Spatial decision-support tools to guide restoration andseed-sourcing in the Desert Southwest

DANIEL F. SHRYOCK ,� LESLEYA. DEFALCO, AND TODD C. ESQUE

U.S. Geological Survey, Western Ecological Research Center, 160 North Stephanie Street, Henderson, Nevada 89074 USA

Citation: Shryock, D. F., L. A. DeFalco, and T. C. Esque. 2018. Spatial decision-support tools to guide restoration andseed-sourcing in the Desert Southwest. Ecosphere 9(10):e02453. 10.1002/ecs2.2453

Abstract. Altered disturbance regimes and shifting climates have increased the need for large-scalerestoration treatments across the western United States. Seed-sourcing remains a considerable challengefor revegetation efforts, particularly on public lands where policy favors the use of native, locally sourcedplant material to avoid maladaptation. An important area of emphasis for public agencies has been thedevelopment of spatial tools to guide selection of genetically appropriate seed. When genetic informationis not available, current seed transfer guidelines stipulate use of climate-based or provisional seed transferzones, which serve as a proxy for local adaptation by representing climate gradients to which plants arecommonly adapted. Despite this guidance, little emphasis has been placed on identifying best practices forderiving provisional seed zones or on incorporating predictions from future climate. We describe a flexible,multivariate procedure for deriving such zones that incorporates a broad range of climatic characteristicswhile accounting for covariation among climate variables. With this approach, we derive provisional seedzones for four regions in the Desert Southwest (the Mojave Desert, Sonoran Desert, Colorado Plateau, andSouthern Great Basin). To facilitate future-resilient restoration designs, we project each zone into its relativeposition in the future climate based on near-term, RCP4.5 and RCP8.5 emissions scenarios. Althoughprovisional seed zones are useful in a variety of contexts, there are also situations in which site-specificguidance is preferable. To meet this need, we implement Climate Distance Mapper, an interactive decision-support tool designed to help practitioners match seed sources with restoration sites through an accessibleonline interface. The application allows users to rank the suitability of seed sources anywhere on the land-scape based on multivariate climate distances. Users can perform calculations for either the current orfuture climates. Additionally, tools are available to guide sample effort in regional-scale seed collections orto partition the landscape into climate clusters representing suitable planting sites for different seedsources. Our tools and analytic procedures represent a flexible and reproducible framework for advancingnative plant development programs in the Desert Southwest and beyond.

Key words: climate change; cluster analysis; decision-support tool; local adaptation; multivariate climate distance;principal components; restoration; seed transfer zones; seed-sourcing; shiny application.

Received 17 May 2018; revised 9 August 2018; accepted 22 August 2018. Corresponding Editor: Dawn M. Browning.Copyright: Published 2018. This article is a U.S. Government work and is in the public domain in the USA. Ecospherepublished by Wiley Periodicals, Inc. on behalf of Ecological Society of America. This is an open access article under theterms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.� E-mail: dshryock@usgs.gov

INTRODUCTION

Deserts of the southwestern United States areincreasingly subject to disturbances affecting largeareas, including invasive species, novel fire

regimes, urbanization and energy development,and shifting climate (D’Antonio and Vitousek1992, Lovich and Bainbridge 1999, Dai 2013).Restoring native vegetation in these environmentsis inherently challenging due to unpredictable

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precipitation (Chesson et al. 2004), competitionby invasive species (DeFalco et al. 2003, Abellaet al. 2012), and granivory (Price and Joyner 1997,DeFalco et al. 2012). These intense environmentalfilters have frequently led to poor restoration out-comes (Knutson et al. 2014, Butterfield 2015),highlighting the need to incorporate plant mate-rial that is adapted to cope with local pressures(Leger and Baughman 2015).

Empirical evidence that local adaptation andhome-site advantages are frequent across a broadspectrum of plant species (Leimu and Fischer2008, Hereford 2009) has increased recognitionthat local adaptation may play a broad role inshaping plant responses to seed transfer (Huffordand Mazer 2003, McKay et al. 2005). Conse-quently, demand has increased for geneticallyappropriate plant materials for use in revegetation(hereafter, we refer to seeds, which are more com-monly used in restoration than cuttings of stemsor other organs). Over the past several decades,both the scope of restoration treatments and theuse of native species have increased on publiclands in the United States (Copeland et al. 2017).Coordinated efforts among public agencies, uni-versities, and other partners, in particular theNational Seed Strategy for Rehabilitation andRestoration (www.blm.gov/seedstrategy), arefocused on developing a sustainable supply ofnative seed to meet future demands (Oldfield andOlwell 2015). Despite these efforts, commercialavailability of locally sourced native plant seedsremains a frequent bottleneck in restorationdesigns, often leaving managers to choosebetween distant seed sources or cultivars of high-priority restoration species (Johnson et al. 2010,Peppin et al. 2010, Jalonen et al. 2017). Seed trans-fer guidelines derived from common garden stud-ies (Wang et al. 2010, Johnson et al. 2012) orlandscape genetics (Shryock et al. 2017) providepertinent information to aid such decisions or tar-get new sites for seed accessions. However, thecost of genetic studies has restricted their applica-tion to relatively few species. Consequently, infor-mation about the scale of local adaptation is rarelyavailable to guide restoration decision-making.

In place of species-specific genetic studies, pro-visional seed zones based on climate have beenthe preferred alternative for seed-sourcing onpublic lands (e.g., Vogel et al. 2005, Bower et al.2014, Omernik and Griffith 2014). These zones are

based on the premise that variation in climatemay serve as a proxy for local adaptation, anassertion for which there is broad evidence (Han-son et al. 2017, Shryock et al. 2017). Althoughplant species are also frequently adapted toedaphic conditions, accounting for soil propertiesin provisional seed zones remains an elusive pro-spect because no spatially contiguous dataset ofappropriate resolution and accuracy is availableto facilitate cross-site comparisons at local toregional scales. Moreover, information concerningfine-scale soil characteristics at seed collectionsites is not generally available to restoration prac-titioners, whereas climate information about aseed collection can be extracted from geographiccoordinates alone.Deriving climate-based provisional seed zones

for ecoregions or broader landscape divisions isnot a trivial task, and past examples have reliedon relatively few climate variables or non-data-driven techniques for assigning zones. A com-mon practice has been to derive provisional seedzones by taking unique combinations of climatevariables that have been placed on an ordinalscale (e.g., 5° temperature bands; Bower et al.2014). However, relying on arbitrary break pointsin the climate variables ignores the distributionalproperties of the climate data (including theirjoint distributions in multivariate climate space;Doherty et al. 2017) and could result in groupswith little relevance on the landscape. This appro-ach also does not account for covariance inclimate gradients or standardize variables to acc-ount for differences in scale. Multivariate appro-aches such as ordination and cluster analysis areclearly better suited for defining groups in multi-dimensional climate space (e.g., Hargrove andHoffman 2005, Potter and Hargrove 2012) andhave already been applied to map local adapta-tion by linking climate variables with geneticinformation (Wang et al. 2010, Fitzpatrick andKeller 2015, Shryock et al. 2017). In the context ofprovisional seed zones, multivariate modelscould generate a more holistic representation ofthe variation in climatic regimes across land-scapes, with potentially broader relevance for dif-ferent species and functional types. Multivariatemodels based on current climate can also be pro-jected into future climate by predicting new val-ues (e.g., principal component scores) for futureclimate variables (Fitzpatrick and Keller 2015,

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Hamann et al. 2015). Such projections could aidforward-thinking restoration designs, includingstrategies for assisted migration.

Despite their popularity and ease of interpreta-tion, there are also situations in which seed trans-fer zones are not optimal for restoration planning.The most obvious case arises when seed sourcesfrom multiple locations need to be compared witha restoration site in a different seed zone. There isno easy way to compare zones, as they typicallydiffer across multiple facets of climate (e.g., bothprecipitation and temperature). This problem iscompounded if available seed sources are locatedat broader distances than are represented by thezones. There is clearly a need for more flexibleapproaches that can compare seed sources andrestoration sites anywhere on the landscape. Amultivariate distance-based approach has beenproposed as one alternative for generating spe-cies-specific seed transfer guidelines (Parker 1992,Lesser and Parker 2006, Doherty et al. 2017, Shry-ock et al. 2017) and can be extended as a generictool for species lacking specific guidelines. Thisapproach is interactive, in that a user specifies afocal point from which multivariate climate dis-tances to surrounding grid cells are calculated.Climate distances allow seed sources to be quanti-tatively ranked in terms of their climate similarityto a restoration site, which presumably impactstheir fitness at that site. The distance-based appro-ach can also accommodate predictions of futureclimate, facilitating predictive seed-sourcing app-roaches (Breed et al. 2013). Two types of futureclimate projections are possible: Forward projec-tions calculate the climate distance from the cur-rent climate at an input site forward to the futureclimate across the landscape, thereby indicatingsuitable areas to plant seed from an input site forfuture climate resilience; backward projectionscalculate the climate distance from the future cli-mate at an input site back to the current climateacross the landscape, thereby indicating suitableareas to collect seed for future resilience at theinput site. These definitions for forward and back-ward projections are analogous to forward andbackward climate change velocity (Hamann et al.2015). Distance-based calculations can also beextended to multiple input points, creating avisual representation of the extent to which a setof locations represents the climatic diversity of aspatial extent (Shryock et al. 2017), which in

turn could guide coordinated seed collectionefforts (Haidet and Olwell 2015) or genetic diver-sity approaches such as admixture provenancing(Broadhurst et al. 2008, Kettenring et al. 2014).However, accessible interfaces are still needed toimplement these approaches for a wider audienceof restoration practitioners.Our aims were to (1) describe a flexible, robust

method for deriving provisional seed transferzones that can be easily extended to any regionor set of climate variables; and (2) provide anaccessible decision-support tool based on multi-variate climate distances for applications involv-ing specific restoration sites or seed sources.

METHODS

All analyses were performed in R version 3.3.2(R Core Team 2016). A script providing functionsfor each step in deriving provisional seed trans-fer zones is available in the supplementary mate-rial (Data S1).

Spatial dataOur study area encompassed four regions in

the arid southwestern United States as defined bycombinations of, or renaming of, the EPA’s Omer-nik level III ecoregion polygons (Omernik andGriffith 2014): the Sonoran Desert (Sonoran Basinand Range combined with Madrean Archipe-lago), Mojave Desert (Mojave Basin and Range),Colorado Plateau (Colorado Plateaus combinedwith Arizona/NewMexico Plateau), and SouthernGreat Basin (Central Basin and Range). To repre-sent the differing climates occurring both withinand between regions, we chose a combination of12 temperature and precipitation variables likelyto influence patterns of local adaptation across abroad range of plant species and functional types(Table 1). These included long-term averages forprecipitation and temperature, as well as mea-sures of annual and interannual variability. Cli-mate data were derived for the study extent on auniform grid of points at an 800 m2 spatial resolu-tion using the program ClimateNA (Wang et al.2016) to downscale PRISM climate data (Dalyet al. 2008) and correct for elevational variation.We used 30-yr averages for the 1980–2010 normalperiod to represent the current climate.We also incorporated scenarios of predicted

future climate in our analyses based on the RCP4.5

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(moderate emissions) and RCP8.5 (high emissions)representative concentration pathways. For bothscenarios, we took predicted 30-yr averages of cli-mate variables for the 2040–2070 period. Climateprojections were based on an ensemble average ofthree models from the Coupled Model Intercom-parison Project phase 5 (CMIP5) database corre-sponding to the 5th IPCC Assessment Report forfuture projections (IPCC 2014). The selectedclimate models included CCSM4 (Community Cli-mate System Model, version 4.0), GFDL-CM3(Geophysical Fluid Dynamics Laboratory ClimateModel, version 3), and HadGEM2-ES (Hadley Cen-tre Global Environmental Model, version 2 EarthSystem). All future climate data were generatedusing ClimateNA at an 800 m2 spatial resolution.

Seed transfer zonesPrincipal components.—Our first step in deriv-

ing provisional seed transfer zones was to reducethe original climate variables into orthogonalaxes representing the major gradients of climatevariability across the four regions. We used prin-cipal components analysis (PCA) to derive fiveprincipal components that together accountedfor more than 90% of the total variability in cli-mate data. The conversion to principal compo-nents both standardizes climate variables andaccounts for covariation, while ensuring that themost important climate gradients are given the

most weight (i.e., because principal componentsare ordered in terms of the variability theyexpress). The first three principal componentscan be visualized as a RGB composite, illustrat-ing climatic variation both within and betweenregions (Fig. 1). Climate variable loadings on allfive principal components are available in thesupplementary material (Appendix S1: Table S1).Cluster analysis.—Next, we performed multi-

variate cluster analysis on the five principal com-ponents to identify zones of similar climate.Multivariate clustering was performed separatelyfor each region. We compared two methods foridentifying clusters: Partitioning Around Medoids(PAM), a robust version of k-means clustering(Kaufman and Rousseeuw 1990), and hierarchicalagglomerative cluster analysis with Euclidean dis-tance and Ward’s linkage method. Partitioning-Around-Medoids was performed using the Rpackage cluster v. 2.0.5 (Maechler et al. 2016). Dueto computational constraints, we performed PAMiteratively on samples of approximately 10,000points. To generate geographically balanced sam-ples, we used the systematic grid sampling func-tion in the R package dismo v. 1.1-1 (Hijmanset al. 2016) to sample points from a uniform5-km2 grid (i.e., one point from each 5-km cell israndomly chosen at each iteration). This proce-dure was repeated 200 times at each value of k(4–16 clusters), and the iteration with the highest

Table 1. Environmental variables derived at 800 m2 resolution for four regions in the Desert southwest.

Environmental variable Code Definition

PrecipitationSummer precipitation (mm) SP Average precipitation received May–OctoberWinter precipitation (mm) WP Average precipitation received November–AprilMean annual precipitation (mm) MAP Average annual precipitationPrecipitation seasonality (%) PCV Coefficient of variation in monthly precipitation totals over the course

of a yearSummer precipitation ratio Pratio The proportion of summer precipitation out of the total precipitation

received in a yearWinter precipitation variability (%) WPCV Coefficient of variation in annual winter precipitation received 1950–2000Summer precipitation variability (%) SPCV Coefficient of variation in annual summer precipitation received 1950–2000

TemperatureMean annual temperature (°C) MAT The mean of the monthly temperature averagesSummer maximum temperature (°C) SMT Maximum temperature of warmest monthWinter minimum temperature (°C) WMT Minimum temperature of coldest monthAnnual temperature range (°C) TD Average of the monthly temperature ranges (monthly maximum minus

monthly minimum)Temperature seasonality (%) TCV Coefficient of variation in monthly average temperatures throughout the

course of a year

Note: Climate variables are averages for the reference period 1980–2010 except where otherwise noted.

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silhouette width was retained (Kaufman andRousseeuw 1990). Silhouette width is a measureof how similar a point is to other points in its owncluster vs. points in the next closest cluster. Forhierarchical clustering, we used an efficient algo-rithm available in the R package fastcluster (M€ull-ner 2013), which enabled us to perform clusteranalysis on the full dataset of more than 1,000,000points for k = 4 to 16. Preliminary analysesrevealed that hierarchical clustering outperformedPAM across regions and for various values of k(Appendix S1: Fig. S1). Therefore, we used thegroups identified by hierarchical clustering for allsubsequent analyses.

We used four criteria to evaluate the quality ofresults from cluster analysis and to select an

optimal number of climate zones, k, for each region.These included two metrics of within-to-between-cluster variability and two simulation-based metrics(Appendix S1: Fig. S2). For the variability-basedmetrics, we first identified the k climate medoidsfor each cluster solution (k = 4–16), where a medoidis defined as the point in each cluster with the low-est average dissimilarity to all other points in thecluster. Next, we derived a metric termed “meanclimate distance” (MCD), defined as the mean ofthe Euclidean distances between grid cells and theirmost similar climate medoid for all cells across agiven spatial extent (here, a region). Mean climatedistance is a measure of group cohesiveness and, ina spatial context, reflects the degree to which gridcells across the four regions are accurately assigned

Fig. 1. Principal components analysis (PCA) composite of climatic variation across the Desert Southwest. Differ-ent colors in the raster layer reflect different climatic regimes, as illustrated by the PCA biplot in the lower left, wherethe same color gradient is applied. The biplot arrows indicate the contribution of environmental variables to eachprincipal component axis (variable codes can be found in Table 1), with longer arrows indicating greater influence.

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to clusters. That is, if a large number of grid cellsare relatively dissimilar to their cluster medoid in aspatial extent, the MCD will be high for that extent.Mean climate distance can also be expressed as apercentage of the total climatic variability if themetric is divided by the maximum possible climatedistance in a spatial extent (Doherty et al. 2017). Wecalculated the maximum climate distance for eachregion by taking the maximum Euclidean distancevalue between points lying on the multivariate con-vex hull of the principal component scores. In addi-tion to MCD, we also calculated the silhouettewidth (Kaufman and Rousseeuw 1990) for clustersolutions as the mean Euclidean distance of gridcells to their closest medoid (MCD) divided by themean Euclidean distance of grid cells to their nextclosest medoid.

For the simulation-based metrics, we formu-lated two types of randomization test to evaluatethe stability of cluster solutions in relation to ran-dom clusters of points. The first test comparedcluster assignments for k = 4–16 to randomassignments of equal k. For each value of k, wecompared MCD from the true cluster assign-ments to the mean and 95% confidence intervalof MCD across 1000 randomly generated clusterassignments of equal k. The test statistic (per-muted P-value) for this simulation is defined as:

Pki MCDr \MCDt

1000

where MCDr is the MCD from a random clusterassignment and MCDt is the MCD of the truecluster assignment. In a second randomizationtest, we compared the difference in MCD betweenki and ki+1 with the difference in MCD from ki toki+r for 1000 iterations, where r represents a ran-dom medoid. The purpose of this simulation is toassess whether increasing k by one medoid resultsin a reduction of MCD that is larger than expectedby chance. Here, the test statistic is defined by

Pki MCDdr [ MCDdt

1000

where MCDdr is the difference in MCD betweenki and ki+r and MCDdt is the observed differencebetween MCD for ki and ki+1, with i ranging from4 to 15.

We used the above criteria to select an appro-priate number of clusters for each region,

representing the different seed zones presentacross the landscape. To project clusters onto ras-ter grids for mapping, we first created raster lay-ers for each principal component. Next, weassigned grid cells to their closest climate medoidbased on the multivariate Euclidean distancesbetween medoid and grid cell values on eachprincipal component. While cluster analysis wasconducted separately for each region, we per-formed this step for the full extent by assigninggrid cells to the climate medoid to which theywere most similar even if that medoid was in adifferent region. This procedure resulted in a clus-ter solution that accounted for the continuoustransitions between regions, since grid cells intransitional areas are sometimes more similar to aclimate medoid from a neighboring region. Forexample, in the transition between the MojaveDesert and the Sonoran Desert, climatic variationis graduated across a broad area that is not wellreflected by the regional boundaries (Fig. 1). Ourflexible approach accounts for this transition.Although we could have performed cluster analy-sis on the entire dataset, we chose to identifymedoids separately for each region both for effi-cient computation and because the regions repre-sent well-established divisions in physiographyand vegetation that we chose to maintain in oursubsequent divisions (Omernik and Griffith 2014).Future climate.—We projected the final seed

zones for each region into their predicted futurelocations based on the RCP4.5 and RCP8.5 cli-mate change scenarios for 2040–2070 (see Spatialdata above). To do so, we first used the PCAmodel to predict new principal componentscores for future climate variables and extractedthese values to the cluster medoids of eachregion. Next, we performed two types of projec-tions: a forward projection, which mapped gridcells in future climate (the predicted PCA scores)to their most similar cluster medoid (i.e., seedzone) in the current climate; and a backward pro-jection, which mapped grid cells in the currentclimate to their most similar cluster medoid infuture climate. These projections allow clustersto shift through space or to disappear completelyif a medoid has no analog in future climate.To display the projected magnitude of climate

change across the study extent independent ofcluster identity, we performed a Procrustes anal-ysis (function procrustes in R package vegan;

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Oksanen et al. 2015) between principal compo-nents in current climate and the predicted PCAscores in future climate for the RCP4.5 scenario.Procrustes analysis measures the similarity oftwo multivariate configurations (Peres-Neto andJackson 2001) and can be used to extract point-wise residuals reflecting the magnitude ofchange through space. We mapped Procrustesresiduals between current and future climate forall points on the uniform 800-m2 spatial grid.Finally, we calculated the average Procrustesresidual for grid cells in each seed zone to com-pare the magnitude of projected climate change.

Decision-support toolsA central objective of our work was to create

an interactive decision-support tool for restora-tion practitioners that leverages climate informa-tion to inform seed-sourcing and related issues inecological restoration. To do so, we coded abrowser-based application with the R packageshiny (Chang et al. 2017), which translates Rcode into html in a reactive programming envi-ronment. The application, which we call ClimateDistance Mapper, allows users to match seedsources with restoration sites by mapping themultivariate climate distance from input pointsto the surrounding landscape. Climate distancesare calculated as multivariate Euclidean dis-tances between input points and other grid cellsbased on the five principal components (see Prin-cipal components) derived from a set of 12 climatevariables (Table 1). Conceptually, multivariateclimate distances calculated on principal compo-nents approximate a multivariate Mahalanobisdistance calculated on the original set of climatevariables. This is because PCA standardizes andaccounts for covariation in the climate variables,resulting in orthogonal components of climatevariation. Climate distance values are also rela-tivized to the 95th percentile of the maximumpossible climate distance in each study extent,allowing the distance values to represent a per-centage of the total climate variability (Dohertyet al. 2017). For example, a climate distance of0.5 is approximately equivalent to 50% of thetotal climate variation in a region. Relativizingdistance values to the 95th percentile of the maxi-mum climate distance reduces the influence ofoutlier grid cells but does result in certain dis-tance values being greater than one. All of the

tools in Climate Distance Mapper support for-ward and backward climate projections based onthe RCP4.5 and RCP8.5 scenarios, where forwardprojections indicate climate distances betweeninput points in current climate and surroundinggrid cells in future climate, and backward projec-tions indicate the reverse. We describe this appli-cation in greater detail in the following section(see Decision-support tools).

RESULTS

Seed transfer zonesCluster analysis.—Performance metrics for clus-

ter analysis gave largely consistent results acrossdifferent values of k (Appendix S1: Fig. S2). Meanclimate distance decreased with increasing num-bers of clusters, but exhibited a pattern ofdecreasing returns in which the difference inMCD became smaller with increasing k. In thesimulation-based metrics, clusters produced bycluster analysis were better than random clustersat all levels of k (P = 0; Appendix S1: Fig. S2).However, there were several cases in which add-ing a cluster to the previous solution did not per-form better than adding a random medoid(Mojave Desert at k = 9; Sonoran Desert at k = 7;Great Basin at k = 13). In all cases, subsequentvalues of k did produce significant results withlower MCD, leading us to conclude that unstabledendrogram cut points were the culprit ratherthan non-significant increases in cluster number.Based on our evaluation of the different met-

rics, we selected a total of 41 provisional seedzones (clusters) across the four regions, includingnine zones in the Colorado Plateau, 10 zones inthe Great Basin, 10 zones in the Mojave Desert,and 12 zones in the Sonoran Desert (Fig. 2; forraster layers, Data S2). Our selections reflectedthe need to balance appropriate climate coveragewith feasibility for land managers (e.g., too manyseed zones would increase staffing costs neededfor seed collecting). As a basic requirement, westipulated that clusters should account for atleast 90% of the total climate variation, corre-sponding to an MCD value that is <10% of themaximum climate distance in each region. Ourfinal seed zone selections all met this criterion,with the Mojave Desert zones explaining 92.3%of the climate variation, the Sonoran Desertzones explaining 94.8%, the Great Basin zones

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explaining 95.1%, and the Colorado Plateauzones explaining 92.6%. Maps of the seed zonesfor each region along with climatographs dis-playing the differences in climatic regimebetween zones are available in the supplemen-tary material (Appendix S2: Figs. S1–S4).

Future climate.—We found considerable land-scape variability in the degree of climatic shiftspredicted by the climate model ensemble for the2040–2070 period. Procrustes residuals for theRCP4.5 scenario were highest in the northernColorado Plateau and central portions of theSouthern Great Basin, along with the westernside of the Mojave Desert and the southern

Sonoran Desert (Fig. 3). Compared amongregions, the Mojave Desert had the largest aver-age Procrustes residual, followed by the SonoranDesert, Southern Great Basin, and Colorado Pla-teau, respectively. At the seed zone level, Zone40 in the Sonoran Desert had the highest averageProcrustes residual, followed by Zone 19 (South-ern Great Basin) and Zone 37 (Sonoran Desert;Fig. 3). In forward climate projections based onthe RCP4.5 scenario, seven seed zones exhibitedmore than a 90% reduction in area (Zones 12, 14,20, 25, 26, 27, and 35). At the same time, otherzones broadly expanded, including five zonesthat more than doubled in area (Zones 3, 10, 21,

Fig. 2. Seed transfer zones for four regions in the Desert Southwest with continuous borders between regions.The zones were derived from a hierarchical agglomerative cluster analysis of five principal components, whichtogether explained more than 90% of the variation in 12 climate variables. Zones are numbered continuouslyfrom 1 to 41, beginning in the Colorado Plateau and ending in the Sonoran Desert. Climatographs of the zonesby region are displayed in Appendix S2.

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Fig. 3. Provisional seed zones in relation to projections of future climate (RCP4.5 ensemble). The top panel(a) illustrates the projected climatic displacement for the study region based on a Procrustes analysis of currentand future climate layers (principal components). Lower panels (b) display, from left to right, the current, for-ward, and backward projections for seed zones in each region. The forward projection indicates areas that will bemore similar to current zones in the future, whereas the backward projection indicates areas that are currentlymore similar to the future climate of a zone. The bar graph (c) displays the average climatic displacement(Procrustes residual) for grid cells within each zone.

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28, and 36). Forward projections did not alwaysmatch the Procrustes residuals, as the formerallow a seed zone to track climate through spaceand retain its area, whereas the latter measuresonly the climate displacement at specific loca-tions. In backward climate projections, we foundthat one zone had no current climate analog toits predicted future climate (Zone 10). Forwardand backward projections for all seed zones areavailable in the supplementary material for boththe RCP4.5 and RCP8.5 scenarios (Data S2).

Decision-support toolsWe developed an interactive, spatial decision-

support tool to aid practitioners in restorationplanning (Fig. 4). Coded as a shiny web applica-tion in R, Climate Distance Mapper is designed tohelp match seed sources with restoration sitesbased on climatic characteristics of the landscape.The application can be accessed at the followinglink: https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/. Underlying data andsource code are provided in a U.S. Geological Sur-vey data release (Shryock et al. 2018), while a fulltutorial for the application is also available in thesupplementary material (Appendix S3). The fun-damental unit of measure in Climate DistanceMapper is the multivariate climate distance,which is defined as the Euclidean distance be-tween climate variables (the five principal compo-nents) at input points and climate variables atother grid cells spread throughout the chosen spa-tial extent. Currently, Climate Distance Mappersupports calculations for the four regions in ourstudy extent (see Spatial data) or for the full extent.Input points can be supplied by clicking on thezoomable map interface, loading a csv file, or bynumeric fields.

Climate Distance Mapper performs three typesof calculations, depending on the number ofinput points, along with forward and backwardclimate projections for each type (Table 2). Whena single input point (e.g., a seed source or res-toration site) is provided, Climate Distance Map-per will calculate and map the multivariateclimate distances from the input point to all othergrid cells across the chosen region at an 800 m2

spatial resolution (Fig. 5a). The climate distancescan be used to quantitatively rank the climaticsuitability of multiple seed sources for a restora-tion site: Seed sources with smaller climate

distances from the restoration site will likelyhave a lower risk of maladaptation when plantedthere. Optionally, Climate Distance Mapperallows the user to constrain climate distances to agiven level of climatic similarity with the inputpoint. For example, the user may choose to onlyinclude areas that are at least 70% similar in cli-mate to the input point. With the forward andbackward projections, Climate Distance Mappercan also guide where to plant seeds from a givencollection site for future climate resilience (for-ward climate projection; Fig. 5b), or where to col-lect seeds that will be resilient to the futureclimate at a given restoration site (backward cli-mate projection; Fig. 5c).When multiple input points are provided, Cli-

mate Distance Mapper will calculate a multipointclimate distance designed to indicate the extentto which the input points represent the climaticvariability of the landscape as a whole. The mul-tipoint climate distance is defined as the mini-mum climate distance from grid cells to one ofmultiple input points (Table 2). For each grid cellin the chosen spatial extent, the climate distanceto each input point is calculated, and the mini-mum distance value is retained in the final raster(Fig. 6a). With this option, users can interactivelyplan seed collections by quickly visualizingwhich areas of the landscape have climates thatare already similar to those of existing seed col-lections, and which areas need additional sam-pling due to poor climate representation. Withthe option to constrain climate distances to agiven similarity level, users can determine, forinstance, areas that are at least 80% similar in cli-mate to an input point (Fig. 6b). In this way, Cli-mate Distance Mapper can facilitate acquisitionsof climatically (and, by extension, genetically)diverse samples of seed sources for use in res-toration projects. Forward and backward climateprojections are also supported with multipleinput points. In this case, the forward multipointprojection is defined as the minimum climate dis-tance between the current climate at any inputpoint and the future climate at other grid cells.This distance indicates the extent to which inputlocations are currently exposed to climates simi-lar to those predicted for the future. The back-ward multipoint projection is defined as theminimum climate distance between the futureclimate at any input point and the current

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Fig. 4. The Climate Distance Mapper online interface (https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/). A toolbar on the left provides options for point input, time period, and type of calculation. Inputpoint(s) may be supplied in a numeric field, by loading a csv file, or by clicking on the zoomable map display at theright. Climate distances can be calculated for either the current or future climate (a dropdown menu to select eitherthe RCP4.5 or RCP8.5 emissions scenarios will appear when a forward or backward projection is selected). Optionsinclude single-point distance, multipoint distance, and clustering (see Table 2 for additional details). Results can beviewed interactively in the display or downloaded as raster files compatible with open-source GIS software.

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climate at other grid cells. This distance indicatesplaces to collect seeds that may be resilient to thefuture climates at the input locations.

The cluster option is designed to quickly parti-tion the landscape into units that best correspondwith each input point based on their climate simi-larity (Table 2). Climate clusters are derived byassigning each grid cell in the chosen region tothe input point from which it has the lowest cli-mate distance (Fig. 6c). With this option, userscan visualize which seed sources are most similarto different parts of the landscape based on theirclimate. If restoration sites rather than seedsources are entered as input, the results indicateclimatically similar areas from which seeds maybe collected for each site. With the option to con-strain climate distances, climate clusters can berestricted to include only areas that are at least agiven level of climate similarity to each inputpoint (Fig. 6d). In this way, seed collection effortsfor multiple restoration sites can be directed toareas that are at least 90% similar in climate to

each site (or 80% similar, etc.). The forward cli-mate projection with the Cluster tool will partitionthe landscape into clusters representing, for eachinput point, areas where the future climate will bemost similar to the current climate at that location.Conversely, the backward climate projection willpartition the landscape into clusters where thecurrent climate is most similar to the predictedfuture climate at the input points. If the inputpoints are restoration sites, the backward projec-tion can be used to select areas for seed collectionwhere populations may be more resilient to thefuture climate predicted at those sites.

DISCUSSION

Flexible spatial planning tools that allow practi-tioners to balance ecological concerns with thecommercial availability of seeds are necessary tosupport the increasing need for restoration of dis-turbed vegetation in the Desert Southwest, wherepolicy broadly supports the use of genetically

Table 2. Available features of Climate Distance Mapper†, a shiny web application for seed-sourcing with nativeplants in the Desert Southwest.

Tool Definition Applications

Climate distance witha single pointSingle-point distance Multivariate climate distance from input point to all other

grid cellsLocal seed-sourcing, assistedmigration

Forward projection Multivariate climate distance from input point in currentclimate forward to future climate at all other grid cells

Predictive seed-sourcing, assistedmigration

Backward projection Multivariate climate distance from input point in futureclimate back to current climate at all other grid cells

Predictive seed-sourcing

Climate distance withmultiple pointsMultipoint distance Minimummultivariate climate distance from grid cells to

any input pointPlanning seed collections,admixture seed-sourcing

Forward projection Minimummultivariate climate distance from future climateat grid cells to current climate of any input point

Planning seed collections,admixture seed-sourcing

Backward projection Minimummultivariate climate distance from currentclimate at grid cells to future climate of any input point

Planning seed collections,admixture seed-sourcing

Climate clustersCluster points Partition grid cells into groups according to their most

climatically similar input pointLocal seed-sourcing

Forward cluster Partition grid cells in future climate into groups accordingto their most similar input point in the current climate

Predictive seed-sourcing

Backward cluster Partition grid cells in current climate into groups accordingto their most similar input point in the future climate

Planning seed collections,predictive seed-sourcing

Extract climate distancesCompare distancesat points

Requires raster output from above tools. Extracts distancevalues (or cluster identity) at additional points provided ina file and adds these values to the map display as popuplabels for each point.

Local seed-sourcing, predictiveseed-sourcing, planning seedcollections

† https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/

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Fig. 5. Examples of single-point distance calculations in Climate Distance Mapper for a topographically com-plex portion of the Southern Great Basin in Nevada. (a) Climate distance is the multivariate Euclidean distancefrom grid cells to an input point calculated over principal components analysis (PCA) climate grids. Distance val-ues range from 0 to 1, with 1 indicating the maximum climate distance in a spatial extent (larger values indicategreater climate distance). Climate Distance Mapper allows both input points and PCA climate grids to varybetween current (1980–2010) and future (2040–2070) climate, facilitating two types of projections. (b) A forwardprojection calculates the climate distance between current climate at an input point and future climate across thelandscape. Minimizing this distance in seed transfer could increase resilience to future climate at planting sites.(c) A backward projection calculates the climate distance between future climate at an input point and current cli-mate across the landscape. Minimizing this distance when collecting seed for a restoration site could increase theseed’s resilience to the future climate at that site. Climate distances are larger in the forward and backward pro-jections relative to the same calculation in current climate (see inset histograms).

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appropriate native seeds. Although methods fordeveloping species-specific genetic guidelines forseed transfer are increasingly efficient (e.g., Shryocket al. 2017), climate-based or “provisional” spatialtools are still needed for planning future-resilientrestoration using many high-priority species. Suchtools are based on the premise that patterns in cli-mate reflect patterns in local adaptation, a conceptfor which there is broad support (Carvalho et al.2011, Manel et al. 2012, Shafer and Wolf 2013,

Hanson et al. 2017). Although species and popula-tions differ with respect to the mechanisms foradaptive divergence, we contend that multivariateclimate models, which capture a holistic represen-tation of the variation in climate, are most likely torepresent relevant environmental variation across abroad range of species and functional types.A standard practice for generating provisional

seed transfer zones has been to derive uniquecombinations of climate variables that are first

Fig. 6. Examples of multipoint climate distance functions available in Climate Distance Mapper. The first panel(a) displays the minimum multivariate climate distance from grid cells to any input point (black circles), suchthat areas with lower climate distances are well represented by the points, and vice versa. This tool denotes areaswith undersampled climates, where further seed collections would improve the climatic coverage availableamong seed accessions (i.e., by providing seeds suitable for a broader range of restoration sites). Optionally, Cli-mate Distance Mapper can constrain the climate distances to only areas that are a given level of similarity to aninput point (b). Areas with larger climate distances could be focused on for additional sampling. The lower leftpanel (c) displays a partitioning of the landscape into climate clusters that are most climatically similar to eachinput point. This tool guides where to plant a set of existing seed collections by indicating areas where the cli-mate is most similar to each collection. Optionally, the climate clusters can be constrained to grid cells that are atleast a given level of climate similarity (here, 80%) to the input points (d).

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split into ordinal categories (Bower et al. 2014).However, this approach does not account fornon-independence of climate variables or for therelative density of climate data in each combina-tion. Hence, zonal boundaries identified withthese combinations represent artificial breaks inthe data rather than observed separation in cli-mate space and could result in poor representa-tion of important climatic divisions. Ourmultivariate procedure for delineating provi-sional seed zones is not susceptible to thesepotential discrepancies and can accommodateany number of variables while preserving natu-ral gradients in climatic variation (Hargrove andHoffman 2005). Our approach first transforms aset of climate data into composite axes of climatevariation using PCA. Next, we identify naturalgroups in the data through hierarchical cluster-ing. This procedure accounts for covariation inthe selected climate variables such that no vari-able has undue influence, while bringing to thefore dominant spatial gradients of the climaticregime. By including a broad set of variables(Table 1), we account for climatic averages,extremes, and ranges of variability. Our variablechoices were based on results from previousgenetic studies incorporating desert species (e.g.,Ehleringer and Cooper 1988, Sandquist andEhleringer 2003, Meyer and Pendleton 2005,Johnson et al. 2012). However, we provide ascript so that others may easily extend our meth-ods to new climate variables or regions and selectan appropriate number of zones (Data S1).

There are legitimate questions regarding thedegree to which populations of various taxa willretain fitness in response to projected shifts in cli-mate (Davis and Shaw 2001, Parmesan 2006,Alberto et al. 2013, Svenning and Sandel 2013).Restoration designs that account for future cli-mate (e.g., predictive provenancing; Breed et al.2013) may be advantageous in locations subjectto rapid selection, where local genotypes arepotentially less optimal than genotypes alreadyexposed to similar pressures at their sites of ori-gin (Sgro et al. 2011). Caution is warranted, how-ever, due to uncertainty in species responses inthe absence of results from field trials (Breedet al. 2013). Our aim is not to contribute to thedebate concerning provenance strategies (e.g.,Broadhurst et al. 2008, Breed et al. 2013), butrather to provide accessible tools for restoration

practitioners to evaluate future climate scenariosat specific sites. We incorporated a near-term,2040–2070 climate change scenario (IPCC 2014)in two ways: first, by projecting provisional seedzones into their relative positions in the futureclimate; and second, by enabling future climateprojections with all of the tools in Climate Dis-tance Mapper (Table 2). We defined two types offuture climate projections: forward projection,which is the climate distance between the currentclimate of seed zones (or input points in ClimateDistance Mapper) and the future climate acrossthe landscape; and backward projection, which isthe climate distance between the future climateof seed zones and the current climate across thelandscape. Our forward projections of provi-sional seed zones indicated that many parts ofthe landscape could experience climatic shifts atleast as large as the differences in climatebetween zones (Fig. 3). We found that manyzones were vastly reduced in size in the futureclimate, while some zones (e.g., Zone 28 in theMojave Desert) broadly expanded. However, onecaveat is that we did not include areas outside ofthe regional boundaries in our forward projec-tions, and such areas could harbor additionalfuture climate analogs to the zones. Backwardprojections indicated that for many areas, currentclimate analogs to the predicted future climateare limited to non-existent (within the currentarea of interest), which could make it difficult tofind seed collections from populations that havealready been exposed to the potential future con-ditions. Moreover, unlike with forward projec-tions, results from backward projections wouldnot likely change if calculations were performedover a broader extent because areas outside ofthe four desert regions we included are almostcertainly more moderate than the future climatespredicted within these regions.Although provisional seed transfer zones sup-

port restoration planning in a variety of contexts,the difficulty in comparing zones poses a chal-lenge for situations requiring broader-scale evalu-ations. In contrast, flexible tools based onmultivariate climate distances provide site-specificguidance for matching seed sources withrestoration sites anywhere on the landscape. Ourimpetus in developing Climate Distance Mapperwas to merge recently proposed climate distancemethods (Doherty et al. 2017, Shryock et al. 2017)

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into an accessible, open-source interface that canbe made available to a wider audience. Within theapplication, a number of different options areavailable (Table 2) to help restoration practition-ers put the right seed in the right place at the righttime (National Seed Strategy for Rehabilitationand Restoration, www.blm.gov/seedstrategy). Cli-mate distances from a single input point allowusers to rank seed sources based on their climatesimilarity to a target restoration site or, alterna-tively, to identify suitable areas for obtaining col-lections. Multipoint climate distances extend thiscalculation to display the minimum climate dis-tance from grid cells to any number of inputpoints. For input points that represent seed collec-tions, the multipoint distance indicates whichareas of the landscape are most climatically simi-lar to the collections, and which areas may needadditional sampling due to poor climatic repre-sentation. This option is aimed at facilitatingbroad-scale seed collections by interactively guid-ing collection teams (e.g., U.S. Bureau of LandManagement’s Seeds of Success Program) to cli-matically undersampled areas. Finally, the clusteroption partitions the landscape into climate clus-ters that are most climatically similar to eachinput point. Given a set of available seed collec-tions, such as those maintained in seed storagefacilities or commercial seed companies, thisoption can quickly display which parts of thelandscape are most suitable for each collection.

The efficacy of climate distances in predictingperformance of seed sources at restoration siteswill be determined by the degree to which cli-mate distances between source and target sitesare associated with adaptive divergence. Thisassociation is likely species-specific and coulddepend on a variety of factors, including effectivepopulation size, strength of selection, and ratesof gene flow (Aitken et al. 2008, Leimu and Fis-cher 2008, Ellstrand 2014). However, local adap-tation to climate (Manel et al. 2012, Alberto et al.2013) and home-site advantages (Leimu and Fis-cher 2008, Hereford 2009) occur with sufficientfrequency that we expect climate distances to bereasonably indicative of performance in a varietyof contexts. Where possible, restoration practi-tioners may still wish to consider additionallandscape features to refine restoration designson a site-specific basis, including soil moisture orother edaphic properties.

We recognize several promising avenues forimproving predictions from climate distance-basedapproaches. First, climate distances might beweighted by results from field trials to capturetrends important to different species, functionalgroups, or geographic regions. While species dis-tribution models provide insight into factors con-straining species at their margins (Butterfield 2015,Crow et al. 2018), common garden trials (Johnsonet al. 2012, Bansal et al. 2015) or landscape genet-ics (Shryock et al. 2015, 2017) still represent the pri-mary sources of data for identifying climaticdrivers of population fitness and adaptive diver-gence. Linking field performance data with climatedistances could allow transformation of the dis-tance measures into units of fitness, therebyenabling direct predictions of performance losswith increasing climate distance. This transforma-tion could be accomplished by using constrainedordination rather than PCA to derive compositeclimate variables for use in distance calculations.For example, generalized dissimilarity modelinghas been used to map genetic variation by trans-forming environmental variables into compositeaxes of genetic variation (Fitzpatrick and Keller2015). In this way, Shryock et al. (2017) developedtools for calculating adaptive distances for twospecies with contrasting life history strategies inthe Mojave Desert. Finally, relating climate vari-ables to local adaptation at the level of functionalgroups would be particularly useful, as the groupscould serve as targeted proxies for a wider numberof species, reducing the need for common gardentrials for every high-priority restoration specieswhen establishing seed transfer guidelines.

ACKNOWLEDGMENTS

We gratefully acknowledge C. Lund (Bureau ofLand Management, California State Office) for fundingsupport. Additional funding was provided by UnitedStates Department of the Interior, Bureau of LandManagement—Native Plant Materials Program. Anyuse of trade, firm, or product names is for descriptivepurposes only and does not imply endorsement by theU.S. Government.

LITERATURE CITED

Abella, S. R., D. J. Craig, S. D. Smith, and A. C. New-ton. 2012. Identifying native vegetation for

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SHRYOCK ET AL.

reducing exotic species during the restoration ofdesert ecosystems. Restoration Ecology 20:781–787.

Aitken, S. N., S. Yeaman, J. A. Holliday, T. Wang, andS. Curtis-McLane. 2008. Adaptation, migration orextirpation: climate change outcomes for tree pop-ulations. Evolutionary Applications 1:95–111.

Alberto, F. J., et al. 2013. Potential for evolutionaryresponses to climate change – evidence fromtree populations. Global Change Biology 19:1645–1661.

Bansal, S., C. A. Harrington, P. J. Gould, and J. B. St.Clair. 2015. Climate-related genetic variation indrought-resistance of Douglas-fir (Pseudotsuga men-ziesii). Global Change Biology 21:947–958.

Bower, A. D., J. B. St. Clair, and V. Erickson. 2014. Gen-eralized provisional seed zones for native plants.Ecological Applications 24:913–919.

Breed, M. F., M. G. Stead, K. M. Ottewell, M. G. Gard-ner, and A. J. Lowe. 2013. Which provenance andwhere? Seed sourcing strategies for revegetation ina changing environment. Conservation Genetics14:1–10.

Broadhurst, L. M., A. Lowe, D. J. Coates, S. A. Cun-ningham, M. McDonald, P. A. Vesk, and C. Yates.2008. Seed supply for broadscale restoration: maxi-mizing evolutionary potential. Evolutionary Appli-cations 1:587–597.

Butterfield, B. J. 2015. Environmental filtering increasesin intensity at both ends of climate gradients,though driven by different factors, across woodyvegetation types of the southwest USA. Oikos124:1374–1382.

Carvalho, S. B., J. C. Brito, E. J. Crespo, and H. P. Poss-ingham. 2011. Incorporating evolutionary pro-cesses into conservation planning using speciesdistribution data: a case study with the westernMediterranean herpetofauna. Diversity and Distri-butions 17:408–421.

Chang, W., J. Cheng, J. J. Allaire, Y. Xie, and J. McPher-son. 2017. shiny: web application framework for R.R package version 1.0.0. https://CRAN.R-project.org/package=shiny

Chesson, P., R. L. E. Gebauer, S. Schwinning, N.Huntly, K. Wiegand, M. S. Ernest, A. Sher, A.Novoplansky, and J. F. Weltzin. 2004. Resourcepulses, species interactions, and diversity mainte-nance in arid and semi-arid environments. Oecolo-gia 141:236–253.

Copeland, S. M., S. M. Munson, D. S. Pilliod, J. L.Welty, J. B. Bradford, and B. J. Butterfield. 2017.Long-term trends in restoration and associatedland treatments in the southwestern United States.Restoration Ecology 26:311–322.

Crow, T. M., S. E. Albeke, C. A. Buerkle, and K. M.Hufford. 2018. Provisional methods to guide

species-specific seed transfer in ecological restora-tion. Ecosphere 9:e02059.

Dai, A. G. 2013. Increasing drought under globalwarming on observations and models. Nature Cli-mate Change 3:52–58.

Daly, C., M. Halbleib, J. J. Smith, W. P. Gibson, M. K.Doggett, G. H. Taylor, J. Curtis, and P. A. Pasteris.2008. Physiographically-sensitive mapping of tem-perature and precipitation across the conterminousUnited States. International Journal of Climatology28:2031–2064.

D’Antonio, C. M., and P. M. Vitousek. 1992. Biologicalinvasions by exotic grasses, the grass/fire cycle,and global change. Annual Review of Ecology andSystematics 23:63–87.

Davis, M. B., and R. G. Shaw. 2001. Range shifts andadaptive responses to quaternary climate change.Science 292:673–679.

DeFalco, L. A., D. R. Bryla, V. Smith-Longozo, and R.S. Nowak. 2003. Are Mojave Desert annual speciesequal? Resource acquisition and allocation for theinvasive grass Bromus madritensis ssp rubens (Poa-ceae) and two native species. American Journal ofBotany 90:1045–1053.

DeFalco, L. A., T. C. Esque, M. B. Nicklas, and J. M.Kane. 2012. Supplementing seed banks to rehabili-tate disturbed Mojave Desert shrublands: Wheredo all the seeds go? Restoration Ecology 20:85–94.

Doherty, K. D., B. J. Butterfield, and T. E. Wood. 2017.Matching seed to site by climate similarity: tech-niques to prioritize plant materials developmentand use in restoration. Ecological Applications27:1010–1023.

Ehleringer, J. R., and T. A. Cooper. 1988. Correlationsbetween carbon isotope ratio and microhabitat indesert plants. Oecologia 76:562–566.

Ellstrand, N. C. 2014. Is gene flow the most importantevolutionary force in plants? American Journal ofBotany 101:737–753.

Fitzpatrick, M. C., and S. R. Keller. 2015. Ecologicalgenomics meets community level modelling of bio-diversity: mapping the genomic landscape of cur-rent and future environmental adaptation. EcologyLetters 18:1–16.

Haidet, M., and P. Olwell. 2015. Seeds of success: anational seed banking program working to achievelong-term conservation goals. Natural Areas Jour-nal 35:165–173.

Hamann, A., D. R. Roberts, Q. E. Barber, C. Carroll,and S. E. Nielsen. 2015. Velocity of climate changealgorithms for guiding conservation and manage-ment. Global Change Biology 21:997–1004.

Hanson, J. O., J. R. Rhodes, C. Riginos, and R. A.Fuller. 2017. Environmental and geographic vari-ables are effective surrogates for genetic variation

❖ www.esajournals.org 17 October 2018 ❖ Volume 9(10) ❖ Article e02453

SHRYOCK ET AL.

in conservation planning. Proceedings of theNational Academy of Sciences of the United Statesof America 114:12755–12760.

Hargrove, W. W., and F. M. Hoffman. 2005. Potential ofmultivariate quantitative methods for delineationand visualization of ecoregions. EnvironmentalManagement 34:S39–S60.

Hereford, J. 2009. A quantitative survey of local adap-tation and fitness trade-offs. American Naturalist173:579–588.

Hijmans, R. J., S. Phillips, J. Leathwick, and J. Elith.2016. dismo: species distribution modelling. Rpackage version 1.1-1. https://CRAN.R-project.org/package=dismo

Hufford, K., and S. Mazer. 2003. Plant ecotypes:genetic differentiation in the age of ecologicalrestoration. Trends in Ecology and Evolution18:147–155.

IPCC. 2014. Climate Change 2014: the Physical ScienceBasis. Contribution of Working Group I to the FifthAssessment Report of the IntergovernmentalPanel on Climate Change. Cambridge UniversityPress, Cambridge, UK and New York, New York,USA.

Jalonen, R., M. Valette, D. Boshier, J. Duminil, and E.Thomas. 2017. Forest and landscape restorationseverely constrained by a lack of attention to thequantity and quality of tree seed: insights from aglobal survey. Conservation Letters 11:e12424.

Johnson, R. C., M. J. Cashman, and K. Vance-Borland.2012. Genecology and seed zones for Indian rice-grass collected in the Southwestern United States.Rangeland Ecology and Management 65:523–532.

Johnson, R., L. Stritch, P. Olwell, S. Lambert, M. E.Horning, and R. Cronn. 2010. What are the bestseed sources for ecosystem restoration on USFSand BLM lands? Native Plants 11:117–131.

Kaufman, L., and P. J. Rousseeuw. 1990. Findinggroups in data: an introduction to cluster analysis.John Wiley & Sons, Hoboken, New Jersey, USA.

Kettenring, K. M., K. L. Mercer, C. R. Adams, and J.Hines. 2014. Application of genetic diversity-eco-system function research to ecological restoration.Journal of Applied Ecology 51:339–348.

Knutson, K. C., D. A. Pyke, T. A. Wirth, R. S. Arkle, D.S. Pilliod, M. L. Brooks, J. C. Chambers, and J. B.Grace. 2014. Long-term effects of seeding afterwildfire on vegetation in Great Basin shrublandecosystems. Journal of Applied Ecology 51:1414–1424.

Leger, E. A., and O. W. Baughman. 2015. What seedsto plant in the Great Basin? Comparing traits prior-itized in native plant cultivars and releases withthose that promote survival in the field. NaturalAreas Journal 35:54–68.

Leimu, R., and M. Fischer. 2008. A meta-analysis oflocal adaptation in plants. PLoS ONE. https://doi.org/10.1371/journal.pone.0004010

Lesser, M. R., and W. H. Parker. 2006. Comparison ofcanonical correlation and regression based focalpoint seed zones of white spruce. Canadian Journalof Forest Research 36:1572–1586.

Lovich, J. E., and D. Bainbridge. 1999. Anthropogenicdegradation of the southern California desertecosystem and prospects for natural recovery andrestoration. Environmental Management 24:309–326.

Maechler, M., P. Rousseeuw, A. Struyf, M. Hubert, andK. Hornik. 2016. cluster: cluster Analysis Basicsand Extensions. R package version 2.0.5. https://cran.r-project.org/package=cluster

Manel, S., F. Gugerli, W. Thuiller, N. Alvarez, P. Legen-dre, R. Holderegger, L. Gielly, and P. Taberlet. 2012.Broad Scale adaptive genetic variation in alpineplants is driven by temperature and precipitation.Molecular Ecology 21:3729–3738.

McKay, J., C. Christian, S. Harrison, and K. Rice. 2005.“How local is local?” A review of practical andconceptual issues in the genetics of restoration.Restoration Ecology 13:432–440.

Meyer, S. E., and B. K. Pendleton. 2005. Factors affect-ing seed germination and seedling establishmentof a long-lived desert shrub (Coleogyne ramosissima:Rosaceae). Plant Ecology 178:171–187.

M€ullner, D. 2013. fastcluster: fast Hierarchical,Agglomerative Clustering Routines for R andPython. Journal of Statistical Software 53:1–18.

Oksanen, J., F. Guillaume, B. Roeland Kindt, P. Legendre,P. R. Minchin, R. B. O’Hara, G. L. Simpson, P. Soly-mos, H. H. Stevens, and H. Wagner. 2015. vegan:community Ecology Package. R package version2.0-8. https://CRAN.R-project.org/package=vegan

Oldfield, S., and P. Olwell. 2015. The right seed in theright place at the right time. BioScience 65:955–956.

Omernik, J. M., and G. E. Griffith. 2014. Ecoregions ofthe conterminous United States: evolution of ahierarchical spatial framework. EnvironmentalManagement 54:1249–1266.

Parker, W. H. 1992. Focal point seed zones: site-specificseed zone delineation using geographic informa-tion systems. Canadian Journal of Forest Research22:267–271.

Parmesan, C. 2006. Ecological and evolutionaryresponses to recent climate change. Annual Reviewof Ecology, Evolution, and Systematics 37:637–669.

Peppin, D. L., P. Z. Ful�e, J. C. Lynn, A. L. Mottek-Lucas, and C. Hull Sieg. 2010. Market perceptionsand opportunities for native plant production onthe southern Colorado Plateau. Restoration Ecol-ogy 18:113–124.

❖ www.esajournals.org 18 October 2018 ❖ Volume 9(10) ❖ Article e02453

SHRYOCK ET AL.

Peres-Neto, P. R., and D. A. Jackson. 2001. How welldo multivariate data sets match? The advantagesof a Procrustean superimposition approach overthe Mantel test. Oecologia 129:169–178.

Potter, K. M., and W. W. Hargrove. 2012. Determiningsuitable locations for seed transfer under climatechange: a global quantitative method. New Forests43:581–599.

Price, M. V., and J. W. Joyner. 1997. What resources areavailable to desert granivores: seed rain or soil seedbank? Ecology 78:764–773.

R Core Team (2016). R: a language and environmentfor statistical computing. R Foundation for Statisti-cal Computing, Vienna, Austria.

Sandquist, D. R., and J. R. Ehleringer. 2003. Popula-tion- and family-level variation of brittlebush(Encelia farinosa, Asteraceae) pubescence: its rela-tion to drought and implications for selection invariable environments. American Journal of Bot-any 90:1481–1486.

Sgro, C. M., A. J. Lowe, and A. A. Hoffmann. 2011.Building evolutionary resilience for conserving bio-diversity under climate change. EvolutionaryApplications 4:326–337.

Shafer, A. B., and J. B. Wolf. 2013. Widespread evi-dence for incipient ecological speciation: a meta-analysis of isolation by ecology. Ecology Letters16:940–950.

Shryock, D. F., L. A. DeFalco, and T. C. Esque. 2018.Principal components of climate variation in

the Desert Southwest for the time periods1980-2010, 2040-2070 (RCP8.5) and (RCP4.5): U.S.Geological Survey data release. https://doi.org/10.5066/p9fcrghf

Shryock, D. F., C. A. Havrilla, L. A. DeFalco, T. C.Esque, N. A. Custer, and T. E. Wood. 2015. Land-scape genomics of Sphaeralcea ambigua in theMojave Desert: a multivariate, spatially-explicitapproach to guide ecological restoration. Conser-vation Genetics 16:1303–1317.

Shryock, D. F., C. A. Havrilla, L. A. DeFalco, T. C.Esque, N. A. Custer, and T. E. Wood. 2017. Land-scape genetic approaches to guide native plantrestoration in the Mojave Desert. Ecological Appli-cations 27:429–445.

Svenning, J. C., and B. Sandel. 2013. Disequilibriumvegetation dynamics under future climate change.American Journal of Botany 100:1266–1286.

Vogel, K. P., M. R. Schmer, and R. B. Mitchell. 2005.Plant adaptation regions: ecological and climaticclassification of plant materials. Rangeland Ecol-ogy and Management 58:315–319.

Wang, T., A. Hamann, D. Spittlehouse, and C. Carroll.2016. Locally downscaled and spatially customiz-able climate data for historical and future periodsfor North America. PLoS ONE 11:e0156720.

Wang, T., G. O’Neill, and S. N. Aitken. 2010. Integrat-ing environmental and genetic effects to predictresponses of tree populations to climate. EcologicalApplications 20:153–163.

SUPPORTING INFORMATION

Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2.2453/full

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