Abstract
The combination of ecosystem stressors, rapid climate change, and increasing landscape‐scale development has necessitated active restoration across large tracts of disturbed habitats in the arid southwestern United States. In this context, programmatic directives such as the National Seed Strategy for Rehabilitation and Restoration have increasingly emphasized improved restoration practices that promote resilient, diverse plant communities, and enhance native seed reserves. While decision‐support tools have been implemented to support genetic diversity by guiding seed transfer decisions based on patterns in local adaptation, less emphasis has been placed on identifying priority seed mixes composed of native species assemblages. Well‐designed seed mixes can provide foundational ecosystem services including resilience to disturbance, resistance to invasive species, plant canopy structure to facilitate natural seedling recruitment, and habitat to support wildlife and pollinator communities. Drawing from a newly developed dataset of species distribution models for priority native plant taxa in the Mojave Desert, we created a novel decision support tool by pairing spatial predictions of species habitat with a database of key species traits including life history, flowering characteristics, pollinator relationships, and propagation methods. This publicly available web application, Mojave Seed Menus, helps restoration practitioners generate customized seed mixes for native plant restoration in the Mojave Desert based on project locations. Our application forms part of an integrated Mojave Desert restoration program designed to help practitioners identify species to include in local seed mixes and nursery stock development while accounting for local adaptation by identifying appropriate seed source locations from key restoration species.
Keywords: decision support tool, Mojave Desert, native plants, priority species list, restoration, seed mix, Shiny application, species distribution model
We describe an integrated decision‐support framework for native plant restoration in the Mojave Desert, including online applications available through the USGS and BLM Mojave Desert Native Plant Program. With this framework, restoration practitioners and resource managers can create seed mixes for restoration sites and identify appropriate seed sources using two complimentary web applications, Mojave Seed Menus and Climate Distance Mapper.
1. INTRODUCTION
Restoring degraded environments to diverse and resilient ecosystems is a fundamental conservation goal, but one that is increasingly challenging due to accelerating human development and rapid climate change across much of the globe. Evidence suggests that diversity is key to ecosystem stability and ability to withstand novel stressors (Isbell et al., 2015; Tilman & Downing, 1994). This pattern holds at multiple levels of organization from the regional to the plant community and genotypic levels (Oliver et al., 2015). Awareness of the fundamental role of diverse, connected ecosystems has resulted in a paradigm shift in restoration ecology, from previous efforts tailored for rapid soil stability and erosion control (e.g., use of cultivars or soil‐stabilizing species from outside regions) to native plant materials development programs aimed at increasing local seed reserves, promoting genetic diversity, and minimizing risk from long‐distance seed transfer (Kettenring et al., 2014; Oldfield & Olwell, 2015; Olwell & Riibe, 2016). Substantial challenges remain for restoration practitioners seeking to apply these principles across a variety of disturbed environments.
The desert ecoregions of the southwestern United States are particularly challenging environments to restore (Lovich & Bainbridge, 1999). These ecoregions are increasingly threatened by stressors such as climate change, which is creating a hotter and drier climate and may shift seasonal precipitation patterns (Dai, 2013; IPCC, 2013), putting local ecotypes at a phenological disadvantage (Kimball et al., 2010). Moreover, widespread invasions of annual grass species (e.g., Bromus tectorum and B. madritensis) have altered shrubland communities across the southwestern United States and contributed to wildfires unprecedented in size and frequency (Brooks et al., 2004; D’Antonio & Vitousek, 1992). Disturbance impacts in deserts are also compounded by the notoriously slow pace of plant community recovery (Engel & Abella, 2011; Webb & Newman, 1982). Recruitment and establishment of desert shrubland plants occurs largely during infrequent resource pulses, with little regeneration outside of these periods (Chesson et al., 2004). Moreover, many desert woody species do not readily resprout after wildfire or surface disturbance (Abella, 2009), and resprouting does not guarantee survival following disturbance (DeFalco et al., 2010). Instead, replenishment of soil seed banks by seeds dispersing from intact areas depends largely on seasonal precipitation pulses that favor reproduction (Bamberg et al., 1976; Meyer & Pendleton, 2015). Persistent soil seed banks have evolved bet‐hedging strategies to circumvent reproductive failure (Angert et al., 2009), yet seedling recruitment often fails because disturbance to the soil surface diminishes seed banks (DeFalco et al., 2009; Esque, Young, et al., 2010) and reduces shrub cover for wildlife and nurse plants that facilitate establishment of native seedlings (Brown & Minnich, 1986; Cave & Patten, 1984), particularly in the presence of invasive species (Esque, Kaye, et al., 2010). In coming decades, the footprint of landscape‐scale disturbance is likely to increase across the southwestern United States, in part due to planned utility‐scale renewable energy development (Bureau of Land Management & U.S. Department of Energy, 2015; Hernandez et al., 2014). Hence, there is a clear need for effective restoration strategies that overcome ecosystem stressors in this region and promote healthy, diverse, and resilient landscapes.
Seeding efforts in the desert southwest have often had limited success (Knutson et al., 2014), even while the frequency and scale of such treatments have increased concomitantly with a shift toward the use of native species (Copeland et al., 2018). Recently, national programs such as the National Seed Strategy (Olwell & Riibe, 2016), Seeds of Success (Haidet & Olwell, 2015), and the National Strategy to Promote the Health of Honeybees and Other Pollinators (Vilsack & McCarthy, 2015) have funded efforts to put “the right seed in the right place at the right time” and supported the development of diverse native seed reserves, along with improved restoration techniques. For example, the Bureau of Land Management (BLM) Mojave Desert Native Plant Program has taken a multi‐faceted approach that uses science to discriminate among best restoration techniques, identify priority restoration species (Esque et al., 2021) and plant functional groups (Shryock et al., 2014), and develop seed transfer zones using landscape genomics and common garden studies (Shryock et al., 2017). However, a topic that has received less emphasis, but has a large potential impact, is the development of geographically appropriate seed mixes that promote diverse native species assemblages. Well‐designed seed mixes can promote community resilience by restoring diversity (Isbell et al., 2015) and functional traits (Balazs et al., 2020), resisting competitive pressure from invasive species (Abella et al., 2012), and providing essential cover and forage for wildlife (Esque et al., 2021). Moreover, custom seed mixes can be tailored toward restoring plant‐pollinator associations in denuded areas, as these relationships are critical to ecosystem function (Bucharova et al., 2021) and support biodiversity across trophic levels (Burghardt & Tallamy, 2013).
Thus far, a key element missing from the restoration practitioner's toolbox is an accessible decision‐support tool that incorporates species trait information and habitat requirements in a geographical context, such that restoration practitioners can easily create species lists – or “seed menus” – based on restoration project locations (but see M’Gonigle et al., 2017). A well‐crafted seed menu can predict suitable species based on their habitat distribution and the environmental characteristics of a restoration site, while also providing species attribute information so that practitioners can emphasize functional traits, pollinator diversity, rapid growth, or other species characteristics in their restoration designs. Although seed menus help to identify suitable native species, one complication is that they do not account for local adaptation. Most desert species are adapted to a particular set of environmental conditions at the population level (Baughman et al., 2019) including climate, soil characteristics, and pollinator associations. Introduction of maladapted genotypes into local populations can have negative consequences such as outbreeding depression or reproductive failure (Hufford & Mazer, 2003; McKay et al., 2005). However, decision‐support tools exist to guide seed transfer decisions in the Mojave and elsewhere (Shryock et al., 2018). We propose an integrated workflow that includes tools to select and prioritize species for a given restoration site as well as to identify appropriate seed sources from each species to account for local adaptation or facilitate alternative seed sourcing designs such as genetic admixture (Broadhurst et al., 2008) or predictive seed sourcing/assisted migration (Breed et al., 2013).
Recently, in partnership with the Bureau of Land Management's Mojave Desert Native Plant Program, Esque et al. (2021) developed a priority native plant species list (hereafter “Mojave PSL”) for the Mojave Desert based on a variety of species traits and wildlife services. This list establishes species targets for large‐scale seed collection programs (e.g., the National Seed Strategy, Seeds of Success) to prioritize the development of native plant materials and seed reserves for future restoration needs in the Mojave. Here, we extend the utility of the Mojave PSL by providing a spatially explicit decision support tool that generates seed menus for project sites in the Mojave Desert. Our new application, Mojave Seed Menus, draws from presence‐only species distribution models (hereafter SDMs) to predict suitable habitat for 49 species from the Mojave PSL. These models predict where species are likely to occur based on climate, topography, or other natural features associated with species occurrence records. By spatially stacking SDMs, we generate interactive lists of priority plant species for any given location within the Mojave. Moreover, Mojave Seed Menus pairs habitat predictions with species attribute information from the Mojave PSL, including life‐history, bloom and flowering traits, pollinator associations, propagation techniques, importance as forage or cover for the Mojave desert tortoise (Gopherus agassizii), and response to disturbance (Esque et al., 2021). We describe how this novel spatial decision‐support tool can be used to create detailed seed menus for Mojave restoration projects, as well as integrate with existing tools for genetically informed seed transfer designs.
2. METHODS
2.1. Study site
The Mojave Desert spans approximately 150,000 km2 in the southwestern United States. This warm desert ecoregion is characterized by north to south trending mountain ranges and interlaying basins (MacMahon, 1988). Elevations range from below sea level in Death Valley to over 3000 m in the Panamint Range and Spring Mountains. Alluvial fans and washes form along mid to lower elevation slopes and contribute to the accumulation of fine particles and salinity in lower basins, forming playas in closed basins. Annual precipitation varies along elevational gradients but averages approximately 135 mm, with much of this occurring during the winter months (Hereford et al., 2006). However, summer precipitation increases along a longitudinal gradient, with higher quantities recorded in the eastern Mojave due to summer tropical storms. As with precipitation, temperatures vary along elevation gradients and range from <0°C in winter to over 50°C in summer at low elevations. Mean annual temperature is approximately 17°C.
2.2. Study species
We selected 49 species for SDMs (Appendix S1) based on their restoration importance and inclusion in the recent Mojave PSL. The species selected here for habitat modeling are a subset of those included in the full Mojave PSL, but include representatives from different growth forms and lifespans, as well as foundational species. Selected species promote overall community recovery from disturbance by providing favorable microsites and attracting animals to increase diversity, such as creosote bush (Larrea tridentata) and the Joshua tree (Yucca brevifolia and Y. jaegeriana) (Hurd & Linsley, 1975; Miller & Stebbins, 1975; respectively).
2.3. Environmental variables
We derived 14 environmental variables to serve as covariates in SDMs, which together characterize climate, topography, plant canopy, and soil surface properties for the Mojave Desert (Table 1). Precipitation and temperature were extracted at collection sites using ClimateNA v. 6.2 (Wang et al., 2016), which downscales PRISM data (Daly et al., 2008) and corrects for elevational variation. Satellite metrics incorporated plant canopy and soil surface data from the moderate‐resolution imaging spectroradiometer (MODIS) satellite averaged across a minimum of ten years (NDVI amplitude and maximum – USGS eMODIS Remote Sensing Phenology, https://doi.org/10.5066/F7PC30G1; other metrics – Inman et al., 2014). Topographic metrics were calculated by aggregating a 30 m digital elevation model at a 1 km2 resolution for modeling (National Elevation Dataset, https://www.usgs.gov/programs/national‐geospatial‐program/national‐map).
TABLE 1.
Environmental variable | Code | Definition |
---|---|---|
Climate | ||
Summer precipitation (mm) | SP | Average precipitation received from May to Oct |
Winter precipitation (mm) | WP | Average precipitation received from Nov to April |
Summer maximum temperature (°C) | T max | Maximum temperature of warmest month |
Winter minimum temperature (°C) | T min | Minimum temperature of coldest month |
Annual temperature range (°C) | T range | Average of the monthly temperature ranges (monthly maximum minus monthly minimum) |
Annual heat/moisture index | AHM | (MAT + 10)/(MAP/1000) |
Climatic moisture deficit | CMD | Difference between potential evapotranspiration (PET) and actual evapotranspiration (AET; Dobrowski et al., 2013) |
Satellite metrics | ||
NDVI amplitude | AMP | Maximum increase in canopy photosynthetic activity above the baseline averaged for the period 2003–2017 |
NDVI maximum | MAXN | Maximum level of photosynthetic activity during the growing season averaged for the period 2003–2017 |
Soil water stress | SWS | Mean of the Shortwave and Infrared Water Stress Index (SIWSI; Fensholt & Sandholt, 2003) from 2001–2010 (Inman et al., 2014). |
Surface texture | ATI | Difference in mean daytime and nighttime surface temperatures for 2001–2010 (Inman et al.,2014) |
Topography | ||
Heat load index | HLI | Aspect/slope transformation index (McCune & Keon, 2002) representing the range in heat load from coolest (northeast slope) to warmest (southwest slope) |
Slope (°) | Slope | Slope in degrees |
Topographic position index | TPI | Steady‐state wetness index expressed as a function of slope and upstream contributing area |
2.4. Species distribution modeling
We used an ensemble modeling approach to create SDMs for 49 native plant species throughout their Mojave Desert ranges. We used a custom R script to control pseudo‐absence selection and model evaluation and to implement parallel processing and model‐averaged response curves. As input data for the SDMs, we assembled species occurrence records from a variety of sources including public databases (Consortium of California Herbaria – http://ucjeps.berkeley.edu/consortium/; SEInet – https://swbiodiversity.org/seinet/), vegetation classification studies (National Park Service vegetation inventory products, https://www.nps.gov/im/vmi‐products.htm), U.S. Bureau of Land Management Seeds of Success collections, and U. S. Geological Survey datasets (Webb et al., 2003). Prior to modeling, all occurrences were visually assessed for georeferencing errors and masked from water bodies. Additionally, we excluded occurrences with positional uncertainty larger than 1 km when noted in the metadata. Occurrences for each species are mapped in Appendix S1.
Our ensemble modeling approach included three algorithms: generalized additive models (R package “mgcv” version 1.8–22; Wood, 2017), random forests (R package “randomForest” version 4.6–12; Liaw & Wiener, 2002), and MaxEnt version 3.3.3k (as implemented in R package “dismo” version 1.1–4; Hijmans et al., 2017; Phillips et al., 2006). We chose to average predictions across different types of algorithms because the choice of algorithm is the largest source of variability in SDM predictions (Watling et al., 2015) and because multi‐model ensembles broaden the types of response functions that can be identified (Araújo & New, 2007). For each individual algorithm, we generated models reflecting all combinations of the 14 environmental variables (Table 1) while restricting the total number of terms within any one model to six to avoid overfitting. Correlated variables (r > |0.7|) were not included in the same models. Due to the lack of surveyed absence points, we created random selections of pseudo‐absences following the recommendations in Barbet‐Massin et al. (2012) for each algorithm. To account for patterns of spatial aggregation/unequal sampling effort in the presence points, which can bias model predictions (Veloz, 2009), we first rasterized presences to the modeling resolution (1 km2) and subsequently applied a spatial thinning procedure (grid sampling) in which a maximum of three points could be sampled from any 10 km2 area (Fourcade et al., 2014). Each model was fit across a series of 50 cross‐validation runs, with each run consisting of a random sample of pseudo‐absences and spatially thinned presence points. For each cross‐validation, a random 20% sample of points was withheld for model evaluation. All GAM models were fit with restricted maximum likelihood (REML) and an extra penalty allowing smooth terms to be penalized to zero (“gam” option select=TRUE in “mgcv” package) to aid model selection. Random forest models were fit with 1000 random trees. MaxEnt models were fit with 10,000 pseudo absences and program defaults.
We considered several metrics of model prediction accuracy to select a candidate list of approximately 10 well‐performing models for each algorithm (30 total candidate models): AUC (i.e., the area under the receiver operating characteristic; Fielding & Bell, 1997), the Boyce Index (Hirzel et al., 2006), and the True Skill Statistic (TSS; Allouche et al., 2006). For GAM and MaxEnt models, we also calculated each model's average AIC (with each model being fit to the same subsets of data) to help identify well‐performing, parsimonious models. AIC values for Maxent models were calculated using the “ENMeval” package in R (Muscarella et al., 2014), which follows the approach developed by Warren and Seifert (2011). To aid model interpretation, we derived relative importance values for each predictor present in the candidate models for each algorithm (Appendix S1). Maxent relative importance values were based on the default permutation importance output for each predictor (Phillips et al., 2006). Relative importance for predictors in random forest models was based on the mean decrease in accuracy from permutations leaving out each term (“importance” function in the R package randomForest; Liaw & Wiener, 2002). For GAM, we used the predictor's average expected degrees of freedom (edf) across all candidate GAM models in which the predictor appeared as the measure of relative importance. We also derived partial variable response curves for each of the top nine predictors present in the candidate models for each species. These curves indicate the shape and direction of relationships between predictors and habitat probability values. For GAM and Maxent models, response curve functions for predictors were averaged across all of the models in which each predictor occurred: in Appendix S1, these model‐averaged curves are overlaid on the individual response curves from candidate models including each predictor. For random forest models, we used the default response curves (“partialPlot” function) fitted to a model with the top nine predictors.
Raster surfaces representing SDM predictions from each model were generated by averaging model predictions across the 50 cross‐validation runs (all surfaces were generated with the “predict” function of the R package “raster”). Next, ensemble predictions for individual algorithms were generated by taking the weighted average among candidate model predictions for each algorithm based on TSS scores, resulting in three ensemble algorithm predictions. For each species, we also calculated a standard error layer based on variation across all candidate models included in the ensemble. Finally, an overall ensemble SDM prediction was generated by taking the average of the three individual algorithm ensembles.
2.4.1. Evaluation of systematic model bias
In largely unpopulated regions of the Mojave Desert, species occurrence records may be biased toward areas with easier human access (e.g., near roads or other developed features), and a pattern of unequal sampling could bias SDM model performance and evaluation (Fourcade et al., 2014; Veloz, 2009). Although our use of occurrence records from vegetation classification and other research studies may partially alleviate this issue, we sought to evaluate systematic spatial sampling bias. To do so, we used a 1 km2 resolution terrestrial development index created for the Western United States (Carr & Leinwand, 2020; Carter et al., 2017) to derive a spatial layer reflecting distance from roads and other developed features. Next, we created spatial layers reflecting the overall mean of the habitat probabilities across all 49 individual species SDMs, as well as the mean of the standard error layers for each species (hereafter referred to as “aggregated habitat probabilities” and “aggregated model standard errors”, respectively). We then assessed whether the aggregated spatial patterns in SDM habitat probabilities and/or model standard errors were associated with distances to developed features, as might occur if there were strong systematic bias in the model suite. To allow for non‐linear associations, we fit generalized additive models in the R package “mgcv” (Wood, 2017) with the default thinplate splines and evaluated models based on these models’ coefficients of determination.
2.5. Mojave Seed Menus application
We developed an interactive spatial decision support tool, Mojave Seed Menus, as a “shiny application” coded using the R package “shiny” v.1.5.0, which generates interactive web pages or dashboards paired with the analytical capabilities of R (Chang et al., 2020). Our application also supports an interactive online map generated using the leaflet package (Cheng et al., 2019) for dynamic user input. The core function of Mojave Seed Menus is to overlay SDMs for species of restoration importance and extract their habitat probability values (probabilities range from 0 to 1, with higher values indicating higher probability of occurrence) based on user input coordinates provided as spreadsheets, shapefiles, or map clicks. Species habitat values for each potential project site are paired with species attribute values from the Mojave PSL, including life‐history, disturbance ecology, pollinator interactions, and propagation techniques. The application outputs a downloadable “seed menu” table with species predicted to have suitable habitat at a given restoration site(s), along with each species’ attribute information. The application also makes available the entire species guide presented in Esque et al. (2021). Used in combination with other restoration tools, e.g., provisional seed transfer zones or climate distance projections (Shryock et al., 2018), the Mojave Seed Menus application presents a powerful new tool for restoration practitioners.
3. RESULTS
3.1. Species distribution models
Our ensemble modeling approach produced SDMs that performed well on average, with AUC ranging from a low of 0.82 for Ambrosia dumosa to a high of 0.97 for Lupinus odoratus, and averaging 0.88 across all species (Table 2). Somewhat counterintuitively, we obtained lower AUC scores for several of the most common species including A. dumosa and Larrea tridentata. However, this is likely due to these species having particularly broad ranges within the Mojave Desert mapping extent, such that random pseudoabsences would more frequently fall within suitable habitat than for species inhabiting a narrower range of conditions. A complete set of species maps is available in Appendix S1, while habitat layers are provided both within Mojave Seed Menus and as a separate U.S. Geological Survey data release (Shryock et al., 2022b).
TABLE 2.
Species | n | Model performance | Relative importance of model terms | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AUC | TSS | AHM | CMD | WP | SP | T max | T min | T range | HLI | Slope | TPI | Text | AMP/MAXN b | SWS | ||
Cacti | ||||||||||||||||
Echinocereus engelmannii | 505 | 0.901 | 0.699 | 13.68 | 3.71 | 7.13 | 7.60 | 7.69 | 5.53 | 14.24 | 0.00 | 0.91 | 9.92 | 14.60 | 10.80 | 4.17 |
Opuntia basilaris | 967 | 0.852 | 0.571 | 5.82 | 11.81 | 9.25 | 7.14 | 5.56 | 8.60 | 12.28 | 0.00 | 8.96 | 10.25 | 14.66 | 2.17 | 3.52 |
Forbs a | ||||||||||||||||
Acmispon humistratus | 196 | 0.888 | 0.678 | 6.95 | 7.04 | 2.22 | 2.08 | 12.99 | 13.60 | 11.50 | 0.48 | 6.55 | 9.00 | 0.46 | 27.14 | 0.00 |
Acmispon strigosus | 352 | 0.902 | 0.677 | 7.72 | 3.70 | 14.98 | 14.82 | 8.09 | 10.58 | 18.85 | 0.00 | 6.95 | 5.50 | 3.60 | 5.21 | 0.00 |
Amsinckia tessellata | 889 | 0.828 | 0.538 | 9.82 | 0.00 | 7.21 | 17.46 | 9.68 | 12.69 | 10.35 | 0.94 | 3.78 | 7.13 | 4.65 | 14.95 | 1.33 |
Asclepias erosa | 108 | 0.894 | 0.733 | 8.26 | 2.62 | 15.25 | 7.85 | 5.79 | 16.67 | 14.05 | 6.93 | 11.43 | 2.16 | 7.56 | 0.81 | 0.64 |
Astragalus didymocarpus | 152 | 0.884 | 0.694 | 9.48 | 0.96 | 6.79 | 22.75 | 17.84 | 12.29 | 7.65 | 3.93 | 0.75 | 5.29 | 8.86 | 2.69 | 0.71 |
Astragalus layneae | 168 | 0.922 | 0.76 | 18.14 | 3.20 | 4.96 | 16.89 | 22.43 | 1.07 | 3.54 | 0.64 | 3.89 | 9.66 | 6.78 | 4.35 | 4.45 |
Baileya multiradiata | 242 | 0.902 | 0.688 | 8.40 | 7.58 | 5.64 | 29.67 | 8.11 | 12.56 | 6.78 | 1.67 | 3.19 | 9.29 | 4.92 | 2.19 | 0.00 |
Chylismia brevipes | 806 | 0.858 | 0.611 | 14.38 | 4.99 | 10.89 | 21.51 | 6.00 | 7.00 | 5.18 | 0.48 | 6.27 | 4.91 | 13.35 | 3.09 | 1.94 |
Cryptantha micrantha | 513 | 0.857 | 0.594 | 8.87 | 3.39 | 16.92 | 10.92 | 8.45 | 12.80 | 13.95 | 0.55 | 3.23 | 6.97 | 6.17 | 7.79 | 0.00 |
Cryptantha nevadensis | 880 | 0.837 | 0.549 | 8.24 | 6.12 | 13.61 | 22.87 | 4.55 | 10.99 | 7.58 | 1.09 | 9.67 | 7.35 | 0.00 | 6.12 | 1.81 |
Descurainia pinnata | 740 | 0.86 | 0.602 | 5.91 | 4.92 | 13.29 | 8.08 | 6.53 | 11.00 | 16.63 | 0.19 | 7.62 | 6.49 | 5.28 | 14.08 | 0.00 |
Eriogonum inflatum | 1395 | 0.858 | 0.584 | 6.51 | 7.45 | 7.71 | 10.35 | 6.50 | 6.27 | 5.79 | 1.61 | 21.25 | 6.28 | 6.88 | 4.99 | 8.40 |
Euphorbia albomarginata | 484 | 0.886 | 0.659 | 8.83 | 4.09 | 14.55 | 8.76 | 9.70 | 14.06 | 19.35 | 4.15 | 2.98 | 3.94 | 3.67 | 3.27 | 2.65 |
Lepidium lasiocarpum | 1210 | 0.863 | 0.606 | 3.11 | 11.16 | 5.87 | 19.39 | 8.81 | 11.87 | 13.00 | 2.79 | 3.48 | 2.86 | 7.96 | 9.70 | 0.00 |
Lupinus odoratus | 45 | 0.97 | 0.919 | 9.97 | 5.68 | 13.09 | 22.51 | 14.41 | 0.00 | 0.00 | 0.00 | 7.93 | 9.52 | 16.88 | 0.00 | 0.00 |
Malacothrix glabrata | 744 | 0.837 | 0.554 | 14.33 | 3.47 | 14.26 | 13.97 | 5.72 | 7.97 | 11.64 | 1.73 | 2.87 | 10.59 | 3.88 | 5.89 | 3.67 |
Mirabilis laevis | 341 | 0.893 | 0.669 | 6.93 | 10.60 | 3.57 | 13.25 | 11.70 | 0.48 | 18.76 | 0.00 | 10.92 | 1.82 | 14.05 | 5.09 | 2.83 |
Oenothera cespitosa | 94 | 0.944 | 0.814 | 6.59 | 1.75 | 14.37 | 10.15 | 27.20 | 8.74 | 6.74 | 1.32 | 4.12 | 3.58 | 15.43 | 0.00 | 0.00 |
Oenothera deltoides | 299 | 0.916 | 0.759 | 11.81 | 12.21 | 5.02 | 11.51 | 5.35 | 18.49 | 3.07 | 12.84 | 7.71 | 4.91 | 1.39 | 4.89 | 0.80 |
Oenothera primiveris | 88 | 0.898 | 0.701 | 2.96 | 20.82 | 4.50 | 9.05 | 8.20 | 12.68 | 2.09 | 3.21 | 13.77 | 15.97 | 2.98 | 3.09 | 0.69 |
Penstemon palmeri | 203 | 0.922 | 0.775 | 5.84 | 3.46 | 7.77 | 30.51 | 10.27 | 5.51 | 0.00 | 1.59 | 6.39 | 9.91 | 16.49 | 2.27 | 0.00 |
Plantago ovata | 1208 | 0.862 | 0.588 | 10.68 | 6.81 | 6.34 | 7.59 | 22.63 | 10.58 | 4.39 | 2.06 | 5.70 | 9.93 | 6.70 | 2.36 | 4.23 |
Salvia columbariae | 687 | 0.864 | 0.596 | 7.31 | 3.59 | 5.38 | 14.98 | 11.18 | 10.62 | 12.13 | 1.67 | 10.80 | 3.73 | 7.94 | 8.55 | 2.12 |
Sphaeralcea ambigua | 1458 | 0.831 | 0.528 | 16.55 | 3.30 | 5.23 | 13.02 | 12.92 | 3.76 | 12.18 | 1.44 | 0.63 | 8.12 | 12.55 | 2.82 | 7.48 |
Stephanomeria exigua | 222 | 0.899 | 0.712 | 1.32 | 4.81 | 16.84 | 10.56 | 5.41 | 6.57 | 28.60 | 0.88 | 3.32 | 15.71 | 1.76 | 3.48 | 0.74 |
Grasses | ||||||||||||||||
Achnatherum hymenoides | 530 | 0.836 | 0.57 | 8.76 | 4.19 | 5.13 | 4.38 | 10.80 | 16.22 | 17.96 | 4.05 | 5.65 | 11.21 | 6.32 | 3.99 | 1.34 |
Hilaria rigida | 658 | 0.871 | 0.611 | 5.22 | 11.10 | 7.86 | 14.76 | 7.19 | 11.47 | 15.20 | 1.77 | 6.22 | 2.57 | 4.93 | 9.89 | 1.82 |
Muhlenbergia porteri | 418 | 0.915 | 0.733 | 11.46 | 7.01 | 5.96 | 23.61 | 7.65 | 10.56 | 16.40 | 0.00 | 0.79 | 0.32 | 7.19 | 8.25 | 0.78 |
Vulpia octoflora | 532 | 0.868 | 0.605 | 3.35 | 8.94 | 9.35 | 9.21 | 9.90 | 5.30 | 12.15 | 4.54 | 16.65 | 13.49 | 1.43 | 0.00 | 5.68 |
Shrubs | ||||||||||||||||
Ambrosia dumosa | 2524 | 0.822 | 0.496 | 21.45 | 6.53 | 7.71 | 15.43 | 5.98 | 4.60 | 8.27 | 2.61 | 7.62 | 9.30 | 4.44 | 6.07 | 0.00 |
Ambrosia salsola | 1280 | 0.831 | 0.531 | 16.83 | 6.40 | 11.18 | 13.55 | 9.85 | 4.54 | 10.11 | 0.27 | 3.43 | 10.90 | 4.85 | 8.09 | 0.00 |
Atriplex hymenelytra | 305 | 0.92 | 0.742 | 0.00 | 5.51 | 0.83 | 19.42 | 17.60 | 10.90 | 12.11 | 0.77 | 13.77 | 2.92 | 4.86 | 7.26 | 4.05 |
Encelia farinosa | 892 | 0.896 | 0.659 | 0.00 | 14.74 | 3.56 | 9.66 | 5.97 | 9.23 | 6.93 | 2.49 | 11.54 | 6.00 | 22.34 | 4.87 | 2.66 |
Ephedra nevadensis | 1972 | 0.84 | 0.557 | 15.78 | 5.72 | 9.41 | 9.26 | 13.06 | 7.21 | 13.40 | 1.16 | 7.51 | 6.10 | 3.83 | 5.54 | 2.02 |
Ericameria cooperi | 433 | 0.921 | 0.727 | 10.48 | 0.00 | 7.19 | 14.46 | 24.13 | 7.02 | 10.56 | 1.45 | 4.79 | 4.76 | 3.59 | 8.96 | 2.62 |
Eriogonum fasciculatum | 1098 | 0.872 | 0.6 | 14.07 | 4.52 | 9.87 | 9.59 | 10.14 | 3.36 | 10.40 | 4.62 | 8.85 | 3.28 | 10.31 | 9.33 | 1.66 |
Krameria bicolor | 272 | 0.92 | 0.732 | 3.75 | 13.60 | 9.45 | 8.08 | 7.26 | 12.26 | 12.89 | 1.10 | 9.29 | 3.39 | 18.93 | 0.00 | 0.00 |
Krameria erecta | 798 | 0.885 | 0.637 | 8.25 | 12.89 | 5.38 | 14.47 | 6.84 | 11.69 | 8.83 | 0.00 | 8.17 | 3.86 | 8.76 | 10.86 | 0.00 |
Larrea tridentata | 2337 | 0.833 | 0.521 | 17.52 | 8.65 | 11.66 | 14.03 | 7.49 | 4.01 | 3.10 | 1.82 | 7.85 | 10.97 | 4.78 | 8.12 | 0.00 |
Lycium andersonii | 653 | 0.861 | 0.583 | 4.76 | 6.20 | 11.35 | 17.20 | 11.12 | 9.70 | 19.77 | 1.41 | 0.84 | 1.49 | 4.12 | 6.28 | 5.77 |
Lycium cooperi | 306 | 0.88 | 0.638 | 10.85 | 12.39 | 1.83 | 14.40 | 19.09 | 11.44 | 14.18 | 0.51 | 5.41 | 6.01 | 0.00 | 3.89 | 0.00 |
Lycium pallidum | 131 | 0.902 | 0.698 | 1.30 | 24.08 | 11.61 | 14.27 | 1.06 | 14.66 | 10.34 | 1.37 | 4.01 | 2.23 | 7.16 | 0.00 | 7.91 |
Psorothamnus fremontii | 444 | 0.894 | 0.672 | 9.24 | 4.83 | 10.46 | 16.23 | 12.72 | 11.90 | 9.40 | 0.75 | 12.51 | 6.26 | 3.39 | 0.00 | 2.31 |
Stephanomeria parryi | 100 | 0.926 | 0.815 | 8.29 | 3.34 | 5.07 | 19.68 | 21.26 | 0.00 | 20.69 | 0.00 | 8.14 | 2.14 | 5.65 | 2.31 | 3.42 |
Trees/Arborescents | ||||||||||||||||
Chilopsis linearis | 330 | 0.9 | 0.688 | 0.00 | 20.18 | 19.65 | 4.70 | 5.16 | 4.60 | 4.47 | 1.72 | 0.72 | 8.84 | 19.62 | 6.20 | 4.13 |
Yucca brevifolia c | 1633 | 0.875 | 0.622 | 10.11 | 3.70 | 5.09 | 10.52 | 8.11 | 12.64 | 15.24 | 0.16 | 6.97 | 9.27 | 4.10 | 4.61 | 9.49 |
Yucca schidigera | 1123 | 0.884 | 0.668 | 10.26 | 7.38 | 5.82 | 15.88 | 6.16 | 7.76 | 11.81 | 0.00 | 11.91 | 3.79 | 13.13 | 6.09 | 0.00 |
Average | 0.881 | 0.652 | 8.78 | 7.17 | 8.74 | 13.84 | 10.46 | 9.06 | 11.11 | 1.73 | 6.89 | 6.73 | 7.62 | 5.68 | 2.20 |
Perennial forbs are in bold.
MAXN was used to represent canopy photosynthetic activity in models including perennial species, while AMP was used to represent vegetation green‐up potential in models including annual species.
This species has two recognized varieties in the ITIS, var. brevifolia and var. jaegeriana, of which the former occurs predominantly in the western Mojave Desert and the latter in the eastern Mojave, with limited transitional areas in southern NV. However, Lenz (2007) reassessed Y. brevifolia and Y. jaegeriana as distinct species.
In terms of environmental variable relative importance, we found that temperature generally outweighed precipitation, with the temperature variables (T max, T min, and T range) showing higher relative importance in aggregate than the precipitation variables (WP and SP) for 39 of 49 species (Table 2). However, given that temperature and precipitation interact to determine the overall aridity of a site, it may be difficult to disentangle these effects. Among the individual climate variables, the amount of summer precipitation (SP) had the greatest relative importance across species (13.84), followed by annual temperature range (11.11). Soil surface texture had the highest average relative importance (7.61) among variables representing topographic and surface characteristics, followed by slope (6.89). We did not observe obvious differences in the relative importance of environmental variables among different growth forms or lifespans, although uneven representation from these groups (e.g., we only considered a low number of cacti and grasses) likely reduces our ability to detect such differences. Response curves for all species are provided in Appendix S1.
3.1.1. Evaluation of systematic model bias
We did not find strong evidence for systematic model bias across the SDMs based on associations between aggregated habitat probabilities, aggregated standard errors, or distance to development. Graphs of the distance to development among binned habitat probability values indicated that habitat probabilities were slightly lower farther from developed areas (Figure 1). However, in GAM models, distance to development explained less than one percent of the variation in aggregated habitat probabilities. For aggregated model standard errors, somewhat higher values were associated with larger distances to development (Figure 1). However, this pattern was again not strong enough to explain more than one percent of the variation in aggregated standard errors in GAM models.
3.2. Seed Menu application
We developed an interactive web application to aid restoration practitioners in creating seed menus for restoration sites. The application, “Mojave Seed Menus”, pairs predicted habitat suitability values for priority native plant species with species attribute information useful for restoration planning at user‐defined locations (Figure 2). Results from the application are provided to users in downloadable table format. Mojave Seed Menus will be freely available over the web (https://rconnect.usgs.gov/MojaveSeedMenu/) and will not require users to install special software or create a user account. The application will also be available as stand‐alone software for users who wish to run Mojave Seed Menus locally through RStudio (Shryock et al., 2022a; https://doi.org/10.5066/P94A2QLK). A list of dependencies for the stand‐alone software version is provided in the linked repository.
We illustrate several key features of Mojave Seed Menu's user interface (Figure 3a–e), including the location input menu, interactive online map, seed menu customization menu, and options for viewing species habitat models and setting habitat probability thresholds for species inclusion in seed menus. Users have several options for selecting the locations (e.g., restoration project sites) for generating seed menus, including uploads from coordinates tables, point or polygon shapefiles, or by zooming in and clicking the online map (Figure 3a, b). Users can also select which species attributes they would like to appear in the table (Figure 3c) and display individual species habitat models in the online map viewer (Figure 3d).
The minimum habitat suitability threshold parameter allows users to select the minimum probability of occurrence allowable in order for a species to be recommended for an input site (Figure 3e), based on the SDMs for each species. For example, if the user selects 0.4 as the threshold, then all species with an SDM occurrence probability value ≥0.4 will be included in the Seed Menu table for that site. When multiple sites are input, setting the threshold parameter to 0.4 would require that all species included have an occurrence probability value ≥0.4 at all input sites. The dropdown menu also includes options for selecting species‐specific thresholds. In this case, habitat suitability value thresholds have been determined separately for each species based on their SDM model sensitivities (proportion of presences correctly predicted) and/or specificities (proportion of absences correctly predicted). For example, the “maximum (sensitivity + specificity)” option provides thresholds that maximize the sum of model sensitivity and specificity for each species.
Based on the initial group of 49 species, Mojave Seed Menus provides strong coverage throughout most of the Mojave Desert (Figure 4). The vast majority of the Mojave Desert is represented by more than 5 modeled priority plant species (i.e., a seed menu created anywhere in the Mojave would likely contain 5 or more recommended plant species). Only scattered and environmentally extreme areas (e.g., lower Death Valley and the highest mountain areas) provide coverage for fewer species.
Detailed online instructions are included with the Mojave Seed Menus application to facilitate proper use. Mojave Seed Menus also provides a web version of the Mojave Desert priority species guide developed in Esque et al. (2021). This guide contains a wealth of information for restoration practitioners, including detail on species propagation, production, cultivation, and recoverability, as well as species importance for the Mojave desert tortoise (Gopherus agassizii). Further, Mojave Seed Menus will display the provisional seed transfer zones for the Mojave Desert developed in Shryock et al. (2018) as a guide for identifying genetically appropriate seed sources.
4. DISCUSSION
Faced with increased development and unprecedented ecosystem stressors, restoration practitioners in the Desert Southwest must balance a need to act with the often‐limited commercial supply of native seeds (Johnson et al., 2010; Peppin et al., 2010) until regionally adapted germplasm is developed for seed increase by commercial growers in the ecoregion. Programmatic directives, national policy, and restoration science all point toward the fundamental importance of promoting native species assemblages, providing habitat for pollinator communities, and maintaining genetic diversity (Olwell & Riibe, 2016; Vilsack & McCarthy, 2015). Meeting these objectives requires careful planning and prioritization among various stakeholders, including government agencies, non‐profits, universities, and commercial seed suppliers. Accessible decision support tools are pivotal to this effort and have already been deployed to guide seed transfer decisions, reducing the risks of maladaptation or loss of genetic diversity (Massatti et al., 2018; Shryock et al., 2017, 2018). However, restoration practitioners must also select a mix of species to seed in degraded areas, a choice that is not trivial given the fundamental role of community assembly on numerous ecological processes (Oliver et al., 2015). To support effective seed mix designs in the Mojave Desert, we developed Mojave Seed Menus, a spatial application that pairs species distribution models (SDMs) for priority native plant species with species trait data, giving restoration practitioners and resource managers an interactive platform to plan seed mixes that can be customized to match project objectives.
Mojave Seed Menus is currently based on a dataset of 49 SDMs for priority plant taxa identified in the Mojave PSL (Esque et al., 2021) using numerous criteria, including their importance as forage or cover for the Mojave desert tortoise (Gopherus agassizii), associations with various pollinators, ability to colonize disturbed areas and/or compete with invasive species, and other metrics. Although presence‐only SDMs have known biases, in particular spatial bias due to aggregation of occurrence records near more easily accessed areas (Fourcade et al., 2014; Veloz, 2009), we did not detect obvious patterns of systematic bias across our SDMs. One might expect habitat suitability predictions from SDMs to show a trend of increasing habitat probabilities near roads or other developmental features if occurrence records were aggregated near such areas rather than more remote locations. However, we did not detect a strong association (linear or non‐linear) between aggregated habitat probabilities and the layer representing distance to development, or between aggregated model standard errors and this layer (Figure 1). In part, our SDMs may have been strengthened by our use of species occurrences from vegetation studies in addition to herbarium records, as the former are likely to be less spatially biased. We also used a grid sampling procedure to disaggregate occurrence records prior to modeling, which reduces the impact of unequal sampling effort (Fourcade et al., 2014). Moreover, we used an ensemble SDM approach to increase accuracy by reducing dependence on individual algorithms (Araújo & New, 2007). Overall, our SDMs provide reasonable accuracy based on the model AUC and TSS scores (Table 2) and predict suitable areas for each species to establish given favorable climate conditions. However, as with all SDMs, we note that our models are subject to bias based on the availability of species occurrence records, which may be spatially incomplete or fail to reflect post‐observation temporal habitat changes.
Mojave Seed Menus provides a number of accessible options for users to create interactive seed mixes for restoration projects (Figure 3). To use the application, the only required input is one or more geographic locations (within the Mojave Desert) from which to derive seed menu(s). These can be provided in multiple ways: users can provide coordinates for a single location, upload a spreadsheet with coordinates and other attributes, click on the online interactive map, or upload a point or polygon shapefile (multiple points and polygons are supported, but we recommend against uploading “multipart” shapefiles, in which multiple spatially distinct polygons are treated as a single feature). Shapefiles can be uploaded in any coordinate system recognized by the “rgdal” library in R (Bivand et al., 2020). Once geographic locations are uploaded, users can customize which species traits to include in the seed menu and download the resulting table. In determining which species can be included, users can optionally set a cut‐off threshold to exclude species that do not meet a given habitat suitability threshold or use a species‐specific habitat cut‐off point already provided as a drop‐down menu in the Mojave Seed Menu program (described above in Methods). Currently, Mojave Seed Menus has a coverage of more than 5 species across the vast majority of the Mojave Desert, with many areas represented by over 10 species (Figure 4). This coverage enables restoration practitioners to devise seed mixes emphasizing a particular suite of functional plant traits, pollinator services, or other characteristics. For example, if pollinator services are a priority, practitioners can select species with the highest pollinator counts or that serve as both larval and adult pollinator hosts. For projects in highly denuded areas, species that are known colonizers may be preferable to establish rapid cover. The detailed species accounts provided in the Mojave PSL and through Mojave Seed Menus afford practitioners broad flexibility to set resource targets and project objectives. In future updates, we hope to expand Mojave Seed Menus to include SDMs and trait data for a larger proportion of species described in the full Mojave PSL (Esque et al., 2021).
Although species selection and seed mix design are important components of native plant restoration, we emphasize that Mojave Seed Menus is part of an integrated restoration program for the Mojave Desert (Figure 5). A second core component of this program aims to increase seeding effectiveness by accounting for within‐species variation. Local adaptation is widespread among plants in arid regions, leading to intraspecific variation in phenology, growth, emergence, and other traits expressed along gradients of climate and topography (Baughman et al., 2019). It is particularly important to account for local adaptation in heterogeneous regions such as the Mojave, which has both large elevational/climate gradients (Hereford et al., 2006) and an extreme climate that grants species narrow windows for regeneration (Reynolds et al., 2012). Seed transfer zones based on genetic studies (landscape genomics or common gardens) are still a primary approach for generating species‐specific guidelines (e.g., Shryock et al., 2017). When genetics studies are unavailable or pending, then climate distances between seed source and planting sites can serve as a generalized proxy for clines in local adaptation across many species (Shryock et al., 2017, 2018). In partnership with the Mojave Desert Native Plant Program, we previously made available a decision support tool (Climate Distance Mapper; https://rconnect.usgs.gov/Climate_Distance_Mapper) for the Mojave and other southwestern U.S. deserts that allows practitioners to rank seed sources for project sites in both current and future predicted climate (e.g., by minimizing the multivariate climate distance between sites; Shryock et al., 2018). By using this application in tandem with Mojave Seed Menus, practitioners can both create seed mixes for a restoration site and rank alternative seed sources for selected species, thereby decreasing potential for maladaptation in the current and future climates (Figure 5). Given the restoration site location and a table of available seed sources, the example workflow in Figure 5 can be rapidly accomplished. Moreover, if seed sources are unknown, Climate Distance Mapper allows users to create focal‐point seed zones surrounding restoration sites, designating areas to target for future seed collections. With such tools becoming widely accessible, restoration practitioners will have more time to focus on other challenges in desert restoration, including the timing of restoration projects to coincide with favorable conditions (Havrilla et al., 2020), strategies to cope with competition from invasive species that often dominate disturbed areas (Leger et al., 2021; Leger & Goergen, 2017), and propagating species for outplanting that may serve as “resource islands” to facilitate shrubland establishment (Badano et al., 2016; Hulvey et al., 2017). Collectively, the Mojave PSL (Esque et al., 2021), Mojave Seed Menus, and Climate Distance Mapper (Shryock et al., 2018) provide a robust and flexible decision support framework for restoration practitioners to create diverse, resilient, and sustainable native plant communities. In addition, these tools can help resource managers set priority targets for seed collection, production, and cultivation efforts that are necessary to sustain future restoration needs.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
Daniel F. Shryock: Conceptualization (equal); Formal analysis (lead); Methodology (equal); Software (lead); Writing – original draft (lead); Writing – review & editing (equal). Lesley A. DeFalco: Conceptualization (lead); Funding acquisition (lead); Methodology (equal); Project administration (equal); Resources (equal); Supervision (equal); Writing – review & editing (equal). Todd C. Esque: Conceptualization (equal); Funding acquisition (equal); Methodology (equal); Project administration (equal); Resources (equal); Writing – review & editing (equal).
Supporting information
ACKNOWLEDGEMENTS
We thank J. Perkins and C. Lund (BLM, California) for providing funding to support the development of seed menus for the Mojave Desert ecoregion. Funding support was also provided through the California BLM Plant Conservation and Restoration Program and BLM Mojave Desert Native Plant Program. We thank Gayle Tyree for assistance in compiling species occurrence records. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Shryock, D. F. , DeFalco, L. A. , & Esque, T. C. (2022). Seed Menus: An integrated decision‐support framework for native plant restoration in the Mojave Desert. Ecology and Evolution, 12, e8805. 10.1002/ece3.8805
DATA AVAILABILITY STATEMENT
Data associated with this manuscript will be available as a USGS data release product on ScienceBase (Shryock et al., 2022b; https://doi.org/10.5066/P9XQJFEL). Software code and documentation for Mojave Seed Menus will be available from the USGS official code repository (Shryock et al., 2022a; https://doi.org/10.5066/P94A2QLK).
REFERENCES
- Abella, S. R. (2009). Post‐fire plant recovery in the Mojave and Sonoran Deserts of western North America. Journal of Arid Environments, 73(8), 699–707. 10.1016/j.jaridenv.2009.03.003 [DOI] [Google Scholar]
- Abella, S. R. , Craig, D. J. , Smith, S. D. , & Newton, A. C. (2012). Identifying native vegetation for reducing exotic species during the restoration of desert ecosystems. Restoration Ecology, 20(6), 781–787. 10.1111/j.1526-100X.2011.00848.x [DOI] [Google Scholar]
- Allouche, O. , Tsoar, A. , & Kadmon, R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223–1232. 10.1111/j.1365-2664.2006.01214.x [DOI] [Google Scholar]
- Angert, A. L. , Huxman, T. E. , Chesson, P. , & Venable, D. L. (2009). Functional tradeoffs determine species coexistence via the storage effect. Proceedings of the National Academy of Sciences of the United States of America, 106(28), 11641–11645. 10.1073/pnas.0904512106 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Araújo, M. B. , & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22(1), 42–47. 10.1016/j.tree.2006.09.010 [DOI] [PubMed] [Google Scholar]
- Badano, E. I. , Samour‐Nieva, O. R. , Flores, J. , Flores‐Flores, J. L. , Flores‐Cano, J. A. , & Rodas‐Ortíz, J. P. (2016). Facilitation by nurse plants contributes to vegetation recovery in human‐disturbed desert ecosystems. Journal of Plant Ecology, 9(5), 485–497. 10.1093/jpe/rtw002 [DOI] [Google Scholar]
- Balazs, K. R. , Kramer, A. T. , Munson, S. M. , Talkington, N. , Still, S. , & Butterfield, B. J. (2020). The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes. Ecological Applications, 30(4), e02110. 10.1002/eap.2110 [DOI] [PubMed] [Google Scholar]
- Bamberg, S. A. , Vollmer, A. T. , Kleinkopf, G. E. , & Ackerman, T. L. (1976). A comparison of seasonal primary production on Mojave Desert shrubs during wet and dry years. The American Midland Naturalist, 95, 398–405. [Google Scholar]
- Barbet‐Massin, M. , Jiguet, F. , Albert, C. H. , & Thuiller, W. (2012). Selecting pseudo‐absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327–338. 10.1111/j.2041-210X.2011.00172.x [DOI] [Google Scholar]
- Baughman, O. W. , Agneray, A. C. , Forister, M. L. , Kilkenny, F. F. , Espeland, E. K. , Fiegener, R. , Horning, M. E. , Johnson, R. C. , Kaye, T. N. , Ott, J. , St. Clair, J. B. , & Leger, E. A. (2019). Strong patterns of intraspecific variation and local adaptation in Great Basin plants revealed through a review of 75 years of experiments. Ecology and Evolution, 9(11), 6259–6275. 10.1002/ece3.5200 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bivand, R. , Keitt, T. , & Rowlingson, B. (2020). rgdal: Bindings for the “Geospatial” Data Abstraction Library. Retrieved from https://cran.r‐project.org/package=rgdal [Google Scholar]
- Breed, M. F. , Stead, M. G. , Ottewell, K. M. , Gardner, M. G. , & Lowe, A. J. (2013). Which provenance and where? Seed sourcing strategies for revegetation in a changing environment. Conservation Genetics, 14(1), 1–10. 10.1007/s10592-012-0425-z [DOI] [Google Scholar]
- Broadhurst, L. M. , Lowe, A. , Coates, D. J. , Cunningham, S. A. , McDonald, M. , Vesk, P. A. , & Yates, C. (2008). Seed supply for broadscale restoration: Maximizing evolutionary potential. Evolutionary Applications, 1(4), 587–597. 10.1111/j.1752-4571.2008.00045.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- Brooks, M. L. , D’Antonio, C. M. , Richardson, D. M. , Grace, J. B. , Keeley, J. E. , DiTomaso, J. M. , & Pyke, D. (2004). Effects of invasive alien plants on fire regimes. BioScience, 54(7), 677–688. [Google Scholar]
- Brown, D. E. , & Minnich, R. A. (1986). Fire and Changes in creosote bush scrub of the western Sonoran desert, California. American Midland Naturalist, 116(2), 411. 10.2307/2425750 [DOI] [Google Scholar]
- Bucharova, A. , Lampei, C. , Conrady, M. , May, E. , Matheja, J. , Meyer, M. , & Ott, D. (2021). Plant provenance affects pollinator network: Implications for ecological restoration. Journal of Applied Ecology, 59(2), 373–383. 10.1111/1365-2664.13866 [DOI] [Google Scholar]
- Bureau of Land Management, & U.S. Department of Energy (2015). Final Programmatic Environmental Impact Statement (PEIS) for Solar Energy Development in Six Southwestern States (FES 12‐24; DOE/EIS‐0403). 1 Statewide Agricultural Land Use Baseline 2015 §.
- Burghardt, K. T. , & Tallamy, D. W. (2013). Plant origin asymmetrically impacts feeding guilds and life stages driving community structure of herbivorous arthropods. Diversity and Distributions, 19(12), 1553–1565. 10.1111/ddi.12122 [DOI] [Google Scholar]
- Carr, N. B. , & Leinwand, I. I. F. (2020). Terrestrial Development Index for the western United States:1‐kilometer moving window: U.S. Geological Survey data releasee. 10.5066/P93ZU0R9 [DOI] [Google Scholar]
- Carter, S. K. , Carr, N. B. , & Miller, K. H. , & Wood, D. J. A. (Eds.). (2017). Multiscale guidance and tools for implementing a landscape approach to resource management in the Bureau of Land Management. In Open‐File Report. 10.3133/ofr20161207 [DOI] [Google Scholar]
- Cave, G. H. , & Patten, D. T. (1984). Short‐term vegetation responses to fire in the upper Sonoran desert. Journal of Range Management, 37(6), 491. 10.2307/3898842 [DOI] [Google Scholar]
- Chang, W. , Cheng, J. , Allaire, J. J. , Xie, Y. , & McPherson, J. (2020). shiny: Web Application Framework for R. Retrieved from https://cran.r‐project.org/package=shiny [Google Scholar]
- Cheng, J. , Karambelkar, B. , & Xie, Y. (2019). leaflet: Create Interactive Web Maps with the JavaScript “Leaflet” Library. Retrieved from https://cran.r‐project.org/package=leaflet [Google Scholar]
- Chesson, P. , Gebauer, R. L. E. , Schwinning, S. , Huntly, N. , Wiegand, K. , Ernest, M. S. K. , Sher, A. , Novoplansky, A. , & Weltzin, J. F. (2004). Resource pulses, species interactions, and diversity maintenance in arid and semi‐arid environments. Oecologia, 141(2), 236–253. 10.1007/s00442-004-1551-1 [DOI] [PubMed] [Google Scholar]
- Copeland, S. M. , Munson, S. M. , Pilliod, D. S. , Welty, J. L. , Bradford, J. B. , & Butterfield, B. J. (2018). Long‐term trends in restoration and associated land treatments in the southwestern United States. Restoration Ecology, 26(2), 311–322. 10.1111/rec.12574 [DOI] [Google Scholar]
- D’Antonio, C. M. , & Vitousek, P. M. (1992). Biological invasions by exotic grasses, the grass/fire cycle, and global change. Annual Review of Ecology and Systematics, 23(1), 63–87. 10.1146/annurev.es.23.110192.000431 [DOI] [Google Scholar]
- Dai, A. (2013). Increasing drought under global warming in observations and models. Nature Climate Change, 3(1), 52–58. 10.1038/nclimate1633 [DOI] [Google Scholar]
- Daly, C. , Halbleib, M. , Smith, J. I. , Gibson, W. P. , Doggett, M. K. , Taylor, G. H. , Curtis, J. , & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031–2064. 10.1002/joc.1688 [DOI] [Google Scholar]
- DeFalco, L. A. , Esque, T. C. , Kane, J. M. , & Nicklas, M. B. (2009). Seed banks in a degraded desert shrubland: Influence of soil surface condition and harvester ant activity on seed abundance. Journal of Arid Environments, 73(10), 885–893. 10.1016/j.jaridenv.2009.04.017 [DOI] [Google Scholar]
- DeFalco, L. A. , Esque, T. C. , Scoles‐Sciulla, S. J. , & Rodgers, J. (2010). Desert wildfire and severe drought diminish survivorship of the long‐lived Joshua tree (Yucca brevifolia; Agavaceae). American Journal of Botany, 97, 243–250. [DOI] [PubMed] [Google Scholar]
- Dobrowski, S. Z. , Abatzoglou, J. , Swanson, A. K. , Greenberg, J. A. , Mynsberge, A. R. , Holden, Z. A. , & Schwartz, M. K. (2013). The climate velocity of the contiguous United States during the 20th century. Global Change Biology, 19(1), 241–251. 10.1111/gcb.12026 [DOI] [PubMed] [Google Scholar]
- Engel, E. C. , & Abella, S. R. (2011). Vegetation recovery in a desert landscape after wildfires: Influences of community type, time since fire and contingency effects. Journal of Applied Ecology, 48(6), 1401–1410. 10.1111/j.1365-2664.2011.02057.x [DOI] [Google Scholar]
- Esque, T. C. , Defalco, L. A. , Tyree, G. L. , Drake, K. K. , Nussear, K. E. , & Wilson, J. S. (2021). Priority species lists to restore desert tortoise and pollinator habitats in Mojave Desert Shrublands. Natural Areas Journal, 41(2), 145–158. 10.3375/043.041.0209 [DOI] [Google Scholar]
- Esque, T. C. , Kaye, J. P. , Eckert, S. E. , DeFalco, L. A. , & Tracy, C. R. (2010). Short‐term soil inorganic N pulse after experimental fire alters invasive and native annual plant production in a Mojave Desert shrubland. Oecologia, 164(1), 253–263. 10.1007/s00442-010-1617-1 [DOI] [PubMed] [Google Scholar]
- Esque, T. C. , Young, J. A. , & Tracy, C. R. (2010). Short‐term effects of experimental fires on a Mojave Desert seed bank. Journal of Arid Environments, 74(10), 1302–1308. 10.1016/j.jaridenv.2010.04.011 [DOI] [Google Scholar]
- Fensholt, R. , & Sandholt, I. (2003). Derivation of a shortwave infrared water stress index from MODIS near‐ and shortwave infrared data in a semiarid environment. Remote Sensing of Environment, 87(1), 111–121. 10.1016/j.rse.2003.07.002 [DOI] [Google Scholar]
- Fielding, A. H. , & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38–49. 10.1017/S0376892997000088 [DOI] [Google Scholar]
- Fourcade, Y. , Engler, J. O. , Rödder, D. , & Secondi, J. (2014). Mapping species distributions with MAXENT using a geographically biased sample of presence data: A performance assessment of methods for correcting sampling bias. PLoS One, 9(5), e97122. 10.1371/journal.pone.0097122 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Haidet, M. , & Olwell, P. (2015). Seeds of success: A national seed banking program working to achieve long‐term conservation goals. Natural Areas Journal, 35(1), 165–173. 10.3375/043.035.0118 [DOI] [Google Scholar]
- Havrilla, C. A. , Munson, S. M. , McCormick, M. L. , Laushman, K. M. , Balazs, K. R. , & Butterfield, B. J. (2020). RestoreNet: An emerging restoration network reveals controls on seeding success across dryland ecosystems. Journal of Applied Ecology, 57(11), 2191–2202. 10.1111/1365-2664.13715 [DOI] [Google Scholar]
- Hereford, R. , Webb, R. H. , & Longpré, C. I. (2006). Precipitation history and ecosystem response to multidecadal precipitation variability in the Mojave Desert region, 1893–2001. Journal of Arid Environments, 67(Suppl.), 13–34. 10.1016/j.jaridenv.2006.09.019 [DOI] [Google Scholar]
- Hernandez, R. R. , Easter, S. B. , Murphy‐Mariscal, M. L. , Maestre, F. T. , Tavassoli, M. , Allen, E. B. , Barrows, C. W. , Belnap, J. , Ochoa‐Hueso, R. , Ravi, S. , & Allen, M. F. (2014). Environmental impacts of utility‐scale solar energy. Renewable and Sustainable Energy Reviews, 29, 766–779. 10.1016/j.rser.2013.08.041 [DOI] [Google Scholar]
- Hijmans, R. J. , Phillips, S. , Leathwick, J. , & Elith, J. (2017). dismo: Species Distribution Modeling. https://cran.r‐project.org/package=dismo [Google Scholar]
- Hirzel, A. H. , Le Lay, G. , Helfer, V. , Randin, C. , & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2), 142–152. 10.1016/j.ecolmodel.2006.05.017 [DOI] [Google Scholar]
- Hufford, K. M. , & Mazer, S. J. (2003). Plant ecotypes: Genetic differentiation in the age of ecological restoration. Trends in Ecology and Evolution, 18(3), 147–155. 10.1016/S0169-5347(03)00002-8 [DOI] [Google Scholar]
- Hulvey, K. B. , Leger, E. A. , Porensky, L. M. , Roche, L. M. , Veblen, K. E. , Fund, A. , Shaw, J. , & Gornish, E. S. (2017). Restoration islands: A tool for efficiently restoring dryland ecosystems? Restoration Ecology, 25, S124–S134. 10.1111/rec.12614 [DOI] [Google Scholar]
- Hurd, P. D., Jr , & Linsley, E. G. (1975). The principal Larrea bees of the Southwestern United States (Hymenoptera: Apoidea). Smithsonian Institution Press. 10.5479/si.00810282.193 [DOI] [Google Scholar]
- Inman, R. D. , Nussear, K. E. , Esque, T. C. , Vandergast, A. G. , Hathaway, S. A. , Wood, D. A. , Barr, K. R. , & Fisher, R. N. (2014). Mapping habitat for multiple species in the Desert Southwest. U.S. Geological Survey Open‐File Report 2014‐1134. 10.3133/ofr20141134 [DOI] [Google Scholar]
- IPCC (2013). Climate change 2013 the physical science basis: Working Group I contribution to the fifth assessment report of the intergovernmental panel on climate change. In Stocker T. F., Qin D., Plattner G. K., Tignor M. M. B., Allen S. K., Boschung J., & Midgley P. M. (Eds.), Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 10.1017/CBO9781107415324 [DOI] [Google Scholar]
- Isbell, F. , Craven, D. , Connolly, J. , Loreau, M. , Schmid, B. , Beierkuhnlein, C. , Bezemer, T. M. , Bonin, C. , Bruelheide, H. , de Luca, E. , Ebeling, A. , Griffin, J. N. , Guo, Q. , Hautier, Y. , Hector, A. , Jentsch, A. , Kreyling, J. , Lanta, V. , Manning, P. , … Eisenhauer, N. (2015). Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature, 526(7574), 574–577. 10.1038/nature15374 [DOI] [PubMed] [Google Scholar]
- Johnson, I. R. , Stritch, L. , Olwell, P. , Lambert, S. , Horning, M. E. , & Cronn, R. (2010). What are the best seed sources for ecosystem restoration on BLM and USFS lands? Native Plants Journal, 11(2), 117–131. 10.2979/NPJ.2010.11.2.117 [DOI] [Google Scholar]
- Kettenring, K. M. , Mercer, K. L. , Reinhardt Adams, C. , & Hines, J. (2014). Application of genetic diversity‐ecosystem function research to ecological restoration. Journal of Applied Ecology, 51(2), 339–348. 10.1111/1365-2664.12202 [DOI] [Google Scholar]
- Kimball, S. , Angert, A. L. , Huxman, T. E. , & Venable, D. L. (2010). Contemporary climate change in the Sonoran Desert favors cold‐adapted species. Global Change Biology, 16(5), 1555–1565. 10.1111/j.1365-2486.2009.02106.x [DOI] [Google Scholar]
- Knutson, K. C. , Pyke, D. A. , Wirth, T. A. , Arkle, R. S. , Pilliod, D. S. , Brooks, M. L. , Chambers, J. C. , & Grace, J. B. (2014). Long‐term effects of seeding after wildfire on vegetation in Great Basin shrubland ecosystems. Journal of Applied Ecology, 51(5), 1414–1424. 10.1111/1365-2664.12309 [DOI] [Google Scholar]
- Leger, E. A. , Barga, S. , Agneray, A. C. , Baughman, O. , Burton, R. , & Williams, M. (2021). Selecting native plants for restoration using rapid screening for adaptive traits: Methods and outcomes in a Great Basin case study. Restoration Ecology, 29(4), e13260. 10.1111/rec.13260 [DOI] [Google Scholar]
- Leger, E. A. , & Goergen, E. M. (2017). Invasive Bromus tectorum alters natural selection in arid systems. Journal of Ecology, 105(6), 1509–1520. 10.1111/1365-2745.12852 [DOI] [Google Scholar]
- Lenz, L. W. (2007). Reassessment of Yucca brevifolia and recognition of Y. jaegeriana as a Distinct Species. Aliso: A Journal of Systematic and Evolutionary Botany, 24(1), 97–104. [Google Scholar]
- Liaw, A. , & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22. http://cran.r‐project.org/doc/Rnews/ [Google Scholar]
- Lovich, J. E. , & Bainbridge, D. (1999). Anthropogenic degradation of the southern California desert ecosystem and prospects for natural recovery and restoration. Environmental Management, 24(3), 309–326. 10.1007/s002679900235 [DOI] [PubMed] [Google Scholar]
- M’Gonigle, L. K. , Williams, N. M. , Lonsdorf, E. , & Kremen, C. (2017). A Tool for Selecting Plants When Restoring Habitat for Pollinators. Conservation Letters, 10, 105–111. 10.1111/conl.12261 [DOI] [Google Scholar]
- Macmahon, J. A. (1988). Warm deserts. In Barbour M. G., & Billings W. D. (Eds.), North American terrestrial vegetation, 2nd ed. (pp. 285–322). Cambridge University Press. [Google Scholar]
- Massatti, R. , Prendeville, H. R. , Larson, S. , Richardson, B. A. , Waldron, B. , & Kilkenny, F. F. (2018). Population history provides foundational knowledge for utilizing and developing native plant restoration materials. Evolutionary Applications, 11(10), 2025–2039. 10.1111/eva.12704 [DOI] [PMC free article] [PubMed] [Google Scholar]
- McCune, B. , & Keon, D. (2002). Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science, 13(4), 603–606. 10.1111/j.1654-1103.2002.tb02087.x [DOI] [Google Scholar]
- McKay, J. K. , Christian, C. E. , Harrison, S. , & Rice, K. J. (2005). “How local is local?” ‐ A review of practical and conceptual issues in the genetics of restoration. Restoration Ecology, 13(3), 432–440. 10.1111/j.1526-100X.2005.00058.x [DOI] [Google Scholar]
- Meyer, S. E. , & Pendleton, B. K. (2015). Evolutionary drivers of mast‐seeding in a long‐lived desert shrub. American Journal of Botany, 102(10), 1666–1675. 10.3732/ajb.1500209 [DOI] [PubMed] [Google Scholar]
- Miller, A. H. , & Stebbins, R. C. (1964). The lives of desert animals in Joshua Tree National Monument (452 pp.). University of California Press. [Google Scholar]
- Muscarella, R. , Galante, P. J. , Soley‐Guardia, M. , Boria, R. A. , Kass, J. M. , Uriarte, M. , & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5(11), 1198–1205. 10.1111/2041-210x.12261 [DOI] [Google Scholar]
- Oldfield, S. , & Olwell, P. (2015). The right seed in the right place at the right time. BioScience, 65, 955–956. 10.1093/biosci/biv127 [DOI] [Google Scholar]
- Oliver, T. H. , Heard, M. S. , Isaac, N. J. B. , Roy, D. B. , Procter, D. , Eigenbrod, F. , Freckleton, R. , Hector, A. , Orme, C. D. L. , Petchey, O. L. , Proença, V. , Raffaelli, D. , Suttle, K. B. , Mace, G. M. , Martín‐López, B. , Woodcock, B. A. , & Bullock, J. M. (2015). Biodiversity and resilience of ecosystem functions. Trends in Ecology and Evolution, 30, 673–684. 10.1016/j.tree.2015.08.009 [DOI] [PubMed] [Google Scholar]
- Olwell, P. , & Riibe, L. (2016). National seed strategy: Restoring pollinator habitat begins with the right seed in the right place at the right time. Natural Areas Journal, 36(4), 363–365. 10.3375/043.036.0403 [DOI] [Google Scholar]
- Peppin, D. L. , Fulé, P. Z. , Lynn, J. C. , Mottek‐Lucas, A. L. , & Hull Sieg, C. (2010). Market perceptions and opportunities for native plant production on the southern Colorado plateau. Restoration Ecology, 18, 113–124. 10.1111/j.1526-100X.2010.00656.x [DOI] [Google Scholar]
- Phillips, S. J. , Anderson, R. P. , & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259. 10.1016/j.ecolmodel.2005.03.026 [DOI] [Google Scholar]
- Reynolds, M. B. J. , DeFalco, L. A. , & Esque, T. C. (2012). Short seed longevity, variable germination conditions, and infrequent establishment events provide a narrow window for Yucca brevifolia (Agavaceae) recruitment. American Journal of Botany, 99(10), 1647–1654. 10.3732/ajb.1200099 [DOI] [PubMed] [Google Scholar]
- Shryock, D. F. , Defalco, L. A. , & Esque, T. C. (2014). Life‐history traits predict perennial species response to fire in a desert ecosystem. Ecology and Evolution, 4(15), 3046–3059. 10.1002/ece3.1159 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shryock, D. F. , DeFalco, L. A. , & Esque, T. C. (2018). Spatial decision‐support tools to guide restoration and seed‐sourcing in the Desert Southwest. Ecosphere, 9(10), e02453. 10.1002/ecs2.2453 [DOI] [Google Scholar]
- Shryock, D. F. , DeFalco, L. A. , & Esque, T. C. (2022a). Mojave Seed Menus: A new spatial tool for restoration. U.S. Geological Survey software release, v1.0.0. 10.5066/P94A2QLK [DOI] [Google Scholar]
- Shryock, D. F. , DeFalco, L. A. , & Esque, T. C. (2022b). Species distribution models for native species in the Mojave Desert. U.S. Geological Survey data release. 10.5066/P9XQJFEL [DOI] [Google Scholar]
- Shryock, D. F. , Havrilla, C. A. , DeFalco, L. A. , Esque, T. C. , Custer, N. A. , & Wood, T. E. (2017). Landscape genetic approaches to guide native plant restoration in the Mojave Desert. Ecological Applications, 27(2), 429–445. 10.1002/eap.1447 [DOI] [PubMed] [Google Scholar]
- Tilman, D. , & Downing, J. A. (1994). Biodiversity and stability in grasslands. Nature, 367(6461), 363–365. 10.1038/367363a0 [DOI] [Google Scholar]
- Veloz, S. D. (2009). Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models. Journal of Biogeography, 36(12), 2290–2299. 10.1111/j.1365-2699.2009.02174.x [DOI] [Google Scholar]
- Vilsack, T. , & McCarthy, G. (2015). National strategy to promote the health of honeybees and other pollinators. Pollinator Health Task Force. [Google Scholar]
- Wang, T. , Hamann, A. , Spittlehouse, D. , & Carroll, C. (2016). Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One, 11(6), e0156720. 10.1371/journal.pone.0156720 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Warren, D. L. , & Seifert, S. N. (2011). Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecological Applications, 21(2), 335–342. 10.1890/10-1171.1 [DOI] [PubMed] [Google Scholar]
- Watling, J. I. , Brandt, L. A. , Bucklin, D. N. , Fujisaki, I. , Mazzotti, F. J. , Romañach, S. S. , & Speroterra, C. (2015). Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecological Modelling, 309–310, 48–59. 10.1016/j.ecolmodel.2015.03.017 [DOI] [Google Scholar]
- Webb, R. H. , Murov, M. B. , Esque, T. C. , Boyer, D. E. , DeFalco, L. A. , Haines, D. F. , & Medica, P. A. (2003). Perennial vegetation data from permanent plots on the Nevada Test Site. In Open‐File Report. 10.3133/ofr03336 [DOI] [Google Scholar]
- Webb, R. H. , & Newman, E. B. (1982). Recovery of soil and vegetation in ghost‐towns in the Mojave Desert, southwestern United States. Environmental Conservation, 9, 245–248. [Google Scholar]
- Wood, S. N. (2017). Generalized additive models: An introduction with R , 2nd ed. CRC Press. 10.1201/9781315370279 [DOI] [Google Scholar]
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Data Availability Statement
Data associated with this manuscript will be available as a USGS data release product on ScienceBase (Shryock et al., 2022b; https://doi.org/10.5066/P9XQJFEL). Software code and documentation for Mojave Seed Menus will be available from the USGS official code repository (Shryock et al., 2022a; https://doi.org/10.5066/P94A2QLK).