<?xml version="1.0" encoding="UTF-8"?>
<STUDY_SET xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
  <STUDY accession="ERP111357" alias="mesc_spa" center_name="Institute of Aquaculture, University of Stirling, Stirling, UK">
    <IDENTIFIERS>
      <PRIMARY_ID>ERP111357</PRIMARY_ID>
      <EXTERNAL_ID namespace="BioProject">PRJEB29086</EXTERNAL_ID>
      <SUBMITTER_ID namespace="IOA">mesc_spa</SUBMITTER_ID>
      <SUBMITTER_ID namespace="Institute of Aquaculture, University of Stirling, Stirling, UK">mesc_spa</SUBMITTER_ID>
    </IDENTIFIERS>
    <DESCRIPTOR>
      <STUDY_TITLE>Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections</STUDY_TITLE>
      <STUDY_TYPE existing_study_type="Other"/>
      <STUDY_ABSTRACT>Local adaptations can determine the potential of populations to respond to environmental changes, yet adaptive genetic variation is commonly ignored in models forecasting species vulnerability and biogeographical shifts under future climate change. Here we integrate genomic and ecological modelling approaches to identify genetic adaptations associated with climate in two cryptic forest bats. We then incorporate this information directly into forecasts of range changes under future climate change and assessment of population persistence through the spread of climate adaptive genetic variation (evolutionary rescue potential). Considering climate adaptive potential reduced range loss projections, suggesting that failure to account for intraspecific variability can result in overestimation of future losses. On the other hand, range overlap between species was projected to increase, indicating that interspecific competition is likely to play an important role in limiting species future ranges. We show that although evolutionary rescue is possible, it depends on population adaptive capacity and connectivity. Hence, we stress the importance of incorporating genomic data and landscape connectivity in climate change vulnerability assessments and conservation management.</STUDY_ABSTRACT>
      <CENTER_PROJECT_NAME>mesc_spa</CENTER_PROJECT_NAME>
      <STUDY_DESCRIPTION>Local adaptations can determine the potential of populations to respond to environmental changes, yet adaptive genetic variation is commonly ignored in models forecasting species vulnerability and biogeographical shifts under future climate change. Here we integrate genomic and ecological modelling approaches to identify genetic adaptations associated with climate in two cryptic forest bats. We then incorporate this information directly into forecasts of range changes under future climate change and assessment of population persistence through the spread of climate adaptive genetic variation (evolutionary rescue potential). Considering climate adaptive potential reduced range loss projections, suggesting that failure to account for intraspecific variability can result in overestimation of future losses. On the other hand, range overlap between species was projected to increase, indicating that interspecific competition is likely to play an important role in limiting species future ranges. We show that although evolutionary rescue is possible, it depends on population adaptive capacity and connectivity. Hence, we stress the importance of incorporating genomic data and landscape connectivity in climate change vulnerability assessments and conservation management.</STUDY_DESCRIPTION>
    </DESCRIPTOR>
    <STUDY_LINKS>
      <STUDY_LINK>
        <XREF_LINK>
          <DB>PUBMED</DB>
          <ID>31061126</ID>
        </XREF_LINK>
      </STUDY_LINK>
    </STUDY_LINKS>
    <STUDY_ATTRIBUTES>
      <STUDY_ATTRIBUTE>
        <TAG>species</TAG>
        <VALUE>Myotis escalerai, Myotis crypticus</VALUE>
      </STUDY_ATTRIBUTE>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-FIRST-PUBLIC</TAG>
        <VALUE>2019-04-11</VALUE>
      </STUDY_ATTRIBUTE>
      <STUDY_ATTRIBUTE>
        <TAG>ENA-LAST-UPDATE</TAG>
        <VALUE>2019-06-10</VALUE>
      </STUDY_ATTRIBUTE>
    </STUDY_ATTRIBUTES>
  </STUDY>
</STUDY_SET>
