Designing optimized multi-species monitoring networks to detect range shifts driven by climate change: a case study with bats in the North of Portugal.
Here we develop a framework to design multi-species monitoring networks using species distribution models and conservation planning tools to optimize the location of monitoring stations to detect potential range shifts driven by climate change. For this study, we focused on seven bat species in Northern Portugal (Western Europe). Maximum entropy modelling was used to predict the likely occurrence of those species under present and future climatic conditions. By comparing present and future predicted distributions, we identified areas where each species is likely to gain, lose or maintain suitable climatic space. We then used a decision support tool (the Marxan software) to design three optimized monitoring networks considering: a) changes in species likely occurrence, b) species conservation status, and c) level of volunteer commitment. For present climatic conditions, species distribution models revealed that areas suitable for most species occur in the north-eastern part of the region. However, areas predicted to become climatically suitable in the future shifted towards west. The three simulated monitoring networks, adaptable for an unpredictable volunteer commitment, included 28, 54 and 110 sampling locations respectively, distributed across the study area and covering the potential full range of conditions where species range shifts may occur. Our results show that our framework outperforms the traditional approach that only considers current species ranges, in allocating monitoring stations distributed across different categories of predicted shifts in species distributions. This study presents a straightforward framework to design monitoring schemes aimed specifically at testing hypotheses about where and when species ranges may shift with climatic changes, while also ensuring surveillance of general population trends.
CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Vairão, Portugal.
Francisco
F
Amorim
CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Vairão, Portugal.
Sílvia B
SB
Carvalho
CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Vairão, Portugal ; Department of Biology, Faculty of Sciences of the University of Porto, Porto, Portugal.
João
J
Honrado
CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Vairão, Portugal ; School of Biological Sciences, University of Bristol, Bristol, United Kingdom.
Hugo
H
Rebelo
D063147:Animal Distribution / Q000502:physiology*; D000818:Animals; D002685:Chiroptera / Q000502:physiology*; D057231:Climate Change; D003198:Computer Simulation; D003247:Conservation of Natural Resources / Q000379:methods*; D003661:Decision Support Techniques; D064308:Ecological Parameter Monitoring / Q000379:methods*; D008954:Models, Biological; D011157:Population Dynamics; D011174:Portugal; D013045:Species Specificity