Assessing the robustness of networks of spatial genetic variation

Habitat transformation is one of the leading drivers of biodiversity loss. The ecological effects of this trans- formation have mainly been addressed at the demographic level, for example, finding extinction thresholds. However, interpopulation genetic variability and the subsequent potential for ad...

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Detalles Bibliográficos
Autores: Albert, Eva M., Fortuna, Miguel A., Godoy, José A., Bascompte, Jordi
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/77436
Acceso en línea:http://hdl.handle.net/10261/77436
Access Level:acceso abierto
Palabra clave:Ecological networks
Amphibian metapopulation
Modularity
resilience
Alytes dickhilleni
Descripción
Sumario:Habitat transformation is one of the leading drivers of biodiversity loss. The ecological effects of this trans- formation have mainly been addressed at the demographic level, for example, finding extinction thresholds. However, interpopulation genetic variability and the subsequent potential for adaptation can be eroded before effects are noticed on species abundances. To what degree this is the case has been difficult to eval- uate, partly because of the lack of both spatially extended genetic data and an appropriate framework to map and analyse such data. Here, we extend recent work on the analysis of networks of spatial genetic var- iation to address the robustness of these networks in the face of perturbations. We illustrate the potential of this framework using the case study of an amphibian metapopulation. Our results show that while the disappearance of some spatial sites barely changes the modular structure of the genetic network, other sites have a much stronger effect. Interestingly, these consequences can not be anticipated using topological, sta- tic measures. Mapping these networks of spatial genetic variation will allow identifying significant evolution- ary units and how they vanish, merge and reorganise following perturbations.