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Assessing abundance-suitability models to prioritize conservation areas for the dwarf caimans in South America
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  • Andres L. Rodriguez-Cordero,
  • Sergio Balaguera-Reina,
  • Brandon Gross,
  • Margaret Munn,
  • Llewellyn Densmore
Andres L. Rodriguez-Cordero
Texas Tech University

Corresponding Author:[email protected]

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Sergio Balaguera-Reina
University of Florida
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Brandon Gross
Texas Tech University
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Margaret Munn
Texas Tech University
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Llewellyn Densmore
Texas Tech University
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Abstract

Species-environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species’ ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance-suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models - GLM, Generalized Additive Models - GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define 4 categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non-consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM-SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns.
09 Aug 2024Submitted to Ecology and Evolution
12 Aug 2024Submission Checks Completed
12 Aug 2024Assigned to Editor
12 Aug 2024Review(s) Completed, Editorial Evaluation Pending
14 Aug 2024Editorial Decision: Accept