza calandra Emberiza cia Emberiza cirlus Emberiza hortulana Emberiza schoeniclus Erithacus rubecula Falco columbarius Falco naumanni Falco peregrinus Falco subbuteo Falco tinnunculus Ficedula hypoleuca Fringilla coelebs Galerida cristata Galerida theklae Gallinago gallinago Gallinula chloropus Hieraaetus pennatus Himantopus himantopus Hippolais polyglotta Hirundo rustica Iduna opaca Jynx torquilla Lanius meridionalis Lanius senator Lophophanes cristatus Lullula arborea Luscinia megarhynchos Melanocorypha calandra Merops apiaster Milvus migrans Milvus milvus Monticola saxatilis Motacilla alba Motacilla cinerea Motacilla flava Muscicapa striata Oenanthe hispanica Oenanthe oenanthe Oriolus oriolus Otus scops Parus major Passer domesticus Passer hispaniolensis Passer montanus Petronia petronia Phoenicurus ochruros Phoenicurus phoenicurus Phylloscopus bonelli Phylloscopus collybita/ibericus Phylloscopus trochilus 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 2 2 3 2 1 2 1 1 1 2 3 1 1 1 2 2 3 2 3 3 2 3 2 2 2 3 1 1 2 2 1 2 2 2 3 1 3 1 3 1 3 2 2 2 1 1 2 3 2 2 1 2 2 2 3 2 10.9 74.5 19.5 77.9 232.5 48.8 23.2 25.6 19.2 24.0 16.0 187.0 152.0 889.2 211.0 232.5 14.5 22.0 41.4 36.8 108.0 326.7 842.0 205.0 11.2 19.3 9.9 33.9 51.4 32.5 11.1 26.1 19.7 64.5 54.9 828.5 1039.7 58.0 21.8 17.8 17.4 17.5 17.8 24.2 70.3 85.7 17.5 28.5 28.2 20.8 30.5 16.2 14.5 7.0 7.5 8.0 9.3 6.0 8.0 5.5 3.5 10.0 0.0 7.0 0.0 0.0 0.0 4.0 4.0 3.5 3.0 4.5 0.0 4.5 10.0 7.0 0.0 14.0 2.0 4.0 4.5 11.2 5.2 12.7 6.0 5.5 6.5 8.0 9.0 9.0 6.5 2.5 2.0 0.0 0.0 0.0 10.0 5.0 9.0 0.0 3.5 4.5 9.7 10.0 10.0 15.0 11.0 0.0 0.0 0.0 0.0 0.0 Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Noct Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W Gr S/W S/W S/W S/W S/W S/W S/W S/W Gr Fw S/W Fw S/W Ua S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W Fa Fa Fa S/W S/W S/W S/W S/W S/W Ua S/W S/W S/W S/W S/W S/W S/W S/W Carn Herb Ins Ins Carn Herb Herb Herb Herb Herb Ins Carn Ins Carn Carn Carn Ins Herb Ins Ins Ins Herb Carn Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Carn Carn Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Herb Herb Herb Herb Ins Ins Ins Ins Ins Yes No No Yes Yes Yes No Yes No No Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes No Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes Yes Yes Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Terr Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast 57 Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Capítulo 1: Atropellos Pica pica Prunella modularis Pyrrhula pyrrhula Regulus ignicapilla Remiz pendulinus Riparia riparia Saxicola rubetra Saxicola rubicola Serinus serinus Streptopelia decaocto Streptopelia turtur Strix aluco Sturnus unicolor Sturnus vulgaris Sylvia atricapilla Sylvia borin Sylvia cantillans Sylvia communis Sylvia conspicillata Sylvia hortensis Sylvia melanocephala Sylvia undata Tachybaptus ruficollis Tetrax tetrax Troglodytes troglodytes Turdus iliacus Turdus merula Turdus philomelos Turdus pilaris Turdus torquatus Turdus viscivorus Tyto alba Upupa epops Vanellus vanellus Mammals Apodemus sylvaticus Arvicola sapidus Bos taurus Canis lupus familiaris Cervus elaphus Crocidura russula Dama dama Eliomys quercinus Eptesicus isabellinus Equus caballus Erinaceus europaeus Felis silvestris catus Felis silvestris silvestris Genetta genetta Herpestes ichneumon Lepus granatensis Lutra lutra Meles meles Microtus duodecimcostatus Miniopterus schreibersii Mus musculus 58 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 2 2 1 2 1 2 2 2 3 2 2 2 3 3 2 2 2 2 1 1 2 2 2 1 2 1 2 2 1 1 1 2 2 2 2 2 3 2 3 2 2 2 1 3 2 2 1 2 2 2 1 2 2 1 2 198.7 19.2 22.4 5.4 9.2 12.9 17.7 15.0 11.9 198.5 132.0 459.8 86.6 77.5 18.0 20.0 9.3 16.5 8.7 21.1 10.9 9.4 162.5 775.0 8.8 60.0 86.1 72.2 91.0 107.5 117.8 271.2 68.6 217.5 6.1 0.0 0.0 0.0 14.0 10.0 0.0 12.5 5.2 4.5 2.0 3.5 9.0 0.0 5.0 0.0 0.0 0.0 8.0 0.0 8.0 8.0 10.0 3.5 13.0 0.0 10.0 0.0 0.0 0.0 10.0 8.2 7.5 4.0 Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Noct Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Diu Noct Diu Diu S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W S/W Fw Fa S/W S/W S/W S/W S/W S/W Gr S/W S/W Gr Ins Ins Herb Ins Ins Ins Ins Ins Herb Herb Herb Carn Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Ins Herb Ins Ins Ins Ins Ins Ins Ins Carn Ins Ins Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes No No Yes Yes No No Yes Yes Yes Yes Yes Yes Yes No Yes Yes No No Yes Yes Yes Yes Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Fly Terr Fly Fly Fly Fly Fly Fly Fly Fly Fly Terr Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo 32.5 205.0 450000.0 13500.0 72000.0 9.3 51500.0 85.0 22.5 400000.0 850.0 3750.0 4250.0 1875.0 2982.5 2300.0 11350.0 11950.0 25.5 12.5 18.5 13.5 9.9 1.0 5.3 1.0 15.7 1.0 11.1 1.0 1.0 5.5 4.0 3.0 2.5 2.5 1.5 2.5 2.5 18.0 1.0 34.0 Noct Noct Diu Diu Noct Noct Noct Noct Noct Diu Noct Noct Noct Noct Diu Noct Noct Noct Noct Noct Noct S/W Fw Gr Ua S/W S/W Gr S/W S/W Gr Fa Ua S/W S/W S/W Gr Fw S/W S/W S/W Ua Herb Herb Herb Carn Herb Ins Herb Ins Ins Herb Ins Carn Carn Carn Carn Herb Carn Carn Herb Ins Herb No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No Yes Terr Terr Terr Terr Terr Terr Terr Terr Fly Terr Terr Terr Terr Terr Terr Terr Terr Terr Terr Fly Terr Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Road-kill patterns in Mediterranean habitats: who, when and where Mus spretus Mustela putorius Nyctalus lasiopterus Nyctalus leisleri Oryctolagus cuniculus Pipistrellus kuhlii Pipistrellus pipistrellus Rattus norvegicus Rattus rattus Suncus etruscus Sus scrofa Tadarida teniotis Vulpes vulpes 1 0 0 0 1 0 1 0 1 0 0 0 1 2 1 1 1 3 1 1 2 2 2 3 1 2 13.0 940.0 58.0 16.5 1200.0 7.5 14.0 300.0 175.0 1.9 49000.0 38.0 6000.0 34.8 7.5 1.0 1.0 7.1 2.0 1.5 81.2 13.4 21.0 4.2 1.0 4.5 Noct Noct Noct Noct Noct Noct Noct Noct Noct Noct Noct Noct Noct S/W S/W S/W S/W S/W S/W S/W Ua S/W S/W S/W S/W S/W Herb Carn Carn Ins Herb Ins Ins Carn Herb Ins Herb Ins Carn No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Terr Terr Fly Fly Terr Fly Fly Terr Terr Terr Terr Fly Terr Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Fast Legend: Abundance: 1 = Rare species; 2 = Common species; 3 = Abundant species (at least during a period of the year). Activity: Diu = diurnal species; Noct = nocturnal species. Habitat preference: Ua = urban areas; Fa = farmlands; Gr = grasslands; S/W = Mediterranean scrubland/woodland; Fw = freshwaters. Food habits: Carn = carnivorous; Ins = insectivorous; Herb = herbivorous. Movement: Terr = terrestrial species; Fly = flying species. Speed: Fast = fast species; Slow = slow species. Metabolic strategy: Ecto = ectotherm species; Endo = endotherm species. The checklist was compiled from the following references: Castroviejo, 1993; Ibáñez et al., 1995; Blanco, 1998a, 1998b; García et al., 2000; Arnold and Ovenden, 2002; Mullarney et al., 2003; Díaz-Paniagua et al., 2005; Ministerio de Medio Ambiente y Medio Rural y Marino, 2007a, 2007b; Salvador and Cassinello, 2010; Salvador and Marco, 2010. The values of predictors for life history analysis were obtained from the bibliography: Valverde, 1958, 1967; Jaksic et al., 1982; Castroviejo, 1993; Ibáñez et al., 1995; Blanco, 1998a, 1998b; Cramp, 1998; Salvador, 1998; García et al., 2000; Arnold and Ovenden, 2002; Díaz-Paniagua et al., 2005; Pérez-Santigosa, 2007; Salvador and Marco, 2010; Salvador and Martínez-Solano, 2010; and also using unpublished data of ESPN. 59 Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Endo Capítulo 1: Atropellos Bibliographic review for the selection of candidate predictors for Life history analyses Abundance: Abundant species can be more road-killed than rare species in birds and mammals (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010; Møller et al., 2011). Size: Small mammals can be more road-killed than large mammals (Caceres, 2011), but medium-sized mammals can be more road-killed than these categories (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010). Small and large passerines can be road-killed with different frequency in different habitats (Ramos et al., 2011). Breding: Juveniles can be more road-killed than adults in reptiles, birds and mammals (Mumme et al., 2000; Ciesiołkiewicz et al., 2006; Conard and Gipson, 2006). Activity: Nocturnal species can be more road-killed than diurnal species (Caceres, 2011; Cook and Blumstein, 2013). Habitat preference: Road-kill frequency can be different in different habitats (Clevenger et al., 2003; Alves da Rosa and Bager, 2012). Food habits: Food habits can affect road-kill probability of birds and mammals (Ford and Fahrig, 2007; Barthelmess and Brooks, 2010; Cook and Blumstein, 2013). Territoriality: Juvenile dispersal has been often associated with road-kill risk in reptiles, mammals and birds, because juvenile individuals have to leave the parental territory, frequently moving through highly anthropized matrix habitats (Bonnet et al., 1999; Massemin et al., 1998; Grilo et al., 2009). For this reason we hypothesized that territorial species can be more road-killed than non-territorial species. Movement: Terrestrial and flying species can be differently affected by road mortality (Teixeira et al., 2013). Speed: Slowness has been suggested to increase road-kill probability of amphibians, reptiles and mammals (Puky, 60 Road-kill patterns in Mediterranean habitats: who, when and where 2005; Laporte et al., 2013). Metabolic strategy: Ectotherms can be more road-killed than endotherms (Puky, 2005; Andrews et al., 2006). Correlations between Life history candidate predictors Table 2: Correlations between continuous candidate predictors. The performed analyses were Pearson correlations between every possible pair of continuous candidate predictors (CORR procedure in SAS). Strong correlations were considered when r > 0.75. Size Breeding Size 1 - Breeding 0.79 1 Table 3: Correlations between categorical candidate predictors. The performed analyses were contingency tables between every possible pair of categorical candidate predictors (FREQ procedure in SAS). When the 50% of dataset cells had expected counts less than 5, the contingency tables could not be performed. In these cases the FREQ procedure provided a Fisher’s exact test (with F in the table). Fisher’s exact test can be performed only for binary predictors, thus in the case in which one of the predictors was not binary, we carried out a GLM (GLIMMIX procedure in SAS, binomial error distribution and logit link function; glm in the table; in the cases of absence of binary predictors we applied poisson error distribution and log link function). Significant variables (p < 0.05) are marked with an asterisk. Abundance Abundance Food habits Territoriality Activity Habitat preference Movement Speed Metabolic strategy - Food habits * 0.04 - Territoriality Activity * 0.02 * 0.048 - 0.60 0.47 0.34 - Habitat preference 0.77 glm 0.88 0.31glm 0.58 glm - Movement * 0.04 0.11 0.07 * < 0.0001 * 0.007 glm - Speed 0.44 glm 0.99 glm * < 0.0001 F * < 0.0001 F * 0.002 glm * < 0.0001 F - 61 Metabolic strategy 0.61 * 0.007 * < 0.0001 * 0.003 * 0.02 glm * < 0.0001 * < 0.0001 F - Capítulo 1: Atropellos Table 4: Correlations between categorical and continuous candidate predictors. P-values of the performed analyses were GLMs between every possible pair of categorical and continuous candidate predictors (GLIMMIX procedure in SAS, binomial error distribution and logit link function; in the cases of absence of binary predictors we applied poisson or negative binomial error distribution and log link function). Significant variables (p < 0.05) are marked with an asterisk. Abundance Food habits Territoriality Activity Habitat preference Movement Speed Metabolic strategy Size * < 0.0001 * < 0.0001 0.11 0.82 * < 0.0001 * 0.01 0.36 0.09 Breeding 0.38 * < 0.0001 0.06 * 0.03 * < 0.0001 * 0.01 * 0.007 * 0.02 References Alves da Rosa, C. and Bager, A. (2012). Seasonality and habitat types affect roadkill of neotropical birds. Journal of Environmental Management. 97: 1-5. Andrews, K.M., Gibbons, J.W. and Jochimsen, D.M. (2006). Literature synthesis of the effects of roads and vehicles on amphibians and reptiles. Federal Highway Administration (FHWA), U.S. Department of Transportation, 151 pp., Washington DC (USA). Barthelmess, E.L. and Brooks, M.S. (2010). The influence of body-size and diet on road-kill trends in mammals. Biodiversity and Conservation. 19: 1611-1629. Arnold, N. and Ovenden, D. (2002). Reptiles y Anfibios. Guía de campo. Todas las especies de España y de Europa [Reptiles and Amphibians. Field guide. All species of Spain and Europe]. 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Italian Journal of Zoology. 78: 379-389. Castroviejo, J. (1993). Memoria del Mapa del Parque Nacional de Doñana [Essay on the Map of Doñana National Park]. Consejo Superior de Investigaciones Cientifícas (CSIC) y Agencia de Medio Ambiente (AMA) de la Junta de Andalucia, 133 pp., Seville (Spain). Ciesiołkiewicz, J., Orłowski, G. and Elżanowski, A. (2006). High juvenile mortality of grass snakes Natrix natrix (L.) on a suburban road. Polish Journal of Ecology. 54: 465-472. Clevenger, A.P., Chruszcz, B. and Gunson, K.E. (2003). Spatial patterns and factors influencing small Vertebrate fauna road-kill aggregations. Biological Conservation. 109: 15-26. 62 Road-kill patterns in Mediterranean habitats: who, when and where Conard, J.M. and Gipson, P.S. (2006). Spatial and seasonal variation in wildlife-vehicle collisions. The Prairie Naturalist. 38: 251-260. Cook, T.C. and Blumstein, D.T. (2013). The omnivore’s dilemma: diet explains variation in vulnerability to vehicle collision mortality. Biological Conservation. 167: 310-315. Cramp, S. (1998). The Complete Birds of the Western Palearctic. Oxford University Press. CD-ROM. Oxford (UK). Díaz-Paniagua, C., Gómez-Rodríguez, C., Portheault, A. and de Vries, W. (2005). Los Anfibios de Doñana [The Amphibians of Doñana]. Organismo Autónomo de Parques Nacionales (Ministerio de Medio Ambiente), 181 pp., Madrid (Spain). Ford, A.T. and Fahrig, L. (2007). Diet and body size of North American mammal road mortalities. Transportation Research Part D: Transport and Environment. 12: 498-505. García, L., Ibáñez, F., Garrido, H., Arroyo, J.L., Máñez, M. and Calderón, J. (2000). Prontuario de las aves de Doñana. Anuario ornitológico de Doñana Nº 0 [Short guide of Doñana Birds. Ornithological Doñana Yearbook Nº 0]. Estación Biológica de Doñana (EBD - CSIC). Ayuntamiento de Almonte, 113 pp., Almonte (Spain). Grilo, C., Bissonette, J. and Santos-Reis, M. (2009). Spatial-temporal patterns in Mediterranean carnivore road casualties: consequences for mitigation. Biological Conservation. 142: 301-313. Ibáñez, C., Guillén, A., Juste, J., Migens, E., Pérez-Jordá, J.L. and Ruiz, C. (1995). Quirópteros del Parque Nacional de Doñana: especies, y tamaño y métodos de estima de sus poblaciones [Chiropterans of Doñana National Park: species, and their population sizes and estimate methods]. Grupo de Investigación de Sistemática y Ecología de Quirópteros (EBD - CSIC), 114 pp., Seville (Spain). Jaksic, F.M., Greene, H.W., Schwenk, K. and Seib, R.L. (1982). Predation upon reptiles in Mediterranean habitats of Chile, Spain and California: a comparative analysis. Oecologia. 53: 152-159. Laporte, M., Silva Beaudry, C.-O. and Angers, B. (2013). Effects of road proximity on genetic diversity and reproductive success of the painted turtle (Chrysemys picta). Conservation Genetics. 14: 21-30. Massemin, S., Le Maho, Y. and Handrich, Y. (1998). Seasonal pattern in age, sex and body condition of barn owls Tyto alba killed on motorways. Ibis. 140: 70-75. Ministerio de Medio Ambiente y Medio Rural y Marino (2007a). Mamíferos [Mammals]: http://www.mma.es/portal/secciones/biodiversidad/inventarios/inb/inventario_vertebrados/mamiferos/ index.htm [online]. In: Inventario Nacional de Biodiversidad, Vertebrados. Madrid (Spain). Ministerio de Medio Ambiente y Medio Rural y Marino (2007). Reptiles [Reptiles]: http://www.mma.es/portal/secciones/biodiversidad/inventarios/inb/inventario_vertebrados/index.htm [online]. In: Inventario Nacional de Biodiversidad, Vertebrados. Madrid (Spain). Møller, A.P., Erritzøe, H. and Erritzøe, J. (2011). A behavioral ecology approach to traffic accidents: interspecific variation in causes of traffic casualties among birds. Zoological Research. 32: 115-127. Mullarney, K., Svensson, L., Zetterström, D. and Grant, P.J. (2003). Guía de Aves. La guía de campo de Aves de España y de Europa más completa [Bird Guide. The most complete Field Guide to the Birds of Spain and Europe]. Ediciones Omega S.A., 400 pp., Barcelona (Spain). Mumme, R.L., Schoech, S.J., Woolfenden, G.E. and Fitzpatrick, J.W. (2000). Life and death in the fast lane: demographic consequences of road mortality in the Florida scrub-jay. Conservation Biology. 14: 501-512. Pérez-Santigosa, N. (2007). Ecología del Galápago exótico, Trachemys scripta elegans, en la Península Ibérica. Efectos sobre las poblaciones autóctonas de Mauremys leprosa y Emys orbicularis [Ecology of the invasive pond turtle, Trachemys scripta elegans, in the Iberian Peninsula. Effects on the autochthonous populations of Mauremys leprosa and Emys orbicularis]. University of Seville (Spain). Puky, M. (2005). Amphibian road kills: a global perspective. In: Proceedings of the 2005 International Conference on Ecology and Transportation (ICOET). Irwin, C.L., Garrett, P. and McDermott, K.P. (Eds.). Center for Transportation and the Environment, North Carolina State University (USA), pp. 325-338. Ramos, C., Pereira de Lima Júnior, D., Zawadzki, C.H. and Benedito, E. (2011). The biology and ecology of birds is an important factor for explain the road kill frequencies? Neotropical Biology and Conservation. 6: 201-212. Salvador, A. and Marco, A. (2010). Reptiles [Reptiles]: http://www.vertebradosibericos.org/reptiles.html [online]. In: Enciclopedia Virtual de los Vertebrados Españoles. Salvador, A. (Ed.). Sociedad de 63 Capítulo 1: Atropellos Amigos del MNCN (SAM) y Museo Nacional de Ciencias Naturales (MNCN - CSIC), Madrid (Spain). Salvador, A. and Martínez-Solano, I. (2010). Anfibios [Amphibians]: http://www.vertebradosibericos.org/anfibios.html [online]. In: Enciclopedia Virtual de los Vertebrados Españoles. Salvador, A. (Ed.). Sociedad de Amigos del MNCN (SAM) y Museo Nacional de Ciencias Naturales (MNCN - CSIC), Madrid (Spain). Teixeira, F.Z., Coelho, I.P., Esperandio, I.B., da Rosa Oliveira, N., Porto Peter, F., Dornelles, S.S., Delazeri, N.R., Tavares, M., Borges Martins, M. and Kindel, A. (2013). Are road-kill hotspots coincident among different vertebrate groups? Oecologia Australis. 17: 36-47. Valverde, J.A. (1958). An ecological sketch of the Coto Doñana. British Birds. 51: 1-23. Valverde, J.A. (1967). Estructura de una comunidad Mediterránea de vertebrados terrestres [Structure of a Mediterranean community of terrestrial vertebrates]. Consejo Superior de Investigaciones Científicas (CSIC), 192 pp., Madrid (Spain). 64 Road-kill patterns in Mediterranean habitats: who, when and where Appendix 2 – Temporal patterns Road-killed species included in Temporal groups Amphibians: Discoglossus galganoi, Epidalea calamita, Pelobates cultripes, Pelophylax perezi, Pleurodeles waltl. Snakes: Coronella girondica, Hemorrhois hippocrepis, Macroprotodon brevis, Malpolon monspessulanus, Natrix maura, Rhinechis scalaris, Vipera latasti. Lizards: Acanthodactylus erythrurus, Podarcis carbonelli, Podarcis vaucheri, Psammodromus manuelae, Tarentola mauritanica, Timon lepidus. Resident birds: Alectoris rufa, Bubo bubo, Buteo buteo, Columba livia domestica, Cyanistes caeruleus, Cyanopica cooki, Emberiza calandra, Fringilla coelebs, Lophophanes cristatus, Lullula arborea, Parus major, Passer domesticus, Passer montanus, Saxicola rubicola, Serinus serinus, Strix aluco, Sylvia melanocephala, Sylvia undata, Troglodytes troglodytes. Migrant nesting birds: Apus pallidus, Caprimulgus ruficollis, Hirundo rustica, Lanius senator. Migrant wintering birds: Erithacus rubecula, Turdus philomelos. Transient migrant birds: Ficedula hypoleuca, Muscicapa striata, Phylloscopus collybita/ibericus, Saxicola rubetra, Sylvia atricapilla, Sylvia cantillans. Small mammals: Apodemus sylvaticus, Crocidura russula, Eliomys quercinus, Mus spretus, Rattus rattus. 65 Capítulo 1: Atropellos Bibliographic review for the selection of candidate predictors for the temporal analysis Photoperiod variation: Temporal distribution of road-kills can vary according to seasonal behaviors (Bonnet et al., 1999; Clevenger et al., 2001; Gryz and Krauze, 2008; Smith-Patten and Patten, 2008) which can be related with photoperiod variation (Fryxell and Sinclair, 1988; Lloyd, 1999; Gwinner and Helm, 2003). Rainfall: Amphibian roadkill peaks can coincide with rainfall (Puky, 2005; Glista et al., 2008). Temperature: Temporal distribution of reptile road-kills can vary according to temperature (Andrews et al., 2006). Seasonal traffic: Temporal distribution of road-kills can vary according to traffic variation (Forman and Alexander, 1998; Trombulak and Frissell, 2000; Forman et al., 2003). Time since previous survey: Temporal distribution of road-kills can be affected by carcass persistence (Antworth et al., 2005; Santos et al., 2011). Correlations between Temporal candidate predictors Table 1: Correlations between continuous candidate predictors. The performed analyses were Pearson correlations between every possible pair of continuous candidate predictors (CORR procedure in SAS). Photoperiod variation Rainfall Temperature Seasonal traffic Time since previous survey 66 Photoperiod variation 1 - Rainfall Temperature -0.29 1 - -0.33 -0.10 1 - Seasonal traffic 0.001 -0.06 0.12 1 - Time since previous survey -0.17 -0.02 0.18 0.06 1 Road-kill patterns in Mediterranean habitats: who, when and where Monthly road-kill patterns for every temporal group Figure 1: Monthly road-kill pattern for amphibians. Casualties per month during the whole study period. Figure 2: Monthly road-kill pattern for snakes. Casualties per month during the whole study period. 67 Capítulo 1: Atropellos Figure 3: Monthly road-kill pattern for lizards. Casualties per month during the whole study period. Figure 4: Monthly road-kill pattern for resident birds. Casualties per month during the whole study period. 68 Road-kill patterns in Mediterranean habitats: who, when and where Figure 5: Monthly road-kill pattern for migrant nesting birds. Casualties per month during the whole study period. Figure 6: Monthly road-kill pattern for migrant wintering birds. Casualties per month during the whole study period. 69 Capítulo 1: Atropellos Figure 7: Monthly road-kill pattern for transient migrant birds. Casualties per month during the whole study period. Figure 8: Monthly road-kill pattern for small mammals. Casualties per month during the whole study period. 70 Road-kill patterns in Mediterranean habitats: who, when and where Factors affecting road-kill temporal patterns Figure 9: Temporal road-kill patterns. Representations with raw data of significant correlations between road-kills and temporal predictors. In the upper two graphs the significant correlations between road-kills and rainfall; in the lower five graphs the significant correlations between road-kills and temperature. 71 Capítulo 1: Atropellos References Andrews, K.M., Gibbons, J.W. and Jochimsen, D.M. (2006). Literature synthesis of the effects of roads and vehicles on amphibians and reptiles. Federal Highway Administration (FHWA), U.S. Department of Transportation, 151 pp., Washington DC (USA). Antworth, R.L., Pike, D.A. and Stevens, E.E. (2005). Hit and run: effects of scavenging on estimates of roadkilled vertebrates. Southeastern Naturalist. 4: 647-656. Bonnet, X., Naulleau, G. and Shine, R. (1999). The dangers of leaving home: dispersal and mortality in snakes. Biological Conservation. 89: 39-50. Clevenger, A.P., McIvor, M., McIvor, D., Chruszcz, B. and Gunson K. (2001). Tiger salamander, Ambystoma tigrinum, movements and mortality on the Trans-Canada highway in southwestern Alberta. Canadian Field-Naturalist. 115: 199-204. Forman, R.T.T. and Alexander, L.E. (1998). Roads and their major ecological effects. Annual Review of Ecology and Systematics. 29: 207-231. Forman, R.T.T., Sperling, D., Bissonette, J., Clevenger, A.P., Cutshall, C.D., Dale, V.H., Fahrig, L., France, R., Goldman, C.R., Heanue, K., Jones, J.A., Swanson, F.J., Turrentine, T. and Winter, T.C. (2003). Road ecology: science and solutions. Island Press, 481 pp., Washington DC (USA). Fryxell, J.M. and Sinclair, A.R.E. (1988). Causes and consequences of migration by large herbivores. Trends in Ecology & Evolution. 3: 237-241. Glista, D.J., DeVault, T.L. and DeWoody, J.A. (2009). A review of mitigation measures for reducing wildlife mortality on roadways. Landscape and Urban Planning. 91: 1-7. Gryz, J. and Krauze, D. (2008). Mortality of vertebrates on a road crossing the Biebrza Valley (NE Poland). European Journal of Wildlife Research. 54: 709-714. Gwinner, E. and Helm, B. (2003). Circannual and circadian contributions to the timing of avian migration. In: Avian Migration. Berthold, P., Gwinner, E. and Sonnenschein, E. (Eds.). Springer Berlin Heidelberg (Germany), pp. 81-95. Lloyd, P. (1999). Rainfall as a breeding stimulus and clutch size determinant in South African arid-zone birds. Ibis. 141: 637-643. Puky, M. (2005). Amphibian road kills: a global perspective. In: Proceedings of the 2005 International Conference on Ecology and Transportation (ICOET). Irwin, C.L., Garrett, P. and McDermott, K.P. (Eds.). Center for Transportation and the Environment, North Carolina State University (USA), pp. 325-338. Santos, S.M., Carvalho, F. and Mira, A. (2011). How long do the dead survive on the road? Carcass persistence probability and implications for road-kill monitoring surveys. PLoS ONE. 6: e25383 [online]. Smith-Patten, B.D. and Patten, M.A. (2008). Diversity, seasonality, and context of mammalian roadkills in the southern Great Plains. Environmental Management. 41: 844-852. Trombulak, S.C. and Frissell, C.A. (2000). Review of ecological effects of roads on terrestrial and aquatic communities. Conservation Biology. 14: 18-30. 72 Road-kill patterns in Mediterranean habitats: who, when and where Appendix 3 – Spatial patterns Road-killed species included in spatial groups Amphibians: Discoglossus galganoi, Epidalea calamita, Pelobates cultripes, Pelophylax perezi, Pleurodeles waltl. Snakes: Coronella girondica, Hemorrhois hippocrepis, Macroprotodon brevis, Malpolon monspessulanus, Natrix maura, Rhinechis scalaris, Vipera latasti. Lizards: Acanthodactylus erythrurus, Podarcis carbonelli, Podarcis vaucheri, Psammodromus manuelae, Tarentola mauritanica, Timon lepidus. Small passerines: Cyanistes caeruleus, Emberiza calandra, Erithacus rubecula, Ficedula hypoleuca, Fringilla coelebs, Lophophanes cristatus, Lullula arborea, Muscicapa striata, Parus major, Passer domesticus, Passer montanus, Phylloscopus collybita/ibericus, Saxicola rubetra, Saxicola rubicola, Serinus serinus, Strix aluco, Sylvia atricapilla, Sylvia cantillans, Sylvia melanocephala, Sylvia undata, Troglodytes troglodytes. Small mammals: Apodemus sylvaticus, Crocidura russula, Eliomys quercinus, Mus spretus, Rattus rattus. Bibliographic review for the selection of candidate predictors for the spatial analyses Macrohabitat: Road-kill frequency can be different in different habitats (Andrews et al., 2006; Alves da Rosa and Bager, 2012; Clevenger et al., 2003). Distance to water: Amphibians can be mainly road-killed close to water bodies (Trombulak and Frissell, 2000; Puky, 2005). Verge habitat: Road-kill frequency can be 73 Capítulo 1: Atropellos different in correspondence to different road verge habitats (Saeki and Macdonald, 2004; Orlowski, 2008; Meek, 2009). Fences: Road-kill frequency can decrease in correspondence to road-fences (Aresco, 2005; Bager and Fontoura, 2013). Culverts: Road-kill frequency can decrease in correspondence to drainage culverts (Clevenger et al., 2001; Aresco, 2005; Dodd et al., 2004). Crossing structures: Road-kill frequency can decrease in correspondence to wildlife road-crossing structures (Clevenger and Waltho, 2000; Grilo et al., 2008; Mata et al., 2008). Road width: Road-kill frequency can decrease or increase according to road width increase (Forman et al., 2003; SmithPatten and Patten, 2008; van Langevelde et al., 2009). Traffic: Road-kill frequency can decrease or increase according to traffic increase (Forman et al., 2003; Coelho et al., 2008; van Langevelde et al., 2009). Speed: Road-kill frequency can decrease or increase according to the increase of maximum allowed speed (Forman et al., 2003; Hobday and Minstrell, 2008; van Langevelde et al., 2009). Shoulders: Road-kill frequency can increase in correspondence to road-shoulders (Ashley and Robinson, 1996; Tanner and Perry, 2007). Curves: Road-kill frequency can decrease or increase in correspondence to curves (Jones, 2000; Forman et al., 2003; Snow et al., 2011). Slope changes: Roadkill frequency can increase in correspondence to slope changes in road (Forman et al., 2003; Snow et al., 2011). Vertical signs: Road-kill frequency can decrease in correspondence to road signs (Hobday and Minstrell, 2008; Glista et al., 2009). Horizontal signs: Road-kill frequency can decrease in correspondence to road signs (Hobday and Minstrell, 2008; Glista et al., 2009). 74 Road-kill patterns in Mediterranean habitats: who, when and where Correlations between Spatial candidate predictors Table 1: Correlations between continuous candidate predictors. The analyses were Pearson correlations between every possible pair of continuous candidate predictors regarding road and landscape (CORR procedure in SAS). Correlations above r > 0.75 are marked with an asterisk. Road width Traffic Speed Distance from water Road width 1 - Traffic Speed * 0.99 1 - * 0.89 * 0.89 1 - Distance to water 0.19 0.14 0.01 1 Table 2: Correlations between categorical candidate predictors regarding road. The performed analyses were contingency tables between every possible pair of categorical candidate predictors regarding road (FREQ procedure in SAS). When 50% of dataset cells had expected counts below 5, the contingency tables could not be performed. In these cases the FREQ procedure provided a Fisher’s exact test (with F in the table). Figures are p-values, marked with an asterisk when p < 0.05. Shoulder Shoulder Slope changes Curves Vertical signs Horizontal signs - Slope changes * 0.03 - Curves 0.66 0.25 F - Vertical signs * 0.01 0.25 F 0.47 - Horizontal signs * < 0.0001 0.26 F * 0.003 * < 0.0001 - 75 Capítulo 1: Atropellos Table 3: Correlations between categorical candidate predictors regarding road verges. The analyses were contingency tables between every possible pair of categorical candidate predictors regarding road verges (FREQ procedure in SAS). When 50% of dataset cells had expected counts below 5, the contingency tables could not be performed. In these cases the FREQ procedure provided a Fisher’s exact test (with F in the table). Figures are p-values, marked with an asterisk when p < 0.05. Verge habitat Fences Culverts Crossing structures Verge habitat - Fences Culverts * < 0.0001 - * 0.03 * 0.009 - Crossing structures * 0.0007 * 0.0004 0.35 F - Table 4: Correlations between categorical candidate predictors regarding road and road verges. The performed analyses were contingency tables between every possible pair of categorical candidate predictors regarding road and road verges (FREQ procedure in SAS). When 50% of dataset cells had expected counts less than 5, the contingency tables could not be performed. In these cases the FREQ procedure provided a Fisher’s exact test (with F in the table). Fisher’s exact test can be performed only for binary predictors, thus in the case in which one of the predictors was not binary, we carried out a GLM (GLIMMIX procedure in SAS, binomial error distribution and logit link function; glm in the table). Figures are p-values, marked with an asterisk when p < 0.05. Shoulder Verge habitat Fences Culverts Crossing structures 76 * < 0.0001 * < 0.0001 * 0.04 * 0.0024 Slope changes * 0.01 glm 0.23 0.33 F * 0.0001 Curves * 0.02 0.19 0.13 F * 0.03 F Vertical signs 0.34 * < 0.0001 0.23 * 0.02 Horizontal signs * 0.0007 * < 0.0001 0.18 F 0.21 F Road-kill patterns in Mediterranean habitats: who, when and where Table 5: Correlations between categorical candidate predictors regarding road, road verges and landscape. The performed analyses were contingency tables between every possible pair of categorical candidate predictors regarding road, roadside and landscape (FREQ procedure in SAS). When 50% of dataset cells had expected counts less than 5, the contingency tables could not be performed. In these cases the FREQ procedure provided a Fisher’s exact test. Fisher’s exact test can be performed only for binary predictors, thus in the case in which one of the predictors was not binary, we carried out a GLM (GLIMMIX procedure in SAS, binomial error distribution and logit link function; glm in the table). Figures are p-values, marked with an asterisk when p < 0.05. Shoulder Slope changes Curves Vertical signs Horizontal signs Verge habitat Fences Culverts Crossing structures Macrohabitat * < 0.0001 * 0.06 glm * < 0.0001 * < 0.0001 * < 0.0001 * < 0.0001 * < 0.0001 * 0.004 * 0.39 glm Table 6: Correlations between categorical and continuous candidate predictors regarding road, road verges and landscape. The performed analyses were GLMs between every possible pair of categorical and continuous candidate predictors regarding road, road verges and landscape (GLIMMIX procedure in SAS, binomial error distribution and logit link function; in the cases of absence of binary predictors we applied poisson error distribution and log link function). Figures are p-values, marked with an asterisk when p < 0.05. Shoulder Slope changes Curves Vertical signs Horizontal signs Verge habitat Fences Culverts Crossing structures Macrohabitat Road width * < 0.0001 * 0.04 0.15 * 0.0003 * < 0.0001 * < 0.0001 * < 0.0001 * < 0.0001 0.47 * < 0.0001 Traffic Speed * < 0.0001 * 0.04 0.08 * 0.001 * < 0.0001 * < 0.0001 * < 0.0001 * < 0.0001 0.53 * < 0.0001 * < 0.0001 * 0.02 0.51 * < 0.0001 * < 0.0001 * < 0.0001 0.22 * < 0.0001 0.23 * < 0.0001 Distance to water * < 0.0001 0.43 * < 0.0001 0.90 * < 0.0001 * < 0.0001 * < 0.0001 0.10 0.87 * < 0.0001 77 Capítulo 1: Atropellos Road-kill distribution for every spatial group Figure 1: Road-kill distribution for amphibians. Black lines represent the paved road-network. The surveyed roads (regional road A494 Matalascañas-Mazagón, forestry/agricultural road of Cabezudos, forestry/agricultural road A483-Hinojos, and forestry/agricultural road A483-Villamanrique de la Condesa) were divided into 50-m long sections in which we sampled the road-kills. These sections are showed in this figure with different width, according with the number of road-killed amphibians accumulated during the study (range 0-20). 78 Road-kill patterns in Mediterranean habitats: who, when and where Figure 2: Road-kill distribution for snakes. Black lines represent the paved road-network. The surveyed roads (regional road A494 Matalascañas-Mazagón, forestry/agricultural road of Cabezudos, forestry/agricultural road A483-Hinojos, and forestry/agricultural road A483-Villamanrique de la Condesa) were divided into 50-m long sections in which we sampled the road-kills. These sections are showed in this figure with different width, according with the number of road-killed snakes accumulated during the study (range 0-4). 79 Capítulo 1: Atropellos Figure 3: Road-kill distribution for lizards. Black lines represent the paved road-network. The surveyed roads (regional road A494 Matalascañas-Mazagón, forestry/agricultural road of Cabezudos, forestry/agricultural road A483-Hinojos, and forestry/agricultural road A483-Villamanrique de la Condesa) were divided into 50-m long sections in which we sampled the road-kills. These sections are showed in this figure with different width, according with the number of road-killed lizards accumulated during the study (range 0-4). 80 Road-kill patterns in Mediterranean habitats: who, when and where Figure 4: Road-kill distribution for small passerines. Black lines represent the paved road-network. The surveyed roads (regional road A494 Matalascañas-Mazagón, forestry/agricultural road of Cabezudos, forestry/agricultural road A483-Hinojos, and forestry/agricultural road A483-Villamanrique de la Condesa) were divided into 50-m long sections in which we sampled the road-kills. These sections are showed in this figure with different width, according with the number of road-killed small passerines accumulated during the study (range 0-4). 81 Capítulo 1: Atropellos Figure 5: Road-kill distribution for small mammals. Black lines represent the paved road-network. The surveyed roads (regional road A494 Matalascañas-Mazagón, forestry/agricultural road of Cabezudos, forestry/agricultural road A483-Hinojos, and forestry/agricultural road A483-Villamanrique de la Condesa) were divided into 50-m long sections in which we sampled the road-kills. These sections are showed in this figure with different width, according with the number of road-killed small mammals accumulated during the study (range 0-3). 82 Road-kill patterns in Mediterranean habitats: who, when and where Factors affecting road-kill spatial patterns. Figure 6: Road-kill spatial distribution and road magnitude. Representations with raw data of significant correlations between road-kills and road magnitude. Road magnitude range corresponds with 5-10 m of Road width (average = 8.79 m), 171-2499 car/day of Traffic (average = 1547 car/day) and 30100 Km/h of Speed (average = 78.69 Km/h). References Alves da Rosa, C. and Bager, A. (2012). Seasonality and habitat types affect roadkill of neotropical birds. Journal of Environmental Management. 97: 1-5. Andrews, K.M., Gibbons, J.W. and Jochimsen, D.M. (2006). Literature synthesis of the effects of roads and vehicles on amphibians and reptiles. Federal Highway Administration (FHWA), U.S. Department of Transportation, 151 pp., Washington DC (USA). Ashley, E.P. and Robinson, J.T. (1996). Road mortality of amphibians, reptiles and other wildlife on the Long Point Causeway, Lake Erie, Ontario. Canadian Field-Naturalist. 110: 404-412. Aresco, M.J. (2005). Mitigation measures to reduce highway mortality of turtles and other herpetofauna at a north Florida lake. Journal of Wildlife Management. 69: 549-560. Bager, A. and Fontoura, V. (2013). Evaluation of the effectiveness of a wildlife roadkill mitigation system in wetland habitat. Ecological Engineering. 53: 31-38. Clevenger, A.P., Chruszcz, B. and Gunson, K. (2001). Drainage culverts as habitat linkages and factors affecting passage by mammals. Journal of Applied Ecology. 38: 1340-1349. Clevenger, A.P., Chruszcz, B. and Gunson, K.E. (2003). Spatial patterns and factors influencing small Vertebrate fauna road-kill aggregations. Biological Conservation. 109: 15-26. 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The impact of heterogeneous road-networks on protected areas. a Estación Biológica de Doñana CSIC, Seville (Spain). b Université Claude-Bernard Lyon 1, Villeurbanne (France). How much traffic is too much? The impact of heterogeneous road-networks on protected areas Resumen: El efecto barrera es un impacto que afecta muchas poblaciones animales y puede ser causado por una respuesta comportamental a carreteras (evitación de superficies viarias o espacios vacíos), a las molestias asociadas (evitación de tráfico) o a vehículos circulantes (evitación de vehículos). Muchos estudios han descrito efecto barrera a lo largo de carreteras principales, pero pocos de ellos han investigado sus causas, como por ejemplo el tráfico o la superficie viaria. Con este propósito medimos el efecto barrera a lo largo de una red viaria heterogénea (i.e. con variación de tráfico y superficie viaria) y además estimamos el consecuente deterioro de hábitat dentro de una emblemática área protegida, la Reserva de la Biosfera de Doñana (España). Nuestras especies de estudio fueron el ciervo Cervus elaphus y el jabalí Sus scrofa, y muestreamos su presencia a lo largo de transectos de 200 m que empezaban en carreteras de referencia (con más de 10 coches diarios) y que frecuentemente intersecaban caminos sin pavimentar. La probabilidad de presencia de jabalíes fue afectada por la mera distancia a la carretera más cercana (en la mayoría de casos caminos sin pavimentación ni tráfico), y los ciervos sufrieron también la cercanía a carreteras de referencias. El efecto barrera debido a la carretera más cercana no puede estar relacionado ni con la superficie viaria ni con el tráfico asociado, y sugiere por primera vez una evitación de espacios vacíos que podría estar basada en la asociación entre estructuras lineares y la posibilidad de vehículos transitando. La consecuencia a nivel de paisaje es una reducción de la probabilidad de presencia del 32% por ciervos y del 45% por jabalíes. Este deterioro de hábitat reduce el valor de un área protegida para nuestras especies de estudio. Por esta razón sugerimos el establecimiento de grandes 87 Capítulo 2: Efecto Barrera áreas sin estructuras lineares, a través de la optimización de la red viaria, especialmente dentro de áreas protegidas, con potenciales efectos positivos a nivel de ecosistema. Abstract: The barrier effect is a road impact which affects several animal populations and can be caused by a behavioral response to roads (surface or gap avoidance), associated disturbances (traffic avoidance) or a circulating vehicle (vehicle avoidance). Most past studies described barrier effect along major roads, but only few of them investigated the causes, such as for example traffic volume or road surface. With this purpose we measured the barrier effect along a heterogeneous road-network (i.e. with variation in traffic volume or road surface), and we also estimated the consequent loss of habitat quality within an emblematic protected area, Doñana Biosphere Reserve (Spain). Our study species were the red deer Cervus elaphus and the wild boar Sus scrofa, and we surveyed their presence of along 200-m transects starting from reference roads (i.e. with more than 10 cars/day) and frequently intersecting unpaved minor roads. The presence probability of wild boars was affected by the mere distance to the nearest road (in most of cases unpaved road without traffic), and red deer also suffered the nearness of reference roads. The barrier effect due to the nearest road cannot be related to road surface or associated traffic, but suggests for the first time a gap avoidance which may be based on the association between linear infrastructures and the possibility of vehicles transiting along them. The consequence at landscape level is a reduction in presence probability of 32% for deer and 45% for wild boar. This decrease of habitat quality affects the conservation value of an emblematic protected area for the selected model species. For this reason we suggest the establishment of large roadless areas through the road-network optimization, especially within protected areas, with potential positive effects for the whole ecosystems. 88 How much traffic is too much? The impact of heterogeneous road-networks on protected areas Introduction Cross a road or avoid it. This individual choice can entail two kinds of impact for animal populations: road-kill or barrier effect, respectively (Forman et al., 2003; Grilo et al., 2012). The barrier effect has been defined as the consequence of a behavioral response towards the road itself (road avoidance), the associated disturbances (traffic avoidance) or a circulating vehicle (vehicle avoidance; Jaeger et al., 2005; McGregor et al., 2008). Road avoidance may depend on the surface material (surface avoidance; McGregor et al., 2008; Brehme et al., 2013) or the artificial clearance in the vegetation (gap avoidance; Oxley et al., 1974; Ford and Fahrig, 2008), while the traffic avoidance may be associated with road-related emissions and other long-ranging disturbances (e.g.: noise, vibrations, lights, dust; Mader, 1984; Reijnen et al., 1995; Forman and Alexander, 1998; Kaseloo, 2005). Traffic avoidance can also results from changes in plant or animal communities due to clearance or treatment of road verges (Oxley et al., 1974; Underhill and Angold, 1999). Finally, vehicle avoidance is a short-range behavioral response of species having enough cognitive ability and movement capacity to avoid being road-killed (Jaeger et al., 2005; Fahrig and Rytwinski, 2009). All these barrier effects typically arise within the road-effect zone, with an extent determined by the features of both road and surrounding environment (Forman and Alexander, 1998; Forman and Deblinger, 2000). Nevertheless, in the last century 89 Capítulo 2: Efecto Barrera road-networks have undergone a synergic development, which has caused the increase and overlap of road-effect zones (Forman, 2000; Jaarsma and Willems, 2002). As a result, the individual behavioral responses towards roads producing the barrier effect can affect animal populations and even species, especially if their distribution is limited to industrialized areas (i.e. with widespread and dense road-networks). The current road-networks indeed fragment and deteriorate natural habitats (Andrews, 1990; Findlay and Houlahan, 1997), imposing a filter on animal movement (Kramer-Schadt et al., 2004; McDonald and St. Clair, 2004b). Large and connected populations become then fragmented into small isolated ones (Vos and Chardon, 1998; Nellemann et al., 2001), with a potential impact on their viability (Mumme et al., 2000; Lesbarrères et al., 2003). Gene flow between isolated populations may also decrease (Keller and Largiadèr, 2003; Riley et al., 2006), entailing a long-term threat to their genetic diversity (Lesbarrères et al., 2006; Holderegger and Di Giulio, 2010). Finally, those isolated populations will also have a lower colonization probability from other areas (Jaeger et al., 2005; Eigenbrod et al., 2008). Barrier effect is one of the best studied road-related impacts (Trombulak and Frissell, 2000; Forman et al., 2003). Nevertheless, there are few studies determining the factors behind the negative response of a given animal species towards certain road features, such as road type or traffic volume. Typical barrier effect studies are usually conducted along a heavily transited major road or highway, making difficult to separate road avoidance from traffic avoidance (McGregor et al., 2008). This distinction could be highlighted by measuring the barrier effect along a heterogeneous road-network, with enough variation in road types (e.g. paved and unpaved roads) and traffic volumes. 90 How much traffic is too much? The impact of heterogeneous road-networks on protected areas Unfortunately, unpaved roads are comparatively less studied than paved ones, although the former are most widespread within natural and protected areas. This entails a potential threat for wildlife, because most species are more abundant in natural areas, and they may be then mostly affected by unpaved roads. The main objective of the present study is to determine for the first time the barrier effect impact of a whole road-network, estimating the related loss of potential habitat quality for a given species. We selected a road-network within a natural protected area, Doñana National and Natural Parks (Spain). This emblematic Biosphere Reserve has a heterogeneous road-network which might facilitate the distinction between road avoidance and traffic avoidances, a distinction of paramount importance to plan mitigation of road impacts (Jaeger et al., 2005). With the aim to optimize the possibilities to detect barrier effects, we selected two ungulates as model species, because ungulates are susceptible to human disturbances (Frid and Dill, 2002; Stankowich, 2008), including road-related ones (Alexander et al., 2005; Ito et al., 2013). These species were the red deer Cervus elaphus Linnaeus, 1758 (which is frequently described as negatively affected by major roads and associated traffic; Gagnon et al., 2007b; St. Clair and Forrest, 2009) and the wild boar Sus scrofa Linnaeus, 1758 (usually less affected by human disturbance; Frantz et al., 2012). The consideration for the first time of a heterogeneous road-network should provide a more complete overview of road-related barrier effects after controlling for 91 Capítulo 2: Efecto Barrera the environmental factors potentially affecting the probability of presence (such as pasture or water availability). The main benefit will be a realistic approach to estimate the loss in habitat quality imposed by the presence of roads, with the consequent reduction in the conservation value of the selected protected area for the two model species. Materials and Methods Study area The fieldwork was carried out within Doñana National and Natural Parks (southwestern Spain: 36º59’ N, 6º26’ W), having the National Park a higher protection degree (Figure 1). Doñana is an assemblage of protected areas characterized by Mediterranean climate, with a mosaic of urban, rural and natural environments, including scrubland, dunes and wetlands. Several regulated human activities are allowed within these protected areas (e.g. cattle breeding, beekeeping, pine-cone harvesting, tourism, research, etc.), aided by the widespread road-network present within the protected area (Figure 1). This is characterized by different road types and traffic volumes, ranging from highly travelled highways to isolated unpaved roads (Román et al., 2010a). 92 How much traffic is too much? The impact of heterogeneous road-networks on protected areas Figure 1: Study area. Into the left section the National Park is in dark grey and the Natural Park is in light grey. In the main section the solid thin lines represent the road-network, and the dashed lines are the reference roads. Black circles indicate the locations of transects. On the right we represent a transect; the arrows A and B represent two examples of the explanatory variables denominated distance to the nearest road and distance to the nearest reference road, respectively. Data collection Red deer and wild boar are present over the whole study area. In the autumn of 2009 (September and October), we randomly established 40 transects 200-m-long and perpendicular to and starting from a reference road (Figure 1), which we defined as 93 Capítulo 2: Efecto Barrera roads with a traffic volume above 10 vehicles per day (Román et al., 2010b). Each transect was subdivided into 20 sections, each 10-m-long (Figure 1). By definition, and given the dense road-network present, transects could intersect with minor roads (i.e. roads with low traffic intensity; Figure 1). We determined the presence of both study species within each transect section by walking along the transect line and georeferring the pellets found within a 1-m-wide buffer. In the case of deer, we only considered those aggregations with more than 10 pellets. We characterized the sections with the distance to the nearest road and distance to the nearest reference road measured from the center of every section; and the traffic volume of the nearest reference road (Table 1; Figure 1). The available road-map was updated using 2007 aerial orthophotos and ground validated in the spring of 2009. Table 1: Factors potentially affecting deer and wild boar presence probabilities. Averages and range values of the potential explanatory variables considered in the analyses. Traffic volume is expressed as average daily cars (ADC). The level of protection of Natural Park is lower than National Park. Variable Environmental Road-related Average (+ SE) Range 9438 (+ 7745) 289-23844 distance to nearest water-body (m) 379 (+ 553) 0-2954 distance to nearest shrub-patch (m) protection level 556 (+ 770) 0-2509 National Park / Natural Park distance to wetland (m) distance to the nearest road (m) 63 (+ 51) 0-197 distance to the nearest reference road (m) 99 (+ 58) 5-197 1018 (+ 2091) 11-6800 traffic volume of the nearest reference road (ADC) 94 How much traffic is too much? The impact of heterogeneous road-networks on protected areas The traffic volume of the nearest reference road was calculated using underground magnetometers (TRAFx Vehicle Counter Generation III; www.trafx.net) deployed during the survey (autumn 2009). Each reference road was sampled during two complete non-consecutive weeks. Traffic in minor roads was always below one car per day. The presence of both species is mediated by habitat quality. We controlled for environmental variables potentially affecting this quality by characterizing transect sections with variables describing pasture availability, which in Doñana is mediated by the ground water level (distance to wetland and distance to nearest water-body; Table 1) and by the presence and proximity to the protection offered by thick vegetation (distance to the nearest shrub-patch; Table 1). Within every section we determined the corresponding values for these environmental variables using 2007 aerial orthophotos. The protection level potentially affects presence and abundance because hunting is allowed in the Natural Park (Table 1). Data analysis We used generalized linear mixed models (GLMM) and evaluated their performance using Akaike information criterion (AIC). For each model the response variable was the presence/absence of the target species within each transect section, 95 Capítulo 2: Efecto Barrera considering the identification code of each transect as a random factor. We used a binomial error distribution and a logit link function. We considered different hypotheses, depending on the assumed impact of roads (Table 2). First, we considered the model with only environmental variables as the null model explaining the presence of both species. The rest of the models contained always the environmental variables. We considered the nearest road hypothesis (model adding the distance to the nearest road), the nearest road reference hypothesis (two models, one including distance to the nearest reference road and another including distance to the nearest reference road plus traffic volume in that reference road), and a combination of previously explained hypothesis (two models, one including both the distance to the nearest road and the distance to the nearest reference road, and the full model; Table 2). We selected the most supported model using AIC, and calculated Akaike weights (wAIC) to estimate the relative support for every given model (ranging from 0 to 1, with larger numbers indicating greater support; Burnham and Anderson 2002). We selected as plausible those models with ΔAIC < 2 (Burnham et al. 2011). Finally, we used the best supported model to calculate the road-network impact on the study area conservation value for each species. We previously measured within every pixel (with a 10 m resolution) of the study area map the values corresponding to each explanatory variable. For each pixel we then calculated deer and wild boar presence probabilities considering both environmental and traffic variables, applying the equation of the best supported model for every study species, respectively. As a result, we obtained two presence probability maps (both of them considering traffic effects): the first for deer and the second for wild boar. We then repeated the presence 96 How much traffic is too much? The impact of heterogeneous road-networks on protected areas probability calculation but replacing within every pixel the values corresponding to each traffic variable with the value assuming there was not road/traffic effect (i.e. a pixel with a distance from the nearest road or reference road corresponding to 200 m). Also in this case we obtained two presence probability maps (both of them without traffic effects) one species. For every map we calculated the overall presence probabilities as a proxy of habitat quality for deer and wild boar, respectively over the entire study area (sum of the presence probability for all pixels over the study area). Finally, for each species we calculated the reduction in habitat quality imposed by the road-network on both species, by obtaining the percentage reduction in habitat quality. Results During the whole survey we detected deer and wild boar excrements in 194 and 30 transect sections, respectively (i.e. 24.25% and 3.75% of the 800 surveyed transect sections). On a wider scale, 32 and 12 transects had at least one transect section with evidences of deer or wild boar presence, respectively (i.e. 80% and 30% of the 40 surveyed transects). Regarding to model selections, for each species there were two supported models according to the ΔAIC < 2 provision (Table 2). In the case of the deer, both supported models belonged to the hypothesis combining the distance to the nearest road and the distance to the nearest reference road (88% of wAIC), highlighting the relevance of these explanatory variables when considered together. The distance to the 97 Capítulo 2: Efecto Barrera nearest road appears to be more relevant than the distance to the nearest reference road, because the only model belonging to the nearest road hypothesis was at least ten times more supported than any model of the nearest reference road hypothesis. The traffic volume of the nearest reference road should be a less relevant explanatory variable, since the addition of this factor to a model decreased the ΔAIC in both of cases. Concerning the wild boar, the first ranked model belonged to the nearest road hypothesis and it was almost three times more supported than the second ranked model, which included both distance to the nearest road and distance to the nearest reference road (Table 2). On the other hand, the distance to the nearest reference road appears to be a marginally relevant explanatory variable, since the models of the nearest reference road hypothesis had not support. 98 How much traffic is too much? The impact of heterogeneous road-networks on protected areas Table 2: Factors affecting red deer and wild boar presence probabilities within each transect section, model ranks by AIC weights (wAIC). In the Model column, the abbreviation env refers to the four environmental variables designated as distance to wetland, distance to nearest water-body, distance to the nearest shrub-patch and protection level; while the abbreviations road, reference and traffic concern the three road-related variables designated as distance to the nearest road, distance to the nearest reference road and traffic volume of the nearest reference road, respectively. See Table 1 for a description of explanatory variables. ΔAIC is the relative difference of a given AIC value compared to the smallest AIC value. The supported models (ΔAIC < 2) are highlighted in grey. AIC weights indicate the relative support for every model (the weights of all the models in the candidate set have the sum of 1). Deer Evidence ratio (ER) is the ratio of wAIC, comparing the best supported model with every competing one. ΔAIC wAIC Rank env 764.8 40.0 0.00 6 env + road env + reference road env + reference road + traffic env + road + reference road env + road + reference road + traffic env env + road env + reference road env + reference road + traffic env + road + reference road env + road + reference road + traffic 727.1 731.9 733.0 723.6 724.8 251.2 248.8 252.7 252.7 250.7 252.0 3.5 8.3 9.4 0.0 1.2 2.4 0.0 3.9 3.9 1.9 3.2 0.09 0.02 0.01 0.57 0.31 0.14 0.46 0.07 0.07 0.18 0.08 3 4 5 1 2 3 1 4 4 2 6 Model Null Nearest road Reference road Combination Wild boar AIC Hypothesis Null Nearest road Reference road Combination ER 888459835 .8 5.8 62.2 110.0 1.0 1.8 3.4 1.0 7.1 7.1 2.6 5.0 Both deer and wild boar presence probabilities increased according to the distance to the nearest road and the distance to the nearest reference road, considering all the best supported models (Table 3; Figure 2). In the case of deer, the presence probability decreased according to the traffic volume of the nearest reference road (Table 3). 99 Capítulo 2: Efecto Barrera Figure 2: Distance to the nearest road. The relation between presence probability and distance to the nearest road, for both deer (on the left) and wild boar (on the right). Dotted lines represent upper and lower standard errors. Table 3: Factors affecting red deer and wild boar presence probabilities within each transect section, parameter estimates for supported models. In the Parameter estimates columns, values in parentheses are standard errors (+ SE). Wild boar Deer Explanatory variables 100 Intercept distance to wetland distance to nearest water-body distance to nearest shrub-patch protection level distance to the nearest road distance to the nearest reference road traffic volume of the nearest reference road Intercept distance to wetland distance to nearest water-body distance to nearest shrub-patch protection level distance to the nearest road distance to the nearest reference road traffic volume of the nearest reference road Variable categories National Park Natural Park National Park Natural Park First ranked model Second ranked model Parameter estimates (+ SE) - 1.511 (+ 0.394) - 0.009 (+ 0.003) - 0.064 (+ 0.032) - 0.043 (+ 0.018) 0.619 (+ 0.324) 0 0.771 (+ 0.248) 0.516 (+ 0.217) - 3.151 (+ 0.736) - 0.001 (+ 0.004) - 0.156 (+ 0.037) - 0.078 (+ 0.045) 0.155 (+ 0.673) 0 0.739 (+ 0.319) - - 1.462 (+ 0.397) - 0.009 (+ 0.003) - 0.045 (+ 0.037) - 0.044 (+ 0.018) 0.723 (+ 0.344) 0 0.768 (+ 0.242) 0.521 (+ 0.215) - 0.048 (+ 0.054) - 3.065 (+ 0.781) - 0.001 (+ 0.005) - 0.152 (+ 0.082) - 0.075 (+ 0.045) 0.128 (+ 0.673) 0 0.854 (+ 0.502) 0.145 (+ 0.483) - How much traffic is too much? The impact of heterogeneous road-networks on protected areas According to model selection, the presence probability maps were obtained through the best supported models (in the case of the deer including both to the distance to the nearest road and the distance to the nearest reference road, while for the wild boar only the first variable). For each species, we used the environmental presence probability map as a reference of habitat quality, in comparison to the map including road-related variables (Figures 3 and 4). The percentage reduction in habitat quality was 32% for deer and 45% for wild boar, respectively. 101 Capítulo 2: Efecto Barrera Figure 3: Deer presence probability maps. On the left the deer presence probability map including only environmental variables, on the right the map regarding the best supported model including road-related variables (see Tables 2 and 3). 102 How much traffic is too much? The impact of heterogeneous road-networks on protected areas Figure 4: Wild boar presence probability maps. On the left the wild boar presence probability map including only environmental variables, on the right the map regarding the best supported model including road-related variables (see Tables 2 and 3). Discussion In the present study we showed the effects of a heterogeneous road-network on the presence probabilities of two model species within a protected area. We observed that, even when considering the environmental factors potentially influencing the spatial behavior of both red deer and wild boar, the presence of a heterogeneous road-network can determine a relevant loss of habitat quality at landscape level, with the consequent reduction in the conservation value of the selected area. Our results showed that deer and wild boar presence probabilities increased according to the mere distance from the nearest road, which in the most of cases refers to unpaved road virtually without traffic. Concerning this variable we could therefore exclude that the study species were suffering traffic avoidance or road avoidance depending on road surface, focusing then on road avoidance due to the artificial clearance in matrix habitat (i.e. gap avoidance). This kind of barrier effect has been previously observed for the red deer (Rost and Bailey, 1979; Meisingset et al., 2013; also in our study area: Suárez-Esteban et al., 2013a), although the authors did not discuss the subjacent mechanisms potentially responsible for it; while for wild boar this is the first reference. The most well-known cases of gap avoidance are in birds and gliding mammals eluding artificial clearances in the forest canopy (Laurance, 2004; van der Ree et al., 2010). Artificial gap avoidance is usually interpreted as an anti-predator 103 Capítulo 2: Efecto Barrera behavior (Shepard et al., 2008; Tremblay and St. Clair, 2009), similarly to the barrier effect produced by a natural clearance in the matrix habitat (Bélisle et al., 2001; Lees and Peres, 2009). In densely vegetated habitats, this anti-predator behavior should be limited to narrow road-effect zones virtually coincident with the linear infra-structures, contrarily to gap avoidance we detected in red deer and wild boar which affected wide areas. This difference probably occurs because Doñana road-network mainly spreads across open habitats (Román et al., 2010c), which vegetation cannot provide the suitable anti-predator cover for large ungulates (Stankowich, 2008). As a consequence, our study species preferentially used the core areas of the patches, as far as possible from the artificial gaps in the matrix habitat. Both red deer and wild boar instinctively avoids humans and vehicles (Gagnon et al., 2007b; Marini et al., 2009; Rogala et al., 2011), and predation attempts consistently amplify the perception of risk in prey species (Stankowich and Blumstein, 2005). These ungulates are a long-lived species with relatively high cognitive capacities (Kloppers et al., 2005; Morelle et al., 2014), and for this reason they could be able to associate humans and vehicles with linear infrastructures. This mechanism might have been also promoted by hunters in the past and poachers in the present, considering that hunting and poaching pressure in natural areas usually spread through and from the road-network (Robinson et al., 1999; Papaioannou and Kati, 2007). Therefore, the gap avoidance we observed in red deer and wild boars might be for the first time classified as prolonged traffic avoidance, based on the association between linear infra-structures and the memory (and related possibility) of vehicle transit along them. The distance from the nearest reference road also affected presence probabilities just as expected, but mainly for the red deer, which has been widely described avoiding 104 How much traffic is too much? The impact of heterogeneous road-networks on protected areas vehicular traffic (Gagnon et al., 2007a, 2007b; St. Clair and Forrest, 2009). Both the model species were then simultaneously affected by the usual traffic avoidance and the prolonged one we suggested above. Both kinds of barrier effect detected in the present study were synergically determining the habitat fragmentation and loss we depicted in the presence probability maps. The environmental maps showed that almost the whole considered protected area could provide good presence probabilities for both deer and wild boars, with a relative importance of the area protection level for deer. Nevertheless since we included road effects the best areas suffered extreme fragmentation, while the previous low quality areas became essentially unavailable. As a consequence, the habitat quality at landscape level decreased almost one-third and one-half for deer and wild boar, respectively. Similarly to main part of large ungulates, red deer and wild boars require sizeable areas to establish viable home ranges (Carranza et al., 1991; Massei et al., 1997). In some cases these areas can include linear infra-structures (Frantz et al., 2012; Meisingset et al., 2013), but it has been widely shown that road fragmentation involves a decrease of habitat quality, with negative consequences on behavior (Guangshun et al., 2006; Ciuti et al., 2012), physiology (Millspaugh et al., 2001; Gasparik et al., 2012), reproduction (Phillips and Alldredge, 2000) and survival (Malo et al., 2004; McCorquodale et al., 2011). Although both red deer and wild boar are globally considered as Least Concern species, the conservation of several of their subspecies and populations are explicitly proclaimed by international legislations and also recommended by scientific literature (Lovari et al., 2008; Oliver and Leus, 2008). On the other hand, the prolonged traffic 105 Capítulo 2: Efecto Barrera avoidance suggested by the present study can probably affect most of ungulate species, which typically show relevant sensitivity to road-related disturbances (Stankowich, 2008; Ito et al., 2013). Mitigation measures should be implemented not only along highways, but also along unpaved road-networks, especially within national and natural parks which ideally should be the source areas for these species. The most suitable mitigation measure for heterogeneous road-networks is their optimization through the decommissioning of high-risk and redundant linear infra-structures (see for example: Switalski et al., 2004; Forman, 2005; Dolan and Whelan, 2007), just as previously suggested for our study area (Román et al., 2010d). This kind of actions or even the establishment of large roadless areas have been already shown to benefit a wide range of species from mammals to plants (Chen and Roberts, 2008; Grant et al., 2011; Switalski and Nelson, 2011). Unfortunately, in most of cases a road-network optimization cannot decommission paved roads, although their negative impacts on ungulates are proven (Stankowich, 2008; Huijser et al., 2009). Despite this, past studies showed that some ungulate species can be able to cross major roads, for example by avoiding traffic rush hours or in presence of extreme foraging or mating motivation (Baofa et al., 2006; Meisingset et al., 2013). Several of these species may also present individual variation in traffic tolerance (Clevenger and Waltho, 2000; Papouchis et al., 2001) and linear infra-structures would represent more a filter than an impenetrable barrier for them, just as previously suggested for other mammals and also birds (Alexander et al., 2005; Jones and Pickvance, 2013). The purpose of possible mitigation measures should be to enhance the permeability of this filter, attempting to not increase at the same time the road-kill probability, which can threaten both ungulate populations (Groot Bruinderink 106 How much traffic is too much? The impact of heterogeneous road-networks on protected areas and Hazebroek, 1996) and human safety, and also imply high economic costs (Conover et al., 1995; Bissonette et al., 2008). The most suitable mitigation measure in this case is the simultaneous provision of a network of wildlife crossing structures supported by road-exclusion fences (Clevenger et al., 2001; Olsson and Widen 2008). Concerning ungulates, crossing structures showed in past studies a mutable effectiveness, from high (Clevenger and Waltho, 2000; 2005; Mata et al., 2008) to relatively low (Rodríguez et al., 1996; Mata et al., 2005). Most of efficient crossing structures were wildlife overpasses (also called eco-ducts or land bridges: Van Wieren and Worm, 2001; Clevenger and Waltho, 2005), which also represent the best solution to improve the landscape connectivity for a large range of other species (McDonald and St. Clair, 2004a; Bond and Jones, 2008). On the other hand, even these efficient crossing structures should be appropriately designed and managed with the purpose to assure their effectiveness. For example, traffic noise and other emissions in the overpass proximities should be properly eliminated or at least reduced (Reijnen et al., 1997; Glista et al., 2009), and the presence of natural vegetation connecting the crossing structure and the surrounding natural habitats should be favored depending on the target species (Clevenger and Waltho, 2005; Jones et al., 2011). Several ungulate species have been defined as keystone species and ecosystem engineer in most part of their distribution range, because of their potential impact on plant species assemblage (Côté et al., 2004; Sandom et al., 2013) and consequences on ecological communities (McShea and Rappole, 2000; Ripple and Larsen, 2000). Therefore, we should consider that the road-network is not only limiting ungulate 107 Capítulo 2: Efecto Barrera presence probabilities and related conservation threats, but also has consequences on the whole ecosystem. For example, in our case study the extreme road-network development through the scrubland areas can probably exacerbates the ungulate habitat selection towards the highly productive ecotone with the wetlands, favoring the overgrazing of these areas (Rogers and Myers, 1980; Soriguer, 1983) and the possible consequences on other species. Another example recently described in Doñana showed that the lack of ungulate herbivory along more travelled unpaved roads is one of the factors facilitating the establishment of fleshy-fruit shrubs (Suárez-Esteban et al., 2013b). This is the first study in which the barrier effect was analyzed along a heterogeneous road-network, and this innovative approach revealed an undescribed category of road avoidance: the prolonged traffic avoidance due to the recognition of linear infra-structures as possibility of motorized transit. We suggest that this type of road avoidance is occurring for red deer and wild boar, but it could affect a large number of species, at least the long-lived ones having high cognitive capacities. For this reason we recommended to mitigate the impact of unpaved road-networks through their optimization, especially within protected areas, with potential positive effects for the whole ecosystem. Acknowledgements This study was funded by the project 2008X0963 (Andalusian Ministry of Environment; Regional Government of Andalusia) and Land-Rover España S.A. kindly 108 How much traffic is too much? The impact of heterogeneous road-networks on protected areas lent two vehicles. MD was supported by JAE-PRE fellowship from CSIC, SP by ICTSRBD project (ICTS-2009-39). Andrea Barón helped with fieldwork and traffic model development; Sofía Conradi-Fernández provided logistic support. 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Effects of habitat fragmentation and road density on the distribution pattern of the moor frog Rana arvalis. Journal of Applied Ecology. 35: 44-56. 114 How much traffic is too much? The impact of heterogeneous road-networks on protected areas 115 Capítulo 3: Medidas de mitigación Comparación de muestreos genéricos y específicos: la eficiencia de diferentes pasos de fauna para micromamíferos. ______________________________________________________________________ D'Amico, M. a, Clevenger, A.P. b, Román, J. a and Revilla, E. a (Manuscript under second review). General versus specific surveys: estimating the effectiveness of different road-crossing structures for small mammals. a Estación Biológica de Doñana CSIC, Seville (Spain). b Western Transportation Institute, Bozeman (USA). General vs specific surveys: the effectiveness of different road-crossing structures for small mammals Resumen: La eficiencia de pasos de fauna se ha controlado menos para micromamíferos que para especies emblemáticas. Además, a causa de las dificultades en la identificación de rastros de micromamíferos, normalmente se realizan muestreos genéricos sin determinación específica. Considerando que las proximidades de las carreteras a menudo están ocupadas por ambientes degradados, nuestra hipótesis es que los tradicionales muestreos genéricos puedan estar sesgados, representando principalmente las especies generalistas de hábitat en detrimento de las especialistas. Por esta razón, comparamos un muestreo genérico con uno específico, seleccionando tres especies modelo: una generalista (Peromyscus maniculatus), una especialista de bosque (Myodes gapperi) y una de pradera (Microtus pennsylvanicus). Muestreamos en cuatro tipos de pasos de fauna (ecoductos, puentes, a sección elíptica y a sección cuadrangular) en el Parque Nacional Banff, Canadá. Posicionamos trampas de huellas a lo largo de los pasos, y como controles también delante de sus entradas y en los bosques colindantes. Como hipotetizamos, el muestreo genérico no detectó diferencias en la ocurrencia de micromamíferos entre pasos de fauna y controles. En cambio, el específico evidenció que la única especie que usaba consistentemente los pasos era la generalista, seleccionando mayoritariamente los ecoductos (que además fueron los únicos pasos usados por las especialistas). Entonces la eficiencia de los pasos de fauna para micromamíferos especialistas podría ser sobrestimada, con implicaciones para su conservación. Como posible solución, sugerimos mejorar el hábitat (por lo menos la disponibilidad de cobertura) a lo largo de los pasos y en el espacio entre estos y los ambientes colindantes. Estudios que como este investiguen la eficacia de los métodos de muestreo tradicionales, son útiles por la correcta interpretación de la eficiencia de nuestros esfuerzos para la conservación, con el propósito último de optimizarlos. 117 Capítulo 3: Medidas de mitigación Abstract: The use of wildlife road-crossing structures (WCS hereafter) is less monitored for small mammals than for emblematic species. Furthermore, due to the objective difficulties in small mammal track identification, most the authors usually carry out general surveys without species recognition. Considering that road proximities are often characterized by degraded environments, our hypothesis was that traditional general surveys may be biased, mainly representing habitat generalist species in detriment to specialist ones. For that, we compared a general small mammal survey with a species-specific one, selecting three study species: one habitat generalist (North American deer mouse Peromyscus maniculatus), one forest specialist (southern redbacked vole Myodes gapperi) and one prairie specialist (meadow vole Microtus pennsylvanicus). Our study was carried out along four types of WCS (overpasses, openspan underpasses, and both elliptical and box culverts), within Banff National Park, Canada. We positioned footprint track-tubes along the WCS, and also as a control in front of their entrances and within the surrounding woodlands. As we suggested, using the traditional general survey we did not detect significant differences in small mammal presence among WCS and controls. Instead, species-specific surveys showed that the only species consistently using the WCS was the generalist one, mainly preferring overpasses (which also were the only WCS used by specialist species). In most cases the inadequacy of WCS for specialist small mammals could therefore be underestimated, with serious conservation implications. As a possible solution, we suggest to improve the habitat (or at least the cover availability) along the WCS and in the space between them and surrounding environments. Studies like the present, verifying traditional survey methods, are useful for the correct interpretation of the effectiveness of our conservation efforts, with the final aim to optimize them. 118 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals Introduction Several studies in the last decades have shown that motorized traffic can negatively affect animal populations; road-kill mortality and barrier effect being the most documented impacts (Forman and Alexander, 1998; Trombulak and Frissell, 2000; Forman et al., 2003). Barrier effect may have especially relevant consequences at population level, limiting animal movements and in some cases gene flow (Merriam et al., 1989; Gerlach and Musolf, 2000; Keller and Largiadèr, 2003; Riley et al., 2006). Wildlife road-crossing structures (WCS) are commonly used to reduce roadrelated mortality and increase landscape connectivity for animal populations (van der Ree et al., 2007; Glista et al., 2009); however, their effectiveness may vary among taxa (Rodríguez et al., 1996; Clevenger and Waltho, 2005). Nevertheless, in most cases the efficacy of WCS has been explored for large mammals (Foster and Humphrey, 1995; Clevenger and Waltho, 2005; Grilo et al., 2008), where small mammals have received less attention (Porto Peter et al., 2013). Furthermore, the objective difficulties in surveying small mammal WCS use cause many authors to consider them as a category, without species identification (Rodríguez et al., 1996; Mata et al., 2003, 2005, 2007; Ascensão and Mira, 2007). Conversely, different species often show distinct habitat selection, territory structure and movement patterns, and all of them can influence species-specific WCS effectiveness. In our opinion, this ambiguity can arise where habitat generalist species are sympatric with specialist ones. Indeed, habitat generalists should be more likely to use 119 Capítulo 3: Medidas de mitigación WCS, which in most cases are characterized by degraded environments typical of road proximities (Umetsu and Pardini, 2007; Freitas et al., 2012). In this context, a traditional general survey of small mammal presence (i.e., without species identification) might only represent the habitat of generalist species, to the detriment of specialist ones, in some cases with relevant conservation implications. With the aim to test this hypothesis, we chose a study system in which are coexisting in sympatry a forest specialist species (southern red-backed vole Myodes gapperi, hereafter referred to as red-backed vole; see Merritt, 1981) with a prairie specialist species (meadow vole Microtus pennsylvanicus; Reich, 1981) and a generalist species (the North American deer mouse Peromyscus maniculatus, hereafter referred to as deer mouse; Baker, 1968; Wywialowski, 1987). In a general survey of WCS effectiveness without species identification, the presence of the ubiquitous deer mouse could hide the absence of the two habitat specialist voles. Indeed, previous studies actually showed that when translocating these three species to the other side of a highway, only few individuals returned to their own territory, and almost all were deer mice (McDonald and St Clair, 2004b). These translocations generated to territorial adults an extreme motivation to cross back the road, but the homing ratios and the WCS efficacy were relatively low anyway, particularly for the two specialist species (McDonald and St Clair, 2004a, 2004b). Therefore, we suggest that WCS use in a natural context (without homing behavior due to translocation) could be even lower than recorded up until now, mainly involving deer mice and almost non-existent for the two vole species. 120 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals The purpose of the present study is therefore to verify whether traditional surveys of WCS efficacy without species identification are actually an appropriate method to sample the whole category of small mammals, or if they are only estimating the use by more generalist species. In addition, we tested whether the suitability of this type of survey depends on the type of WCS, with the aim to better understand which WCS feature can improve across-road connectivity for the habitat specialist species. Materials and Methods Study area Banff National Park (51º15’ N, 115º30º W) is situated in Alberta (Canada), along the Bow River Valley (within Canadian Rocky Mountains, elevation range: 13003400 m). The valley floor (always below 2000 m of elevation) is characterized by the presence of the Trans-Canada Highway (Figure 1). This is the major transportation corridor through Banff and Yoho National Parks, with a traffic volume relatively high for the region (an average of 17970 vehicles per day in 2008 and increasing 2.5% per year: Highway Service Centre, Parks Canada, unpublished data). WCS were built along the Trans-Canada Highway, the first 27 km (Phases 1 and 2) were completed in 1988, the next 18 km (Phase 3A) in 1997, the final 30 km (Phase 3B) are nearly finished. The climate of the park is continental with relatively long winters and short summers (Janz and Storr, 1977), and the vegetation encompasses montane, subalpine and alpine ecoregions (Achuff & Corns 1983; Holland and Coen, 1983). The transportation corridor traverses the montane ecoregion, mostly characterized by 121 Capítulo 3: Medidas de mitigación coniferous forests (Douglas fir Pseudotsuga menziesii, Canadian white spruce Picea glauca, lodgepole pine Pinus contorta, and American quaking aspen Populus tremuloides) and natural grasslands (Achuff & Corns 1983; Holland and Coen, 1983). Deer mice and red-backed voles are the most abundant rodents in Banff National Park (Holland and Coen, 1983), meadow voles are also present (McDonald and St Clair, 2004a, 2004b). Figure 1: Study area map, with locations of the surveyed WCS (overpasses, open-span bridge passes, elliptical culverts and box culverts). 122 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals Data collection During September and October 2010, we sampled the presence/absence of the three study species within and adjacent to four different types of WCS. We surveyed two 50-m-wide wildlife overpasses, two open-span bridge underpasses (ca. 3 m high, 11 m wide), three elliptical metal culverts (ca. 4 m high, 7 m wide) and five concrete box culverts (2.6 m high, 3.2 m wide; Figure 2) along the Phases 1, 2 and 3A of the TransCanada Highway (Figure 1). Overpass habitat consists of sparse young trees, shrubs and open grassland, whereas the wildlife underpasses are void of vegetation. There was no abrupt habitat edge between overpass habitat and surrounding woodland, in contrast with the wildlife underpasses which have grassland verge habitat (Figure 2). Surveys were carried out using track-plates (Mayer, 1957; Zielinski and Truex, 1995; Clevenger et al., 2001). Every track-plate consisted of a tube of 30 cm in length and 10 cm in diameter, with a sooted metal sheet as floor (Figure 2). We disposed these track-tubes as sample lines along every surveyed WCS and also in their respective controls (Figure 2). Along each sample line, we placed one track-tube every 10 m. The WCS sample lines were composed by five track-tubes (50 m length), while the control sample lines had four track-tubes (40 m length). Within every surveyed WCS we placed two sample lines, one for each WCS side. The first control sample lines coincided to the WCS entrances, where they were disposed in parallel to the highway and perpendicularly to the WCS. In correspondence to overpasses, the entrance controls 123 Capítulo 3: Medidas de mitigación were characterized by the same habitat of the WCS, whereas in underpasses and culverts these controls were located along grassland verge habitat. The second control sample lines were placed inside the contiguous woodland, one of them in each side of the highway, 50 m away from the WCS. Also the woodland controls were positioned parallel to the highway and perpendicularly to the WCS (Figure 2). The survey period consisted of two continuous weeks for every WCS (concentrated during September-October 2010), that is enough time for our study species to get used to track-tubes (Nams and Gillis, 2003). The track-tubes were checked only at the end of the survey period. Species-specific track identification was carried out according to bibliography (Murie, 1974; Elbroch, 2003; Kays and Wilson, 2009). Furthermore, we also trapped some individuals for every study species, with the aim to analyze in visu their footprints on our track-tubes. 124 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals Figure 2: Surveyed WCS types: 1) overpass; 2) open-span bridge pass; 3) elliptical culvert; 4) box culvert. Tree symbols represents the woodland, while grass symbols correspond to roadside grasslands. Circles represent WCS track-tubes (black circles along the overpass, grey circles within underpasses). Rhombus represent both control track-tubes (WCS entrances and surrounding woodland). 125 Capítulo 3: Medidas de mitigación Data analysis We performed two analyses based on the presence/absence of footprints within the track-tubes. In the first analysis we verified whether small mammals used the WCS, while in the second one we analyzed the small mammal WCS preferences. In both cases, we firstly considered the small mammals as a whole category without species identification, and afterwards we separated them by species. The first analysis (i.e. WCS use) was constituted by generalized linear mixed models (GLMMs; McCullagh and Nelder, 1989) with binomial error distribution and logit link function. In the first GLMM, the response variable was small mammal presence/absence in every given track-tube (without species identification). The only explanatory variable was the location of the track-tube, constituted by three categories: WCS, entrance control or woodland control. The random variable was the identification code of every WCS. In the following species-specific three models of WCS use, the response variables were deer mouse, red-backed vole and meadow vole presence/absence in every given track-tube, respectively. The explanatory and random variables were the same of the previous general model. The second analysis (i.e. WCS preference) was also constituted by GLMMs with binomial error distribution and logit link function. In the first GLMM, the response variable was small mammal presence/absence in every given WCS track-tube (without species identification). The explanatory variables were WCS type (four categories: 126 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals overpass, open-span, elliptical culvert or box culvert) and track-tube continuous distance from the centre of the WCS. We used the identification code of every WCS as a random variable. In the following species-specific models of WCS preference, we only considered the species that effectively used the WCS. The response variable was the species presence/absence in every given WCS track-tube. The explanatory and random variables were the same of the previous general model. Results In the 85% of track-tubes (within WCS and both controls; n = 310) we detected the presence of at least one of our three study species. In 28 track-tubes we found the simultaneous presence of two species. Deer mice were the most commonly detected species (60% of total captures, present in 56% of track-tubes), followed by red-backed vole (25% and 23%) and meadow vole (16% and 15%). In 86% of WCS track-tubes (n = 119) we detected small mammal tracks (without species identification). Both controls showed similar small mammal occurrence, since we detected footprints in 83% of WCS entrance track-tubes (n = 95) and 84% in the woodland track-tubes (n = 96). Indeed, we did not find significant differences in small mammal presence among WCS and both controls (parameter estimates and standard errors for WCS, WCS entrances and woodland: 1.78 + 0.38; 1.69 + 0.39; 1.61 + 0.29; Figure 3). 127 Capítulo 3: Medidas de mitigación Focusing on single species WCS use, we found that 98% of small mammal occurrences within WCS were deer mice. This species occurred in 84% of WCS tracktubes, but it was also widely recorded in both controls, being detected in 76% of WCS entrance track-tubes and 33% of woodland track-tubes. The specific GLMM showed higher presence probability along WCS track-tubes than in control track-tubes (parameter estimates and standard errors for WCS, WCS entrances and woodland: 1.65 + 0.33; -0.25 + 0.30; -0.69 + 0.22; F = 27.33; p < 0.001; Figure 3). Red-backed vole scarcely used the WCS, representing only 2% of WCS small mammal records. It was present only in 6% of WCS entrance track-tubes. Nevertheless, red-backed vole occurrence along woodland controls was high, amounting to 66% of track-tubes. Indeed, the specific GLMM showed that red-backed voles used more frequently woodland track-tubes than other ones (parameter estimates and standard errors for WCS, WCS entrances and woodland: -3.86 + 0.65; -2.89 + 0.50; 0.71 + 0.32; F = 42.02; p < 0.001; Figure 3). Finally, for meadow vole we only detected one record in WCS track-tubes. On the other hand, this species occurred in 46% of WCS entrance track-tubes and there were not records on woodlands. Its specific GLMM confirmed than WCS entrance track-tubes were significantly more used compared with other ones (parameter estimates and standard errors for WCS, WCS entrances and woodland: -4.91 + 1.42; -0.19 + 1.03; -4.70 + 1.04; F = 19.19; p < 0.001; Figure 3). 128 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals Figure 3: General and species-specific small mammal presence probability among WCS and controls. Results from the WCS use analysis. We detected small mammals at all WCS (without species identification). All WCS types were highly used, amounting the small mammal presence to 85% along overpass track-tubes, 75% in open-span underpasses and respectively 90% and 87% within elliptical and box culverts (Figure 4). In fact, into the second analysis, the general GLMM confirmed the lack of differences in small mammal occurrence among different WCS types (parameter estimates and standard errors for overpasses, bridges, elliptical culverts and box culverts: 1.27 + 0.73; 0.57 + 0.82; 0.84 + 0.82; 1.13 + 0.83; Figure 4). The distance from the centre of the WCS did not affect small mammal occurrence. 129 Capítulo 3: Medidas de mitigación Figure 4: General and species-specific small mammal presence probability among different WCS types. Columns represent the percentage of used track-tubes: white columns correspond to the whole category of small mammals without species identification, lighter grey columns to deer mouse, darker grey columns to red-backed vole, and black columns to meadow vole. Least mean squares with error bars represent presence probabilities resulting from the WCS preference analysis, only for the categories with enough data (i.e. small mammals and deer mice). Focusing on single species, the deer mouse commonly used WCS, with presence proportions of 75% in track-tubes of both overpasses and bridges, 90% in elliptical culverts and 87% in box culverts (Figure 4). Indeed, the specific GLMM did not show significant differences among WCS types (parameter estimates and standard errors for overpasses, bridges, elliptical culverts and box culverts: 0.61 + 0.64; 0.56 + 0.74; -0.47 130 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals + 0.71; -0.18 + 0.75; Figure 4). Additionally, the distance from the centre of the WCS did not affect the deer mouse presence. Concerning both red-backed and meadow voles, all their scarce WCS records (3 and 1 for 119 WCS track-tubes, respectively) regarded overpasses (Figure 4), but they were insufficient to statistically estimate their specific WCS preferences. Discussion Our results highlighted strong discrepancies between general and specific surveys of WCS effectiveness for small mammals, questioning the suitability of the former method. Indeed, our first analysis series showed that surveying without species identification we could infer that small mammals widely used WCS, without significant differences compared with controls. Nevertheless, as a result of the specific models, we found that the only species actually using the WCS was the habitat generalist deer mouse. This species commonly uses both wildlife overpasses and underpasses (Clevenger et al., 2001; McDonald and St Clair, 2004a; Meaney et al., 2007), and past studies also recorded its relative indifference towards road disturbances (Yale-Conrey and Mills, 2001; McDonald and St Clair, 2004b; and also see for the similar whitefooted mouse Peromyscus leucopus: Rytwinski and Fahrig, 2007; McGregor et al., 2008). Furthermore, the deer mouse is a nocturnal species, and therefore its activity usually coincides with the hours of lower traffic volume and consequent disturbance (Goosem, 2002; McDonald and St Clair, 2004a; McGregor et al., 2008). 131 Capítulo 3: Medidas de mitigación Our analyses also showed significantly more deer mouse presence within WCS than along controls, but also past studies on this species found an apparent preference towards degraded habitats compared with more conserved ones (Yale-Conrey and Mills, 2001; Bissonette and Rosa, 2009; Brehme et al., 2013). In our case this finding could be consequence of interspecific displacement by dominant red-backed and meadow voles (Grant, 1971; Bowker and Pearson, 1975; Crowell and Pimm, 1976), which were widely present along surrounding woodlands and roadside grasslands. Other factors could also explain this result, such as for example the relative low densities of predators along roads, compared with more natural habitats (Lovallo and Anderson, 1996; Riley et al., 2006; van Langevelde et al., 2009). The generalist deer mouse could then actively select these road-related degraded areas, in a trade-off context between habitat quality and predation risk (Rytwinski and Fahrig, 2007, 2013; Planillo and Malo, 2013). The effectiveness of WCS for red-backed and meadow voles was then extremely low, because both of these species were virtually absent along them, despite being frequently found along contiguous habitats (woodlands and roadside grasslands, respectively). As we hypothesized, their WCS use in this natural context was even lower than recorded in previous studies which entailed translocations of individuals (McDonald and St Clair, 2004a). The main cause of such recorded barrier effect should be the well-known road-associated disturbances (Oxley et al., 1974; Kozel and Fleharty 1979; Mader, 1984), also amplified in the case of the meadow vole because its circadian activity includes daily hours, which coincide with higher traffic intensity (McDonald and St Clair, 2004a, 2004b). Both vole species usually tend to avoid the road surface, including translocated territorial adults with a consequent extreme motivation to homing 132 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals behavior (Yale-Conrey and Mills, 2001; McDonald and St Clair, 2004a, 2004b). On the other hand, this road avoidance could also depend to a more general rejection towards unattractive habitats (Nams and Gillis, 1998; Witt and Huntly, 2001; Russell et al., 2007). Our findings also confirm the already described potential importance of roadside grasslands as habitat for meadow vole (Getz et al., 1978; Kirsch, 1997), especially in heavily forested landscapes just as our study area (Clevenger et al., 2001). More in general, this result is an additional evidence of the potential relevance of verge habitats for small mammals (Bellamy et al., 2000; Ascensão et al., 2012; Ruiz-Capillas et al., 2013). Effectiveness surveys without species identification would errantly suggest WCS suitability for all the small mammals, but we found significant differences between species, being the more generalist one (i.e. the deer mouse) the only actually using the WCS. Such misinterpretation produces an overestimation of WCS use by habitat specialist species, as observed in our study concerning red-backed and meadow voles. The consequences can entail obvious conservation implications, in the case of endangered or keystone species. For example, some authors consider the red-backed vole as a forest keystone species for its role dispersing woodland ectomycorrhizal spores (Terwilliger and Pastor, 1999; Cook and MacDonald, 2001). In this context, the long term viability of its fragmented populations could be threatened by the barrier effect caused by roads and associated traffic (Browne and Ferree, 2007), with possible repercussions on the ecosystem health (Cook and MacDonald, 2001). All that considered, we want to highlight the necessity to improve WCS effectiveness for habitat 133 Capítulo 3: Medidas de mitigación specialist species, with special regard to endangered and keystone ones, especially across heavily fragmented landscapes. The improvement of WCS efficacy principally depends on the understanding of specific preferences regarding these infra-structures. In our study, the only WCS type used by red-backed and meadow voles was the wildlife overpass. Previous published experiments showed that our study species preferred underpasses in comparison to overpasses, possibly because the latter ones at that time, recently constructed, were scarcely vegetated (McDonald and St Clair, 2004a). These species selected then smaller underpasses in comparison with larger ones, probably because the former can be perceived as safer from predators (Rodriguez et al., 1996; McDonald and St Clair, 2004a). In our study the wildlife overpasses were already characterized by a mosaic of young woodland and grasslands, which for both habitat specialist voles potentially represents a matrix environment with the suitable vegetative cover to minimize the perception of predation risk (Merritt, 1981; Reich, 1981). We suggest that translocated voles probably would return to their territories through the shorter way or the safer one (in the study of McDonald and St Clair, 2004a it was the wildlife underpasses); while in a more natural context without translocations, they would choose to cross the highway only in correspondence to suitable conditions (in our study provided by vegetated wildlife overpasses). Our findings suggest the importance of species-specific habitat quality and principally of vegetative cover in determine the occurrence of habitat specialist rodents along WCS. The first inference should be encouraging the establishment of overpasses 134 General vs specific surveys: the effectiveness of different road-crossing structures for small mammals as privileged WCS building choice, when local topography make it possible; especially considering the efficacy of such infra-structures also for other road-sensitive species (Clevenger and Waltho, 2005; Bond and Jones, 2008). On the other hand, wildlife underpasses are comparatively much more widespread than overpasses, and a strategy focused to improve their effectiveness for small mammals would considerably optimize the landscape connectivity for these species. A possible suggestion in this sense can be the improvement of underpass habitat quality, providing dead woods and brushes as artificial cover (Clevenger et al. 2001, McDonald and St Clair, 2004a). Finally, another complementary way to improve both overpass and underpass attractiveness for small mammals can be to avoid the vegetation cutting at the entrance of the WCS, decreasing then predation risk perception while approaching the WCS (Rodriguez et al., 1996; Clevenger et al., 2001; McDonald and St Clair, 2004a). All these possible solutions have been often suggested in the past; nevertheless their organic application is rarely carried out. For this reason we suggest to associate them to experimental studies focused on validate their effectiveness and the actual improvement of the WCS. The present study highlights the unsuitability of WCS effectiveness surveys for small mammals without species identification, showing that they may be strongly biased towards more generalist species. Therefore, we resolutely recommend the application of species-specific surveys. Concerning our case of study, we also discussed the reasons of WCS inefficiency for two habitat specialist species (i.e. red-backed and meadow vole), and consequently we suggested several possible solutions. This kind of studies, entailing the verification of traditional survey methods, can help in the correct 135 Capítulo 3: Medidas de mitigación interpretation of the effectiveness of our conservation efforts, with the final aim to optimize them. Acknowledgements This research was partially supported by the JAE-PRE PhD stay fellowship granted to MD by the CSIC. 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Motor traffic as a stressor eliciting parental care in birds Resumen: Algunas aves acuden a carreteras para la búsqueda de alimento y lugares de nidificación, pero el estrés asociado al tráfico puede amenazar su éxito individual. Entender estos procesos puede ser relevante dentro de áreas protegidas, donde el poco tráfico debido a actividades de conservación puede paradójicamente transformar hábitats adecuados en trampas ecológicas. En este estudio averiguamos si los abejarucos Merops apiaster que crían en las áreas laterales de las vías perciben el tráfico como un elemento de estrés que estimule alarmas, estampidas y un decremento de cuidados parentales, o si alternativamente demuestran habituación. Comparamos individuos sometidos a diferentes niveles de tráfico dentro de una Reserva de la Biosfera en España. Los marcados cambios diarios y semanales en niveles de tránsito proporcionaban un diseño cuasi-experimental para discernir los efectos del tráfico per se. Alarmas y estampidas fueron mayores en zonas de tráfico intenso, donde también se detectaron atropellos. Los abejarucos perciben el tránsito como un elemento de estrés y son incapaces de modular respuestas de evitación de riesgo. Contrariamente a nuestras predicciones las tasas de ceba parentales fueron mayores en carreteras de tránsito elevado, aumentando durante días laborales y con picos en horas punta, y aumentando potencialmente el éxito reproductivo de los abejarucos. Por otro lado, hasta que no se determinen las consecuencias netas sobre salud individual, niveles de estrés y eficacia biológica, se deberían mantener los actuales niveles de tráfico y reducir los límites de velocidad (en la temporada de cría) para disminuir la mortalidad. Debido a que los resultados esperados pueden parecer opuestos si monitoreados durante el apareamiento o la cría, aconsejamos cautela a la hora de inferir eficacia biológica a partir de comportamientos aislados o de determinados estadios del ciclo vital de una especie. 141 Capítulo 4: Reproducción y comportamiento Abstract: Several avian species are attracted to roads for foraging or nesting, but individual performance can be jeopardized by exposure to traffic. Understanding the effects of traffic can be relevant within protected areas, where a relative low level of transit allowing conservation-oriented activities could paradoxically transform suitable habitats into ecological traps. Here we questioned whether roadside breeding bee-eaters Merops apiaster perceive traffic as a stressor eliciting predation-avoidance behaviors and decreasing parental care, or they alternatively show habituation. Comparisons were established between birds exposed to contrasting traffic levels within a Biosphere Reserve in Spain. The marked daily and weekly changes in road use provided a quasiexperimental design to disentangle the effects of traffic per se from other sources of variability. Alarm calls and risk-avoidance behaviors were related to traffic intensity, being considerably higher in breeding colonies beside high-traffic roads compared to controls. Road-kills were only detected in colonies exposed to high traffic. Bee-eaters perceive traffic as a stressor and were unable to down-modulate risk-avoidance responses. Contrary to our predictions parental feeding rates were highest along hightraffic roads, increasing during weekdays and peaking during traffic rush hours. Traffic positively affects nesting feeding rates, potentially improving the breeding performance. Until the net consequences of traffic on individual health, stress levels and fitness are determined, managers are encouraged to maintain the present transit levels and reduce speed limits (at least during breeding season) to minimize mortality. Because the expected outcome of traffic on performance can be opposed when responses are monitored during mating or brooding, caution should be taken before inferring fitness consequences from isolated behaviors or life history sub-stages. 142 Motor traffic as a stressor eliciting parental care in birds Introduction The development of motor vehicles and the associated expansion of road networks throughout the last century have largely contributed to the dramatic increase of anthropogenic impacts currently affecting most habitats worldwide (Trombulak and Frissell, 2000; Forman et al., 2003). Among other effects, roads and motor traffic often result in increased wildlife casualties (Clevenger et al., 2003; Coelho and Kindel, 2008) to the extent of changing demographic parameters (McLellan and Shackleton, 1988; Ferreras et al., 1992), facilitate the introduction of exotic species (Mortensen et al., 2009; D'Amico et al., 2013) and reduce the availability of suitable habitats through barrier effects and fragmentation (Vos and Chardon, 1998; McGregor et al., 2008), thereby reducing biodiversity (Findlay and Bourdages, 2000; Blasco et al., 2008). However, wild birds and also other vertebrates can show contrasting behavioral responses to roads and motor traffic. While some species display behavioral plasticity in response to this kind of human disturbance (e.g. progressive reduction of flushing distances; Stankowich and Blumstein, 2005; Rees et al., 2005) and may even benefit from traffic (Peris and Pescador, 2004; Fahrig and Rytwinski, 2009), others show disrupted activity patterns and use of space (Bautista et al., 2004; Bennett et al., 2013), sexual and territorial behavior (Bee and Swanson, 2007; Dalal et al., 2011), and parental care (McGowan and Simons, 2006; Schroeder et al., 2012), as well as elevated stress levels (Crino et al., 2011; Strasser and Heath, 2013), potentially reducing individual fitness and long-term population viability (Frid and Dill, 2002; Gibbs and Shriver, 2002). 143 Capítulo 4: Reproducción y comportamiento A major goal of the global Biosphere Reserve network is the preservation of biodiversity, but many reserves are affected by the impacts of roads and associated traffic (e.g. Ramp Wilson and Croft, 2006; Coelho and Kindel, 2008; Velando and Munilla, 2011). Even if public access is regulated or restricted, management and research activities often involve the transit of motor vehicles within the reserves. This provides grounds for a paradox that has been rarely tested: that conservation-related road traffic can actually become a perturbation potentially jeopardizing the preservation of biodiversity. Management of human disturbance thus requires a prior understanding of the mechanisms by which anthropogenic stressors promote changes in individual behavior, and how they may affect population distribution and abundance (Boyle and Samson, 1985; Sorice et al., 2006). Here, we test whether moderate levels of road traffic associated to management and research activities within a Biosphere Reserve modify the behavior of roadside breeding birds. As study model we used a population of European bee-eaters Merops apiaster Linnaeus, 1758, a burrow-nesting insectivorous bird, present in Doñana National Park and MAB Biosphere Reserve (Spain). The particularities of this bird population offer a unique model to study the effects of traffic on wildlife behavior for several reasons. First, because the flat landscape lacks natural soil cliffs (typically selected by bee-eaters for nesting elsewhere; Casas-Crivillé and Valera, 2005; Smalley et al., 2013), birds readily dig their burrows in the slight slopes of the edges and ditches of unpaved roads (thus literally breeding beside vehicle transit areas). Second, because access to 144 Motor traffic as a stressor eliciting parental care in birds most areas within the National park is restricted to authorized park staff and researchers, our model is suitable for assessing the specific impact of scientific and management activities within a Biosphere Reserve. Finally, because recent studies have revealed high variability in the spatial and temporal dynamics of road transit within the study area (Revilla et al., 2010) we can (i) perform an a priori selection of sampling locations exposed to contrasting vehicle transit levels, and also (ii) perform an a priori, quasiexperimental sampling design aimed at comparing behaviors within and between days differing in traffic intensity (e.g. weekdays vs weekends; rush vs post-rush traffic hours; see details below). We addressed the following hypotheses: (1) road traffic is perceived by breeding bee-eaters as a predatory threat (risk-disturbance hypothesis; Frid and Dill, 2002; Fernández-Juricic et al., 2003), and (2) increased levels of traffic interfere with normal breeding activities reducing parental care. According to the risk-disturbance hypothesis, we predicted a higher frequency of risk-avoidance responses (such as those elicited by the presence of potential predators, e.g. vocalization of alarm calls, massive flushing and temporary abandonment of nesting activities; Kossenko and Fry, 1998; Petrescu and Adam, 2001) among birds breeding beside high-traffic roads compared to birds breeding beside low-traffic roads. We also predicted a direct positive association between traffic intensity and risk-avoidance behaviors. Our second hypothesis is an offset of the risk-disturbance hypothesis based on ample evidence that wildlife exposed to stressful stimuli require shifting energy and time away from behaviors such as foraging (Gill et al., 1996; Williams et al., 2006) and parental care (McGowan and 145 Capítulo 4: Reproducción y comportamiento Simons, 2006; Baudains and Lloyd, 2007), allowing us predicting a negative association between traffic intensity and nestling feeding rates. Materials and Methods Study area The study was carried out in Doñana National Park (southwestern Spain; Figure 1), a Biosphere Reserve and UNESCO World Heritage Site, Ramsar Site and European Community Special Protection Area considered one of the most important wetlands in the world (García-Novo and Marín, 2006). Here some 427 species of vertebrates (including 6 million migratory birds) coexist with traditional human activities such as open-range cattle-raising, agriculture, shell-fishing and agro-environmental tourism. The main habitats are marshlands, Mediterranean scrublands and coastal sand dunes (Valverde, 1958). The European bee-eater is a medium-sized (27-29 cm), migratory, monogamous and highly gregarious bird (Cramp, 1985; Valera, 2009). Bee-eaters are colonial breeders nesting inside ground cavities excavated at the end of 70-200 cm deep burrows, generally in vertical or very sloped cliffs (Casas-Criville and Valera, 2005) although in our study area they nest in nearly flat sandy soils (Alvarez and Hiraldo, 1974). Birds are present in Doñana between March and August, starting socio-sexual activity and nest-digging in April-May, laying one asynchronous clutch (4-7 eggs) in May-June, and brooding the nestlings in June-July. The species shows little sexual 146 Motor traffic as a stressor eliciting parental care in birds dimorphism, and both sexes incubate, brood and care for the nestlings (Cramp, 1985; Jones et al., 1991). Bee-eaters feed almost exclusively on flying insects caught on the wing (Krebs and Avery, 1984; 1985; Kossenko and Fry, 1998). Figure 1: Map of the study area, with the Iberian Peninsula and Doñana Biosphere Reserve outlined in grey, and enlarged study area showing the four main bee-eater colonies located beside high- or low-traffic roads (dashed and dotted lines, respectively). Note that for ease of presentation not all the roads are represented. 147 Capítulo 4: Reproducción y comportamiento Data collection Access to the study area is restricted to park staff and authorized researchers, implying that traffic levels are exclusively related to management and scientific activities. Recent studies have revealed a high variability in the spatial and temporal dynamics of road transit therein, allowing us selecting a priori bee-eaters colonies with similar habitat but exposed to contrasting vehicle transit levels. The highest traffic intensity of the unpaved road-network occurs through the gravel road leading to the Palacio de Doñana (i.e. Research Station in Figure 1). Here vehicle transit during spring averages 80.9 cars/day (Revilla et al., 2010) and shows the following particularities: (i) traffic intensity dramatically decreases during weekends compared to weekdays, and (ii) weekday traffic shows a marked daily peak between 14:00-15:00 (traffic rush time onwards). Bee-eaters nests are frequent along the ditches of this road and show several colonial aggregations that will be further referred to as high-traffic colonies. On the other extreme, the lowest intensity of traffic in the park occurs in secondary, dirt roads, where average transit is less than one order of magnitude below the gravel road described above (less than two cars/day; Revilla et al., 2010). For comparative purposes we chose two low-traffic bee-eaters colonies located in the ranch of Hato Barrera (Figure 1). In order to have a more specific knowledge of the traffic affecting the selected study colonies, we also placed automatic magnetometers (TRAFx Vehicle Counter Generation III) beside the roads crossing our high-traffic and low-traffic bee-eaters colonies during two weeks of May 2012. 148 Motor traffic as a stressor eliciting parental care in birds During the early stages of the reproductive season (May 2012), when bee-eaters were engaged in nest-digging and high socio-sexual activity we performed 26 behavioral observation sessions in four focal breeding sites (two high- and two lowtraffic colonies). The observations were performed from camouflaged blinds during 60 minutes, recording (i) total number of birds present in the colony (maximum number of birds counted every other minute), (ii) number of passing predators (both raptors and carnivores), (iii) number of passing vehicles, (iv) number of alarm calls, (v) number of flock flushing events (sudden take-off of all the birds present in the colony or at least 10 birds simultaneously, followed by rapid spiral upwards described as panic-flight in Cramp, 1985). No other source of disturbance was detected during the observations. Walking transects were performed upon finalization of the observation sessions with the aim of recording road kills and injured birds along the road segments adjacent to the colonies. During the late stages of the reproductive season (July 2012), when bee-eaters were brooding the nestlings, we monitored parental feeding rates in nests located beside the high (N = 75) and low (N = 29) traffic roads described above. Feeding events were recorded using a small size video camera (GoPro HD Hero ®) attached to 15-20 cm long sticks and placed on the ground, 50-70 cm away from the entrance of each focal nest. During the 24 hours prior to data collection, dummy cameras were left in recording position to allow habituation before actual data recording. The resulting 30minutes long video clips (60 fps; 848x480 pixels) were visualized on color computer 149 Capítulo 4: Reproducción y comportamiento screens to count the number of feeding events. These occurred at the entrance of the tunnel or inside the burrow, but in all cases we verified adults carrying a prey upon arrival and leaving the nest without it. All the recordings were performed in the afternoons (14:00- 19:30 h), allowing us to establish comparisons between a priori rush (14:00- 15:00 h) and post-rush traffic hours (15:00-19:30 h) as well as between days differing in traffic intensity (i.e. weekdays vs weekends) in nests exposed to high traffic. Data analysis Differences in traffic intensity (i.e. average daily or hourly vehicle passages) between and within colonies (data from the TRAFx Vehicle Counter, see above) were analyzed by means of t-tests aimed at verifying consistency with the patterns described by Revilla et al., (2010), which were considered the a priori working hypotheses (i.e. higher traffic in high-traffic roads, during weekdays and rush hour). The frequency of potential risks (i.e. either predator and vehicle passages per hour) and risk-avoidance behaviors (i.e. either alarm calls and flock flushing events per hour) recorded through direct observations as well as the nestling feeding frequencies extracted from video recordings were analyzed through generalized linear mixed models (GLMMs; SAS; Littel et al., 1996). Models used normal or poisson error distribution and identity or logarithmic link function, respectively; depending on the fit of response variable to normal function (Kolmogorov-Smirnov test; McCullagh and Nelder, 1989; 150 Motor traffic as a stressor eliciting parental care in birds Zuur et al., 2007). The models included colony identity as a random factor, and considered sampling date and hour as covariates. All the GLMMs regarding the frequencies of potential risks (i.e. either predator and vehicle passages per hour) and risk-avoidance behaviors (i.e. either alarm calls and flock flushing events per hour) tested the differences between high- and low-traffic colonies, while the models concerning nestling feeding frequency tested the differences between: high- and low-traffic colonies, weekdays and weekend (only considering hightraffic colonies), rush hours and post-rush hours (only considering weekdays of hightraffic colonies). Results The average daily vehicle passages through high-traffic bee-eater colonies were more than twenty times higher than through low-traffic colonies (mean and SE = 22.43 ± 5.86 vs 0.86 ± 1.29 vehicles/day; t = 3.63; p < 0.01; Figure 2). Restricting the analyses to the high-traffic colonies, the average daily vehicle passages during weekdays were more than two times higher than during weekends (27.30 ± 7.71 vs 10.25 ± 2.52 vehicles/day; t = 2.102; one tailed p = 0.03). In the lowtraffic colonies, weekday effects were absent. In the high-traffic colonies, the average hourly vehicle passages were almost three times higher at rush traffic hours compared to post-rush hours during weekdays (8.40 ± 1.17 vs 2.83 ± 0.24 vehicles/hour; t = 4.67; p < 0.001), but not during weekends. In the low-traffic colonies there were no rush hours. 151 Capítulo 4: Reproducción y comportamiento Figure 2: Frequency of vehicle passage (mean ± SE vehicles per hour) beside the bee-eater nesting grounds characterized as high-traffic areas (A) and low-traffic areas (B) within Doñana National Park. Light grey and dark grey bars indicate weekdays and weekends respectively. The estimated number of adults in the four focal breeding sites was 35 and 40 in the high-traffic colonies, and 20 and 45 in the low-traffic ones. There were not differences comparing predator passage per hour (black kites Milvus migrans in all the observations) between high- and low-traffic areas, while high-traffic areas experienced significantly more vehicle passage per hour than low-traffic areas during the observations sessions (F1,21 = 13.83; p < 0.005). Bee-eaters responded to predators with alarm calls (in 88% of the occasions), flock flushing (88%) or both risk-avoidance behaviors simultaneously (81%). Vehicle passage elicited the vocalization of alarm calls (62%), flock flushing (100%) or both risk-avoidance behaviors simultaneously (in 62% of the occasions). 152 Motor traffic as a stressor eliciting parental care in birds Alarm calls per hour were unrelated to predator passage per hour (and also to sampling hour), but positively related to vehicle passage per hour (F1,20 = 11.52; p < 0.01; Figure 4A) and negatively related to sampling date (F1,20 = 5.22; p = 0.03). Flock flushing events per hour were positively associated to both predator and vehicle passage per hour (F1,18 = 18.79; p < 0.001 and F1,18 = 53.05; p < 0.001 respectively; Figure 4B) and negatively related to sampling date and hour (F1,18 = 12.60 ; p < 0.01 and F1,18 = 4.96; p = 0.04). Although no road fatalities were recorded in the low-traffic colonies, four birds were found road-killed in the high-traffic ones. Figure 3: Frequency (mean ± SE events per hour) of predators and vehicle passages, alarm calls and flock flushing events, recorded in bee-eater colonies located in high-traffic areas (dark circles) and low-traffic areas (light circles). 153 Capítulo 4: Reproducción y comportamiento Figure 4: Frequency of alarm calls (A) and flock flushing events (B) occurring in bee-eater colonies in relation to the number of vehicles registered during one-hour observation sessions (N = 26 sessions). Black and white dots indicate observations performed in high-traffic areas and low-traffic areas respectively. Nestling feeding frequency was statistically higher in nests located in high-traffic colonies compared to nests located in low-traffic colonies (F1,63 = 51.06; p < 0.01; Figure 5A), decreased with sampling date (F1,63 = 34.57; p < 0.01) and was independent of the sampling hour. Restricting the analyses to high-traffic colonies, nestling feeding frequency was higher during weekdays compared to weekends (F1,65 = 35.73; p < 0.01 ; Figure 5B), even accounting for the effects of both sampling date and hour (F1,65 = 10.84; p < 0. 01 and F1,65 = 8.57; P < 0.01). During the weekdays the effect of traffic rush time was statistically significant, whit nestling feeding frequency increasing at rush traffic hours compared to post-rush hours (F1,51 = 16.78; p < 0.01; Figure 5C). 154 Motor traffic as a stressor eliciting parental care in birds Figure 5: Feeding rate (mean ± SE feeds per 30 minutes) in bee-eater nests as a function of (A) study area (high- and low-traffic areas respectively), (B) day of the week (only nests in high-traffic areas), and (C) rush traffic time (only nests in high-traffic areas, triangles and circles indicate weekdays and weekends respectively). Discussion Our results concerning alarm calls and flock flushing events as a response to both predators and vehicles indicate that motor traffic is perceived by European beeeaters as a threat comparable to a predator, supporting the risk-disturbance hypothesis (Frid and Dill, 2002; Fernández-Juricic et al., 2003). Thus, even when our study sites did not differ in the abundance of natural predators, individuals living in colonies subjected to different degree of vehicle traffic experienced variable levels of risk which can have consequences for their fitness. Alarm calls are typically used by bee-eaters when detecting predators and in response to conspecific distress calls (Jilka and Ursprung, 1980; Kossenko and Fry, 1998). As a response, the typical reactions are risk-avoidance flights and the rapid 155 Capítulo 4: Reproducción y comportamiento retreat of nestlings inside the burrows (Reid, 1974; Jilka and Ursprung, 1980) likely imposing energetic costs to signalers and receptors (Bergstrom and Lachmann, 2001). Although alarm vocalizations were not systematically elicited by vehicle passage, they were generally associated to this perturbation and their frequency was positively and statistically related to traffic intensity. Moreover, flock flushing consistently occurred when we observed passing vehicles (100%, N = 92). This result is expected according to the risk-disturbance hypothesis which posits that fleeing probability increases when the threats approach more directly, more quickly and are larger in size (Frid and Dill, 2002; Fernández-Juricic et al., 2003). The road-kills recorded in high-traffic colonies may reinforce a lack of habituation to this stressor (Lima et al., 2014), but even in absence of fatal outcomes bee-eaters likely maximize fitness by overestimating rather than underestimating risks, because the costs of overestimation (e.g. resource utilization in traffic-avoidance behaviors) have milder fitness consequences than the cost of underestimating (which might be immediate death; Bouskila and Blumstein, 1992; Byrnes et al., 2012). The recorded responses to traffic may impose other less immediate costs, such as larger energy expenditure. The effects of traffic on parental provisioning rates were completely opposed to our second hypothesis. Breeding bee-eaters provided more, rather than less food to nestlings in colonies located beside high-traffic roads, and here feeding rates increased during the days of the week experiencing more traffic and during rush traffic hours. The quasi-experimental nature of our sampling design allowed us to rule out the possibility 156 Motor traffic as a stressor eliciting parental care in birds that these results were an artifact of daily rhythms in adult hunting activities (Inglisa and Galeotti, 1993) or parental adjustments in food provisioning as nestlings’ growth (Lessells and Avery, 1989). Although both factors could affect feeding rates (explaining why sampling date and/or hour were statistically significant in some of our models) they would be unable to account for the decreased parental provisioning recorded during weekends (when local traffic decreases), and the recorded increase at rush traffic times (which does not occur on weekends, when rush traffic peaks are negligible). Previous studies on other avian species have reported increased foraging efficiency along roads or roadside habitats, but to our knowledge no study has previously shown a positive effect of traffic intensity per se. For example, Florida scrub-jays Aphelocoma coerulescens show higher rates of energy intake on roadside habitats (Morgan et al., 2010) and male white-crowned sparrows Zonitrichia leucophrys oriantha increase nestling feeding rates with decreased distance to roads (Crino et al., 2011). At least three alternative, non-mutually exclusive hypotheses may explain the positive association we found between traffic levels and parental feeding in bee-eaters, namely (1) facilitation, (2) unmatched cue and (3) interference-displacement hypothesis. First, road transit could elevate the availability or detectability of food resources, facilitating (improving) the foraging efficiency of adult birds and hence nest provisioning rates. This is the most frequently invoked mechanism explaining the foraging benefits associated to roadside habitats: vehicle-killed animals including arthropods facilitate foraging to a number of avian species, mostly corvids (e.g. Morgan 157 Capítulo 4: Reproducción y comportamiento et al., 2010; Yamac and Kirazli, 2012) and the abundance of live prey such as arthropods and reptiles can also increase along road edges (Dangerfield et al., 2003; Andrews et al., 2006). Contrary to the above mentioned avian species, bee-eaters feed almost exclusively on live flying insects captured on the air (Krebs and Avery, 1984; 1985; Kossenko and Fry, 1998). Although a traffic-mediated increase in the abundance or detectability of flying insects was not visually evident to us in Doñana, closely related species such as the northern carmine bee-eater Merops nubicus have been reported to feed on insects flushed by wild and domesticated mammals in Africa (Grimwood, 1964; Boswall, 1970; Cunningham Van Someren, 1970), and to forage in association with large birds, man and motor vehicles (Jackson, 1945). Similarly, southern carmine bee-eaters Merops nubicoides have been observed feeding on insects flushed by mammals and vehicles in the Kruger National Park (Dean and MacDonald, 1981). A second hypothesis potentially explaining the reported pattern is that bee-eaters would be attracted to traffic because this stimulus is an effective hunting clue in locations other than our study site (unmatched cue hypothesis). The lack of asphalt pavements in the roads of Doñana generates conspicuous clouds of dust behind passing vehicles, and birds could have learned to use this cue for detecting foraging patches in their African winter areas (i.e. herds of African ungulates or running cars where flushed insects may abound; see references above). The learned behavior would lead to a trafficrelated increase of hunting effort in Doñana regardless of any real effects on local prey availability. 158 Motor traffic as a stressor eliciting parental care in birds Finally, a third potential hypothesis is that passing vehicles interrupt the normal progression of adults’ daily activities (e.g. resting, preening, brooding, hunting for selfmaintenance) and disturbed birds redirect their behavior towards nestling feeding (interference-displacement hypothesis). Here motor traffic would increase feeding rates only secondarily, through displacement behaviors triggered by the perturbation. For example, adults hunting for self-maintenance could deliver prey to nestlings just because the alarm calls of conspecific (elicited by vehicle passage) triggered their return to the colony. In the same line of arguments, adults resting, preening or brooding at the colony would take-off in response to vehicle passage. Once on the air, a hunting mode could be naturally activated in this primarily aerial predator. In the latter two examples traffic can indirectly result in increased feeding rates despite the primary responses were predator-avoidance behaviors. Our results provide evidence that motor traffic in Doñana Biosphere Reserve exerts relevant effects on the behavior of roadside breeding bee-eaters, but its net consequences on individual health and performance remain unclear. On the one hand, repeated exposure to traffic could bring fitness costs. Extrapolating the reported association between vehicle passage and predator-avoidance responses, bee-eaters breeding beside the studied high-traffic areas experienced 379 flock flushing events and 235 alarm call episodes during one single week, compared to 14 flock flushing events and nine alarm calls in low-traffic colonies. In addition to the direct energy demands associated to the alarm responses themselves (e.g. Bergstrom and Lachmann, 2001), disturbance stimuli can indirectly affect individual health and fitness through trade-offs 159 Capítulo 4: Reproducción y comportamiento between perceived risk and energy intake or lost opportunity costs. For example, passerine birds may decrease singing activity in response to human disturbance, tradingoff predation-avoidance behaviors with the ability to defend territories and attract mates (Gutzwiller et al., 1994; Slabbekoorn and Peet, 2003). Similarly, lekking species such as the great snipe Gallinago media respond to human disturbance fleeing away from the display grounds, trading-off access to mates (Kålås et al., 1995). Vehicle exposure and road proximity has been shown to elevate physiological stress in birds (i.e. corticosterone levels, Sapolsky et al., 2000) including spotted owls Strix occidentalis (Hayward and Wasser, 2006), adult and nestling white-crowned sparrows Zonitrichia leucophrys oriantha (Crino et al., 2006) and female American kestrels Falco sparverius (Strasser and Heathn, 2013). We may thus expect increased mating efforts and higher levels of corticosterone in bee-eaters breeding beside high-traffic roads. In the shortterm, corticosterone elevations may allow traffic-exposed bee-eaters meeting the increased energy demands of stress-copying mechanisms (such as the recorded predation-avoidance behaviors) through its effects on gluconeogenesis, mobilization of body energy stores and locomotion (Sapolsky et al., 2000). However, in the long-term sustained corticosterone elevations are known to exert deleterious effects on health, reproduction and survival (Wingfield and Sapolski, 2003) and we may predict reduced fat depots and body condition with potential cascading effects on reproductive function (Miller et al., 2009; Strasser and Heath, 2013). On the other hand, the recorded positive effects of traffic on parental feeding rates could bring fitness benefits to bee-eaters. Parental feeding rates were 2.75 times higher in traffic exposed nests compared to control areas, and this might result in overall 160 Motor traffic as a stressor eliciting parental care in birds higher offspring quality (e.g. improved body condition and growth rate; Ardia, 2007) and quantity (Clutton-Brock, 1991). However, the consequences for adults’ health and condition would be very different depending on the preponderant mechanism mediating the traffic-related increase in feeding rates. If the facilitation hypothesis underlies our results, adult bee-eaters would optimize hunting efficiency without necessarily increasing hunting effort, and higher reproductive outcome could be achieved without compromising adult health and condition. This mechanism may explain why breeding success in magpies Pica pica is significantly higher near highways (Yamac and Kirazli, 2012). The net fitness outcome for adult birds should nonetheless depend on the relative contribution of the costs associated to predator-avoidance behaviors (e.g. increased stress levels, decreased body condition, higher mortality risk) and the benefits associated to increased offspring quality or quantity. Alternatively, if the unmatched cue or the interference-displacement hypotheses underlie our results, traffic-related elevations in feeding rates would be a consequence of increased feeding effort at a time when prey is neither more abundant nor more detectable. Energy expenditure has been shown to increase exponentially with food provisioning rates in closely related bee-eater species (i.e. blue-throated bee-eaters Merops viridis; Bryant, 1988). Under constant conditions of food availability, increased feeding rates could lead to a loss in parental mass, increasing the chances of starvation or the susceptibility to parasites and disease. The expected reproductive benefits would then occur at the expense of compromising adult health and condition in birds already suffering the costs of traffic. Disentangling which of the three hypotheses above explain the reported traffic-related increase in feeding rates will have important consequences for management decisions, as it will 161 Capítulo 4: Reproducción y comportamiento allow determining if the study roadside environments are ecological traps or not (Kuitunen et al., 2003; Orlowski, 2008). Whether the final outcome of road traffic is a positive, negative or neutral effect on bee-eaters’ net fitness is therefore difficult to predict, and should be the focus of future ad-hoc research. Our results should be interpreted in context with the actual traffic pressure exerted by management and research activities within a biosphere reserve. This pressure was moderate even in the high-traffic colonies, with a maximum recorded count of 91 vehicles/day during our survey, while in contrast the adjacent public road A483 intersecting with our high-traffic study road (see Figure 1) holds an average traffic above 6500 vehicles/day (Revilla et al., 2010). Bee-eaters clearly avoid this public road and nearby areas (with the closest nests in the intersecting study road being located 380 m away) likely due to the elevated transit, high vehicle speed and asphalt pavement. Although we are not aware of the species’ thresholds of traffic tolerance, bee-eaters modified their behavior in response to management- and research-related traffic, showing lack of habituation and experiencing mortality through vehicle collisions. Road kills were never detected in our low-traffic colonies, but 3-7 deaths are normally reported on a yearly basis along the studied high-traffic road leading to the Palacio de Doñana (authors pers. obs.). The real impact of management- and researchrelated road traffic on bee-eater mortality should be higher than currently appreciated due to the rich community of scavengers and predators (e.g. wild boars, carnivores, corvids, raptors; Sebastián-González et al., 2013) likely removing injured and dead roadside birds (Santos et al., 2011; Teixeira et al., 2013). Bee-eaters probably collide 162 Motor traffic as a stressor eliciting parental care in birds with vehicles as a consequence of their risk-avoidance flushing responses. This risk seems more evident during the mating and nest-digging period when flock size is typically larger, birds are distracted by the intense socio-sexual activities of the colonies and flushing distances decrease (Lessells et al., 1994; Petrescu and Adam, 2001) forcing drivers to reduce vehicle speed below the current limit of park regulations (i.e. 40 Km/h) in order to avoid collisions (authors pers. obs.). Until the net consequences of traffic on individual health, stress levels and fitness are determined, managers are encouraged to maintain the present traffic levels (i.e. below 100 vehicles/day on roads presenting bee-eater colonies). Furthermore, lowering speed limits below 40 Km/h beside colony locations could minimize trafficrelated mortality even if this measure is applied only a few weeks per year. Our results illustrate how an avian population can show contrasting behavioral responses to the same type of disturbance occurring at different times of its life cycle. Because the expected outcome of traffic on individual performance can be opposed if the responses are only monitored during either mating or brooding, we recommend caution before inferring net fitness consequences of traffic from isolated behavioral responses or specific life history sub-stages. 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Springer, 523 pp., New York (USA). 167 Capítulo 5: Invasiones Biológicas Invasores en ruta: aves sinantrópicas alimentándose en las carreteras. ______________________________________________________________________ D'Amico, M. a, Rouco, C. b, Russell, J.C. c, Román, J. a and Revilla, E. a (2013). Invaders on the road: synanthropic bird foraging along highways. Oecologia Australis. 17(1 - Road Ecology Special Issue), 86-95. a Doñana Biological Station (EBD-CSIC), Seville (Spain). b Manaaki Whenua - Landcare Research, Alexandra (New Zealand). c University of Auckland, Auckland (New Zealand). Invaders on the road: synanthropic bird foraging along highways Resumen: Las carreteras intermunicipales ofrecen potencialmente alta disponibilidad de alimentos de fácil acceso y pueden representar un área preferente para la alimentación de aves sinantrópicas. Como consecuencia, una red de carreteras podría actuar como corredor de invasión de aves urbanas no nativas. Este estudio es una primera aproximación para verificar tal patrón de dispersión, determinando si la búsqueda de alimento es realmente la principal actividad desarrollada a lo largo de carreteras intermunicipales. Con este objetivo muestreamos 200 puntos de la red de vías intermunicipales de Nueva Zelanda, realizando observaciones sobre cuatro especies de aves sinantrópicas invasoras (gorrión Passer domesticus, mirlo Turdus merula y estornino Sturnus vulgaris, todos euroasiáticos; y miná Acridotheres tristis, de origen indio). Para cada especie también investigamos: preferencias alimenticias, características de carretera que puedan afectar la búsqueda de alimento, y selección de micro-hábitat de alimentación. Los comportamientos observados predominantemente estuvieron relacionados con actividad de alimentación, con diferencias en estrategia de cada especie. Los gorriones fueron observados alimentándose principalmente de semillas e invertebrados, mientras estorninos y minás mostraron comportamientos alimenticios más generalistas. Estas tres especies fueron observadas principalmente a lo largo del borde de carreteras, mientras que los mirlos fueron detectados principalmente depredando invertebrados en prados de las cunetas. De forma semejante a las áreas urbanas, las carreteras intermunicipales proveen alimentos de fácil acceso, disponibles para las especies de estudio. Proponemos la existencia de un patrón de invasión por aves sinantrópicas mediado por carreteras, e incentivamos futuros estudios para optimizar estrategias de manejo, focalizado hacia carreteras intermunicipales como áreas de control más adecuadas para evitar la propagación de esas especies. 169 Capítulo 5: Invasiones Biológicas Abstract: Intercity roads potentially offer a high availability of easily accessible food and could therefore be expected to represent a preferential area for synanthropic bird foraging. As a consequence, a road-network could act as an invasion corridor for non-native urban birds. This study is a first approximation to verify such pattern of spread, by determining if foraging is actually the main activity carried out along intercity roads. With this aim, during the spring of 2011 we surveyed 200 points along the New Zealand intercity road-network, carrying out observations on four invasive synanthropic bird species (Eurasian house sparrow Passer domesticus, Eurasian blackbird Turdus merula, Eurasian starling Sturnus vulgaris and Indian myna Acridotheres tristis). For every species we also investigated: food preferences, the possibility that certain road features could affect the occurrence of feeding activity along roads, and foraging micro-habitat selection along roads. Predominant observed behaviors related to feeding activity, with differences concerning species-specific foraging strategies. Sparrows were mainly observed feeding on small-sized items (such as seeds and invertebrates), while both starlings and mynas showed more generalist feeding habits. These three species were mostly observed along the side of the lane, in contrast with blackbirds, mainly detected preying on invertebrates along the mown grassy verges of the road. Similarly to urban areas, intercity roads provide easily accessible food, available for all the study species. We propose a road-mediated invasion pattern for synanthropic birds, and encourage future studies to optimize management strategies, and targeting intercity roads as the most suitable control areas to prevent spread. 170 Invaders on the road: synanthropic bird foraging along highways Introduction Human dominated landscapes are increasing worldwide, with associated potential threats to biodiversity and ecosystems (Vitousek et al., 1997; Hooper et al., 2012). Paradoxically, there are species taking advantage of global change, shifting their trophic and spatial preferences to the novel niches of recently degraded habitats (Rebele, 1994; Marzluff, 2001). In the case of urban and rural areas, these species are usually defined as synanthropic (di Castri et al., 1990), including human commensal birds (Luniak et al., 2004; Bonier et al., 2007). Urbanized environments are also selected by synanthropic birds because they have lower predator diversity and milder winter temperatures compared with natural habitats (Jokimäki et al., 1996; Gering and Blair, 1999; McKinney, 2006). Synanthropic bird species have been introduced worldwide for food, pest control and nostalgic purposes, and due to their adaptation to coexist with humans they have proliferated and also spread (Marzluff, 2001; McKinney, 2006). Intercity roads usually cross rural and natural habitats, and probably show to a lesser extent the above mentioned characteristics of urbanized areas, including the availability of easily accessible feeding resources. Food availability is one of the main drivers of dispersal (Bowler and Benton, 2005), and for urban birds it should be easier to forage along intercity roads than in certain natural habitats. As a consequence, urban bird dispersal routes might not be randomly selected, and these species could preferentially move near this kind of linear infrastructure. In this case, road-networks might therefore act as invasion corridors for synanthropic birds. A first approximation 171 Capítulo 5: Invasiones Biológicas to verify this spreading pattern would be an observational study describing the synanthropic bird behaviors along intercity roads, trying to confirm the relative significance of feeding activities. A positive association between foraging and roads would encourage a simplification of current management strategies for these introduced species, by determining the suitable target control areas for carrying out actions focused on their containment. New Zealand is an insular country with a distinctive biota mainly characterized by its endemic bird communities (Cooper and Millener 1993; Daugherty et al., 1993). The principal threats to New Zealand biodiversity are biological invasions (Clout, 2001; Norton, 2009), including the introduction of synanthropic birds mostly carried out since 1860 by Acclimatization Societies (Druett, 1983; McDowall, 1994). Urban birds compete with native species and prey on endemic fauna (Meads et al., 1984; Greene and Jones, 2003). Moreover, they contribute to the spread of infectious diseases and invasive plants (Williams and Karl, 1996; Tompkins and Gleeson, 2006), and they also impact several human activities such as orchard cultivation (Dawson and Bull, 1970, Kross et al., 2012). Introduced synanthropic birds represent the most abundant and widespread avian species in New Zealand, particularly the Eurasian house sparrow Passer domesticus, the Eurasian blackbird Turdus merula, the Eurasian starling Sturnus vulgaris and the Indian myna Acridotheres tristis (Heather and Robertson, 2005). They are regionally subjected to lethal control methods including shooting, trapping and baiting by avicides, principally alphachloralose and DRC-1339 (Nelson, 1990; 1994). All these methods and especially the use of poison also entail mortality risk for nontarget bird species (Nelson 1990, 1994), but the society generally supports control 172 Invaders on the road: synanthropic bird foraging along highways programs (Green and Rohan, 2012). However, their planning nonetheless requires prudence. The main purpose of the present study is to determine the principal activity carried out by these four synanthropic bird species along the intercity roads of New Zealand, where they are abundant. Our hypothesis advances that urban birds use such areas mostly to forage, with implications for their establishment and spread patterns and consequent management strategies. Moreover, with the aim of further improving the control and containment of these introduced birds, for every species we also investigated: food preferences, the possibility that certain road features could affect the occurrence of feeding activity along roads, and foraging micro-habitat selection along roads. All that considered, this species-specific observational study about the feeding habits of non-native synanthropic birds is of high interest to understand their activity along intercity roads, with several possible implications for the improved planning of their management. Materials and Methods Study area and Data collection Our study was carried out along several New Zealand intercity roads, across both the North and the South Islands (Figure 1), during three weeks of the Austral spring 2011. We selected 200 sample points (50 for every surveyed species: sparrows, blackbirds, starlings and mynas) depending on features characterizing road and surrounding environment: traffic intensity (high or low), habitat (open or with 173 Capítulo 5: Invasiones Biológicas shrubs/trees) and presence/absence of mown grassy verges. Each variable level was equally sampled for all the four species. Every sample point was at least five kilometers from another to avoid resurveying the same individual. At each sample point the observer waited inside a car for the arrival of an individual of our study species. The individual detection was the beginning of a binocular focal observation of five minutes, recording the main activity carried out. In the case of foraging behaviors, we also registered the most frequent feeding choice and the section of the road in which the action was mostly carried out. Data analysis To determine the principal activity carried out by introduced synanthropic birds along New Zealand intercity roads we carried out a first Pearson χ2 test (goodness-offit; SAS Institute Inc., 2008) for each study species. In such test we analyzed if the observed frequency of every detected behavior significantly diverged from the distribution of behaviors if they were random. Furthermore, selecting only the observations of foraging activities, with a second Pearson χ2 test (goodness-of-fit) we also determined the food resources most often consumed by every study species along roads. With the aim to analyze if certain roadway and surrounding environment features were affecting the appearance of feeding activity along intercity roads, we carried out a logistic generalized linear model (GLM: McCullagh and Nelder, 1989) for each study species. The response variable was the presence/absence of foraging behaviors as the main detected activity of the observation, while the explanatory 174 Invaders on the road: synanthropic bird foraging along highways categorical variables were: traffic volume (high or low), habitat (open or with shrubs/ trees) and presence/absence of mown grassy verges. In all cases, we applied a binomial error distribution and logit link function. Finally, the fourth analyses also were Pearson χ2 tests (goodness-of-fit), in this case with the aim to determine in which sections of the road (middle, shoulders and verges) the species carried out the main part of their foraging activities. Also for these analyses, we performed a test for each study species. 175 Capítulo 5: Invasiones Biológicas Figure 1: Sample point distribution for each study species. 1) sparrows, 2) blackbirds, 3) starlings and 4) mynas. 176 Invaders on the road: synanthropic bird foraging along highways Results We mostly detected four types of activities along the New Zealand intercity roads: foraging, searching, perching and interacting. The latter included attacks and fighting, courtship, copulations, and also parental care of fledglings. Other activities were also detected (passing, alert, escaping, preening, defecating), but none of them reached the status of predominant behavior during an observation. Sparrows were observed carrying out all the four main activities, especially foraging (76% of the observations: Figure 2). The first Pearson χ2 test indicated that such behavior was more frequent (p < 0.0001) than searching, perching and interacting (respectively 8%, 10% and 6% of the observations). We did not detect interactions among blackbirds, and also in their case foraging was more frequent (74% of the observations; p < 0.0001) than both searching and perching (16% and 10%). Starlings carried out all the four main activities, with feeding being more frequent (80% of the observations; p < 0.0001) than the other behaviors (searching 8%, perching 4% and interacting 8%). The first Pearson χ2 test for mynas also revealed the same pattern (foraging 74%, searching 4%, perching 12% and interacting 10%; p < 0.0001; Figure 2). 177 Capítulo 5: Invasiones Biológicas Figure 2: Non-native urban bird main activity for each of 50 observations. For all the four species, foraging activity significantly represents the main observed behavior. Concerning the main food resources consumed along intercity roads, we detected six categories: garbage, identifiable carrions (mostly introduced mammals; hedgehogs Erinaceus europaeus, possums Trichosurus vulpecula and rabbits Oryctolagus cuniculus), flattened organic matter, invertebrates, seeds/fruits and unrecognizable items. We could not identify the most part of the items selected by sparrows (55% of foraging observations; Table 1). The second Pearson χ2 test showed that foraging activity on unrecognizable resources was more frequent (p = 0.0003) than on garbage, flattened organic matter and seeds (respectively the 11%, 18% and 16%). For sparrows, we did not detect scavenging behavior and identifiable predation on invertebrates. Conversely, blackbirds mainly fed on invertebrates, and also on unrecognizable items (respectively 35% and 46% of foraging observations). Flattened organic matter and 178 Invaders on the road: synanthropic bird foraging along highways seeds/fruits were consumed less (8% and 11%; p = 0.0016), and foraging on garbage and carrions was not detected. We could not categorize the most part of items selected by starlings (50% of the feeding observations). Foraging activity on flattened organic matter and invertebrates was less frequent (both of them respectively 20%; p < 0.0001), followed by consumption of garbage and seeds/fruits (both of them respectively 5%). Scavenging behavior was not detected for starlings. Finally, there were no significant differences in the food resources consumed by mynas along intercity roads. The most exploited item was flattened organic matter (30% of foraging observations), followed by invertebrates (17%), garbage, carrions, seeds/fruits and unrecognizable items (each of them respectively 14%; Table 1). Table 1: Number of foraging observations for every main consumed food item. The meaning of the acronym FOM is flattened organic matter, and for NII is not identified items. Total represents the overall number of foraging observation per species. The only species without significant differences among consumed items is the myna. Garbage Carrions FOM Invertebrates Seeds/Fruits NII Total Sparrows 4 0 7 0 6 21 38 Blackbirds 0 0 3 13 4 17 37 Starlings 2 0 8 8 2 20 40 Mynas 5 5 11 6 5 5 32 179 Capítulo 5: Invasiones Biológicas Regarding the GLMs carried out, the only candidate variable affecting the appearance of synanthropic birds foraging along intercity roads was the presence of mown grassy verges, increasing the occurrence of blackbirds (p = 0.007; Figure 3). Figure 3: Probability to observe a blackbird foraging along the highway during an observation, in relation to the presence/ absence of mown grassy verges. Finally, about the road sections preferably used for feeding activities by introduced synanthropic birds, the third Pearson χ2 test for sparrows revealed they significantly selected the shoulders of the lanes (76% of foraging observations; p < 0.0001; Figure 4), compared with the middle of the roadway and its verges (respectively 11% and 13%). Conversely, blackbirds are the only species actively selecting the verges (76%; p < 0.0001), followed by the sides (21%) and the middle of the road (3%). On the other hand, similarly to sparrows, the starlings also preferred the shoulders (60%; p = 0.0005), and used the middle of the road and the verges less (10% and 30%). The mynas 180 Invaders on the road: synanthropic bird foraging along highways mostly selected the external sides of the lanes (81%; p < 0.0001), compared with the middle of the roadway or its verges (respectively 16% and 3% of foraging observations). Figure 4: Number of foraging observations for each species and area of the road (verges, shoulders and the middle of the roadway). 181 Capítulo 5: Invasiones Biológicas Discussion The present study represents a simple approximation to the use of behavioral observations with the aim to understand habitat use and diet, and optimize control strategies of invasive species. With a moderate effort we recorded that introduced synanthropic birds are generally using intercity roads mainly for foraging purposes, affirming the plausibility of a road-mediated invasion pattern for synanthropic birds. This could have consequences on the current management strategies for these species. Our results show that the four study species were detected carrying out feeding activities during approximately 75% of the observations. This proportion might also increase considering that two of the three other detected behaviors (both searching and perching) are probably related to foraging in several cases. We did not categorize such behaviors as feeding activities because searching can also concern grit and nest materials, and perching may just correspond to a resting activity. Such prevalence of foraging activities along intercity roads can be related to the easily accessible food availability that we also detected during the fieldwork of the present study. This availability represents a pronounced feature of urban areas as well, and probably one of the main causes of synanthropization of several bird species (Marzluff, 2001; Davies et al.; 2009, Evans et al.; 2009). Therefore, considering the importance of feeding resources for dispersal (Bowler and Benton, 2005), intercity roads might represent a preferential first step of invasion spread for urban birds, prior to the colonization of natural habitats. The confirmation of the road-mediated invasion pattern that we suggest could enable the optimization of introduced synanthropic bird containment strategies, 182 Invaders on the road: synanthropic bird foraging along highways which might be mainly focused on urban areas and the surrounding intercity roads. This management choice would have less probability to accidentally target native New Zealand birds, which tend to avoid human dominated environments (van Heezik et al., 2008). All that considered, we strongly recommend further research regarding our suggestion of road-mediated invasion by non-native synanthropic bird species. Concerning the main food items consumed along intercity roads, we can differentiate them in two categories, directly and indirectly related with human activity. The first category includes garbage and carrions resulting from road-kills and the flattened organic matter consequent to the repeated transit of vehicles. The second category consists of invertebrates, seeds/fruits and unrecognizable items. We could not identify the latter items due to their small size, and therefore we suspect they should also correspond with invertebrates and seeds. We consider that invertebrates and seeds/fruits availability along verges were indirectly related with human activity because these might respectively depend on roadway/roadside building materials and verge vegetation management. Indeed, temperatures are usually higher along paved roads than in natural surrounding environments, attracting arthropods and their predators (Hannay, 2001; Jackson, 2003). Moreover, roadside vegetation management can affect invertebrate availability for insectivorous birds, facilitating their foraging activities in the presence of mown grassy verges (Romanowski and Zmihorski, 2008, Devereux et al., 2006). Roadside hedgerows and plantations also determine seed and fruit availability along the verges. All of our four study species fed along intercity roads according to their previously known diet preferences (Moeed, 1976; 1980; MacMillan, 1981; Williams and Karl, 1996), but we also detected the consumption of food items 183 Capítulo 5: Invasiones Biológicas directly related with human activity. Mynas were observed feeding on road-killed carrions, and all the species consumed garbage (excluding blackbirds) and flattened organic matter. This exploitation of human-related food items suggests a plausible lack of neophobia among urban birds, which could be capitalized by wildlife managers selecting live-trapping as the main strategy to control such invasive species, also eliminating mortality risk for autochthonous species. The previously mentioned baiting with avicides (Nelson, 1990; 1994) could then be limited to particular situations, such as intercity roads crossing degraded environments with consequent low densities of native avifauna (van Heezik et al., 2008). Regarding the influence of certain features of roadway and surrounding environment on the presence of urban bird foraging along intercity roads, the only significant variable was the presence of mown grassy verges increasing the occurrence of blackbirds. As previously suggested, this kind of roadside management could facilitate the feeding activities of insectivorous birds (Romanowski and Zmihorski, 2008, Devereux et al., 2006), and we largely detected blackbirds preying on invertebrates (especially earthworms) during our observations. The mowing of grassy verges could therefore be an effective strategy to attract blackbird individuals in order to expose their populations to management, should it be required. We also observed blackbird preference for verges through the analysis of foraging micro-habitat selection, as a further confirmation of their relative dependence on roadside food resources. Conversely, the other three study species significantly used road shoulders to carry out their feeding activities, probably because there they can 184 Invaders on the road: synanthropic bird foraging along highways access both the roadway and roadside food resources. Moreover, comparing mynas with sparrows and starlings, we can appreciate how they additionally used the middle of the roads too, confirming their preference for human-related food items. All that considered, the use of the roadway shows us that road-kill mortality seems to be a tolerable risk for these three species, denoting a boldness that can indicate again the pertinence of trapping as a main control method. The results of the present study imply relevant suggestions for the management of these introduced species. We also reaffirmed the plausibility of a road-mediated invasion pattern for synanthropic birds and we strongly suggest its confirmation through future studies, due to its potential power of optimization for the containment of these species, especially determining if urban areas and intercity roads are the most suitable control target areas. We also encourage further and more exhaustive studies regarding environmental variables affecting the presence of introduced synanthropic birds, with the aim to improve management protocols and consequently to limit associated risks for New Zealand native avifauna. Acknowledgements This research was supported by the JAE-PRE PhD stay fellowship granted to MD by the CSIC. MD was also supported by a JAE-PRE fellowship from CSIC. 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New Zealand Journal of Ecology. 20: 127-145. 187 Discusión general y Conclusiones Discusión general y Conclusiones Síntesis y Perspectivas Esta tesis doctoral consta de cinco estudios que pretenden describir patrones de impactos viarios sobre poblaciones animales, tratando de investigar los mecanismos que generan estos patrones. Cada estudio se refiere a uno de los temas principales de la que se define como Ecología de carreteras: atropello, efecto barrera, medidas de mitigación, impacto sobre comportamiento y reproducción, e invasiones biológicas. En el primer capítulo investigamos los factores específicos intrínsecos, temporales y espaciales que afectaban la probabilidad y los patrones de atropello en vertebrados mediterráneos. Tanto las especies comunes como las abundantes tuvieron más probabilidad de ser atropelladas que las raras, y dentro del conjunto de especies comunes y abundantes el rasgo de historia natural que más afectó a la probabilidad de colisión fue la estrategia metabólica, siendo los ectotermos más atropellados de los endotermos. Las lluvias se asociaron a los picos de atropello de anfibios, mientras las altas temperaturas coincidieron con un aumento de atropellos en serpientes, lagartijas y aves nidificantes. Lagartijas y micromamíferos fueron atropellados sobre todo a lo largo de grandes vías con mucho tráfico y altos límites de velocidad. Sugerimos priorizar las medidas de mitigación capaces de reducir permanentemente la mortalidad de ectotermos (anfibios y reptiles), como pasos de fauna específicos y cercados impermeables en correspondencia de sus puntos calientes de atropello. Al mismo tiempo, recomendamos aplicar las medidas temporales de mitigación durante los picos estacionales de atropellos. 188 Discusión general y Conclusiones En el segundo capítulo medimos el efecto barrera en ungulados (ciervo Cervus elaphus y jabalí Sus scrofa) a lo largo de una red viaria heterogénea (i.e. con variación de tráfico y superficie viaria) y estimamos el consecuente deterioro de hábitat dentro de una emblemática área protegida, el Espacio Natural de Doñana. La probabilidad de presencia de ciervos y jabalíes fue afectada por la mera distancia a la carretera más cercana (en la mayoría de casos caminos sin pavimentación ni tráfico), y los primeros sufrieron también la cercanía a carreteras de más envergadura y tránsito. El efecto barrera debido a la carretera más cercana sugiere una evitación de espacios artificiales vacíos que podría estar basada en la asociación entre estructuras lineares y la posibilidad de vehículos transitando. La consecuencia a nivel de paisaje es una reducción de la probabilidad de presencia del 32% por ciervos y del 45% por jabalíes, con una consecuente reducción de calidad de hábitat, muy grave dentro de un enclave protegido. Por esta razón sugerimos el establecimiento de grandes áreas sin infra-estructuras lineares, a través de una optimización de la red viaria que incluía también el cierre de caminos no pavimentados. En el tercer capítulo hipotetizamos los tradicionales muestreos genéricos de eficacia de pasos de fauna para micromamíferos pudieran estar sesgados hacía las especies generalistas de hábitat en detrimento de las especialistas, considerado que las carreteras a menudo están rodeadas por ambientes degradados. Por esta razón, comparamos un muestreo genérico con uno específico, seleccionando tres especies modelo: una generalista (Peromyscus maniculatus), una especialista de bosque (Myodes gapperi) y una de pradera (Microtus pennsylvanicus). Muestreamos en cuatro tipos de pasos de fauna (ecoductos, puentes, a sección elíptica y a sección cuadrangular) en el Parque Nacional Banff (Canadá). Posicionamos trampas de huellas a lo largo de los pasos, y como controles también delante de sus entradas y en los bosques colindantes. 189 Discusión general y Conclusiones El muestreo genérico no detectó diferencias en la ocurrencia de micromamíferos entre pasos de fauna y controles, mientras que el específico evidenció que la única especie que usaba consistentemente los pasos era la generalista, seleccionando principalmente los ecoductos (que además fueron los únicos pasos usados por las especialistas). La eficiencia de los pasos de fauna para micromamíferos especialistas podría ser sobrestimada, con posibles implicaciones para su conservación. Sugerimos entonces mejorar el hábitat (o por lo menos la disponibilidad de cobertura) a lo largo de los pasos y en el espacio entre estos y los ambientes colindantes. En el cuarto capítulo investigamos como el estrés asociado al tráfico puede amenazar el éxito individual de aves que acuden a carreteras para la búsqueda de alimento y lugares de nidificación. Intentamos averiguar si los abejarucos Merops apiaster que crían en las áreas laterales de los caminos perciben el tráfico como un elemento de estrés que estimule alarmas, estampidas y un decremento de los cuidados parentales, o si en cambio demuestran habituación. Comparamos individuos sometidos a diferentes niveles de tráfico dentro de la Reserva de la Biosfera de Doñana. Alarma y estampidas resultaron aumentar en presencia de tráfico a motor, siendo mayores en las colonias de cría establecidas en zonas de tránsito intenso. Sin embargo, las tasas de ceba parentales también cambiaron de acuerdo al nivel de tráfico, pero contrariamente a las predicciones fueron mayores en los nidos situados en carreteras de tránsito elevado, aumentando durante los días laborales y con picos en las horas punta. El tráfico parece afectar positivamente las tasas de ceba, aumentando potencialmente el éxito reproductivo de los abejarucos. Por otro lado, hasta que no se determinen las consecuencias netas del tráfico sobre salud individual, niveles de estrés y eficacia biológica, sugerimos mantener los actuales niveles de tráfico. 190 Discusión general y Conclusiones En el quinto capítulo hipotetizamos que las carreteras intermunicipales ofrecen una alta disponibilidad de alimentos de fácil acceso y pueden representar un área preferente para la alimentación de aves sinantrópicas. Como consecuencia, una red de carreteras podría actuar como corredor de invasión de aves urbanas no nativas. Por esta razón intentamos proporcionar esta primera aproximación para verificar la posible existencia de este patrón de dispersión, determinando si la búsqueda de alimento es realmente la principal actividad desarrollada a lo largo de las carreteras intermunicipales. Muestreamos entonces 200 puntos de la red de vías intermunicipales de Nueva Zelanda, realizando observaciones sobre cuatro especies de aves sinantrópicas invasoras (el gorrión Passer domesticus, el mirlo Turdus merula y el estornino Sturnus vulgaris, todos de origen euroasiático; y el miná Acridotheres tristis, de origen indio). Para cada especie también observamos: preferencias alimenticias, características de las carreteras que puedan afectar la búsqueda de alimento, y selección de micro-hábitat de alimentación. Los comportamientos observados estuvieron predominantemente relacionados con actividad de alimentación, con diferencias en las estrategias de cada especie. Los gorriones fueron observados alimentándose principalmente de semillas e invertebrados, los estorninos y los minás mostraron comportamientos alimenticios más generalistas, mientras que los mirlos fueron detectados principalmente depredando invertebrados en los prados de las cunetas. Las carreteras intermunicipales realmente proveen alimentos de fácil acceso, disponibles para todas las especies de estudio. Proponemos entonces la existencia de un probable patrón de invasión por aves sinantrópicas mediado por carreteras, e recomendamos futuros estudios para optimizar el manejo focalizado hacia las carreteras intermunicipales como las áreas de control más adecuadas para evitar la propagación de esas especies. 191 Discusión general y Conclusiones Esta tesis doctoral proporciona cinco ejemplos de estudios que no se limitan a describir patrones, como a menudo se ha venido haciendo en Ecología de carreteras, sino que pretenden investigar los mecanismos que generan esos patrones. El primer capítulo, por ejemplo, trataba el tema de los atropellos de fauna intentando proporcionar una información más completa que un listado de especies afectadas y las posibles causas, ya que pretendía entender que factores climáticos, ambientales y de tráfico determinaban la distribución temporal y espacial de las colisiones de distintos grupos funcionales de especies. El enfoque al estudio de mecanismos nos ha permitido sugerir medidas de mitigación que pudieran integrar las que han sido realizadas hasta ahora, y que podrían resultar muy efectivas, ya que pretenden resolver los impactos a través de un conocimiento más profundo de su forma de afectar especies y ecosistemas. En el segundo capítulo, por ejemplo, vimos que no es el tráfico motorizado el único factor que determina efecto barrera en ungulados, considerado que estos han mostrado una gran tendencia a evitar incluso caminos que apenas tenían tránsito. Como consecuencia, sugerimos que en estos casos habría que integrar los pasos de fauna a menudo ya existentes a lo largo de las carreteras principales con acciones de optimización de las redes viarias, eliminando los caminos redundantes por lo menos dentro de las áreas protegidas. Por otro lado, este enfoque al estudio de mecanismos puede ser positivo no solo para la resolución de problemas concretos como el de las medidas de mitigación, sino que más en general puede contribuir considerablemente al avance del conocimiento en Ecología de carreteras, ya que este tipo de enfoque a menudo proporciona la posibilidad de hacer inferencia a una escala más amplia. En el tercer capítulo, por ejemplo, observamos que los micromamíferos generalistas de hábitat eran los únicos que 192 Discusión general y Conclusiones realmente usaban los pasos de fauna en los bosques de las Montañas Rocosas Canadienses, con toda probabilidad gracias precisamente a su tolerancia a entornos degradados, mientras los especialistas de hábitat se quedaban confinados en los ambientes que les eran más propicios. Haber diseñado este estudio con la finalidad de entender que uso hacían de los pasos de fauna tanto las micromamíferos generalistas como los especialistas nos ha hecho entender el probable mecanismo que determina la eficiencia de los pasos de fauna para estos grupos de especies, y nos permite inferir que estos patrones se podrían repetir también en micromamíferos de otras áreas e incluso con otros taxa, siempre que sean generalistas y especialistas de hábitat. Finalmente, los estudios enfocados en investigar mecanismos proporcionarán un tipo de información que podrá resultar extremadamente útil para el desarrollo de la Ecología de carreteras, que probablemente se centrará en un futuro próximo en efectos crónicos como por ejemplo la distribución a nivel de paisaje de contaminantes y personalidades, para luego enfocarse finalmente en estudios de síntesis y meta-análisis necesarios para obtener patrones más globales y fiables de cómo funcionan los impactos asociados a las redes viarias. 193 Discusión general y Conclusiones Conclusiones de esta tesis doctoral 1) Existen distintos factores intrínsecos a las especies animales que hacen que algunas de ellas sean más susceptibles a ser atropelladas que otras, tanto en nuestro caso de estudio (los vertebrados mediterráneos) como con toda seguridad también en el resto de vertebrados e invertebrados del mundo. El más lógico entre estos factores intrínsecos es la abundancia de una especie, pero pueden haber otros factores, como los relacionados con su capacidad de movimiento y su estrategia metabólica (endotermia versus ectotermia). 2) Existen factores temporales como el fotoperiodo, las precipitaciones y la temperatura que determinan cambios en actividad y abundancia de algunos grupos funcionales de especies a lo largo del año y consecuentemente la variación de su probabilidad de atropello. 3) Existen factores espaciales como las dimensiones de una carretera, su tráfico y la velocidad máxima permitida en ella que hacen que los atropellos de algunos grupos funcionales de especies se concentren en algunos puntos concretos. Por otro lado, también existen otros factores espaciales relacionados con la variabilidad de abundancia de algunos grupos funcionales de especies (e.g. la distancia a un cuerpo de agua) que contribuyen a la variabilidad de la distribución espacial de los atropellos. 194 Discusión general y Conclusiones 4) Algunas especies de ungulados (y probablemente muchas otras especies de larga vida y con suficiente capacidad cognitiva) tienen tendencia a evitar las infra-estructuras lineares, aunque estas sean simple caminos sin pavimentación ni tráfico motorizado circulante; lo que comporta una reducción sensible de la calidad de hábitat de grandes áreas naturales (incluso protegidas, como en nuestro caso de estudio). 5) En algunas especies de ungulados (y probablemente en muchas otras especies) a la evitación de meras infra-estructuras lineares sin transito se puede sumar la evitación de tráfico motorizado, que puede contribuir a su vez a la mencionada reducción de la calidad de hábitat. 6) Las especies generalistas de hábitat (en nuestro caso de estudio micromamíferos) pueden usar las áreas degradadas como las carreteras y sus alrededores con más frecuencia que las especialistas de hábitat; lo que puede comportar sesgos en los muestreos de eficiencia de pasos de fauna que no diferencien a nivel de especie. 7) Como se ha descrito ya en otras especies, también los micromamíferos parecen preferir los ecoductos a los pasos de fauna subterráneos. Aún así, las especies generalistas de hábitat los usan con más frecuencia que las especialistas, así que para aumentar la eficacia de los ecoductos son necesarias acciones específicas de mejora de su hábitat. 195 Discusión general y Conclusiones 8) Algunas especies que potencialmente se benefician de la presencia de carreteras (como los abejarucos en nuestro caso de estudio, que llegan a nidificar en taludes de los caminos de mucho tráfico) siguen reaccionando frente a los vehículos a motor como si estuvieran delante de depredadores naturales (e.g. alarmas y estampidas), lo que podría provocar a largo plazo que su eficacia biológica pueda verse afectada. 9) Los estudios de comportamiento animal a lo largo de infra-estructuras viarias pueden revelar patrones sorprendentes, como en nuestro caso de estudio en que el tránsito de vehículos a motor parece facilitar (directamente o indirectamente) el forrajeo de aves insectívoras y por lo tanto sus tasas de ceba al nido. 10) Las aves sinantrópicas que son invasoras en Nueva Zelanda (y probablemente la mayoría de vertebrados sinantrópicos e invasores a lo largo del globo) usan las carreteras para la búsqueda de alimento, lo que podría facilitar en estas especies un patrón de invasión biológica mediado por la presencia de infra-estructuras viarias. 196