10.1016/j.ecolmodel.2017.06.006

10.1016/j.ecolmodel.2017.06.006 Article history: We develop a new perspective on the uncertainties affecting the predictions of coastal species distribu- Received 12 September 2016 tions using patterns of genetic diversity to assess the congruence of hindcasted distribution models. We Received in revised form 27 May 2017 model the niche of the subtidal seagrass Cymodocea nodosa, for which previous phylogeographic findings Accepted 7 June 2017 are used to contrast hypotheses for the Last Glacial Maximum (LGM) in the Mediterranean and adjacent Available online 14 June 2017 Atlantic coastal regions. We focus on amelioration of sampling bias, and explore the influence of other sources of uncertainty such as the number of variables, Ocean General Circulation Models (OGCMs), Keywords: and thresholds used. To do that, we test geographical and environmental filtering of presences, and a Ecological niche modelling species-specific weighted filter related to political boundaries for background data. Contrary to our ini- Genetic diversity tial hypothesis that reducing sampling bias by means of geographical, environmental or background Last glacial maximum filtering would enhance predictive power and reliability of the models, none of these approaches consis- Ocean general circulation models Sampling bias tently improved performance. These counter-intuitive results might be explained by the higher relative Threshold http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

10.1016/j.ecolmodel.2017.06.006

Elsevier — Jun 11, 2020

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Abstract

Article history: We develop a new perspective on the uncertainties affecting the predictions of coastal species distribu- Received 12 September 2016 tions using patterns of genetic diversity to assess the congruence of hindcasted distribution models. We Received in revised form 27 May 2017 model the niche of the subtidal seagrass Cymodocea nodosa, for which previous phylogeographic findings Accepted 7 June 2017 are used to contrast hypotheses for the Last Glacial Maximum (LGM) in the Mediterranean and adjacent Available online 14 June 2017 Atlantic coastal regions. We focus on amelioration of sampling bias, and explore the influence of other sources of uncertainty such as the number of variables, Ocean General Circulation Models (OGCMs), Keywords: and thresholds used. To do that, we test geographical and environmental filtering of presences, and a Ecological niche modelling species-specific weighted filter related to political boundaries for background data. Contrary to our ini- Genetic diversity tial hypothesis that reducing sampling bias by means of geographical, environmental or background Last glacial maximum filtering would enhance predictive power and reliability of the models, none of these approaches consis- Ocean general circulation models Sampling bias tently improved performance. These counter-intuitive results might be explained by the higher relative Threshold

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