DOI: 10.5061/dryad.dfn2z357c
title: A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA

description: Understanding the suitable habitat of endangered species is crucial for
 agencies such as the Bureau of Land Management to plan management and
 conservation. However, few species distribution models are directly
 validated, potentially limiting their application. In preparation for a
 Species Status Assessment of clay‐loving wild buckwheat (Eriogonum
 pelinophilum), an endangered subshrub found in southwest Colorado, we ran
 a series of species distribution models to estimate the species'
 potential occupied habitat and validated these models in the field. A
 1‐meter resolution digital elevation model derived from LiDAR and a
 high‐resolution geology mapping helped identify biologically relevant
 characteristics of the species' habitat. We employed a weighted
 ensemble model based on two Random Forest and one Boosted Regression Tree
 model, and the discrimination performance of the ensemble model was high
 (AUC-PR = 0.793). We then conducted a systematic field survey of model
 habitat suitability predictions, during which we discovered 55 new
 subpopulations of the species and demonstrated that new species
 observations were strongly associated with model predictions
 (p &lt; .0001, Cliff's delta = 0.575). We then further refined
 our original models by incorporating the additional species occurrences
 collected in the field survey, a new explanatory variable, and a more
 diverse set of models. These iterative changes to the model marginally
 improved performance (AUC‐PR = 0.825). Direct validation of species
 distribution models is extremely rare, and our field survey provides
 strong validation of our model results. This helps increase confidence in
 utilizing predictions in planning. The final model predictions greatly
 improve the Bureau of Land Management's understanding of the
 species' habitat and increase our ability to consider potential
 habitat in planning land use activities such as road development and
 travel management.
description: This dataset includes LiDAR data collected in Delta and Montrose
 counties in Colorado, United States. A digital elevation model (DEM) from
 this LiDAR collection is included here. Slope, eastness, and northness
 derived from the DEM are also included. This dataset was available at 1-m
 resolution. A dataset of geologic mapping was included.
 Only one geologic stratum, the Smoky Hill member of the Mancos Shale
 formation (formation 'kms'), was relevant for this analysis. We
 selected only this stratum, then calculated a buffer of distance from it.
 We transformed this into four classes: 0 to 1 meters from the formation, 1
 to 10 meters from it, 10 to 100 meters away, and farther than 100 meters.
 This buffer was then rasterized at 1-m resolution.
 30-year normal precipitation data (1991-2020) from PRISM are
 included too (PRISM Climate Group. 2016. PRISM climate data. Oregon State
 University). Originally available at 800-m resolution, this was resampled
 to 1-m resolution by nearest neighbor interpolation. A
 clear Landsat scene from the study area was utilized to calculate the soil
 color index. This was calculated using the Red and Green bands as follows:
 (Red - Green) / (Red + Green). Originally available at 30-m resolution,
 this was resampled to 1-m resolution by nearest neighbor
 interpolation. Points of species presence used in the
 analysis are provided, but have had a 1-km buffer applied to them because
 the species of interest is an endangered species in an accessible area
 with recreation and other land uses that may threaten the
 species.
description: # Clay-loving wild buckwheat (Eriogonum pelinophilum), BLM Uncompahgre
 Field Office This dataset includes environmental covariates and buffered
 species presence observations of clay-loving wild buckwheat (Eriogonum
 pelinophilum), an endangered species in Western Colorado, USA. This data
 was used to run a series of species distribution models and ensemble
 models to better distinguish the species' suitable habitat. After
 running our first set of models, we then conducted a field survey using
 the model results to search for new species occurrences. We found 55 new
 subpopulations in this survey. With the new species observations and a new
 environmental covariate, we hypothesized would improve the model (soil
 color index), we then ran a second set of species distribution models.
 This improved model performance. ## Description of the data and file
 structure The data provided here are mainly raster datasets of the
 environmental covariates used in modeling. Due to the large size of these
 files, each covariate was split into four geographic sections for
 analysis. Therefore, each covariate has four instances (eg: eastness_1,
 eastness_2, eastness_3, and eastness_4). Buffered points of species
 presence are also provided in two zip files. One zip file includes the
 presence points used in the original modeling effort, and one includes
 points used in the final modeling effort. ## Sharing/Access information
 Data was derived from the following sources: * LiDAR mapping of the study
 area * Geology mapping of the study area * Precipitation data from PRISM *
 Soil color index derived from a Landsat scene ## Code/Software All
 analysis was carried out in R 4.1.2.