DOI: 10.5061/dryad.r2280gbfm
title: Low elevation species richness and biodiversity in the eastern Mojave desert, Clark County

description: Global loss of biodiversity is a well-known concern for conservationists
 and managers, but detailed spatial maps of local biodiversity for use by
 local managers are often lacking. We used a suite of existing species
 distribution models to calculate spatial variation in low-elevation
 species richness across Clark County, Nevada, USA, comprising much of the
 eastern Mojave Desert. We then used a macroecological model to estimate
 true latent low-elevation biodiversity across the county, correcting for
 potential taxonomic bias in the estimates of species richness. We found
 that species richness and biodiversity tended to be higher along the Muddy
 and Virgin rivers and in the Las Vegas valley. Biodiversity was positively
 associated with flat, rocky landforms, low elevation, late seasonal
 greenup, and lower differences between winter and summer temperatures. We
 present a brief example for local managers to apply the new publicly
 available low-elevation species richness and biodiversity spatial layers.
description: <em>Study area</em> The Mojave
 Desert is the smallest North American desert and is generally arid.
 Precipitation patterns exist along a gradient, with the western Mojave
 drier and experiencing winter rains and the eastern Mojave wetter with
 both winter rain and snow and summer monsoon rains (Germano et al. 1994,
 Keeler-Wolf 2003, Pietrasiak et al. 2014). At low elevations in the
 eastern Mojave Desert, precipitation can average 11 cm per year (Abella et
 al. 2009).  Mojave Desert landforms are typically alluvial fans and
 basins, dominated by the creosote bush (<em>Larrea</em>
 <em>tridentata</em>) and white bursage
 (<em>Ambrosia</em> <em>dumosa</em>) shrub alliance
 at lower elevations, blackbrush (<em>Coleogyne</em>
 <em>ramosissima</em>) shrub communities at middle elevations,
 and pinyon-juniper
 (<em>Pinus</em>-<em>Juniperus</em>) forests at
 higher elevations (Keeler-Wolf 2003, Abella et al. 2009).
 Clark County is the southernmost county in the U.S. state of
 Nevada and encompasses ~20,800 km<sup>2</sup>, covering much
 of the eastern Mojave Desert (Figure 1). Approximately 88.9% is federally
 owned and managed with the remainder being state or private land.
 Elevation ranges from 137 m to 3,634 m (Abella et al. 2009). Riparian
 shrub communities (e.g., <em>Salix</em> spp., invasive
 <em>Tamarix</em> <em>ramosissima</em>) occur along
 the Virgin and Muddy rivers in the northeastern portion of the county.
 Approximately 12,800 km<sup>2</sup> are designated under some
 minimal type of conservation management, such as wilderness areas,
 federally protected lands, military lands, state parks, etc. (Clark County
 Desert Conservation Program, unpublished data). Included within the
 conservation acreage is the Boulder City Conservation Easement (BCCE), a
 353 km<sup>2</sup> area managed by Clark County to protect
 habitat for Mojave desert tortoises and other species and which we used as
 an example of applying the species richness and biodiversity layers
 derived here (Figure 1). Anthropogenic activities on public lands can be
 extensive, including off-road vehicles, shooting, and illegal dumping. As
 one example of quantifying anthropogenic activities, from 1995–2006 an
 average of nine million people annually visited the Lake Mead National
 Recreation Area along the eastern border of Clark County (Figure 1; Abella
 et al. 2009). <em>Conceptual treatment of
 SDMs</em> As of 2018, the Clark County Desert
 Conservation Program had either commissioned or had access to SDMs for 55
 plant, reptile, mammal, avian, and invertebrate species. The list of
 species chosen for deriving SDMs was not comprehensive of all species
 present within Clark County, but was reflective of species occurring at
 low elevations that were most subject to degradation from private land
 development and were covered under the multiple species habitat
 conservation plan. Therefore, the resultant estimates of richness should
 be interpreted as richness of low elevation species, hereafter ‘species
 richness’. Importantly, we then used the species richness layer and
 landscape data to generate a macroecological model generalized to all
 low-elevation species, and hereafter refer to this predictive layer as
 ‘biodiversity’ (Calabrese et al. 2013). <em>Data
 processing</em> Our first step in data processing
 was to classify all SDMs into one of three categories: suitable for use as
 provided (i.e., a continuous estimate of the relative probability of
 occurrence, scaled between zero and one), suitable for use after
 processing (e.g., species density estimates that can be scaled between
 zero and one), or unsuitable for this task (e.g., vector layers with
 qualitative classification of species’ occurrence or a probability raster
 that was only generated for a small portion of Clark County). For SDMs
 with density estimates that were all less than 1, the direct density
 estimate was retained as approximately equivalent to the probability of
 occurrence in that habitat cover type (i.e., low average observed
 densities = low probability of occurrence in a given plot). For density
 estimates that exceeded 1, densities were rescaled to a zero-to-one scale
 by dividing the observed value by the maximum value rounded up to the
 nearest tenth place to ensure all values were less than 1. For ‘suitable
 after processing’ SDMs we assumed that within-species variation in density
 was a proxy variable for variation in probability of occurrence.
 Simple spatial re-projection, raster snapping, or raster
 resampling were applied where necessary to standardize all of the SDMs to
 the same resolution (i.e., 250m x 250m), raster origin coordinates, and
 coordinate system (i.e., NAD83 UTM Z11N). All NoData cells were assigned a
 value of 0 prior to stacking. Some SDMs were resampled to a higher
 resolution (250m) than that of the original SDM (1km). In practice, this
 meant that a grid cell that was previously 1km x 1km with a single raster
 value now was comprised of four grid cells, each 250m x 250m, and each
 with the same original raster value. In terms of spatial accuracy, this
 falsely implies a higher resolution at the level of the individual SDM,
 but we decided it was acceptable to do at the level of the SSDM because it
 allowed them to accommodate the resolution of the 250m SDMs. This approach
 maximized inclusion of all information from the highest-resolution SDMs.
 Some of the avian SDMs were generated at an even higher resolution than
 250m x 250m. These were resampled to a 250m x 250m resolution taking the
 maximum value within the window to reflect the highest probability that a
 species was observed within the larger window. After processing, all SDMs
 were a continuous, probabilistic probability of species occurrence, or
 p-SDM (Calabrese et al. 2013). <em>Stacking
 p-SDMs and macroecological model</em> Next we
 ‘stacked’ all of the p-SDMs together via summation over overlapping grid
 cells, yielding a continuous index of species richness (a p-SSDM; Figure
 2), a method that has been found to be robust and accurate when compared
 with validation data (Calabrese et al. 2013, Zellmer et al. 2019, Zurrell
 et al. 2020). The stacked p-SSDM is useful in that it retains information
 on the input species, but there is a potential for bias in the p-SSDM with
 regards to true biodiversity depending on the level of latent bias in
 which species were chosen for creation of the individual SDMs (Distler et
 al. 2015). For example, if only reptile species were used for the species
 richness model, that model may not represent biodiversity of plants. To
 resolve this we conducted a second analysis, whereby we built a
 macroecological model that sought general rules in the eastern Mojave
 Desert driving species assemblages (Calabrese et al. 2013, Distler et al.
 2015). We used linear regression to model the p-SSDM species index as a
 function of environmental predictors. The environmental predictor
 variables were chosen from the six most important variables identified by
 Inman et al. (2014) in a study on SDMs for 15 species across the Mojave
 Ecoregion. The five predictor variables used in this macroecological model
 were elevation, seasonal thermal difference (i.e., the difference between
 winter and summer surface temperatures), surface texture (i.e., an index
 of sandy versus rocky surface), topographic position (i.e., high values
 equal valley bottom while low values equal mountain or ridge top), and
 seasonal greenness timing (i.e., the average date of maximum vegetation
 greenness; Table 1). Winter precipitation was a sixth environmental
 predictor variable initially included. However, winter precipitation was
 highly correlated with elevation (r = 0.863) and was excluded from further
 analysis because previous work has shown elevation to be a primary driver
 of regional variation in biodiversity (Mateo et al. 2012).
 We then generated a systematic grid of sample points 2.25 km
 apart from each other to sample the p-SSDM species richness raster and the
 environmental predictor variable rasters while ensuring even coverage
 across the county and minimizing spatial autocorrelation. After removing
 points from the sample grid that fell within ‘disturbed’ areas (i.e.,
 graded or paved construction as determined from aerial imagery analysis,
 L. Bice unpublished data) there were 3,827 sample points. We then used
 linear regression in Program R v.3.5 (R Core Team 2018) to derive the
 macroecological statistical model. Finally, we used the coefficients from
 the statistical model in Raster Calculator in ArcGIS 10.4 (ESRI, Inc.) to
 create the continuous biodiversity predictive surface. All environmental
 raster layers were available at a resolution of 1 km, so the output
 macroecological layer is at this resolution as well.