Accounting for geographic variation in species‐habitat associations during habitat suitability modeling

Abstract

Range‐wide species conservation efforts are facilitated by spatially explicit estimates of habitat suitability. However, species‐environment relationships often vary geographically and models assuming geographically constant relationships may result in misleading inferences. We present the first range‐wide habitat suitability model (HSM) for the federally threatened eastern indigo snake (Drymarchon couperi) as a case study illustrating an approach to account for known latitudinal variation in habitat associations. Specifically, we modeled habitat suitability using interactive relationships between minimum winter temperature and several a priori environmental covariates and compared our results to those from models assuming geographically constant relationships. We found that multi‐scale models including interactive effects with winter temperature outperformed single‐scale models and models not including interactive effects with winter temperature. Our top‐ranked model had suitable range‐wide predictive performance and identified numerous large (i.e., ≥1000 ha) potential habitat patches throughout the indigo snake range. Predictive performance was greatest in southern Georgia and northern Florida likely reflecting more restrictive indigo snake habitat associations in these regions. This study illustrates how modeling interactive effects between temperature and environmental covariates can improve the performance of HSMs across geographically varying environmental gradients.

Modeling a hot, dry future: Substantial range reductions in suitable environment projected under climate change for a semiarid riparian predator guild

“…Part of our predictor selection process included using a priori information from expertise and relevant literature [ 92 ]. Several predictors identified herein, e.g., maximum summer and minimum winter temperature, have worked well for SDMs of snakes in similar latitudes or regions, including congenerics and conspecifics [ 53 , 100 – 104 ]. Overall, our high performance evaluation scores suggest that our selected variables and ensemble modeling process adequately reflect aridland Thamnophis -environmental relationships.…”

A cross-regional analysis of red-backed shrike responses to agri-environmental schemes in Europe

“…Our results showed that the positive effect of AES on shrike occurrence probability diminished at high levels of structural landscape complexity (SHRUBS), though at varying degrees across regions. Differences in the environmental subniches among shrike populations may be the cause (Chandler et al 2022). The red-backed shrike is considered a farmland species throughout Europe, but has been shown to inhabit Relationships were graphed for each predictor with all other covariates held at their medians.…”

Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake

“…1), excluding areas west of Mobile Bay, Alabama (e.g., southern Mississippi, Enge et al 2013; U. S. Fish and Wildlife Service 2019). The habitat suitability model we used was developed over the approximate contemporary distribution of DRCO in south-central and southeast Georgia and peninsular Florida as described in Chandler et al (2022). Our study area included a diversity of natural vegetation communities, including xeric sandhill and scrub, mesic pine flatwoods, hardwood hammocks, coastal hammocks and dunes, and forested and non-forested wetlands.…”

“…Furthermore, defining conservation units solely on genetic data may fail to prioritize local, yet important, populations (Taylor and Dizon 1999). Chandler et al (2022) published a range-wide multi-scale habitat suitability model for DRCO that accounts for known latitudinal differences in habitat associations (Hyslop et al 2014;, which could be used to define inter-connected habitat-based conservation units.…”

“…Our objectives were to: (1) use the habitat suitability model of Chandler et al (2022) and empirical DRCO movement data to calibrate a resistance surface and resistant kernel reflecting annual nondispersing movements of adult DRCO, (2) use resistant kernels to create a range-wide DRCO connectivity surface, (3) threshold this connectivity surface to identify potential corridors and delineate habitatbased conservation units, and (4) compare our habitat-based conservation to the population units identified in the DRCO species status assessment (SSA; U. S. Fish and Wildlife Service 2019).…”