El Colegio de la Frontera Sur

El Colegio de la Frontera Sur
Hormigas en cafetales: Estudio sobre la calidad de la matriz
en fincas cafetaleras en el Soconusco, Chiapas.
TESIS
presentada como requisito parcial para optar al grado de
Doctorado en Ciencias en Ecología y Desarrollo Sustentable
por
Aldo Alejandro de la Mora Rodríguez
2014
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DEDICATORIA
A un ser superior.
A mis genios preferidos:
Orlando López Báez
Álvaro García Ballinas
Gabriela Pérez-Lachaud
Jean Paul Lachaud
Stacy Philpott
A mi pequeña familia.
En especial, mi madre, mis hermanos, abuelos, papás, mamás.
A los primos y sobrinos.
¡Gracias a la vida ¡
A los compañeros poetas.
A lo que se llama soledad.
Por lo que nunca fue, ni será.
Por las tazas de café sin terminar, los abrazos y cervezas prometidas,
por las hormigas sacrificadas durante este trabajo…
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RECONOCIMIENTOS
Al Consejo Nacional de Ciencia y Tecnología (CONACYT), por la beca de manutención del
programa de posgrado (Becario 168970); así como, la beca mixta otorgada para la estancia
académica en la Universidad de Toledo, en Estados Unidos.
A Conservation International-Rapid Assessment Program, por el valiosos apoyo económico
otorgado para la realización de este proyecto de vida. De manera especial a Leeanne Alonso por
creer en mí.
A los dueños de las fincas Irlanda, Hamburgo, San Francisco, Génova, Rancho Alegre, Chiripa,
Maravillas, Santa Anita, San Enrique y Rancho Cuilco (Rogers S.A. de C.V.
y la empresa Rogers Family Company), por permitirnos realizar esta investigación en cada una
de ellas. Especialmente mi reconocimiento y eterno agradecimiento, al Sr. Walter Peters por
compartir una vida entera entre sorbos de café.
Al apoyado parcial económico de la National Science Foundation (número DEB-1262086)
otorgado a S.M. Philpott.
Finalmente, y no menos importante, a ECOSUR, por el apoyo brindado y la oportunidad de
seguir aprendiendo y desarrollarme académicamente durante mi estancia en el posgrado.
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AGRADECIMIENTOS
A Stacy Philpott por compartir sus conocimientos, por la paciencia a mis errores y por ser una
amiga en todo momento.
A los miembros de mi comité tutelar: Al Dr. Guillermo Ibarra Núñez, Dr. Jean Paul
Lachaud, Dra. Gabriela Pérez, Dra. Lorena Soto, por su contribución al desarrollo de esta
tesis, muchas gracias por los comentarios y enseñanzas. También, agradezco al Dr. Jaime
Gómez, Dr. Pablo Liedo Fernández y al Dr. Alfredo Castillo Vera, por tomarse el tiempo
para leer este trabajo y aceptar ser revisores externos.
Al M.C. Javier Valle Mora, por su apoyo estadístico, pero sobre todo, por compartir los
conocimientos dentro y fuera de las aulas. A L.I. Higinio López por el aporte a esta tesis, así
como la amistad que se genero durante este tiempo fuera y dentro de aulas. No olvidar, en estos
agradecimientos a la M.C. Guadalupe Nieto, por su apoyo y amistad incondicional durante este
tiempo. A las L.I. Beatriz Romero y Paulina González por el apoyo logístico y eficiente para la
culminación de esta tesis. A Ana María Galindo y Margarita Hernández por el apoyo en todo
momento en la adquision de libros en la bilbioteca. Así también, a Eduardo Chamé por el
apoyo brindado en la colección biológica y M.C. Enoc Cabrera por el apoyo logístico.
A mis madrinas: Sra. Rosalba Morales y Doña Lucy, por el apoyo moral y humano en todo
momento durante mi estancia en Ecosur. Así tambien, a Doña Rosi Morales por apoyarme
administrativa y logistivamente durante la realización de esta tesis.
Al personal administrativo de Ecosur –Tapachula: Carmina, Rosario, Mónica, Magda, Ada,
Adriana, Lilia, Elizabeth y Jesús Éboli.
A Ivette Perfecto y Jhon Vandermeer. Así también a Francisco Infante, Julio Rojas,
Benigno Gómez, Jorge Toledo, Anne Damon y Cristian Tovilla.
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A los incansables y extrañables: G. Domínguez, G. López Bautista, B. Chilel, P. Bichier, G.
Livingston, C. Repetto, K. Mathis, C. Murnen, E. Sintes, E. Sintes-Garcia, R. Becker, J. Heraty,
E.A. Murray, F. Jiménez, G. Pardee, J. Santis, U. Pérez Vásquez, S. Poncio, I. Santos, R.
Pérez, K. Williams-Guillen, K. Zemenick, E. Schüller, S. Arming, C. Hochreiter, G. Mejía, F.
Rocha, D. Gonthier, K. Ennis, D. Allen, K. Fisher, D. Jackson, J. Remfert, Estelí Jiménez, H.
Hsieh, Amanda Ayala, R. Cuevas, J. Burdine, A. Rodríguez, C. Guadarrama, I. Armbrecht, A.
Niño, P. Gillette, M. Pintin, F. Chaufon, J. de Queiroz, G. Pascasio, A. Roblero, J.L. Enríquez,
Rocío Gómez y Sara Mendoza.
! Muchas gracias!
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ÍNDICE DE CONTENIDO
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RESUMEN
CAPÍTULO I.
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1.1. Introducción General
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1.1.1. Contexto global de la biodiversidad
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1.1.2. Agroecosistemas de café: Factores de intensificación y de paisaje
que inciden en la funcionalidad del agroecosistema
12
1.1.3. Hormigas como indicadoras de diversidad y su importancia ecológica
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1.2 Hipótesis y Objetivos
1.2.1. Hipótesis general e hipótesis particulares
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1.2.2 Objetivo general y objetivos particulares
18
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1.3. Metodología General
1.3.1. Caracterización del área de estudio y técnicas
de muestreo utilizadas
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1.3.2. Análisis estadísticos
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CAPÍTULO II. Factores locales y de paisaje promueven la biodiversidad
de cuatro grupos de hormigas en paisajes cafetaleros (Local and landscape
drivers of biodiversity of four guilds of ants in coffee landscapes. Biodiversity
Conservation, 22:871-888).
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CAPÍTULO III. Factores locales, del paisaje y de diversidad promueven los servicios
ecosistemicos de depredación por hormigas en cafetales (Local, landscape, and diversity
drivers of predation services provided by ants in coffee landscapes. Agriculture, Ecosystems and
Environment, Manuscript re-submitted.
Number : AGEE10440).
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CAPÍTULO IV. Factores locales y de paisaje promueven el parasitismo en un paisaje de
7
café (Local and landscape drivers of ant parasitism
in a coffee landscape. Environmental Entomology, Manuscrito sometido).
84
CAPÍTULO V. CONCLUSIONES Y RECOMENDACIONES
126
CAPÍTULO VI.- LITERATURA CITADA
135
ANEXO 1.
Acuse de artículo aceptado: Local and landscape drivers of biodiversity
of four guilds of ants in coffee landscapes. Biodiversity Conservation, 22:871-888.
144
ANEXO 2.
Normas editoriales de Agriculture, Ecosystems, and Environment
8
145
RESUMEN
La intensificación de agroecosistemas afecta la diversidad de la flora y fauna. Los
objetivos fueron: conocer los efectos de la intensificación agronómica de cafetales y de los
remanentes de bosque sobre la diversidad de hormigas, la relación entre la diversidad de
hormigas y la remoción de insectos presa por parte de éstas y finalmente, analizar cómo afectan
las variables locales y de paisaje a la tasa de parasitismo en las diferentes especies de hormigas
en un agroecosistema de café.
Se establecieron 40 sitios experimentales (400 m² cada uno) en siete fincas de café y en
fragmentos de bosque en el Soconusco, Chiapas. Se calculó un índice de complejidad de
vegetación (IVC), y mediante encuestas al personal de las fincas sobre el uso y frecuencia de
aplicación de agroquímicos se elaboró un índice de agroquímicos (IA). Con los datos de la
vegetación se clasificaron los 40 sitios experimentales en diferentes manejos del área de estudio
en: monocultivos (17 sitios), policultivos (13 sitios) y bosques (10 sitios). Con los programas
ArcView, y ArcMap se calcularon las siguientes variables de paisaje: distancia de cada sitio al
borde del bosque, áreas con radios de 50, 200 y 500 m para sitios de bosques y de café de tipo
rústico. En cada sitio, en las temporadas de secas y lluvias, se colectaron las hormigas del suelo,
cafetos y arboles con diversos métodos de muestreo.
Los resultados de esta investigación sugieren que los factores de manejo agronómico y la
influencia de fragmentos de bosque afectan la diversidad y abundancia de hormigas e
indirectamente influyen positivamente en los servicios ecosistémicos que las hormigas ofrecen.
Sin embargo, la intensidad de parasitismo sufrido por las hormigas es afectada principalmente
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por factores del paisaje, la cantidad de humus y numero de pupas para ciertos grupos de
hormigas.
Se propone realizar acciones de diversificación de las zonas de café empleando diferentes
especies de árboles para sombra (preferentemente nativos) o plantar diversas especies de Inga,
con el fin de proveer refugio y alimentación para los diferentes organismos que existen y así
favorecer las interacciones ecológicas entre ellos, favoreciendo una mejor calidad de la matriz
agrícola que promovería sustentabilidad, economía y salud de los productores en la región.
Palabras clave: diversidad, servicios ecosistémicos, parasitoides, paisaje, hormigas.
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CAPÍTULO I.
Introducción general
1.1.1. – Contexto global de la biodiversidad
Globalmente, dos tercios de los bosques originales se han dteriorado por diversas
acciones humanas como la agricultura (Richards y Tucker 1988). La pérdida de la diversidad
local y las extinciones a nivel global son consecuencias de la disminución y/o deterioro del
hábitat original (Jenkins, 2003; Baillie et al., 2004). A partir de estudios recientes, se ha
demostrado que la biodiversidad global está en declive desde hace alrededor de 40 años; y si el
ser humano no cambia o modifica las acciones en el uso de los recursos naturales, el mismo
panorama negativo para la biodiversidad continuará en las siguientes décadas (Butchart et al.,
2010).
Existen diversos estudios que modelan las consecuencias de la pérdida de diversidad y su
repercusión en la funcionalidad de los agroecosistemas (Tscharntke et al., 2002; van Nouhuys,
2005; Vandermeer y Perfecto, 2007). Entre los estudios realizados para conocer las
consecuencias de la pérdida de los componentes de la biodiversidad (ver Altieri, 1999) y su
relación con diversas funciones ecológicas en paisajes fragmentados, se encuentran aquellos que
han estudiado la polinización, la depredación, y el parasitismo (Landis et al., 2000; Wilby y
Thomas, 2002; Mayfield et al., 2006; Tscharntke y Lewis, 2007). Otros estudios relacionaron el
impacto de la simplificación de paisajes agrícolas con procesos ecológicos y ambientales que
intervienen en la funcionalidad de algunos agroecosistemas (Wilby y Thomas, 2002; Tscharntke
et al., 2008) como los de café y de cacao, los cuales poseen alta riqueza biológica y son refugio
de gran cantidad de organismos (Moguel y Toledo 1999).
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1.1.2. Agroecosistemas de café: Factores de intensificación y de paisaje que inciden en la
funcionalidad del agroecosistema.
En los últimos años, la tendencia global a la intensificación o tecnificación del café, con
el fin de aumentar los rendimientos, han transformado algunos cafetales en monocultivos de
sombra (Perfecto y Snelling 1995). La problemática ocasionada por estas acciones locales
(podas, uso de agroquímicos, etc.), es que se genera una matriz agrícola de baja calidad y/o la
fragmentación del paisaje, afectando diversos procesos ecológicos y funcionales a nivel del
paisaje, al disminuir la conectividad entre bosques (Vandermeer y Perfecto, 2007).
En México, los cultivos de café están altamente correlacionados con regiones de alta
diversidad biológica (Moguel y Toledo, 1999). Particularmente en Chiapas, tercer estado
productor de café a nivel nacional, se ha documentado que algunas fincas en la región del
Soconusco, que poseían gran riqueza y abundancia de insectos (Ibarra-Núñez; 1990, Perfecto y
Vandermeer, 2002) están sufriendo cambios en su manejo agronómico, lo cual puede afectar la
riqueza de especies que albergan estos sitios que están dentro de las áreas denominadas “hotspots” (puntos calientes) de diversidad (Moguel y Toledo 1999; De la Mora et al. 2013a). El
paisaje de café en el Soconusco se caracteriza por un gradiente de intensificación de este cultivo,
delimitado por fragmentos de bosque con la flora original de la región. La matriz, considerada
como un hábitat uniformemente hostil que no ofrece ningún recurso a las especies aisladas en los
fragmentos agrícolas en esta zona está representada por fincas de café rústico (alta diversidad),
policultivo tradicional, policultivo comercial, monocultivo de sombra, y cafetales sin sombra
(cultivo intensivo), delimitadas con parches de bosques con poca conectividad entre ellos (De la
Mora et al. 2013a). Las prácticas agronómicas locales en algunas fincas, como la reducción de
sombra, recepas en plantas de café, siembra de variedades de alto rendimiento y aplicación de
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agroquímicos, provocan cambios drásticos en la matriz, afectando en menor o mayor grado a
diversos taxa (Perfecto et al., 2003; Philpott, 2005; García Estrada et al., 2006; Williams-Guillén
et al., 2008); por ejemplo, a mayor intensificación se observan efectos negativos en la diversidad
de mariposas, aves, murciélagos y hormigas (Perfecto et al., 2003; García Estrada et al., 2006;
Perfecto et al., 2007). En teoría, la pérdida de diversidad biológica por la intensificación del
cultivo provoca una simplificación en las relaciones ecológicas y funcionales del agroecosistema
(Vandermeer y Perfecto, 2007, Philpott et al., 2008c). Así también, en los últimos años se
encuentran fincas que han sembrado árboles de sombra alta y/o han dejado áreas para la
conservación de flora y fauna de la zona, lo que podría aumentar la conectividad entre
fragmentos de bosques, mejoraría la calidad de matriz y por lo tanto, mejoraría la funcionalidad
del agroecosistema (ver Vandermeer y Perfecto, 2007). Por lo anterior, el paisaje de café en el
Soconusco, Chiapas es un escenario ideal para estudiar la diversidad, los servicios ecosistemicos,
las relaciones ecológicas y el efecto de cambios en la intensificación a nivel local y de paisaje,
propósitos de este estudio.
En esta región cafetalera del Soconusco se han realizado investigaciones sobre la calidad
de la matriz y su efecto en la diversidad, funcionalidad y relaciones ecológicas. Por ejemplo, se
ha comparando la biodiversidad de aves y los servicios ecosistémicos brindados por este grupo
(Perfecto et al., 2004); el efecto de la intensificación del cultivo de café en la diversidad de
mariposas (Perfecto et al., 2003); y la diversidad de hormigas y su efecto por depredación sobre
otros insectos en fincas con diferentes condiciones de manejo agronómico (Perfecto et al., 2003;
Armbrecht y Perfecto, 2003; Philpott y Armbrecht, 2006; Philpott et al., 2008a). Como resultado,
se ha determinado que algunos organismos responden en diferente forma a los cambios de la
calidad de la matriz; por ejemplo, cambios en la composición y diversidad de especies (Perfecto
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et al., 2003; García Estrada et al., 2006; Philpott et al., 2008b; Jha, 2009). Sin embargo, casi no
se ha estudiado el efecto sobre la diversidad y abundancia a gran escala (al nivel de paisaje), o
los efectos del manejo agronómico y de cambios en el paisaje y las relaciones ecológicas con
otros organismos (polinización, depredación, parasitismo). De igual manera, poco se ha
estudiado el efecto de cambios a nivel de paisaje en la funcionalidad de un agroecosistema en
México.
1.1.3. Hormigas como indicadoras de diversidad y su importancia ecológica
No obstante que las hormigas ocupan un amplio rango de nichos ecológicos y son
importantes para el funcionamiento de los ecosistemas (Hölldobler y Wilson, 1990; Folgarait,
1998; Agosti y Alonso, 2000) son escasos los estudios que han abordado a nivel del paisaje, a
este grupo taxonómico en agroecosistemas de regiones tropicales (Longino y Colwell, 1997;
Lachaud y García-Ballinas, 2001; Ribas et al., 2005; Jiménez et al., 2008). Existen estudios sobre
la diversidad y la abundancia de gremios de hormigas con fines taxonómicos y
comportamentales, desde hormigas que recolectan una gran diversidad de alimentos, hasta
especialistas que hacen uso de un solo recurso (Rojas-Fernández, 2001; Jiménez et al., 2008). Sin
embargo, debido a que existen diferentes especies de hormigas, con diferentes estrategias de
forrajeo, de depredación y diferentes niveles de especialización en presas (Yanoviak y Kaspari,
2000; De la Mora et al., 2008b), es interesante conocer si los cambios en la diversidad y
abundancia de las hormigas (con sus diferentes estrategias o adaptaciones morfológicas,
químicas, comportamentales y su complementariedad con otros taxa) por efecto de la
intensificación agrícola, pueden impactar en los servicios ecosistémicos que éstas ofrecen; como
por ejemplo, la remoción de insectos-plaga en los diferentes estratos de agroecosistemas como
los cafetales.
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Por otro lado, las hormigas son parasitadas por numerosos organismos (ver Lachaud y
Pérez-Lachaud, 2012), en particular por himenópteros de la familia Eucharitidae (Heraty, 1997;
Lachaud y Pérez-Lachaud 2012). Algunas especies de esta familia de avispas son consideradas
agentes potenciales para el control biológico de hormigas que son consideradas plagas (Lachaud
y Pérez-Lachaud 2012); sin embargo, otras especies parasitan hormigas que son consideradas
benéficas en ciertos agroecosistemas (Lachaud et al., 1998; Pérez-Lachaud et al., 2006a;
Lachaud y Pérez-Lachaud, 2009; Pérez-Lachaud et al., 2010) como algunas hormigas de las
subfamilias Ponerinae y Ectatomminae conocidas como hormigas depredadoras generalistas en
agroecosistemas tropicales. Paralelamente, el parasitismo que sufren las hormigas puede estar
influenciado por el tipo de hábitat donde se encuentran (Vásquez-Ordoñez et al., 2012), la época
del año (Pérez-Lachaud et al., 2006b; Lachaud y Pérez-Lachaud, 2009; De la Mora y Philpott
2010; Pérez-Lachaud et al., 2010) y la disponibilidad de pupas (Lachaud y Pérez-Lachaud,
2009). Sin embargo, poco se ha estudiado el efecto de características del paisaje sobre el
parasitismo en hormigas; únicamente se conoce que el tipo de hábitat puede influir en la tasa de
parasitismo (De la Mora y Philpott 2010; Vázquez-Ordóñez et al., 2012), y que la abundancia
relativa de diferentes especies de parasitoides puede responder a cambios estacionales (Reese y
Philpott, 2012).
Se sabe que la diversidad y abundancia de comunidades de hormigas es afectada por la
intensificación en sistemas de café (Perfecto y Snelling, 1995; Perfecto et al., 2003), pero se
requiere información sobre las consecuencias en los servicios que brindan las hormigas y en sus
interacciones con otros organismos. Por ejemplo, sobre la depredación de otros insectos por las
hormigas en estos agroecosistemas, y/o sobre el parasitismo que las hormigas sufren por sus
enemigos naturales a nivel de paisaje. En el caso de los servicios ecológicos, tanto la diversidad
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como la abundancia de hormigas son importantes en la regulación de insectos-plaga para algunos
agroecosistemas (Jiménez et al., 2008; De la Mora et al., 2008a). En estudios a nivel de paisaje,
se conoce que en sitios con matrices de calidad alta (cafetales de sombra), algunas especies de
hormigas pueden presentar un efecto regulador sobre la plaga principal de granos de café
Hypothenemus hampei (Coleoptera: Curculionidae) (Armbrecht y Gallego, 2007), y que la
distancia a los bosques afecta la remoción de estos coleópteros (Armbrecht y Perfecto, 2003).
Por ejemplo, Azteca sericeasur Longino (Formicidae: Dolichoderinae), una hormiga arbórea
dominante, es un insecto con potencial en la regulación de herbívoros en cafetales con abundante
sombra (Vandermeer et al., 2002).
En sistemas de café y cacao en el Soconusco se ha reportado la existencia de diversas
especies de avispas de la familia Eucharitidae que parasitan a varios géneros de hormigas
(Lachaud et al., 1998; Lachaud y Pérez-Lachaud, 2001; Pérez-Lachaud et al., 2006a,b), pero
existe poca información sobre la tasa de parasitismo por eucarítidos en hormigas y su posible
variación por efecto del manejo agronómico local en cafetales (De la Mora y Philpott, 2010).
Además, se desconoce la influencia de los remanentes de bosque en los paisajes cafetaleros
como posibles fuentes de biodiversidad de himenópteros, reportado en otros sistemas (Thies et
al., 2003; Sperber et al., 2004).
Por lo anterior, este trabajo trata contestar las siguientes preguntas: 1) ¿Está la diversidad
de hormigas en agroecosistemas cafetaleros influenciada por factores de manejo agronómico
locales y por factores de paisaje? 2) ¿Los servicios ecológicos, como la depredación, están
influenciados por factores locales y de paisaje? 3) ¿El parasitismo de hormigas es afectado por
factores locales o de paisaje?
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1.2- Hipótesis y Objetivos
1.2.1.- Hipótesis general e hipótesis particulares
Hipótesis General
Los factores de manejo agronómico y la influencia de cambios en el paisaje afectan la diversidad
y abundancia de hormigas, influyen en los servicios ecosistémicos de remoción de presas que
este grupo ofrece y afectan la relación de parasitismo que sufren por parasitoides.
Hipótesis particulares
La diversidad y abundancia de hormigas en fincas cafetaleras es afectada por la intensificación
del manejo agronómico y/o por la influencia de cambios en el paisaje.
La remoción de insectos por hormigas en fincas cafetaleras es influenciada por la intensificación
del manejo agronómico y/o por la influencia de cambios en el paisaje.
El parasitismo sufrido por hormigas es afectado por la intensificación del manejo agronómico
y/o por la influencia de cambios en el paisaje.
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1.2.2. Objetivo general y objetivos particulares
Objetivo General
Determinar si la intensificación del manejo agronómico y los cambios en el paisaje afectan la
diversidad y abundancia de hormigas, influyen en los servicios ecosistémicos de remoción de
presas que este grupo ofrece, y modifican la relación de parasitismo que sufren por parasitoides.
Objetivos Particulares
Determinar si la diversidad y abundancia de hormigas en fincas cafetaleras son afectadas por la
intensificación del manejo agronómico y/o por los cambios en el paisje.
Identificar los factores locales o cambios en el paisaje que influyen en la remoción de insectos
por hormigas en fincas cafetaleras.
Determinar si el parasitismo sufrido por hormigas está relacionado con factores locales de del
manejo agronómico y/o con cambios en el paisaje.
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1.3. Metodología General
1.3.1. Caracterización del área de estudio y técnicas de muestreo utilizadas
El estudio de campo se llevó a cabo en siete fincas de café y sus fragmentos de bosque en
el Soconusco, Chiapas, México (ver Capítulo II, Fig. 1). La sombra en estos cafetales es
proporcionada predominantemente por diferentes especies del género Inga, mientras que en los
fragmentos de bosque existen aún árboles que se consideran como representativos de la
vegetación original como son: Virola guatemalensis (Myristicaceae), Terminalia amazonia
(Combretaceae) y Quercus spp. (Fagaceae). Se seleccionaron y se caracterizaron 40 sitios en 52
km2 aproximadamente (30 en cafetales y 10 en bosque) con las siguientes condiciones: 1) estar
entre 850 a 1450 msnm, 2) no tener presencia de Azteca sericeasur, por ser una hormiga
arborícola dominante, 3) estar en presencia de café Coffea arabica (para sitios en cafetales) y 4)
distancia entre sitios mayor de 200 m. Posteriormente se realizaron al menos 26 mediciones in
situ de las características físicas y de vegetación en cada sitio experimental (por ejemplo: altura
de árboles, sombra, grosor de la copa y peso de humus, densidad de cafetos, altitud). Así
también, mediante encuestas al personal de las fincas se obtuvo información sobre las
actividades agronómicas que se realizan durante el año para la regulación de plagas, hongos y el
control de malezas, entre otros aspectos de manejo. Para las variables de paisaje en los sitios
experimentales de muestreo, se cuantificó el área de remanentes de bosque y cafetales de tipo
rústico en cada sitio (creación de áreas con radios de 50, 200, y 500m); es decir, cuanta área de
bosque y café de tipo rústico había en los diferentes puntos de muestreo en las áreas creadas.
Finalmente se cuantifico la distancia de cada sitio experimental al boorde del bosque más
cercano. Las mediciones de paisaje se realizó mediante el programa ArcGIS (ver Capítulo II; De
la Mora et al., 2013b).
19
Para conocer la riqueza y la abundancia de las hormigas, la intensidad relativa de
remoción de insectos por hormigas, y el parasitismo que sufre este grupo, se utilizaron diferentes
métodos de muestreo. Se muestrearon cuatro grupos de hormigas de acuerdo con su hábitat, su
sitio de anidación y su estrato de forrajeo durante la época seca (febrero-abril 2010 y febrero
2011) y temporada lluviosa (junio-julio 2010). Para las hormigas de la hojarasca, se colectaron
muestras de hojarasca y se depositaron en sacos tipo Mini-Winkler por 72 horas. Para las
hormigas que forrajean en el suelo, se colectaron adultos mediante la técnica destructiva en
troncos podridos (donde se colectaron también pupas) y se colocaron como cebo 20 moscas
muertas de Anastrepha obliqua Macquart (Diptera: Tephritidae) sobre el suelo. Para colectar
hormigas que nidifican en ramas de café, se realizó la apertura de ramas huecas de café y se
utilizó como cebo 20 brocas de café Hypothenemus hampei Ferrari (Coleoptera: Curculionidae)
muertas colocadas sobre ramas de cafetos. Por último, para hormigas arbóreas se colocaron 20
moscas muertas de la fruta sobre la corteza de árboles de sombra del género Inga, a una altura de
1.30 m aproximadamente. La determinación de las hormigas se realizó mediante las claves de
Bolton (1994), Longino (2009) y páginas especializadas (Longino, 2011).
Para relacionar la diversidad y abundancia de hormigas con factores locales y de paisaje,
se utilizaron datos obtenidos de las trampas Mini-Winkler, del método destructivo en troncos
podridos, del método destructivo de ramas de café y de los cebos en árboles (ver Capítulo II).
Para conocer los servicios ecosistémicos en relación con la remoción de presas por hormigas, se
utilizaron los datos de los cebos constituidos de moscas muertas de la fruta en suelo y en arboles
de sombra y de brocas del café muertas para ramas de café (Capítulo III). Por último, el
parasitismo que sufren las hormigas en sistemas agroforestales fue examinado mediante la
colecta de pupas de hormigas en troncos podridos (Capítulo IV).
20
1.3.2. Análisis estadísticos
Inicialmente se analizaron y se crearon índices de complejidad de vegetación (IVC) de
acuerdo a las características físicas de sombra principalmente (porcentaje de sombra, riqueza de
especies, densidad de cafetos, entre otros) y un índice de agroquímicos (IA) de acuerdo a las
entrevistas realizadas sobre el uso de agroquímicos. Utilizando el IVC y el IA, se agruparon y
clasificaron los 40 sitios experimentales en: café de baja sombra (17 sitios), café de alta sombra
(13 sitios) y bosque (10 sitios). Con el análisis multivariado de la varianza (MANOVA), y
utilizando 19 factores locales y siete factores del paisaje como variables independientes se
encontraron diferencias significativas entre hábitats (ver Capítulo II, Tabla 1). Con el fin de
conocer las diferencias de factores físicos entre los hábitats, se realizó un análisis de varianza
(ANOVA univariada), seguido de la prueba de Tukey cuando las comparaciones fueron
estadísticamente significativas entre hábitats.
Como segundo análisis, para conocer el efecto del manejo local en cada una de nuestras
variables dependientes, se utilizaron análisis de regresión, así como análisis de modelos lineales
generalizados mixtos (GLMM) y modelos lineales generalizados (GLM). Particularmente, en el
Capítulo II, los análisis de diversidad y abundancia de hormigas y la relación entre los valores
del índice de complejidad de vegetación (IVC) y el índice de agroquímicos se realizó mediante
análisis de regresión. Para elegir la línea de tendencia que mejor se ajustó a cada modelo de
regresión, se utilizó el criterio de información de Akaike (AIC). Por último, debido al gran
número de regresiones para cada análisis en este capítulo, se utilizó el criterio de tasa de falso
descubrimiento (FDR), análisis basado en ajustar la significancia de cada regresión y evitar el
error de tipo 1 (Benjamini y Hochberg, 1995). Los análisis del capítulo II se realizaron con el
paquete estadístico SPSS V.18. Para conocer el efecto de la proporción de presas removidas
21
(Capítulo III) y la proporción en parasitismo sufrido por este grupo (Capítulo IV), se realizaron
análisis con el programa R (The R Development Core Team 2011) mediante modelos lineales
generalizados (GLM) y modelos lineales generalizados mixtos (GLMM). De igual forma que en
el Capítulo II, para elegir la mejor combinación que se ajustó a cada modelo se utilizó el criterio
de información de Akaike (AIC), análisis calculado con el paquete de "MASS" en el programa R
(The R Development Core Team, 2011; Venables y Ripley, 2002). Por último, para evitar el
error de tipo 1 (Benjamini y Hochberg, 1995) en estos dos capítulos, se utilizó la función
“CBIND” para cada análisis en los modelos generalizados (en el programa R).
Para examinar qué factores locales y de paisaje influyeron sobre las diferentes variables
dependientes se crearon, para cada capítulo, modelos de árboles de inferencia con el paquete
"PARTY" en el programa R (Hothorn et al., 2006; The R Development Core Team, 2011). De
acuerdo a Olden y colaboradores (2008) los árboles de inferencia examinan el grado en que las
variables independientes predicen una variable dependiente (Strobl et al., 2009), así también
estiman y clasifican las relaciones entre variables mediante la utilización de un algoritmo
recursivo de datos binarios de partición (Hothorn et al., 2006) el cual examina todas las variables
y busca el mejor predictor de la variable dependiente, divide los datos en grupos y
posteriormente repite la variable de selección hasta encontrar predictores que caractericen el
fenómeno presentado (Hothorn et al., 2006; Jha, 2009). Debido a que muchas características
locales pueden estar correlacionadas, se intentó reducir el número de variables con un análisis de
componentes principales y correlaciones mediante el criterio de Pearson (Uno et al., 2010). Sin
embargo, la mayoría de los factores no se correlacionaron con los componentes principales PC1
o PC2 (P=0.05), por lo que se utilizaron las 26 variables descriptivas previamente mencionadas
en árboles de inferencia para cada capítulo. El análisis de componentes principales se realizó con
22
el programa “R” utilizando la librería “VEGAN”. Con el fin de examinar un efecto de
autocorrelación, es decir, si la proximidad de las parcelas de muestreo o grado de solapamiento
entre zonas de café rustico y bosque de 50, 200, 500 m influía significativamente en los
diferentes experimentos (datos generados), se realizó la prueba de autocorrelación espacial de las
variables dependientes (ver Dormann et al., 2007). Específicamente, se realizaron análisis de
autocorrelación espacial con los residuales de todas las regresiones y árboles de inferencia
condicional con correlogramas espaciales (con el paquete “NCF” y “SPDEP”en R) (Bivand et
al., 2012; Bjornstad 2009; The R Development Core Team 2011). Para el cálculo de los
predictores de Moran, utilizamos las distancias entre parcelas experimentales más cercanos y la
opción de prueba de permutación. De las variables examinadas, particularmente en las
regresiones y los árboles de inferencia condicional, ninguna mostró autocorrelación espacial,
solo se presentó significancia de autocorrelacion en dos variables para el capítulo de
parasitismo (ver Capítulo IV).
23
Capítulo II
Factores locales y de paisaje promueven la biodiversidad de cuatro grupos de hormigas en
paisajes cafetaleros
De la Mora A, Murnen CJ, Philpott SM (2013). Local and landscape drivers of biodiversity of
four groups of ants in coffee landscapes. Biodiversity and Conservation 22:871-888
Articulo publicado en febrero de 2013 en:
Biodiversity Conservation
Factor de impacto: 2.065
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Capítulo III.
Factores locales, del paisaje y de diversidad promueven los servicios ecosistemicos de
depredación por hormigas en cafetales.
De la Mora A, García-Ballinas A, Philpott SM. Local, landscape, and diversity drivers of
predation services provided by ants in coffee landscapes. Agriculture, Ecosystems and
Environment, Manuscript re-submitted after completing revisions. (Number: AGEE10440).
Articulo en revision en Agriculture Ecosystem and Environment
Running Head: Prey removal by ants in coffee landscapes
Factor de impacto: 3.24
43
Local, landscape, and diversity drivers of predation services provided by ants in coffee
landscapes
De la Mora, A.1
García-Ballinas J.A.¹
Philpott, S. M.2,3
¹ECOSUR, Ecología de Artrópodos y Manejo de Plagas, Carretera Antiguo Aeropuerto Km 2.5
Tapachula, Chiapas, México
2
Environmental Studies Department, University of California, Santa Cruz, 1156 High St., Santa
Cruz, CA 95064, U.S.A.
3
Corresponding author, e-mail: [email protected]
44
Abstract
Agricultural management and the landscape surrounding farms impact biological
diversity and ecosystem services, such as predation in agroecosystems. Diversified coffee
agroecosystems harbor biodiversity, and maintain ecosystem services, especially when in
complex landscapes, and when diversity of organisms providing services is maintained. But few
have examined whether biological diversity, per se, or the local and landscape habitat features
are stronger drivers of the services provided. We studied the relationships between local
characteristics associated with agricultural management (vegetation complexity and
agrochemical use), landscape surroundings of farms, abundance and richness of ants, and
predation services provided by ants in a tropical coffee landscape. Specifically, we examined: 1)
Do ants remove introduced prey items, and does prey removal differ in the wet and dry seasons?
2) Do vegetation complexity and agrochemical use on farms affect prey removal rates by ants?
3) Do richness or abundance of ants correlate with prey removal rates? and 4) Which local and
landscape factors correlate with prey removal rates by ants, and do local, landscape, or ant
community characteristics more strongly influence predation? We established sites across a
range of coffee management systems, at varying distances to forest fragments, and in forest
fragments and examined prey removal by ants on the ground, coffee branches, and shade tree
trunks in the wet and dry season. Prey removal did not differ with season. Prey removal on the
ground increased within increases in ant abundance and richness, and number of hollow coffee
twigs. Prey removal on coffee plants decreased with vegetation complexity, but increased with
ant abundance and richness, coffee density (a local factor), and rustic coffee area in the
landscape (a landscape factor). Prey removal on trees was higher in the two coffee management
systems than in forests, declined with vegetation complexity, but increased with abundance and
45
richness of ants. Characteristics of the ant community were consistent predictors of prey
removal, whereas local and landscape habitat features were less important, and only three of the
wide array of habitat features correlated with prey removal. Thus, promoting pest control
services within coffee agroecosystems might be best accomplished by manipulating ant
abundance and richness, or specific vegetation factors that correlate with ants.
Keywords: Biodiversity and ecosystem function, biological control, conservation, ecosystem
services, habitat complexity
1. Introduction
1.1. Biodiversity, agricultural intensification and landscape change
Ecosystem services in complex, biodiverse ecosystems, such as forests, may be more
robust to disturbance in comparison to simplified landscapes such as those under agricultural
production. However, the drivers of the maintenance of ecosystem services remain unclear. Few
studies have examined whether biodiversity per se or local or landscape complexity are the
drivers of ecosystem service maintenance (Klein et al. 2002, Matson et al. 1997, Perfecto et al.
1997). Additionally, not all species within ecosystems harbor characteristics that make them
resilient to disturbance and thus may be more susceptible to local extinctions if disturbances are
severe or frequent (Stork et al. 2009). An ideal setting to study the effect of intensification on
ecosystem services is in agroecosystems ranging in management from diverse, complex systems
to monocultures.
Agricultural intensification and industrialization result in extensive, input dependent
monocultures that simplify the agricultural environment, while aiming to increase agricultural
46
profits (Bisseleua et al., 2009; Matson et al., 1997; Swift et al., 1996). This agricultural
intensification process creates expansive landscapes consisting of a matrix of homogeneous
monocultures with interspersed fragments of natural vegetation (Fahrig, 2003; Vandermeer and
Carvajal, 2001; Vandermeer and Perfecto, 2007). Agricultural intensification results in habitat
loss, alteration of ecological communities, and changes in species interactions (Fischer et al.,
2006; Vandermeer et al., 2010) and transforms landscapes by affecting physical characteristics
such as microclimate, structure and diversity of native vegetation, size of forest patches, matrix
quality, the distribution of habitat patches, and the ecological relationships in the new landscape
(Dauber et al., 2003; Fahrig, 2003; Tscharntke et al., 2002). Changes in habitats, as well as
landscape changes may significantly impact trophic interactions (Vandermeer and Carvajal,
2001). Our work aims to study the relationship between habitat and landscape modification,
predator abundance and richness, and predation services in a coffee landscape.
1.2. Agricultural landscape change, biodiversity, and ecosystem services
Changes in agricultural landscapes result in rapid biodiversity loss with implications for
ecosystem services. Some agroecosystems are important reservoirs native flora and fauna, but
management intensification, including reducing the shade tree structure and complexity from
coffee and cacao agroecosystems, can affect the potential for these systems to harbor biodiversity
(Bisseleua et al., 2009; Klein et al., 2003; Moguel and Toledo, 1999). Further, intensification
includes increased use of agrochemicals that impact biodiversity (De la Mora et al., 2013; García
Estrada et al., 2006). Transformation of complex agricultural systems to intensive monocultures
results in losses of bird, ant, bee, and bat diversity (García-Estrada et al., 2006; Klein et al., 2002;
Perfecto et al., 1997; Perfecto and Vandermeer, 2002; Philpott et al., 2008a). Such species losses
47
can affect trophic interactions and food webs (Dauber et al., 2005; Klein et al., 2002; Perfecto
and Vandermeer, 2006), and have further consequences for ecosystem function. Specifically,
losses of pollinator, seed disperser, and predator abundance and richness may decline, affecting
agroecosystem function (Klein et al., 2003; Wilby and Thomas, 2002; Zhang et al., 2007). With
agricultural intensification, a loss of taxonomic or functional diversity, or shifts in
complementarity of organisms within ecological networks can alter provisioning of basic
ecosystem services such as pollination, predation, and parasitism needed in agroecosystems
(Klein et al., 2002; Philpott et al., 2008c; Wilby and Thomas, 2002).
Yet, conserving biodiversity in agricultural habitats may promote biological control
(Fischer et al., 2006; Vandermeer et al., 2010). Habitat and landscape complexity and increases
in species richness promote complex ecological networks (Dauber et al., 2003, 2005; IbarraNúñez, 1990; Moguel and Toledo, 1999), and ecosystem services (Dauber et al., 2005; Jha and
Vandermeer, 2010; Klein et al., 2003; Loreau et al., 2001; Vandermeer et al., 2010; Zhang et al.,
2007). A conservation approach may enhance agricultural matrix quality by providing additional
food resources or nesting sites, or by providing protection from predators, thereby favoring the
survival of natural enemies (Gallego-Ropero et al., 2009; Landis et al., 2000; Vandermeer et al.,
2010). Practices like providing artificial nesting sites, a diverse plant species selection, or
alternative food resources positively impact ecosystem services by increasing abundance of
predators that regulate different potential pest insect populations (Folgarait, 1998; Landis et al.,
2000; Philpott and Foster, 2005; Teodoro et al., 2008). Thus, we must understand which local
and landscape factors drive ecosystem services in order to best promote specific pest control
practices (Avelino et al., 2012; Tscharntke et al., 2005).
48
1.3. Ants as potential predators in coffee farms
Ants are predators in natural ecosystems and agroecosystems (De la Mora et al. 2008;
Folgarait, 1998; Larsen and Philpott, 2010; Vandermeer et al., 2010; Way and Khoo, 1992) and
there is a negative impact of agricultural intensification on ant diversity and on pest control
services provided by ants (Philpott and Armbrecht, 2006). In agroecosystems, such as coffee,
diversity and abundance of ants positively correlate with habitat complexity, and pest removal
tends to be higher in complex coffee habitats (Armbrecht and Perfecto, 2003). However, local
agricultural practices in coffee farms, like shade tree pruning, will change structural
characteristics of agroecosystems that may affect the efficiency of ants as predators (De la Mora
et al., 2008; Philpott and Armbrecht, 2006). In addition, removal of coffee twigs when coffee
plants are pruned may reduce abundance and richness of twig-nesting ants, important predators
of coffee pests (Larsen and Philpott, 2010; Philpott and Foster, 2005). Some studies comparing
local and landscape influences on ants find that habitat characteristics, such as insolation and soil
conditions, more strongly affect ant communities than landscape factors (Dauber et al., 2005).
Yet, little is known about whether landscape variables influence predation services provided by
ants.
Predatory ants, including those that nest and forage on the ground, coffee branches, or on
trees, may positively affect coffee as biological control agents (Folgarait, 1998; Jiménez et al.,
2008). In coffee agroecosystems, specifically, ants control coffee pests (Ibarra-Núñez et al.,
2001; Philpott and Armbrecht, 2006). Ground- and coffee-foraging ants prey on the coffee berry
borer (Armbrecht and Perfecto, 2003; Larsen and Philpott, 2010) and limit attacks by the coffee
leaf miner (De la Mora et al., 2008). Some of the effects of ants on coffee pests depend on both
taxonomic diversity and functional richness of ants in the community, and may also depend on
49
the presence of specialized parasites of aggressively dominant ants in highly complex ecological
networks (Philpott and Armbrecht, 2006). Further, coffee intensification negatively impacts
control of coffee pests by ants (Armbrecht and Gallego, 2007; Philpott and Armbrecht, 2006)
and predatory impacts of ants are higher closer to forest fragments (Armbrecht and Perfecto,
2003). Less is known, for coffee or for any other agroecosystem, about the relative influences of
local agricultural management, components of the surrounding landscape, and the ant
community, on predatory impacts of ants on pests.
1.4. Hypothesis and goals
The goal of this study is to examine the relative impacts of local, landscape, and ant
community effects on predation services provided by ants in coffee landscapes. We also aimed to
examine the effects of seasonality on predation effects because the population sizes and locations
(e.g., on coffee plants or on the ground) of pests in the system can shift seasonally (Barrera
2002). For example, the coffee berry borer (Hypothenemus hampei Ferrari), the most damaging
insect pest of coffee, is more abundant on coffee plants during the rainy season, and more
common on the ground during the dry season (Barrera 2002). We examined how changes in
agricultural management at the local level (e.g. vegetation characteristics, agrochemical use) and
at the landscape level (e.g. distance from forest fragments, area of forest cover surrounding study
sites) affect predatory activities of ants in coffee agroecosystems. We also examined whether
changes in ant abundance or richness correlate with changes in predation services provided by
ants. Specifically, we asked: 1) Do ants remove introduced prey items, and does prey removal
differ in the wet and dry seasons? 2) Do vegetation complexity and agrochemical use on farms
affect prey removal rates by ants? 3) Do richness or abundance of ants correlate with prey
50
removal? and 4) Which local and landscape factors correlate with prey removal by ants, and do
local, landscape, or ant community characteristics more strongly influence predation?
2. Methodology
2.1. Study Site
We conducted research in the Soconusco region of Chiapas, Mexico in an area dominated
by coffee (~ 94% of the landscape) and forest fragments (~ 6% of the landscape) (Philpott et al.,
2008b). Study sites were located 30 – 40 km NE of Tapachula between 800 – 1450 m above sea
level. Rainfall averages 4500 mm per year with a dry season between December and April. We
established forty 20 x 20 m sites; 10 in forest fragments and 30 in coffee sites. According to the
coffee management classification described in Philpott et al. (2008a), our 30 study sites
corresponded to 3 rustic coffee sites, 4 traditional polyculture sites, 7 commercial polyculture
sites, 15 shade monoculture sites, and 1 sun coffee site.
2.2. Local and Landscape factors
The local and landscape characteristics of each study site are developed in detail in De la
Mora et al. (2013), but we summarize those findings here and in the supplemental information
(S1). At the local scale, we measured 19 variables related to site location, vegetation, and
agrochemical use. For site location, we measured altitude and slope of the terrain. We measured
13 vegetation factors: percent canopy cover, tree height, tree circumference, tree density, tree
richness, number of coffee plants, number of hollow coffee twigs, number of rotten logs on the
ground, length of rotten logs, circumference of rotten logs, degree of decay of rotten logs, leaf
litter mass, and humus mass. With the vegetation data, we created a vegetation complexity index
51
(VCI) that summarized vegetation changes across sites and also allowed us to separate coffee
farm sites into 13 high- and 17 low-shade coffee habitats (Mas and Dietsch, 2005; De la Mora et
al., 2013). We measured 4 agrochemical factors: frequency of use of fertilizers, pesticides,
fungicides, and herbicides. We likewise used agrochemical data to create an agrochemical index
(AI). With a Geographic Information System of the study region and data analysis in ArcGIS, we
calculated 7 landscape-level factors: distance to the nearest forest patch and the percent of rustic
coffee and forest within 50 m, 200 m, and 500 m buffer zones (see Philpott et al 2008b). Habitat
types differed for both local and landscape characteristics (De la Mora et al., 2013) (S1).
2.3. Predation trials
We examined ant predatory impacts during both the wet (June - August 2010) and dry
seasons (February - April 2011). We assessed ant predatory impact by examining removal rates
of two sentinel prey items, West Indian fruit flies (Anastrepha obliqua Macquart) and coffee
berry borers that were added to forest and coffee sites. We quantified prey removal in three
different strata – on the ground, on coffee plants (or on shrubs in forest sites), and on tree trunks
within farms and forests. For ground trials we placed 20 dead fly adults on an index card (10 x 7
cm) and then placed cards directly on the ground. For coffee (or shrub) trials, we placed 20 dead
coffee berry borer adults (CBB) on an index card, and balanced cards between two coffee
branches 1-1.50 m above ground. For tree trunk trials, we put 20 dead fly adults on an index card
and balanced cards 1.3 m above the ground on low branches of Inga spp. shade trees. For both
coffee shrub and shade tree trials, we took great care placing the cards to ensure that any missing
prey were due to predation and not falling off. In addition, we bent index cards slightly to lower
the risk that prey would fall off. We conducted two trials (between 8 AM and 2 PM) in each
52
study site during both the dry and wet seasons. Each trial was conducted in a different
haphazardly selected location within the 20 x 20 m site. Thus for each site we observed two
cards during each season (40 prey total per site per season), and recorded the number of prey
items removed after 30 min. There were two observers for each trial, thus one person watched
each card continuously for 30 min.
In addition, we recorded the number of ant individuals on the cards, and the species (or
morphospecies of ants) on the cards. We tallied the total species richness of ants visiting the two
cards in each site during each season, and the mean number of individual ants per minute across
the 30 min. observations. At the end of the trial (after 30 min) we collected all ant individuals
seen on cards, or that had recently left cards, for identification. Ants were stored in 70% ethanol
and identified to species (or morphospecies) following Bolton (1994) and Longino (2009, 2011).
2.4. Data Analyses
We compared prey removal rates in the three habitat types and during the two study
seasons. We summed the number of prey items removed across the two trials in each site during
each season and then compared the number of prey items removed on the ground, on coffee
plants (or forest shrubs), and on tree trunks for the three habitat types during the two seasons
with generalized linear mixed models (GLMM) with ‘glmer’ in the ‘lme4’ package in R (R
Development Core Team, 2012). We used number of prey items (out of 40) as the dependent
variable, and used the ‘cbind’ function, a binomial error distribution and logit link. We included
habitat type and season, and the interaction between them as fixed factors and included the
number of ant species, individuals, or both counted on cards as random factors. We compared the
three models with AIC, and selected the one with the lowest value (in all cases the model that
53
included the number of ant individuals on cards as a random factor).
We next examined relationships between prey removal on the ground, coffee or shrubs,
and shade trees and vegetation complexity (VCI), agrochemical use (AI), ant richness, and ant
abundance. We taking the mean number of prey items removed across the two seasons and used
site as a replicate. We examined ant abundance and richness in two ways. First, we examined
richness and abundance observed on index cards. For abundance, we calculated the mean
number of individuals recorded during each minute over the 30 min observation. For richness,
we counted the number of different ant morphospecies visiting cards in 30 min. Second, we
examined total ant richness and abundance at each study site as determined with extensive ant
surveys (De la Mora et al., 2013). We used data from mini-Winkler traps and rotten log samples
for ground ant abundance and richness, data from coffee twig surveys for coffee/shrub ant
abundance and richness, and data from baits placed on tree trunks for tree ant abundance and
richness (De la Mora et al., 2013). We then examined relationships between prey removal and
explanatory variables with generalized linear models (GLMs) in R with the ‘cbind’ function, a
binomial error distribution and logit link. Based on the GLM results, we used ANOVA with chisquare statistics to estimate the P-values, and used a Bonferonni correction to account for
multiple tests (6) per dependent variable (adjusted α = 0.008).
To examine which factors most highly influenced prey removal rates we created
conditional inference trees with the ‘party’ package in R (Hothorn et al., 2006; R Development
Core Team, 2012). We ran three sets of conditional inference trees, one set for each prey
removal strata (ground, coffee or shrubs, and tree trunks). For each stratum, we used three
separate models. In the first model, we included the 19 local- and the 7 landscape-scale habitat
variables as predictors of prey removal. In the second model, we included the 19 local- and 7
54
landscape-scale variables and abundance and richness of ants observed on cards as predictors of
prey removal. In the third model, we included the 19 local- and 7 landscape-scale variables and
total abundance and richness (measured in extensive ant surveys) as predictors of prey removal.
For all tests, we used the minimum criterion of 0.95 (P = 0.05), and used the site as a replicate.
We examined for spatial autocorrelation (Dorman et al., 2007) in the residuals of the
conditional inference trees and GLMs with (1) spatial correlograms (with the ‘ncf’ package in R)
and (2) the Moran's test for spatial autocorrelation using a spatial weights matrix (with the
‘spdep’ package in R) (Bivand et al., 2012; Bjornstad, 2009; R Development Core Team, 2012).
For the correlograms, we computed 100 permutations using the resamp argument in the correlog
function to examine the distance, if any, at which variables were spatially autocorrelated. For the
calculation of Moran’s I, we used nearest neighbor distances as the metric, and used the
permutation test option. One of 27 of the statistical tests examined displayed significant spatial
autocorrelation at any distance (Table S1), this spatial autocorrelation is likely not pervasive.
3. Results
3.1. Prey removal rates in different habitats and during different seasons
Prey removal on the ground and coffee or shrubs did not differ with habitat type (forest,
high-shade coffee, low-shade coffee), but there were differences in prey removal for tree
predation (Table 1). On average, there were 3.15 ± 1.20 (SE) prey items removed by ants in
forests, 5.0 ± 1.14 removed in high-shade coffee, and 3.62 ± 0.88 removed in low-shade coffee.
Ants removed 4.0 ± 0.71 prey in the dry season and 3.9 ± 0.99 prey items in the wet season.
Neither habitat type nor season influenced prey removal on the ground. On coffee and forest
shrubs, ants removed on average 0.20 ± 0.12 prey items in forests, 2.35 ± 0.74 in high-shade
55
coffee, and 2.62 ± 1.08 in low-shade coffee. Ants on coffee and shrubs removed 2.10 ± 0.79 prey
items in the dry season and 1.75 ± 0.71 prey items in the wet season. Again, neither habitat type
nor season influenced prey removal on the coffee or shrubs. On shade tree trunks, ants removed
1.45 ± 0.99 prey in forests, 2.88 ± 1.12 in high-shade coffee, and 3.38 ± 0.81 prey in low-shade
coffee. Ants removed on average 3.73 ± 0.89 prey items in the dry season and 1.75 ± 0.64 prey
in the wet season. Prey removal was higher in the high-shade coffee than in forest (P=0.001) and
was also higher in low-shade coffee than in forest (P=0.035). There were no differences in prey
removal from the trees in different seasons.
3.2. Vegetation complexity, agrochemical use and prey removal
Vegetation complexity, but not agrochemical use correlated with prey removal on
coffee/shrubs and on shade trees (Fig. 1). According to the GLM, prey removal on the ground
was not significantly correlated with VCI (z = -1328, Χ2 = 1.785, df = 1, 38, P = 0.1815), but was
negatively correlated with prey removal on coffee (z = - 3.899, Χ2 = 16.683, df = 1, 38, P
<0.001), and trees (z = -2.812, Χ2 = 8.273, df = 1, 38, P = 0.004). There was no influence of AI
on prey removal on the ground (z = -0.395, Χ2 = 0.157, df = 1, 38, P = 0.692), on coffee (z = 0.134, Χ2 = 0.018, df = 1, 38, P = 0.893), or on trees (z = 0.09, Χ2 = 0.008, df = 1, 38, P = 0.929).
3.3. Ant abundance and richness and prey removal
Local abundance and richness of ants (observed on cards) and total abundance correlated
significantly with prey removal for all removal strata (Fig. 1). In removal trials, we observed 50
different morphospecies of ants from 21 genera: Pheidole was the most species rich genus with
19 morphospecies observed. Overall, ants removed 239 prey items, and 11 morphospecies
56
removed 62% of prey items removed during trials (Table 2). Prey removal was positively
correlated with the number of ant individuals on cards on the ground (z = 7.304, Χ2 = 52.361, df
= 1, 38, P <0.001), on coffee (z = 9.892, Χ2 = 85.439, df = 1, 38, P <0.001), and on trees (z =
6.274, Χ2 = 34.598, df = 1, 38, P <0.001). In addition, prey removal was positively correlated
with the number of ant species that visited cards on the ground (z = 6.946, Χ2 = 54.376, df = 1,
38, P <0.001), on coffee and shrubs (z = 5.335, Χ2 = 27.933, df = 1, 38, P < 0.001), and trees (z =
6.668, Χ2 = 43.279, df = 1, 38, P <0.001).
Total ant richness and abundance also correlated with prey removal on trees, but not on
the ground or on coffee or shrubs. Prey removal on trees was significantly correlated with total
ant richness (z = 4.693, Χ2 = 20.705, df = 1, 38, P <0.001) and total ant abundance (z = 4.68, Χ2 =
20.429, df = 1, 38, P <0.001). Prey removal on the ground, however, did not correlate with total
richness (z = -0.753, Χ2 = 0.566, df = 1, 38, P = 0.452) or abundance (z = 0.810, Χ2 = 0.660, df =
1, 38, P = 0.417). Likewise, prey removal from coffee plants and shrubs did not vary with total
richness (z = 1.79, Χ2 = 3.21, df = 1, 38, P = 0.07) or abundance (z = 0.896, Χ2 = 0.782, df = 1,
38, P = 0.376).
3.4. Local and landscape factors and prey removal rates
The results from the conditional inference trees revealed that abundance and richness of
ants (on cards and total richness measured in more extensive surveys) were the most important
factors driving predation rates when included in the regression tree model. But when these ant
variables were excluded from the analysis, local vegetation factors were more important
predictors of prey removal than landscape factors. For the regression tree script that only
included local and landscape level variables as predictors, ground prey removal positively
57
correlated with number of hollow coffee twigs (Fig. 2a), coffee/shrub prey removal was
influenced by one local factor (coffee plant density) and one regional factor (rustic coffee area
within 200 m) (Fig. 2b), and tree prey removal was not influenced by any local or regional
factor. For the regression tree model that included local and landscape habitat factors, plus local
ant richness and abundance (observed on cards) as predictors, ground prey removal positively
correlated with ant abundance (Fig. 2c), and both coffee/shrub prey removal (Fig. 2d) and tree
prey removal (Fig. 2e) was positively correlated with ant abundance and ant richness. For the
regression model that included local and landscape predictors and total ant richness and
abundance, prey removal on the ground and in coffee/shrubs did not differ from the first model;
however, tree prey removal was positively associated with increases in ant richness (Fig. 2f).
4. Discussion
4.1. Season, habitat, and vegetation and agrochemical indices
We found that prey removal on trees was higher in the coffee farms than in forests, and
that prey removal on both coffee plants and shade trees declined with increases in vegetation
complexity. However, there were no differences in habitat type or with vegetation complexity for
prey removal on the ground. These results were not expected, as other studies have found
differences in ant predation activities in different coffee farm habitats that differ with vegetation
complexity. For example, in Colombia, simplification of coffee systems (e.g. reductions in the
amount of shade canopy or shade tree diversity) may result in lower prey removal rates by ants
(e.g., Armbrecht and Perfecto, 2003; Armbrecht and Gallego, 2007). In other studies in Mexico,
CBB removal rates from traditional polyculture coffee farms, with a high shade tree diversity
and density, are higher than in other management systems with fewer shade trees (Larsen and
58
Philpott, 2010). Yet others have found, at least in some environments, that habitat complexity at
the ground level such as that provided by moss, rocks, and leaf litter can slow ant foraging (Gibb
and Parr, 2010), and thus might lower prey removal rates. In the case of arboreal ants foraging on
the coffee plants or shade trees, where we observed lower removal rates, added complexity in the
canopy may provide more constant or abundant resources that reduces the likelihood that ants
will drop down to coffee plants or shade tree trunks to forage for baits, thus reducing overall
removal rates. Interestingly, most previous studies have not isolated whether differences in
removal rates in different coffee habitats are due to structural changes in habitat complexity or
due to differences in the predatory ant community.
Surprisingly, we did not find a relationship between prey removal and agrochemical use.
Agrochemical applications negatively affect ant richness and abundance (Andersen et al., 2002;
McCoy et al., 2001) and have non-target impacts on other arthropods, including natural enemies
that thereby reduces predation rates (Leslie, 1977; McCoy et al., 2001). In our study area,
agrochemicals including endosulfan, chlorpyrifos, and copper sulfate are sprayed to control
insect pests (e.g. CBB) and fungal diseases (García-Estrada et al., 2006; Jaramillo et al., 2006).
All of these chemicals can affect predatory ants (Andersen et al., 2002; McCoy et al., 2001). In
our study, Pheidole spp. removed a high (60%) fraction of prey, yet chemicals sprayed (e.g.
glyphosate) can negatively impact Pheidole abundance (Leslie, 1977). In fact, in our study, the
number of Pheidole colonies in study sites was negatively correlated with increases in
agrochemical use (R2 = 0.12, F1, 38 = 4.925, P = 0.033). In stark contrast, other agrochemicals,
such as fertilizer, may indirectly increase ant predatory activities. For example, with synthetic
fertilizers, populations of hemipteran pests like aphids and scale insects may increase, resulting
in increased activity of hemipteran-tending ants, many of which are also predatory (Strauss,
59
1987). In addition, certain agrochemicals may have slightly larger or weaker effects on ants and
predatory activity (Andersen et al., 2002). Because we combined the effects of all agrochemicals
on into one index, the individual, slightly nuanced effects of the different chemicals may have
been thus masked.
Ant predatory activities often differ in the tropical wet and dry seasons, but we did not
observe any seasonal changes in prey removal. In tropical rainforests, removal rates of protein
baits by arboreal and ground-foraging ants differ with season because protein requirements may
also differ with season (Davidson et al., 2003; Hahn and Wheeler, 2002). In forest habitats,
identities of ant species attracted to baits vary considerably in different seasons with terrestrial
ants moving onto arboreal baits slightly more often in the wet season (Hahn and Wheeler, 2002).
In addition, arboreal ants more strongly prefer protein baits compared to their terrestrial
counterparts (Yanoviak and Kaspari, 2000; Hahn and Wheeler, 2002). In agroecosystems,
management can indirectly influence the life cycle or emergence times of pest species (Teodoro
et al., 2009) and this variation can influence susceptibility of prey to predators (Armbrecht and
Gallego, 2007). In studies in South America, CBB removal rate is higher in habitats with greater
shade cover and in the rainy season (Armbrecht and Gallego, 2007), perhaps because CBB
densities are higher in the rainy season (May-July) (Teodoro et al., 2009). Despite that ant
foraging and predation may differ with season in other systems, we did not observe differences
in removal rates between seasons. This may be due to a mix of factors influencing foraging and
prey removal rates during both the wet and dry seasons. First, environmental factors, such as soil
temperature and moisture can influence ant foraging rates (Baccaro et al., 2010; Porter and
Tschinkel, 1987; Teodoro et al., 2010; Ruano et al., 2000), and desiccation risk for ants (Baccaro
et al., 2010). Thus, during the dry season, foraging and predation activity may be lowered.
60
Likewise, foraging activity may decline drastically during heavy rains (Porter and Tschinkel,
1987), so both rainy and dry conditions may negatively affect prey removal. There may have
been differences in the composition of ants on baits during the wet and dry season, as well as
different nutritional requirements of ants during times of high colony growth that make
interpreting a lack of differences more difficult.
4.2. Local and landscape factors and prey removal rates
When ant variables were not included in regression tree models, two local vegetation
factors, and one landscape factor were positively associated with prey removal rates. Changes in
structural characteristics of agroecosystems, including reductions in habitat or landscape
complexity, may result in decreased in ecological services like predation, parasitism, and
pollination (Chaplin-Kramer et al., 2011; Fahrig, 2003; Tscharntke et al., 2005). Generally,
changes to the physical structure and vegetation complexity of farms may impact richness and
abundance of ants because this alters microclimatic conditions, food availability, and availability
of nesting sites (Moguel and Toledo, 1999; Andersen and Majer, 2004; Perfecto and
Vandermeer, 1996; Philpott and Armbrecht, 2006; Teodoro et al., 2010). We demonstrated that
very specific vegetation characteristics of coffee habitats (numbers of hollow coffee twigs and
number of coffee plants in a site) positively correlate with prey removal from the ground and
from coffee plants; no local habitat factors influenced prey removal on trees. Increased numbers
of hollow coffee twigs leads to increases in abundance of arboreal twig-nesting ants (Philpott and
Foster, 2005), and perhaps where coffee twigs are abundant, there is a high density of hollow
twigs on the ground. In other coffee systems, increases in the density and diversity of hollow
twigs on the ground increase the abundance and richness of ground-foraging predatory ants
61
(Armbrecht et al., 2004), and thus may increase predation rates. It is possible that the number of
tree species and coffee plants in our habitats may influence the solar radiation or the soil
temperatures (Lin, 2007), and both of those factors may influence ant activity and prey removal
rates (Kaspari and Weiser, 2000). At the landscape scale, rustic coffee within 200 m was
negatively correlated with prey removal on coffee and shrubs. In our sites, rustic coffee sites
were closer to forest fragments, and in addition may have higher abundance of prey and other
resources for ants (e.g. nectar, canopy hemipterans) that may pull arboreal-foraging ants away
from the introduced prey resources. In other studies, however, proximity to more vegetatively
complex areas enhances predation (Armbrecht and Perfecto, 2003; Dauber et al., 2003).
4.3. Ant abundance, richness and prey removal
Our data support the hypothesis that richness and abundance of ants observed in
predation trials correlated with prey removal rates in a coffee agroecosystem, but total richness
and abundance of ants had a more limited influence on prey removal. We found that local
abundance and richness of ants was positively and strongly correlated with proportion of prey
removed from the ground, coffee, and trees, and trumped the effects of local and landscape
factors when included in the same predictive models. Total ant richness in a site was correlated
with prey removal on trees, but not on the ground or coffee plants and total colony abundance
did not influence prey removal in any strata. Our experiments focused on observing prey
removal, and thus the group of ants that we observed on cards act as predators of the specific
prey items that we studied. Within the overall community in a site, many species may not be
predatory, may forage in different strata, may forage at other times of day or year that we did not
test, and still others may specialize on other prey items. Thus in effect, we have isolated that
62
fraction of the ant community preying on fly and CBB prey items in coffee habitats. This
distinction between actually observing predators, and attempting to correlate overall arthropod
populations on plants with or without ants may be one reason why some other studies did not
observe strong correlations between ant richness and arthropod reduction (e.g. Larsen and
Philpott, 2010). Of course, it is logical that increased removal of prey by ants due to increases in
abundance or richness could actually by mediated by other changes, such as differences in
vegetation, temperature, or humidity. The fact that ant community variables top vegetation and
landscape variables in the inference trees is a strong indication that these variables play an
important role in determining the degree to which prey are removed.
There are several mechanisms that may link increased ant richness to increased prey
removal. First, increased richness may result in increased chance of presence by a single
important predator or the sampling effect (Huston, 1997). We cannot rule out that
explanation, but because we observed several genera of ants preying on pests in each strata
(e.g. Pseudomyrmex, Camponotus, Gnamptogenys, Pheidole). This points to the fact that the
assemblage, rather than a single important species, is suppressing potential pests in our coffee
and forest sites. Second, the complementarity among species may lead to increased predation
(Ibarra-Núñez et al., 2001; Loreau and Hector, 2001; Loreau et al., 2001). Complementarity is
based on the idea that different species differ in their resource requirements, foraging behavior,
or morphological features which results in greater overall use of available resources (see Loreau
and Hector, 2001; Philpott et al., 2009). It is likely that the different ant species and genera that
we observed preying on flies and the CBB vastly differ in terms of eye and mandible
morphology (Jiménez et al., 2008), as well as their specific foraging behaviors (Traniello, 1989)
both of which could influence predation (Armbrecht and Perfecto, 2003; Philpott and Armbrecht,
63
2006). These differences may lead to complementarity among the different ant species observed.
Certainly in coffee agroecosystems, other predators are complementary both in time at which
predation occurs (e.g. birds and bats, ants and spiders) (Williams-Guillén and Perfecto, 2010;
Ibarra-Núñez et al., 2001) and in terms of foraging strategy, strata, and morphology (e.g. birds,
Philpott et al., 2009) leading to increases in removal of arthropods from coffee plants (Van Bael
et al., 2008).
In general, agroecosystem intensification can lead to decreases in functional diversity
organisms responsible for provisioning of ecosystem services such as predation, pollination, and
seed dispersal (Tscharntke et al., 2005). For predation services in particular, we have been able
to identify that the richness and abundance of ants, more so than differences in habitat type,
vegetation complexity, or landscape characteristics play an extremely important role in
determining overall predation rates. Because it is likely difficult to manipulate ant richness
directly, steps could be taken to examine which vegetation factors relate to increases in richness
and abundance of predatory ants, and then to change management to increase predation services.
Other studies have documented specific factors that correlate with increases in abundance and
species richness of ants in coffee agroecosystems (e.g. Philpott et al. 2008a, De la Mora et al.
2013), but have not explicitly focused on ants involved in predatory functions.
5. Conclusions
In sum, we documented that species richness and abundance of ants participating in pest
removal services was highly positively correlated with prey removal rates. Thus, biological
diversity of predators in coffee farms is related to increases in the level of pest control services
provided in this economically-important agroforest crop. A few management factors (number of
64
hollow coffee twigs, coffee density) and only one landscape factor (rustic coffee within 500 m)
correlated with increased prey removal. Interestingly, at least increasing coffee density might
also increase yields for farmers. But when ant richness and abundance were included in models,
they were overwhelmingly the most important factors. We did find direct negative correlations
between vegetation complexity and prey removal, but no relationships with agrochemical use.
Thus, it appears that changes in the vegetation or habitat type predominantly affect prey removal
rates via indirect effects on the ant community, effects that have not been closely documented in
other studies. Thus, we recommend taking management actions to alter coffee farms in a way
that increases ant richness and abundance in order to promote predation services.
6. Acknowledgements
J. Santis, G. Dominguez, U. Pérez Vásquez, G. López Bautista, B. Chilel, E. Schüller, S.
Arming, and E. Sintes assisted with fieldwork. R. Becker, R. John, and J.H. López Urbina
assisted with GIS. G. Ibarra Núñez, J. Rojas, J. Valle-Mora, G. Rodas and E. Chamé Vásquez of
El Colegio de la Frontera Sur (ECOSUR) provided logistical support. C. Hochreiter, D.
Gonthier, K. Ennis, G. Ibarra Núñez, J.-P. Lachaud, G. Pérez-Lachaud, L. Soto-Pinto, D. Allen,
D. Jackson, J. Remfert, and H. Hsieh provided comments on the manuscript. We thank Fincas
Irlanda, Argovia, Hamburgo, San Francisco, Genova, Rancho Alegre, Cuilco, Chiripa,
Maravillas, Santa Anita, and San Enrique for allowing us to conduct research on their farms.
Special thanks are due to Don Walter Peters and Finca Irlanda for providing housing. A.D.M was
funded by scholarship number 168970 granted by the National Council of Science and
Technology (CONACYT) in Mexico and a Conservation International Rapid Assessment
Program award. Additional funding was provided by NSF DEB-1020096 to S.M.P.
65
Table S1. Results from Moran's test for spatial autocorrelation using a spatial weights matrix.
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74
Tree prey
removal
Coffee prey
removal
Dependent
Variable
Ground
prey
removal
Habitat High-shade
Habitat Low-shade
Season Wet
Habitat High-shade: Season
Wet
Habitat Low-shade: Season
Wet
Intercept
-1.4343
75
0.5552
4297.1588
15.9993
-1.8265
1.1057
1.0816
4297.1586
4297.1587
0.7675
0.5036
-15.3851
15.8198
1.0047
1.0535
0.5073
0.6806
0.4159
0.2821
Habitat High-shade
Habitat Low-shade
Season Wet
Habitat High-shade: Season
Wet
Habitat Low-shade: Season
Wet
Intercept
-3.7312
0.6613
0.6108
0.8714
0.9631
-3.1798
Intercept
Standard
Error
0.5589
Estimate
Fixed Effects
agroecosystems.
-3.29
0.004
0.694
0.466
-0.004
0.004
-3.714
-1.361
0.767
1.14
0.477
0.293
-5.689
Z
0.001
0.997
0.488
0.641
0.997
0.997
<0.001
0.173
0.443
0.265
0.633
0.77
<0.001
P(z)
No. of
individuals
on cards
No. of
individuals
on cards
Random
effects
No. of
individuals
on cards
3.2478
4.2129
2.5022
Variance
1.8022
2.0525
Standard
error
1.5818
shrubs and on shade trees in different habitat types (forest, high-shade coffee, low-shade coffee) and seasons (wet, dry) in coffee
Table 1. Results of a generalized linear mixed model testing for differences in prey removal by ants on the ground, on coffee plants/
Habitat High-shade
Habitat Low-shade
Season Wet
Habitat High-shade: Season
Wet
Habitat Low-shade: Season
Wet
1.3366
1.9675
76
0.4748
0.5977
1.1704
1.2551
-1.5169
-1.2624
-1.9449
0.923
1.472
-3.195
-2.112
-1.662
0.735
0.141
0.001
0.035
0.097
0.462
Table 2. Top eleven ant morphospecies that removed sentinel prey from index cards in three
habitats (forest, high-shade coffee, low-shade coffee) in Soconusco, Chiapas.
Ant species§
Removal from Removal from
Removal from
tree trunks¶
the ground
coffee or shrubs
Pheidole protensa
1
0
34
Pheidole synanthropica
7
2
17
Pheidole sp. 16
0
5
11
Pheidole sp. 11
4
4
6
Pheidole cf. pubiventris
6
2
3
Gnamptogenys striatula
1
1
7
Pheidole sp. 10
0
0
9
Nylanderia sp. 1
2
3
2
Pheidole sp. 1
0
0
7
Solenopsis globularia
0
0
6
Wasmannia auropunctata
1
1
4
§
Ant species are organized from the morphospecies that removed the most prey items across all
sites to those that removed fewer prey items.
Numbers show the total numbers of prey items removed by that ant species in that stratum across
all plots and all habitat types.
77
Figure Legends
Fig. 1. Relationships between ant abundance and ant richness on observation cards and prey
removal on (a, d) the ground, (b, e) coffee plants and shrubs, and (c, f) tree trunks in forests and
coffee agroecosystems in Chiapas, Mexico. Panels show results for 40 sites including low-high
shade and forest. Trend lines and regressions are shown for significant relationships as
determined by GLM: * where P < 0.01, and ** where P < 0.001.
Fig. 2. Conditional inference trees examining relationships between 19 local and 7 regional
factors and ant richness and abundance and prey removal. Graphs show the influences of local
and landscape predictors only on prey removal from (a) ground and (b) coffee plants, the
influences of local and landscape predictors, plus local ant richness and abundance on prey
removal on the (c) ground, (d) coffee plants, and (e) shade trees, and influences of local and
landscape predictors, plus total ant richness and abundance on prey removal on the (a) ground,
(b) coffee plants, and (f) shade trees. Predictor variables in boxes are ranked (top, highest
correlation with dependent variable). P-values indicate the significance at 95% of confidence in
the relationship tested. Box plots include the inner quartiles (grey box), the median values (solid
black line), and 1.5 x the inner quartiles (error bars) of ant abundance associated with each string
of predictor variables. Sample sizes at each terminal node show the number of sites in that box
plot.
78
Figure 1.
79
Figure 2.
80
Supplemental information.
S1. Habitat types differed for both local and landscape characteristics (De la Mora et al., 2013).
Forest sites were more vegetatively complex than both high- and low-shade coffee sites, and
high-shade coffee was more vegetatively complex than low-shade coffee. Tree richness and
abundance was greater in forests than in coffee sites. Tree height was greater in forest and highshade than in low-shade coffee. Tree circumference was greater in high-shade coffee than in
forest or in low-shade coffee. Canopy cover was highest in forest, followed by high-shade and
then low-shade coffee. Coffee plant density was greater in low- than in high-shade coffee. The
number and circumference of rotten logs on the ground did not differ between sites, but logs
tended to be longer in forest and high-shade coffee compared with low-shade coffee. The
number of hollow twigs on coffee plants did not differ between sites. Leaf litter mass was higher
in low- than in high-shade coffee or forest. Agrochemicals were not applied in the forest sites,
but agrochemical use, overall, and for each type of agrochemical, was significantly more
frequent in low- than in high-shade coffee sites. Forest sites were surrounded by more forest than
coffee sites, however, the distance from forest, and percent of forest at 50 m, 200 m, or 500 m
did not differ between the two coffee habitats.
81
Table S1. Results from Moran's test for spatial autocorrelation using a spatial weights matrix.
Dependent variable (test)§
Coffee/Shrub (GLM - AI)
Coffee/Shrub (GLM - VCI)
Coffee/Shrub (GLM - no.
individuals on cards)
Coffee/Shrub (GLM - species on
cards)
Coffee/Shrub (GLM - total ant
abundance)
Coffee/Shrub (GLM - total
species richness)
Coffee/Shrub (Tree - local ants
model)
Coffee/Shrub (Tree - total ants
model)
Coffee/Shrub (Tree - vegetation
model)
Ground (GLM - AI)
Ground (GLM - VCI)
Ground (GLM - no. individuals
on cards)
Ground (GLM - species on cards)
Ground (GLM - total ant
abundance)
Ground (GLM - total species
richness)
Ground (Tree - local ants model)
Ground (Tree - total ants model)
Ground (Tree - vegetation model)
Tree trunk (GLM - AI)
Tree trunk (GLM - VCI)
Tree trunk (GLM - no. individuals
on cards)
Tree trunk (GLM - species on
cards)
Tree trunk (GLM - total ant
abundance)
Tree trunk (GLM - total species
Moran
’s
statisti
c
standa
rd
deviate
-1.3120
-1.7761
-1.7753
Moran I
statistic
Expectatio
n
Variance
P
-0.185499
-0.254814
-0.260610
-0.025641
-0.025641
-0.025641
0.014846
0.016649
0.017519
0.905
0.962
0.962
-1.5322
-0.226483
-0.025641
0.017183
0.937
-1.3244
-0.182212
-0.025641
0.013976
0.907
-1.1378
-0.156522
-0.025641
0.013231
0.872
-0.5126
-0.082907
-0.025641
0.012481
0.696
-1.6015
-0.225928
-0.025641
0.015641
0.945
-1.6015
-0.225928
-0.025641
0.015641
0.945
0.6082
0.5533
1.9830
0.058930
0.052058
0.237268
-0.025641
-0.025641
-0.025641
0.019336
0.019720
0.017577
0.272
0.290
0.024
-0.0500
0.5743
-0.032505
0.053520
-0.025641
-0.025641
0.018734
0.018998
0.520
0.283
0.5903
0.056976
-0.025641
0.019587
0.278
-0.1964
0.4076
0.4076
-0.4036
-0.5943
-0.4552
-0.052692
0.030464
0.030464
-0.080535
-0.106070
-0.086966
-0.025641
-0.025641
-0.025641
-0.025641
-0.025641
-0.025641
0.018974
0.018949
0.018949
0.018499
0.018313
0.018138
0.578
0.342
0.342
0.657
0.724
0.676
-0.5496
-0.097852
-0.025641
0.017262
0.709
-0.4917
-0.092268
-0.025641
0.018361
0.689
-0.5249
-0.097065
-0.025641
0.018519
0.700
82
richness)
Tree trunk (Tree - local ants
0.5247
0.045456
-0.025641
0.018361
0.300
model)
Tree trunk (Tree - total ants
0.3429
0.020998
-0.025641
0.018501
0.366
model)
Tree trunk (Tree - vegetation
-0.4127 -0.081794
-0.025641
0.018509
0.660
model)
§ All variables show the strata from which prey items were removed. AI = Agrochemical Index,
VCI = Vegetation Complexity Index, Tree = conditional inference trees.
83
Capítulo IV.
Factores locales y de paisaje promueven el parasitismo en un paisaje de café.
De la Mora A, Pérez-Lachaud G, Lachaud J-P, Philpott SM. Local and landscape drivers of ant
parasitism in a coffee landscape. Manuscript in preparation for Environmental Entomology.
Sometido a Environmental Entomology
Factor de imparcto: 1.24
84
RRH: Ant parasitism in a coffee landscape
Local and landscape drivers of ant parasitism in a coffee landscape
Aldo De la Moraª1
Gabriela Pérez-Lachaud*
Jean-Paul Lachaud*b
Stacy M. Philpott †
ªEl Colegio de la Frontera Sur, Ecología de Artrópodos y Manejo de Plagas, Carretera Antiguo
Aeropuerto Km 2.5 Tapachula 30700, Chiapas, México.
*
El Colegio de la Frontera Sur, Conservación de la Biodiversidad, Avenida Centenario km 5.5,
Chetumal 77014, Quintana Roo, México.
b
Centre de Recherches sur la Cognition Animale, CNRS-UMR 5169, Université de Toulouse
UPS, 118 route de Narbonne, 31062 Toulouse Cedex 09, France.
†
Environmental Studies Department, University of California, Santa Cruz, 1156 High St., Santa
Cruz, CA 95064, U.S.A.
1
Corresponding author, E-mail: [email protected], Tel: 831-459-1549
85
Abstract
Diversity loss due to agroecosystem simplification can influence ecological relationships.
We studied effects of local and landscape factors on one ecological interaction, parasitism of ants
that nest in rotting wood by eucharitid wasps. Our objectives were to examine whether a) habitat
and season influence ant parasitism, b) vegetation complexity and agrochemical use correlate
with ant parasitism, and c) specific local and landscape factors correlate with changes in ant
parasitism. We worked in a landscape dominated by coffee farms with scattered forest
fragments. In that landscape, we selected 30 coffee and 10 forest sites in which we characterized
local management (e.g. vegetation, agrochemical use) and landscape features (e.g. distance to
forest, percent of rustic coffee nearby). We opened rotten logs to locate ant cocoons, collected
and monitored cocoons for emergence of adult parasitoids, then dissected cocoons to search for
developing parasitoids. Across all sites sampled, we found 16 ant morphospecies in three ant
subfamilies (Ectatomminae, Ponerinae and Formicinae). Seven ant species were parasitized by
two genera of Eucharitidae parasitoids (Kapala and Obeza), some of them by different eucharitid
species, and at least seven new eucharitid-ant associations were reported. We examined for
differences in presence of parasitism, proportion of parasitized species, and proportion of
parasitized cocoons of common species and genera with season, habitat, vegetation complexity,
agrochemical use, and with changes in local and landscape factors. Parasitism did not differ with
habitat (forest, high-shade coffee, low-shade coffee), but parasitism increased in the dry season
for Gnamptogenys ants. Parasitism increased with vegetation complexity for two genera and one
common species (Pachycondyla, Gnamptogenys and G. sulcata), and was high in sites with both
high and low agrochemical use for most parasitism metrics examined. Two landscape variables
(rustic coffee and forest area within 200 m) and two local factors (humus mass and number of
86
ant cocoons) positively correlated with parasitism for some ant genera and species. Landscape
factors ranked as more important predictors of parasitism than local factors. Thus differences in
vegetation complexity at the local and landscape scale and agrochemical use in coffee landscapes
alter ecological interactions between parasitoids and their ant hosts.
Key words
Coffee, Ectatomminae, Formicinae, Ponerinae, Eucharitidae, vegetation complexity.
Deforestation and intensification of agroecosystems contribute to global loss of diversity
and to alterations in species interactions and ecosystem services provided. In agroecosystems,
richness and abundance of some organisms decrease due to habitat simplification and landscape
modification (e.g. Dauber et al. 2005, García Estrada et al. 2006). Some implications of
biodiversity loss are the disruption of ecological processes and alteration of interaction networks
that support mutualisms and ecosystem services (Naeem 2002, Fischer et al. 2006). Species
interactions are modified by landscape changes including fragmentation (Tscharntke et al. 2002),
changes to matrix quality (Vandermeer and Carvajal 2001, Steffan-Dewenter et al. 2002),
modifications to forest shape, edge or area (Tscharntke et al. 2002) and habitat loss or
degradation (Fischer and Lindenmayer 2007). In any habitat where interaction networks are
simplified, there may be reductions in ecosystem functioning (Dobson et al. 2006, Jonsson et al.
2012) and hence in the provisioning of services. Thus, habitat loss and landscape simplification
drive biodiversity loss that in turn drives declines in ecosystem processes and functionality
(Clergue et al. 2005, Tscharntke et al. 2005, Fischer and Lindenmayer 2007, Philpott et al. 2009).
87
In contrast, some agroecosystems harbor high levels of biodiversity and may act as
refuges for biological diversity that can provide ecosystem services such as biological pest
control and pollination (Perfecto et al. 1996, Moguel and Toledo 1999, Tscharntke et al. 2005,
Jose 2009, Power 2010, Vandermeer et al. 2010). Different taxa vary in response to increases in
tree diversity, canopy shade and decreased agrochemical use (Moguel and Toledo 1999, Mas and
Dietsch 2003, García Estrada et al. 2006, Gagic et al. 2012), thus some taxa may be more
sensitive to agricultural landscape changes and act as indicators of habitat simplification (e.g.
Pocock and Jennings 2008, De la Mora and Philpott 2010). There are now numerous examples of
increases in ecosystem services provided in less intensive agricultural systems, such as shaded
coffee, compared with more intensive farms (e.g. Jha et al. 2014). Thus, we must focus on
understanding how to promote less intensive agricultural production in order to prevent the
increase of insect pests, reduced pollination, and loss of other ecological services (Kruess and
Tscharntke 1994, Steffan-Dewenter 2002, Jha and Vandermeer 2010). Agricultural
intensification and habitat complexity may strongly affect host-parasitoid interactions
(Wilkinson and Feener 2007; 2012, Visser et al. 2009, Jonsson et al. 2012). Natural enemies,
especially the parasitic Hymenoptera, play an important role in pest regulation (Rodríguez and
Hawkins 2000, Varone and Briano 2009) and parasitoids and parasitism are affected by local
habitat changes and changes at the landscape level (Klein et al. 2002, Fischer and Lindenmayer
2007). In addition, hymenopteran parasitoids are sensitive to agrochemical use and thus
examining different host-parasitoid interactions where agrochemical use varies is necessary.
At the habitat level, two factors are important when parasites search for potential hosts:
habitat preference (Townes 1960, Quilici and Rousse 2012, Mathis and Philpott 2012) and
habitat complexity (Tylianakis et al. 2005; 2007, Wilkinson and Feener 2007; 2012, Vásquez-
88
Ordóñez et al. 2012). Different habitat types vary spatially and temporally in terms of
microclimate (Hsieh and Perfecto 2012), abundance and density of host colonies (Henne et al.
2008), and habitat complexity, each of which may promote greater diversity of parasitoids
(Steffan-Dewenter 2002, Sperber et al. 2004, Hsieh and Perfecto 2012). For example, parasitism
rate on immature stages of the Ectatomminae ant Ectatomma ruidum by eucharitid wasps is
higher in more complex habitats (woodlands) than in simple habitat (grasslands) that differ in
vegetation composition and structure (Vásquez-Ordóñez et al. 2012). Moreover, agricultural
intensification simplifies food web complexity and may affect host-parasitoid relationships
(Jonsson et al. 2012). At the landscape level, other factors such as distance from forest habitat,
hedgerows, or wooded fields may influence parasitism rate (Marino and Landis 1996, Klein et al.
2006), but landscape features are not always important for parasitism (e.g. Coudrain et al. 2013).
In addition, agricultural matrix composition may alter host-parasitoid dynamics (Cronin 2007,
Monmany and Aide 2009, Macfadyen and Muller 2013). In particular, landscapes with low
habitat complexity may have lower diversity of natural enemies and therefore lower
complementarity between natural enemies, which thereby alters ecological processes, and
interactions (Menalled et al. 1999, Steffan-Dewenter 2002, Wilby and Thomas 2002, Jonsson et
al. 2012).
In coffee and cacao agroecosystems, parasitism of ants may differ with agricultural
management type (De la Mora and Philpott 2010) and with vegetation complexity (VásquezOrdóñez et al. 2012) but little attention has been given to differences in agroecosystems
depending on landscape context. A study in Chiapas, Mexico showed that the percentage of
parasitism in ants is higher in primary forest than in different coffee management systems (De la
Mora and Philpott 2010). Yet no studies have examined the relative importance of local and
89
landscape drivers of ant parasitism in coffee agroecosystems. This interaction is important to
investigate given the role of ants as biological control agents and predators in coffee
agroecosystems (Ibarra-Núñez et al. 2001, Philpott and Armbrecht 2006, Lachaud and PérezLachaud 2009). Ants are also indicators of habitat quality (Andersen et al. 2002, Bution et al.
2010), and are sensitive to landscape fragmentation (Perfecto and Vandermeer 2002, Armbrecht
and Perfecto 2003, Paolucci et al. 2012, De la Mora et al. 2013). Therefore, tropical agroforestry
systems are an ideal system to study how both local and landscape changes influence ant
parasitism with potential impacts for natural crop pest control.
Ants are parasitized by several organisms including eucharitid wasps (Hymenoptera:
Chalcidoidea) (Johnson 1988, Heraty 1994, Lachaud and Pérez-Lachaud 2012) and some species
of Eucharitidae have been considered for biological control of pest-ant species (Johnson 1988,
Heraty 1994, Varone and Briano 2009, Lachaud and Pérez-Lachaud 2012). However for
predatory ants, such as genera of the subfamilies Ectatomminae and Ponerinae that sometimes
prey on pest species in tropical agroecosystems, parasitism by eucharitids may interfere with
biological control provided by ants (Pérez-Lachaud et al. 2006b, 2010, Lachaud and PérezLachaud 2009). Parasitism of ants by eucharitids may be influenced by habitat type (VásquezOrdóñez et al. 2012), season (Pérez-Lachaud et al. 2006a) and host cocoon availability (Lachaud
and Pérez-Lachaud 2009, Pérez-Lachaud et al. 2010). All of these factors may vary with changes
in coffee management and in coffee landscapes. Here, we studied ant-parasitoid relationships in
a coffee landscape to examine whether a) habitat and season influence ant parasitism, b)
vegetation complexity and agrochemical use correlate with ant parasitism, and c) specific local
and landscape factors correlate with changes in ant parasitism.
Methods
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Study site, local and landscape characteristics. We performed our study in 40 sites (30
coffee sites and 10 forest sites) within a coffee landscape in the Soconusco region of Chiapas,
Mexico. The landscape is predominantly comprised of coffee farms (93.7% of land cover) with
scattered small forest fragments (6.3% of land area) (Philpott et al. 2008b). In the specific zone
where research was conducted, coffee area can be further classified into management types
categorized by Moguel and Toledo (1999): rustic/traditional polyculture (0.22% of the study
area), traditional/commercial polyculture (36.91%), commercial polyculture (36.84%),
commercial polyculture / shade monoculture (5.58%), shade monoculture (17.5%), and sun
coffee (1.11%) (Philpott et al. 2008b). Each site consisted of a 20 x 20 m plot. We conducted
observations during the dry (February - March) and wet seasons (June - July) of 2010. All sites
were located between 900-1400 masl and averaged 4000 mm rain per year. Coffee sites were
organically- or conventionally-managed and also varied both in tree diversity and density and in
canopy cover (De la Mora et al. 2013). The most common coffee shade trees in the study region
include Inga micheliana Harms (Fabaceae), Inga sapindoides Willd (Fabaceae), Trema
micrantha (L.) Blume (Ulmaceae) and Alchornea latifolia Swartz (Euphorbiaceae).
We characterized the local and landscape factors of each site. At the local scale, we
examined site characteristics and vegetation and interviewed farmers regarding agrochemical
use. The 13 specific local factors included were: elevation, humus mass, tree circumference, tree
height, percent canopy cover, richness of shade trees, density of shade trees, number of rotten
logs, coffee plant density, and use of pesticides, herbicides, fertilizers, and fungicides (see De la
Mora et al. 2013 for a complete description of how each variable was measured). We included
seven landscape variables including distance to the nearest forest edge, the area of forest within
50 m, 200 m, and 500 m of study sites, and the area of rustic coffee within 50 m, 200 m, and 500
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m of study sites as determined with ArcGIS (Philpott et al. 2008b, De la Mora et al. 2013). To
summarize vegetation data and agrochemical use we created two indices: a) a vegetation
complexity index (VCI) and b) an agrochemical index (AI) (Philpott et al. 2008a, De la Mora et
al. 2013). Both indices range from 0 (low vegetation complexity or agrochemical use) to 1 (high
vegetation complexity or agrochemical use). Further, we used VCI values to classify coffee sites
into high-shade coffee (13 sites with VCI between 0.37-0.609) and low-shade coffee (17 sites
with VCI between 0.029-0.35).
Ant and parasitoid sampling. Within each site, we sampled rotten logs to find ants and
their parasitoids. In this study, we chose to study the subfamilies Formicinae (but only the genus
Camponotus), Ponerinae, and Ectatomminae. We chose these subfamilies and genus because
these ants have pupae protected by a cocoon that is spun by the larvae prior to pupation (Wheeler
1915). Additionally, many species with cocoons are predators in coffee agroecosystems, and
understanding the relationships between these ants and their parasitoids may shed light on
biological control processes in farms. We opened rotten log pieces with an ax and knives,
specifically focusing on collection of cocoons. We collected all cocoons encountered for all
species of Ponerinae, Ectatomminae, and the Formicinae genus Camponotus, counted cocoons,
and then placed cocoons into plastic cups covered with mesh. A representative sample of
workers (10-15 individuals per colony) was collected and stored in vials with alcohol and later
identified according to Bolton (1994) and Longino (2011). We examined for parasitism in two
ways. First, we waited for parasitoid emergence, and second, we dissected ant cocoons to search
for parasitoids. We checked cocoons daily for 10 days for emergence of parasitoids. After 10
days, remaining cocoons were dissected to determine the total parasitism rate in each colony.
Voucher specimens of ants were deposited in the Entomology Collection at El Colegio de la
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Frontera Sur in Tapachula, Chiapas, Mexico, and parasitoids were deposited in the Department
of Entomology at University of California, Riverside.
Data Analysis. We examined ant parasitism with several metrics. First, we used the
presence or absence of parasitism. Second, we used the proportion of species parasitized. Third,
we examined the proportion of cocoons parasitized for common genera and species. Common
genera and species were defined as those that were found from at least two colonies in each of
the three habitat types sampled (forest, high-shade coffee, low-shade coffee) and from at least
two colonies in each season sampled (wet, dry).
To examine whether habitat and season influence ant parasitism, we used generalized
linear mixed models (GLMM) with ‘glmer’ in the ‘lme4’ package in R (R Development Core
Team 2012). GLMMs allow analysis with count or proportional responses (Bolker et al. 2008)
and our dependent variables included were presence of parasitism, proportion of species
parasitized, proportion of cocoons of common genera (Gnamptogenys, Odontomachus,
Pachycondyla) that were parasitized, and proportion of cocoons of common species
(Gnamptogenys sulcata (F. Smith), Odontomachus chelifer (Latreille)) that were parasitized in
each site during each season. All of these common genera and species are known hosts of
eucharitids. We included habitat type (forest, high-shade coffee, low-shade coffee), season (wet,
dry), and the interaction between habitat and season as fixed effects, the number of cocoons
(total, or for each genus or species) as a random effect, and used the binomial error distribution
with the logit link. For all dependent variables that were proportions, we used the ‘cbind’
function with proportion parasitized and proportion not parasitized as input variables.
To examine whether vegetation complexity and agrochemical use correlate with
parasitism, we used general linear models (GLM) in R (R Development Core Team 2012). The
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dependent variables included were presence of parasitism, proportion of species parasitized,
proportion of cocoons of Gnamptogenys, Odontomachus, Pachycondyla, G. sulcata, and O.
chelifer that were parasitized in each site. For each dependent variable we tested 8 different
predictive models including: 1) VCI as a linear predictor, 2) VCI as a quadratic predictor 3) AI
as a linear predictor, 4) AI as a quadratic predictor, 5) both VCI and AI as linear predictors, 6)
VCI as a linear predictor and AI as a quadratic predictor, 7) VCI as a quadratic predictor and AI
as a linear predictor, and 8) both VCI and AI as quadratic predictors. All models were tested with
and without the number of cocoons (overall, or for each genus or species tested) as an additional
explanatory variable for a total of 16 total models tested for each dependent variable. To select
the best model among the 16 possible models, we used the Akaike's Information Criterion (AIC)
computed with the ‘mass’ package (Venables and Ripley 2002). For each GLM we used a
binomial error distribution and logit link and for all dependent variables that were proportions,
we used the ‘cbind’ function with proportion parasitized and proportion not parasitized as input.
To examine whether specific local and landscape factors correlate with changes in ant
parasitism we created conditional inference trees with the ‘party’ package in R (Hothorn et al.
2006, Strobl et al. 2009). The dependent variables included were presence of parasitism,
proportion of species parasitized, proportion of cocoons of Gnamptogenys, Odontomachus,
Pachycondyla, G. sulcata, and O. chelifer that were parasitized in each site. The predictor
variables included the 13 local and 7 regional factors measured for each site, plus the number of
cocoons and number of ant colonies (overall or for individual genera or species examined).
Because some study plots were located within 250 m of one another, we also examined
the degree of spatial autocorrelation in the residuals of the best-fit GLM models and conditional
inference trees with: (1) spatial correlograms (with the ‘ncf’ package in R) and (2) the Moran's
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test for spatial autocorrelation using a spatial weights matrix with the ‘spdep’ package in R
(Bivand et al. 2012, R Development Core Team 2012). For the correlograms, we computed 100
permutations using the resamp argument in the correlog function to examine the distance, if any,
at which variables were spatially autocorrelated. For the calculation of Moran’s I, we used
nearest neighbor distances as the metric, and used the permutation test option. In those analysis
that revealed significant spatial autocorrelation, we included longitude and latitude as additional
predictor variables, but they did not improve the fit of any of the tested models, and were thus
removed from the models presented in the results (Table S1).
Results
Across all sites sampled, we collected a total of 4006 cocoons from 16 ant species
belonging to the subfamilies Ponerinae (9 species), Ectatomminae (5 species) and Formicinae (2
species) (Table 1). Only seven of the 16 species that we encountered (or 43.8% of species) were
parasitized. Gnamptogenys sulcata, the only species parasitized in high-shade and low-shade
coffee and forest habitats, was attacked by at least three different parasitoid species but its
overall parasitism rate was the lowest (1.05% of encountered cocoons) of all the parasitized
species (Table 1). All parasitoids we found were Eucharitidae (E.A. Murray and J.M. Heraty,
pers. comm.). Of these, adults and pharate adults encountered belonged to at least three species
in the genus Kapala Cameron and to one new, undescribed species in the genus Obeza Heraty
(Table S2). Camponotus atriceps (F. Smith) was parasitized by Obeza n. sp., Gnamptogenys sp.
1 was parasitized by K. izapa (Carmichael) and Kapala sp., G. sulcata was parasitized by Kapala
nr. sulcifacies, K. izapa and Kapala sp., and Odontomachus chelifer, Pachycondyla harpax
(Fabricius) and P. impressa (Roger) were parasitized by Kapala nr. sulcifacies (Table S2).
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Odontomachus meinerti Forel was parasitized by an eucharitid species that could not be
determined to species because only juvenile stages were found.
According to the GLMM, ant parasitism by eucharitid wasps did not differ with habitat
type, and there was no interaction between habitat and season for any dependent variable, but
parasitism of ants in one genus (Gnamptogenys) and one species within that genus (G. sulcata)
varied with season (Table 2). Presence of parasitism, the proportion of parasitized species, and
proportion of parasitized cocoons of Gnamptogenys, Odontomachus, Pachycondyla, G. sulcata,
and O. chelifer did not differ in forest, high-shade, or low-shade coffee, and for none of those
variables was there a significant habitat by season interaction (Table 2). The proportion of
parasitized Gnamptogenys cocoons was 1.5 times higher in the dry season than in the wet season
and the proportion of parasitized G. sulcata cocoons was more than ten times higher in the dry
season than in the wet season (Table 2). Parasitism, as defined by the other metrics (presence of
parasitism, proportion of parasitized species, and proportion of parasitized cocoons of
Odontomachus, Pachycondyla, and O. chelifer) did not differ with season.
According to the GLMs, the agrochemical index (AI) was frequently included in the bestfit model predicting parasitism, and vegetation complexity (VCI) and number of cocoons were
also sometimes included (Table 3). The model that best predicted presence of parasitism
included AI and number of cocoons, and parasitism was highest at low and high AI values
(Table 3, Fig. 1). Presence of parasitism increased as the number of cocoons increased (Table 3).
The best model for the proportion of parasitized species and for the proportion of parasitized
cocoons of Odontomachus, G. sulcata, and O. chelifer included only the AI, and for all variables,
parasitism was highest at low and high AI values (Table 3, Fig. 1, 2). The model that best
predicted proportion of parasitized Gnamptogenys cocoons included VCI, AI, and the number of
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Gnamptogenys cocoons. Gnamptogenys parasitism increased with VCI, decreased with number
of cocoons, and was high with low and high AI values (Table 3, Fig. 2). Finally, parasitism of
Pachycondyla cocoons increased with VCI (Table 3, Fig. 2).
The conditional inference tree analyses revealed one local, two landscape, and one ant
density predictor of parasitism. The presence of parasitism increased in sites with more rustic
coffee area within 200 m and with more cocoons (Fig. 3a). The proportion of parasitized species
increased in sites with more forest area within 200 m and with more cocoons (Fig. 3b). The
proportion of parasitized Gnamptogenys cocoons increased in sites with higher humus mass (Fig.
3c). The proportion of parasitized Odontomachus, Pachycondyla, G. sulcata, and O. chelifer
cocoons did not correlate with any local, landscape, or ant density variable.
Discussion
We found seven ant species that were parasitized by parasitoids in the family
Eucharitidae, and encountered at least seven previously unknown host-parasitoid relationships:
C. atriceps – Obeza n. sp., Gnamptogenys sp. 1 – K. izapa, Gnamptogenys sp. 1 – Kapala sp., G.
sulcata – Kapala nr. sulcifacies, O. chelifer – Kapala nr. sulcifacies, P. harpax – Kapala nr.
sulcifacies, and P. impressa – Kapala nr. sulcifacies. One other association, G. sulcata – K. izapa
has already been reported in a previous study (Pérez-Lachaud et al. 2006b) and the associations
G. sulcata – Kapala sp. and O. meinerti – undetermined eucharitid species, probably correspond
to previous reports involving K. iridicolor and Kapala sp., respectively (Pérez-Lachaud et al.
2006b, De la Mora and Philpott 2010). Most of the parasitized ant species that we collected were
in the Ponerinae and Ectatomminae subfamilies, but we also report the first case of parasitism of
C. atriceps (Formicinae subfamily) by a parasitoid in the genus Obeza Heraty. This is the second
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report of the genus Obeza attacking a Camponotus species for the state of Chiapas (see PérezLachaud and Lachaud 2014). We documented that Kapala izapa parasitized two species of
Gnamptogenys raising to at least three the number of its potential hosts within this genus (see
Pérez-Lachaud et al. 2006b), and that Kapala nr. sulcifacies parasitized four species of ants from
three different genera (Table S2). There was one ant species (G. sulcata) parasitized by more
than one species of parasitoid from the genus Kapala (Table S2). Most of the ant species
encountered were not parasitized in our study sites, although some of them (Gnamptogenys
striatula Mayr, Pachycondyla apicalis (Latreille), and Typhlomyrmex rogenhoferi Mayr) are
parasitized by eucharitid parasitoids in other habitats (Pérez-Lachaud et al. 2006b, De la Mora
and Philpott 2010). Unfortunately, many of the specific ant-parasitoid relationships could not be
determined at the species level as many parasitoids were only collected as larvae or pre-pupae
that could not be identified to the genus or species level. Other recent studies, however,
documented that ant species can be parasitized by multiple species of parasitoids (Pérez-Lachaud
et al. 2006b, Torréns 2013), sometimes as multiparasitism where the same individual host is
parasitized by multiple species of parasitoids (Pérez-Lachaud et al. 2006a). In addition, several
parasitoid species can parasitize multiple species of host ants (Pérez-Lachaud et al. 2006b,
Lachaud and Pérez-Lachaud 2012) as we found for K. izapa and Kapala nr. sulcifacies.
Parasitism and local habitat and vegetation features. We aimed to study whether ant
parasitism by Eucharitidae wasps varied in different coffee management systems and forests, and
whether parasitism varied with vegetation features and agrochemical use in coffee farms and
forests. We did not find any differences in the presence of parasitism, the proportion of species
parasitized, or the proportion of parasitized cocoons of any genus or species parasitized in
different habitat types. One reason that we may not have encountered differences in habitat type
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is because there was very high within-habitat variability in vegetation characteristics measured in
high-shade and low-shade coffee sites. Thus the VCI may be a better measure of the specific
vegetation changes in the plots sampled. In fact, we did find that parasitism of Gnamptogenys
and Pachycondyla cocoons increased as vegetation complexity increased, and these were the two
genera with the highest numbers of cocoons collected and colonies encountered. For the other
genus (Odontomachus) and species tested, the low numbers of cocoons collected may have
limited our ability to detect significant differences between habitat types or correlations with
vegetation complexity. Other studies have documented that changes in habitat type may affect
parasitism of ants (De la Mora and Philpott 2010, Vásquez-Ordoñez et al. 2012). Because adult
parasitoids often seek non-host food sources in agroecosystems (e.g. honeydew, nectar or
pollen), adding vegetation complexity may increase resource availability within agricultural
habitats and therefore may increase the presence of certain species or groups of parasitoids
(Landis et al. 2000, Sperber et al. 2004). However, we did not find differences with habitat or
VCI and presence or absence of parasitism or the proportion of ant species that were parasitized.
This may be because different ant species or genera (or their parasitoids) respond to changes in
habitat or vegetation in different ways, and examining species together may not have represented
taxonomic variability in response to habitat or vegetation changes.
We found high presence of parasitism, high proportion of parasitized species, and high
proportions of parasitized Gnamptogenys, Odontomachus, G. sulcata, and O. chelifer cocoons in
sites with low and high agrochemical use. Agrochemical use can be a more important predictor
of decreases in parasitism rate than change in floral resources or alternative hosts (Jonsson et al.
2012). We may expect in sites with low agrochemical use, that parasitoid abundance and
diversity would be highest, resulting in higher parasitism rates. Abundance and richness of
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parasitoids is negatively affected by application of nematocides and insecticides (Matlock and de
la Cruz 2002). Use of the insecticide endosulfan (e.g. brand name Thiodan) is common in the
study region and may have negatively affected hymenopteran parasitoids (Damon 2000). Given
that almost all of the sites with zero agrochemical use were forest sites, increases in resources for
parasitoids in those habitats may be correlated with lower agrochemical use, contributing to the
observed pattern.
Explaining increases in parasitism in sites with high agrochemical use is less intuitive,
but may relate to one or more factors including changes in understory vegetation or weed cover
with herbicide application, impacts of fertilizers on ant species richness, influences of pesticides
on insect behavior, and increases in presence of trees with extrafloral nectaries in sites with high
agrochemical use. First, parasitoid-ant interactions are strongly impacted by herbaceous
vegetation (Clausen 1940) and regular use of herbicides in some of the study sites may have
resulted in changes in parasitism. Composition of herbaceous plants (e.g. weeds) may differ in
sites with high and low herbicide use (A. De la Mora, Pers. obsv.). Although we did not quantify
weed cover, qualitative differences observed indicate that in sites with high agrochemical use,
weed cover tends to be dominated by Impatiens spp., whereas in other sites with less
agrochemical use, common herbaceous plants include Lantana spp., Ipomea spp., and Vernonia
spp., as well as other plants in the family Asteraceae and Gramineae. Such changes in
herbaceous plant composition and other ground cover may influence ant foraging patterns
(Lachaud and Pérez-Lachaud 2009, Gibb and Parr 2010) and eucharitid oviposition (Johnson
1988, Pérez-Lachaud et al. 2006b). All eucharitid species oviposit away from the host in or on
plant tissues (Clausen 1940). For example, some Kapala species oviposit on leaves, flower buds,
or on seed receptacles (Clausen 1940, 1941; Wojcik 1989, Pérez-Lachaud et al. 2006b, Lachaud
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and Pérez-Lachaud 2009), and herbicide use may alter oviposition site availability for Kapala
thereby negatively influencing parasitism rates. We did not measure weed cover, or the
abundance or richness of weed species within our study sites, but this should be explicitly
considered in future studies to detail relationships between parasitism and herbaceous vegetation.
Second, changes in parasitism related to agrochemical use might relate to changes in ant species
richness due to fertilizer use. De la Mora et al. (2013) found that increased fertilizer use
correlates with decreased species richness of ants that nest in rotting logs (e.g. including
Pachycondyla, Gnamptogenys, and Odontomachus species). Changes in species richness and
abundance of certain ant species in different sites may have influenced parasitism. For example,
in agricultural landscapes in Ecuador, increases in agricultural intensification increased
hymenopteran parasitoid diversity, reduced hymenopteran host diversity, increased host
specificity for some parasitoids, and reduced attacks on alternative hosts (Tylianakis et al. 2007).
Translated for this system, increases in agrochemical use may have reduced ant diversity, while
at the same time increasing the host specificity of parasitoids for remaining ant species, thus
leading to higher parasitism. Third, pesticide spraying could potentially affect oviposition
behavior of Eucharitidae females decreasing the likelihood of host encounter (Wojcik 1989).
Fourth, some parasitoids respond positively to presence of extrafloral nectaries on shade trees
within coffee agroecosystems (Rezende et al. 2014). The study sites that used high levels of
agrochemicals and also experienced high parasitism levels also had very high percentages (e.g.
~70.0 %) of Inga spp. individuals that contain extrafloral nectaries. Other sites with lower use of
agrochemcials had fewer trees in the genus Inga (e.g. 56.8 %), and vegetation in those sites may
provide fewer resources for adult parasitoids. In the study sites, Kapala spp. parasitoids have
been observed visiting extrafloral nectaries on Inga vera Wild (M. E. Jiménez-Soto, pers.
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comm.). Increases in nectar resources may have counteracted negative impacts of agrochemicals
in those sites. Finally, all of the sites with high agrochemical use and high parasitism rates were
found in a single, large farm (Finca Hamburgo). Thus, there remains the possibility that some
property of management, history of chemical use at that site may have influenced the observed
results.
Parasitism and season. We aimed to examine if parasitism changes with season, and
found that for most metrics of parasitism there were no differences in parasitism in the wet and
dry season. However, parasitism of Gnamptogenys, the most frequently encountered genus, and
G. sulcata, the most frequently encountered species, was higher in the dry season than in the wet
season. Seasonal changes in parasitism may relate to changes in ant reproductive cycles, or
changes in brood production (see Lachaud and Pérez-Lachaud 2009, Pérez-Lachaud et al. 2010).
For example, some ant species susceptible to eucharitid parasitism, such as Ectatomma ruidum,
produce fewer cocoons in the dry season, and in that season have lower rates of parasitism by
Kapala izapa and K. iridicolor -- thus some eucharitid parasitoids demonstrate positive densitydependence with increases in number of available cocoons (Lachaud and Pérez-Lachaud 2009).
Yet, we did not find positive density dependence in parasitism for Gnamptogenys, as the number
of Gnamptogenys spp. cocoons was negatively related to Gnamptogenys parasitism and the
number of G. sulcata cocoons did not correlate with G. sulcata parasitism. Seasonal changes in
food and nesting resources for ants and parasitoids in different seasons may alter ant-parasitoid
interactions (Clausen 1940; 1941). Shifts in parasitism rate with season are often due to changes
in physical characteristics of sampled habitats (Tylianakis et al. 2005). Additionally, flowering of
certain common herbaceous weeds, such as Melampodium divaricatum (Asteraceae), a known
oviposition site for Kapala iridicolor (Pérez-Lachaud et al. 2006b), occurs in the dry season (A.
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De la Mora, pers. obsv.) which may explain increased parasitism rates for Gnamptogenys spp.
and G. sulcata during the dry season. For example, Pérez-Lachaud et al. (2006a), suggest that the
presence or absence of certain herbaceous plants throughout the year may affect the spatial
distribution of eucharitid wasps that parasitize Ectatomma tuberculatum. Oviposition sites are an
important factor determining parasitism of ants by eucharitids. Adult wasps are not highly
mobile and rely on foraging ants to pick up a planidium and transport it to the nest. At the same
time, increases in ant foraging activity in patches of vegetation with high resources may facilitate
parasitism success (Clausen 1940; 1941, Johnson 1988, Wojcik 1989). One further possibility is
that the abundance of eucharitid predators may shift in the wet and dry season, thereby
influencing the abundance of the eucharitids. For example, eucharitids may be trapped in spider
webs, and changes in abundance of web-building spiders in the wet and dry season may increase
predation risk for the parasitoids during one season or the other. However, available data from
the study sites indicates that web-building spider abundance is higher in the dry season (Pinkus
et al. 2006), which would presumably lower eucharitid abundance and result in lower ant
parasitism. Finding differences in season for other ant species and genera, and for the proportion
of ant species parasitized may have been hindered by low numbers of colonies and specifically
parasitized colonies (as low as 0.6% and only as high as 2.5% of cocoons parasitized on average
for some species in some sites).
Parasitism and local and landscape factors. A final study objective was to determine
relationships between parasitism of ants by eucharitids and local and landscape features of coffee
landscapes. We found that presence of parasitism and the proportion of species parasitized was
most strongly correlated with two landscape factors and with the number of ant cocoons found in
those study sites. The presence of parasitized cocoons increased with rustic coffee (e.g. complex
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coffee habitat) area within 200 m and the proportion of parasitized ant species increased with
increases in forest (e.g. non-crop or natural habitat) area within 200 m. Several studies have
investigated the importance of landscape composition and complexity for hymenopteran
parasitoid abundance and parasitism, documenting that both factors can strongly influence
parasitism rates (e.g. Marino and Landis 1996, Thies et al. 2003, Tylianakis et al. 2005, 2007, but
see Menalled et al. 1999). For instance, greater vegetation complexity in a landscape, such as
that found in forest and rustic coffee sites, can promote mobility of Hymenoptera across habitats
(Tylianakis et al. 2005) and non-crop and diverse habitats can thus act as sources to other nearby
areas. In addition, increases in habitat complexity at the landscape scale can translate into
increased parasitoid diversity (Sperber et al. 2004, Menalled et al. 1999), and the presence of a
diverse group of complementary parasitoids may relate to higher parasitism for other host taxa
(Wilby and Thomas 2002). Increases in the amount of non-crop (e.g. forest) habitat and of
complex habitats (e.g. rustic coffee) in the landscape may provide alternative hosts and resources
for parasitoids leading to higher parasitoid abundance and parasitism rates (Thies et al. 2003).
One open question is exactly why landscape changes at the 200 m scale (as opposed to other
scales examined) emerged as significant predictors of parasitism. Different studies have
documented differences in parasitoids and in parasitoid-host interactions, but no consensus yet
exists on which spatial scale results in maximum change in which system. Thies et al. (2003)
examined the influence of non-crop habitat within 0.5 - 6 km on parasitism for different
hymenopteran parasitoid species. They found that non-crop habitat influenced parasitism rate
most highly at spatial scales of between 1-2 km (Thies et al. 2003). An additional study found
that parasitoid diversity responded to changes in habitat composition at the 300 m scale, but did
not address parasitism rates specifically (Bennett and Gratton 2012).
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We also found that the parasitism of Gnamptogenys spp. in particular was positively
associated with increases in one local habitat factor -- humus mass. Certain disturbances in
agricultural systems that lower humus mass and therefore diminish nesting resources for grounddwelling ant species will decrease the abundance or richness of certain species (Mertl et al.
2009). But supplementing litter and humus biomass will increase insect prey resources and
thereby increase brood production for ground-dwelling ants (McGlynn et al. 2009, Shik and
Kaspari 2010), which may in turn lead to increases in parasitism rate by these density-dependent
parasitoids. In contrast to previous studies with eucharitid parasitoids, we did not document
positive relationships between Gnamptogenys parasitism and number of cocoons. Another
explanation for this pattern is that increased humus mass might prevent parasitoid desiccation or
might provide other necessary, but unknown, resources for parasitoids.
Conclusions. In summary, the local habitat and landscape predictors of ant parasitism by
eucharitid parasitoids is not well studied and we found evidence that changes in local and
landscape complexity can influence parasitism for certain groups of ants. Overall, presence of
parasitism in the landscape was influenced by the amount of rustic coffee and forest habitat
nearby and parasitism of two abundant genera increased with local vegetation complexity. We
found that presence of parasitism increased with increases in the number of cocoons, but did not
find positive density-dependence in parasitism of any ant species or genus examined. Parasitism
for Gnamptogenys and G. sulcata was higher in the dry season.. We did find influences of both
low and high agrochemical use on parasitism of nearly all ant groups studied, but the reasons for
these patterns remain somewhat elusive. We suggest that future studies on ant-parasitoid
relationships should examine changes in availability of eucharitid oviposition sites and
herbaceous vegetation in study sites, as well as changes in ant-parasitoid specificity, as these
105
differences may explain the results observed in this study. Differences between ant parasitism in
different locations within the landscape may have implications for other trophic interactions and
ecological services in agricultural landscapes.
Acknowledgements
A. García Ballinas, J. Santis, G. Dominguez, U. Pérez Vásquez, G. López Bautista, B.E.
Chilel, E. Sintes assisted with field work. R. Becker, R. John, and J.H. López Urbina assisted
with the GIS analysis. G. Ibarra Núñez, J. Rojas, and E. Chamé Vásquez of El Colegio de la
Frontera Sur (ECOSUR) provided logistic support. E.A. Murray and J.H. Heraty from the Dept.
of Entomology at the University of California, Riverside identified the parasitoids. We thank the
owners of Fincas Irlanda, Argovia, Hamburgo, San Francisco, Genova, Rancho Alegre, Chiripa,
Maravillas, Santa Anita, San Enrique and Rancho Cuilco for allowing us to conduct research on
their farms. ADM was funded by scholarship number 168970 granted by the National Council of
Science and Technology (CONACYT) in Mexico and a Conservation International Rapid
Assessment Program award. Additional funding was provided by NSF DEB-1020096 to SMP.
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Table 1. Ant morphospecies in the subfamilies Ectatomminae, Formicinae, and Ponerinae
examined for eucharitid parasitoids in forest and coffee habitats in Chiapas, Mexico §¶. Data show
total numbers of cocoons, parasitized cocoons, and the proportion of parasitized cocoons for 10
forest (F) sites, 13 high-shade coffee sites (HSC), and 17 low-shade coffee sites (LSC) sampled
during the dry and the rainy season of 2010.
No. cocoons
No. parasitized
Parasitized cocoons
collected
cocoons
(%)§§
Morphospecies
F
HSC
LSC
F
HSC
LSC
F
HSC
LSC
Camponotus atriceps
0
153
88
NA
11
0
NA
7.19
0
29
0
0
0
NA
NA
0
NA
NA
Gnamptogenys sp. 1
79
184
0
13
0
NA
16.46
0
NA
Gnamptogenys striatula
66
58
211
0
0
0
0
0
0
135
871
425
2
10
3
1.48
1.15
0.71
0
81
13
NA
0
0
NA
0
0
Leptogenys sp. 1*
13
7
3
0
0
0
0
0
0
Odontomachus chelifer
11
43
421
0
3
14
0
6.98
3.33
0
29
0
NA
2
NA
NA
6.90
NA
(F. Smith)
Camponotus striatus
(F. Smith)
Mayr*
Gnamptogenys sulcata (F.
Smith)*
Gnamptogenys wheeleri
(Santschi)
(Latreille)*
Odontomachus meinerti
117
Forel
Pachycondyla apicalis
21
42
39
0
0
0
0
0
0
8
173
442
0
0
0
0
0
0
41
0
49
0
NA
0
0
NA
0
32
79
18
2
0
0
6.25
0
0
Pachycondyla sp. 1
0
7
0
NA
0
NA
NA
0
NA
Platythyrea punctata (F.
0
12
13
NA
0
0
NA
0
0
0
105
0
NA
0
NA
NA
0
NA
(Latreille)*
Pachycondyla cf. cognata
(Emery)*
Pachycondyla harpax
(Fabricius)
Pachycondyla impressa
(Roger)*
Smith)
Typhlomyrmex rogenhoferi
Mayr
§
Morphospecies in bold print were parasitized.
*Species with cocoons found in all three habitats
¶
NA indicates that there were no cocoons in that habitat that could have been parasitized.
§§
Calculated based on total number of parasitized and unparasitized cocoons across all sites
sampled.
118
1
7
0±0
cocoons parasitized
Prop. of O. chelifer
0.023±0.01
3
0.003±0.00
0±0
6
0.023±0.01
2
0.002±0.00
3
0.026±0.01
2
0.002±0.00
6
0.006±0.00
4
0.035±0.01
4
0.015±0.01
3
0.133±0.04
1
0.281±0.08
Wet
Dry
0.024±0.02
0.03±0.023
0±0
6
0.016±0.01
9
0.024±0.01
5
0.286±0.12
5
0.286±0.12
Season
11
17
20
12
27
46
46
N
0.402
0.762
<0.001
0.673
0.523
0.450
0.446
t
Habita
0.297
6.726*
<0.001
0.510
1.424*
0.139
0.016
n
Seaso
F-value
§
cocoons parasitized
7
6
4
4
Presence or absence in indiivdual sites was coded as 0 or 1, and the mean across all sites was calculated, and reported here.
* P < 0.05
0.048±0.02
0.025±0.02
0.007±0.00
Prop. of G. sulcata
0±0
1
0.055±0.02
5
8
0.018±0.01
0±0
9
0.017±0.01
6
0.078±0.04
5
4
0.232±0.08
0.176±0.09
Low-shade
0.368±0.11
shade
High-
cocoons parasitized
Prop. of Pachycondyla
cocoons parasitized
Prop. of Odontomachus
cocoons parasitized
Prop. of Gnamptogenys
0.043±0.03
0.25±0.134
Prop. of species
parasitized
0.3±0.153
Presence of parasitism§
Forest
Habitat
shade coffee agroecosystems and in the wet and dry season in coffee landscapes in Chiapas, Mexico.
119
<0.001
<0.001
<0.001
<0.001
0.453
0.009
0.005
Season
Habitat x
Table 2. Results of a generalized linear mixed model testing for differences in ant parasitism by Eucharitidae in forest, high-, and low-
<0.001
VCI + AI2 + number of
NA
0.038
NA
NA
AI2
VCI2
AI2
AI2
Prop. of Odontomachus cocoons parasitized
Prop. of Pachycondyla cocoons parasitized
Prop. of G. sulcata cocoons parasitized
Prop. of O. chelifer cocoons parasitized
Prop. of Gnamptogenys cocoons parasitized
NA
NA
<0.001
NA
NA
NA
NA
VCI2
0.313
0.087
NA
0.641
0.237
0.376
0.329
AI
†
NA indicates that variable was not included in the best model.
Numbers show p-values for predictor variables as determined with generalized linear mixed models.
§
NA
AI2
Gnamptogenys cocoons
NA
AI2 + number of cocoons
Presence of parasitism
Prop. of species parasitized
VCI
Best Model
Dependent Variable
agrochemical index (AI) and the number of ant cocoons found in a site. §†
0.003
<0.001
NA
0.001
<0.001
0.002
<0.001
AI2
NA
NA
NA
NA
<0.001
NA
0.025
cocoons
Number of
120
Table 3. Results of generalized linear models testing relationships between dependent variables, the vegetation complexity index (VCI),
Figure legends.
Figure 1. Relationships between presence of parasitism of ants by eucharitid parasitoids (a, b) and
proportion of species parasitized by eucharitids (c, d) and vegetation complexity and agrochemical
use in coffee landscapes.
Figure 2. Relationship between proportion of Gnamptogenys (a, b), Odontomachus (c, d),
Pachycondyla (e, f), G. sulcata (g, h) and O. chelifer (i, j) and vegetation complexity (VCI) and
agrochemical use (AI) in coffee landscapes.
Figure 3. Conditional inference trees testing relationships between 13 local and 7 regional factors
and: a) presence of parasitism, b) proportion of parasitized ant species, and c) proportion of
parasitized Gnamptogenys cocoons in coffee landscapes. Predictor variables in boxes are ranked
(top, highest correlation with dependent variable). P-values indicate the significance at 95% of
confidence in the relationship tested. Box plots include the inner quartiles (grey box), the median
values (solid black line), and 1.5 x the inner quartiles (error bars) of cocoons parasitized
associated with each string of predictor variables. Sample sizes at each terminal node show the
number of sites in that box plot.
0.0
0.2
0.2
0.6
0.2
0.8
1.0
c
1.0
0.4
0.4
VCI
VCI
0.6
0.6
0.8
0.6
0.8
0.2
0.4
Prop species
0.4
0.0
0.0
0.0
Prop. parasitized species
0.0
0.0
0.2
0.2
0.6
0.4
0.6
Presence of parasitism
0.4
0.8
0.8
Presence or absence of parasitism
1.0
1.0
Presence of parasitism
Figure 1.
a
b
VCI
0.8
0.0
0.0
0.2
0.2
0.4
AI
0.4
AI
AI
0.6
0.6
0.8
d
0.8
Figure 2.
Figure 3.
Table S1. Results from Moran’s test for spatial autocorrelation using a spatial weights matrix.
Results are shown for best-fit model GLMs and for the conditional inference trees (CIT).
Dependent variable (test)§
Moran’s statistic
Moran I
Expectation
Variance
P
standard deviate
statistic
Presence of parasitism (GLM)
1.7286
0.131114
-0.029411
0.008623
0.042
Prop. of species parasitized (GLM)
1.006
0.060946
-0.02941
0.00806
0.157
Prop. of Gnamptogenys cocoons
0.5664
-0.022905
-0.04761
0.00190
0.285
-0.1685
-0.146028
-0.1000
0.0746
0.567
0.5664
-0.022905
-0.04761
0.00190
0.286
3.6474
0.248209
-0.07142
0.00767
<0.001
0.7071
-0.3088
-0.333333
0.001201
0.240
Presence of parasitism (CIT)
-0.8663
-0.11139
0.02941
0.00895
0.807
Prop. of species parasitized (CIT)
0.4778
0.01493
-0.02941
0.008614
0.316
Prop. of Gnamptogenys cocoons
0.4266
-0.01478
-0.052631
0.00787
0.335
-1.3745
-0.48874
-0.10000
0.07998
0.915
-0.7734
-0.085185
-0.05555
0.00146
0.780
-1.0477
-0.16272
-0.07142
0.007593
0.853
-0.7071
-0.9999
-0.33333
0.88888
0.760
parasitized (GLM)
Prop. of Odontomachus cocoons
parasitized (GLM)
Prop. of Pachycondyla cocoons
parasitized (GLM)
Prop. of G. sulcata cocoons
parasitized (GLM)
Prop. of O. chelifer cocoons
parasitized (GLM)
parasitized (CIT)
Prop. of Odontomachus cocoons
parasitized (CIT)
Prop. of Pachycondyla cocoons
parasitized (CIT)
Prop. of G. sulcata cocoons
parasitized(CIT)
Prop. of O. chelifer cocoons
parasitized(CIT)
§
The tests autocorrelation for general linear models with the vegetation complexity index (VCI),
agrochemical index (AI) and for conditional inference trees (CIT) for each dependent variable
tested.
Table S2. Ant species and morphospecies and their eucharitid parasitoids encountered in three
habitats in a coffee landscape.
Ant morphospecies
Eucharitid
morphospecies
Seaso
Habitat(s)
Stage/Sex
N†
collected¶
n
Camponotus atriceps
Obeza n. sp.¶¶
Wet
High-shade
adult F
11
Gnamptogenys sp. 1
Kapala izapa
Wet
Forest
adult M
12
Gnamptogenys sp. 1
Kapala sp.
Wet
Forest
pharate adult
1
Gnamptogenys sulcata
Kapala izapa
Dry
High-shade
adult M, adult
8
F
Gnamptogenys sulcata
Kapala nr sulcifacies§
Wet
Low-shade
adult F
2
Gnamptogenys sulcata
Kapala sp.
Wet
High-shade
pharate adult
1
Gnamptogenys sulcata
Eucharitidae
Dry
High-shade
pharate adult
1
Gnamptogenys sulcata
Eucharitidae
Wet
Low-shade
pupa
1
Odontomachus chelifer
Kapala nr sulcifacies§
Wet
High-shade
adult M
3
Odontomachus chelifer
Kapala nr sulcifacies§
Wet
Low-shade
adult F
14
Odontomachus meinerti
Eucharitidae
Wet
High-shade
pharate adult
2
Pachycondyla harpax
Kapala nr sulcifacies§
Wet
Low-shade
adult F
1
Pachycondyla harpax
Eucharitidae
Wet
Low-shade
pupa
1
Pachycondyla impressa
Kapala nr sulcifacies
Wet
Forest
adult F
2
¶
Some pharate and pupal forms could not be identified to genus, species, or to sex.
Indicates the number of instances of the relationship was encountered.
¶¶
Undescribed species
§
All individuals found belonged to the same morphospecies.
†
Capítulo V. DISCUSION Y CONCLUSIONES
Este trabajo tuvo como propósito estudiar la diversidad y abundancia de hormigas, servicios
ecosistemicos que este grupo ofrece a través del parasitismo que ciertos géneros de parasitoides en
un paisaje cafetalero. Las preguntas centrales de esta investigación fueron: ¿Cuáles son los
factores locales y paisaje que afectan la diversidad y abundancia de hormigas? ¿Qué factores
influyen en los servicios ecosistémicos de remoción de presas que este grupo ofrece? ¿Cúales
afectan la relación de parasitismo que sufren las hormigas por parasitoides? Este trabajo se centra
en la hipótesis de que la intensificación de hábitats reduce la diversidad y abundancia de ciertos
grupos de insectos en menor o mayor grado, lo que afectará los servicios ecosistémicos debido a
la simplificación de grupos funcionales y se encontrará un menor parasitismo en sitios con mayor
intensificación. En los análisis, además de medir variables de complejidad en cada sitio, se
incluyeron el efecto del uso y frecuencia de agroquímicos en las diferentes labores de control y
nutrición, así como factores de paisaje: distancia a los parches de bosque, mediciones espaciales
del porcentaje de áreas conservadas (bosque y café rustico) en cada sitio de muestreo, variables
poco estudiadas en agroecosistemas tropicales. Con base a los resultados generados en esta
investigación, encontramos que la diversidad y abundancia de los diferentes grupos de hormigas
responde en mayor grado a características locales de vegetación (por ejemplo, humus,
circunferencia de árboles) y en menor, pero no de mínima importancia, a las características de
paisaje (proporción de áreas de bosque y cafetales de café rustico). Por lo tanto, el grado de
complejidad del hábitat está fuertemente correlacionada con la diversidad y abundancia de
hormigas (Armbrecht et al 2005, Rivera y Armbrecht 2005, Teodoro et al 2010), debido a las
condiciones microclimaticas, las labores culturales (podas, fertilización) que se realizan en menor
o mayor grado (Philpott 2005, MacGlynn et al 2009), las condiciones nutrimentales del suelo,
sitios de anidación, entre otros factores, y están directamente relacionadas con cambios en la
comunidad de hormigas (Perfecto y Vandermeer 1996). Similares resultados se encontraron en
estudios con abejas en la misma zona de estudio, donde la complejidad del hábitat favorece la
riqueza, abundancia y composición de una comunidad de abejas (Jha y Vandermeer 2010), y no
los efectos de paisaje como ha sido estudiado en otros agroecosistemas con diversos grupos de
himenópteros (Tylianakys et al 2005). Un factor local de manejo agronómico (fertilización)
afectó negativamente la abundancia de hormigas del suelo, este dato no había sido reportado en
sistemas tropicales de café; pero se debe continuar estudiando detalladamente e incluso con otros
organismos, por ejemplo evaluando la residualidad de los productos aplicados (Matlock y de la
Cruz 2003). A pesar de que factores de paisaje no son estadísticamente importantes para algunas
hormigas, otros factores biológicos no considerados, como la capacidad de dispersión de las
diferentes especies, están relacionados estrechamente a cambios en el paisaje que indirectamente
afectan la diversidad y abundancia de hormigas (Livinston et al. 2013). Por ejemplo, se conoce
que las hormigas que anidan en ramas dependen de la disponibilidad de ramas huecas (factor
local) y son consideradas relativamente sésiles (Philpott y Foster 2005); en este estudio, contrario
a la predicción inicial, se encontró que la distancia al bosque fue el factor que influye en la
diversidad de este grupo de hormigas, a mayor distancia al bosque, aumenta la diversidad de
hormigas que anidan en ramas y esto se debe que existe una mayor disponibilidad de ramas
huecas a mayor distancia al bosque. Este trabajo reporta, que en agroecosistemas de café el
hábitat (con manejo agronómico moderado) es el factor importante para este grupo de hormigas;
en sitios con intensificación moderada (por ejemplo cafetal de tipo policultivo comercial) la
diversidad es mayor en comparación con agroecosistemas más complejos como café rústico o
bosque, como se esperaba encontrar. Analizando detalladamente patrones locales (disponibilidad
de ramas huecas) y de paisaje (distancia al bosque) en esta investigación, encontramos que existe
una relación positiva entre la disponibilidad de ramas huecas y la distancia al bosque, hay mas
ramas disponibles a mayor distancia al bosque. Así también, recientemente se ha demostrado que
la altitud es un factor de paisaje que influye en la diversidad de este grupo de hormigas, aspecto
considerado para esta investigación pero significativamente no importante (Gillette et al en
revisión).
Paralelamente, se ha estudiado las implicaciones por pérdida o aumento de algunas
especies de hormigas (o su abundancia) en la remoción de presas potenciales en cafetales para
diversas plagas como broca y minador de la hoja de café (De la Mora et al 2008, Larsen y
Philpott 2010). Este trabajo apoya la hipótesis de que la riqueza y abundancia de hormigas están
correlacionadas postivimanente con las tasas de remoción de presas en agroecosístemas de café.
Se encontró que la abundancia y la riqueza de hormigas tuvieron una tendencia positiva y
fuertemente correlacionada con la proporción de presas removidas, incluso cuando otros factores
locales y el paisaje fueron incluidos en los análisis. Sin embargo, de acuerdo con nuestros datos la
remoción de presas puede estar influenciada indirectamente por características locales del sitio;
por ejemplo, la riqueza de árboles, el número de ramas huecas de café y un factor del paisaje (café
rústico a 500 m); estas características físicas promueven una mayor riqueza y abundancia de
hormiga que directa y positivamente se correlacionan con la remoción de presas. No se encontró
una relación positiva con la distancia al bosque, contrario a estudios previos en la región de
estudio, donde la distancia al bosque es inversamente proporcional al número de presas removidas
(Armbrecht y Perfecto 2003); sin embargo en este estudio se concluye que la diversidad de
hormigas está correlacionada y es mayor a distancias menores al bosque (un efecto indirecto de la
diversidad). Así, los servicios de control de plagas en paisajes cafetaleros están fuertemente
impulsados por la diversidad biológica de depredadores, con cierta influencia de factores físicos
que promueven la diversidad y abundancia de grupos de depredadores (como las hormigas, aves y
murciélagos) y complementan la regulación de insectos nocivos potenciales (Garcia Estrada
2006, Philpott et al 2009, Larsen and Philpott 2010).
Inicialmente se esperaba encontrar una relación negativa entre el porcentaje de pupas
parasitadas y cafetales complejos, así como en sitios sin uso de agroquímicos. Sin embargo un
resultado importante de este trabajo es que la frecuencia por el uso de algún agroquímico afecta el
parasitismo en hormigas. Por otro lado, la focalización sobre un determinado grupo de pupas
(cocones) limitó el conocimiento sobre el parasitismo real que afecta a hormigas en un paisaje de
café. A partir de esto, los análisis realizados señalaron que el factor importante para el parasitismo
en hormigas es el uso de agroquímicos, seguido de la cantidad de humus y dos factores de paisaje.
Las variables de paisajes corroboran la importancia de mantener sitios con mayor complejidad que
provean refugios, sitios de alimentación y anidación para otros himenópteros, como abejas y
avispas demostrado en estudios previamente revisados (Landis et al 2000, Sperber et al 2004).
Adicionalmente, como parte de los resultados del estudio de parasitismo, se encontró un
parasitoide perteneciente a la subfamilia Eucharitidae (género Obeza spp.) en la hormiga
Camponotus atriceps Mayr, que de acuerdo con la literatura ha sido reportado previamente para
otra especie de Camponotus (posiblemente no descrita) (ver Pérez-Lachaud y Lachaud 2014).
Como discusión final, se puede argumentar que los diferentes hábitats no fueron un factor
determinante para nuestros estudios, pero si, ciertas características físicas locales y de paisaje para
cada grupo de hormigas. El índice de agroquímicos únicamente fue significativo para la
abundancia de hormigas que anidan en troncos de café y para la relación hormiga-parasitoide,
pero es necesario estudiar más a fondo esta relación, pues a nivel mundial está poco abordada. La
diversidad y abundancia de hormigas son los principales factores que determinaron la remoción,
así también factores locales y de paisaje están indirectamente relacionados con el fenómeno de
remoción. Finalmente, este y otros estudios demuestran que la intensificación en agroecosistemas
afecta la diversidad y abundancia de diferentes organismos. Así también, se conoce que la pérdida
de biodiversidad afectará los servicios ecosistemicos que los sistemas complejos proveen, por lo
que se debe considerar evaluar desde diferentes perspectivas el impacto del manejo agronómico
que se realice en los agroecosistemas. Es deseable incentivar áreas de amortiguamiento o
corredores biológicos con árboles que promuevan la conservación de diferentes organismos a
través de la matriz agrícola en paisajes de café.
CONCLUSIONES
1.- La presente investigación utilizó al grupo taxonómico Hymenoptera para relacionar el
efecto de factores locales y de paisaje sobre la diversidad y abundancia de hormigas en tres
diferentes hábitats (café de baja sombra, café de sombra, y bosque). Encontramos que los factores
locales afectan principalmente a la diversidad de hormigas, pero cada grupo responde de manera
diferente a ciertos aspectos de manejo agrícola (hormigas de hojarasca, hormigas que anidan en
ramas de café, en troncos podridos y hormigas arbóreas). Particularmente, en este trabajo se
determinó que la cantidad de humus, el número de ramas secas de café, la circunferencia de los
árboles, el número de troncos podridos, son factores locales que promueven los patrones de
diversidad y abundancia para las especies de los cuatro grupos de hormigas. Sin embargo, cada
grupo de hormigas responde en abundancia y riqueza a factores específicos. Interesantemente, el
uso y frecuencia de la aplicación de fertilizantes afecta negativamente la abundancia de hormigas
que anidan en trocos podridos.
2.- Interesantemente, el uso y frecuencia de la aplicación de fertilizantes afecta
negativamente la abundancia de hormigas que anidan en trocos podridos. Se demostró un efecto
negativo de la fertilización sobre la abundancia de un grupo de hormigas. Este resultado
contribuye al número de estudios que evidencian una de las problemáticas que representa la
intensificación de los agroecosistemas por el uso de productos químicos que afectan
indirectamente a insectos benéficos o susceptibles a ciertos productos químicos.
3.- Este trabajo tuvo como propósito estudiar los servicios ecosistémicos, con un enfoque
sobre la remoción de presas por hormigas en un paisaje de café. Nos apoyamos en la hipótesis de
que la intensificación del manejo agronómico disminuye los servicios ecosistémicos y a partir de
una serie de análisis realizados en esta investigación, se puede argumentar que no hay efectos
negativos directos de la reducción de la complejidad de la vegetación o por el uso de
agroquímicos sobre la remoción de presas por las hormigas. Sin embargo, este trabajo apoya la
hipótesis de que la riqueza y abundancia de hormigas están correlacionadas con las tasas de
remoción de presas en
agroecosístemas de café. Se encontró que la abundancia y la riqueza de hormigas tuvieron una
tendencia positiva y fuertemente correlacionada con la proporción de presas removidas, incluso
cuando otros factores locales y del paisaje fueron incluidos en los análisis. Sin embargo, la
remoción de presas puede estar influenciada indirectamente por características locales del sitio
(troncos podridos, disponibilidad de ramas secas en cafetos) y factores del paisaje (distancia y
porcentaje de bosque en cada sitio) pues estas características físicas promueven una mayor riqueza
y abundancia de hormigas (ver Capítulo II) que directamente se correlacionan positivamente con
la remoción de presas.
5.-No se encontró un efecto positivo entre la proporción de parasitismo y los índices de
complejidad para la mayoría de nuestras variables dependientes, únicamente para el género
Gnamptogenys spp. Sin embargo un resultado importante en este trabajo ha sido demostrar que la
frecuencia del uso de algún agroquímico afecta el parasitismo en hormigas (Capítulo IV). La
focalización del estudio sobre las especies de hormigas para las cuales la pupación se realiza
dentro de un capullo limitó el conocimiento sobre el parasitismo real que afecta a las hormigas en
general en un paisaje de café. Los análisis realizados señalaron que los factores importantes para
el parasitismo en hormigas son el uso de agroquímicos, la cantidad de humus y dos factores de
paisaje (proporción de cafetal rústico y bosque en cada sitio), siendo el primero (uso de
agroquímicos) el más significativo estadísticamente (ver Capítulo IV).
6.- Como parte de los resultados del estudio de parasitismo, se encontraron varias
asociaciones nuevas de eucharítidos con Ponerinae y Ectatomminae. Se encontró un parasitoide
perteneciente a la subfamilia Eucharitidae (género Obeza sp.) en la hormiga Camponotus atriceps
Mayr, que de acuerdo con la literatura, recientemente este género de Eucharitidae ha sido
reportado atacando Camponotus aff. textor por parte de una especie no descrita de este género.
Lo anrerior, puede generar futuros estudios para conocer la diversidad de parasitoides y sus
posibles asociaciones entre Obeza sp. y diferentes especies de Camponotus.
RECOMENDACIONES
1.- Sustentado por los datos generados en esta investigación, se recomienda conservar una
diversidad de árboles de sombra en cafetales, lo que generaría directamente refugio o sitios de
anidación y alimentación para las hormigas (como ramas huecas, troncos podridos y exudados
florales); así como también aumentaría la complejidad de interacciones tróficas y relaciones
ecológicas con otros organismos (depredación, parasitismo y competencia inter e intra especifica).
2.- Se propone implementar una diversidad estructural, promoviendo la diversidad de especies
para la sombra de café con la vegetación original, con el fin de aumentar la riqueza y abundancia
de hormigas para promover los servicios ecosistémicos de depredación sin intervenir con las
metas de producción. Con acciones en la diversificación estructural de la sombra de café, otros
taxa como los murciélagos, aves, arañas complementarán las labores de regulación de insectos
potenciales, y se enfatiza la importancia de la diversidad biológica de los depredadores en las
fincas de café que está altamente relacionada con la regulación de insectos-plaga.
3.- Algunos factores de paisaje (por ejemplo áreas de bosque y cafetal rustico) promueven la
relación ecológica de las hormigas con sus depredadores, por lo cual, la implementación de zonas
de amortiguamiento y conectividad entre fragmentos de bosque puede ayudar a mantener
reguladas las comunidades de hormigas, dentro de ellas, las consideradas como insectos-plaga.
CAPÍTULO VI. LITERATURA CITADA
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Leverington, F., Loh, J., McGeoch, M. A., McRae, L., Minasyan, A., Hernández Morcillo,
M., Oldfield, T. E. E., Pauly, D., Quader, S., Revenga, C., Sauer, J. R., Skolnik, B., Spear,
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ANEXO 1.
Acuse de recibido del artículo aceptado: Local and landscape drivers of biodiversity of four guilds
of ants in coffee landscapes. Biodiversity Conservation, 22:871-888.
ANEXO 2.
Normas editoriales del articulo sometido de la revista
Agriculture, Ecosystems, and Environment
AGRICULTURE, ECOSYSTEMS &
ENVIRONMENT
An International Journal for Scientific Research on the Interaction Between
Agroecosystems and the Environment
AUTHOR INFORMATION PACK
TABLE OF CONTENTS
.
•
•
•
•
•
•
.
Description
Audience
Impact Factor
Abstracting and Indexing
Editorial Board
Guide for Authors
DESCRIPTION
.
Agriculture, Ecosystems & Environment publishes scientific articles dealing with the interface between
agroecosystems and the natural environment, specifically how agriculture influences the environment
and how changes in that environment impact agroecosystems. Preference is given to papers from
experimental and observational research at the field, system or landscape level, complemented
as appropriate by dynamic and statistical modelling, that bridge scientific disciplines, integrate
knowledge, and are placed in an international or wide comparative context.
The focus is on the following areas:• Biological and physical characteristics of agroecosystems
including land, air, and water quality.• Ecology, diversity and sustainability of agricultural systems.•
Relationships between agroecosystems and the natural environment.• Agroecosystem and global
environmental changes including climate change and air pollution.• Ecological consequences of
intensification, soil degradation, waste application, irrigation, and mitigation options. • Environmental
implications of agricultural land use and land use change.
All manuscripts are initially screened on their topic suitability and linguistic quality. The following
topics are discouraged unless they provide new information regarding processes operating at
the agroecosystem-environment interface: inventory and survey analysis and impact assessment,
including life cycle and emergy analysis; greenhouse or laboratory-based studies; development of
models or methodologies; studies that are purely agronomic, socio-economic, or political.
A section of this journal is published as the companion journal Applied Soil Ecology. Please
bookmark this page as: http://www.elsevier.com/locate/agee
Fore more information/suggestions/comments please contact [email protected]
AUDIENCE
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Scientists in Agriculture, Forestry, Ecology and the Environment, Administrators and Policy-Makers in
these fields.
IMPACT FACTOR
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2009: 3.130 © Thomson Reuters Journal Citation Reports 2010
ABSTRACTING AND INDEXING
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Biological and Agricultural Index
Current Contents/Agriculture, Biology & Environmental Sciences
EMBiology
Ecological Abstracts
Elsevier BIOBASE
Environmental
Abstracts
Environmental Periodicals Bibliography
GEOBASE
Science Citation
Index Scopus
TROPAG/RURAL Database
EDITORIAL BOARD
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Editor-in-Chief:
J. Fuhrer, Air Pollution/Climate Group, Agroscope Reckenholz-Taenikon ART, Reckenholzstrasse
191, 8046 Zürich, Switzerland, Fax: 41 1 3777 201, Email: [email protected]
Associate Editors:
J.P. Aeschlimann, Montpellier, France
M. Carter, Charlottetown, PE, Canada
A. de Rouw, Paris, France
M.J. Jones, Dorset, UK
S. Saggar, Palmerston North, New Zealand
G. Seneviratne, Kandy, Sri Lanka
T.A. Veldkamp, Enschede, Netherlands
Editorial Advisory Board:
B. Amon, Universität für Bodenkultur Wien (BOKU), Wien, Austria
A. Arunachalam, North Eastern Regional Inst. of Sci. & Technology, Nirjuli, Arunachal Pradesh, India
restoration ecology, crop production, natural resource management, remote sensing and
GIS, soil nutrient management
P. Audet, University of Ottawa, Ottawa, ON, Canada
E. Baggs, University of Aberdeen, Aberdeen, UK
K.L. Bailey, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
biopesticides, biological control, plant pathology, microbiology, plant diseases and weeds
N. Batjes, ISRIC - World Soil Information, Wageningen, Netherlands
C. Boutin, National Wildlife Research Centre, Hull, QC, Canada
plant and weed diversity, non-crop habitats, organic farming, phytotoxicity, landscapes
F. Burel, Université de Rennes I, Rennes, France
landscape ecology, biodiversity, connectivity, invertebrates, hedgerows
K.N. Cahill, Lund University, Lund, Sweden
I. Cardoso, Federal University of Vicosa, Vicosa, Brazil
M. Centritto, Consiglio Nazionale delle Ricerche (CNR), Monterotondo Scalo RM, Italy
plant physiological ecology, gas-exchange (carbon assimilation, transpiration and isoprenoid
emission), physiological responses of plants to environmental stress, climate-vegetation interactions,
climate change impacts
K.Y. Chan, University of New South Wales, Wagga Wagga, NSW, Australia
soil structure, soil health, soil organic carbon, conservation tillage, earthworms
X. Chen, Zhejiang University, Zhejiang, China
F. Conen, Universität Basel, Basel, Switzerland
C. Drury, Agriculture and Agri-Food Canada, Harrow, ON, Canada
A.C. Edwards, Peterhead, Aberdeenshire, UK
soil, nutrients, cycles, impacts, agriculture
F. Ewert, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
climate change (impacts), agroecosystems (physiology), resource use and management (land,
water), systems analysis and modelling, agricultural sustainability assessment
L.G. Firbank, Institute of Grasslands and Environmental Research, Okehampton, UK
biodiversity, agro-ecology, ecosystem services, land use, sustainable farming systems
A.J. Franzluebbers, U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS),
Watkinsville, GA, USA
M. Frei, Japan International Research Center for Agricultural, Tsukuba, Ibaraki, Japan
J. Helenius, University of Helsinki, University of Helsinki, Finland
agroecology, biodiversity (in farmland), ecosystem services, food systems, agroecosystem
W.B. Hoogmoed, Wageningen University, Wageningen, Netherlands
Y. Huang, Chinese Academy of Sciences (CAS), Beijing, China
M.B. Kirkham, Kansas State University, Manhattan, KS, USA
A. Légère, Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
J. Leifeld, Research Station Agroscope Reckenholz, Zürich, Switzerland
soil organic matter quality and turnover in agricultural systems, soil responses to land-use and
management, analytical techniques in soil chemistry, greenhouse gases in agricultural systems
(measurement and modelling), environmental performance of farming systems
F-M. Li, Lanzhou University, Lanzhou, Gansu Province, China
Y. Li, Chinese Academy of Agricultural Sciences, Beijing, China
N.Z. Lupwayi, Agriculture and Agri-Food Canada, Lethbridge, AB, Canada
Soil Microbiology
E.J.P. Marshall, Marshall Agroecology Limited, Winscombe, UK
agroecology, biodiversity, vegetation management, field margins, weeds, dispersal
W.J. McConnell, Michigan State University (MSU), East Lansing, MI, USA
G. Moreno Marcos, Universidad de Extremadura, Plasencia, Spain
K. Mueller, AgeResearch Ltd., Hamilton, New Zealand
J. Nyamangara, Int. Crops Research Institute for the Semi Arid Tropics (ICRISAT), Bulawayo,
Zimbabwe
O. Oenema, Wageningen University, Wageningen, Netherlands
D.M. Olszyk, Corvalis, OR, USA
A. Ouin, INP ENSAT, Castanet-Tolosan, France
J. Pant, The WorldFish Center, Bayan Lepas, Penang, Malaysia
D. Pimentel, Cornell University, Ithaca, NY, USA
H. Pleijel, Göteborgs Universitet, Göteborg, Sweden
S.L. Poggio, Universidad de Buenos Aires, Buenos Aires, Argentina
D.D. Poudel, University of Louisiana at Lafayette, Lafayette, LA, USA
A. Reenberg, University of Copenhagen, Copenhagen K, Denmark
M. Shiyomi, Ibaraki University, Mito, Japan
P. Smith, University of Aberdeen, Aberdeen, UK
L. Sparrow, Tasmanian Institute of Agricultural Research, Kings Meadows, TAS, Australia
M. Sperow, West Virginia University, Morgantown, WV, USA
F. Van der Pol, Royal Tropical Institute, Amsterdam, Netherlands
B. Vanlauwe, TSBF-CIAT, Nairobi, Kenya
E.F. Viglizzo, INTA Centro Regional La Pampa, Santa Rosa, La Pampa, Argentina
C. Wellstein, Universität Bayreuth, Bayreuth, Germany
H. Xiubin, Chinese Academy of Sciences (CAS), Yangling Shaanxi, China
H. Yao, Zhejiang University, Hangzhou, China
GUIDE FOR AUTHORS
.
INTRODUCTION
Agriculture, Ecosystems and Environment deals with the interface between agriculture and the
environment. Preference is given to papers that develop and apply interdisciplinarity, bridge scientific
disciplines, integrate scientific analyses derived from different perspectives of agroecosystem
sustainability, and are put in as wide an international or comparative context as possible. It is
addressed to scientists in agriculture, food production, agroforestry, ecology, environment, earth and
resource management, and administrators and policy-makers in these fields.
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agricultural production methods on the environment, including soil, water and air quality, and use
of energy and non-renewable resources; agroecosystem management, functioning, health, and
complexity, including agro-biodiversity and response of multi-species ecosystems to environmental
stress; the effect of pollutants on agriculture; agro-landscape values and changes, landscape
indicators and sustainable land use; farming system changes and dynamics; integrated pest
management and crop protection; and problems of agroecosystems from a biological, physical,
economic, and socio-cultural standpoint.
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material should not have been published previously elsewhere, except in a preliminary form.
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stated. Please see http://www.elsevier.com/funding.
Funding body agreements and policies
Elsevier has established agreements and developed policies to allow authors whose articles appear in
journals published by Elsevier, to comply with potential manuscript archiving requirements as specified as
conditions of their grant awards. To learn more about existing agreements and policies please visit
http://www.elsevier.com/fundingbodies.
Open access
This journal offers you the option of making your article freely available to all via the ScienceDirect
platform. To prevent any conflict of interest, you can only make this choice after receiving notification
that your article has been accepted for publication. The fee of $3,000 excludes taxes and other
potential author fees such as color charges. In some cases, institutions and funding bodies have
entered into agreement with Elsevier to meet these fees on behalf of their authors. Details of these
agreements are available at http://www.elsevier.com/fundingbodies. Authors of accepted articles,
who wish to take advantage of this option, should complete and submit the order form (available at
http://www.elsevier.com/locate/openaccessform.pdf). Whatever access option you choose, you retain
many rights as an author, including the right to post a revised personal version of your article on your
own website. More information can be found here: http://www.elsevier.com/authorsrights .
Language and language services
Please write your text in good English (American or British usage is accepted, but not a mixture of
these). Authors who require information about language editing and copyediting services pre- and
post-submission please visit http://webshop.elsevier.com/languageediting or our customer support
site at http://support.elsevier.com for more information.
Full Online Submission
Submission to this journal proceeds totally online and you will be guided stepwise through the creation
and uploading of your files. The system automatically converts source files to a single PDF file of the
article, which is used in the peer-review process. Please note that even though manuscript source
files are converted to PDF files at submission for the review process, these source files are needed for
further processing after acceptance. All correspondence, including notification of the Editor's decision
and requests for revision, takes place by e-mail removing the need for a paper trail.
All submissions must be accompanied by a cover letter detailing what you are submitting. Please
indicate:
• The author to whom we should address our correspondence (in the event of multiple authors, a
single 'Corresponding Author' must be named)
• A contact address, telephone/fax numbers and e-mail address
• Details of any previous or concurrent submissions. Please see our Authors' Rights section for more
copyright information.
• It is also useful to provide the Editor-in-Chief with any information that will support your submission
(e.g. original or confirmatory data, relevance, topicality).
Referees
Authors are required to identify four persons who are qualified to serve as reviewers. Authors are
requested not to suggest reviewers with whom they have a personal or professional relationship,
especially if that relationship would prevent the reviewer from having an unbiased opinion of the work of
the authors. A working e-mail address for each reviewer is essential for rapid review in the event that
reviewer is selected from those that are identified by the authors. You may also select reviewers you do
not want to review your manuscript, but please state your reason for doing so.
PREPARATION
Use of wordprocessing software
It is important that the file be saved in the native format of the wordprocessor used. The text should be
in single-column format. Keep the layout of the text as simple as possible. Most formatting codes will be
removed and replaced on processing the article. In particular, do not use the wordprocessor's options
to justify text or to hyphenate words. However, do use bold face, italics, subscripts,
superscripts etc. When preparing tables, if you are using a table grid, use only one grid for each
individual table and not a grid for each row. If no grid is used, use tabs, not spaces, to align columns. The
electronic text should be prepared in a way very similar to that of conventional manuscripts (see
also the Guide to Publishing with Elsevier: http://www.elsevier.com/guidepublication). Note that source
files of figures, tables and text graphics will be required whether or not you embed your figures in the
text. See also the section on Electronic illustrations.
To avoid unnecessary errors you are strongly advised to use the "spell-check" and "grammar-check"
functions of your wordprocessor.
Article structure
Subdivision - numbered sections
Divide your article into clearly defined and numbered sections. Subsections should be numbered
1.1 (then 1.1.1, 1.1.2, ...), 1.2, etc. (the abstract is not included in section numbering). Use this
numbering also for internal cross-referencing: do not just refer to "the text". Any subsection may be
given a brief heading. Each heading should appear on its own separate line.
Introduction
State the objectives of the work and provide an adequate background, avoiding a detailed literature
survey or a summary of the results.
Results
This should explore the significance of the results of the work, not repeat them. A combined Results
and Discussion section is often appropriate. Avoid extensive citations and discussion of published
literature.
Conclusions
The main conclusions of the study may be presented in a short Conclusions section, which may stand
alone or form a subsection of a Discussion or Results and Discussion section.
Appendices
If there is more than one appendix, they should be identified as A, B, etc. Formulae and equations in
appendices should be given separate numbering: Eq. (A.1), Eq. (A.2), etc.; in a subsequent appendix, Eq.
(B.1) and so on. Similarly for tables and figures: Table A.1; Fig. A.1, etc.
Essential title page information
Title. Concise and informative. Titles are often used in information-retrieval systems. Avoid
abbreviations and formulae where possible.
• Author names and affiliations. Where the family name may be ambiguous (e.g., a double
name), please indicate this clearly. Present the authors' affiliation addresses (where the actual work
was done) below the names. Indicate all affiliations with a lower-case superscript letter immediately
after the author's name and in front of the appropriate address. Provide the full postal address of
each affiliation, including the country name, and, if available, the e-mail address of each author.
•
• Corresponding author. Clearly indicate who will handle correspondence at all stages of
refereeing and publication, also post-publication. Ensure that telephone and fax
numbers (with country and area code) are provided in addition to the e-mail
address and the complete postal address. Contact details must be kept up to
date by the corresponding author.
• Present/permanent address. If an author has moved since the work described in the article was
done, or was visiting at the time, a "Present address" (or "Permanent address") may be indicated as a
footnote to that author's name. The address at which the author actually did the work must be
retained as the main, affiliation address. Superscript Arabic numerals are used for such footnotes.
Abstract
A concise and factual abstract is required. The abstract should state briefly the purpose of the
research, the principal results and major conclusions. An abstract is often presented separately from the
article, so it must be able to stand alone. For this reason, References should be avoided, but if
essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be
avoided, but if essential they must be defined at their first mention in the abstract itself.
Optional graphical abstract
A Graphical abstract is optional and should summarize the contents of the paper in a concise, pictorial
form designed to capture the attention of a wide readership online. Authors must provide images
that clearly represent the work described in the paper. Graphical abstracts should be submitted
with a caption. Supply captions separately, not attached to the graphical abstract. A caption should
comprise a brief title (not on the graphical abstract itself). Graphical abstracts should be submitted as
a separate file in the online submission system. Maximum image size: 400 × 600 pixels (h × w,
recommended size 200 × 500 pixels). Preferred file types: TIFF, EPS, PDF or MS Office files. See
http://www.elsevier.com/graphicalabstracts for examples.
Highlights
Highlights are mandatory for this journal. They consist of a short collection of bullet points
that convey the core findings of the article and should be submitted in a separate file in the
online submission system. Please use 'Highlights' in the file name and include 3 to 5 bullet
points (maximum 85 characters including spaces, or, maximum 20 words per bullet point). See
http://www.elsevier.com/highlights for examples.
Keywords
Immediately after the abstract, provide a maximum of 6 keywords, using American spelling and
avoiding general and plural terms and multiple concepts (avoid, for example, "and", "of"). Be sparing
with abbreviations: only abbreviations firmly established in the field may be eligible. These keywords will
be used for indexing purposes.
Acknowledgements
Collate acknowledgements in a separate section at the end of the article before the references and do
not, therefore, include them on the title page, as a footnote to the title or otherwise. List here those
individuals who provided help during the research (e.g., providing language help, writing assistance or
proof reading the article, etc.).
Math formulae
Present simple formulae in the line of normal text where possible and use the solidus (/) instead of a
horizontal line for small fractional terms, e.g., X/Y. In principle, variables are to be presented in
italics. Powers of e are often more conveniently denoted by exp. Number consecutively any equations
that have to be displayed separately from the text (if referred to explicitly in the text).
Footnotes
Footnotes should be used sparingly. Number them consecutively throughout the article, using
superscript Arabic numbers. Many wordprocessors build footnotes into the text, and this feature may be
used. Should this not be the case, indicate the position of footnotes in the text and present the
footnotes themselves separately at the end of the article. Do not include footnotes in the Reference
list.
Table footnotes
Indicate each footnote in a table with a superscript lowercase letter.
Artwok
Electronic
artwork
General
points
• Make sure you use uniform lettering and sizing of your original artwork.
• Save text in illustrations as "graphics" or enclose the font.
• Only use the following fonts in your illustrations: Arial, Courier, Times, Symbol.
• Number the illustrations according to their sequence in the text.
• Use a logical naming convention for your artwork files.
• Provide captions to illustrations separately.
• Produce images near to the desired size of the printed version.
• Submit each figure as a separate file.
A detailed guide on electronic artwork is available on our website:
http://www.elsevier.com/artworkinstructions
You are urged to visit this site; some excerpts from the detailed information are
given here.
Formats
Regardless of the application used, when your electronic artwork is finalised, please "save as" or
convert the images to one of the following formats (note the resolution requirements for line drawings,
halftones, and line/halftone combinations given below):
EPS: Vector drawings. Embed the font or save the text as "graphics".
TIFF: color or grayscale photographs (halftones): always use a minimum of 300 dpi. TIFF:
Bitmapped line drawings: use a minimum of 1000 dpi.
TIFF: Combinations bitmapped line/half-tone (color or grayscale): a minimum of 500 dpi is required. If
your electronic artwork is created in a Microsoft Office application (Word, PowerPoint, Excel) then
please supply "as is".
Please do not:
• Supply files that are optimised for screen use (like GIF, BMP, PICT, WPG); the resolution is too low;
• Supply files that are too low in resolution;
• Submit graphics that are disproportionately large for the content.
Color artwork
Please make sure that artwork files are in an acceptable format (TIFF, EPS or MS Office files) and with the
correct resolution. If, together with your accepted article, you submit usable color figures then
Elsevier will ensure, at no additional charge, that these figures will appear in color on the Web (e.g.,
ScienceDirect and other sites) regardless of whether or not these illustrations are reproduced in color in
the printed version. For color reproduction in print, you will receive information regarding the costs
from Elsevier after receipt of your accepted article. Please indicate your preference for color in print
or on the Web only. For further information on the preparation of electronic artwork, please see
http://www.elsevier.com/artworkinstructions.
Please note: Because of technical complications which can arise by converting color figures to "gray
scale" (for the printed version should you not opt for color in print) please submit in addition usable
black and white versions of all the color illustrations.
Figure captions
Ensure that each illustration has a caption. Supply captions separately, not attached to the figure. A
caption should comprise a brief title (not on the figure itself) and a description of the illustration. Keep text
in the illustrations themselves to a minimum but explain all symbols and abbreviations used.
Tables
Number tables consecutively in accordance with their appearance in the text. Place footnotes to tables
below the table body and indicate them with superscript lowercase letters. Avoid vertical rules. Be
sparing in the use of tables and ensure that the data presented in tables do not duplicate results
described elsewhere in the article.
References
Citation in text
Please ensure that every reference cited in the text is also present in the reference list (and vice
versa). Any references cited in the abstract must be given in full. Unpublished results and personal
communications are not recommended in the reference list, but may be mentioned in the text. If
these references are included in the reference list they should follow the standard reference style of
the journal and should include a substitution of the publication date with either "Unpublished results" or
"Personal communication" Citation of a reference as "in press" implies that the item has been
accepted for publication.
Web references
As a minimum, the full URL should be given and the date when the reference was last accessed. Any
further information, if known (DOI, author names, dates, reference to a source publication, etc.),
should also be given. Web references can be listed separately (e.g., after the reference list) under a
different heading if desired, or can be included in the reference list.
References in a special issue
Please ensure that the words 'this issue' are added to any references in the list (and any citations in the
text) to other articles in the same Special Issue.
Reference management software
This journal has standard
templates
available
in
key
reference
management packages
EndNote
(http://www.endnote.com/support/enstyles.asp)
and
Reference
Manager
(http://refman.com/support/rmstyles.asp). Using plug-ins to wordprocessing packages, authors only
need to select the appropriate journal template when preparing their article and the list of references
and citations to these will be formatted according to the journal style which is described below.
Reference style
Text: All citations in the text should refer to:
1. Single author: the author's name (without initials, unless there is ambiguity) and the year of
publication;
2. Two authors: both authors' names and the year of publication;
3. Three or more authors: first author's name followed by "et al." and the year of publication.
Citations may be made directly (or parenthetically). Groups of references should be listed first
alphabetically, then chronologically.
Examples: "as demonstrated (Allan, 1996a, 1996b, 1999; Allan and Jones, 1995). Kramer et al.
(2000) have recently shown ...."
List: References should be arranged first alphabetically and then further sorted chronologically if
necessary. More than one reference from the same author(s) in the same year must be identified by the
letters "a", "b", "c", etc., placed after the year of publication.
Examples:
Reference to a journal publication:
Van der Geer, J., Hanraads, J.A.J., Lupton, R.A., 2000. The art of writing a scientific article. J. Sci.
Commun. 163, 51–59.
Reference to a book:
Strunk Jr., W., White, E.B., 1979. The Elements of Style, third ed. Macmillan, New York.
Reference to a chapter in an edited book:
Mettam, G.R., Adams, L.B., 1999. How to prepare an electronic version of your article, in: Jones, B.S.,
Smith , R.Z. (Eds.), Introduction to the Electronic Age. E-Publishing Inc., New York, pp. 281–304.
Video data
Elsevier accepts video material and animation sequences to support and enhance your scientific
research. Authors who have video or animation files that they wish to submit with their article are
strongly encouraged to include these within the body of the article. This can be done in the same way as
a figure or table by referring to the video or animation content and noting in the body text where it
should be placed. All submitted files should be properly labeled so that they directly relate to the video
file's content. In order to ensure that your video or animation material is directly usable, please provide the
files in one of our recommended file formats with a preferred maximum size of 50 MB. Video and
animation files supplied will be published online in the electronic version of your article in Elsevier
Web products, including ScienceDirect: http://www.sciencedirect.com. Please supply 'stills' with your
files: you can choose any frame from the video or animation or make a separate image. These will be
used instead of standard icons and will personalize the link to your video data. For more detailed
instructions please visit our video instruction pages at http://www.elsevier.com/artworkinstructions.
Note: since video and animation cannot be embedded in the print version of the journal, please
provide text for both the electronic and the print version for the portions of the article that refer to
this content.
Supplementary data
Elsevier accepts electronic supplementary material to support and enhance your scientific research.
Supplementary files offer the author additional possibilities to publish supporting applications, highresolution images, background datasets, sound clips and more. Supplementary files supplied will be
published online alongside the electronic version of your article in Elsevier Web products, including
ScienceDirect: http://www.sciencedirect.com. In order to ensure that your submitted material is
directly usable, please provide the data in one of our recommended file formats. Authors should
submit the material in electronic format together with the article and supply a concise and descriptive
caption for each file. For more detailed instructions please visit our artwork instruction pages at
http://www.elsevier.com/artworkinstructions.
Submission checklist
The following list will be useful during the final checking of an article prior to sending it to the journal for
review. Please consult this Guide for Authors for further details of any item.
Ensure that the following items are present:
One Author designated as corresponding Author:
• E-mail address
• Full postal address
• Telephone and fax numbers
All necessary files have been uploaded
• Keywords
• All figure captions
• All tables (including title, description,
footnotes) Further considerations
• Manuscript has been "spellchecked" and "grammar-checked"
• References are in the correct format for this journal
• All references mentioned in the Reference list are cited in the text, and vice versa
• Permission has been obtained for use of copyrighted material from other sources (including the Web)
• Color figures are clearly marked as being intended for color reproduction on the Web (free of charge)
and in print or to be reproduced in color on the Web (free of charge) and in black-and-white in print
• If only color on the Web is required, black and white versions of the figures are also supplied for
printing purposes
For any further information please visit our customer support site at http://support.elsevier.com.
AFTER ACCEPTANCE
Use of the Digital Object Identifier
The Digital Object Identifier (DOI) may be used to cite and link to electronic documents. The DOI
consists of a unique alpha-numeric character string which is assigned to a document by the publisher
upon the initial electronic publication. The assigned DOI never changes. Therefore, it is an ideal
medium for citing a document, particularly 'Articles in press' because they have not yet received their
full bibliographic information. The correct format for citing a DOI is shown as follows (example taken
from a document in the journal Physics Letters B):
doi:10.1016/j.physletb.2010.09.059
When you use the DOI to create URL hyperlinks to documents on the web, they are guaranteed never to
change.
Proofs
One set of page proofs (as PDF files) will be sent by e-mail to the corresponding author (if we do
not have an e-mail address then paper proofs will be sent by post) or, a link will be provided in
the e-mail so that authors can download the files themselves. Elsevier now provides authors with
PDF proofs which can be annotated; for this you will need to download Adobe Reader version 7 (or
higher) available free from http://get.adobe.com/reader. Instructions on how to annotate PDF files
will accompany the proofs (also given online). The exact system requirements are given at the Adobe
site:
http://www.adobe.com/products/reader/tech-specs.html.
If you do not wish to use the PDF annotations function, you may list the corrections (including
replies to the Query Form) and return them to Elsevier in an e-mail. Please list your corrections
quoting line number. If, for any reason, this is not possible, then mark the corrections and any other
comments (including replies to the Query Form) on a printout of your proof and return by fax, or scan
the pages and e-mail, or by post. Please use this proof only for checking the typesetting, editing,
completeness and correctness of the text, tables and figures. Significant changes to the article as
accepted for publication will only be considered at this stage with permission from the Editor. We will
do everything possible to get your article published quickly and accurately – please let us have all your
corrections within 48 hours. It is important to ensure that all corrections are sent back to us in one
communication: please check carefully before replying, as inclusion of any subsequent corrections
cannot be guaranteed. Proofreading is solely your responsibility. Note that Elsevier may proceed with
the publication of your article if no response is received.
Offprints
The corresponding author, at no cost, will be provided with a PDF file of the article via e-mail. For an
extra charge, paper offprints can be ordered via the offprint order form which is sent once the article is
accepted for publication. The PDF file is a watermarked version of the published article and includes a
cover sheet with the journal cover image and a disclaimer outlining the terms and conditions of use.
AUTHOR INQUIRIES
For inquiries relating to the submission of articles (including electronic submission) please visit
this journal's homepage. Contact details for questions arising after acceptance of an
article, especially those relating to proofs, will be provided by the publisher. You can
track accepted articles at http://www.elsevier.com/trackarticle. You can also check our Author
and/or
contact
Customer
Support
via
FAQs
(http://www.elsevier.com/authorFAQ)
http://support.elsevier.com.
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