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Translating large‐scale prioritization models for vultures to local‐scale decision‐making: response to Santangeli et al. 2019
Author(s) -
Efrat Ron,
Hatzofe Ohad,
BergerTal Oded
Publication year - 2020
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/cobi.13557
Subject(s) - desert (philosophy) , prioritization , geography , ecology , archaeology , scale (ratio) , library science , political science , biology , cartography , engineering , law , management science , computer science
Santangeli et al. (2019) present an extensive analysis that aims to answer one of the most important questions in conservation: where should we target conservation efforts? They try to answer this question of resource allocation for Old World vulture conservation, a group of species of great importance because of their unique functional role in most ecosystems, significance to many human cultures, and recent rapid decline worldwide (Markandya et al. 2008; Ogada et al. 2012; O’Bryan et al. 2018). A prioritization map for the conservation of Old World vultures could assist ongoing conservation efforts directed at halting, and even reversing, these species declines internationally. Furthermore, as demonstrated by the authors, a global analysis is important for this group because of the shared threats throughout most of its global distribution. For these reasons, we highly appreciate Santangeli et al.’s work, and believe it is of great importance to the conservation of Old World vultures. However, such large-scale analyses typically require generalizations that exclude relevant data for smaller scale conservation efforts. We believe it is of crucial importance to be aware of the assumptions and limitations of such generalizations when attempting to implement large-scale prioritization maps at the local scale. We acknowledge that “all models are wrong, some are useful” (Box 1979) and that when performing largescale analyses, it is impossible to consider all factors that are relevant for the entire region (Mouquet et al. 2015). Therefore, our goal is not to look for flaws in the original model, but rather to complement it with notes regarding

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