Search for: All records

Abstract Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles (Chelonia mydas). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence‐only, presence‐absence, mark‐recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa. 

Abstract Invasive species provide powerful in situ experimental systems for studying evolution in response to selective pressures in novel habitats. While research has shown that phenotypic evolution can occur rapidly in nature, few examples exist of genomewide adaptation on short “ecological” timescales. Burmese pythons (Python molurus bivittatus) have become a successful and impactful invasive species in Florida over the last 30 years despite major freeze events that caused high python mortality. We sampled Florida Burmese pythons before and after a major freeze event in 2010 and found evidence for directional selection in genomic regions enriched for genes associated with thermosensation, behaviour and physiology. Several of these genes are linked to regenerative organ growth, an adaptive response that modulates organ size and function with feeding and fasting in pythons. Independent histological and functional genomic data sets provide additional layers of support for a contemporary shift in invasive Burmese python physiology. In the Florida population, a shift towards maintaining an active digestive system may be driven by the fitness benefits of maintaining higher metabolic rates and body temperature during freeze events. Our results suggest that a synergistic interaction between ecological and climatic selection pressures has driven adaptation in Florida Burmese pythons, demonstrating the often‐overlooked potential of rapid adaptation to influence the success of invasive species.