Abstract
Biological invasions are one of the greatest existing threats to biodiversity. Invasive species can cause economic and environmental damage. Callithrix penicillata is naturally found in the Brazilian savanna and Caatinga. Its introduced populations have become a conservation problem due to its high occupancy potential, native fauna predation, competition with native primates, congeners and hybridization. We used Species Distribution Modeling (SDM) through the Maxent software in this study in order to identify areas with a higher probability of C. penicillata occurrence. The AUC value was close to 1 (AUC=0.966), with a curve value close to 1. Through the Jackknife test we observed that temperature seasonality was the variable most related to distribution (AUC=0.86), which agrees with other studies that show climatic variables influencing primate distribution. The Atlantic Forest in the Southeast and South regions of Brazil was indicated as susceptible to invasion by C. penicillata. The marmoset C. penicillata has become a successful invader of Atlantic Forest areas, causing depreciation in many native species and other problems. However, biological invasions might be mitigated or even extinguished through successful interventions and management.
Highlights
Biological invasions are responsible for significant environmental alterations and are one of the greatest existing threats to biodiversity [1]
Our goals were to discriminate the actual distribution of the C. penicillata and through the Species Distribution Modeling (SDM) modelling Maxent (Maximum Entropy) software to predict which areas are more probable to invasions by this species and to discuss the ecological relevance of invaded areas, as well as the losses caused by the marmoset in such sites
Our results show that the sites which are more susceptible to C. penicillata invasion outside their likely occurrence area are in the Southeast of the Atlantic Forest (Figure 2)
Summary
Biological invasions are responsible for significant environmental alterations and are one of the greatest existing threats to biodiversity [1]. Species Distribution Modeling (SDM) has become increasingly important for predict biological invasions [3, 4]. Species distribution models have been used in biogeography, conservation, ecological and paleontological studies [5]. Identifying areas that may be successfully occupied by invasive species is one of the greatest challenges when studying biological invasions [1]. Data used to determine the distribution of a species in a given geographical area is usually scarce and incomplete, which hinders conservation and management projects [7]. These projects are only made possible by knowing which areas have already been
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