United States Department of Agriculture

Forest 

Service

Pacific Southwest 

Research Station

Research Paper

PSW-RP-270 July

2018

Using Species Distribution Models 

With Climate Change Scenarios 

to Aid Ecological Restoration 

Decisionmaking for Southern 

California Shrublands

Erin C. Riordan, Arlee M. Montalvo, and Jan L. Beyers
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Authors

Erin C. Riordan is a postdoctoral intern, Riverside-Corona Resource Conservation District, 4500 Glenwood Drive Building A, Riverside, CA 92501 and assistant 

project scientist, Department of Ecology and Evolutionary Biology, University of 

California, Los Angeles, CA 90095; Arlee M. Montalvo is a senior plant restoration ecologist, Riverside-Corona Resource Conservation District, 4500 Glenwood 

Drive Building A, Riverside, CA 92501 and an associate at the Agricultural Experiment Station, Department of Botany and Plant Sciences, University of California, 

Riverside, CA 92521; and Jan L. Beyers is a research plant ecologist, U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 4955 

Canyon Crest Drive, Riverside, CA 92507.
Abstract

Riordan, Erin C.; Montalvo, Arlee M.; Beyers, Jan L. 2018. Using species 

distribution models with climate change scenarios to aid ecological restoration 

decisionmaking for southern California shrublands. Res. Rep. PSW-RP-270. 

Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 130 p.

Sourcing appropriate plant material for restoration within the heterogeneous 

landscape of southern California is a nuanced task further complicated by climate 

change. We generated species distribution models (SDMs) that may be useful tools 

for incorporating climate change scenarios into ecological restoration decisionmaking for southern California scrub and shrubland habitats. We modeled regional 

patterns of suitable habitat under baseline (1951–1980) and midcentury (2040–2069) 

climate conditions for 44 focal plant taxa under five future climate scenarios. 

Projected changes in habitat suitability varied across taxa and climate scenarios. 

Future climate scenarios with the most extreme directional changes in precipitation 

(increase or decrease) resulted in the greatest projected loss of suitable habitat for 

most taxa. The majority of plant taxa we modeled had a high degree of stable future 

habitat suitability, with 31 taxa projected to have ≥75 percent of baseline suitable 

habitat maintaining suitability midcentury under at least three future climate scenarios. Infraspecific differences in projected midcentury suitable habitat highlight 

the importance of considering varieties and subspecies when applying modeling 

results to conservation and natural resource management decisionmaking. While 

we did not explicitly model plant communities, multitaxon suitability overlays 

revealed patterns for alluvial scrub, coastal sage scrub, mixed chaparral-sage scrub, 

and low-elevation chaparral vegetation groups that were less readily apparent in 

individual taxon maps. We discuss caveats regarding SDMs and suggest that they 

could be used as part of an integrated toolset for successful application.

Keywords: Alluvial scrub, chaparral, climate change, coastal sage scrub, 

ecological restoration, seed transfer, shrubland, southern California, species distribution modeling.
Preface

This study provides the background and methodology for species distribution 

modeling and habitat suitability maps generated as part of a collaborative effort by 

the Riverside-Corona Resource Conservation District and the USDA Forest Service 

Pacific Southwest Research Station to develop decision-support tools for use in 

ecological restoration within southern California ecosystems. The maps are being 

incorporated into taxon-specific plant profiles with detailed ecological and genetic 

information on foundation plant taxa from alluvial scrub, coastal sage scrub, and 

low-elevation chaparral. The expanded plant profiles will be available online and 

are intended to help inform species selection, seed sourcing, and decisions about 

seed transfer for ecological restoration and other native planting projects.

Summary

Sourcing appropriate plant material for restoration within southern California’s 

heterogeneous landscape is a nuanced task further complicated by climate change. 

It entails a detailed understanding of species distribution, dispersal ecology, 

population genetics, and biotic and abiotic interactions. Generalized tools used to 

inform restoration, such as provisional seed zones, are typically static with respect 

to environmental change and may become less effective in the context of climate 

change. We generated species distribution models (SDMs) that may be useful tools 

for incorporating climate change scenarios into recommendations for ecological 

restoration decisionmaking for southern California scrub and shrubland habitats. 

We modeled regional patterns of suitable habitat under baseline (1951–1980) and 

projected midcentury (2040–2069) climate conditions for 44 focal plant taxa under 

five future climate scenarios. Projected changes in habitat suitability varied across 

taxa and climate scenarios. Future climate scenarios with the most extreme directional changes in precipitation (projected increase or decrease) had the greatest projected loss of suitable habitat for most taxa. The majority of plant taxa we modeled 

had a high degree of stable future habitat suitability, with 31 taxa projected to have 

≥75 percent of baseline suitable habitat maintaining suitability midcentury under at 

least three future climate scenarios. Infraspecific differences in midcentury suitable 

habitat projections highlight the importance of considering within-species variation 

when applying modeling results to conservation and natural resource management 

planning, especially in areas of high environmental heterogeneity such as southern 

California. Although we did not explicitly model plant communities, multitaxon 
suitability overlays revealed patterns for alluvial scrub, coastal sage scrub, mixed 

chaparral-sage scrub, and low-elevation chaparral vegetation groups that were less 

readily apparent in individual species maps. We discuss caveats regarding SDMs 

and suggest their use as part of an integrated toolset for successful application. This 

modeling effort is part of a greater collaborative project between the RiversideCorona Resource Conservation District and the USDA Forest Service Pacific 

Southwest Research Station to provide decision-support tools guiding plant selection for use in ecological restorations within southern California shrub-dominated 

ecosystems.
Contents

1 Introduction

2 Climate Change Context

4 A Species Distribution Modeling Approach

6 Methods

6 Study Area

6 Focal Taxa and Occurrence Data

14 Climate Data

15 Land Use

17 Modeling Approach

20 Model Evaluation

20 Results

20 Midcentury Climate Exposure in Southern California

22 Species Distribution Model Performance

23 Projected Changes in Habitat Suitability

29 Discussion

30 Habitat Suitability Under Projected Climate Change

32 Climate Change, Land Use, and Fire

35 Management Implications

37 Acknowledgments

38 U.S. Equivalents

38 References

59 Appendix 1: Projected Midcentury Change in Southern

California Climate

61 Appendix 2: Detailed Species Distribution Modeling Methods

61 Infraspecies Name Assignments

63 Parent Geology

64 Treatment of Sampling Bias
66 Appendix 3: Detailed Species Distribution Modeling Results

73 Appendix 4: Baseline and Projected Midcentury Suitable Habitat Maps

for Focal Species

118 Appendix 5: Projected Midcentury Suitable Habitat for Scrub and

Shrubland Vegetation Groups

123 Appendix 6: Species Distribution Modeling Caveats

123 What Do SDM Habitat Suitability Maps Represent?

123 Factors to Consider When Applying SDM in Decisionmaking

128 Glossary
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Introduction

Land managers in southern California often face the challenge of restoring wildland 

habitat degraded by various combinations of habitat fragmentation, disruption of 

dispersal processes and biotic interactions, human-caused disturbance, and humanaltered fire regimes. The region’s diverse shrubland communities are of particular 

concern. For example, urbanization, recreational development, and increased fire 

frequency have contributed to the widespread loss of coastal sage scrub (including 

alluvial fan scrub), which has as little as 15 percent of its original extent remaining 

today (O’Leary 1990, 1995; Westman 1981). Restoration efforts to maintain habitat 

for rare species of these plant communities have been carried out for many years 

(Allen et al. 2000, Buck-Diaz et al. 2011, DeSimone 2006, Hanes et al. 1989). Lowelevation chaparral has been degraded by nonnative annual grass invasion and 

reduced shrub density under shortened fire-return intervals (Keeley and Brennan 

2012) and by construction projects, leading to more interest in methods to restore 

chaparral shrubs (Engel 2014, Wilkin et al. 2013) and the important ecosystem services they provide, such as hillslope stability (Gabet and Dunne 2002) and carbon 

sequestration (Rundel and Vankat 1989). Selecting appropriate species and plant 

material for restoration in the heterogeneous landscape of southern California is a 

complex undertaking that entails an understanding of species distribution, habitat 

relationships, ecological genetics, and biotic and abiotic interactions; this task is 

further complicated by changing climate.

Scientists have already documented ecological responses to climate change 

from species to ecosystems (Parmesan and Hanley 2015, Walther 2010). In southern 

California, Kelly and Goulden (2008) attributed elevational shifts in dominant 

plant species in the Santa Rosa Mountains to changes in regional climate from 

1977 through 2007, though past fire disturbance may have also contributed to the 

observed shifts (Schwilk and Keeley 2012). In California’s forests, climate-driven 

changes in water balance have been linked to changes in structure and species composition during the 20th century (McIntyre et al. 2015) and increased tree mortality 

during the 2012–2015 drought (Young et al. 2017). Projected changes in climate are 

expected to dramatically alter natural systems within the 21st century (Thomas et al. 

2004). Many species could face local extirpation, and even extinction, if the rate of 

climate change exceeds their capacity to adapt locally or migrate to track suitable 

conditions (Christmas et al. 2016, Corlett and Westcott 2013, Malcolm et al. 2002). 

Consideration of the potential effects of future climate change on reference or target 

ecosystems, species, and populations will likely become increasingly important for 

the long-term success of ecological restoration projects (Breed et al. 2013, Harris et 

al. 2006, Havens et al. 2015, Jackson and Hobbs 2009).

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RESEARCH PAPER PSW-RP-270

Climate Change Context

California has experienced an overall warming trend over the past century, 

although patterns vary spatially and temporally (Cordero et al. 2011, Rapacciuolo 

et al. 2014). Weather station data show warming trends for statewide minimum and 

maximum temperatures from 1918 through 2006, accelerating after 1970 to rates 

of +0.31 °C and +0.27 °C per decade, respectively (Cordero et al. 2011). Southern 

California has experienced some of the greatest regional increases in temperature 

across the state, particularly interior mountain areas where minimum and maximum temperatures have increased 0.42 °C and 0.26 °C per decade, respectively, 

from 1970 through 2006 (Cordero et al. 2011). Intensely urbanized areas have 

experienced especially large increases in minimum and maximum temperature 

(LaDochy et al. 2007), in part from a heat island effect (Kalnay and Cai 2003). Los 

Angeles, for example, experienced as much as a 5 °C increase in minimum temperature and a 2 °C increase in maximum temperature from 1950 to 2000 (LaDochy 

et al. 2007). 

For vegetation, water availability in particular plays a central role in shaping 

plant distribution (Rehfeldt et al. 2006; Stephenson 1990, 1998; Woodward 1987). 

Patterns in precipitation have also changed in California, although high spatial 

and temporal variability make detecting trends difficult. For much of northern and 

central California, annual precipitation increased in 1970–2009 relative to 1900–

1939, whereas precipitation decreased slightly in southern California during the 

same time period (Rapacciuolo et al. 2014). Shifts in temperature and precipitation 

affected water balance in southern California, causing large increases in climatic 

water deficit (CWD) for 1970–2009 relative to 1900–1939 (Rapacciuolo et al. 2014). 

CWD is the evaporative demand not met by available water, defined as the difference between potential evapotranspiration (PET)—the amount of water that could 

potentially evaporate or transpire from vegetation given unlimited water—and 

actual evapotranspiration (AET)—the evaporative water loss from vegetation based 

on actual water availability (Stephenson 1990). Temperature and precipitation (as 

it affects soil moisture storage) both influence AET. Together, these variables are 

important indicators of water availability and climate stress experienced by plants 

(Stephenson 1998). 

Temperatures are projected to continue warming in California throughout the 

21st century (Cayan et al. 2008, Hayhoe et al. 2004, Pierce et al. 2013). Statewide 

mean temperature could increase as much as 5.8 °C by 2070–2099 relative to 

1971–2000 under continued high greenhouse gas emissions (Mastrandrea and Luers 

2
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

2012). In the Los Angeles Basin and surrounding mountains, mean temperature 

is projected to increase anywhere from 2.8 to 6.2 °C by the end of the 21st century 

relative to 1981–2000 (Sun et al. 2015). Without measures to reduce greenhouse 

gas emissions, a new regional climate state could emerge in southern California 

where average temperatures at the end of the 21st century are outside the range of 

historical interannual variability (Sun et al. 2015). Future change in precipitation for 

California is less certain, with both increases and decreases in annual precipitation 

projected, depending on the general circulation model (GCM) considered (Berg 

and Hall 2015, Dettinger et al. 2015, Pierce et al. 2013). A recent analysis of many 

GCMs under a “business-as-usual” (continued high) emission scenario suggests 

that California will experience precipitation increases, though with less increase 

toward the south (Allen and Luptowitz 2017). Extreme dry and extreme wet events 

are projected to increase in frequency by the end of the 21st century (Berg and Hall 

2015). Mid-21st-century projected change in precipitation regionally downscaled for 

the Los Angeles Basin and surrounding mountains ranges from +19 mm to -25 mm 

relative to 1981–2000 (Berg et al. 2015). Regardless of the direction of precipitation change in southern California, the change will likely be small compared to the 

naturally high interannual variability (Berg and Hall 2015, Pierce et al. 2013). 

Warming temperatures since the mid-20th century are also significantly altering 

the hydrologic cycle across the Western United States (Barnett et al. 2008, Hamlet 

et al. 2007) through decreased snowpack (Mote et al. 2005), increased proportion of 

precipitation falling as rain rather than snow (Knowles et al. 2006), and changes in 

the timing and variability of spring snowmelt and streamflow (Maurer et al. 2007, 

Pagano and Garen 2005, Stewart et al. 2005). Projected warming and hydrological shifts will likely increase evaporative demand in the 21st century (Dettinger 

et al. 2015), while changes in snowmelt, resulting effects on streamflow (Hayhoe 

et al. 2004), and increases in the frequency of extreme dry and wet hydrological 

years (Berg and Hall 2015, Kim 2005) could severely stress already-strained water 

resources and infrastructure. In the mountains of southern California, widespread 

decline in both snowfall and snowpack are projected for the end of the 21st century 

(Sun et al. 2016). If greenhouse gas emissions continue to increase at current rates, 

less than 50 percent of snowfall and 26 percent of snowpack relative to 1981–2010 

levels are projected to remain at elevations under 2400 m by the mid-21st century 

(Sun et al. 2016). Warming-accelerated spring snowmelt and declines in snowpack 

will likely affect streamflow for the region. Drought risk in California may also 

increase. Although periodic severe drought is common in California, the recent 

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RESEARCH PAPER PSW-RP-270

2011–2015 drought was exceptionally severe, driven by low, albeit not unprecedented, precipitation and record high temperatures (Griffin and Anchukaitis 2014). 

Anthropogenic warming is increasing the probability that low-precipitation years 

will coincide with warm conditions (Diffenbaugh et al. 2015) and contribute to 

extreme drought (Williams et al. 2015). 

A Species Distribution Modeling Approach

When carefully constructed and interpreted, species distribution models (SDMs) 

can be a useful tool for incorporating climate change into natural resource decisionmaking (Araújo and Peterson 2012, Guisan et al. 2013, Schwartz 2012), including choices of species and source locations for restoration (Havens et al. 2015). 

Species distribution models correlate species occurrence data with environmental 

information to predict habitat suitability in space and time (Elith et al. 2006, 

Guisan and Thuiller 2005). Model algorithms tailored for presence-only data and 

small sample sizes (e.g., Maxent) (Phillips et al. 2006), combined with statistical 

techniques addressing sampling bias (e.g., Phillips et al. 2009), allow the investigation of the geographic distribution of suitable habitat for species even with limited 

or incomplete occurrence information. This is particularly valuable when time or 

other resource constraints prevent the collection of detailed field data. Gastón et al. 

(2014) found that SDMs were as effective as local experts in ranking the suitability 

of different plant taxa at forest restoration sites and proposed SDMs as a fast and 

cost-effective method to inform species selection.

Some species and plant communities may be affected more than others by 

changes in climate. A species’ vulnerability to climate change can be evaluated 

based on climate exposure (the degree to which a system is exposed to climate 

change) and factors related to its sensitivity (how severely it is affected) and adaptive capacity (ability to adjust and maintain fitness), based on life history traits, 

dispersal capacity, pattern of genetic diversity, and history of population fragmentation (Butt et al. 2016, Dawson et al. 2011, Fordham et al. 2012, Williams et al. 2008). 

When combined with future climate scenarios, SDMs can provide an estimate of 

climate exposure, measured in terms of projected change in suitable habitat. These 

future suitable habitat projections can then help inform various aspects of decisionmaking in restoration. For example, SDMs have recently been applied to improve 

long-term restoration success by identifying target species having suitable habitat 

both now and in the future (Butterfield et al. 2016, López-Tirado and Hidalgo 

2016). Gelviz-Gelvez et al. (2015) considered ecological characteristics together 

with SDM-suitable habitat projections under future climate scenarios to select plant 

4
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

species for restoration in a semiarid region of central Mexico. Species distribution 

models have also been used to assess the need for and potential success of intervention such as assisted migration (e.g., Hällfors et al. 2016a). If such intervention is 

warranted, SDM used in concert with maps that show how climate variables may 

shift spatially can inform the implementation of climate-adjusted provenancing 

(sourcing) scenarios such as those described by Prober et al. (2015). 

Most SDM approaches focus at the species level, pooling occurrence data 

throughout a species’ range and assuming uniform environmental tolerance 

(Hällfors et al. 2016b). This practice obscures patterns of within-species variation 

that could be important for restoration success. Modeling approaches that address 

intraspecific variation in niche structure reveal distinct patterns in predicted 

distributions as well as forecasts of change under future climate scenarios (Hällfors 

et al. 2016b, Pearman et al. 2010, Valladares et al. 2014). Ecological and genetics 

studies that include multiple named infraspecies (variety or subspecies) commonly 

reveal differences in habitat affinities (Cole 1967, Montalvo and Ellstrand 2000, 

Stebbins 1942, Wang et al. 1999). In southern California, the presence of geographic 

variation in environmental tolerance and local adaptation across populations of 

many plant species suggests that intraspecific variation would be useful to take into 

account when modeling species distributions to inform restoration decisionmaking. 

Ideally, information on genetic variation in adaptive traits would be used to delineate populations into groups or ecotypes for modeling at an intraspecific level (e.g., 

Richardson et al. 2014). However, these data are unavailable for many plant taxa. 

In the absence of detailed population-level genetic data, infraspecies level models 

(Pearman et al. 2010, Rehfeldt et al. 2014) or models based on groups of populations 

delineated by climate or geography (Hällfors et al. 2016b, Hamann and Aitken 

2013) may be more informative than pooled species-level models.

Focusing on the South Coast hydrologic region (Calwater 2.2.1) (CIWMC 

2004), we used SDMs to create habitat suitability maps to aid species and infraspecies selection and to help inform decisions about seed sourcing for ecological 

restoration in scrub and shrubland habitats. First, we modeled suitable habitat for 

44 focal plant taxa under baseline (1951–1980) climate conditions. We restricted 

occurrence and climate data to the study area to model regional patterns of habitat 

suitability and, when possible, modeled at the infraspecific (variety or subspecies) level. We then projected baseline suitability models under five future climate 

scenarios for mid 21st century (2040–2069) conditions to identify potential shifts 

in suitable habitat and how the shifts differ in their severity and spatial context 

under the different climate change scenarios. We also created multitaxon suitability 

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RESEARCH PAPER PSW-RP-270

overlays to highlight potential effects of climate change on southern California 

alluvial scrub, coastal sage scrub, mixed chaparral-sage scrub, and low-elevation 

chaparral vegetation groups. Finally, we discuss potential climate change effects in 

the context of additional nonclimatic drivers known to strongly influence patterns 

of vegetation structure in southern California and which are expected to interact 

with climate change—namely fire, invasive species, and land use—and we highlight caveats regarding SDM use in planning and decisionmaking. 

Methods

Study Area

Our study area encompasses the South Coast hydrologic region (Calwater 2.2.1) 

(CIWMC 2004), excluding the Channel Islands but including adjacent hydrologic 

subareas from the Colorado River hydrologic region plus a 30-km buffer (fig. 1). 

This region lies within a globally recognized biodiversity hotspot (Myers et al. 

2000) where high plant diversity and endemism is intermixed with high human 

impact. Of California’s 39-million population, an estimated 18 million people (46 

percent) inhabit Los Angeles, Orange, Riverside, and San Diego Counties (U.S. 

Census Bureau 2015). Human activities and land use are concentrated in coastal 

lowland and low-elevation shrubland habitats. 

The climate in the region is typical of Mediterranean-type ecosystems with 

cool moist winters and hot dry summers. Oceanic influences moderate winter and 

summer temperatures along the coast (fig. 2), and precipitation declines sharply to 

the east of the mountains in the bordering arid desert ecoregions. Comparisons of 

30-year climate averages show recent warming in 1981–2010 relative to 1951–1980 

(fig. 2). Although precipitation was variable during this time period, CWD estimated from the California Basin Characterization Model (CA-BCM) (Flint et al. 

2013) increased throughout most of our study area because warming temperatures 

increased evaporative demand. Climatic water deficit, which can be thought of as a 

measure of drought stress, can increase even with increased precipitation (Thorne 

et al. 2012).

Focal Taxa and Occurrence Data

We modeled suitable habitat for 44 plant taxa that are characteristic of southern 

California scrub and shrubland habitats and are important for ecological restoration 

(table 1). We chose taxa used in habitat restoration that are considered to be foundational: either dominants or codominants, or indicator species of low-elevation 

shrubland communities. We categorized taxa into vegetation groups based on the 

Text continues on page 11.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 1—Southern California study area with (A) ecological sections (color key) and subsections (letters) and (B) South 

Coast hydrologic region and adjacent watersheds with 30-km buffer used for modeling extent. Data sources: ecological 

sections are from Goudey and Smith (1994) updated by Cleland et al. (2007); hydrologic regions with watersheds are from 

Calwater 2.2.1 (CIWMC 2004).

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RESEARCH PAPER PSW-RP-270

Figure 2—Baseline (1951–1980) climate averages and recent change (1951–1980) relative to baseline in southern California. Climate 

variables: T = minimum winter temperature December through February, T = maximum summer temperature June through min max

August, PPT = annual precipitation, CWD = climatic water deficit. Black lines indicate ecological section boundaries; dark gray lines 

indicate ecological subsection boundaries. Climate data are based on PRISM 30-year averages, downscaled by the California Basin 

Characterization Model (Flint et al. 2013).

8
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Table 1—List of modeled plant taxa

Scientific namea Common name Family Life formb

Fire c response

Acmispon glaber var. brevialatus (Ottley) 

Brouillet

Deerweed, California 

broom

Fabaceae Subshrub S

Acmispon glaber (Vogel) Brouillet var. glaber Deerweed, California 

broom

Fabaceae Subshrub S

Adenostoma fasciculatum Hook. & Arn. var. 

fasciculatum

Chamise Roseaceae Shrub FS

Arctostaphylos glandulosa subsp. cushingiana 

(Eastw.) J.E. Keeley, M.C. Vasey & V.T. Parker

Cushing’s manzanita Ericaceae Shrub OR

Arctostaphylos glandulosa Eastw. subsp. 

glandulosa

Eastwood’s manzanita Ericaceae Shrub OR(FSd

)

Arctostaphylos glandulosa subsp. mollis 

(J.E. Adams) P.V. Wells

Transverse Range 

manzanita

Ericaceae Shrub OR

Arctostaphylos glauca Lindl. Big-berry manzanita Ericaceae Shrub S

Artemisia californica Less. California sagebrush Asteraceae Subshrub FS

Ceanothus crassifolius Torr. var. crassifolius Hoaryleaf ceanothus Rhamnaceae Shrub S

Ceanothus cuneatus (Hook.) Nutt. var. cuneatus Buck brush Rhamnaceae Shrub S

Ceanothus leucodermis Greene Chaparral whitethorn Rhamnaceae Shrub FS

Ceanothus megacarpus Nutt. var. megacarpus Big-pod ceanothus Rhamnaceae Shrub S

Ceanothus oliganthus Nutt. Hairy ceanothus Rhamnaceae Shrub S

Ceanothus perplexans Trel. Cupped leaf ceanothus Rhamnaceae Shrub S

Ceanothus tomentosus Parry Woolly leaf ceanothus Rhamnaceae Shrub S

Ceanothus vestitus Greenea Mojave ceanothus Rhamnaceae Shrub S

Cercocarpus betuloides Nutt. var. betuloides Birch-leaf mountain 

mahogany

Rosaceae Shrub OR

Corethrogyne filaginifolia (Hook. & Arn.) Nutt. Common sandaster Asteraceae Suffru

perennial 

OR

Deinandra fasciculata (DC.) Greene Fascicled tarplant Boraginaceae Annual herb S

Encelia californica Nutt. California encelia, 

bush sunflower

Asteraceae Subshrub OR

Encelia farinosa A. Gray ex Torr. Brittlebush, incienso Asteraceae Subshrub FS

Eriodictyon crassifolium Benth. var. crassifolium Thickleaf yerba santa Boraginaceae Subshrub FS

Eriodictyon crassifolium var. nigrescens Brand Bicolored yerba santa Boraginaceae Subshrub FS

Eriodictyon trichocalyx var. lanatum

(Brand) Jeps.

San Diego yerba santa Boraginaceae Subshrub FS

Eriodictyon trichocalyx A. Heller var. trichocalyx Hairy yerba santa Boraginaceae Subshrub FS

Eriogonum fasciculatum Benth. var. fasciculatum Coastal California 

buckwheat

Polygonaceae Subshrub FS

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Table 1—List of modeled plant taxa (continued)

Scientific namea Common name Family Life formb

Fire c response

Eriogonum fasciculatum var. foliolosum (Nutt.) 

Abrams

Leafy California 

buckwheat

Polygonaceae Subshrub FS

Eriogonum fasciculatum var. polifolium (Benth.) 

Torr. & A. Gray

Mojave desert 

California buckwheat

Polygonaceae Subshrub FS

Eriophyllum confertiflorum (DC.) A. Gray var. 

confertiflorum

Golden yarrow Polygonaceae Suffru

 perennial 

FS

Hesperoyucca whipplei (Torr.) Trel. Chaparral yucca Agavaceae Suffru rosette OR

Heteromeles arbutifolia (Lindl.) M. Roem. Toyon Roseaceae Shrub OR

Lepidospartum squamatum (A. Gray) A. Gray Scalebroom Asteraceae Shrub OR

Malacothamnus fasciculatus (Nutt. ex Torr. & A. 

Gray) Greene var. fasciculatus

Chaparral mallow Malvaceae Subshrub FS

Malosma laurina (Nutt.) Nutt. ex Abrams Laurel sumac Anacardiaceae Shrub FS

Phacelia minor (Harv.) Thell. ex F. Zimm. California bells Boraginaceae Annual herb S

Plantago erecta E. Morris California plantain, 

dot-seed plantain

Plantaginaceae Annual herb S

Prunus ilicifolia (Nutt. ex Hook. & Arn.) 

D. Dietr. subsp. ilicifolia

Islay, hollyleaf cherry Roseaceae Shrub OR

Quercus berberidifolia Liebm. Scrub oak Fagaceae Shrub OR

Rhamnus crocea Nutt. Spiny redberry Rhamnaceae Shrub OR

Rhamnus ilicifolia Kellogg Hollyleaf redberry Rhamnaceae Shrub OR

Rhus ovata S. Watson Sugar bush Anacardiaceae Shrub FS

Salvia apiana Jeps. White sage Lamiaceae Subshrub FS

Salvia mellifera Greene Black sage Lamiaceae Subshrub/

shrub

FS

Stipa pulchra Hitchc. Purple needlegrass Poaceae Bunchgrass FS

a Taxonomy follows the Jepson eFlora (Jepson Flora Project 2017) except for Ceanothus vestitus, where we follow the pervious treatment in Baldwin et 

al. (2012). The most recent eFlora treatment of Ceanothus vestitus places the taxon in Ceanothus pauciflorus DC. (Jepson Flora Project 2017).

b

 Growth forms: subshrubs are stiff to somewhat woody at base, stems do not die back each year; suffru perennial = suffrutescent perennial, 

branches stiff and sometimes somewhat woody at base but with substantial annual die-back of stems; suffru rosette = suffrutescent rosette, 

monocots with stiff leaves in a rosette from a semiwoody crown (no true wood).

c Fire response: S = obligate seeder; FS = facultative seeder (both seeds and resprouts); OR = obligate resprouter (seeds killed by fire) 

(Keeley et al. 2006).

d Some populations reseed as well as resprout after fire (Jan Beyers, personal observation).

10
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

plant communities with which they are commonly associated (Sawyer et al. 2009): 

alluvial scrub, coastal sage scrub, mixed chaparral-sage scrub, and low-elevation 

chaparral (table 2). We define low-elevation chaparral as occurring on the lower 

slopes of southern California mountains from 300- to 1500-m elevation (lower 

chaparral sensu Schoenherr 1992) and mixed chaparral-sage scrub where the two 

communities intergrade along low elevations or south-facing slopes in chaparral. 

Vegetation groups are not mutually exclusive, and taxa listed in a group do not necessarily occur together in local vegetation alliances or associations. Predominance 

in a particular vegetation association can depend upon successional stage, postfire 

disturbance, or both. Additionally, different assemblages within a vegetation group 

may occur within contrasting exposures or geographic regions.

We obtained occurrence data from the Consortium of California Herbaria, or 

(CCH) (CCH 2016), Calflora (Calflora 2016), alluvial scrub vegetation surveys 

from southern California (Buck-Diaz et al. 2011, Wirka 1997), and field surveys in 

chaparral and coastal sage scrub that we conducted in 2015. CCH provides a record 

of taxon occurrences based on herbarium specimens for which collection goals 

and collection intensity vary across taxa and locations. To capture taxon occurrences across ecological gradients, we augmented the CCH data with survey data 

that recorded the presence of all taxa. For the 2015 surveys, we first mapped the 

density of herbarium collections of the focal taxa to identify under-collected areas 

in the lower elevations of the Santa Ana Mountains, San Bernardino Mountains, 

San Jacinto Mountains, and the Gavilan Hills in Riverside County. We then used 

provisional seed zone boundaries (Bower et al. 2014) and ecological subsection 

boundaries (Cleland et al. 2007) to select survey locations that maximized sampling 

across topographic, climatic, and ecological gradients. At each survey location we 

recorded geographic coordinates and elevation using a global positioning system 

(GPS) (Garmin Oregon 6001

), slope, aspect, and general soil characteristics. We 

noted the dominant vegetation and presence of any focal taxa within 10 m. We 

recorded additional focal taxa on contrasting slopes within 250 m, using binoculars 

for identifications in rugged terrain, and noted the direction and estimated distance 

from the GPS waypoint. 

1

 The use of trade or firm names in this publication is for reader information and does 

not imply endorsement by the U.S. Department of Agriculture or the Riverside-Corona 

Resource Conservation District of any product or service.

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RESEARCH PAPER PSW-RP-270

Table 2—Vegetation group assignments for focal plant taxa

Scientific name

a Vegetation group

ALSC CSS CHAP MIXED

Acmispon glaber var. brevialatusb X X X X

Acmispon glaber var. glaberb X X X X

Adenostoma fasciculatum var. fasciculatum X X X

Arctostaphylos glandulosa subsp. cushingiana X

Arctostaphylos glandulosa subsp. glandulosa X

Arctostaphylos glandulosa subsp. mollis X

Arctostaphylos glauca X

Artemisia californica X X X

Ceanothus crassifolius var. crassifolius X

Ceanothus cuneatus var. cuneatus X

Ceanothus leucodermis X

Ceanothus megacarpus var. megacarpus X

Ceanothus oliganthus X

Ceanothus perplexans X

Ceanothus tomentosus X

Ceanothus vestitus X

Cercocarpus betuloides var. betuloides X X

Corethrogyne filaginifolia X X X X

Deinandra fasciculata X

Encelia californica X

Encelia farinosa X

Eriodictyon crassifolium var. crassifolium X X X

Eriodictyon crassifolium var. nigrescens X X X

Eriodictyon trichocalyx var. lanatum X X

Eriodictyon trichocalyx var. trichocalyx X X X

Eriogonum fasciculatum var. fasciculatum X X

Eriogonum fasciculatum var. foliolosum X X X X

Eriogonum fasciculatum var. polifolium X X

Eriophyllum confertiflorum var. confertiflorumb X X X

Hesperoyucca whipplei X X X X

Heteromeles arbutifolia X X

Lepidospartum squamatum X

Malacothamnus fasciculatus var. fasciculatusb X X X

Malosma laurina X X X X

Phacelia minorb X X X

12
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Table 2—Vegetation group assignments for focal plant taxa (continued)

Scientific name

Vegetation groupa

ALSC CSS CHAP MIXED

Plantago erecta X

Prunus ilicifolia subsp. ilicifolia X X

Quercus berberidifolia X

Rhamnus crocea X X X

Rhamnus ilicifolia X

Rhus ovata X X

Salvia apiana X X X

Salvia mellifera X X X X

Stipa pulchra X

a Vegetation groups are based upon the plant communities in which each taxon predominantly occurs: ALSC 

= alluvial scrub, CSS = coastal sage scrub, CHAP = low-elevation chaparral (305 to 1524 m), MIXED = 

chaparral-sage scrub.

b Common in early postfire chaparral.

We used the following criteria to ensure a high quality of occurrence records. 

We omitted herbarium record occurrences (1) having ≥2.5 km error or uncertainty 

associated with the geographic coordinates; (2) annotated or otherwise identified 

as hybrids or having introgression; or (3) planted or cultivated, including suspected 

roadside revegetation plantings or introductions. We only used Calflora records that 

were contributed by the rapid vegetation assessments conducted by the California 

Department of Fish and Wildlife and the U.S. Geological Survey Central Mojave 

Vegetation Mapping project. Because the majority of taxa modeled are long-lived, 

we included records collected from 1951 to present, assuming that herbarium 

collections in years after the 1951–1980 baseline climate period were likely from 

plants established during or close to the baseline time period. Including these recent 

collections increased our sample of occurrences with high-quality location data and 

was necessary to ensure the best possible model accuracy in representing species’ 

climatic niches (e.g., Feeley and Silman 2011). Similarly, we excluded all occurrences collected before 1950 because climate in southern California has already 

shifted since the early 20th century, increasing the likelihood that older records 

could reflect climatic conditions at plant establishment that differ from the baseline 

climate used in SDMs. A more recent time period for the baseline climate (e.g., 

1981–2010) could include climate conditions that have already changed since the 

establishment of the collected specimen and are no longer suitable for successful 

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RESEARCH PAPER PSW-RP-270

reproduction and establishment (Svenning and Sandel 2013). We discuss this issue 

of vegetation disequilibrium, climate change, and SDM assumptions in appendix 6. 

We identified climatic outliers as any record occurring in an area of mean annual 

temperature or annual precipitation greater or equal to five standard deviations 

from the species’ mean. All climatic and geographic outliers were examined and 

excluded if the mapped coordinates did not reasonably match the locality description in the database. 

As species misidentification errors affect the accuracy and spatial predictions of 

SDMs (Costa et al. 2015), we visited major data contributors to CCH—the University of California and Jepson Herbaria at the University of California, Berkeley; the 

California Academy of Sciences Herbarium; Rancho Santa Ana Botanic Garden 

(RSA); and the Herbarium at the University of California, Riverside (UCR)—to 

verify the identification of the following easily confused Ceanothus taxa: C. 

cuneatus, C. perplexans, C. vestitus, C. tomentosus, and C. oliganthus. Previous 

taxonomic treatments list C. perplexans and C. vestitus as varieties of C. greggii. 

Where possible, we identified records of C. greggii missing an infraspecies determination to either C. perplexans or C. vestitus. At RSA and UCR, we also examined specimens of C. crassifolius, Acmispon glaber, and Arctostaphylos glandulosa

to verify varietal status. At these two herbaria, we checked any focal taxa specimens with dubious geographic locations for misidentification or labeling errors. 

Finally, we assigned infraspecific names for any record missing an infraspecific 

determination in our occurrence database when the infraspecies could reasonably 

be inferred from the record’s geographic location and the taxon description (app. 2). 

Climate Data

We downloaded 30-year averages of baseline (1951–1980) and midcentury 

(2040–2069) climate data at 270 m resolution from the CA-BCM (Flint et al. 2013). 

The CA-BCM applies a regional water-balance model to simulate hydrological 

responses to climate across the California hydrologic region (Flint et al. 2013). 

Monthly historical temperature and precipitation data are based on ParameterElevation Relationships on Independent Slopes Model (PRISM) data (Daly et 

al. 1994) spatially downscaled by the gradient-inverse distance squared (GIDS) 

approach. We selected five general circulation models (GCMs) from phase five of 

the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012) under a 

business-as-usual greenhouse gas emission scenario, the representative concentration pathway (RCP) 8.5. The RCP 8.5 scenario corresponds to an increase in global 

radiative forcing of 8.5 W m-2 from preindustrial times through 2100 (van Vuuren 

14
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

et al. 2011) and represents a high greenhouse gas future that most closely tracks our 

current emissions trajectory. The five GCMs were chosen to represent the range in 

projected precipitation and temperature change: CCSM4, CNRM-CM5, FGOALSG2, IPSL-CM5A-LR, and MIROC-ESM (fig. 3, app. 1). 

We calculated 19 bioclimatic variables (Busby 1991) from monthly temperature and precipitation 30-year averages. We then examined correlations among 

bioclimatic and hydrological CA-BCM variables, selecting a subset of variables 

for modeling that (1) are important in driving plant distributions in the Western 

United States (Rehfeldt et al. 2006, Stephenson 1998), (2) minimized correlations 

between variables (Pearson’s |r| < 0.8) (table 3), and (3) maximized model performance. These variables were minimum winter temperature (T ) calculated as min

the minimum temperature averaged over the coldest months (December through 

February), summer maximum temperature (T ) calculated as the maximum max

temperature averaged over the hottest months (June through August), temperature 

seasonality (T ) calculated as the standard deviation of the weekly mean tem- seas

peratures, precipitation seasonality (PPT ) calculated as the standard deviation of seas

the weekly precipitation estimates expressed as a percentage of the annual mean of 

those estimates, precipitation of the warmest quarter (PPT ), precipitation of summer

the coldest quarter (PPT ), CWD, and AET. The last two variables, CWD and winter

AET, reflect biologically relevant estimates of water balance that have been associated with vegetation distribution, structure, and mortality (Das et al. 2013; McIntyre 

et al. 2015; Stephenson 1990, 1998). We also included a variable for soil parent geology (app. 2). Although different variables within the subset may contribute more or 

less to the habitat suitability for individual taxa, we included all eight variables for 

comparability of models across taxa. 

Land Use

We obtained contemporary urban and agricultural land use from the California 

State Department of Forestry and Fire Protection Fire and Resource Assessment 

Program vegetation map (FRAP 2015). This vegetation map is a compilation of 

best available land cover data from about 1990 to 2014 and is available as a raster 

of 30 m resolution. We used spatial overlays to exclude area already converted to 

human land-cover types from habitat suitability maps and habitat change calculations. We did not include projections for future land use. 

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Figure 3—Range of projected midcentury (2040–2069) change in southern California climate relative to baseline (1951–1980) climate averages for 18 climate scenarios downscaled by 

the California Basin Characterization Model. Points show mean ± standard deviation change in annual precipitation and minimum winter temperature summarized for (A) Southern 

California Coast (261B) and (B) Southern California Mountains and Valleys (M262B) ecological sections of Goudey and Smith (1994) updated by Cleland et al. (2007). The climate 

scenarios used for species distribution modeling and climate change visualizations are circled in red. 

16
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Table 3—Pairwise Pearson’s correlation coefficients for climatic variables used in species 

distribution modeling

PPTseas PPTsummer PPTwinter Tseas AET CWD Tmin Tmax

PPTseas 1.000

PPTsummer -0.699 1.000

PPTwinter 0.520 0.075 1.000

Tseas -0.443 0.604 0.133 1.000

AET 0.231 0.088 0.552 -0.110 1.000

CWD 0.090 -0.494 -0.589 -0.326 -0.384 1.000

Tmin 0.511 -0.677 -0.139 -0.777 0.163 0.625 1.000

Tmax -0.226 0.060 -0.300 0.405 -0.035 0.449 -0.046 1.000

Pearson’s correlation coefficients calculated across occurrence localities. Climate variables: PPTseas = precipitation seasonality, 

PPTsummer = precipitation of the warmest quarter, PPTwinter = precipitation of the coldest quarter, Tseas = temperature seasonality, 

AET = actual evapotranspiration, CWD = climatic water deficit, Tmin = minimum winter temperature December–February, Tmax = 

summer maximum temperature June–August.

Modeling Approach

Species distribution models correlate occurrence data and spatially explicit environmental data to predict species distributions in space and time (Elith and Leathwick 

2009, Guisan and Thuiller 2005). Using the modeling algorithm Maxent (version 

3.3.3 k) (Phillips et al. 2006), we used the occurrence data and environmental layers 

described above to predict suitable habitat for focal plant taxa under baseline and 

midcentury climate conditions. Maxent is a high-performing SDM method tailored 

to presence-only data (Elith et al. 2006, Merow et al. 2013, Phillips et al. 2006). For 

each taxon, we modeled the baseline (1951–1980) taxon-climate relationship, then 

forecast that model onto each of the five future climate scenarios to map future 

habitat suitability under midcentury (2040–2069) conditions. We ran models with 

the following settings: (1) linear, quadratic, and product features only; (2) default 

beta-regularization; (3) 10,000 bias-weighted background points (details below); and 

(4) logistic output ranging from zero to one. The modeling extent was restricted to

the study area described above (fig. 1). We implemented Maxent in R (R Core Team

2016) with the “dismo” package (Hijmans et al. 2016).

Spatial and temporal biases in natural history collections can reduce SDM 

predictive performance (Kadmon et al. 2004, Kramer-Schadt et al. 2013, Syfert et 

al. 2013). We corrected for sampling bias by weighting background sampling with 

a bias grid based on a target group (Phillips et al. 2009) consisting of digitized 

herbarium records for all vascular plants in southern California collected after 1950 

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RESEARCH PAPER PSW-RP-270

(CCH 2016). Our aim was to produce geographic sampling bias in the background 

data that was similar to that of the occurrence data. The bias grid was derived from 

a Gaussian-kernel density map of the target group occurrences with a standard 

deviation of 100 km, rescaled from 1 to 20 (Elith et al. 2010). We then used this 

grid to weight background sampling within the study area. We restricted background sampling to within 100 km of known occurrences inside the study area. We 

investigated two additional methods to control for sampling bias but report results 

only for the bias grid approach, as it gave the highest performance with the least 

overfitting for the majority of focal taxa (see app. 2 for a detailed comparison of 

methods). Model complexity also affects predictive performance and transferability 

across space and time (Merow et al. 2014, Moreno-Amat et al. 2015, Warren and 

Seifert 2011). Overly complex (overfit) models produce predictions that reflect noise 

or sampling idiosyncrasies rather than a species’ environmental tolerances. Thus, 

we used only linear, quadratic, and product features to limit the complexity of the 

climate response curves created in Maxent.

We calculated future changes in habitat suitability from binary suitable 

habitat maps created using the maximum sensitivity plus specificity threshold 

(Jimenez-Valverde and Lobo 2007, Liu et al. 2013). For each taxon by future climate scenario, we calculated the percentage change in suitable habitat relative to 

baseline. We defined changes in future suitable habitat as stable = suitable under 

baseline and future conditions, loss = suitable under baseline but unsuitable under 

future conditions, or gain = unsuitable under baseline and becoming suitable under 

future conditions (fig. 4). We assumed unlimited dispersal within the study area 

for projections of suitability gain and assumed no dispersal (species are unable to 

disperse to new areas of suitable habitat) for projections of suitability loss. These 

dispersal assumptions are meant to cap the two extremes of potential habitat change 

under future climate. Expansion into newly suitable habitat will depend on intrinsic 

species traits and habitat connectivity. We excluded areas already converted to 

human land cover types from all suitable habitat calculations. To show how forecasted habitat suitability varies with climate scenario, we created consensus maps 

for each species. Consensus maps show the degree of agreement in future suitable 

habitat across the five climate scenarios, where values range from 1 (no agreement) 

to 5 (full agreement). To visualize potential climate change effects on different 

vegetation groups, we overlaid the suitability maps of the individual taxa associated 

with each group: alluvial scrub, coastal sage scrub, mixed chaparral-sage scrub, and 

low-elevation chaparral (table 2). We excluded bunchgrass and annual growth forms 

from vegetation group maps, as these are not structurally dominant elements of our 

vegetation groups. 

18
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 4—Example of occurrence data and baseline (1951–1980) and projected midcentury 

(2040–2069) suitable habitat for Rhus ovata. Midcentury suitable habitat maps show agreement 

across the five climate model scenarios and range from one (no agreement) to five (full agreement). 

Projected change in suitable habitat is defined as follows: stable = suitable under baseline and future 

conditions; loss = suitable under baseline but unsuitable under future conditions; gain = unsuitable 

under baseline and becoming suitable under future conditions. In all maps, suitable habitat already 

converted to human land uses (FRAP 2015) is masked in dark gray.

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RESEARCH PAPER PSW-RP-270

Model Evaluation

We evaluated overall model performance by the area under the receiver operator 

curve statistic (AUC) (Fielding and Bell 1997, Lobo et al. 2008) averaged over 

fivefold cross-validation replicates. AUC ranges from 0 to 1, where a score of 1 

indicates perfect discrimination between presence and background, 0.5 indicates 

predictive performance no better than random, and < 0.5 indicates predictive 

performance worse than random. To evaluate model overfitting, we calculated the 

difference between the training and test AUC (AUC ); the smaller the difference, diff

the lesser the degree of overfitting in the model (Warren and Seifert 2011). We also 

included two threshold-dependent measurements of performance, the omission 

rate at the 10 percent test threshold (OR ) and the omission rate at the maximum 10p

sensitivity plus specificity threshold (OR ) used to create binary suitability mSSS

maps. The omission (or false negative) rate is the proportion of test occurrences 

incorrectly classified and provides information about the discriminatory ability of 

the model at a particular threshold. We identified important climatic variables from 

Maxent’s percentage of permutation importance metric. Permutation importance is 

calculated in Maxent by randomly permuting the values of each variable among the 

training points and measuring the drop in training AUC. A large decrease indicates 

heavy model dependence on a particular variable and high permutation importance. 

Results

Midcentury Climate Exposure in Southern California

Under the five climate scenarios we considered, projected midcentury (2040–2069) 

increase in minimum winter and summer maximum temperatures averaged across 

the Southern California Coast (261B) ecological section ranged from 2.0 ± 0.3 °C 

to 3.0 ± 0.2 °C (mean ±SD) and 2.2 ± 0.3 °C to 3.0 ± 0.3 °C, respectively, relative 

to 1951–1980 averages (table 4, fig. 3). The direction and magnitude of precipitation change varied across the five climate scenarios, ranging from a 27 ± 3 percent 

decrease (MIROC-ESM) to a 16 ± 6 percent increase (CNRM-CM5). Projected 

increases in winter minimum and summer maximum temperatures tended to be 

slightly higher in the Southern California Mountains and Valleys (M262B) ecological section compared to the Southern California Coast (261B), ranging from 2.2 

± 0.3 °C to 3.0 ± 0.3 °C for winter and 2.4 ± 0.4 °C to 3.6 ± 0.4 °C for summer, 

relative to 1951–1980 averages. Projected change in precipitation ranged from a 29 

± 4 percent decrease (MIROC-ESM) to a 12 ± 7 percent increase (CNRM-CM5). 

Climatic water deficit increased in both ecological sections even under scenarios of 

increased precipitation (table 4, app. 1). The MIROC-ESM scenario had the greatest 

20
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Table 4—Recent and projected midcentury (2040–2069) climate change in southern California 

climate variables relative to baseline (1951–1980) conditions, summarized (mean ± standard 

deviation) by ecological section

Variable

Baseline 

climate

Change in climate relative to baseline 

Historical Projected midcentury (2040–2069

1951–1980 1981–2010 CCSM4 CNRM FGOALS IPSL MIROC

Southern California Coast (261B):

AET (mm) 322 ± 44 11 ± 7 -12 ± 12 21 ± 15 -12 ± 15 2 ± 12 -57 ± 24

CWD (mm) 1034 ± 70 28 ± 12 82 ± 14 60 ± 17 102 ± 17 101 ± 15 150 ± 24

PPTseas 97 ± 5 5 ± 2 -1 ± 2 25 ± 3 3 ± 5 -3 ± 1 -5 ± 3

PPTsummer (mm, %) 10 ± 2 -29 ± 12 33 ± 16 24 ± 34 66 ± 36 -1 ± 27 -33 ± 11

PPTwinter (mm, %) 236 ± 67 8 ± 3 0 ± 5 35 ± 7 -7 ± 6 -6 ± 4 -31 ± 5

Tmin (°C) 6.3 ± 1.5 0.7 ± 0.3 2.0 ± 0.3 2.4 ± 0.3 2.3 ± 0.3 2.9 ± 0.3 3 ± 0.2

Tmax (°C) 27 ± 3.2 0.4 ± 0.4 2.2 ± 0.3 2.5 ± 0.3 2.8 ± 0.3 3.5 ± 0.3 3 ± 0.4

Tseas 362 ± 77 -3 ± 7 13 ± 9 27 ± 7 27 ± 8 32 ± 8 6 ± 9

Southern California Mountains and Valleys (M262B):

AET (mm) 291 ± 79 13 ± 11 3 ± 27 31 ± 22 16 ± 26 17 ± 30 -32 ± 42

CWD (mm) 996 ± 167 32 ± 15 96 ± 24 84 ± 27 107 ± 27 123 ± 32 160 ± 33

PPTseas 88 ± 11 -16 ± 15 29 ± 12 33 ± 21 83 ± 33 -10 ± 11 -9 ± 22

PPTsummer (mm, %) 24 ± 14 9 ± 7 -1 ± 6 33 ± 8 -9 ± 6 -8 ± 5 -35 ± 4

PPTwinter (mm, %) 256 ± 122 19 ± 12 -4 ± 15 83 ± 47 -21 ± 18 -22 ± 17 -89 ± 43

Tmin (°C) 2.0 ± 3.0 0.7 ± 0.3 2.2 ± 0.3 2.6 ± 0.3 2.4 ± 0.3 3.0 ± 0.3 3.0 ± 0.3

Tmax (°C) 31.2 ± 3.3 0.3 ± 0.3 2.4 ± 0.4 2.6 ± 0.4 2.8 ± 0.4 3.6 ± 0.4 2.9 ± 0.4

Tseas 591 ± 75 -1 ± 9 14 ± 10 25 ± 8 24 ± 8 31 ± 8 3 ± 9

Ecological sections from Goudey and Smith (1994) updated by Cleland et al. (2007).

Climate variables: AET = actual evapotranspiration, CWD = climatic water deficit, PPTseas = precipitation seasonality,

PPTsummer = precipitation of the warmest quarter, PPTwinter = precipitation of the coldest quarter, Tmin = minimum winter 

temperature December–February, Tmax = maximum summer temperature June–August, Tseas = temperature seasonality. 

Change in annual precipitation is calculated as the percentage of change relative to baseline.

Global circulation models used: CCSM4, CNRM (= CNRM-CM5 in text), FGOALS (= FSGOALS-G2 in text), IPSL (= IPSLCM5A-LR in text), and MIROC (= MIROC-ESM in text). 

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RESEARCH PAPER PSW-RP-270

projected increase in CWD throughout our southern California study area. Across 

all the future scenarios we considered, CWD increased notably in section M262B 

subsections g, h–San Gorgonio Mountains, l–San Jacinto Foothills-Cahuilla Mountains (eastern portion), m–San Jacinto Mountains, and o–Palomar-Cuyamaca Peak 

(eastern portion of the Peninsular Ranges, especially the Laguna Mountains).

Species Distribution Model Performance

Overall, Maxent models had high performance (app. 3). For the 44 taxa modeled, 

AUC scores ranged from 0.728 to 0.959 with a median score of 0.830. Omission test

rate at the 10 percent test threshold (OR ) ranged from 0.10 to 0.18. Higher 10p

AUC scores did not necessarily translate to higher model performance in terms test

of omission rate. For example, Arctostaphylos glandulosa subsp. mollis had among 

the highest overall model performance in terms of AUC (0.926) but lowest test

performance in terms of omission rate at the 10 percent test threshold (0.18). The 

importance of environmental variables in models varied across taxa (app. 3). Temperature seasonality (Tseas) was most frequently important, ranking most important 

for 13 taxa. Climatic water deficit ranked most important for nine taxa, and winter 

rainfall (PPTwinter) ranked most important for six taxa. Also worth noting is 

precipitation seasonality (PPTseas). Although ranked most important for only three 

species, two of those species were projected to have among the greatest losses in 

habitat suitability (Ceanothus perplexans and Eriodictyon trichocalyx var. lanatum).

Baseline suitable habitat maps generally matched the known distribution of 

most taxa, with a few exceptions (app. 4). Models of Eriodictyon crassifolium var. 

nigrescens predicted baseline suitable habitat in areas of San Diego County where 

occurrence data were excluded from our models and where E. crassifolium var. 

crassifolium occurs (see app. 2). The model of Ceanothus vestitus may overpredict 

baseline suitable habitat in the Peninsular Ranges or may reflect the taxonomic 

uncertainty between C. vestitus and C. perplexans in the area. Previously considered varieties of C. greggii, these taxa are now treated as separate species. Herbarium records suggest C. perplexans is more common in the Peninsular Ranges, 

but the degree to which the species overlap in this region has not yet been closely 

studied. Therefore, it is unclear whether our model is picking up suitable habitat in 

the Peninsular Ranges that is indeed occupied by the C. vestitus or overpredicting 

into the range of C. perplexans. In both cases, taxonomic uncertainties may be 

affecting model predictions. Two of the 44 taxa, Encelia farinosa and Eriogonum 

fasciculatum var. polifolium, have geographic distributions that extend significantly

into desert ecoregions much more arid than the portion of the species range we 

model in our study area. These range truncations, as well as those to the north and 

22
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

south of the study area, may affect future habitat suitability projections. Lastly, 

suitable habitat maps for Lepidospartum squamatum should be interpreted with 

caution. Lepidospartum squamatum is sensitive to the substrate and fluvial dynamics of alluvial fans and confined outwashes, factors that were not directly measured 

by our environmental predictors. 

Projected Changes in Habitat Suitability

Projected midcentury (2040–2069) changes in suitable habitat varied widely across 

taxa and climate scenarios (table 5, apps. 3 and 4), including among taxa in the 

same genus and family. For example, in the family Rhamnaceae, the chaparral 

shrub C. crassifolius var. crassifolius was projected to have among the highest 

degree of stable suitable habitat of all modeled taxa, with >90 percent of baseline 

habitat maintaining future suitability under all five GCMs. In contrast, C. perplexans was projected to have among the greatest losses of suitable habitat, >50 percent, 

under three of five GCMs. Rhamnus ilicifolia, another chaparral shrub, had highly 

variable suitability projections (32 to 99 percent stable) (fig. 5). Rhamnus crocea, 

which occurs in alluvial scrub, chaparral, and mixed chaparral-sage scrub, had even 

greater variability in suitability projections (2 to 99 percent stability) across the 

GCMs we considered. 

The climate scenarios with the greatest projected loss of suitable habitat for 

most taxa were the two scenarios with the most extreme directional changes in 

precipitation: CNRM-CM5 (wettest) and MIROC-ESM (driest). Taxa differed 

in the direction of the precipitation shift that resulted in the greatest loss of suitable habitat. Twelve taxa showed projected losses of ≥50 percent under the driest 

climate scenario, most of which were woody chaparral shrubs (app. 3). Fourteen 

taxa showed projected losses of ≥50 percent under the wettest climate scenario, 

including a number of taxa associated with coastal sage scrub such as Acmispon 

glaber var. glaber, Encelia californica, Eriogonum fasciculatum var. fasciculatum, 

Deinandra fasciculata, and Salvia apiana. 

Overall, the majority of plant taxa we modeled had a high degree of stable 

future suitable habitat, with 31 taxa projected to have ≥75 percent of baseline 

suitable habitat maintaining suitability midcentury under at least three future 

climate scenarios (table 5). Eleven taxa had ≥75 percent of baseline suitable habitat 

maintaining suitability across all five GCMs (Artemisia californica, Ceanothus 

crassifolius var. crassifolius, Encelia farinosa, Eriodictyon crassifolium var. crassifolium, Eriogonum fasciculatum var. foliolosum, Hesperoyucca whipplei, Malosma 

laurina, Phacelia minor, Quercus berberidifolia, Rhus ovata, and Salvia mellifera). 

Seven taxa had projected losses of baseline suitable habitat of >50 percent under at 

23
Table 5—Baseline (1951–1980) climatically suitable habitat and percentage of change in suitable habitat under projected 

midcentury (2040–2069) climate scenarios for taxa arranged from highest to lowest percentage loss of suitable habitata

Baseline suitable habitatb Projected percentage of change in suitab

Unconverted Converted Stable Loss 

Square kilometers – – – – – – – – – – – – – – – Percent – – – – – – – – – – – – 

Eriodictyon trichocalyx var. lanatum 3 563 7 16 (0–64) 84 (36–100) 

Acmispon glaber var. glaber 4 572 53 37 (1–58) 63 (42–99) 

Corethrogyne filaginifolia 13 041 35 38 (21–50) 62 (50–79) 

Ceanothus vestitus 10 857 9 38 (26–47) 62 (53–74) 

Eriophyllum confertiflorum var. confertiflorum 13 482 25 39 (4–58) 61 (42–96) 

Arctostaphylos glandulosa subsp. cushingiana 4 922 4 44 (29–58) 56 (42–71) 

Ceanothus perplexans 6 392 10 48 (1–67) 52 (33–99) 

Eriodictyon crassifolium var. nigrescens 8 553 22 51 (34–84) 49 (16–66) 

Ceanothus cuneatus var. cuneatus 8 558 19 58 (27–82) 42 (18–73) 

Ceanothus oliganthus 8 078 23 67 (31–91) 33 (9–69) 

Deinandra fasciculata 6 001 62 68 (11–93) 32 (7–89) 

Arctostaphylos glandulosa subsp. mollis 8 022 8 68 (49–91) 32 (9–51) 

Arctostaphylos glauca 18 060 11 71 (41–96) 29 (4–59) 

Arctostaphylos glandulosa subsp. glandulosa 8 751 20 75 (17–95) 25 (5–83) 

Eriodictyon trichocalyx var. trichocalyx 6 171 21 77 (27–91) 23 (9–73) 

Stipa pulchra 6 486 56 77 (64–94) 23 (6–36) 

Ceanothus leucodermis 8 542 12 78 (35–91) 22 (9–65) 

Plantago erecta 4 607 62 79 (36–89) 21 (11–64) 

Rhamnus ilicifolia 13 131 22 80 (32–99) 20 (1–68) 

Eriogonum fasciculatum var. fasciculatum 4 133 60 81 (8–100) 19 (0–92) 

Ceanothus megacarpus var. megacarpus 3 471 52 82 (58–100) 18 (0–42) 

Encelia farinosa 7 204 33 84 (79–95) 16 (5–21) 1

Eriogonum fasciculatum var. polifolium 16 033 11 86 (62–95) 14 (5–38) 

Ceanothus tomentosus 3 300 42 89 (50–98) 11 (2–50) 

Eriodictyon crassifolium var. crassifolium 8 073 28 89 (81–92) 11 (8–19) 

Acmispon glaber var. brevialatus 8 459 33 90 (65–98) 10 (2–35) 

Lepidospartum squamatum 6 143 51 92 (12–100) 8 (0–88) 

24
Table 5—Baseline (1951–1980) climatically suitable habitat and percentage of change in suitable habitat under pro

midcentury (2040–2069) climate scenarios for taxa arranged from highest to lowest percentage loss of suitable ha

Baseline suitable habitatb Projected percentage of change 

Unconverted Converted Stable Loss 

Square kilometers – – – – – – – – – – – – – – – Percent – – – – – – – 

Rhamnus crocea 5 532 63 95 (2–99) 5 (1–98

Cercocarpus betuloides var. betuloides 16 762 11 96 (53–100) 4 (0–47

Encelia californica 3 560 67 97 (33–100) 3 (0–67

Prunus ilicifolia subsp. ilicifolia 10 889 28 98 (57–100) 2 (0–43

Adenostoma fasciculatum var. fasciculatum 12 064 32 99 (53–100) 1 (0–47

Heteromeles arbutifolia 6 961 43 99 (69–100) 1 (0–3

Malacothamnus fasciculatus var. fasciculatus 7 422 59 99 (69–100) 1 (0–3

Hesperoyucca whipplei 9 406 29 99 (81–100) 1 (0–19

Eriogonum fasciculatum var. foliolosum 11 751 47 99 (84–100) 1 (0–16

Rhus ovata 10 492 29 99 (85–99) 1 (1–15

Salvia apiana 10 461 36 100 (48–100) 0 (0–52

Quercus berberidifolia 13 145 28 100 (75–100) 0 (0–25

Ceanothus crassifolius var. crassifolius 7 768 35 100 (93–100) 0 (0–7

Malosma laurina 6 310 58 100 (95–100) 0 (0–5

Artemisia californica 8 411 58 100 (96–100) 0 (0–4

Phacelia minor 7 802 41 100 (96–100) 0 (0–4

Salvia mellifera 5 600 55 100 (97–100) 0 (0–3

a Converted is the area of baseline habitat that is climatically suitable but has already been converted to human land uses. Unconverted is the area of baseli

land cover types.

b Reported percentage of change of suitable habitat where at least three climate scenarios are in agreement with the range (minimum–maximum) across all 

scenarios. 

All habitat suitability calculations exclude areas that have already been converted to human land uses (FRAP 2015). 

Stable suitable habitat can also be interpreted as the percentage of spatial overlap of suitable area under baseline and future climate conditions. 

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Figure 5—Examples of contrasting patterns of projected change in suitable habitat under midcentury (2040–2069) climate for three 

species, (A) Ceanothus perplexans (high loss), (B) Rhamnus ilicifolia (variable), and (C) C. crassifolius var. crassifolius (high stability). 

Maps show agreement across the five climate model scenarios and range from one (no agreement) to five (full agreement). Projected 

change in suitable habitat is defined as follows: stable = suitable under baseline and future conditions; loss = suitable under baseline but 

unsuitable under future conditions; gain = unsuitable under baseline and becoming suitable under future conditions. In all maps, suitable 

habitat already converted to human land uses (FRAP 2015) is masked in dark gray.

26
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

least three GCMs (Acmispon glaber var. glaber, Arctostaphylos glandulosa subsp. 

cushingiana, Ceanothus perplexans, C. vestitus, Corethrogyne filaginifolia, E.

trichocalyx var. lanatum, Eriophyllum confertiflorum var. confertiflorum). Some 

of these taxa with large losses in suitable habitat also occur in areas of high CWD 

exposure (e.g., ecological section M262B subsection near the Palomar Mountains 

and Cuyamaca Peak), such as Ceanothus perplexans and Eriodictyon trichocalyx 

var. lanatum. Corethrogyne filaginifolia, however, is widespread in our study area, 

yet had among the greatest projected loss in suitable habitat of all modeled taxa (50 

to 79 percent loss). 

We found examples of contrasting patterns of projected habitat suitability 

change for varieties within the same species. Acmispon glaber var. glaber had much 

greater projected loss of suitable habitat (42 to 99 percent) compared to A. glaber 

var. brevialatus (2 to 35 percent). Both varieties had the greatest loss of suitable 

habitat under the wettest climate scenario, but A. g. var. brevialatus fared better 

with 35 percent loss compared to 99 percent loss for A. g. var. glaber. Similarly, 

Eriodictyon trichocalyx var. lanatum had a much greater loss in suitable habitat 

(36 to 100 percent) compared to E. trichocalyx var. trichocalyx (9 to 73 percent). 

Interestingly, these varieties strongly differed as to which climate scenario resulted 

in the greatest suitable habitat loss. The wettest scenario resulted in 100 percent loss 

of suitable habitat for E. t. var. lanatum, but only 9 percent loss for E. t. var. trichocalyx, while the driest scenario resulted in 36 percent loss for E. t. var. lanatum and 

73 percent loss of suitable habitat for E. t. var. trichocalyx. 

Although we did not explicitly model plant communities, the multitaxon suitable habitat overlays revealed patterns for associated vegetation groups that were 

less readily apparent in maps of individual taxa (fig. 6, app. 5). Spatial patterns of 

projected suitable habitat loss, or climate exposure, varied across future climate 

scenarios and vegetation groups. All vegetation groups—alluvial scrub, coastal 

sage scrub, low-elevation chaparral, and mixed chaparral-sage scrub—had large 

projected suitability losses under the wettest future climate scenario (CNRMCM5). Many taxa associated with low-elevation chaparral also had large projected 

suitable habitat losses under the driest scenario (MIROC-ESM). For low-elevation 

chaparral, suitable habitat losses were most pronounced in the low elevations of the 

Southern California Mountains and Valleys (M262B) ecological section. Coastal 

sage scrub taxa, in contrast, had the most pronounced suitable habitat losses in the 

Southern California Coast (261B) ecological section. Baseline and future suitable 

habitat for coastal sage scrub taxa also coincided geographically with a high degree 

of human land use. Larger unconverted areas of stable habitat suitability occurred 

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Figure 6—Number of taxa with suitable habitat under baseline (1951–1980) and midcentury (2040–2060) climate conditions for (A) alluvial scrub, (B) coastal sage scrub, (C) low-elevation chaparral, and (D) mixed chaparral-sage scrub vegetation groups. Midcentury maps show areas of agreement for least three of the five future climatescenarios. Projected change in suitable habitat is defined as stable = suitable under baseline and future conditions; loss = suitable under baseline but unsuitable under futureconditions; gain = unsuitable under baseline and becoming suitable under future conditions. See appendix 5 for maps for individual climate scenarios. Vegetation groupassignments are not mutually exclusive (see table 2). In all maps, suitable habitat already converted to human land uses (FRAP 2015) is masked in dark gray.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

in coastal Orange and San Diego Counties for alluvial scrub and coastal sage scrub 

taxa and in portions of the Santa Monica, San Gabriel, and Santa Ana Mountains 

for low-elevation chaparral and mixed chaparral-sage scrub taxa. Overlays for all 

vegetation groups showed potential gains in suitable habitat with increasing elevation in the Transverse and Peninsular Ranges under all five future climate scenarios 

(app. 5). 

Discussion

Species distribution models can be a valuable tool for conservation and natural 

resource management decisionmaking when applied thoughtfully and in concert 

with expert knowledge about the biology, ecological relationships, population 

dynamics, and demographics of the species of interest. Habitat suitability maps 

provide insights about which species might be used most successfully for restoration in the face of an uncertain future, and, when paired with climate-adjusted 

provenancing strategies (e.g., Prober et al. 2015), provide information on where 

one might collect material for any particular restoration site. One of the largest 

hurdles to incorporating climate change into decisionmaking is the uncertainty 

in future climate. We explored this uncertainty by considering five scenarios that 

span a range of future conditions for southern California, including both increases 

and decreases in precipitation. Note, however, that the actual climate conditions at 

midcentury may not be encompassed by these five scenarios. 

Habitat suitability forecasts are best interpreted as an estimate of potential 

climate exposure (the nature and degree of projected change in climate at midcentury), as our models focus on climatic predictors rather than other factors that may 

influence species distribution, such as biotic interactions or disturbance regime. 

Projected loss in suitability suggests high future climate stress or exposure, but 

inferring extinction risk or population persistence requires coupling SDMs with 

demographic models (Dawson et al. 2011; Franklin et al. 2014, 2016), which we 

were unable to do given the lack of detailed demographic data for nearly all taxa. 

Climate exposure estimates from SDM projections can be evaluated in combination 

with factors related to the sensitivity and adaptive capacity of a species to assess 

vulnerability under climate change (Butt et al. 2016, Estrada et al. 2015). However, 

we caution against transferring our regional model results outside our focal area, 

as those taxon-climate relationships may differ from the ones we modeled. Additionally, our restricted occurrence dataset could result in underpredicting suitable 

climate space and overpredicting climate-driven suitability loss (e.g., Barbet-Massin 

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et al. 2010). Finally, there are a number of methodological assumptions for SDMs 

that should be taken into consideration when using modeling results for practical 

applications. These are explicitly recognized in appendix 6. 

Habitat Suitability Under Projected Climate Change

In California’s environmentally heterogeneous landscapes, the geographic ranges of 

species can vary tremendously in extent and habitat attributes. Our regional models 

focus on projected changes in suitable habitat under climate change specifically 

for southern California. Projections of future habitat suitability for focal shrubland 

taxa varied widely depending on the climate scenario considered. Future climate 

scenarios with the most extreme directional changes in precipitation at midcentury 

had the greatest projected losses of suitable habitat for most taxa. The wettest 

scenario (CNMR-CM5) had the greatest departure from baseline conditions with 

respect to novel climate combinations. Our choice to limit SDM extrapolation in 

areas of novel climates may have contributed to the high projected losses for some 

taxa under this scenario. The driest scenario (MIROC-ESM) produced high losses 

of suitable habitat for many plant taxa. It also had the greatest increase in climatic 

water deficit (CWD) of all five future climate scenarios. 

Given the strong association between water availability and vegetation structure 

and composition in California (e.g., Jin and Goulden 2014), shifts toward a hotter, 

drier climate like that projected by MIROC-ESM could be expected to have large 

effects on southern California vegetation. The majority of taxa with high suitability 

loss under the MIROC-ESM scenario were woody chaparral shrubs, suggesting that 

low-elevation chaparral shrublands may be vulnerable to increased drought stress 

in southern California. Thorne et al. (2016) also predicted high climate exposure 

and loss of suitability for chaparral at the macro-group level, particularly in low-elevation areas in southern California. Differential response to drought stress among 

chaparral species (Coates et al. 2015, Frazer and Davis 1988, Venturas et al. 2016) 

may result in changes in chaparral structure and composition as climate shifts. 

Future increases in precipitation extremes and interannual variability (Berg and 

Hall 2015, Berg et al. 2015) may also strongly affect southern California vegetation, 

although we did not directly address these changes in our modeling. SDM overlays 

of multiple taxa provide a first pass at predicting climate change effects on shrubland vegetation groups. Areas that maintain suitable habitat for a large number of 

taxa in vegetation groups may act as important refugia under future climate change. 

Individualistic shifts of taxa in response to climate change (Huntley 1991), however, 

will have implications for the structure and composition of plant communities and 

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

ecosystems, such as the potential for emergent novel species assemblages with no 

modern analog or novel biotic interactions (Jackson and Overpeck 2000, Williams 

and Jackson 2007).

Many of the taxa we modeled showed high stability in future suitable habitat 

under projected midcentury climate conditions, which contrasts with recent modeling studies predicting large habitat losses for southern California plant taxa under 

climate change (e.g., Principe et al. 2013, Riordan and Rundel 2014). For example, 

we predicted a high degree of stable future suitable habitat under midcentury climate for Artemisia californica in southern California (92 to 100 percent of baseline 

suitable habitat), while Principe et al. (2013) predicted a 78 precent loss. Thorne 

et al. (2016) and Riordan and Rundel (2014) also predicted greater losses of suitable habitat for dominant coastal sage scrub taxa both at mid and late 21st century 

compared to our regional modeling results. Factors that may have contributed to 

the differences between studies include, but are not limited to, GCM and emission scenario selection, study area extent, source of occurrence data, and spatial 

resolution of climatic data. Our models used higher resolution (270 m) climate data 

that may better identify topographically buffered areas maintaining stable climate 

conditions (Dobrowski et al. 2009, Franklin et al. 2013, Meineri and Hylander 2017) 

compared to models built on coarser 1 km resolution climate data, such as Riordan 

and Rundel (2014) and Principe et al. (2013). 

The infraspecific differences in projected midcentury suitable habitat that we 

found highlight the need to consider within-species variation when forecasting 

future climate change effects in southern California. Modeling at the infraspecies 

level can improve model performance, revealing divergent patterns of potential 

effects of climate change that are masked when modeling at the species level (e.g., 

Pearman et al. 2010). Furthermore, incorporating intraspecific genetic information 

into SDMs can provide more realistic distribution models and improve forecasts 

of range shifts, especially if infraspecies are adapted to different environments 

(Gotelli and Stanton-Geddes 2015, Marcer et al. 2016). Plant populations are often 

adapted to their local environments and can vary in genetic structure and traits 

across even relatively short distances within a species’ range (reviewed in Linhart 

and Grant 1996). Valladares et al. (2014) showed that when population differentiation (phenotypic plasticity and local adaptation) and dispersal restrictions are 

accounted for in SDMs, models predict greater losses in suitable habitat under 

climate change than when assuming a homogenous species range with unlimited 

dispersal. Species’ traits and interactions with dispersal agents also ought to be 

considered, as both can profoundly affect migration rates, isolation, expansion of 

species range, and the extent to which populations can adapt to new conditions 

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(Aitken et al. 2008). Although we were unable to incorporate population-level 

genetic variation in our SDMs, we emphasize the importance of considering genetic 

variation and dispersal ability, where available, when applying modeling results for 

restoration in areas of high environmental heterogeneity like southern California. 

For example, Richardson et al. (2014) showed that genetically distinct warm- and 

cold-adapted ecotypes in blackbrush (Coleogyne ramosissima Torr.) differed in 

their predicted occupancy of contemporary and future climate space, with the 

warm-adapted ecotype dominating contemporary climate space and the coldadapted ecotype dominating future climate space and newly suitable habitat that 

became available at higher latitudes and elevations. 

Climate Change, Land Use, and Fire

Fire, invasive species, and human land use are major drivers of vegetation change in 

southern California. For example, a combination of human land use, frequent fire, 

invasion by exotic annuals, grazing, and air pollution have greatly reduced coastal 

sage scrub (Minnich and Dezzani 1998; O’Leary 1990, 1995; Westman 1981). In 

southern California, contemporary fire-return intervals for coastal sage scrub and 

chaparral are 30 to 40 years (Keeley and Fotheringham 2001), which is already 

shorter than pre-European settlement return intervals (Safford and Van de Water 

2014). Too-frequent fires and other human disturbance can lead to a shift in dominance to nonnative annual grasses (Fleming et al. 2009, Talluto and Suding 2008). 

Nitrogen deposition from air pollution and severe drought enhance these shifts in 

coastal sage scrub, slowing postfire succession and increasing nonnative grass cover 

(Kimball et al. 2014). Once established, nonnative grasses can further promote high 

fire frequencies by carrying fire easily where ignitions are plentiful, as they are in 

most of southern California (D’Antonio and Vitousek 1992, Faivre et al. 2014, Keeley 

and Syphard 2017). In addition, interacting drivers of vegetation change such as fire 

can complicate and confound climate change signals (e.g., Schwilk and Keeley 2012). 

Thus, fire, land use, and invasive species could interact with changing climate to 

cause vegetation change, even if the projected vulnerability to climate change alone 

is relatively low. Novel climate conditions may also facilitate new invasive species or 

affect existing biotic interactions (Schweiger et al. 2010).

In the Western United States, warming temperatures exacerbate water deficit 

and drought (Williams et al. 2015), which may affect fuel conditions and increase 

wildfire activity (Westerling et al. 2006), including fire severity and intensity (van 

Mantgem et al. 2013). In southern California, the worst fire conditions occur during 

Santa Ana wind events in autumn, but projected warmer and drier conditions midcentury could lead to a longer fire season and cause an increase in the area burned 

32
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

by fire, whether or not it occurs during a Santa Ana event (Jin et al. 2015, Yue et al. 

2014). Because climatic conditions are already suitable for massive fires most years 

in southern California, human activity has a large effect on ignition frequency and 

wildfire activity (Faivre et al. 2014, Keeley and Syphard 2016). A projected increase 

in southern California’s population of over 50 percent by midcentury (Sanstad et al. 

2011) will likely further alter fire regimes and contribute to additional habitat loss 

and fragmentation, all of which could affect vegetation response to climate change. 

Habitat fragmentation poses a significant threat to many species’ persistence. 

Much of the lower elevation of the southern California landscape has a high degree 

of habitat conversion and fragmentation, the result of interrelated effects of urbanization, altered fire regimes, and invasive species (especially nonnative grasses and 

forbs). Local extinctions can stem from direct habitat loss owing to land development, but also indirectly from the effects of habitat degradation and fragmentation. 

Reduced population sizes in habitat fragments are more susceptible to inbreeding 

depression and extirpation from demographic stochasticity (Frankham 1995, Neel 

2008, Richards 2000). The high degree of human land use in southern California 

is a considerable barrier to dispersal and gene flow and could negatively affect 

the adaptive capacity and ability of some species to move across the landscape in 

response to changing conditions (Fahrig 1997, Krosby et al. 2010). 

Life history traits, especially postfire regeneration response, are also important 

in understanding the influence of different environmental drivers of vegetation 

change (Swab et al. 2012, Syphard et al. 2013). In southern California, seedling 

recruitment of many shrubs is stimulated by fire (Keeley et al. 2006, Keeley and 

Keeley 1981, Keeley and Zedler 1978). Obligate resprouters regenerate after fire 

solely by vegetative resprouts, while postfire seeders can either be obligate seeders 

that cannot resprout after fire or facultative seeders that recruit postfire through 

seeds and resprouting (Keeley et al. 2012). Too-frequent fires can cause shifts in 

dominance by reducing obligate seeders on a site (Syphard et al. 2006, Zedler et 

al. 1983) or lead to type conversion to grass-dominated vegetation (Haidinger and 

Keeley 1993, Lippitt et al. 2013). Modeling studies linking population dynamics, 

climate change, land use, and fire highlight the substantial threat of short-interval 

fire regimes on obligate seeder persistence in southern California (Syphard et al. 

2013). The interaction between fire and drought can also affect postfire regeneration in chaparral. Facultative seeding and obligate resprouting shrub species may 

be able to tolerate shorter fire-return intervals than obligate seeding species (Lucas 

et al. 2013), but they are susceptible to severe drought following fire that causes 

high seedling mortality (Frazer and Davis 1988) as well as increased mortality 

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of resprouts (Pratt et al. 2014). Seedlings of obligate seeders have comparatively 

higher tolerance to water stress and postfire drought (Pratt et al. 2008), but they are 

particularly vulnerable to short fire-return intervals. 

Despite uncertainty in projections of future drought and precipitation, warming 

temperatures are predicted to increase annual climatic water deficits regardless of 

the direction in future precipitation change (Thorne et al. 2012). Recent extreme 

and prolonged drought in California could be indicative of a trend of continuing 

hot droughts in the Anthropocene characterized by high plant mortality rates as 

physiological thresholds are exceeded (Allen et al. 2015, Overpeck 2013). Although 

our modeling approach is best used to understand potential shifts in climate exposure, rather than to infer mechanisms of species response to climate change, high 

projected losses in habitat suitability may suggest increased mortality for some 

chaparral species. Chaparral shrubs tend to have lower drought tolerance and to 

occur in more mesic and higher elevation areas than drought-deciduous sage scrub 

species (Harrison et al. 1971, Miller and Poole 1979, Mooney and Dunn 1970). Thus 

high projected habitat suitability losses for low-elevation chaparral species under the 

driest future scenario may translate to future increases in mortality. Structure and 

composition within vegetation types may shift depending on differential species tolerance to drought (Pratt et al. 2007, 2014; Venturas et al. 2016), as well as differential 

responses to changes in wildfire frequency and intensity (Keeley et al. 2005, 2006).

Spatial context is also important when evaluating the vulnerability of southern 

California plant species to multiple interacting threats. At high fire-return intervals, fire tends to be the most serious threat for obligate seeding shrub species, 

compared to projected land use or climate change (Franklin et al. 2014, Regan et al. 

2010, Syphard et al. 2013). At longer fire-return intervals, however, the rankings of 

projected threats vary by species and depend on the spatial context (Syphard et al. 

2013). Regan et al. (2010) found that a small degree of habitat fragmentation may 

actually be beneficial for the obligate seeder Ceanothus perplexans if it spatially 

and temporally decouples fire events, though the negative impact of habitat loss 

typically outweighed this benefit as fragmentation increased. For species that are 

already embedded in urban landscapes, such as the rare obligate seeder C. verrucosus (not modeled in our study), climate change may pose the greatest threat, resulting in population decline and increased local extirpation (Lawson et al. 2010). It is 

also possible that climate change may benefit some plant taxa. Our modeling results 

suggest that many southern California shrub taxa may retain and even gain suitable 

habitat under projected climate change. Drought-tolerant species such as C. crassifolius, whose seedlings are particularly tolerant of water stress (Pratt et al. 2008, 

2014), may be able to expand with moderate increases in temperature and climatic 

34
Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

water deficit so long as fire frequencies do not exceed the threshold needed for the 

species to reach reproductive maturity. Therefore, it is important to consider SDM 

results such as ours within a broader spatial framework of environmental change. 

Management Implications

Effective land management under rapid environmental change may call for managers to consider multiple spatial and temporal scales, address diverse threats and 

drivers of environmental change, and incorporate scenarios of climate change into 

many aspects of planning and action (e.g., Abrahms et al. 2017, Heller and Zavaleta 

2009, Lawler 2009). For ecological restoration, practitioners are beginning to 

redefine project goals and procedures in the context of recent and projected future 

climate change, from the selection of reference targets to sourcing of plant material (Breed et al. 2013, Harris et al. 2006, Havens et al. 2015, Jackson and Hobbs 

2009). Understanding how climate is projected to change in southern California 

and the potential effects on target species are important considerations for restoration efforts in the region. Fortunately, a variety of tools and frameworks have been 

developed to help facilitate the incorporation of climate change into planning and 

decisionmaking (reviewed in Abrahms et al. 2017). The SDM results from this 

study can be consulted to address some important questions raised in the Abrahms 

et al. (2017) review, including (1) how climate is projected to change in the project 

area, (2) how the suitable habitat of a potential project taxon is expected to shift, 

(3) how vulnerable potential combinations of taxa are to the projected changes, (4)

what suite of taxa is projected to remain or become appropriate for the project site,

(5) and what management strategies might affect project outcomes.

Uncertainty, whether from a lack of knowledge in how species and ecosystems

will respond to change (e.g., Walther 2010) or due to uncertainty about the magnitude and direction of future climate change itself (e.g., Stott and Kettleborough 

2002), can impede decisionmaking. Scenario planning facilitates incorporating 

uncertainty into decisionmaking by providing a framework to evaluate the robustness of a decision across a range of plausible but contrasting futures (Peterson 

et al. 2003). Considering multiple climate scenarios allows planners to address 

uncertainty about future climate change and identify strategies that are successful across a range of future conditions. Our projections of future suitable habitat 

provide information about possible climate change effects on focal shrubland plant 

taxa that can be incorporated into a scenario planning framework. For example, 

taxa projected to maintain suitability at a site under all future scenarios may have 

high restoration value even given uncertain future conditions. For taxa with high 

projected suitable habitat loss and climate exposure, evaluation of their ecological 

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RESEARCH PAPER PSW-RP-270

relationships, genetics, and life-history traits will help estimate vulnerability to 

climate change and guide the development of taxon- and site-specific sourcing 

strategies. Because nonclimatic factors are important drivers of southern California 

vegetation distribution, we suggest that SDM outputs could be used in a broader 

context that combines future habitat suitability under climate change with alternative futures of varying stressors (e.g., fire, land use, invasive species) and species 

response (e.g., dispersal capacity, physiological tolerances). 

Species distribution model forecasts can be used to inform restoration goals and 

reference targets, as well as to evaluate restoration feasibility, under projected climate 

change. Setting realistic and effective goals for ecological restoration under changing 

environmental conditions remains a significant challenge, despite numerous recent 

advancements in the field (Hobbs 2007, Perring et al. 2015). Ecological restoration 

in the United States has often used historical data from the site to be restored and 

contemporary data from pristine analogous sites to establish reference conditions 

for restoration (Moore et al. 1999, White and Walker 1997). Although this approach 

may lose effectiveness under mounting climate and environmental change, restoring 

historical ecosystems may remain a viable goal in some areas (Jackson and Hobbs 

2009). For example, historical targets may remain useful at sites projected to maintain suitability for many species across a range of future scenarios, whereas sites 

projected to lose suitability may be more difficult to restore or necessitate greater 

human intervention for success. Managers may want to consider alternative restoration goals as well as more dynamic targets in areas of high future climate exposure, 

especially if other ecological stressors (e.g., invasive species, disturbance) are also 

high. Species distribution models can also help managers to evaluate species-site 

suitability and identify specific taxa with projected high stability of suitable habitat, 

or the reverse, under climate change to aid in species selection (Butterfield et al. 

2016, Gelviz-Gelvez et al. 2015, López-Tirado and Hidalgo 2016). 

For SDMs to most effectively assist restoration efforts, we recommend that models take into account intraspecific variability. As found in our study, this often leads 

to very different predictions of risk and vulnerability under projected climate change, 

compared to traditional SDM approaches that pool records across a species’ range 

(Hällfors et al. 2016b, Pearman et al. 2010, Valladares et al. 2014). By partitioning 

species’ ranges into groups of populations at landscape units, Hamann and Aitken 

(2013) showed that climate change could negatively affect populations throughout a 

species’ range, not just at the leading or trailing edges. When Valladares et al. (2014) 

accounted for population-level differentiation and restricted dispersal capacity, they 

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

forecasted greater losses under climate change compared to conventional forecasts 

assuming uniformly high plasticity across a species’ range. Wherever possible, we 

recommend interpreting SDM results in a broader context that combines variation in 

habitat attributes, genetics, and adaptive and life-history traits.

One of the biggest challenges in ecological restoration is accounting for variation among populations within taxa. Our study takes a step toward incorporating 

such variation by modeling at the level of infraspecies over a limited ecological 

region. Within a species’ range, natural plant populations are often adapted to their 

local environments and can differ in genetic structure and phenotypic characters 

over large or small spatial scales (reviewed in Linhart and Grant 1996). This variation has implications both for sourcing appropriate plant material for restoration and 

for understanding how a species may respond to changing climate. In the absence of 

taxon-specific seed transfer guidelines, using local seed sources for restoration has 

been recommended to minimize the risk of maladaptation and outbreeding depression to the extent that the deleterious effects of small populations and inbreeding 

can be avoided (Hufford and Mazer 2003, McKay et al. 2005, Rogers 2004, Rogers 

and Montalvo 2004). Rapidly changing environmental conditions, however, can 

threaten the adaptive capacity of species at local spatial scales (Christmas et al. 

2016, Nicotra et al. 2015), suggesting a more complex, but carefully determined, 

form of seed sourcing may be needed on a case-by-case basis (Havens et al. 2015, 

Prober et al. 2015). Population-level genetic variation is also important in understanding species vulnerability to climate change. Variation in adaptive traits and 

intrinsic traits can profoundly affect migration rates, isolation, expansion of species 

range, and the extent to which populations migrate to suitable conditions or adapt 

to new conditions (Aitken et al. 2008, Estrada et al. 2015). Any guidelines for 

determining the direction and extent of expanded seed sourcing areas will need to 

consider the risks of moving too far or too soon. The SDM results we provide can 

be combined with other information to begin the genesis of such guidelines and 

stimulate research to test their utility.

Acknowledgments

The University of California, Riverside and Los Angeles campuses, provided 

library services. Motivation for this work was provided by participants of the Plant 

Conservation Alliance and the National Seed Strategy for Rehabilitation and Restoration 2015–2020. Nels Johnson, Eric Knapp, Shannon Still, and Jessica Wright 

provided valuable reviews of an earlier version of this manuscript.

37
RESEARCH PAPER PSW-RP-270

U.S. Equivalents

When you know: Multiply by: To find:

Millimeters (mm) 0.0394 Inches

Meters (m) 3.28 Feet

Kilometers (km) 0.621 Miles

2 Square kilometers (km ) .386 Square miles

Degrees Celsius (°C) 1.8 °C + 32 Degrees Fahrenheit

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Appendix 1: Projected Midcentury Change in Southern 

California Climate

Appendix 1 provides maps (fig. 7 A–E) of projected midcentury climate change 

relative to baseline climate conditions. Change in climatic variables was calculated 

as the difference between midcentury (2040–2069) and baseline (1951–1980) 

30-year climate averages under five climate scenarios. Change in annual precipitation was calculated as percentage of change relative to baseline. Abbreviations: 

Tmin = minimum winter temperature (December through February); Tmax = 

maximum summer temperature (June through August); PPT = annual precipitation; 

CWD = climatic water deficit. Black lines indicate ecological section boundaries; 

dark gray lines indicate ecological subsection boundaries. Climate data are from the 

Basin Characterization Model (CA-BCM) (Flint et al. 2013). See figure 3 for full 

name and source of the general circulation models for the five climate scenarios.

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Figure 7—Projected midcentury (2040–2069) change in climate under five future climate scenarios (A-E) relative to baseline 

(1951–1980) climate.

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Appendix 2: Detailed Species Distribution Modeling 

Methods

Infraspecies Name Assignments

Species distribution models (SDMs) use locality data from species occurrences and 

spatially explicit environmental layers to predict species distributions in space and 

time (Elith and Leathwick 2009, Guisan and Thuiller 2005). Spatial and temporal 

biases in natural history collections (e.g., Kadmon et al. 2004) can affect model 

performance, requiring more collections than previously thought to accurately 

represent a species range or climatic niche (Feeley and Silman 2011). To maximize 

the number of occurrence records used in infraspecies models, we assigned missing 

infraspecific names where the variety or subspecies could reasonably be inferred 

from the geographic location of the occurrence data based on the taxonomic 

description. For some taxa, we also checked herbarium specimens at the Herbarium 

at the University of California, Riverside (UCR). Taxonomy and nomenclature 

follow Baldwin et al. (2012), unless otherwise noted. 

Acmispon glaber— 

Two varieties are recognized for Acmispon glaber: A. g. var. brevialatus and A. g.

var. glaber. Acmispon g. var. brevialatus is restricted to southern California and 

Baja California unless planted or introduced, whereas A. g. var. glaber occurs not 

just in southern California, but also in coastal portions of northern California, the 

North Coast Ranges, and the Sierra Nevada foothills. In southern California, A. g. 

var. brevialatus occurs primarily in hotter and drier interior regions of Riverside, 

Los Angeles, western San Bernardino, and eastern San Diego Counties, and A. g. 

var. glaber primarily occurs in coastal southern California, the western Transverse 

Ranges, and Peninsular Ranges. After checking and annotating herbarium specimens at UCR, we assigned other records missing a variety determination that occurred outside southern California and were not obvious roadside plantings or introductions to A. g. var. glaber (north of the western Transverse Ranges, including the 

Tehachapi Mountains, Sierra Nevada foothills, Coast Ranges, and the central and 

northern California coast). We did not use A. glaber records lacking variety determinations that were collected in southern California where both varieties occur. 

Adenostoma fasciculatum var. fasciculatum— 

There are three varieties recognized in Adenostoma fasciculatum: A. f. var. fasciculatum, A. f. var. prostratum, and A. f. var. obtusifolium. Adenostoma f. var.

fasciculatum is common and broadly distributed throughout shrublands below 

1830 m in the California Floristic Province. After checking specimens at UCR, we 

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assigned all records missing a variety determination to A. f. var. fasciculatum. This 

may include a small number of records from two other less common varieties, A. 

f. var. prostratum (restricted to a limited area in the central coast and outside our

study extent) and A. f. var. obtusifolium (occurring in the south coast and southwest

Peninsular Ranges in San Diego County).

Ceanothus crassifolius var. crassifolius—

There are two varieties recognized in Ceanothus crassifolius: C. c. var. crassifolius and C. c. var. planus. The varieties intergrade in Santa Barbara and Ventura 

Counties and may represent the extremes of an intergrading network that needs 

more study to see if varietal rank is appropriate. We included all records missing varietal determination as C. c. var. crassifolius, but recognize this may include 

some records near the northern limit of our study area that either are C. c. var. planus or have introgression with C. c. var. planus.

Ceanothus cuneatus var. cuneatus—

There are three varieties in Ceanothus cuneatus: C. c. var. cuneatus, C. c. var. fascicularis, and C. c. var. ramulosus. C. c. var. cuneatus is widespread throughout the 

California Floristic Province below 2133 m. The two other varieties are restricted 

to the central coast (C. c. var. fascicularis in Santa Barbara and San Luis Obispo 

Counties) and the Coast Ranges from the San Francisco Bay Area south into the 

western Transverse Ranges (C. c. var. ramulosus), outside the geographic extent of 

this study. We assigned records missing a variety determination collected outside 

these areas of overlap to C. c. var. cuneatus. 

Ceanothus megacarpus var. megacarpus—

There are two varieties recognized in Ceanothus megacarpus: C. m. var. insularis,

which is restricted to the Channel Islands, and C. m. var. megacarpus, which occurs 

in southern California. We assigned records missing variety determination collected on the mainland to C. m. var. megacarpus. 

Cercocarpus betuloides var. betuloides—

There are three varieties recognized in Cercocarpus betuloides: C. b. var.

betuloides, C. b. var. blanchaea, and C. b. var. macrourus. Cercocarpus. b. var. 

betuloides overlaps in distribution with C. b. var. blanchaea in the Chanel Islands 

and the southern western Transverse Ranges and with C. b. var. macrourus in 

northern California in the Klamath Ranges, Cascade Range, and Modoc Plateau. 

We assigned records missing a variety determination collected outside these areas 

of overlap to C. b. var. betuloides. 

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Eriodictyon crassifolium— 

There are two varieties recognized in Eriodictyon crassifolium: E. c. var. crassifolium and E. c. var. nigrescens. Eriodictyon c. var. crassifolium occurs in cismontane 

southern California, including the South Coast Ranges and drainages, from eastern 

Santa Barbara County to northwestern Baja California. Eriodictyon c. var. nigrescens also occurs in cismontane southern California, but primarily in the western 

Transverse Ranges and southern South Coast Ranges. However, there are specimens from areas of southern California that are hard to place into either variety; 

and there are some specimens from the southwestern Peninsular Ranges that have 

been identified as E. c. var. nigrescens. We assigned all southern California records 

missing variety determination collected from San Bernardino, Riverside, Orange, 

and San Diego Counties to E. c. var. crassifolium. We excluded records of E. c. var. 

nigrescens collected in the southwestern Peninsular Ranges in San Diego County 

from models, but recognize this may truncate the taxon’s range if those collections 

are indeed correct and if the range description in the current taxonomic treatment 

needs to be updated. 

Malacothamnus fasciculatus var. fasciculatus—

There are four varieties currently recognized in Malacothamnus fasciculatus, including M. f. var. fasciculatus, which is most common and widespread in southern 

California. Two subspecies, M. f. var. catalinensis and M. f. var. nesioticus, occur 

only on Catalina and Santa Cruz Islands, respectively. One variety, M. f. var. nuttallii, overlaps with M. f. var. fasciculatus on the mainland in the South Coast Ranges 

and western Transverse Ranges in Santa Barbara and Ventura Counties. We include 

all mainland records missing a variety determination as M. f. var. fasciulatus, but 

recognize that this may include a few records of M. f. var. nuttallii within a small 

zone of overlap for the study extent. 

Prunus ilicifolia subsp. ilicifolia—

There are two subspecies recognized in Prunus ilicifolia: P. i. subsp. ilicifolia and 

P. i. subsp. lyonii. As P. i. subsp. lyonnii is restricted to the Channel Islands, we assigned any records missing a subspecies determination occurring on the mainland

to P. i. subsp. ilicifolia.

Parent Geology

The parent geology data included in SDMs (fig. 8) were obtained from the California Basin Characterization Model (CA-BCM) (Flint et al. 2013), based on the 

Geologic Map of California (Jennings et al. 2010). We simplified the CA-BCM 

parent geology layer into 18 categories. All different types of alluvium parent 

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Figure 8—Parent geology map simplified to 18 categories used in species distribution models. 

geology, such as playas, valley fill, and channels, were grouped into a single 

“alluvium” category. Different types of granites, volcanics, and sandstone were 

also combined into single groups by those names. 

Treatment of Sampling Bias

We compared three methods for correcting sampling bias based on the manipulation of background data to produce geographical sampling bias similar to that of our 

occurrence data. Each approach used target group sampling (Phillips et al. 2009) of 

digitized herbarium records for all vascular plants in southern California collected 

after 1950 (CCH 2016). In the first method, “target background,” we substituted 

all target group occurrences thinned to one record per environmental grid cell for 

Maxent’s default random background. In the second method, “target-weighted,” we 

created a grid of relative sampling frequency by summing the number of occurrences in each grid cell and dividing by the total number of occurrences (Merow 

et al. 2013). We then used this grid to weight background sampling in proportion 

to the relative sampling frequency of our target group. In the third method, “bias 

grid,” we created a bias grid derived from a Gaussian-kernel density map of the 

target group, rescaled from 1 to 20 (Elith et al. 2010). As in the second method, we 

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

used this grid to weight background sampling. We tested model performance of six 

different bias grids of varying standard deviation distances of the Gaussian-kernel 

(15 km, 20 km, 30 km, 50 km, 75 km, and 100 km). To evaluate model performance, we ran each modeling method with fivefold cross validation and compared 

three evaluation metrics: sample size corrected Akaike information criterion (AICc) 

(Akaike 1974, Burnham and Anderson 2002), area under the receiver operating 

curve of the test data (AUC ) and the difference between the calibration and test test

AUC (AUC ). We included AICc as an additional measure of overall model per- diff

formance when comparing methods because AUC can produce inflated estimates 

of model performance when both training and test data are spatially biased, nonindependent, or both (Hijmans 2012, Veloz 2009). We calculated AICc in R by the 

calc.aicc function in the ENMeval package (Muscarella et al. 2014). Of the different 

distance-based bias grids, the 100 km standard deviation distance produced models 

with the lowest AICc (highest model performance) for the majority of taxa modeled. When compared to the target background and target weighted methods, the 

100 km bias grid had significantly higher performance, as measured by AICc and 

AUC (all P < 0.0001; paired Student’s t-test), and less overfitting, as measured by test

AUC (all P < 0.0001; paired Student’s t-test). diff

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Appendix 3: Detailed Species Distribution Modeling 

Results

Detailed model performance metrics and summaries of projected habitat suitability 

change are presented for all focal plant taxa. Table 6 reports model performance 

(mean ± standard deviation) summarized across fivefold cross-validation replicates 

for four different metrics: area under the receiver operator curve for the test data 

(AUC ); difference between the calibration and test AUC (AUC ); omission test diff

rate at the 10 percent test threshold (OR ); and the omission rate at the maximum 10P

sensitivity plus specificity threshold (OR ). AUC provides a threshold-inde- mSSS test

pendent measures of overall model performance. AUC measures model overfit- diff

ting with high values indicate high overfitting to the calibration data. The omission 

(or false negative) rate gives the proportion of test occurrences incorrectly identified 

and provides information about the discriminatory ability of the model at a particular threshold. Table 7 reports the relative significance of individual environmental 

variables to Maxent models for each taxon. Variable significance is estimated as 

the percentage of permutation importance. A high value of permutation importance 

indicates a strong model dependence on that particular variable. Bold font indicates 

the variable with the greatest permutation importance. Table 8 reports the percentage of change in suitable habitat under projected midcentury (2040–2069) climate 

conditions relative to baseline (1951–1980) suitable habitat for each of the five 

climate scenarios. Projected change in suitable habitat is defined as follows: stable 

= suitable under baseline and future conditions; loss = suitable under baseline but 

unsuitable under future conditions; gain = unsuitable under baseline and becoming 

suitable under future conditions. 

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Table 6—Model performance metrics (mean ± standard deviation) for focal taxa

Scientific name N a AUCtest

b AUCdiffc OR10Pd ORmSSS

Acmispon glaber var. brevialatus 303 0.780 ± 0.027 0.014 ± 0.035 0.13 ± 0.06 0.20 ± 0.09

Acmispon glaber var. glaber 246 0.859 ± 0.021 0.003 ± 0.026 0.11 ± 0.03 0.24 ± 0.08

Adenostoma fasciculatum var. fasciculatum 609 0.762 ± 0.014 0.001 ± 0.017 0.11 ± 0.04 0.18 ± 0.02

Arctostaphylos glandulosa subsp. cushingiana 39 0.914 ± 0.014 0.023 ± 0.016 0.13 ± 0.09 0.15 ± 0.14

Arctostaphylos glandulosa subsp. glandulosa 204 0.899 ± 0.014 0.006 ± 0.017 0.13 ± 0.09 0.12 ± 0.08

Arctostaphylos glandulosa subsp. mollis 137 0.926 ± 0.023 0.011 ± 0.027 0.18 ± 0.11 0.15 ± 0.11

Arctostaphylos glauca 503 0.785 ± 0.012 0.006 ± 0.013 0.12 ± 0.03 0.21 ± 0.06

Artemisia californica 345 0.825 ± 0.020 0.003 ± 0.023 0.12 ± 0.03 0.06 ± 0.03

Ceanothus crassifolius var. crassifolius 357 0.873 ± 0.013 -0.002 ± 0.016 0.12 ± 0.05 0.22 ± 0.07

Ceanothus cuneatus var. cuneatus 135 0.827 ± 0.040 0.033 ± 0.049 0.16 ± 0.11 0.24 ± 0.14

Ceanothus leucodermis 404 0.842 ± 0.019 0.001 ± 0.021 0.12 ± 0.04 0.20 ± 0.09

Ceanothus megacarpus var. megacarpus 88 0.959 ± 0.029 0.001 ± 0.035 0.13 ± 0.16 0.07 ± 0.13

Ceanothus oliganthus 235 0.913 ± 0.017 0.001 ± 0.021 0.11 ± 0.04 0.12 ± 0.06

Ceanothus perplexans 229 0.881 ± 0.023 0.004 ± 0.028 0.11 ± 0.05 0.14 ± 0.05

Ceanothus tomentosus 201 0.917 ± 0.011 0.001 ± 0.014 0.11 ± 0.05 0.11 ± 0.05

Ceanothus vestitus 97 0.916 ± 0.032 0.013 ± 0.039 0.17 ± 0.11 0.09 ± 0.10

Cercocarpus betuloides var. betuloides 398 0.792 ± 0.019 0.006 ± 0.022 0.12 ± 0.06 0.19 ± 0.09

Corethrogyne filaginifolia 456 0.728 ± 0.022 0.007 ± 0.028 0.12 ± 0.03 0.27 ± 0.09

Deinandra fasciculata 347 0.841 ± 0.013 -0.002 ± 0.016 0.11 ± 0.05 0.17 ± 0.08

Encelia californica 195 0.915 ± 0.024 0.001 ± 0.030 0.13 ± 0.07 0.14 ± 0.09

Encelia farinosa 256 0.890 ± 0.017 -0.002 ± 0.021 0.12 ± 0.05 0.10 ± 0.03

Eriodictyon crassifolium var. crassifolium 274 0.818 ± 0.029 0.008 ± 0.036 0.11 ± 0.01 0.26 ± 0.10

Eriodictyon crassifolium var. nigrescens 121 0.854 ± 0.023 0.025 ± 0.026 0.17 ± 0.09 0.18 ± 0.09

Eriodictyon trichocalyx var. lanatum 62 0.862 ± 0.040 0.029 ± 0.046 0.13 ± 0.09 0.19 ± 0.11

Eriodictyon trichocalyx var. trichocalyx 148 0.880 ± 0.022 0.018 ± 0.025 0.13 ± 0.07 0.24 ± 0.07

Eriogonum fasciculatum var. fasciculatum 79 0.934 ± 0.012 0.010 ± 0.013 0.18 ± 0.08 0.18 ± 0.08

Eriogonum fasciculatum var. foliolosum 477 0.770 ± 0.025 0.004 ± 0.028 0.12 ± 0.06 0.21 ± 0.05

Eriogonum fasciculatum var. polifolium 426 0.740 ± 0.027 0.011 ± 0.033 0.13 ± 0.03 0.27 ± 0.06

Eriophyllum confertiflorum var. confertiflorum 478 0.751 ± 0.021 0.002 ± 0.026 0.10 ± 0.04 0.26 ± 0.06

Hesperoyucca whipplei 459 0.763 ± 0.036 0.005 ± 0.046 0.11 ± 0.04 0.30 ± 0.12

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Table 6—Model performance metrics (mean ± standard deviation) for focal taxa (continued)

Scientific name N a AUCtest

b AUCdiffc OR10Pd ORmSSS

Heteromeles arbutifolia 361 0.836 ± 0.012 0.002 ± 0.015 0.11 ± 0.03 0.20 ± 0.03

Lepidospartum squamatum 338 0.757 ± 0.041 0.016 ± 0.050 0.14 ± 0.07 0.33 ± 0.10

Malacothamnus fasciculatus var. fasciculatus 346 0.789 ± 0.018 0.007 ± 0.021 0.12 ± 0.03 0.21 ± 0.09

Malosma laurina 365 0.871 ± 0.007 -0.003 ± 0.008 0.12 ± 0.04 0.11 ± 0.05

Phacelia minor 282 0.801 ± 0.025 0.009 ± 0.030 0.12 ± 0.03 0.36 ± 0.14

Plantago erecta 274 0.830 ± 0.018 0.008 ± 0.021 0.12 ± 0.07 0.21 ± 0.07

Prunus ilicifolia subsp. ilicifolia 377 0.751 ± 0.019 0.010 ± 0.021 0.13 ± 0.03 0.29 ± 0.01

Quercus berberidifolia 386 0.782 ± 0.018 0.010 ± 0.020 0.11 ± 0.03 0.19 ± 0.07

Rhamnus crocea 229 0.815 ± 0.025 0.015 ± 0.030 0.13 ± 0.12 0.15 ± 0.13

Rhamnus ilicifolia 471 0.766 ± 0.021 0.007 ± 0.027 0.13 ± 0.03 0.27 ± 0.13

Rhus ovata 512 0.798 ± 0.037 -0.003 ± 0.043 0.11 ± 0.07 0.22 ± 0.08

Salvia apiana 435 0.752 ± 0.012 0.013 ± 0.016 0.12 ± 0.06 0.22 ± 0.07

Salvia mellifera 470 0.838 ± 0.025 -0.005 ± 0.030 0.13 ± 0.06 0.23 ± 0.07

Stipa pulchra 187 0.831 ± 0.014 0.020 ± 0.017 0.11 ± 0.05 0.28 ± 0.10

N = total number of taxon occurrences.

a

 AUC = area under the receiver operator curve for the test data. test b

 AUC = difference between the calibration and test AUC. diff c

 OR = omission rate at the 10 percent test threshold. 10P d OR = omission rate at the maximum sensitivity plus specificity threshold. mSSS

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Table 7—Relative significance of individual environmental variables to Maxent models measured as percentage of pe

importance

Scientific name AET CWD PPTseas PPTsummer PPTwinter Tseas Tm

Acmispon glaber var. brevialatus 2 3 7 15 3 38 

Acmispon glaber var. glaber 2 21 13 12 2 4 

Adenostoma fasciculatum var. fasciculatum 5 14 19 0 20 5 

Arctostaphylos glandulosa subsp. cushingiana 0 67 0 12 2 6 

Arctostaphylos glandulosa subsp. glandulosa 10 21 8 1 13 7 

Arctostaphylos glandulosa subsp. mollis 0 20 0 2 27 23 

Arctostaphylos glauca 2 26 9 5 19 14 

Artemisia californica 1 9 7 7 3 41 

Ceanothus crassifolius var. crassifolius 0 6 24 11 4 8 

Ceanothus cuneatus var. cuneatus 24 6 7 14 27 5 

Ceanothus leucodermis 1 19 2 5 37 16 

Ceanothus megacarpus var. megacarpus 4 8 1 9 17 38 

Ceanothus oliganthus 5 26 1 1 33 7 

Ceanothus perplexans 4 25 24 2 8 6 

Ceanothus tomentosus 10 23 4 4 0 35 

Ceanothus vestitus 6 5 3 11 6 22 

Cercocarpus betuloides var. betuloides 4 16 7 29 10 12 

Corethrogyne filaginifolia 4 36 8 3 9 13 

Deinandra fasciculata 5 1 4 10 3 63 

Encelia californica 0 5 7 17 1 54 

Encelia farinosa 5 9 11 5 2 8 

Eriodictyon crassifolium var. crassifolium 16 25 2 12 0 12 

Eriodictyon crassifolium var. nigrescens 1 22 0 41 11 6 

Eriodictyon trichocalyx var. lanatum 3 21 40 0 0 9 

Eriodictyon trichocalyx var. trichocalyx 13 8 22 24 7 6 

Eriogonum fasciculatum var. fasciculatum 4 20 13 10 1 20 

Eriogonum fasciculatum var. foliolosum 2 11 9 4 8 36 

Eriogonum fasciculatum var. polifolium 3 35 6 16 2 10 

Eriophyllum confertiflorum var. confertiflorum 3 37 19 6 8 15 

Hesperoyucca whipplei 40 3 9 4 12 1 

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Table 7—Relative significance of individual environmental variables to Maxent models measured as percentage of p

importance (continued) 

Scientific name AET CWD PPTseas PPTsummer PPTwinter Tseas T

Heteromeles arbutifolia 2 22 7 3 3 22 

Lepidospartum squamatum 11 1 33 15 4 10 

Malacothamnus fasciculatus var. fasciculatus 0 0 7 1 1 77 

Malosma laurina 7 15 4 2 1 43 

Phacelia minor 7 11 18 18 0 14 

Plantago erecta 1 0 2 7 20 55 

Prunus ilicifolia subsp. ilicifolia 1 6 2 7 23 18 

Quercus berberidifolia 3 10 1 12 24 8 

Rhamnus crocea 1 1 13 2 3 56 

Rhamnus ilicifolia 1 28 7 9 14 0 

Rhus ovata 5 14 8 15 8 18 

Salvia apiana 2 0 18 8 13 31 

Salvia mellifera 8 4 8 2 2 31 

Stipa pulchra 15 4 2 8 26 14 

Environmental variables: AET = actual evapotranspiration; CWD = climatic water deficit; PPTseas = precipitation seasonality; PPTsummer = precipitation of wa

= precipitation of coldest quarter; Tseas, temperature seasonality; Tmin = winter minimum temperature over the coldest months (December through February); T

temperature over the warmest months (June through August); GEOL = soil parent geology.

Bold font = variable with greatest permutation importance.

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Table 8—Percentage of change in suitable habitat under projected midcentury (2040–2069) climate conditions re

baseline (1951–1980) suitable habitat for each of five climate scenarios

CCSM4 CNRM FGOALS IPSL 

Scientific name S L G S L G S L G S L G 

Acmispon glaber var. brevialatus 98 2 32 65 35 13 74 26 28 83 17 28 

Acmispon glaber var. glaber 39 61 0 1 99 0 6 94 0 56 44 4 

Adenostoma fasciculatum var. fasciculatum 100 0 34 53 47 6 99 1 18 100 0 44 

Arctostaphylos glandulosa subsp. cushingiana 45 55 3 58 42 9 29 71 0 52 48 6 

Arctostaphylos glandulosa subsp. glandulosa 95 5 16 17 83 0 69 31 3 78 22 6 

Arctostaphylos glandulosa subsp. mollis 69 31 2 91 9 38 62 38 1 74 26 4 

Arctostaphylos glauca 75 25 2 96 4 14 64 36 1 62 38 7 

Artemisia californica 100 0 88 96 4 80 97 3 37 100 0 84 

Ceanothus crassifolius var. crassifolius 100 0 111 100 0 219 100 0 93 100 0 92 

Ceanothus cuneatus var. cuneatus 59 41 12 56 44 21 50 50 13 82 18 53 

Ceanothus leucodermis 87 13 9 91 9 15 77 23 5 63 37 9 

Ceanothus megacarpus var. megacarpus 85 15 5 100 0 67 58 42 3 80 20 3 

Ceanothus oliganthus 82 18 22 91 9 37 66 34 20 64 36 17 

Ceanothus perplexans 63 37 10 1 99 0 67 33 9 44 56 9 

Ceanothus tomentosus 98 2 61 50 50 1 89 11 41 79 21 14 

Ceanothus vestitus 41 59 0 47 53 0 43 57 0 33 67 5 

Cercocarpus betuloides var. betuloides 98 2 15 100 0 50 86 14 27 87 13 6 

Corethrogyne filaginifolia 50 50 0 28 72 0 21 79 0 47 53 0 

Deinandra fasciculata 68 32 0 11 89 0 40 60 0 86 14 7 

Encelia californica 97 3 8 33 67 0 65 35 0 100 0 42 

Encelia farinosa 88 12 148 84 16 95 95 5 170 79 21 175 

Eriodictyon crassifolium var. crassifolium 92 8 80 81 19 42 85 15 90 90 10 57 

Eriodictyon crassifolium var. nigrescens 48 52 1 60 40 4 34 66 1 84 16 7 

Eriodictyon trichocalyx var. lanatum 15 85 0 0 100 0 34 66 2 13 87 0 

Eriodictyon trichocalyx var. trichocalyx 85 15 60 91 9 181 64 36 102 76 24 15 

Eriogonum fasciculatum var. fasciculatum 81 19 6 8 92 3 21 79 0 98 2 31 

Eriogonum fasciculatum var. foliolosum 100 0 20 84 16 4 85 15 5 100 0 24 

Eriogonum fasciculatum var. polifolium 95 5 17 79 21 19 62 38 26 84 16 10 

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Table 8—Percentage of change in suitable habitat under projected midcentury (2040–2069) climate conditions rel

baseline (1951–1980) suitable habitat for each of five climate scenarios (continued)

CCSM4 CNRM FGOALS IPSL 

Scientific name S L G S L G S L G S L G 

Eriophyllum confertiflorum var. confertiflorum 47 53 0 4 96 0 12 88 0 58 42 0 

Hesperoyucca whipplei 100 0 64 88 12 41 81 19 49 100 0 94 

Heteromeles arbutifolia 100 0 56 69 31 3 97 3 42 99 1 46 

Lepidospartum squamatum 96 4 71 100 0 329 86 14 147 70 30 37 

Malacothamnus fasciculatus var. fasciculatus 100 0 17 69 31 5 97 3 8 98 2 19 

Malosma laurina 100 0 30 95 5 7 95 5 10 100 0 25 1

Phacelia minor 100 0 143 100 0 247 96 4 185 100 0 119 

Plantago erecta 89 11 14 36 64 1 85 15 13 58 42 5 

Prunus ilicifolia subsp. ilicifolia 100 0 30 99 1 34 87 13 21 94 6 24 

Quercus berberidifolia 100 0 32 96 4 44 100 0 32 100 0 26 

Rhamnus crocea 95 5 28 2 98 0 68 32 2 97 3 51 

Rhamnus ilicifolia 85 15 15 99 1 45 72 28 19 72 28 16 

Rhus ovata 99 1 55 95 5 48 85 15 55 99 1 48 

Salvia apiana 100 0 54 48 52 15 93 7 41 99 1 49 1

Salvia mellifera 100 0 66 97 3 66 100 0 53 100 0 97 1

Stipa pulchra 81 19 8 68 32 38 94 6 32 64 36 6 

Climate scenarios CCSM4, CNRM, FGOALS, IPSL, and MIROC represent a range of midcentury climate conditions with MIROC and CCSM4 models spann

directional changes in precipitation increase and decrease, respectively (table 4, fig. 3).

Percentage of habitat change categories: S = percentage of stable suitable habitat, L = percentage of loss in suitable habitat, G = percentage of gain in suitable h

Habitat suitability calculations exclude areas that have already been converted to human land uses (FRAP 2015).

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Appendix 4: Baseline and Projected Midcentury 

Suitable Habitat Maps for Focal Species

Baseline maps illustrate areas with suitable habitat in southern California under 

historical (1951–1980) climate conditions. Midcentury (2040–2069) suitable habitat 

maps show projected changes in suitable habitat under each of five future climate 

scenarios relative to baseline suitable habitat. Projected change in suitable habitat 

is defined as stable = suitable under baseline and future conditions; loss = suitable 

under baseline but unsuitable under future conditions; gain = unsuitable under 

baseline and becoming suitable under future conditions. Climate model consensus 

maps show agreement in projected suitable habitat change across the five climate 

scenarios and range from one (no agreement) to five (full agreement). In all maps, 

suitable habitat that has already been converted to urban and agriculture land uses 

is masked in dark gray (FRAP 2015). Projections for future land use change were 

not included.

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Figure 9—Acmispon glaber var. brevialatus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 10—Acmispon glaber var. glaber baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 11—Adenostoma fasciculatum var. fasciculatum baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 12—Arctostaphylos glandulosa subsp. cushingiana baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps

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Figure 13—Arctostaphylos glandulosa subsp. glandulosa baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 14—Arctostaphylos glandulosa subsp. mollis baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 15—Arctostaphylos glauca baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 16—Artemisia californica baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 17—Ceanothus crassifolius var. crassifolius baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 18—Ceanothus cuneatus var. cuneatus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 19—Ceanothus leucodermis baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 20—Ceanothus megacarpus var. megacarpus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 21—Ceanothus oliganthus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 22—Ceanothus perplexans baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 23—Ceanothus tomentosus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 24—Ceanothus vestitus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 25—Cercocarpus betuloides var. betuloides baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 26—Corethrogyne filaginifolia baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 27—Deinandra fasciculata baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 28—Encelia californica baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 29— Encelia farinosa baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 30—Eriodictyon crassifolium var. crassifolium baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 31—Eriodictyon crassifolium var. nigrescens baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 32—Eriodictyon trichocalyx var. lanatum baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps

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Figure 33—Eriodictyon trichocalyx var. trichocalyx baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 34—Eriogonum fasciculatum var. fasciculatum baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 35—Eriogonum fasciculatum var. foliolosum baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Using Species Distribution Models with Climate Change Scenarios to Aid Ecological Restoration Decisionmaking

Figure 36—Eriogonum fasciculatum var. polifolium baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 37—Eriophyllum confertiflorum var. confertiflorum baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 38—Hesperoyucca whipplei baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 39—Heteromeles arbutifolia baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 40—Lepidospartum squamatum baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 41—Malacothamnus fasciculatus var. fasciculatus baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 42— Malosma laurina baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 43—Phacelia minor baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 44—Plantago erecta baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 45—Prunus ilicifolia subsp. ilicifolia baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 46—Quercus berberidifolia baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 47—Rhamnus crocea baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 48—Rhamnus ilicifolia baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 49—Rhus ovata baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 50—Salvia apiana baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 51—Salvia mellifera baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Figure 52—Stipa pulchra baseline (1951–1980) and midcentury (2040–2069) suitable habitat maps.

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Appendix 5: Projected Midcentury Suitable Habitat for 

Scrub and Shrubland Vegetation Groups

Multiple-taxon overlay maps show the number of taxa with projected suitable 

habitat under midcentury (2040–2060) climate conditions for alluvial scrub, coastal 

sage scrub, low-elevation chaparral, and mixed chaparral-sage scrub vegetation 

groups (figs. 53 through 56). Projected change in suitable habitat is defined as: 

stable = suitable under baseline and future conditions; loss = suitable under baseline but unsuitable under future conditions; gain = unsuitable under baseline and 

becoming suitable under future conditions. Vegetation group assignments are not 

mutually exclusive (see table 2). For baseline suitability maps, see figure 6. In all 

maps, suitable habitat that has already been converted to urban and agriculture land 

uses is masked in dark gray (FRAP 2015).

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Figure 53—Projected habitat suitability for plant taxa associated with alluvial scrub under five future climate 

scenarios (A–E).

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Figure 54—Projected habitat suitability for plant taxa associated with coastal sage scrub under five future climate 

scenarios (A–E).

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Figure 55—Projected habitat suitability for plant taxa associated with low-elevation chaparral under five future 

climate scenarios (A–E).

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Figure 56—Projected habitat suitability for plant taxa associated with mixed chaparral-sage scrub under five future 

climate scenarios (A–E).

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Appendix 6: Species Distribution Modeling Caveats

What Do SDM Habitat Suitability Maps Represent?

Predictions of baseline and future habitat suitability should be interpreted cautiously and are best applied in concert with expert knowledge about the biology, 

ecological relationships, population dynamics, and demographics of the species. 

Baseline maps identify the geographic distribution of suitable habitat meeting the 

abiotic (climate and parent geology) requirements of individual taxa under climate 

conditions averaged from 1951 to 1980. While not included in our models, biotic 

interactions, dispersal, human land use, and fire also influence plant distribution 

in southern California and affect the degree to which habitat suitability models 

match the known distribution, or realized niche, of the species modeled (Araújo 

and Peterson 2012). Future suitability maps show how spatial patterns in habitat 

suitability may change under midcentury climate conditions averaged from 2040 to 

2069. We assumed unlimited dispersal ability within the study area for projections 

of suitability gain and assumed no dispersal (where species are unable to disperse 

to new areas of suitable habitat), for projections of suitability loss. Though biologically unrealistic, these dispersal assumptions are meant to cap the two extremes of 

potential habitat change under future climate. Suitable habitat projections are best 

interpreted as estimates of projected climate exposure rather than predictions of 

species persistence, which would require integrating species distribution models 

(SDMs) with models of population dynamics, physiological tolerances, and genetic 

variation (Dawson et al. 2011, Franklin et al. 2016, Richardson et al. 2014). For 

example, a prediction of suitability loss can identify high climate exposure and 

potentially stressful environmental conditions midcentury, but it does not indicate 

how the species may respond or if populations within the species range would 

respond differently. 

Factors to Consider When Applying SDM in Decisionmaking

Spatial scale— 

It is important to consider spatial scale (extent and resolution) when applying modeling results in decisionmaking. We modeled species-environment relationships at a 

regional extent and results are best used to inform decisionmaking within southern 

California. Our results are not transferable to other regions that may differ in the 

dynamics controlling habitat suitability and species distribution. When the underlying input data to the California Basin Characterization Model (CA-BCM) has high 

accuracy, spatial applications of the climatic and hydrologic data can be quite small, 

as fine as hillslope scale (Flint et al. 2013). However, uncertainties in the CA-BCM 

input variables, such as coarse resolution of soil data and difficulty incorporating 

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the impacts of human activities on hydrology for some areas in California (Flint et 

al. 2013) or measurement errors in the temperature and precipitation data (Oyler et 

al. 2015, Stoklosa et al. 2015), can limit the appropriate spatial resolution for applications. The occurrence data used in SDMs can introduce additional spatial uncertainties, such as spatial biases in collections and error in georeferencing accuracy 

(Barry and Elith 2006, Graham et al. 2008). Misidentifications (Costa et al. 2015) 

and even taxonomic ambiguities that arise as taxon concepts are refined (Romero 

et al. 2014) can affect model predictions and forecasts. Although our output habitat 

suitability maps have a high spatial resolution (270 m), patterns of suitability are 

best interpreted as summaries or trends at the ecological subsection to section level.

Uncertainty in future climate—

Uncertainty in future climate projections stems from a variety of sources. Climate 

change projections depend on general circulation models (GCMs)—mathematical representations of the Earth’s climate system—that vary in their sensitivity 

of climate to greenhouse gases and radiative forcing (Daniels et al. 2012, Stott 

and Kettleborough 2002). Combined with scenarios for future greenhouse gas 

emissions, such as the representative concentration pathways (RCPs) used in the 

Intergovernmental Panel on Climate Change (IPCC) 5th assessment report (IPCC 

2013), GCMs provide a range of possible future climate conditions for the 21st

century. These projections tend to diverge dramatically across emission scenarios 

after the mid-21st century. Downscaling, which is necessary to convert the coarse 

scales of GCMs (250 km resolution) to finer regional-to-local scales relevant 

for land management, is another source of uncertainty in future climate projections. Precipitation, for example, is particularly challenging to forecast in southern 

California, as coarse-resolution GCMs are unable to represent the fine-scale variability introduced by the region’s complex topography (Hughes et al. 2009). We 

use five GCMs under a ‘business-as-usual’ scenario that tracks our present high 

greenhouse gas emission trajectory (IPCC scenario RCP 8.5) to show how climate 

may change in southern California and highlight some of the uncertainty in these 

changes. It is possible that the five scenarios we selected may not encompass the 

actual climate conditions in southern California at midcentury. Temperature is projected to warm under continued greenhouse gas emissions, but to what degree and 

how quickly depends on a number of factors, including human actions to reduce 

those emissions (IPCC 2013). The magnitude and direction of precipitation change 

are also uncertain, with different GCMs projecting either increases or decreases in 

precipitation relative to the 20th century. Our selection of climate models spans both 

wetter (CNRM-CM5) and drier (MIROC-ESM) future conditions and can be used 

to explore sensitivity of habitat suitability to the direction in precipitation change. 

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Novel climates—

Climate change will likely result in novel future climate combinations that have no 

present-day analog, especially if greenhouse gas emissions continue to increase at 

current levels (Williams and Jackson 2007). Therefore, projecting SDM predictions 

under future climate often involves extrapolating models to novel environmental 

space outside the reference conditions used to train the models (Elith et al. 2010, 

Merow et al. 2013). In Maxent, the species-environment relationship can either be 

“clamped” to a constant probability when projected into novel space or simply extended, neither of which is likely to reflect a biologically realistic response (Merow 

et al. 2013). Consequently, suitability forecasts under novel conditions should be 

treated with extreme caution. Of the five future climate scenarios we considered, 

the CNRM-CM5 model had the greatest departure from the baseline 1951–1980 climate conditions. Novel climate conditions also introduce biotic uncertainty. Studies 

of past vegetation dynamics during the late Quaternary suggest that plant communities are unlikely to remain intact as species shift individualistically, with nonanalog conditions leading to nonanalog communities (Jackson and Overpeck 2000). 

Climate change, land use change, fire, and invasive species may all contribute to 

the rise of novel ecosystems, creating implications for conservation and restoration 

(Hobbs et al. 2009).

Uncertainty in SDM— 

Uncertainty in correlative SDMs arises from a number of sources, some of which 

have already been discussed above. Model outputs depend on the quality of the input data, and errors in the species occurrences (e.g., Graham et al. 2008) and environmental predictors (e.g., Fernández et al. 2013, Stoklosa et al. 2015) introduce uncertainty into model predictions. Mismatches in the resolution between occurrence 

and environmental layers can compound these uncertainties (Wiens and Bachelet 

2010); model resolution should ideally be matched to the scale of the conservation 

and management applications. For example, models built with coarse-scale environmental data may not be appropriate to inform decisionmaking at local or fine scales. 

Species geographical and ecological characteristics can also affect model performance and predictions (reviewed in Heikkinen et al. 2006). Methodological decisions and errors introduce uncertainty in SDMs, including choice of modeling algorithm, model misspecification, and missing covariates (Barry and Elith 2006, Beale 

and Lennon 2012). Methodological decisions in model parameterization affect the 

transferability of models in both space and time and are important to consider when 

projecting predictions under future climate change (García-Callejas and Araújo 

2016). Comprehensive reviews on the sources of SDM uncertainty can be found 

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in Barry and Elith (2006), Beale and Lennon (2012), Heikkinen et al. (2006), and 

Wiens et al. (2009). Despite these challenges, SDMs can be a powerful tool to support decisionmaking. We emphasize the importance of close collaboration between 

modelers and data users to ensure that models are designed to address appropriate 

management questions and that uncertainties and limitations in application and interpretation are well-communicated.

Model assumptions—

Numerous papers have reviewed SDM assumptions and practical applications 

in conservation and management (Araújo and Peterson 2012, Guisan et al. 2013, 

Jarnevich et al. 2015, Schwartz 2012, Sinclair et al. 2010, Wiens et al. 2009). The 

degree to which a species violates certain SDM assumptions can lead to both overestimation and underestimation of risk under projected climate change (e.g., Early 

and Sax 2014, Schwartz 2012, Valladares et al. 2014). We highlight the fundamental 

assumptions underlying SDMs to better guide appropriate use of SDM results.

Correlation—SDMs assume that a species’ distribution is limited by the environmental variables included in the model; in our case climatic and geologic factors. 

For some species, the approach used to relate distributional data with environmental conditions may not correlate with the population-level processes driving species persistence. This is particularly relevant when extinction risk is sensitive to life 

history traits (e.g., recruitment response to fire) (Fordham et al. 2012, Lawson et 

al. 2010). Ultimately, SDMs linked to population dynamics are needed to address 

population persistence or extinction risk under projected climate change (Franklin 

et al. 2016). 

Equilibrium with climate—Species distribution models assume that the current 

distribution of a species is in equilibrium with the environment, meaning all suitable habitat is occupied by the species. However, dispersal, demographics, longevity, and biotic interactions (including humans) can affect the degree to which a 

species is in equilibrium with the current environment. For long-lived taxa, individuals and populations may continue to persist in areas where climate has become 

unfavorable for reproduction or seedling establishment. Projected climate change is 

expected to induce vegetation disequilibrium with climate at both leading and trailing edges of species ranges (Svenning and Sandel 2013). While unavoidable, this 

assumption should be acknowledged when interpreting SDM results. 

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Niche conservatism—Species distribution models assume that the species-environment relationship remains fixed, or conserved, over space and time (niche conservatism) (Wiens and Graham 2005). That is, the model assumes that there is no local 

adaptation within a species range and that species will not have any adaptive response to changing conditions. Although an assumption of niche conservatism may 

be realistic for species with high dispersal ability and little population structure, or 

where the rate of climate change exceeds a species’ adaptive capacity, it is likely to 

be violated for many species. For the widespread species we analyzed, our regionalscale modeling aims to capture relationships specific to southern California without including potentially confounding dynamics from other regions in the state. 

However, we are not able to address patterns of local adaptation and differential 

response to climate change without more detailed data on genetic variability of individual taxa across the landscape.

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Glossary

actual evapotranspiration (AET)—Evaporative water loss (a function of temperature and radiation) from the ground surface and transpired by vegetation given prevailing water availability. Available water is defined as the amount of water held in 

the soil between wilting point and field capacity multiplied by soil depth (affected 

by precipitation over time) in the California Basin Characterization Model. 

adaptive capacity—Ability of a system to adjust to climate change (including 

climate variability and extremes) to moderate potential damages, to take advantage 

of opportunities, or to cope with the consequences (IPCC 2007). At the population 

level, the ability of populations to adjust to climate change stimuli and maintain fitness through genetic change (evolutionary response), behavioral response, or plastic 

responses in physiology. 

California Basin Characterization Model (CA-BCM)—A spatially explicit, 

regional water balance model that integrates physical watershed characteristics 

with historical or projected climate data to predict watershed-specific hydrologic 

responses for the California hydrologic region (Flint et al. 2013). It mechanistically 

models the pathway of precipitation into evapotranspiration, infiltration into soil, 

runoff, and the ability of water to percolate through the root zone and recharge 

groundwater, producing high-resolution surfaces (270 m) for the following variables: precipitation, air temperature, April 1st snowpack, recharge, runoff, potential 

evapotranspiration (PET), actual evapotranspiration (AET), and climatic water 

deficit (CWD).

climate scenario—Plausible and often simplified representation of the future 

climate based on a set of coherent and internally consistent assumptions about key 

driving forces and relationships (IPCC 2013). 

climate exposure—Degree and nature to which a system is exposed to climate 

change. Exposure refers to extrinsic factors of climate change such as character, 

magnitude, and rate of change (Glick et al. 2011). Loss in climatic suitability 

predicted from correlative species distribution models based on climate variables 

can be interpreted in terms of climate exposure.

climate model—Numerical representation of the climate system based on the 

physical, chemical, and biological properties of its components, their interactions 

with feedback processes, and accounting for all or some of its known properties 

(IPCC 2013). See general circulation model.

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climate projection—Simulated response of the climate system to a scenario of 

future emission or concentration of greenhouse gases and aerosols, or radiative 

forcing scenarios, often derived using climate models (IPCC 2013). 

climatic water deficit (CWD)—Evaporative demand that exceeds water availability. Calculated as the difference between potential evapotranspiration (PET) and 

actual evapotranspiration (AET) summed annually in the California Basin Characterization Model (Flint et al. 2013).

downscaling—A method that derives local- to regional-scale (10 to 100 km) information from larger scale models or data analyses (IPCC 2013).

emission scenario—Plausible representation of the future development of emissions of substances that are potentially radiatively active (e.g., greenhouse gases, 

aerosols) based on a set of coherent and internally consistent assumptions about 

driving forces and their relationships. Driving forces can include demographic 

and sociological development, technological change, etc. Concentration scenarios 

derived from emission scenarios are used as input to a climate model to compute 

climate projections (IPCC 2013). See representative concentration pathways.

general circulation model (GCM)—Comprehensive, three-dimensional global 

climate model that reflects the latest scientific understanding of complex physical, 

chemical, and biological properties of the climate system, as well as their interactions with feedback properties (IPCC 2013). 

habitat suitability—Potential of a site to support a particular taxon. In species 

distribution modeling, the degree to which the environmental variables considered 

in the model (e.g., climate) are predicted to support the presence of a particular 

taxon in geographic space and time. 

infraspecific—Refering to classification below the species level, as in subspecies 

and variety.

intraspecific—Refering to variation within a species, such as variation among 

populations or among individuals within populations.

Maxent—Species distribution modeling method tailored for presence-only data and 

based on a maximum entropy algorithm (Phillips et al. 2006).

potential evapotranspiration (PET)—Amount of water that could evaporate from 

the ground surface or transpire from vegetation given unlimited water, summed 

annually. In the California Basin Characterization Model, it is modeled from solar 

radiation and air temperature and partitioned to represent bare-soil evaporation and 

evapotranspiration owing to vegetation (Flint et al. 2013).

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radiative forcing—Change in net radiative flux (expressed in W m-2) at the top 

of the atmosphere owing to a change in a driver of the climate system, such as a 

change in the concentration of carbon dioxide or output of the Sun (IPCC 2013). 

resilience—Degree to which a system rebounds, recoups, or recovers from a 

disturbance or hazard.

representative concentration pathway (RCP)—Scenarios of future emissions and 

concentrations of greenhouse gases, aerosols and chemically active gases, and land 

use/land cover. Each RCP provides just one of many possible future scenarios that 

could lead to the specific radiative forcing characteristics. Four RCPs were used to 

represent climate projections in the Intergovernmental Panel on Climate Change 

(IPPC) 5th climate change assessment, RCP2.6, RCP4.5, RCP6.0, and RCP8.5 

(IPCC 2013). 

RCP8.5—One possible high-concentration pathway in which radiative forcing 

reaches greater than 8.5 W m-2 by 2100. 

sensitivity—Degree to which a species or system is affected, either adversely or 

beneficially, by climate variability or change (Glick et al. 2011, IPCC 2007). The 

effect may be direct (e.g., a change in crop yield in response to a change in the 

mean, range or variability of temperature) or indirect (e.g., damages caused by an 

increase in the frequency of coastal flooding owing to sea level rise) (IPCC 2007). 

species distribution model (SDM)—Statistical model relating observations of 

species occurrence with environmental information to predict habitat suitability in 

space and time.

vulnerability—Degree to which a system is susceptible to, or unable to cope with, 

adverse effects of climate change, including climate variability and extremes. 

Vulnerability is a function of the climate exposure experienced by the system, its 

sensitivity, and its adaptive capacity (Glick et al. 2011). 

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