Assessing abundance-suitability models to prioritize conservation areas
for the dwarf caimans in South America
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.