Tesis Daniela Rivera final - Repositorio UC

ORGANIZACIÓN SOCIAL DE Octodontomys gliroides (Gervais y
d'Orbigny, 1844) Y SUS IMPLICANCIAS SOBRE EL ORIGEN Y
EVOLUCIÓN DE LA SOCIABILIDAD EN ROEDORES
OCTODÓNTIDOS
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PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE
FACULTAD DE CIENCIAS BIOLÓGICAS
PROGRAMA DOCTORADO EN CIENCIAS BIOLÓGICAS
MENCIÓN ECOLOGÍA
ORGANIZACIÓN SOCIAL DE Octodontomys gliroides (Gervais y d'Orbigny, 1844)
Y SUS IMPLICANCIAS SOBRE EL ORIGEN Y EVOLUCIÓN DE LA
SOCIABILIDAD EN ROEDORES OCTODÓNTIDOS
Por
DANIELA SUZANA RIVERA ROCABADO
Tesis presentada a la Facultad de Ciencias Biológicas de la Pontificia Universidad Católica
de Chile para optar al grado académico de Doctor en Ciencias Biológicas con mención en
Ecología.
Dirigida por:
Luis A. Ebensperger PhD.
R. Eduardo Palma PhD.
Septiembre, 2013
Santiago, Chile
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RESUMEN…………………………...………………………………………………….. ix
INTRODUCCIÓN GENERAL………………...…………………………………...……… 1
MARCO TÉORICO……………………………...………………………………………… 2
Modelo de estudio, Hipótesis y predicciones……….…………………………..... 6
REFERENCIAS……………………………………………………………………..……. 10
CAPÍTULO I
PHYLOGEOGRAPHY AND GENETIC STRUCTURE OF THE ANDEAN DEGU,
OCTODONTOMYS GLIROIDES (RODENTIA: OCTODONTIDAE) .………......... 19
ABSTRACT……………………...…………………………………………………….… 20
INTRODUCTION….…………………………………………………............................ 21
METHODS……….………………………………………………………………............ 20
RESULTS…………………..…………………………………………............................ 30
DISCUSSION………………………………………………………………...….............. 34
AKNOWLEDGMENTS………………………………………………………................ 41
REFERENCES…………………………………………………………………………...42
TABLES………………………………………………………………………................. 55
FIGURES…………………………………………………………………..…..……...…. 59
CAPÍTULO II
WITHIN BUT NOT BETWEEN VARIATION IN ECOLOGY PREDICT
POPULATION DIFFERENCES IN OCTODONTOMYS GLIROIDES GROUPLIVING………………………………………………………………………………….. 70
ABSTRACT……………………………………………………………………………… 71
INTRODUCTION…………………………………………………….............................. 72
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METHODS…………………………………………………………….............................. 77
RESULTS……………………………………………………………................................ 87
DISCUSSION…………….………………………..…………………………………..… 90
AKNOWLEDGMENTS………..………………………………………............................ 94
REFERENCES…………………………………………….……………..……………….. 95
TABLES………………………………………………….……………………………… 109
FIGURES………………………………….…………………………………………….. 113
CONCLUSIONES GENERALES……………………………………………………….117
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A mis papás, pilares fundamentales en mi vida, soy lo que
soy gracias a ustedes
v
AGRADECIMIENTOS
Esta tesis no hubiera sido posible llevarla a cabo si no hubiera contado con el apoyo de
varias personas que en cierto punto, ya sea académico o personal, de lejos o de cerca
estuvieron presentes en momentos claves del desarrollo de este trabajo. Es por tanto que en
las siguientes líneas tratare de manifestar un agradecimiento general a todos ellos…
Al Dr. Luis A. Ebensperger, por la ayuda y confianza que me brindo como asesor de esta
tesis, por las ideas, las correcciones e interés que demostró en esta investigación. Por tener
siempre el tiempo de leer lo que le enviaba y por sobre todo por enseñarme el mundo de la
sociabilidad y de los degus. Al Dr. Eduardo Palma, como cotutor de este trabajo, por
abrirme las puertas de su laboratorio y hacer que siempre me sintiera bienvenida, por su
constante apoyo y sus comentarios y correcciones a los resultados.
A los miembros de mi comisión asesora de tesis integrada por los doctores Rodrigo
Vásquez, María Fernanda Pérez y Claudio Latorre, por toda su buena disposición,
correcciones y aportes a esta tesis.
A Mauricio Lima, quien me dio la oportunidad de poder postular al programa de doctorado.
A Pancho Bozinovic por abrirme las puertas de su laboratorio desde el comienzo.
A todas y cada una de las fuentes de financiamiento que hicieron posible la presente tesis.
A la Organización de Estados Americanos (OEA), la beca con la cual llegue a Chile y con
la que inicie mis estudios de doctorado. A Comisión Nacional Científica y Tecnológica
(CONICYT), por los dos años de beca doctoral. A la Vicerrectoría de Investigación (VRIUC) y Dirección de Investigación y Postgrado (DIP-UC). Al proyecto FONDECYT
1090302 “A mechanistic model to explain direct fitness consequences of sociality in the
rodent Octodon degus”. Al Programa 1 del Centro de Estudios Avanzados en Ecología y
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Biodiversidad (FONDAP 1501–001). Al Animal Behaviour Society (Developing Nations
Award) y American Society of Mammalogists por los fondos para cubrir parte de los
terrenos. Al Departamento de Ecología de la Facultad de Ciencias Biológicas de la
Pontificia Universidad Católica de Chile, en la cual desarrolle mis estudios y pase gran
parte de mi tiempo.
A todas las instituciones y colegas biólogos que me brindaron y enviaron muestras de tejido
para los análisis genéticos: Al Field Museum of Natural History; Sam Noble Oklahoma
Museum of Natural History; Colección de Flora y Fauna Profesor Patricio Sánchez Reyes,
UC; Instituto de Ecología y Evolución, UACh; Museo de Ciencias Naturales y Tradicional
de Mar del Plata "Lorenzo Scaglia"; a Agustina y Ricardo Ojeda de la Colección de
Mamíferos del Instituto Argentino de Investigaciones de zonas Áridas (IADIZA), Jorge
Jayat y Raúl Sobrero.
Al Centro de Biodiversidad y Genética por todo el material de campo. A las comunidades
de “Oploca” y “Chusmiza” donde realice el terreno, a tanta gente linda que nos brindaron
su ayuda, recibiéndonos con las puertas abiertas.
A todos los valientes voluntarios de campo, Ariel, Marco Antonio, Alejo, Mauri, que
aguantaron frio, calor, cebar trampas de madrugada y revisarlas varias veces en la noche.
A Huber por su valentía y destreza en el campo, por permanecer despierto tantas noches
cuidando de que no nos pasara nada y por aguantar dos meses aislado. Por sus
conocimientos de botánica y ayuda con la identificación de las especies vegetales.
A Gabi Villanueva por ser amiga y un 10 en el campo, por tantos momentos lindos que
compartimos despiertas tantas noches durante el trampeo y telemetría. Por su entusiasmo y
predisposición, por seguirme en todas mis ocurrencias y por su amistad.
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A Sebas Abades, por sus valiosos aportes para el análisis estadístico de los datos. A Andrés
Parada y Juliana Vianna, por su paciencia y ayuda con los programas filogenéticos.
A mis compañeros y ex compañeros del lab Ebensperger. A Ricardo Cancino por iniciarme
en el mundo de las pipetas y de los PCRs. A Marlene Manzano por ser mi hada madrina de
los análisis genéticos y una verdadera amiga.
A Sabri, Li, Bea, por tantos lindos momentos. A los grandes amigos que ya migraron a
tierras lejanas y otras más cercanas: Ana, Sergio, Claudiña, David, Fernanda, Felipe, Stella
gracias por tantos recuerdos.
A mis amigos del día a día: Feñita, Felipe, Lore, Sebas y a los amigos que a pesar de la
distancia se que están presentes. Al Sr. Fernando y Sra. Daniela por estar siempre
preocupados y pendientes de mí.
A mi familia, a mis papás por brindarme su apoyo incondicional y darme ánimos cuando
más lo necesitaba. A mis hermanos, porque a pesar de la distancia siempre me acompañan
y me sacan de apuro. A mi mamá por ser mi pilar y fortaleza y saber transmitirme sus
sabios consejos a lo largo de toda mi carrera y formación profesional. A mi abuelita
“mămaie” que me dejo durante mi primer año en el programa, pero que siempre me ha
acompañado. A mi abuelo “tătaie” que me acompaña a pesar de la distancia.
A mi cable a tierra, Fernando Alfaro, por aguantarme y por estar siempre, por su infinita
paciencia y por brindarme su apoyo incondicional. Por tantos momentos amargos y dulces
compartidos en toda esta etapa.
A todos ustedes muchas gracias!!!
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Resumen
El comportamiento social es variable entre especies de roedores de la familia Octodontidae,
y donde el número de especies sociales es más frecuente en las ramas terminales de este
grupo. Determinar si la vida social en las formas más derivadas evolucionó en respuesta a
cambios ambientales recientes dependen fundamentalmente de las características de la
estructura social de Octodontomys gliroides, el taxón hermano del clado compuesto por la
mayoría de las especies sociales. Por lo tanto, la ocurrencia de hábitos solitarios en O.
gliroides indicaría que la vida social en la familia Octodontidae es un rasgo derivado. En
cambio, la ocurrencia de hábitos sociales en O. gliroides indicaría que este rasgo
evolucionó temprano y que la sociabilidad en Octodontidae es el producto de presiones de
selección históricas. El objetivo central de esta tesis fue determinar el grado de sociabilidad
de O. gliroides, la importancia de factores ecológicos como causas de su posible variación,
y las implicancias de esto sobre la evolución de la vida en grupo en Octodontidae. Para ello
se realizó un análisis filogeográfico que permitió determinar el grado de estructuración
genética de las poblaciones de O. gliroides a lo largo de su rango de distribución. Los
resultados indicaron que las poblaciones de esta especie están estructuradas genéticamente.
En base a estos resultados, se seleccionaron dos de estas poblaciones genéticamente
distintas pero que habitan ambientes con distinta productividad primaria determinada por
un gradiente de precipitación. La sociabilidad cuantificada como tamaño de grupo y grado
de cohesión social entre individuos del mismo grupo no presentó variación entre las
poblaciones estudiadas, a pesar de diferencias marcadas en condiciones ecológicas
(distribución de alimento y refugio, riesgo de depredación, costos asociados a excavar
madrigueras). En cambio, se registró una asociación entre diferencias ecológicas y la
variación en el tamaño de los grupos dentro de estas poblaciones. En conjunto, los
resultados son consistentes con que la sociabilidad en esta especie evolucionó en respuesta
a las condiciones de aridez en el pasado y que esta se han mantenido relativamente
invariable a lo largo del tiempo a pesar de las diferencias ecológicas entre sus poblaciones
actuales. Tomando en cuenta la posición filogenética de O. gliroides es posible inferir que
la sociabilidad en Octodontidae evolucionó temprano en el clado “social”.
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INTRODUCCIÓN GENERAL
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MARCO TÉORICO
La sociabilidad, definida como la tendencia de los individuos a vivir gran parte de su ciclo
vital con otros individuos de la misma especie y a interactuar más frecuentemente con estos
(Alexander 1974; Blumstein & Armitage 1998; Lacey & Sherman 2007; Wey et al. 2008),
es relativamente común en insectos y otros invertebrados (Seeley 1989; Avilés & Tufiño
1998), así como en peces (Heg et al. 2005; Reddom et al. 2011), aves y mamíferos (Lott
1991; Heinsohn 1992; Brown & Brown 1996; Ebensperger 2001; Lacey & Sherman 2007).
La tendencia de los individuos a agruparse puede resultar a partir del estímulo generado por
recursos externos a éstos (e.g. refugio y/o alimento), o por una atracción generada entre los
propios individuos (Parrish et al. 1997; Ebensperger 2001). Esta tendencia no es un rasgo
invariante, tal como lo demuestran estudios que han documentado cambios en el tamaño de
grupo, estructura y grado de cohesión social tanto dentro como en diferentes poblaciones de
vertebrados (Lott 1984; 1991; Brashares & Arcese 2002; Schradin & Pillay 2005). La
ocurrencia de variación en el comportamiento social es evidente incluso dentro de especies
con una estructura social cohesionada y permanente (Lott 1991), un aspecto esencial para
estudios cuyo objetivo es determinar las causas ecológicas y significancia funcional de la
sociabilidad (Lott 1991; Foster 1999, Johnson et al. 2002). En particular, comparaciones
entre poblaciones tienen la ventaja de abarcar un rango más amplio de condiciones
ecológicas, en particular cuando estas poblaciones están asociadas a diferencias importantes
en condiciones ecológicas (Ebensperger et al. 1995; Travis et al. 1995; Shradin & Pillay
2005), lo que sin embargo permite “controlar” otras fuentes de variación como filogenia y
nicho ecológico (Clutton-Brock & Harvey 1978; Lott 1991; Maher 1994). A diferencia de
los estudios en una sola población, los estudios comparativos a lo largo de gradientes
ambientales (i.e., variación entre poblaciones), representan “experimentos naturales” que
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permitan evaluar la contribución relativa de las condiciones ecológicas sobre la estructura y
tamaño de los grupos sociales (Johnson et al. 2002). Así entonces, comparaciones entre
poblaciones pueden contribuir a determinar si variación en condiciones ecológicas se han
traducido en diferencias en el grado de sociabilidad (Foster 1999; Ebensperger et al. 2012).
La variación intraespecífica en el grado de sociabilidad es a menudo atribuida al
efecto de diferencias en condiciones ecológicas (e.g., clima, riesgo de depredación,
densidad, disponibilidad de lugares para nidificar, calidad, cantidad o distribución de
alimento) (Lott 1991), las cuales determinarían los beneficios y costos que emergen de las
decisiones de los individuos por asociarse o a abandonar un grupo (Slobodchikoff 1984;
Lacey 2000; Brashares & Arcese 2002; Ebensperger et al. 2012). Entre los potenciales
beneficios que se atribuyen a la formación y mantención de la vida en grupo se incluyen
una disminución en el riesgo per cápita de depredación, un incremento en la eficiencia de
forrajeo debido a una localización y/o defensa más eficiente de los recursos, así como un
incremento en el ahorro energético producto de termorregulación social entre otros
(Alexander 1974; Bertram 1978; Armitage 1981; Krebs & Davies 1993; Ebensperger 2001;
Lacey & Sherman 2007). Por otro lado, los costos asociados a la vida en grupo, incluyen un
incremento en la trasmisión de parásitos y competencia por recursos entre otros (Alexander
1974; Loehtle 1995; Altizer et al. 2003).
Son numerosos los estudios que han documentado una relación estrecha entre el
grado de sociabilidad y variación en factores ecológicos asociados a beneficios de la vida
en grupos. Por ejemplo, hembras del venado de cola blanca (Odocoileus virginianus) que
habitan ambientes abiertos y de mayor susceptibilidad a depredadores forman grupos más
numerosos comparados con grupos de la misma especie en ambientes más cerrados (Hirth
1977; Lott 1991). Por otra parte, la distribución de recursos críticos es uno de los
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principales agentes ecológicos de la conducta social. En particular, recursos que se
encuentran distribuidos de manera heterogénea en espacio y tiempo pueden promover la
vida en grupo debido a que los grupos más numerosos son más eficientes en localizar o
defender recursos de alta calidad (Slobodchikoff 1984; Travis et al. 1995; Brashares &
Arcese 2002; Maher & Burger 2011). Por ejemplo, la rata topo común (Cryptomys
hottentotus hottentotus) forma colonias más numerosas en ambientes áridos donde el
alimento está distribuido en forma heterogénea que en ambientes más húmedos donde el
alimento está distribuido en forma más uniforme. Un mayor número de individuos en
ambientes áridos determina una localización y defensa más eficiente de estos recursos
(Spinks & Plagányi 1999; Spinks et al. 2000). De manera similar, los perritos de la pradera
de Gunnison (Cynomys gunnisoni) incrementan el tamaño de grupo y solapamiento de sus
ámbitos de hogar cuando los recursos se encuentran más dispersos en espacio y tiempo
(Verdolin 1999). Además de la distribución, la abundancia de los recursos también parece
tener algún efecto sobe la tendencia a formar grupos. Así por ejemplo, variación en el
tamaño de grupo durante la actividad de forrajeo en mamíferos carnívoros (Canis lupus,
Crocuta crocuta) está asociada a diferencias estacionales o temporales en la disponibilidad
de presas (Mech 1970; Kruuk, 1972).
En contraste con un escenario en el cual las condiciones ecológicas son atribuidas
como causas de la evolución de la vida en grupo, diferencias en el grado de sociabilidad
entre especies dentro de un clado también pueden representar rasgos heredados a partir de
formas ancestrales. En este escenario, los rasgos pueden permanecer invariantes a pesar de
diferencias entre los ambientes de las formas ancestrales y actuales. Es decir, el rasgo o
estado del rasgo en las formas actuales representarían inercia filogenética. Esta hipótesis es
apoyada por comparaciones entre especies. Por ejemplo, la vida social en miembros de la
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super-familia Cavioidea ha sido atribuida a los efectos filogenéticos dentro de este grupo,
es decir, que el rasgo social habría estado presente también en las formas ancestrales,
independiente de diferencias en los ambientes usados por estas especies (Rowe &
Honeycutt 2002). Del mismo modo, la organización social y espacial en especies de
caballos salvajes (familia Equidae) es uniforme a pesar de diferencias ambientales y
demográficas, lo que apoya que la estructura social observada sería un rasgo ancestral y un
caso de inercia filogenética (Linklater2000). En aves se ha enfatizado el posible rol de
factores ecológicos (e.g., depredación, dispersión limitada) como causa evolutiva de la vida
social. Sin embargo, existe evidencia que apoya que la vida social es el estado ancestral en
varias familias de aves, lo que no descarta un componente filogenético (Arnold & Owens
1998; 1999; Covas & Griesser 2007). Además, estudios en los que inicialmente se atribuyó
el efecto de causas ecológicas a la sociabilidad han sido re-evaluados encontrando un fuerte
componente filogenético. Por ejemplo, Van Schaik & Van Noordwijk (1986) indicaron que
diferencias en riesgo de depredación serían la causa de diferencias en el tamaño de grupo
en poblaciones de macacos de cola larga (Macaca fascicularis). Sin embargo, la evidencia
más reciente a partir de estudios comparados es más consistente con que la sociabilidad en
especies del género Macaca estarían está vinculada a inercia filogenética (Thierry et al.
2000). En conjunto, estos estudios indican que no es claro en qué medida la variación social
observada en poblaciones de especies actuales es el resultado de adaptación a variación en
condiciones ecológicas o el legado de formas ancestrales.
Por otra parte, un escenario de inercia también puede ser evaluado indirectamente a
partir de compasiones intraespecíficas. En particular, una ausencia de variación social en
poblaciones genéticamente distintas y asociadas a condiciones ambientales diferentes sería
más consistente con un escenario de inercia. Solo un estudio reciente ha examinado esta
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posibilidad en el tejón europeo (Meles meles), y donde una ausencia de variación social está
vinculada con variación ecológica significativa.
Modelo de estudio, Hipótesis y predicciones
El orden Rodentia constituye un grupo de especies particularmente apropiado para
examinar los roles de condiciones ecológicas y de inercia filogenética en el origen y
evolución de la sociabilidad. En particular, en este orden prevalecen clados monofiléticos
bien sustentados y donde las especies exhiben una gran diversidad ecológica y morfológica
(Nedbal et al. 1994; Rowe & Honeycutt 2002). Es así, que la vida social en este taxón se ha
registrado en no menos de 70 especies, representantes de 39 géneros y 18 familias
ampliamente distribuidas (Lacey & Sherman 2007).
El orden Rodentia está representado por tres grupos taxonómicos: los sciurognatos,
los miomorfos y los hystricognatos (Wilson & Reeder 2005). Los hystricognatos
sudamericanos (degus, cururos, cobayos, ratas vizcachas, tuco-tucos) incluyen especies que
varían morfológica y fisiológicamente y que se han adaptado a diferentes modos de vida
(e.g., sociales, escansoriales, fosoriales, semifosoriales), pudiendo encontrarse en casi todo
tipo de ambientes (Contreras et al. 1987; Mares & Ojeda 1982; Eisenberg & Redford 1999).
Estos patrones de diversidad morfológica, fisiológica y ecológica tienden a covariar
con patrones de organización social y comportamiento, donde la estructura social varía
entre especies solitarias y/o especies altamente sociales y gregarias (Ebensperger & Cofré
2001; Lacey & Ebensperger 2007; Ebensperger et al. 2008). Dentro de los hystricognatos
sudamericanos, los octodontidos (Octodontidae) corresponden a uno de los grupos más
característicos en ambientes áridos y semiáridos del cono sur de Sudamérica (Contreras et
al. 1987; Lacey & Ebensperger 2007; Honeycutt et al. 2007). Los Octodontidae constituyen
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un grupo monofilético (Honeycutt et al. 2003; Lessa et al. 2008) con 13 especies divididas
en siete géneros (Aconaemys, Octodon, Octodontomys, Octomys, Spalacopus,
Tympanoctomys y Pipanacoctomys; Wilson & Reeder 2005; Ojeda et al. 2013). Al mismo
tiempo, se trata de un grupo ecológicamente diverso que incluye especies con hábitos de
vida escansorial, semisubterráneos y subterráneos (Redford & Eisenberg 1992; Lacey &
Ebensperger 2007; Ebensperger et al. 2008). Estas especies se encuentran distribuidas a lo
largo de la Cordillera de los Andes desde los 16° S a 41° S, especialmente en las laderas
occidentales (Contreras et al. 1987; Ojeda et al. 1996; Verzi 2001), ocupando una gran
diversidad de ambientes que incluyen pastizales abiertos, bosques ralos y bordes de salares
extremadamente áridos (Redford & Eisenberg 1992; Lacey & Ebensperger 2007).
Si bien la información disponible sobre la estructura social para la mayoría de las
especies de octodóntidos es poco conocida, se ha establecido que el clado chileno
compuesto por las especies Octodon degus (Fulk 1976; Lacey & Ebensperger 2007),
Spalacopus cyanus (Reig 1970; Lacey & Ebensperger 2007), Aconaemys fuscus (Reise &
Gallardo 1989) y O. lunatus (Sobrero 2013) serían sociales. Por otra parte, las especies del
clado argentino: Tympanoctomys barrerae, Octomys mimax y Pipanacoctomys aureus
presentan hábitos solitarios (Mares et al. 1997; Ebensperger et al. 2008). Se ha planteado
que este patrón de diversificación podría haber evolucionado en respuesta a cambios
climáticos (Reig 1986; Ojeda & Tabeni 2009). La evaluación de esta hipótesis depende
fundamentalmente de la estructura social de la especie Octodontomys gliroides (soco,
choschori, rata cola de pincel o degu Andino), un aspecto completamente desconocido
hasta ahora.
Diversas observaciones sugieren que la historia evolutiva de O. gliroides pudo estar
marcada por respuestas a variación en las condiciones ecológicas de los ambientes usados
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por estos animales. Se trata e una especie que se encuentra en la región tropical de
Sudamérica (Gallardo et al. 2007), en ambientes con características climáticas y
geográficas contrastantes (e.g., desiertos extensos, montañas elevadas, extensos salares)
(Ojeda et al. 2000). La distribución geográfica actual de esta especie coincide con un
gradiente de precipitación de este a oeste creado por el efecto de “sombra de lluvia” de los
Andes. La evidencia disponible apoya que esta zona se caracteriza por una historia
compleja de cambios asociados con períodos secos-húmedos que ocurrieron en el
Pleistoceno-Holoceno (Nester et al. 2007; Santoro & Latorre 2009). Esta variación
climática ha causado cambios en la distribución de la flora local y la localización de zonas
andinas xéricas donde O. gliroides se encuentra en la actualidad (Ribichich 2002; Teta &
Ortiz 2002; Barquez et al. 2006).
Por otra parte, la mayoría de las reconstrucciones filogenéticas recientes sitúan a O.
gliroides como el taxón hermano del clado compuesto por la mayoría de las especies
sociales (Honeycutt et al., 2003; Gallardo et al. 2004; Opazo 2005; Rowe et al. 2010). Por
lo tanto, la ocurrencia de hábitos solitarios en O. gliroides apoyaría que la vida social en
Octodontidae sería un rasgo derivado, y potencialmente en respuesta a condiciones
eclógicas recientes. En contraste, la ocurrencia de hábitos sociales en O. gliroides indicaría
que este rasgo evolucionó temprano en Octodontidae y que es el resultado de presiones de
selección históricas. De esta manera, el objetivo central de esta tesis fue determinar el grado
de sociabilidad de O. gliroides, examinando la importancia de factores ecológicos (e.g.,
distribución de recursos, riesgo de depredación) como posibles causas, así como sus
implicancias sobre la evolución de la vida en grupo en el resto de los octodóntidos sociales.
Para esto, se realizó primero un análisis filogeográfico que permitió determinar el grado de
diferenciación genética entre poblaciones de O. gliroides a lo largo de su rango de
8
distribución. En base a estos resultados, se seleccionaron dos poblaciones genéticamente
distintas pero que ocurren en ambientes con distinta productividad primaria determinada
por un gradiente de precipitación. En ambas poblaciones se evaluó la importancia de
diferentes factores ecológicos, en particular, la distribución de recursos, riesgo de
depredación y costos asociados a cavar madrigueras como predictores de la variación en el
grado de sociabilidad dentro y entre poblaciones.
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17
CAPITULO I
Phylogeography and genetic structure of the Andean degu, Octodontomys
gliroides (Rodentia: Octodontidae)
Daniela S. Rivera1 *Juliana A. Vianna2Luis A. Ebensperger1and R. Eduardo Palma1
18
To be summited to Journal of Evolutionary Biology
Phylogeography and genetic structure of the Andean degu, Octodontomys
gliroides (Rodentia: Octodontidae)
Daniela S. Rivera1 * Juliana A. Vianna2 Luis A. Ebensperger1 and R. Eduardo Palma1
1
Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad
Católica de Chile, Casilla 114-D, Santiago, Chile
2
Departamento de Ecosistemas y Medio Ambiente, Facultad de Agronomía e Ingeniería
Forestal, Pontificia Universidad Católica de Chile, Santiago, Chile
Short title: Phylogeography of Octodontomys gliroides
*Correspondence:
Address: Daniela S Rivera, Departamento de Ecología, Pontificia Universidad Católica de
Chile, Santiago. Chile.
Phone: (56-2) 686 2950
Email: [email protected]
19
Abstract
The Andean degu, Octodontomys gliroides constitutes the only species of the family
Octodontidae inhabiting pre-Andean Prepuna and Puna environments of tropical South
America. To gain insights into its phylogenetic relationships, phylogeographic patterns, and
origin, 21 populations of O. gliroides across its entire distributional range were studied
through a 579-bp fragment of the mitochondrial DNA control region. We evaluated the
intraspecific genealogy, the population structure, the demographic history as well as the
reconstruction of the ancestral distributionof the species, by means of different Likelihood,
Bayesian, Network and Statistical Dispersal-Vicariance analyses.Our results showed that O.
gliroides is characterized by a geographical structure, which is in agreement with major
geographical barriers (e.g., rivers, salt flats, deserts and mountain systems). The haplotype
network analysis inferred three haplogroups along the distribution of O. gliroides, the same
that were corroborated by the population structure analysis. The mismatch distributions and
neutrality test along with Bayesian Skyline Plots suggested contrasting histories for
different cluster of populations, with some cluster showing demographic stability and no
significant departures from neutrality. Others were fitted more with a contraction-expansion
model coincident with dry-wet events during Pleistocene. Finally, we suggest that one
possible ancestral area in the diversification of O. gliroides populations which includes the
Andean Puna-Prepuna ecoregion.
Keywords: Andean degu; Octodontomys gliroides; historical demography; phylogeography;
Andean Puna- Prepuna.
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INTRODUCTION
Octodontidae (degus, cururos, viscacha rats) is an endemic family of Hystricognath rodents
distributed along South America (Reig, 1981; Mares & Ojeda, 1982). Interestingly, these
rodents include species that are ecologically diverse, includingcursorial, rock-dwelling,
semi-subterranean and truly subterranean forms (Lacey & Ebensperger, 2007), that occur in
a wide array of habitats.They also show variation in social behavior with species ranging
from solitary-living to highly gregarious species (Mares & Ojeda, 1982; Reig, 1986;
Contreras et al., 1987; Lacey & Ebensperger, 2007). Octodontid rodents represent one of
the most characteristic groups in the arid lands of southern South America (Ojeda, 2010),
ranging along both sides of the Andes in Argentina, Bolivia and Chile between 15° and
40°S (Ojeda et al., 2013). Within this narrow geographical range, the Octodontidae occurs
in a great array of habitats, including coastal areas in central Chile, pre-Andean and Andean
regions, desert and semi-desert scrublands, and extremely arid salt flats (Contreras et al.,
1987; Mares & Ojeda, 1982; Redford & Eisenberg, 1992; Gallardo et al., 2007). Living
Octodontidae contains 13 recognized species (Woods & Kilpatrick, 2005) assigned to six
monotypic and two polytypic genera (Ojeda et al., 2013). Eight of the 13 species occur
exclusively in the Andes of Chile, while four of them inhabit extremely arid salt flats in
Argentina and a single species (Octodontomys gliroides) occupies simultaneously the
Andean region of northern Argentina and Chile, and central-southern Bolivia (Contreras et
al., 1987; Ojeda et al., 2013).
O. gliroides (the Andean degu) is the only octodontid living in tropical South
America, in environments with contrasting climatic and geographical characteristics,
ranging from the north mesic Puna in Bolivia, to the northern boundary of the Atacama
21
Desert in Chile (Ojeda et al., 2000). The current geographical distribution of O. gliroides
matches with a rainfall gradient from east to west created by the rain shadow effect of the
Andes. While, the Quaternary history of the rest of octodontids has been punctuated by a
sequence of glacial advances and retreats that resulted in extreme environmental changes
from Andean Puna to Tierra del Fuego (Verzi et al., 2002; Verzi & Quintana, 2005;
Gallardo et al., 2013), recent evidence suggests that actual distribution of O. gliroides could
be associated to a complex history of dry-wet periods during the Pleistocene-Holocene in
the central Andean Puna (Nester et al., 2007; Placzek et al., 2011). In particular, because
extensive flood cycles in the last 0.02 Mya produced large paleo-lakes that divided the
western and eastern sides of Andean Puna (Placzek et al., 2013), affecting the local climate
on this region, which in turn affected the distribution of vegetation. How these changes in
climate and connectivity in the Andean Puna have affected the current distribution of
populations and phylogeographic history of O. gliroides, it has never been evaluated.
To clarify aspects of population differentiation it is critical to elucidate the historical
and geographical context in which the evolution of the O. gliroides took place.
Phylogeography is an excellent approach to gain further insights to investigate the historical
factors that have shaped current diversity patterns and to document the evolution and
biogeography of this unique species. Therefore, in the present study, we examine the
genetic structure and phylogeographic patterns of O. gliroides. To achieve this objective,
we sequenced the mitochondrial DNA (mtDNA) control region from specimens of this
species across its entire distributional range. We used the latter mtDNA marker because it
does have a number of advantages such as the lacking of recombination (Avise, 2000;
Rokas et al., 2003), its maternally inherited property, and it does a lower effective
population size compared to nuclear markers. Therefore it takes less time for
22
polymorphisms to become fixed in a population and evolves relatively fast which allows
greater resolution at intra-specific scale (Brown et al., 1979; Moore, 1995; Avise, 2000). In
addition, we analyzed the biogeographic history that shaped the current distribution of
living populations of O. gliroides, to propose a potential scenario for inferring the past
environment in which O. gliroides evolved.
METHODS
Study area and sample collections
Specimens were collected in the field from 11 localities along the entire range of the
species across northern, central and southern Bolivia. Skulls, skins and tissues from Bolivia
localities were deposited at the collection of the Colección Boliviana de Fauna, La Paz,
Bolivia and the Museo de Historia Natural Alcides d’Orbigny, Cochabamba, Bolivia. This
study followed the American Society of Mammalogists guidelines (Animal Care and Use
Committee, 1998; Gannon et al., 2007; Sikes et al., 2011) and adhered to Bolivian and
Chilean laws (permit number MMAyA-VMA-DGBAP N 0937/11 by the Dirección
General de Biodiversidad y Áreas Protegidas, and by the authorization number 1-62-2012
[2373] by the ServicioAgrícola y Ganadero, Chile). All procedures that involved handling
of live animals were approved by the Bioethic Committee of the Faculty of Biological
Sciences at Pontificia Universidad Católica de Chile (CBB-040-2011). Additionally, to
optimize the number of samples and cover the full geographic range of the species, tissues
and/or skin were requested from Collections and Museums of Argentina (Museo de
Ciencias Naturales y Tradicional de Mar del Plata "Lorenzo Scaglia"; Colección de
Mamíferos del Instituto Argentino de Investigaciones de zonas Áridas , IADIZA and
Catálogo de campo de Jorge Pablo Jayat), Chile (Colección de Flora y Fauna Profesor
23
Patricio Sánchez Reyes, Pontificia Universidad Católica de Chile and Instituto de Ecología
y Evolución, Universidad Austral de Chile) and the United States (Field Museum of
Natural History, Chicago, Illinois and the Sam Noble Oklahoma Museum of Natural
History, The University of Oklahoma). All samples are listed and documented in Table 1.
Extraction of DNA, amplification, and sequencing
Genomic DNA extractions were performed using the phenol-chloroform method
(Sambrook et al., 1989). An 800-base pair (bp) fragment of the mitochondrial DNA control
region (CR) was amplified by the polymerase chain reaction technique (PCR) from 97
individuals using primers FVAL (5’ GAA AAC AAA CTC CTC AAA TGA AG 3’) – and
H191 (5’ ATT ATG CGG GCT AAG GGA ACT G 3’), designed to amplify the same
mtDNA region for the sister species of O. gliroides, Tympanoctomys barrerae (Ojeda,
2010) and Octodon degus (Valladares, 2009), respectively. Amplifications were performed
using a thermal cycler (Applied Biosystems 2720) with the following parameters: initial
denaturation at 94°C for 3 minutes; 30 cycles of 45 s at 94°C; 1 minute at 50°C; 1 minute at
72°C and a final extension for 5 minutes at 72°C. All amplifications, including positive and
negative controls were checked in a 2% agarose gelwith TAE buffer, using the proper
molecular weight ladder and gels were visualized in a UV transiluminator. Double-stranded
PCR products were purified with Wizard SV gel Gel and PCR Clean-Up System
(Promega). All PCR products weresequenced by Macrogen Inc. (Seoul, South Korea;
www.macrogen.com).
24
Sequence analyses
Sequences were aligned manually using CodonCode Aligner v. 1.5.1 (Codon Code
Corporation, Dedham, MA) and collapsed into haplotypes using the FaBox package
(Villesen, 2007) (http://users-birc.au.dk/biopv/php/fabox/). Analyses were performed
across populations, which corresponded to:, (Llacasa, Bo1; Eucaliptus, Bo2; Villa Ventilla,
Bo3; Jirira, Bo4; Hara, Bo5; Castilluma, Bo6; Uyuni, Bo7; Oploca, Bo8; Tupiza, Bo9;
Villa Abecia, Bo10; Cieneguillas, Bo11; Iscayachi, Bo12; Putre-Murmutani, Ch1; PutreChungará, Ch2; Camiña-Chusmiza, Ch3; Chusmiza, Ch4; Pucara del Tilcara, Ar1; Susques,
Ar2; San Antonio de los Cobres, Ar3; Santa Victoria, Ar4; and Cachi, Ar5, see Fig. 1).
Analyses were also performed across two major ecogeographic ecoregions: the Andean
Puna and the Andean Prepuna (Table 1). For the subdivision into ecoregions the elevation
was taken into consideration. In this way, for the Andean Puna we considered elevations
between 3400 and 4500 m (Cabrera, 1957; 1968; Cabrera & Willink, 1973; Fernández &
Busso, 1999; Aagesen et al., 2009), whereas for the Andean Prepuna we considered
altitudes of 2000 to 3400 m (Cabrera & Willink, 1973; López & Beck, 2002; López et al.,
2006; Aagesen et al., 2009). Additionally, we followed to Beck, (1985); Cabrera, (1968);
Davis et al., (1997); Ortuño et al., (2011) and subdivided the Andean Puna considering the
rainfall gradient from north to south and from east to west of Andes into: Northern Puna or
Moist Puna, Central Puna and Southern Puna or Dry Puna (Table 1).
A single sequence for each haplotype was used for further phylogenetic analyses.
The output from FaBox was then used as the input file for the DnaSP v5.0 program
(Librado & Rozas, 2009) and for ARLEQUIN (Excoffier et al., 2005) to estimate levels of
genetic diversity, including numbers of haplotypes (H), haplotype diversity (Hd),
nucleotide diversity (π) and the number of polymorphic sites (PS). At this point, we
25
combined different sampling localities based on geographic proximity, similarity of
habitats and sample sizes (Table 1).
Population Genetic Analyses
To evaluate the presence of population structure for O. gliroides, we used the program
GENELAND v.4.0.3 (Guillot et al., 2005a, b) in the R-Package, a Bayesian approach
which estimates the number of classes (genetic clusters) present in the data set and
incorporates the geographical coordinates of the individuals to detect and locate genetic
discontinuities (Guillot et al., 2005a). The number of clusters was determined by running
MCMC (Markov chain Monte Carlo) iterations five times, allowing K (i.e., the most
probable number of populations) to vary, with the following parameters: 5 × 106 MCMC
iterations, maximum rate of the Poisson process fixed to 100 (equal to sample size as
recommended by Guillot et al., 2005a). The minimum K fixed to 1 and maximum K fixed
to 10, the maximum number of nuclei in the Poisson-Voronoi tessellation was fixed to 300
(roughly three times the sample size as suggested by Guillot et al., 2005a). After inferring
the number of populations in the data set from these five runs, the MCMC was run 30 times
with K fixed to the inferred number of clusters, with the other parameters the same as
above. We ranked the models by mean logarithm of posterior probability and conducted
post-processing analyses for the three runs with the highest values. We used a burn-in
period of 1000 x 100 iterations, a spatial domain of 400 pixels along the X-axis and 200
pixels along the Y-axis and checked the runs visually for consistency.
Relationship between haplotypes and geography in order to infer the historical
processes that shaped the sample populations were examined in a haplotype network. The
network was built using the median-joining approach implemented in Network 4.6.1.1
26
software (http://www.fluxus-technology.com), with all characters weighted equally. The
median-joining method uses a maximum parsimony (MP) approach to search for the
shortest phylogenetic trees (Bandelt et al., 1999). MP is particularly suitable for interpreting
intraspecific phylogenies because they explicitly allow for the co-existence of ancestral and
descendant haplotypes in a sample (Posada & Crandall, 2001).
Historical Demography and Biogeographic Analysis
Sudden demographic history of expansion (Rogers & Harpending, 1992) and spatial
expansion (Excoffier 2004) models of the resulting GENELAND cluster analysis were
fitted to the observed mismatch distribution using 1000 bootstrap and the sum of square
deviations (SSD) between observed mismatch distribution and simulated data as a test
statistic (i.e., test of goodness of fit; Schneider & Excoffier, 1999; Excoffier, 2004)
implemented in ARLEQUIN software (Excoffier & Lischer 2010). This analysis assumes
that signatures in the distribution of pairwise nucleotide differences result from episodes of
population growth and decline. Thus, when a population has undergone sudden
demographic expansion, it should display an unimodal and smooth distribution, whereas a
population that either is subdivided or in demographic equilibrium is expected to exhibit a
multimodal or random and rough distribution (Rogers & Harpending, 1992). Tajima’s D
neutrality statistic (Tajima, 1989) and Fu’s FS values (Fu, 1997) were calculated to detect
deviations from a neutral Wright-Fisher model of mutation-drift equilibrium. These
neutrality tests assume that the population has been in mutation-drift balance for a long
period of evolutionary time (Nei & Kumar, 2000). When the population is not under
mutation-drift equilibrium due to sudden expansion, these indexes tend to have
significantly negative values and an excess of polymorphisms at low frequency can be
27
expected. By contrast, positive values reflect the elimination of rare alleles after genetic
bottlenecks (Ramos-Onsins & Rozas, 2002).
Although D, Fs and mismatch distributions are able to provide insights into whether
or not population growth has been expansive, they are not able to provide information
about the shape of population growth over time. For example, a no significant negative
values of D and Fs should indicate that populations have not undergone expansive growth
(i.e., population stability). However, such values are agnostic as to whether population are
expanding slowly, are contracting or remaining relatively constant size (Fontanella et al.,
2008). Therefore, to estimate the shape of population growth through time we constructed a
Bayesian Skyline Plot (BSP) as implemented in the software BEAST v 1.7.4 (Drummond
& Rambaut, 2007). This Bayesian approach incorporates the uncertainty in the genealogy
by using Markov chain Monte Carlo integration under a coalescent model, providing
information about effective population sizes through time (Drummond et al., 2005). The
best-fit substitution model for each cluster was estimated in MODELGENERATOR 0.85
(Keane et al., 2006). BSP was performed for each of the clusters recovered with
GENELAND. The running conditions include 3.0 x 107 iterations, of which the model
parameters were sampled every 1000 steps under a relaxed lognormal molecular clock with
a fixed rate calibration and uniformly distributed priors. The first 10% of the steps were
discarded to allow for burn-in. To assess the robustness of parameter estimates, 2
independent chains were run with identical settings. Log-files were visualized using Tracer
v1.5 (Drummond & Rambaut, 2007). If population sizes are constant through time then the
slope of skyline plot should not be significantly different than zero.
28
Divergence time
In order to estimate divergence we used a Bayesian analysis in BEAST 2.0.2 (Drummond
& Rambaut, 2007). This software relies on a Bayesian Markov chain Monte Carlo
(MCMC) method to infer a relaxed phylogenetic topology that allows us to co-estimate
phylogeny and divergence times (Drummond et al., 2006). Because no fossils or reliable
geological evidence were available to calibrate a local molecular clock for populations of
Octodontomys we used external calibrations of that put the origin of crown Octodontidae
dates back to the late Miocene: 9 (6.7, 11.6) Mya. And we follow Opazo (2005) estimations
that put the divergence of O. gliroides and Octodon + Spalacopus + Aconaemys genera
have occurred around 6.07 ± 1.3 Mya. As outgroups we used mtDNA control region
sequences from seven species of the family Octodontidae from GenBank: Aconaemys
fuscus (AY836575); Octodon degus (GQ168717); Octomys mimax (GQ168718);
Spalacopus cyanus (AY836572) and Tympanoctomys barrerae (GQ168701), and other
sequenced by us: Octodon lunatus and Pipanacoctomys aureus. Phylogenetic estimates
were constructed using the best-fitting model selected using the AIC criterion implemented
in MODELGENERATOR 0.85 (Keane et al., 2006). To estimate the mutation rate, an
uncorrelated lognormal relaxed molecular clock model was used to allow rate variation
among branches (Drummond et al., 2006) and the Yule speciation process which is more
appropriate when considering sequences from different species (Drummond & Rambaut,
2007). Both normal prior calibrations had a mean of 9.0, with standard deviations of 1.4 for
the first calibration of Upham & Patterson (2012) and 6.04 and 0.6 for the second
calibration of Opazo (2005). Analyses were run for 2 x 107 generations, with parameters
logged every 1000 generations following a pre burn-in of 5000 generations. Multiple runs
were conducted to check for stationarity and that independent runs were converging on a
29
similar result. Output from BEAST was examined in Tracer 1.5 with 10% burn-in and the
tree results were summarized using TreeAnnotator 1.7 (included in the BEAST package).
The tree was visualized using FIGTREE 1.3.1 (Rambaut, 2010).
Biogeographic Analysis
To reconstruct ancestral area distributions we performed a Statistical Dispersal-Vicariance
Analysis (S-DIVA) analysis implemented in the program RASP (Reconstruct Ancestral
State in Phylogenies) 2.1 alpha (Yu et al., 2010; 2011). For this, the combined output from
three BEAST runs (62499 trees) served as the input file. To account for uncertainties in
phylogeny, we used all of the post burn-in trees obtained with BEAST for the combined
dataset. Distribution areas of sister species of O. gliroides were assigned to geographical
region as defined by Ojeda et al., (2013). For population of O. gliroides we distinguish
between two major phytogeographic ecoregions: Andean Puna and Andean Prepuna. The
number of maximum areas allowed at the nodes was kept as either three or two whereas
outgroups distributions were not considered. Ten Markov chains were run with the default
setting for 5 x 105 generations, sampling every 100 generations and discarding a bur-in of
100 samples.
RESULTS
Mitochondrial DNA Sequence Variation
Amplification and sequencing of the mtDNA control region yielded a minimal consensus of
579 bp for 101 individuals of O. gliroides. Sequences comparisons yielded a total of 24
variable sites including 3 singletons and 21 parsimony informative sites, which determined
27 haplotypes (Table 2, Fig. 1). The CR mtDNA sequences of O. gliroides showed a
30
pattern of high haplotype diversity (0.921 ± 0.01) but moderate nucleotide diversity (π)
(0.0059 ± 0.0005, Table 2). At the population level, the highest estimates haplotype and
nucleotide diversity were found in combination of population Bo11 and Bo12 (CI) and Ar1
and Ar4 (ArgN) whereas the lowest values of haplotype and nucleotide diversity was found
in Bo5 and Bo1 populations respectively (Table 2). Five populations, Bo4, Bo6, Bo9, Ch1
and Ch2, were invariant (Table 2). Among subdivisions into phytogeographic regions the
Andean Prepuna presented the lowest values of haplotype diversity, whereas Andean Puna
showed the highest value of nucleotide diversity (Table 2). Most of the 27 haplotypes were
restricted to a single phytogeographic ecoregion (81% private haplotypes); only five
haplotypes (H3, H6, H8, H9 and H10) were shared among two or more region. The most
widely distributed haplotypes in the entire sample were H3, H8 and H10. H3 was restricted
to Andean Puna at Bolivia and Chile (Central and Southern Puna) and Andean Prepuna at
Chile. H8 also vas restricted to Andean Puna occurring in both Central and Southern Puna.
H10 was present in three regions and was restricted principally at Andean Prepuna and in a
low proportion at Andean Puna (Central and Southern Puna).
The Median-joining network topology generated three geographically wellstructured haplogroups among the entire range of O. gliroides distribution (Fig. 2). Only
haplogroup III, which contains sequences from Argentina and only a sequence of southern
Bolivia are clearly isolated and separated by a six mutational steps from the rest of
haplogroups. Haplogroups I and II were separated only by one mutation step, with a single
shared haplotype (H20). Two of the most widely distributed haplotype (H3 and H10) were
found in haplogroup I, whereas H8 was found in haplogroup II. Our results did not
illustrate a clear phytogeographic region separation indicated the existence of admixture
haplotypes in this level between all of the three haplotype groups (Fig. 2).
31
Along with this, the population structure for O. gliroides estimated by the
GENELAND program inferred three most probable genetic clusters (k = 3, Fig. 3a), that
were congruent with the network analysis in recognizing the same three major groups.
Although haplogroup I and II was only separated by one mutational step, and also we
identified one shared haplotype between both groups the GENELAND analysis appaeared
to confirm the separation of these groups. Cluster I (haplogroup I) comprised all samples
from northern Chile (Ch1 to Ch4), central and southern Bolivia (Bo6, Bo7, Bo8, Bo9, Bo10
and Bo12) and the northernmost population of Argentina (Ar4, Fig. 3b). The second cluster
(haplogroup II) joined sequences representing the northernmost population of Bolivia (Bo1)
and four populations from central Bolivia (Bo2, Bo3, Bo4 and B5, Fig. 3c). The third
cluster (haplogroup III) comprised the four southernmost populations from Argentina (Ar1,
Ar2, Ar3 and Ar5) and one population of southern of Bolivia (Bo11, Fig. 3d). Similarly to
network topology, GENELAND analysisi did not illustrate a clear phytogeographic region
separation.
Population demographic history
Neutrality test were performed to detect evidence of population growth expansion. Results
for the entire sample Fu´s Fs test of neutrality was statistically negative (Fs = -11.93, P =
0.001), suggesting a demographic range expansion process. In contrast, the Tajima´s D- test
did not reveal a signal of demographic range expansion for entire samples level. At the
haplotype group level, we found a signal of population range expansion only in
haplogroups I and III (Fs = -5.78, P = 0.003 and Fs = -3.027, P = 0.012). Similarly, signals
of population expansion were not detected in any of all three haplogroups using Tajima’s
D-test (Table 3). The discrepancy between D and Fs test is likely due to the decreased
32
statistical power of D in detecting significant changes in population sizes (Ramos-Onsins &
Rozas, 2002). These results suggest that the historical demographic processes of range
expansion for some areas differed within the distribution of O. gliroides. We found signals
of stability only for haplogroup II. The mismatch distribution of pairwise nucleotide
differences for all entire sample level and for each haplogroups was roughly multimodal
(Fig. 4 left) and we did not found departures from an equilibrium model (Table 3),
suggesting constant population size or structuring or stable or shrinking population.
Similarly, mismatch distributions for each cluster no showed evidence of sudden expansion
model (see pSSD and pSSD* in table 3).
Finally the Bayesian skyline plots (BSP) suggested relatively stable population size
for all entire sample level and for each haplogroup over the last (0.1 – 0.3 Mya). Congruent
with the mismatch analyses, BSP for all entire sample level showed an increase in the
effective population size starting about 0.03 Mya (Fig. 4b), about 0.025 Mya for the cluster
I and III (although the pattern is not very clear for the latter, Fig 4d, h respectively). After
this population expansion, a recent decrease of Ne occurred when all cluster are considered
(Fig. 4b) and in a low proportion in cluster I (Fig. 4d). This pattern of expansion was not
evident in cluster II (Fig. 4f).
Clade-dating
Divergence dating indicates that the genus Octodon split up from the O. gliroides
during Early Pliocene approximately 4.70 Mya (95% CI = 3.18-6.28 Mya). The most recent
common ancestor for the main lineages of O. gliroides was estimated to live during Early
Pleistocene around 2.43 Mya (95% CI = 0.98-4.03 Mya). the divergence estimations
33
obtained for the remaining lineages, suggested that thye appeared during the Middle
Pleistocene (Fig. 5)
Biogeographic Analysis
Reconstruction of ancestral biogeography using S-DIVA analysis implemented in
RASP suggests one possible ancestral area for the most recent common ancestor of
Octodontomys population (node 48 in Fig. 7) which includes Andean Puna-Prepuna
ecoregions (AB) with a support value of 100%. This analysis also showed that dispersal
had more influence on lineage diversification than vicariance (17 events compared to 7,
Fig. 6).
DISCUSSION
Haplotype and nucleotide diversity
This study is the first to assess the phylogeography of unknown Andean degu over its entire
range distribution yielding important insights into the evolutionary history of this species.
The network topology and GENELAND analyses showed that O. gliroides is characterized
by geographical subdivision of variation in mtDNA. This translates into three well resolved
haplotype groups or clusters along O. gliroides entire range distribution: haplogroup I
comprises populations from northern slopes of the Chile Andean Puna, Andean Puna and
Prepuna population from southern Bolivia and northernmost Andean Prepuna population of
Argentina. Haplogroup II is formed by northern and central Andean Puna and Prepuna
populations of Bolivia. Finally the third group comprises populations of Argentina Andean
Puna and Prepuna and one Andean Prepuna population of southern Bolivia.
34
All of three genetic haplogroups are in agreement with the major barriers present in
the area (i.e., major rivers, salt flats, the Atacama Desert and orogeny formations of the
Andes in Bolivia), and have been associated with strong level of genetic differentiation
(Fig. 7). The Andean Puna is flanked by high mountains in East Andes Cordillera and by
West Andes Cordillera (Fernández & Buso, 1999). These geographic barriers determine
that lacustrine and rivers system conform an extensive endorheic basin constituted for
Titicaca and Poopo lakes and Coipasa and Uyuni salt flats. Mayor rivers connecting lakes
and salt flats are Desaguadero and Lacajahuira (Fig.7). The Titicaca and Poopo lakes are
connected by Desaguadero River that flowing permanently, meanwhile, Lacajahuira River
connect Poopo Lake with Coipasa salt flat. These two rivers are permanents with large
increase in caudal during wet season in austral summer (Roche et al., 1991) and constituted
the principal barrier between cluster II and I (Fig. 7). A critical characteristic of these two
Andean rivers is the large river-floodplain systems that which advance parallel to the river,
in particular, in Desaguadero River that undergoes strong floods when water level increases
in Titicaca Lake and floodplain has around 1km of width. Floodplain is constituted by large
sand deposits without vegetation, locally known as “arenales”. Therefore these river
systems separate the populations of haplogroup II (the northernmost and central Andean
Puna population at Bolivia) from haplogroup I (Andean Puna and Prepuna at northern Chile
and southern Bolivia populations). On the other hand, the San Juan River originate in high
mountains near to Jujuy, Argentina, and flows from northern Argentina to south-west
Bolivia through varied environments is the principal barrier that separated Argentina
populations of haplogroup III from central and southern Bolivia populations (Fig. 7).The
San Juan River is the larger river in the East Andes and is the principal tributary of
Pilcomayo River. Additionally, Argentina populations are separated from the Chilean group
35
by presence of the Atacama Desert and also by the high mountain range (more than 4800
m) found in the border to Chile and Argentina (Fig. 7). Another potential barrier is the
mountain range of The Cordillera de los Frailes which borders Poopó Lake in the northwest
and extends to south in the northeast of the department of Potosi (Montes de Oca, 2005).
Our analyses of genetic diversity showed that that O. gliroides has high haplotype
diversity for all of three clusters, but low nucleotide diversity for each of the clusters,
suggesting recent differentiation with rapid population growth (Grant & Bowen, 1998).
This is particularly true for haplogroup III where we obtained the highest haplotype
diversity and in haplogroup II where we recovered the lowest nucleotide diversity. A
similar pattern of nucleotide and haplotype diversity has been reported for population of
Octodon degus (Valladares, 2009), Spalacopus cyanus (Opazo, 2008) and Tymapnoctomys
barrerae (Gallardo et al., 2013). This pattern of genetic variability suggests population
growth following a period of decrease of effective population size (Grant & Bowen, 1998;
Avise, 2000; Cope, 2004). Low genetic divergence among most of O. gliroides populations
(i.e., low values of nucleotide diversity among haplotypes) suggests that colonization into
recently available ranges occurred rapidly (Grant & Bowen, 1998; Palma et al., 2012). Our
analyses consistently show a history of long-term demographic stability for O. gliroides in
most of its current distributional range. Neutrality tests indicated that the demographic
histories of some areas differed, suggesting that O. gliroides have experimented different
demographic events in recent past. In this way, the roughly multimodal mismatch
distribution for all entire sample level and haplogroup I (Fig. 4b, d) suggested sudden
expansion events. Our analyses of the Fu’s Fs test are consistent with this scenario. BSP
analyses for all entire sample level and all of three haplogroups showed that the effective
population sizes of O. gliroides seem to have remained constant until a recent increase in
36
population size between 0.03 Mya for the all entire sample level and a more recent
expansion period (approximately 0.025 Mya) for haplogroup I. We also found a signal of
population range expansion for group III (statistical significant values for the Fu’s Fs test),
however while BSP analysis suggests slightly evidence of recent expansion (Fig. 4h) this
pattern is not clear probably due to the limited sample size. Our results are in agreement
with evidence of other octodontids: Valladares, (2009) and Gallardo et al., (2013) found a
similar evidence of recent population expansion in Octodon degus and Tympanoctomys
barrerae respectively.
Biogeographic scenario
Knowledge about the major climatic changes that occurred at the Andean Puna region
during Pleistocene wet-dry events (Placzek et al., 2009) may help understand the pattern of
demographic expansion occurred in population of O. gliroides. At least three major events
associated with dramatic changes in precipitation have been reported for the Andean Puna
during the last 0.12 Mya (Risacher & Fritz, 2000; Placzek et al., 2011), which have been
associated to important modification of the biota of the area (Placzek et al., 2009) and the
primary habitat for O. gliroides. During this period, climatic oscillations contributed to
formation of large paleo-lakes that covered endorheic basin of Titicaca and Poopo lakes
and Coipasa and Uyuni salt flats (Placzek et al., 2009). Potential causes that be argument
for explain this climate changes are attributed to potential role of interannual and millenial
scale variability of tropical Pacific sea-surface temperature gradients (Garreaud et al., 2003;
Cane, 2005), and recently Plazcek et al., (2013) suggested a critical role of the North
Atlantic sea-surface temperature on moisture arriving to Central Andean Puna. The first
maximum pale-lake expansion denominated Ouki-Salinas started around 0.12 Mya and
37
ended about 0.08 Mya (Placzek et al., 2013), with ca 80 m of deep (Placzek et al., 2009).
After this prorogated period, the lake retreated and the Andean Puna environments were
relatively dry and cold during the next 0.04 Mya (Placzek et al., 2006). Posterior, a new
shorter wet period took place around 0.02 Mya with formation of the Tauca lake cycle that
reached a maximum around 0.015 Mya and resulting in the deepest (ca. 140 m) and largest
lake in the basin over the past 0.12 Mya (Placzek et al., 2009). The last period known as the
Coipasa lake cycle with ages between 0.013 and 0.011 Mya (Placzek et al., 2006), together,
both Tauca and Coipasa lake cycles as referred as the Central Andean Pluvial Event
(CAPE) (Quade et al., 2008). Our results support the above biogeographic scenario with
relatively stable population size for all clades during the Ouki lake cycles, and a posterior
moderate population growth starting around 0.04 Mya in accordance with the drier wet
stable period. After this population expansion a relatively recent decrease of population size
occurred during CAPE phase (approximately 0.015 Mya). This pattern of expansion and
recent decrease was evident only when we considered all three clusters (all entire sample
level) and for cluster I. Interestingly, expansion on populations coincides with the inter-lake
time (0.08 to 0.025 years) that was the longest period of dry conditions in Andean Puna in
last 1.3 Mya (Placzek et al., 2013). Past hydrologic change recorded in two sediment cores
(Fornary et al., 2004; Chepstow-Lusty et al., 2005) evidence an increase in vegetation
species with presence of Polylepis/Acaena pollen, high abundance of Asteraceae,
Chenopodiaceae, Cyperaceae, Myriophyllum and Pediastrum inter alia during this period.
38
Divergence-date estimates
Calibration estimates derived from mtDNA control region suggest that the most recent
common ancestor between O. gliroides and genus Octodon took place during that the mid
Pliocene (4.7 Mya, range 3.18 to 7.22 Mya), whereas the origin of Andean degu is
estimated to have occurred at approximately 2.43 Mya. In general, our divergence estimates
are slightly earlier than the estimate calculated by Upham & Patterson (2012) in
Octodontoidea, using growth hormone receptor and 12S rRNA markers. They are slightly
later than estimations reported by Opazo (2005) and are consistent with values reported by
Rowe et al., (2010).
Ancestral biogeographic reconstruction
The contemporary genetic population structure can be strongly influenced by both their
history and current ecological conditions (Shaikano et al., 2012). Based on our analysis of
optimal S-DIVA reconstruction for the history of O. gliroides we proposed that Andean
Puna-Prepuna region as the most likely ancestral area for where Andean degu diversified
(node 48, Fig. 7). Our geographical reconstruction of the ancestral area of Andean degu is
consistent with reconstruction of ancestral area for South America octodontid rodents
(Ojeda et al., 2013). These authors suggested an ambiguous ancestral area for the clade
containing Andean degu and Chilean octodontid species: High Monte or “protopuna”,
Valdivian Temperate Forest, Central Andean Puna and Central Andean Dry Puna. Given O.
gliroides extends its distribution to pre-Andean Prepuna and Puna habitats in northern
Argentina, southern Bolivia and north-east Chile (Contreras et al., 1987), and also because
the adaptation for arid environment had been proposal for occurred early in the
Octodontidae (Honeycutt et al., 2003), we propose that ancestral forms of O. gliroides
39
evolved in aridlands with posterior colonization and diversification to more mesic
environments.
40
ACKNOWLEDGMENTS
We thank G. Villanueva, H. Villca, M. Orellana and A. Coca for their assistance during
data collection. DSR is extremely thankful to F. Alfaro for his continuously assistance. We
thanks to R. Ojeda, A. Ojeda, P. Jayat, D. Flores and R. Sobrero for provided tissue
samples. We also want to thank the following institutions who collaborated with the
shipping and loan of tissue and skin samples: Field Museum of Natural History, Sam Noble
Oklahoma Museum of Natural History Instituto de Ecología y Evolución, Universidad
Austral de Chile, Colección de Flora y Fauna Profesor Patricio Sánchez Reyes, P.
Universidad Católica de Chile, Museo de Ciencias Naturales y Tradicional de Mar del Plata
"Lorenzo Scaglia", Mar del Plata and Colección de Mamíferos del Instituto Argentino de
Investigaciones de zonas Áridas (IADIZA). DSR is really thankful to personal of
Laboratory of Evolutionary Biology of prof. E. Palma and M. Manzano for their laboratory
assistance. DSR is also thankful to A. Parada for his assistance with molecular analysis. We
thank the Dirección General de Biodiversidad, Bolivia and Servicio Agrícola y Ganadero,
Chile for permission to work and capture specimens in Bolivia and Chile respectively. The
Centro de Biodiversidad y Genética- Universidad Mayor de San Simón and Laboratorio de
Ecología Conductual, Departamento de Ecología-Pontificia Universidad Católica provided
traps and field equipment. DSR was supported by the Organización de los Estados
Americanos (OEA), Comisión Nacional Científica y Tecnológica (CONICYT),
Vicerrectoría de Investigación and Dirección de Investigación y Postgrado-Pontificia
Universidad Católica de Chile (VRI-UC and DIP-UC), the Animal Behaviour Society
(Developing Nations Award), the American Society of Mammalogists, and the Program 1
of Centro de Estudios Avanzados en Ecología y Biodiversidad (FONDAP 1501–001). LAE
and REP were supported by FONDECTY grant (#1090302 and #1100558 respectively).
41
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Table1 1. Description of sampling localities of Octodontomys gliroides specimens in Bolivia (Bo), Chile (Ch) and Argentina (Ar).
Localities and habitat type as shown in Fig. 1. Some of the localities have been combined and their abbreviations are showed. For all
specimens we show the Id code/museum or collection source.
Map references
Bolivia
Bo1
Bo2
Bo3
Bo4
Bo5
Bo6
Bo7
Bo8
Bo9
Bo10
Bo11
Bo12
Chile
Ch1
Ch2
Ch3
Ch4
Argentina
Ar1
Ar2
Ar3
Locality
Llacasa
Eucaliptus
Villa Ventilla
Jirira
Hara
Castilluma
Uyuni
Oploca
Tupiza
Villa Abecia
Cieneguillas
Iscayachi
Abbreviation Latitude Longitude Phytogeographical Id code/Source Haplotypes
regions
EVva
EVva
CIb
CIb
Putre Murmutani
Putre Chungará
Camiña Chusmiza
Chusmiza
Pucara del Tilcara
Susques
San Antonio los Cobres
ArgNc
ArgSd
ArgSd
16°40’S
17°35’S
17°58’S
19°50’S
19°46’S
19°56’S
20°26’S
21°19’S
21°25’S
20°59’S
21°19’S
21°29’S
68°01’W
67°33’W
67°09’W
67°37’W
67°34’W
68°15’W
66°45’W
65°57’W
65°43’W
65°14’W
65°02’W
64°58’W
Northern Puna
Central Puna
Central Puna
Southern Puna
Southern Puna
Southern Puna
Southern Puna
Prepuna
Prepuna
Prepuna
Prepuna
Central Puna
DSR
DSR
FMNH1
DSR
DSR
DSR
DSR
DSR
DSR
DSR
FMNH
DSR
6, 9, 12, 13
5-9
27
8
8
3
10, 20 - 23
10, 14, 15
10
10, 24 - 26
4
6, 10
18°12’S
18°22’S
18°13’S
19°41’S
69°49’W
69°33’W
69’15’W
69°10’W
Central Puna
Central Puna
Prepuna
Prepuna
IEEUACH2
IEEUACH
IEEUACH
SSUC3/DSR
3
3
1, 2
1, 2, 3
23°35’S
24°00’S
24°16’S
65°24’W
66°30’W
66°30’W
Prepuna
Southern Puna
Southern Puna
MMPMa4
OMNH5
CMI-RAO6
16, 17
11
19
55
Ar4
Ar5
Santa Victoria
Cachi
ArgNc
ArgSd
22°13’S
25°01’S
65°12’W
66°14’W
Prepuna
Prepuna
JPJ7
OMNH
10
18
1
Field Museum of Natural History, Chicago, Illinois 60605
Instituto de Ecología y Evolución, Universidad Austral de Chile, Valdivia
3
Colección de Flora y Fauna Profesor Patricio Sánchez Reyes, P. Universidad Católica de Chile, Santiago, Chile
4
Museo de Ciencias Naturales y Tradicional de Mar del Plata "Lorenzo Scaglia", Mar del Plata
5
Sam Noble Oklahoma Museum of Natural History, The University of Oklahoma, Norman, Oklahoma
6
Colección de Mamíferos del Instituto Argentino de Investigaciones de zonas Áridas (IADIZA)
7
Catálogo de campo de Jorge Pablo Jayat.
a
EVv: Eucaliptus-Villa Ventilla; bCI: Cieneguillas - Iscayachi; cArgN: Pucara del Tilcara - Santa Victoria; dArgS: Susques - San Antonio los
Cobres -Cachi
2
56
Table 2. Number of sequences, haplotypes, polymorphic sites by population, haplotype diversity (H) and nucleotide (π) from
Octodontomys gliroides. Some of the localities have been combined for diversity calculation. Abbreviations of localities are defined in
Table 1.
Sample locality
Bo1
EVv
Bo4
Bo5
Bo6
Bo7
Bo8
Bo9
Bo10
CI
Ch1
Ch2
Ch3
Ch4
ArgN
ArgS
Andean Puna
Andean Prepuna
Total
Number of sequences Haplotype Polymorphic Haplotype Standard Nucleotide diversity Standard
sites
diversity H desviation
π (10-3)
desviation
12
18
5
7
5
11
6
5
7
3
2
3
2
7
3
4
66
35
101
4
6
1
2
1
5
3
1
4
3
1
1
3
3
3
3
16
14
27
3
8
0
3
0
7
3
0
6
9
0
0
3
3
12
3
19
18
24
0.77
0.82
0.00
0.47
0.00
0.85
0.73
0.00
0.71
1.00
0.00
0.00
0.67
0.67
1.00
0.83
0.88
0.84
0.92
0.07
0.05
0.00
0.17
0.00
0.06
0.15
0.00
0.18
0.27
0.00
0.00
0.31
0.15
0.27
0.22
0.02
0.05
0.01
2.04
3.85
0.00
2.47
0.00
4.90
2.07
0.00
2.96
10.36
0.00
0.00
3.45
1.97
13.82
2.59
5.74
6.01
5.97
0.31
0.70
0.00
0.89
0.00
0.75
0.74
0.00
1.11
4.38
0.00
0.00
1.63
0.76
5.54
0.95
0.58
1.26
0.59
57
Table 3. Characteristics of all three defined groups by Network and GENELAND analysis including the number of sequences (N),
number of haplotypes (Hap), polymorphic sites (S), haplotype and nucleotide diversity, values of Tajima’s D and Fu’s FS tests, and
Mismatch distribution demographic (SSD) and spatial (SSD*).
Tajima’s D
Group
I
II
III
Geographic
N Hap S
Haplotype Nucleotide diversity
D
P (Ds < Dobs)
location
diversity h
π (10-3)
18°12’ - 21°29’ 52 16 14 0.833 (0.04)
3.42 (0.43)
-1.354
0.076
16°40’ - 19°50’ 42 11
9 0.827 (0.04)
3.39 (0.38)
-0.696
0.251
21°19’ - 25°01’ 7
6
6 0.952 (0.09)
3.78 (0.76)
-0.536
0.343
Fu’s Fs
F
-5.785
-1.545
-3.027
Mismatch
distribution
P (Fs < Fobs) pSSD pSSD*
0.003
0.251
0.012
0.56
0.16
0.52
0.48
0.20
0.56
58
FIGURE LEGEND
Figure 1. Map of Octodontomys gliroides sampling locations along its range distribution
(see Table 1 for Abbreviations of localities). Pie charts displaying the frequency of
occurrence of each haplotype in each locality; pie chart size is proportional to population
size. A total of 27 mtDNA control region haplotypes are represented.
Figure 2. Median-joining network (MJN) of the 27 O. gliroides mtDNA control region
haplotypes recovered from 21 populations. Each circle represents a different haplotype with
size proportional to frequencies, with the largest circles representing the most abundant
haplotypes. Shading indicates localities and black small lines denote the number of
mutational steps between haplotypes. Absence of black small lines is equivalent to a single
mutational event. Small red circles represent missing or unsampled haplotypes. The
coloration of each haplotype represents the four major phytogeographical region of O.
gliroides distribution. The three dashed circles represent each one of the GENELAND
clusters.
Figure 3. Map of cluster membership and posterior probability for each cluster based on
the GENELAND analysis. a) the estimated cluster membership represent the modal cluster
assignment of each pixel, the rest of the inset maps show the posterior probability of
individuals of O. gliroides in Argentina, Bolivia and Chile. Region with the greatest
probability of inclusion are indicated by light yellow, whereas diminishing probabilities of
inclusion are proportional to the degree of red. Black dots represent sampling localities.
The three clusters are b) continuous population extending from northern of Chile, central
59
and southern of Bolivia and northern of Argentina, c) northern and central of Bolivia and d)
rest of Argentina populations and one population from southern of Bolivia.
Figure 4. Pairwise mismatch distribution (left) and Bayesian skyline plots (right) depicting
the demographic history for a-b) the entire sample level, c-d) cluster I, e-f) cluster II and gh) cluster III. For the mismatch distributions, white circles represent the observed
distribution of Pairwise differences and the black circles represent theoretical expected
distribution under a population expansion model. For the skyline plot, black lines represent
median estimation and gray lines represent the upper and lower 95% credible intervals. The
x-axes of these figures are the time before present and the y-axis is the estimated effective
population size.
Figure 5. Divergence time estimates in million years (above the branches) and 95%
credible intervals (below the braches) for principal nodes for each clade of O.gliroides and
other sister species of family Octodontidae.
Figure 6. Ancestral areas reconstruction of Andean degu, O. gliroides along its entire
distribution range, obtained by Statistical Dispersal-Vicariance Analysis (S-DIVA), using
mt-DNA CR. Color legend represents possible ancestral ranges at different nodes; black
with asterisk represent other ancestral ranges. Areas: A: Andean Prepuna; B: Andean Puna;
C: Valdivian Temperate Forest; D: Chilean Scrublands; E: Dry Chaco; F: Argentina Monte
Desert; G: Patagonian Steppe. See Table 1 for Abbreviations of localities.
60
Figure 7. Most important barriers separating the population of O. gliroides when
considered each one of the haplogroups or GENELAND clusters 1) and 2) are principal
barrier separating cluster II from cluster I 3); 4) and 6) are the principal barrier separating
Argentina populations from southern Bolivia and Chilean populations respectively 5) is
major barrier separating the northernmost and central Bolivia populations from the rest of
southern Bolivia populations.
61
Figure 1.
62
Figure 2.
63
Figure 3.
64
Figure 4.
65
Figure 5.
66
Figure 6.
67
Figure 7.
68
CAPITULO II
Within but not between variation in ecology predict population
differences in Octodontomys gliroides group-living
Daniela S. Rivera1,2* Sebastian Abades1,3 and Luis A. Ebensperger1
69
Within but not between variation in ecology predict population
differences in Octodontomys gliroides group-living
Daniela S. Rivera1* Sebastian Abades1,2 and Luis A. Ebensperger1
1
Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad
Católica de Chile, Casilla 114-D, Santiago, Chile
2
Centro de Investigación e Innovación para el Cambio Climático (CIICC), Universidad
Santo Tomás, Santiago, Chile
Short title: Ecological drivers of group-living
*Correspondence:
Address: Daniela S Rivera, Departamento de Ecología, Pontificia Universidad Católica de
Chile, Santiago. Chile.
Phone: (56-2) 686 2950
Email: [email protected]
70
ABSTRACT
Given that variation in sociality is thought to be driven by multiple ecological factors,
among and within population comparisons linked to different environmental conditions
remain a powerful approach to identify costs and benefits of group-living. We studied two
Andean degu, Octodontomys gliroides populations located at two extremes of the climatevegetation gradient across the Andes range. We evaluated how ecological variation in terms
of abundance and distribution of resources, predation risk, and burrowing costs predicted
intraspecific variation in group size and range area overlap (two proxies of sociality). We
found that multiple population differences in ecology (i.e., abundance and distribution of
resources, predation, burrowing costs, and Andean degu density) did not relate to variation
in sociality. In contrast, within population variation in ecology predicted significant
variation in group size, and where larger social groups were in patches with greater density
of vegetation patches and density of burrow openings. In addition, more socially cohesive
groups (as indicated from higher overlap in same group range areas) used areas with lower
soil hardness and density of vegetation patches. Thus, costs and benefits linked to living
socially in O. gliroides seem fine tuned to within but not to between population differences
in ecology. The social behavior of O. gliroides supports that sociality across octodontids
rodents evolved early in the clade and represents historical selection pressures. Thus, an
uncoupling between a relatively ancient social phenotype and current day population
differences in ecology cannot be ruled out.
Keywords: Andean degu; distribution of resources; predation risk; burrowing costs; group
size; range area overlap; sociality.
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INTRODUCTION
Intra-specific variation in the number and composition of group members characterizes
socially flexible, but also socially cohesive groups in vertebrates (Lott 1991; Maher &
Burger 2011). This variation in sociality is thought to be driven by ecological conditions
(e.g., weather, predation pressure, density, nest-site availability, and quality, quantity, or
distribution of food) (Lott 1991), which in turn determine fitness benefits and costs
associated to individuals’ decision to join or leave groups (Ebensperger et al. 2012). Groupliving benefits social individuals through several ways. Individuals in larger groups are
known to decrease predation risk, locate and defend food resources more efficiently,
decrease costs of burrow or nest construction, decrease thermoregulatory costs, or decrease
risk of infanticide (Alexander 1974; Wrangham 1979; Ebensperger 2001; Ebensperger &
Cofré 2001; Ebensperger & Blumstein 2006). These benefits typically come to fitness
costs, including greater competition over food resources and parasitism (Alexander 1974;
Wrangham 1979; Freeland, 1979; Loehle 1995; Altizer et al. 2003). Given that most of
these benefits and costs may be influenced by ecological conditions, ecological variation
remains a major, ultimate cause of intraspecific variation in social group size (Lott 1991;
Slobodchikoff 1984; Brashares & Arcese 2002; Ebensperger et al. 2012).
Predation risk remains a commonly reported ecological correlate of variation in the
size of social groups (Lott 1991; Brashares & Arcese 2002; Isvaran 2007; Maher & Burger
2011). Individuals in larger groups can reduce per capita predation risk through several
mechanisms, including dilution, predator confusion effects, earlier detection and warning,
or through cooperative defense (Pulliam & Caraco 1984; Krause & Ruxton 2002;
Ebensperger et al. 2006). As a consequence, individuals in groups can spend more time
feeding and less time scanning for predators (Alexander 1974; Roberts 1996; Lacey 2000;
72
Ebensperger 2001; Ebensperger et al. 2006). Distribution of critical resources (e.g., food,
nest sites, shelter) is an additional ecological driver of social behavior (Johnson et al. 2002).
Resources that are patchily (heterogeneously) distributed through space and time promote
group-living because larger groups attain preferential access to high-quality resources
(Slobodchikoff 1984; Travis et al. 1995; Brashares & Arcese 2002; Maher & Burger 2011).
Alternatively, relatively large patches of food may relax intraspecific competition and
promote recruitment of individuals, which in turn favor greater sociality without any
apparent fitness benefit (Macdonald 1983; Johnson et al. 2002; Verdolin 2009). Other
ecological factors have been suggested to explain variation in social systems, including
ambient temperature (e.g., group-living decreases thermoregulation costs through huddling)
(Berteaux et al. 1996; Canals et al. 1998; Kaufman et al. 2003), or physical conditions that
influence the costs of burrow or nest construction (e.g., soil harness to burrowing
organisms) (Lacey 2000; Ebensperger 2001; Ebensperger & Cofré 2001).
One powerful approach to examine how intraspecific variation in sociality tracks
ecological variation is to examine populations facing different environmental conditions.
Population comparisons have the advantage of spanning a greater range of ecological
conditions, particularly near the extremes of a species’ distribution (Travis & Slobodchikoff
1995; Spinks et al. 2000; Schradin & Pillay 2005). However, recent evidence from these
studies has not revealed consistent relationships between ecological variation and groupliving (Ebensperger et al. 2012). For example, the effect on larger group size in populations
of cercopithecoid primates living in a gradient of predation risk was greater in populations
under high predation risk (Hill & Lee 1998). In contrast, Clutton-Brock et al. (1999) did not
find population variation in group size and composition in social groups of mongoose
facing different predation regimes. Regarding the effect of food resources, larger social
73
groups of some rodents and ungulates are associated with greater patchiness and abundance
food across populations (Travis et al. 1995; Brashares & Arcese 2002). In contrast, studies
on European badgers (Meles meles) showed no consistent relationship between territory
size and resource dispersion, or between group size and resource richness (Johnson et al.
2001). In another study, Kruuk & Parish (1987) found that a decrease in food availability
was followed by territory expansion rather than by the expected group size reduction.
However, most of these studies have focused on each of these factors individually (Kruuk
& Parish1987; Hill & Lee 1998; Clutton & Brock et al. 1999; Johnson et al. 2001; Hass &
Valenzuela 2002) and only a few studies examined simultaneously and quantitatively the
link between ecological conditions and social behavior (Brashares & Arcese2002; Isvaran
2007; Ebensperger et al. 2012).
Model species, hypotheses and predictions
A potentially informative group of model organisms to understand how sociality and social
organization tracks ecological conditions is the New World hystricognath (or caviomorph)
rodents (Ebensperger 1998, 2001; Lacey & Sherman 2007; Maher & Burger 2011).
Caviomorph rodents exhibit relatively large differences in social behavior, with species
ranging from solitary living to highly gregarious (Lacey & Ebensperger 2007). This
variation also occurs across populations within species (Maher & Burger 2011), providing a
natural system to examine the extent to which different ecological conditions influence
social behavior (Ebensperger et al. 2012). Within octodontids, the social behavior of
Octodontomys gliroides (Andean degu) remains scarcely known, yet this knowledge is
critical to determine the extent to which social behavior in these animals is maintained by
current ecological conditions. According to phylogenetic hypothesis (Honeycutt et al.,
74
2003; Gallardo et al. 2004; Opazo, 2005; Rowe et al. 2010), O. gliroides is basal to Chilean
clade composed of more social octodontids (Ebensperger et al. 2004; Gallardo et al. 2007;
Lacey & Ebensperger 2007). Therefore, solitary living in O. gliroides would suggest that
social living is a more derived trait. On the contrary, if O. gliroides presents social habits,
would support that that solitary living evolved rather recently through the clade.
The aim of this study was to examine the relative importance of different ecological
factors as predictors of variation in group size within and across two populations of O.
gliroides. We selected two populations that are genetically similar based on molecular
markers (Rivera 2013) but represent two extremes of the characteristic climate-vegetation
east to west gradient throughout the distribution of this species across the Andes range. In
particular, we assessed how variables linked to resource distribution and food abundance,
predation risk, and burrowing costs predicted population variation in group size.
Ecological variation faced by O. gliroides suggests differences in the abundance and
distribution of food, which in turn predict an effect on social behavior in this species. The
geographic distribution of Andean degu faces an east to west gradient of decreasing
precipitation caused by a rain shadow effect across the Andes Mountains. This area is
characterized by a complex history of changes associated with dry-wet periods that took
place in the Pleistocene-Holocene (Nester et al. 2007; Santoro & Latorre 2009). This
variation has driven changes in the distribution and cover of the local flora used of xeric
Andean regions (Puna, Prepuna, Monte and high Andes) used by O. gliroides (Ribichich
2002; Teta & Ortiz 2002; Barquez et al. 2006). In particular, O. gliroides burrows in
patches of columnar and ground-level cacti and/or shrubs where these rodents feed on the
vegetation, hide from predators, and rear their offspring (Rivera 2013). Because resources
in arid or semi arid ecosystems such as food and shelter are distributed in a mosaic of
75
patches with high plant cover interspersed in a low-cover matrix (Sala & Aguilar 1996;
Aguilar & Sala 1999), larger groups would be expected in patches with relatively low plant
cover (Ebensperger 2001).
Even though the role of predation risk as a driver of social behavior in O. gliroides
has not been examined, its influence cannot be ruled out. Population studies in the sister
species Octodon degus indicated predation risk influences the size of foraging groups
(Ebensperger & Wallem 2002; Ebensperger et al. 2006; 2012). In addition, vegetation
overhead cover has been shown to influence predation risk (Ebensperger & Hurtado 2005).
Thus, the ecological variation in the distribution and cover of the local flora faced by
Andean degus may have influenced variation in social behavior of these animals. Given
that predation risk is thought to increase in areas with relatively low shrub cover because
shrubs provide overhead protective cover (Vásquez et al. 2002; Ebensperger & Hurtado
2005; Taraborelli 2009), larger social groups would be expected in patches with lower plant
overhead cover. In addition, we considered predation risk to increase with distance to the
nearest burrow system, and decrease with density of burrow openings (i.e., abundance of
safe havens) (Ebensperger et al. 2012). Thus, larger groups would be expected in the
population with greater distance between burrow systems and with greater density of
burrow openings.
Given that burrowing is energetically expensive and this cost generally increases
with soil harness (Ebensperger & Bozinovic 2000), we also examined the prediction of
larger groups using patches with increasing soil hardness.
To summarize, we examined the above predictions to determine the relative
contribution of different ecological factors in driving the size of Andean degu social
groups. The observation of larger social groups and range area overlap (two proxies of
76
sociality) in the population holding more clumped resources would support resource
abundance and distribution is a driver of Andean degu sociality. The observation of larger
social groups and greater range area overlap in the population with greater overhead cover
would support a role for predation risk. Finally, larger social groups and greater range area
overlap in the population with harder soil conditions would support burrowing costs are
relevant to Andean degu sociality.
METHODS
Study populations
We examined two Andean degu populations located 400 km apart in two habitats with
extreme differences in ecology. The Oploca population (hereafter Oploca) was located in
southwest Bolivia (21°20´S 65°50´W; 3,121 m of altitude); the Chusmiza population
(hereafter Chusmiza) was located in the high Andean plateau of northern Chile (19°40´S
69°10´W; 3,460 m of altitude).
Differences in rainfall between these populations lead to further differences in
vegetation types and plant cover (HilleRisLambers et al. 2001; Schradin 2005). Oploca
ecosystem is characterized by dry climate with rainfall ranging from 200 to 650 mm; most
rain falls during summer months (November–February), and mean temperature ranges from
14 to19° C (López 2003; SENAMHI 2012). The study area of this population was
characterized by xerophytic vegetation, where the succulent Trichocereus tacaquirensis,
Oreocereus celsianus, Opuntia boliviensis and the thorn scrub Acacia feddeana, Cercidium
andicola, and Prosopis ferox were the most common species (Ibisch et al. 2003; López
2000; 2009; 2010). In contrast, Chusmiza population exhibits an arid climate, with a mean
annual precipitation of 101 mm (Ministerio de Obras Públicas, MOP 2012) that
77
concentrates during the austral summer months (January- March; "invierno boliviano")
(Garreaud et al. 2009). Mean ambient temperature ranges from -0.2 to 6.8° C (Ministerio de
Obras Publicas, MOP 2012). Vegetation at the study site was dominated by small bushes
and cacti of Tarapacá Precordillera (Atriplex sp., Bacharis boliviensis, Senna birostris,
Lophopappus tarapacanus, Corryocactus brevistylus, Oreocereus leuvotrichus,
Trichocereus atacamensis, Opuntia soehrensii, Opuntia camachoi) (Philippi 1941;
Villagran et al. 1999; Moreira-Muñoz 2011).
Trapping and marking of animals
The study was conducted in both populations during the spring-summer transition (i.e.,
before the rainy season). Thus, Oploca was examined during October through December
2011, and Chusmiza was studied during November 2012-January 2013. We trapped
Octodontomys using a combination of Tomahawk (Tomahawk Live Trap Co., Tomahawk,
Wisconsin) and locally produced medium and large sized Sherman traps, all baited with
mixture of tuna’s fruit (Opuntia fus-indica) and grated apple. We set traps at previously
identified burrow systems. Occupancy was based on the presence of finger prints, "paths",
fresh droppings or fresh brands of consumption of cacti leaves or seedpods in the vicinity
of burrows. We placed a total of 220 traps at Oploca and 160 traps at Chusmiza. The total
area examined for both populations was similar and reached 11 ha. A similar combination
of Sherman and Tomahawk traps were distributed at each burrow systems. The number of
traps used per day at each burrow systems averaged 8 ± 0.2 at Oploca and 6 ± 0.0 at
Chusmiza. The total number of burrow systems trapped at Oploca was 45 and 40 at
Chusmiza. These burrows were found primarily on the base of shrubs, columnar, and
ground-level cacti at Oploca, but were associated only to columnar cacti at Chusmiza.
78
We trapped Andean degus during 26 consecutive days at Oploca and 18 consecutive
days at Chusmiza. Traps were opened during most daylight hours and checked
approximately every 2 hours. Traps were closed between 11:00 until 16:30 to prevent
individuals dying through overheating. The sex, body mass, and reproductive condition
(e.g., whether females were perforated, pregnant or lactating and males had descended
testes) were recorded for all animals captured. Upon first capture, individuals were given a
unique identification with the use of metal ear tags coded with numbers (National Band and
Tag Co. Newport, USA). In addition, a small sample of ear tissue was taken first caught
subjects for subsequent genetic analyses.
To characterize spatial relationships among individuals, all adult sized individuals
(≥ 140 g) caught during this trapping period were fitted with 6-7 g radio-collars (AVM
Instrument Co., USA). Weight of radio-collars represented 4-5% of study subjects’ body
weight. At the end of data collection (see below) all radio-collared animals were recaptured
and transmitters were removed.
This study followed the American Society of Mammalogists guidelines (Animal
Care and Use Committee 1998; Gannon et al. 2007), and adhered to Bolivian and Chilean
laws (permit number MMAyA-VMA-DGBAP N 0937/11 by the Dirección General de
Biodiversidad y Áreas Protegidas and by the permit number 1-62-2012 [2373] by the
Servicio Agrícola y Ganadero). All procedures that involved handling of live animals were
approved by the Faculty of Biological Sciences at Pontificia Universidad Católica de Chile
(CBB-040-2011).
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Temporal activity and range area
The Andean degu has been presumed to be active at nighttime by some (Ipinza et al. 1971;
Nowak 1991; Pedreros & Valenzuela 2009), but diurnally active by others (Mann 1978).
Therefore, we first needed to confirm this discrepancy and examined patterns of temporal
activity. We used the homing technique (Kenward 2001) to follow all radio-collared
animals with the use of LA 12-Q receivers and hand held, and 3-element Yagi antennas
(tuned to 150.000-151.999 MHz; AVM Instrument Co., USA). In particular, two observers,
each holding a receiver and an antenna, tracked all radio-collared animals every two hours
during nighttime and daytime. Once located, the position of each animal was marked with
coded flags. Each fixing location was then geo-referenced with a Garmin portable GPS
(Garmin International Inc., Olathe, Kansas, USA). The rugged micro-topography of both
study sites caused frequent signal bounce and precluded the use of long-range radio-fixings
(i.e., triangulation) (Kenward 2001). To prevent observers affecting the movements of
animals and disrupt their behavior, each observer was trained to avoid stepping loudly
when near each radio-collared subject, and to left swiftly once its location was confirmed.
Daily patterns of activity were then characterized from how activity varied with time of
night and day on both populations.
Social group identification
Given that activity of Andean degu rats concentrated their activity mostly during night
time, the criterion to assign Andean degu rats to social groups was based on the sharing of
burrow systems (in which they rest and interact) during day-time (e.g., Hayes et al. 2009).
During a total of 10 days we determined resting locations at burrows systems three times
per day: in the morning (09:30 to 11:30 h), afternoon (12:30 to 14:30 h) and before sunset
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(15:30 to 17:30 h). This effort has been adequate to determine group membership in other
octodontids rodents (Ebensperger et al. 2004).
The determination of group composition required the compilation of a symmetric
similarity matrix of pairwise association of the burrow locations of all adult Andean degus
during daytime telemetry (Whitehead 2008). We determined the association (overlap)
between any two individuals by dividing the number of days that these two animals were
tracked with telemetry to the same burrow system by the number of days that both
individuals were tracked with telemetry on the same day (Ebensperger et al. 2004). We
determined group composition using the SOCPROG software (Whitehead 2009). We
performed hierarchical cluster analysis of the association matrix. We confirmed the fit of
data with the cophenetic correlation coefficient, a correlation between the actual association
indices and the levels of clustering in the diagram. Under this procedure, values above 0.8
would indicate that hierarchical cluster analysis has provided an effective representation of
the data (Whitehead 2008). We chose maximum modularity criteria (Newman 2004) to cut
off the dendrogram and define social groups. Group size was then calculated as the number
of Andean degus assigned to a same social group.
Range areas and overlap
To determine whether Andean degus assigned to the social unit formed a socially cohesive
group when active above ground, we used night radio locations to monitor patterns of space
use. Location of radio fixes for each individual were first transferred to an X-Y system of
coordinates, and then mapped using the 95% minimum convex polygon algorithm (MCP)
with the software Ranges VI (Kenward et al. 2003). Pairwise estimates of percentage range
81
area overlap among individuals and nesting associations were also calculated using Ranges
VI (Ebensperger et al. 2006; Kenward et al. 2003).
Ecological differences across populations
At the level of population, availability of food resources was estimated based on abundance
of ground-level cacti biomass. We previously determined that Octodontomys gliroides
feeds principally on ground-level cacti at Chusmiza, but on cacti and shrubs (Prosopis
flexuosa) at Oploca (Villanueva 2013). One 100 m transect was established randomly in
each study population: each transect was then divided into ten 10 x 2 m plots. Within each
plot we used from 1 to 7 0.25 m2 quadrants at Oploca and from 4 to 13 0.25 m2 quadrants
per plot at the Chusmiza to quantify the abundance of cacti leaves. From each 0.25 m2
quadrant we randomly removed five leaves that were stored inside paper bags, and
transported to the laboratory. All cacti leaf samples were oven-dried at 60° C for 10
consecutive days to determine its dry mass (biomass in g).
A second measure of food availability was vegetation cover (Reus et al. 2013). We
estimated vegetation cover based on ground-level cacti, columnar cacti, and shrubs at each
population. To do so, we established other 15 (50 x 2 m) randomly located transects.
Vegetation cover was recorded every 10 m using the line point intercept method (Bonham
1989). Percent cover was calculated as the number of hits for each plant species or ground
cover class divided by the total number of points per transect.
We used Poisson variance-to-mean ratios of distance between patches with groundlevel cacti, columnar cacti, and shrubs to estimate the distribution of resources within each
population. To do this, all patches in the total area (11 ha) were georeferenced in each
population. We then divided the variance of distance between patches by its mean. Values
82
<1 were indicated a relatively uniform distribution; values >1 were taken to represent a
relatively clumped distribution (Travis & Slobodchikoff 1993; Krebs 1999).
At the level of populations, predation risk was recorded first from the active burrow
entrances at each burrow system. Active burrow entrances were identified from the
presence of fresh droppings, urine marks, signs of recent soil digging, or remains of
recently consumed cacti leaves. Number of active burrow entrances per square meter at
each burrow system was calculated as the number of active burrow openings divided by the
area covered by the burrow system (hereafter density of burrow openings). A second
measure of predation risk was distance (meters) from each burrow system to the nearest
shrub or a cactus (distance to cover). Finally, predation risk was recorded from vegetation
overhead cover as suggested by several previous studies (Hill & Dumbar 1998; Jensen et al.
2003; Ebensperger & Hurtado 2005). For Oploca population we considered the groundlevel cacti as cover because we observed that these species of cacti are large enough to
provide coverage against predators. On the other hand, in Chusmiza the ground-level cacti
were too small to provide any protection, so we did not considered in this analysis.
We estimated soil penetrability as an index of soil hardness and therefore, energy
costs associated with building burrows (Lacey & Wieczorek 2003; Ebensperger et al.
2012). At the level of the population we established 15 randomly located transects (50 x 2
m). Soil penetrability was recorded twice every 10 m. Soil penetrability was recorded with
the use of a hand-held soil compaction meter (Lang Penetrometer Inc., Gulf Shores, AL,
USA).
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Ecological predictors within populations
At the level of burrow systems used by Andean degus, distribution of food resources was
measured as distance between patches with shrub and/or ground-level and columnar cacti
and density of vegetation patches. Both, distance between patches and density of vegetation
patches were calculated as the distance and number of locations with shrub and/or groundlevel or columnar cacti on an area of 1 ha around each burrow systems used by varying
number of these rodents.
To examine predation risk, we considered the density of burrow openings (number
of active burrow entrances per m2), overhead vegetation cover, and distance (meters) from
each burrow system used to the nearest overhead shrub or cacti. In the context of energy
costs associated with burrow digging, we recorded the soil penetrability around burrow
systems of social groups. Soil penetrability was recorded five times in each of four points
located in north, east, west, and south orientation and on the perimeter of each burrow
systems used by Andean degus. The five measures per point were averaged for subsequent
analyses.
Andean degu abundance
We used data from burrow trapping to calculate the abundance of Andean degu assuming a
closed population (no emigration, immigration, death or birth). We restricted this analysis
to the first 18 days of burrow trapping at each population. These analyses were performed
using the CAPTURE software (Otis et al. 1978; White et al. 1982; Rexstad & Burnham
1991).
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Statistical analysis
Unless stated differently, statistical analyses were performed using Statistica 9.0 (StatSoft
Inc., Tulsa, Oklahoma, USA). We used General Linear Models (GLM) when data did not
violate the assumptions of normal distribution and homogeneity of variances, or could be
transformed to meet these assumptions. Percentage values were arcsine square-root
transformed (Zar 1996). Alternatively, we used Generalized Linear Model (GZLM)
assuming a Poisson distribution and a log link function.
To examine how group size varied between populations, we used GZLM with
population entered as a categorical factor (Oploca vs. Chusmiza) and group size entered as
the dependent variable. To examine range area overlap, we used a GLM where population
was entered as a categorical factor (Oploca vs. Chusmiza), group size was entered as a
covariate, and range area overlap was the dependent variable. For descriptive purposes, we
also used GLM approach to examine the effects of population (Oploca vs. Chusmiza), sex
(males vs. females), and population by sex interaction on the size of range areas.
To determine differences in ecological conditions between populations we used
GZLM with Poisson distribution. During these analyses population was entered as a
categorical factor and ecological predictors (i.e., measures of food availability, Poisson
variance to mean ratio, predation risk, and, soil hardness) represented dependent variables.
To quantify how variation in ecology predicted variation in sociality within
populations we used two different and complementary approaches, including partial least
squares regression analysis (PLSR; Abdi 2007), and regression commonality analysis
(Kraha et al. 2012). Both techniques are robust to relatively low sample sizes, a relatively
high number of predictors, or to relatively high degree of correlation between predictor
variables (i.e., multi-collinearity) (Carrascal et al. 2009; Kraha et al. 2012). The PLSR
85
analysis reduced the set of ecological predictor variables of resource distribution and
predation risk to a few components that exhibit maximum covariance with the dependent
variables. We performed separate PLSR analyses for group size and range area overlap as
dependent variables but used all same ecological predictors: distance between patches and
density of vegetation patches as measures of resource distribution; density of burrow
openings, vegetation cover and distance to cover as measures of predation risk; soil
hardness as a measure of borrowing costs. Additionally, we consider population level as
categorical factor for both analyses. Only components that were significant after a fivefold
validation procedure were retained (Carrascal et al. 2012). Prior to PLSR analyses, we
transformed predictor and dependent variables in order to make their distributions be fairly
symmetrical. We followed Wold et al. (2004) and log-transformed variables whose range of
values spanned over ten orders of magnitude. Variables whose values spanned over less
than ten orders of magnitude were fourth rooted transformed. All PLSR analyses were
performed with both dependent and explanatory variables centered and standardized.
The Regression Commonality Analysis partitions the total explained variance
accounted for by the full regression model (R2) into unique and non-unique contributions
made by each ecological predictor variables (Mood 1969; 1971; Newton & Spurrell 1967),
a procedure that makes collinearity assessment an informative part of the analysis instead
of a methodological nuisance. We implemented Regression Commonality Analysis after
fitting a full model to both group size and range area overlap with all ecological predictors.
We focused on the extent to which R2 was partitioned into unique, common and total
contributions of each predictor to the overall fit of the model, as a way to estimate the
partial contribution of each variable to the total variance explained (Kraha et al. 2012).
86
Regression Commonality Analysis was conducted with the of “yhat” module (Interpreting
Regression Effects) of R package, version 1.0-5 (Nimon & Roberts 2012).
Data are reported as mean ± SE. Statistical significance was determined at P < 0.05.
RESULTS
Population differences in sociality
At Oploca we monitored 6 females and 9 males, representing a total of 5 social groups. At
Chusmiza we monitored 5 females and 6 males, and determined 5 additional social groups.
Social group sizes at Oploca (3.0 ± 0.32 adults) and Chusmiza (2.2 ± 0.2 adult) were not
statistically different (GZLM: χ21 = 0.61, P = 0.435; Fig. 1a). Group size ranged from 2 to 4
adults at Oploca and from 2 to 3 adults at Chusmiza. Of these, the number of adult females
ranged from 1 to 2 individuals at Oploca. Social groups at Chusmiza always had only one
adult female (Fig. 1b, c). Andean degus from same social groups at both populations
always used one burrow system as resting and hiding place.
Individual range areas of Andean degus in the Chusmiza (0.37 ± 0.07 ha) were
significantly larger than range areas at the Oploca (0.12 ± 0.03 ha) (GLM: F1,22 = 20.652, P
< 0.001). In addition, range areas varied significantly with sex (GLM: F1,22 = 7.62, P <
0.011). Male range areas (0.29 ± 0.06) were larger than those of females (0.14 ± 0.03).
There was not a statistically significant population by sex interaction on range areas (GLM:
F1,22 = 0.25, P < 0.620).
Range area overlap among same group members (50.9 ± 6.9%) was greater than
overlap among individuals from different social groups (0.6 ± 0.4%) at Chusmiza (GZLM:
χ21 = 56.96, P < 0.001; Fig. 2). Likewise, overlap among same group members (32.8 ±
8.9%) was greater than overlap among individuals from different social groups (0%) at
87
Oploca (Fig. 2). Overlap of range areas among same group members at Oploca (32.8 ±
8.9%) and Chusmiza (50.9 ± 6.9) did not differ when group size was controlled for (GLM:
F1,7 = 1.64, P = 0.242 and F1,7 = 0.02, P = 0.896, respectively).
Abundance of Andean degus
The abundance of Andean degus was greater at Chusmiza than at Oploca. There were 23 ±
4 adults at Chusmiza, and 19 ± 4 adults at Oploca.
Ecological differences between populations
Abundance of ground-level cacti biomass (GZLM: χ21 = 358.27, P < 0.001) and vegetation
cover (GLM: F1,28 = 4.55, P = 0.042), two measures of food availability, were higher at
Oploca than at Chusmiza (Table 2). Regarding the distribution of food resources, the
Poisson variance to mean ratio of distance between patches was higher at Chusmiza than at
Oploca (GZLM: χ21 = 314.8, P < 0.001; Table 2), implying a more clumped distribution of
resources at Chusmiza.
Two out of the three measures of predation risk examined differed between
populations. Distance to overhead cover (GLM: F1,665 = 192.27, P < 0.001) was greater at
Chusmiza than at Oploca, but the overhead vegetation cover was higher at Oploca than at
Chusmiza (GLM: F1,28 = 21.71, P < 0.001) (Table 2). Even though the absolute number of
active burrow entrances per burrow system was greater at Bolivian Prepuna (5.4 ± 0.89)
compared with Chilean Prepuna (1.62 ± 0.14), density of burrow openings (number of
burrow entrances per m2) was similar between populations (GZLM: χ21 = 0.1, P = 0.749;
Table 2).
88
There was a statistically significant difference in soil hardness (GZLM: χ21 = 7.11, P
< 0.001), in which soil was harder at Oploca compared with Chusmiza (Table 2).
Ecological predictors of sociality
The PLSR analysis revealed that group size was explained by two partial least regression
components that accounted for 75% of the variation in this measure of sociality (Table 2).
However, only Component 1 was statistically significant and explained 47.9% of the total
variance. Component 1 associated variation in group size to density of vegetation patches
and density of burrow openings. Therefore, larger social groups were using areas with
greater density of vegetation patches and greater density of burrow openings.
Regarding the effects of ecological predictors on overlap in range areas, the PLSR
analysis revealed two components, accounting for 31.69%, a relatively low proportion of
the original variance. Only Component 1 was significant and explained 25.3% of variance
in this additional measure of group-living (Table 2). The most influential ecological factors
were soil hardness and density of vegetation patches. Greater range overlap within social
groups was associated with low soil hardness and with low density of vegetation patches.
The Regression Commonality Analysis revealed an overall significant fit for group
size (R2 = 0.87, F6,39 = 52.51, P < 0.001), but not for overlap in range areas (R2 = 0.099,
F6,39= 0.72, P = 0.636). In particular, density of vegetation patches and density of burrow
openings showed the highest total contribution to variation in group size. In contrast,
distance between vegetation patches, vegetation cover, distance to cover, and soil hardness
were less important (Table 3).
89
DISCUSSION
General findings
Our study revealed ecological differences between populations. First, preferred food
resources (ground-level cacti biomass) were more abundant at Oploca than at Chusmiza.
Similarly, vegetation cover as a second measure of food abundance based on ground-level
cacti, columnar cacti, and shrubs at each population was higher at Oploca. A measure based
on the Poisson variance to mean ratio indicated that preferred food and other resources
(e.g., hiding or resting places) were patchier at Chusmiza. In addition, amount of distance
to cover was higher at Chusmiza, and overhead vegetation cover was greater at Oploca,
implying that refuge against predators was less abundant, yet more patchily distributed, at
Chusmiza. Thirdly, soil hardness was higher at Oploca suggesting that burrowing costs to
degus from this population were higher. Fourthly, Andean degu abundance was higher at
Chusmiza than at Oploca. Taken together, ecological conditions in terms of Andean degu
abundance, resource distribution and predation risk were more challenging at Chusmiza,
but the opposite was true in terms of burrowing costs, and food abundance. These
population differences did not translate into social differences in terms of mean group size,
a raw measure of sociality, and within group range area overlap, a measure of social
cohesion during activity. In contrast, within population variation in ecology predicted
significant variation in group size, and where larger social groups were in locations with
greater density of vegetation patches and density of burrow openings (number of burrow
entrances per m2). However, more socially cohesive groups (as indicated from higher
overlap in same group range areas) used areas with lower soil hardness and density of
vegetation patches.
90
Within vs. between population variation in ecological conditions
Intra and inter-specific variation in the number and composition of group members
characterizes more socially cohesive groups in vertebrates (Lott 1991; Maher & Burger
2011), and both sources of variation should reflect trade-offs between current fitness
benefits and costs that emerge from individuals' decisions to join or leave groups
(Ebensperger et al. 2012). Thus, variation in sociality should mirror within and between
population differences in ecology. While both sources of variation have been frequent
subjects of previous studies, these are rarely addressed simultaneously (Brashares & Arcese
2002; Isvaran 2007; Ebensperger et al. 2012). Unexpectedly, our study on O. gliroides
revealed group size and range area overlap within groups did not vary with population
differences food abundance and distribution, predation risk, or burrowing costs. Thus, costs
and benefits linked to living socially in O. gliroides are not fined tuned to current day
population differences in ecology. This lack of social variation may need further
examination on the basis of a larger number of social groups and populations, yet our
current findings imply a small effect size if any. In contrast, variation in group size within
populations was coupled to spatial variation in density of vegetation patches and density of
burrow openings. In addition, range area overlap among group members was associated to
soil hardness and density of vegetation patches. The lack of population level differences in
sociality departs from what has been recorded in other social octodontids (Ebensperger et
al. 2012), other social rodents (Travis et al. 1995, Schradin & Pillay 2005), and other social
mammals (e.g., ungulates) (Rowe-Rowe et al. 1992; Brashares & Arcese 2002).
Very likely, the evolution of octodontids, including O. gliroides, has been shaped by
changes in the landscape and habitat fragmentation in response to Andean uplift and
increasing aridity about 7.8 Mya (Reig 1986; Contreras et al. 1987; Honeycutt et al. 2003).
91
However, a lack of covariation between population differences in ecology and social
behavior in O. gliroides could suggest that group-living in current day populations reflects
historical but not current day selective pressures. We lack direct evidence to support this
possibility, yet patterns of sociality across Octododontids provide some indirect support.
According to phylogenetic hypothesis (Honeycutt et al. 2003; Gallardo et al. 2004; Opazo
2005; Rowe et al. 2010), O. gliroides is basal to social-Chilean clade (Ebensperger et al.
2004; Lacey & Ebensperger 2007) of living octodontids. The observation that O. gliroides
exhibit group-living supports that sociality in O. gliroides and the Chilean clade
octodontids evolved early in the clade and represents historical selection pressures. Thus,
an uncoupling between a relatively ancient social phenotype and current day population
differences in ecology cannot be ruled out.
Within population predictors of group-living O. gliroides
Overall, our results support multiple factors may be important in explaining variation in
sociality within populations of Andean degus. Resource availability in particular may
predispose some organisms to adopt social living (Rolland et al. 1998; Beauchamp 2002)
through benefits derived from the collective defense of resources (Travis et al. 1995;
Brashares & Arcese 2002), or through mutual attraction to resources per se with no benefits
involved (Carr & Macdonald 1986; Johnson et al. 2001). Either way, the size of social
groups is expected to increase with increasing abundance, quality and distribution
heterogeneity of food resources (Travis et al. 1995; Brashares & Arcese 2002; Verdolin
2007). Within populations of O. gliroides, group size was positively and range area overlap
negatively associated with density of vegetation patches, implying that resource distribution
(food, shelter) remains a driver of group-living at Andean degus. This findings supports
92
previous studies based on within population correlates other mammals (Taber &
Macdonald 1992; Brashares & Arcese 2002; Isvaran 2007).
Within population variation in predation risk also had an influence on group size of
Andean degu at Oploca. Larger (yet not more socially cohesive) social groups were
recorded in locations with greater density of burrow openings, implying that larger groups
decrease predation risk through building more safe heavens. The abundance of safe havens
has been noted to decrease vulnerability to predators of other ground-dwelling rodents
(Bonenfant & Kramer 1996; Ebensperger & Hurtado 2005). Thus, results from our study
converge upon studies on other social mammals (rodents) in that predation risk remains a
driver of sociality within populations (Asher et al. 2004; Ebensperger & Wallem 2002;
Hayes et al. 2007), but also provide unique evidence on a social enhancement of safe
heaven abundance.
Burrow systems play an important role in the life of many rodent species in arid
environments (Shenbrot et al. 2002). Since the construction and maintenance of burrows
systems is energetically costly (Ebensperger & Bozinovic 2000), animals may be forced to
live in groups to minimize these costs (Ebensperger 2001). Indeed, active burrow digging
has been linked to the evolution of group-living of New World histricognath rodents
(Ebensperger & Cofré 2001; Ebensperger & Blumstein 2006). The results of this study do
not provide strong support to this hypothesis. Within population variation in group size was
unrelated to spatial variation in soil hardness. Moreover, social cohesion within social
groups (as evidence from range area overlap) decreased in patches with harder soil
conditions, and this association had a relatively low effect size.
93
ACKNOWLEDGMENTS
We thank G. Villanueva, H. Villca, A. Galarza and M.A Jaldin for their assistance during
data collection. DSR is extremely thankful to F. Alfaro for his continuously assistance. We
thank to Oploca and Chusmiza community who welcomed us and facilitated our study. We
thank the Dirección General de Biodiversidad, Bolivia and Servicio Agrícola y Ganadero,
Chile for permission to work and capture specimens in Bolivia and Chile respectively.
Comments and suggestions made by L. Hayes and helped us to improve an early version of
this article. The Centro de Biodiversidad y Genética- Universidad Mayor de San Simón and
Laboratorio de Ecología Conductual, Departamento de Ecología-Pontificia Universidad
Católica provided traps and field equipment. DSR was supported by the Organización de
los Estados Americanos (OEA), Comisión Nacional Científica y Tecnológica (CONICYT),
Vicerrectoría de Investigación and Dirección de Investigación y Postgrado-Pontificia
Universidad Católica de Chile (VRI-UC and DIP-UC), the Animal Behaviour Society
(Developing Nations Award), the American Society of Mammalogists, and the Program 1
of Centro de Estudios Avanzados en Ecología y Biodiversidad (FONDAP 1501–001). LAE
was supported by FONDECTY grant (#1090302).
94
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Table 1. Mean (±SE) estimates and statistical comparisons of ecological conditions between Oploca and Chusmiza populations.
Values in bold indicate statistical significant differences at P < 0.05.
Ecological condition/variables
Populations
Oploca
Chusmiza
P-value
141.19± 42.48
77.64± 5.82
<0.001
Vegetation cover (%)
46.67
37.19
0.04
Distribution of resources
31.44 ± 0.44
39.21 ± 1.21
<0.001
46.67
24.67
<0.001
27.93 ± 0.43
37.77 ± 0.57
<0.001
0.1 ± 0.02
0.14 ± 0.03
0.749
29.42 ± 0.22
24.36 ± 0.25
<0.001
Food Abundance
Abundance of food biomass (g/m2)
Poisson variance to mean ratio of
distance between patches
Predation risk
Overhead vegetation cover (%)
Distance to cover (m)
2
Density of burrow openings (number/m )
Burrowing costs
Soil hardness (Kg/cm2)a
a
2
2
2
2
To convert lbf/in into kg/m we used 1 lbf/in = 0.07031 kg/cm (Pennycuick 1988).
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Table 2. Results of the Partial least squares regression (PLSR) model for most influential variables from three hypotheses explaining
two measures of Andean degu sociality (group size, range area overlap); w COMP 1 and 2 represent the weights of each variable in the
first and second PLSR components, respectively; R2 is the percentage of variance in the response variable accounted for by each
component of the PLSR. PLSR weights whose squares are larger than 0.2 for the significant component are shown in bold.
Ecological predictor variable
Sociality measure
PLSR
Group size
PLSR
Range area overlap
w
w
COMP1
COMP2
w
COMP1
w
COMP2
Distance between patches
0.2875
0.1765
-0.2557
0.1045
Density of vegetation patches
0.4734
-0.0313
-0.4053
0.2234
Density of burrow openings
0.4656
0.6128
-0.1233
0.7933
Distance to cover (m)
0.0899
-0.0996
-0.0925
0.2974
Vegetation cover
-0.1674
0.3062
0.3113
0.4095
Resource Distribution
Predation risk
Burrowing costs
110
Soil harness
0.2678
-0.6752
-0.4911
0.2316
R2 (%)
47.893
27.126
25.34
6.359
P*
* The PLSR model was not significant (P > 0.05).
<0.0001
*
<0.0001
*
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Table 3. Results from regression commonality analysis for ecological variables explaining two measures of sociality (group size, range
area overlap) in the Andean degu. Values in bold indicate independent predictors whose contribution was larger than 20%, when
overall fit of multiple regression model was statistically significant.
Hypothesis
Sociality measures
Group size
Range area overlap
Unique
Common
Total
%R2
Unique
Common
Total
%R2
Distance between patches
0.0550
0.1010
0.1560
17.871
0.0013
0.0157
0.0170
17.172
Density of vegetation patches
0.3528
0.0278
0.3806
43.602
0.0315
0.0454
0.0769
77.677
Burrow density
0.0009
0.2576
0.2585
29.614
0.0121
-0.0030
0.0091
9.191
Distance to cover (m)
0.0174
-0.0172
0.0002
0.023
0.0012
-0.0011
0.0001
0.101
Vegetation cover
0.000
0.1006
0.1006
11.525
0.0000
0.0248
0.0248
25.050
Soil harness
0.2881
-0.2069
0.0812
9.302
0.0034
0.0470
2
Note: Unique, x’s unique effect; Common, Σ x’s common effects; Total = Unique + Common; % of R = Total/R2
0.0504
50.909
Resource Distribution
Predation risk
Burrowing costs
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FIGURE LEGENDS
Figure 1. Mean (± SE) (a) total group size, (b) number of female group members, and (c)
number of male group members of O. gliroides at Oploca population (white squares) and
Chusmiza population (black squares).
Figure 2. Mean (± SE) range area overlap among individuals of same social groups at
Oploca and Chusmiza. Pie graphs on top of the bars are used to compare range area overlap
among individuals from same social groups (white proportion) and overlap among
individuals from different social groups (black proportion). There was no overlap in range
areas among individuals from different social groups at Chusmiza.
113
Figure 1.
Total group size
5
a)
4
3
2
1
0
Chusmiza
Oploca
Localities
Number of females
5
b)
4
3
2
1
0
Oploca
Chusmiza
Localities
Number of males
5
c)
4
3
2
1
0
Oploca
Chusmiza
Localities
114
Figure 2.
Intra-group
home range overlap (%)
100
80
60
40
20
0
Oploca
Chusmiza
Localities
115
CONCLUSIONES GENERALES
116
CONCLUSIONES
Los resultados de esta tesis son los primeros en dilucidar las relaciones filogeográficas,
comportamiento social y ecología del roedor octodóntido, Octodontomys gliroides. Los
resultados indicaron que las poblaciones actuales de O. gliroides presentan una alta
estructuración genética, con tres grupos principales y que son congruentes con las
principales barreras geográficas postuladas para la distribución de esta especie. Tanto los
análisis de diferencias pareadas entre sitios nucleotídicos como las pruebas de neutralidad y
BSP señalan historias contrastantes para cada uno de los tres grupos. Por un lado el grupo I,
representado por poblaciones al Este y Oeste de la cordillera de los Andes (Chile, Bolivia y
Argentina) evidenció una expansión demográfica reciente, la cual estaría asociada a los
periodos secos-húmedos que tuvieron lugar durante el Pleistoceno-Holoceno. Por otro lado,
el grupo II incluyó a las poblaciones del norte-centro de Bolivia, y el grupo III
representando por poblaciones argentinas se caracterizaron por presentar estabilidad
demográfica. Mientras que la historia del Cuaternario para el resto de los octodóntidos ha
sido asociada a eventos de avance y retraída de ultimo máximo glacial (LGM), los
resultados de estas tesis apoyan que la actual distribución de O. gliroides está asociada a
una alternancia de periodos secos y húmedos que tuvieron lugar durante el Pleistoceno en la
Puna Andina. La reconstrucción del escenario biogeográfico más plausible indica que el
nodo de origen de Octodontomys tuvo lugar en un ambiente con características tanto de la
Puna como la Prepuna Andina de hace aproximadamente 2.43 millones de años atrás
(Plesitoceno), a partir del cual esta especie diversificó.
En base a estos resultados, se seleccionaron dos poblaciones genéticamente distintas
sometidas a distinta productividad primaria determinada por un gradiente de precipitación
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Este-Oeste generado por el efecto de “sombra de lluvia” de los Andes. Se determinó y
comparó el grado de sociabilidad entre poblaciones de O. gliroides y cómo una posible
variación en este aspecto del comportamiento este se relaciona con factores ecológicos en
términos de abundancia y distribución de recursos, riesgo de depredación y costos
asociados a cavar madrigueras. Los resultados indicaron que la sociabilidad cuantificada a
partir del tamaño de grupo y porcentaje de solapamiento de espacial entre individuos del
mismo grupo (una medida de cohesión social) no varió significativamente entre las dos
poblaciones estudiadas, a pesar de diferencias marcadas en las condiciones ecológicas entra
ambas. En cambio, se registró una asociación entre diferencias ecológicas y variación en el
tamaño de los grupos dentro de estas poblaciones. En conjunto, los resultados son
consistentes con que la sociabilidad en esta especie evolucionó en respuesta a las
condiciones de aridez en el pasado y que esta se han mantenido relativamente invariable a
lo largo del tiempo a pesar de las diferencias ecológicas entre sus poblaciones actuales.
Finalmente, y tomando en cuenta la posición filogenética de O. gliroides, los resultados de
esta tesis son consistentes con un escenario donde la sociabilidad en la familia
Octodontidae evolucionó temprano en el clado “social”. La verificación de esta hipótesis
requiere de un análisis de reconstrucción del estado ancestral en Octodontidae.
118