Florida, U.S.A. is a hotspot of biological invasions with over 500 non-native species reported. Reptiles encompass the majority of non-native wildlife with over 50 species established, many of which are sympatric and are identified as invasive due to their impacts to the environment, economy, and human health and safety. Reports of new non-native reptiles occur and many established non-native reptiles continue to expand their ranges in Florida, increasing the need for multi-taxa detection and monitoring capabilities. Invasive constrictor snakes are a primary focus of management efforts due to life history traits that favor successful establishment and dispersal in Florida as well as their impacts to native wildlife and Everglades restoration efforts. While traditional survey methods that rely on visual detections fail to reliably detect invasive constrictors, environmental DNA (eDNA) has proven to be a promising method for detection of cryptic and rare species across the landscape. To address emerging needs for multi-species detection and monitoring in Florida we developed the first tetraplex dPCR assay designed for detection of four species of invasive constrictor snakes, including Burmese pythons (Python bivittatus), northern African pythons (P. sebae), boa constrictors (Boa constrictor), and rainbow boas (Epicrates cenchria). In this tetraplex assay, no cross-amplification across species was documented. This assay serves as a valuable tool for faster and more accurate monitoring efforts of these invasive species in south Florida.

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.