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

Original Paper

Abstract

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.

Keywords

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

Abbreviations

IPCC

Interngovernmental Panel on Climate Change

SDM

Species distribution model

BIO5

Maximum temperature of the warmest month

BIO6

Minimum temperature of the coldest month

BIO13

Precipitation of the wettest month

BIO14

Precipitation of the driest month

BIO15

Precipitation seasonality

AUC

Area under the receiver operating characteristic curve

TSS

True skills statistics

ANN

Artificial neural networks

CTA

Classification tree analysis

GAM

Generalized additive models

GBM

Generalized boosting models

GLM

Generalized linear models

MARS

Multivariate adaptive regression splines

MDA

Mixture discriminant analysis

RF

Random forests

SRE

Surface range envelopes

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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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