DOI: 10.5061/dryad.5hqbzkh41
title: Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts

description: Ecological niche models (ENMs) have classically operated under the
 simplifying assumptions that there are no barriers to gene flow, species
 are genetically homogeneous (i.e., no population-specific local
 adaptation), and all individuals share the same niche. Yet, these
 assumptions are violated for most broadly distributed species. Here we
 incorporate genetic data from the widespread riparian tree species
 narrowleaf cottonwood (Populus angustifolia) to examine whether including
 intraspecific genetic variation can alter model performance and
 predictions of climate change impacts. We found that (1) P. angustifolia
 is differentiated into six genetic groups across its range from México to
 Canada, and (2) different populations occupy distinct climate niches
 representing unique ecotypes. Comparing model discriminatory power, (3)
 all genetically-informed ecological niche models (gENMs) outperformed the
 standard species-level ENM (3-14% increase in AUC; 1-23% increase in
 pROC). Furthermore, (4) gENMs predicted large differences among ecotypes
 in both the direction and magnitude of responses to climate change, and
 (5) revealed evidence of niche divergence, particularly for the Eastern
 Rocky Mountain ecotype. (6) Models also predicted progressively increasing
 fragmentation and decreasing overlap between ecotypes. Contact zones are
 often hotspots of diversity that are critical for supporting species’
 capacity to respond to present and future climate change, thus predicted
 reductions in connectivity among ecotypes is of conservation concern. We
 further examined the generality of our findings by comparing our model
 developed for a higher elevation Rocky Mountain species with a related
 desert riparian cottonwood, P. fremontii. Together our results suggest
 that incorporating intraspecific genetic information can improve model
 performance by addressing this important source of variance. gENMs bring
 an evolutionary perspective to niche modeling and provide a truly
 “adaptive management” approach to support conservation genetic management
 of species facing global change.
description: <strong>NL_MSAT_STRUCTURE_756.csv</strong> - Data for
 this study included 756 narrowleaf cottonwood samples collected from 36
 different sampling locations across the species' range from Arizona
 to Alberta, Canada. Each tree was individually GPSed; latitude and
 longitude were recorded in the North American Datum 1983 coordinate system
 (X, Y). Three sampling locations with very small sample sizes, close
 proximity and high genetic identity (&gt;0.9) with neighboring
 collection sites were combined, such that the Site column reflects 33
 sites used to assess population genetic structure. Remaining columns
 indicate diploid microsatellite allele calls for 12 loci originally
 described by Tuskan et al. (2004, 2006). Missing data are indicated as -9.
 We note that site BWR was strongly differentiated and exhited unusually
 high heterozygosity. We did not include this site in subsequent ecological
 niche modeling, and suspect possible introgression from <em>P.
 trichocarpa</em>.  <strong>Occurrence
 records (genetic groups + GBIF).zip - </strong>This folder contains
 .csv files, one for each of the population genetic groups, and
 an <em>AllPops</em> file that contains the complete list of
 occurrence records. Each ecotype is augmented with occurrence reccords
 from the Global Biodiversity Information Facility (GBIF) that exhibited
 &gt;70% probability of belonging to a given genetic group, based on
 spatial population genetic maps developed in Geneland (Guillot et al.
 2005). Data are in the WGS84 coordinate system. 
 <strong>Pruned_gENM_rasters_(continuous).zip
 - </strong>This folder contains continuous ecological niche model
 output generated in MaxEnt (Phillips et al. 2004, 2006). Raster grids are
 included for each of the seven <em>P.
 angustifolia</em> ecotypes (<em>pop1-7</em>) and a
 species-level model (<em>all</em>). Naming conventions are as
 follows: <em>curr</em> refers to baseline climate (30-year
 averages for 1961-1990); <em>a1b</em> and
 <em>a2 </em>indicate niche models based on IPCC AR4 moderate
 and high emissions scenarios, respectively (IPCC
 2007); <em>2050 </em>and <em>2080 </em>refer to
 30-year average climate forecasts for the periods 2040-2069 and 2070-2099,
 respectively. Rasters represent ecological niche model averages based on
 10-fold cross-validation (e.g., each model was run 10X, with a different
 random subset of 10% of occurrence records left out for testing each run).
 For future 2050 and 2080 predictions, model output represents a 5-GCM
 ensemble mean, also derived from 10-fold cross-validation of each of the
 five individual GCMs for each time period (individual model means for each
 of the five GCMs can be found below). MaxEnt model output was pruned using
 the over-prediction correction algorithm of Kremen et al. (2008); this
 served to restrict model output within a biologically meaningful region
 encompassing potential pollen dispersal and gene flow from genetic
 populations. We used the following pruning
 parameters: <em>r </em>= 10<sup>th</sup>
 percentile training presence threshold for each model (Pearson et al.,
 2007), <em>b </em>= 80 grid cell
 buffer, <em>f </em>= 160 grid cell width of fading
 buffer. Rasters are in the WGS84 coordinate system and at ~1km
 resolution.
 <strong>Pruned_Rasters_Threshold_10T.zip
 - </strong>This folder contains binary ENM raster grids, with
 suitable habitat (1) based on the 10<sup>th</sup> percentile
 training presence threshold for each model (Pearson et al., 2007). Naming
 conventions, pruning methodology, and coordinate system (WGS84) are the
 same as described above.
 <strong>Individual_GCM_mean_rasters_(continuous).zip
 - </strong>This folder contains raster .tif files of continuous
 ecological niche model output based on future climate as predicted by five
 different global climate models (GCMs). Whereas the files
 in <strong>Pruned_gENM_rasters_(continuous).zip </strong>above
 represent averages of the 5-GCM ensemble mean, the files included here are
 10-model averages for each of the niche models generated from the five
 different individual GCMs. Future ENMs are based on 30-year average
 climate forecasts for 2070-2099. GCMs are abbreviated as: <em>cnrm
 </em>= CNRM-CM3, <em>csiro </em>=
 CSIRO-MK3.0, <em>mpi </em>=
 ECHAM5-MPI, <em>ncar </em>= NCAR-CCSM3,
 and <em>ukmo </em>= UKMO-HADGEM1. Other naming conventions,
 pruning methodology, and coordinate system (WGS84) are the same as
 described above.
 <strong>Individual_GCM_rasters_Threshold_10T.zip
 - </strong>This folder contains raster .tif files of binary ENM
 output, with suitable habitat (1) based on the 10<sup>th</sup>
 percentile training presence threshold for each model (Pearson et al.,
 2007). Naming conventions, pruning methodology, and coordinate system are
 the same as described above. Suitable habitat rasters ending with
 <em>avg_10T </em>represent the average of 10-fold
 cross-validation for each of the five GCMs and emissions scenario
 (<i>A1B, A2</i>). Files ending
 with <em>summed</em> illustrate where predictions of the five
 GCMs are in agreement or diverge. For example, a value of 1 indicates that
 only a single GCM predicts suitable habitat in a given location by 2080,
 whereas a value of 5 indicates that all five GCMs predict suitable habitat
 for a given location. Regions of high agreement are indicative of low
 future risk, and thus suggest regions where conservation and restoration
 efforts are likely to have high long-term success.
 <strong>Temporal_corridors.zip - </strong> This
 folder contains raster grids of temporal corridors (i.e., climate refugia)
 where habitat is predicted to remain continuously suitable from the
 present through 2099. Naming conventions and pruning methodology are as
 described above with WGS84 coordinate system. These regions are important
 for conservation as they represent relatively low stress areas where trees
 are not expected to have to adapt or migrate away from, but rather may
 remain <em>in situ</em> in the face of climate
 change.