Biodiversity and Conservation

, Volume 19, Issue 9, pp 2651–2666 | Cite as

Potential loss of genetic variability despite well established network of reserves: the case of the Iberian endemic lizard Lacerta schreiberi

  • Dennis RödderEmail author
  • Ulrich Schulte
Original Paper


Although future anthropogenic climate change is recognized as one of the major threats to European species, its implementation during reserve planning has only been started recently. We here describe climate change impacts on the Iberian endemic lizard Lacerta schreiberi expecting serious declines and range reductions due to a loss of suitable climate space in the next future. We apply species distribution models to assess possible future changes in the lizard’s range, identify areas with high extinction risk meriting conservation efforts and analyze whether the Natura 2000 network in its current stage will offer a sufficient protection for the genetically most valuable lineages. Despite a very good coverage and connectivity of the most valuable populations of L. schreiberi with the existing protected sites network, our results predict a strong loss of genetic variability by 2080. Also, two main patterns become evident: While the genetically less diverse north-western populations may be less affected by climate change, the climate change effects on the southern isolates and the genetically most diverse populations within the Central System may be devastating. To improve a successful prospective conservation of L. schreiberi the management of protected sites needs to consider the processes that threaten this species. Furthermore, our study highlights the urgent need to consider climate change effects on evolutionary significant units within the Natura 2000 framework.


Lacerta schreiberi BIOMOD Climate change Extinction risk Iberian Peninsula Species distribution models 



Interngovernmental Panel on Climate Change


Species distribution model


Maximum temperature of the warmest month


Minimum temperature of the coldest month


Precipitation of the wettest month


Precipitation of the driest month


Precipitation seasonality


Area under the receiver operating characteristic curve


True skills statistics


Artificial neural networks


Classification tree analysis


Generalized additive models


Generalized boosting models


Generalized linear models


Multivariate adaptive regression splines


Mixture discriminant analysis


Random forests


Surface range envelopes



This work benefited from a grant of the ‘Forschungsinitiative’ of the Ministry of Education, Science, Youth and Culture of the Rhineland-Palatinate state of Germany ‘Die Folgen des Global Change für Bioressourcen, Gesetzgebung und Standardsetzung’ as well as a grant of the ‘Deutsche Bundesstiftung Umwelt’ (DBU). Two anonymous referees kindly improved the manuscript.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of BiogeographyTrier UniversityTrierGermany

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