ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

Abstract

24Species distribution modeling is a valuable tool with many applications across ecology and 25 evolutionary biology. The selection of biologically meaningful environmental variables that 26 determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 27 bioclimatic variables from WorldClim are frequently employed, primarily because they are 28 easily accessible and available globally for past, present and future climate scenarios. Yet, the 29 availability of relatively few other comparable environmental datasets potentially limits our 30 ability to select appropriate variables that will most successfully characterize a species' 31 distribution. We identified a set of 16 climatic and two topographic variables in the literature, 32which we call the ENVIREM dataset, many of which are likely to have direct relevance to 33 ecological or physiological processes determining species distributions. We generated this set 34 of variables at the same resolutions as WorldClim, for the present, mid-Holocene, and Last 35Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed 36 whether including the ENVIREM variables led to improved species distribution models 37 compared to models using only the existing WorldClim variables. We found that including the 38 ENVIREM dataset in the pool of variables to select from led to substantial improvements in 39 niche modeling performance in 17 out of 20 species. We also show that, when comparing 40 models constructed with different environmental variables, differences in projected 41 distributions were often greater in the LGM than in the present. These variables are worth 42 consideration in species distribution modeling applications, especially as many of the 43 variables have direct links to processes important for species ecology. We provide these 44 variables for download at multiple resolutions and for several time periods at 45 3 envirem.github.io. Furthermore, we have written the 'envirem' R package to facilitate the 46 generation of these variables from other input datasets. 47 48 49

Predicting range shifts of Asian elephants under global change

“…We also included various climatic factors and their seasonal variation (Hijmans, Cameron, Parra, Jones, & Jarvis, ; Title & Bemmels, ; Supporting information Table ). We obtained a total of 115 predictor variables: 60 climatic, 16 human disturbance, 29 forest and vegetation and 10 topographic variables.…”

Seasonal occurrence and abundance of dabbling ducks across the continental United States: Joint spatio‐temporal modelling for the GenusAnas

“…Both the NLCD and Cropland datasets were categorical rasters at 30‐m resolution. A topographical roughness index, a topographical wetness index and a continentality raster (30 arc‐seconds continuous rasters) from the ENVIREM dataset (Title & Bemmels, ) were adopted to model topographical heterogeneity across the study domain. Indices for topographical roughness and wetness were intended to help differentiate wetlands and surface waters from other landscape components while continentality (average coldest month temperature subtracted from average warmest month temperature in °C) aided in distinguishing between terrestrial and marine climate regimes (Sayre, Comer, Warner, & Cress, ).…”

Known and estimated distribution in Mexico of Batrachochytrium dendrobatidis, a pathogenic fungus of amphibians

“…Using 100 random repetitions, we excluded the points that were separated by a distance of <10 km, keeping as many records as possible. This distance was selected to reduce the associated geographic bias, as a function of the heterogeneity of the study area and the performance values reported in the literature (Anderson & Raza, ; Boria et al., ; Galante et al., ; Title & Bemmels, ). After filtering, we obtained 48 presence records that we used to construct the model.…”

“…Given that there is evidence that the impact of human activities contributes to the prevalence and distribution of the pathogen (Adams et al., ; James et al., ; O'Hanlon et al., ), we included a layer corresponding to the ecological footprint of human activities (Sanderson et al., ). The ENVIREM set of climate variables was also included, as they notably improve the performance of the models (Title & Bemmels, ). These layers correspond to potential evapotranspiration (PET), which is a measure of the loss of water from the land's surface via evaporation and plant transpiration for a certain area over a specific period of time.…”