Projecting invasion risk of non-native watersnakes (Nerodia fasciata and Nerodia sipedon) in the western United States

The Phylogenomic and Biogeographic History of the Gartersnakes, Watersnakes, and Allies (Natricidae: Thamnophiini)

North American Thamnophiini (gartersnakes, watersnakes, brownsnakes, and swampsnakes) are an ecologically and phenotypically diverse temperate clade of snakes representing 61 species across 10 genera. In this study, we estimate phylogenetic trees using ∼3,700 ultraconserved elements (UCEs) for 76 specimens representing 75% of all Thamnophiini species. We infer phylogenies using multispecies coalescent methods and time calibrate them using the fossil record. We also conducted ancestral area estimation to identify how major biogeographic boundaries in North America affect broadscale diversification in the group. While most nodes exhibited strong statistical support, analysis of concordant data across gene trees reveals substantial heterogeneity. Ancestral area estimation demonstrated that the genus Thamnophis was the only taxon in this subfamily to cross the Western Continental Divide, even as other taxa dispersed southward toward the tropics. Additionally, levels of gene tree discordance are overall higher in transition zones between bioregions, including the Rocky Mountains. Therefore, the Western Continental Divide may be a significant transition zone structuring the diversification of Thamnophiini during the Neogene and Pleistocene. Here we show that despite high levels of discordance across gene trees, we were able to infer a highly resolved and well-supported phylogeny for Thamnophiini, which allows us to understand broadscale patterns of diversity and biogeography.

Using citizen science data to identify the sensitivity of species to human land use

Conservation practitioners must contend with an increasing array of threats that affect biodiversity. Citizen scientists can provide timely and expansive information for addressing these threats across large scales, but their data may contain sampling biases. We used randomization procedures to account for possible sampling biases in opportunistically reported citizen science data to identify species' sensitivities to human land use. We analyzed 21,044 records of 143 native reptile and amphibian species reported to the Carolina Herp Atlas from North Carolina and South Carolina between 1 January 1990 and 12 July 2014. Sensitive species significantly associated with natural landscapes were 3.4 times more likely to be legally protected or treated as of conservation concern by state resource agencies than less sensitive species significantly associated with human-dominated landscapes. Many of the species significantly associated with natural landscapes occurred primarily in habitats that had been nearly eradicated or otherwise altered in the Carolinas, including isolated wetlands, longleaf pine savannas, and Appalachian forests. Rare species with few reports were more likely to be associated with natural landscapes and 3.2 times more likely to be legally protected or treated as of conservation concern than species with at least 20 reported occurrences. Our results suggest that opportunistically reported citizen science data can be used to identify sensitive species and that species currently restricted primarily to natural landscapes are likely at greatest risk of decline from future losses of natural habitat. Our approach demonstrates the usefulness of citizen science data in prioritizing conservation and in helping practitioners address species declines and extinctions at large extents.

Simulation of potential habitat overlap between red deer (Cervus elaphus) and roe deer (Capreolus capreolus) in northeastern China

Background. Understanding species distribution, especially areas of overlapping habitat between sympatric species, is essential for informing conservation through natural habitat protection. New protection strategies should simultaneously consider conservation efforts for multiple species that exist within the same landscape, which requires studies that include habitat overlap analysis.

Methods. We estimated the potential habitat of cervids, which are typical ungulates in northern China, using the present locations of red deer (Cervus elaphus; N = 90) and roe deer (Capreolus capreolus; N = 106) in a Maximum Entropy (MaxEnt) model. Our study area was a human-dominated landscape in the Tieli Forestry Bureau located at the southern slope of the Lesser Xing’an Mountains. We grouped 17 environmental predictor variables into five predictor classes (terrain, habitat accessibility, land cover, vegetation feature, and interference), which were used to build habitat suitability models.

Results. Habitat accessibility and human interferences were found to have the strongest influence on habitat suitability among the five variable classes. Among the environmental factors, distance to farmland (26.8%), distance to bush-grass land (14.6%), elevation (13.5%), and distance to water source (12.2%) were most important for red deer, distance to farmland (22.9%), distance to settlement (21.4%), elevation (11.6%), and coverage of shrub-grass (8%) were most important for roe deer. Model accuracy was high for both species (mean area under the curve (AUC) = 0.936 for red deer and 0.924 for roe deer). The overlapping habitat comprised 89.93 km² within the study area, which occupied 94% of potentially suitable habitat for red deer and 27% for roe deer.

Conclusions. In terms of habitat suitability, roe deer showed greater selectivity than red deer. The overlapping habitat was mostly located in the eastern mountains. The southwestern plain was not a suitable habitat for deer because it was close to Tieli City. Regarding management measures, we suggest that priority protection should be given to the potential areas of overlapping deer habitats found in this study.

SDMdata: A Web-Based Software Tool for Collecting Species Occurrence Records

It is important to easily and efficiently obtain high quality species distribution data for predicting the potential distribution of species using species distribution models (SDMs). There is a need for a powerful software tool to automatically or semi-automatically assist in identifying and correcting errors. Here, we use Python to develop a web-based software tool (SDMdata) to easily collect occurrence data from the Global Biodiversity Information Facility (GBIF) and check species names and the accuracy of coordinates (latitude and longitude). It is an open source software (GNU Affero General Public License/AGPL licensed) allowing anyone to access and manipulate the source code. SDMdata is available online free of charge from <http://www.sdmserialsoftware.org/sdmdata/>.