Table Of ContentMadrono, Vol. 60, No. 3, pp. 193-210, 2013
CLIMATE CHANGE VULNERABILITY ASSESSMENT OF RARE PLANTS
IN CALIFORNIA
Brian L. Anacker1 2 Melanie Gogol-Prokurat2 Krystal Leidholm2 and
, , ,
Steve Schoenig2
department of Evolution and Ecology, University of California, Davis, CA 95616
[email protected]
California Department of Fish and Game, 1807 13th St., Sacramento, CA 95611
Abstract
We assessed thevulnerability to climatechange of 156 rare plant species. The species were selected
from the 1625 rare species in California to comprise eight rarity types, classified according to range
size, population size, and habitat specificity. For each ofthe 156 species, we first assigned a climate
changevulnerability scoreusinglifehistoryattributes and speciesdistribution models, as specified by
the Climate Change Vulnerability Index (CCVI) of NatureServe. The resulting CCVI scores were
extremely vulnerable (n = 2), highly vulnerable (n = 40), moderatelyvulnerable (n = 57), presumed
stable (n = 32), increase likely (n = 16), and insufficient evidence (n = 9). Piperia yadonii Rand,
Morgan & Ackerman and Mimulus purpureus A. L. Grant were the species scored as extremely
vulnerable. There was no correlation of the CCVI scores with rarity type, suggesting that climate
change vulnerability cannot be inferred by simple categorizations based on geographic range and
habitat preference. Second, we conducted a follow-up species distribution model sensitivity analysis
that showed that themodelingresults were highly dependent upon bothmodel algorithm and choice
of predictor variables. However, 60 of the 156 species were predicted to have declines in climatic
suitability, regardlessofmodelingtechnique. Third, asanindependentassessmentofvulnerability,we
calculated the topographic complexity around known occurrences of each species. Species in
topographically dissected landscapes may be less vulnerable to climate change because they can find
suitable climates locally as climate changes. We found that topographic complexity varied
substantially, even within a single CCVI score level, and therefore provides unique information on
vulnerability. Our results can be used to guide monitoring, management, and conservation plans for
rare plant species.
KeyWords: California, climate change, NatureServe, rare plants, vulnerability.
Climate change may negatively impact the flora change that is time-efficient, repeatable, and is
ofCalifornia, abiodiversityhotspotwithover2000 directly comparable across species. Investigators
A
endemic plant species (Myers et al. 2000). can use vulnerability assessments to rank a list of
changing climate may reduce and extirpate popu- species with regard to their relative expected
lations (Pounds et al. 2006), cause species to sensitivity to shifts in climate (Gardali et al.
migrate north and upslope (Parmesan 1996; Kelly 2012). Most studies are focused upon individual
and Goulden 2008; Loarie et al. 2009), advance species; however, recent work has also considered
floweringtimes, promote speciesinvasion, increase the vulnerability oflandscapes (Klausmeyer et al.
disturbance (e.g., fire), and cause community 2011). For example, topographically complex
reorganization (Walther et al. 2002; Burkett et al. landscapes may provide refugia or paths for
2005). Severaltoolshavebeendevelopedtoidentify movement to more suitable areas underchanging
which species and habitats are most imperiled by temperature and moisture regimes (Hunter et al.
the negative impacts ofclimate change (Schnieder 1988; Beier and Brost 2010). Moreover, under-
et al. 2007; Williams et al. 2008; Heller and standing how regional and local processes inter-
Zavaleta 2009; EPA 2009; Byers and Norris 2011; act to create spatial heterogeneity in climate may
Glicketal. 2011; Schlesingeret al. 2011), underthe help predict the direction and rate of climate
assumptionthattheworldwillcontinuetowarmin change (Ackerlyet al. 2010). Further, assessments
the near term, even if emissions are immediately of geophysical diversity (i.e., the number of
reduced (IPCC 2007). By identifying species or bedrock types) may be a useful alternative to
habitatsmostatriskfromeffectsofclimatechange, species-level assessments, since high levels of
conservation and management efforts can be geological diversity are often associated with
targeted to reduce these impacts, such as by habitat heterogeneity and species diversity (An-
protecting existing habitat or through assisted derson and Ferree 2010).
migration (Hunter 2007; McLachlan et al. 2007). Species-level vulnerability assessments are typ-
Vulnerability assessments provide a standard- ically based on intrinsic life history traits, species
ized method to assess sensitivity to climate distribution models (SDMs), or both. The trait-
—
MADRONO
194 [Vol. 60
based approach identifies and scores species recognizes 1625 plant taxa as rare or endangered,
attributes relevant to avoiding or tolerating as ofMarch 1, 2011. Whilemany ofthe 1625 taxa
climate change, such as movement ability (i.e., are subspecies and varieties, and thus the words
dispersal rates) or sensitivity to changes in “taxa” and “taxon” are appropriate than “spe-
temperature or moisture. The sum ofthese scores cies,” we use “species” throughout the text for
represents the species’ overall vulnerability to simplicity. There are 26 California Rare Plant
—
climate change. Trait-based indices were largely Rank 1A—presumed extinct in California; 1132 I
developed with animals as a primary focus. For Rank IB rare or endanger—ed in California and
example, the International Union for Conserva- elsewhere; and 492 Rank 2 rare or endangered
tion of Nature (IUCN) analyzed the life history, in California, but more common elsewhere
ecology, behavior, physiology, and genetic make- (CNPS 2001). These rare species may have
up of “red list” animal species to assess a species narrow ranges, small population sizes, or narrow
vulnerabilitytoclimatechange(Fodenetal. 2009). habitat preferences (or all of the above) for
A second set oftools used to assess vulnerabil- natural or anthropogenic reasons (CNPS 2001).
ity to climate change is SDMs (Pearson and The potential impacts ofclimate change were not
Dawson 2003; Loarie et al. 2008; Stralberg et al. a factorconsidered by CNPS when assigningrare
2009). Typically, point occurrence data for a plant ranks; thus, which of the 1650 species will
species are used to create a statistical model of be most vulnerable to climate change has been
climatic suitabilityusinghistorical (often 30-year- largely uninvestigated.
mean) climate data. This model is then used to Climate-only SDMs suggest California plants
predict the species’ contemporaryrange, basedon may be in trouble: 66% will experience 80%
agrid ofhistoricalclimate, and the species’ future reductions in range size within a century (Loarie
range, based on agrid ofpredicted futureclimate. et al. 2008). In addition, plants may be unable to
Finally, thechangeinpredictedrangesizeandthe adjust their ranges fast enough to spatially track
amount of range overlap is calculated. Species shiftingclimates (Loarie et al. 2009). It is possible
with large range reductions and/or low range that rare species may be even more sensitive to
overlapareconsideredtobemorevulnerablethan climate change, given their limited geographic
species with small range reductions and/or high ranges and small population sizes. Or, perhaps
range overlap. However, there are a largenumber they will be less sensitive to climate change, given
ofmodeling techniques used to describe climatic their specialized ecologies. These attributes also
suitability, and differences in model algorithms make it more difficult to accurately model rare
and assumptions can greatly influence the quality species than common species. Given our conser-
ofmodel predictions (Araujo and New 2007). vation concerns, it is our hope to create a
A leading example ofcombining species traits meaningful vulnerability ranking for rare species
and SDMs into a single vulnerability analysis and to identify which spatial and life history
comes fromNatureServe (Arlington, VA), a non- factors contribute most to that vulnerability.
profit organization whose mission is to provide Due to the large number of rare plants in
the scientific basis for effective conservation California, we sought to determine whether the
action. NatureServe developed the Climate level of climate change vulnerability could be
Change Vulnerability Index (CCVI) to serve as inferred forcertain groups ofrare plants based on
a standardized methodology for assessing vulner- rarity type, life history traits, or biogeographic
ability to climate change at the species level affinity. Our work can be divided into three
(Young et al. 2012). The CCVI consists of a complementary parts. First, for a subset of the
Microsoft Excel —document with four main 1650rareplants(n = 156),wecompiledasetoflife
sections: Section A direct exposure to ch—anging history attributes and created distribution models
temperature and precipitation; Section B indi- to rank vulnerability as specified by the CCVI of
rect exposure to climate change, including sea NatureServe. Second, we conducted a SDM
level rise, natural and human barriers, and land sensitivity analysis to determine how choice of
impacts from climate mitigation; Section C model algorithm and predictor variables influ-
sensitivity factors (hereafter refer—red to as “life enced distribution model predictions of habitat
history traits”); and Section D modeled re- suitability in future climates. Third, we calculated
sponse to climate change. an index of vulnerability based on topographic
Our goal was to assess the vulnerability of complexity around known occurrences.
California rare plant species to climate change
and to evaluate the application of the Nature- Methods
Serve CCVI method to rare plants. Rarity is a
major feature of California’s botanical heritage. Species Selection
The California Native Plant Society (CNPS)
Rare Plant Program, which works in coordina- To create a list of focal species that was
tion with the California Department ofFish and representative ofCalifornia rareplant speciesasa
Game’s (CDFG) Natural Diversity Database, whole, we first classified each ofthe 1625 species
2013] ANACKER ET AL.: CLIMATE CHANGE AND RARE PLANTS IN CALIFORNIA 195
Table 1. TypesofRarity. Thetypesofrarity,modifiedfromRabinowitz(1981), comefromintersectingrange
size (small or large), population size (small or large), and habitat specificity (habitat specialist or generalist). For
each category, the number ofspecies in our sample and an example species is provided. While only seven ofthe
eight groups are “rare,” species with relatively large ranges, large populations, and generalist habitat preferences
among our sample are still relatively rare with respect to the average species in the flora. Thus, we used all eight
groups for our selection.
Large range Small range
Population size Habitat generalist Habitat specialist Habitat generalist Habitat specialist
n = 28 n = 25 n = 24 n = 17
Large populations
Californiamacrophylla Streptanthusmorrisonii Mimuluspurpureas Allium tuolumnense
n = 24 n = 15 n = 17 n = 6
Small populations Liliumparryi Calochortusplummerae Taraxacum Monardella
californicum stebbinsii
into one of the eight types or forms of rarity natural history, and conservation status of rare
(Rabinowitz 1981) (Table 1). Following Rabino- species from CDFG, CNPS, and NatureServe.
witz’s (1981) definitions, only seven of the eight We then conducted a literature review, mapped
groups are “rare,” because the combination species distributions, and modeled responses to
“large range, large population, and habitat climate change. Biologists and botanists were
generalist” is considered common. However, consulted to fill data gaps as needed for
since our sample pool was made up ofonly rare particular species. Collected data and sources
species, even the species within this pool with for each species are available online at www.dfg.
large ranges, large populations, and generalist ca.gov/biogeodata/.
habitat preferences were rare relative to the
average species in the flora. Thus, we sampled CCVISectionA: Directexposure (twofactors).
across alleightgroups to obtain oursubset of156 Direct exposure was scored based on the
species (Appendix 1). Nomenclature for these percentage of the species’ range that falls into
species follows the CNPS Inventory (2001). NatureServe’s recommended categories of pro-
We used information from the California jected changes of temperature or moisture. The
Natural Diversity Data Base (CNDDB) to temperature change categories (decreasing in
attribute each species with the three variables severity) were >3.1°C, 2.8-3.1°C, 2.5-2.7°C, 2.2-
2.4°C, and <2.2°C; the moisture change categories
ro(1en)quraiarnemgdiefnsoiirzmeru—amrittoyctoatlnyvpaeerxecalpaosofsliysfgpiceoacntiieosenn,rcaaonsmgpfeaolsblsaoiswnesgd: (-d0e.c1r1e9a,si—n0g.0in74s—e—v0e.ri0t9y6),w-e0r.e05<1——00..101793,, -—00..002987--
mapped occurr—ences from the CNDDB; (2) —0.050, and >—0.028. Climate data (Fig. la-d)
population size the median population number and projections for the year 2080 were derived by
ooffiCndNivDiDduBal;s,aenxdtra(c3t)edhfabriotmatthsepeccoifmimcietny—t fsiuebl-d TthheeirNCaltiumrateeCWoinzsaerrdva(nMcoydealn:dEndsoewmnblloeadAevderfargoe,m
strate affinity, extracted from the habitat field of emission scenario [ES] A2; www.climatewizard.
CNDDB. For range size and population size, org). Temperature change was the predicted
speciesweredesignatedaslarge orsmallbased on change in annual temperature by2080, calculated
their value relative to the median of the over the range of the species in California.
distribution ofvalues. We then randomly selected Moisture change was the predicted net change in
species from each of the eight rarity types moisture based on the Hamon AET:PET Mois-
(Table 1). Our list of 156 species includes 139 ture Metric, calculated over the range of the
nCaaonlmdiifntoahrlrnleiyea rRRaararenekanPdl3atnsht.usRWhadionlkenoItrBascn,oknt1r33ibRsuataenrkteo2ntohste, aaspcneqdcuiieSrseDidnMfCoasrleinftsohiretniivmaio.tdyAedaldneiadltyisroienssapl(oIcnIl)siemfar(tSoeemcdtaWitooanrwlDad)s-
1625 described above, we included three ofthese Clim, as described below.
species to see if they had remarkably different CCVI Section B: Indirect exposure (four
vulnerability scores. factors). Indirect exposure evaluated landscape
configuration factors that may affect the vulner-
Climate Change Vulnerability Index (CCVI) abilityofa speciesto climatechange: Exposureto
sea level rise, distribution relative to natural
CCVI overview. The NatureServe CCVI (re- barriers, distribution relative to anthropogenic
lease 2.01) assesses 24 climate change vulnerabil- barriers, and predicted impact of land use
ity risk factors, placed in four categories: direct changes resulting specifically from human re-
exposure, indirect exposure, life history traits, sponses to climate change. To evaluate these
and modeled response. To complete the CCVI, factors, we compared the distribution of the
we collected information on the distribution. known occurrences ofeach species with a map of
MADRONO
196 [Vol. 60
Fig. 1. Map of future temperature change (a), future drying (b), historical temperature variation (c), and
historical annual precipitation (d).
predicted sea level rise (Strauss et al. 2012), species’ range occurring in areas subject to sea
topographic maps that depicted landscape fea- level rise were placed into five broad categories
tures, and maps of proposed renewable energy (decreasing in vulnerability): (1) >90%, (2) 50-
(e.g., solar power stations, wind farms, geother- 90%, (3) 10-49, (4) <10, and (5) predicted
mal wells; California Department of Fish and increasein extent (intertidal specieswhosehabitat
Game 2011). For sea level rise, the percent ofthe may increase with sea level rise). For barriers,
2013] ANACKER ET AL.: CLIMATE CHANGE AND RARE PLANTS IN CALIFORNIA 197
Table 2. Life History Traits Usedin Determining Climate Change Vulnerability Index (CCVI) in
THIS Study. Assumptions and data sources are listed for each determining factor.
Factor Assumption Data source
Dispersal and movements Lower dispersal ability leads to Dispersal mechanisms from scientific
high vulnerability. literature and expert opinion.
Predicted sensitivity to Narrow historical climate exposure Historical temperature and
temperature and moisture and special microclimatic precipitationvariationfromClimate
changes preferences leads to high Wizard. Physiological hydrological
vulnerability. niche and physiological thermal
niche from scientific literature and
expert opinion.
Dependence on a specific Dependence on a particular Adaptation and affinity for fire and
disturbance regime disturbance regime leads to flood-prone habitats from scientific
high vulnerability. literature and expert opinion.
Restriction to uncommon Habitat specialization leads to Substrate affinity from CNDDB.
geological features or high vulnerability. SSURGOsoildatafromtheNRCS.
derivatives
Reliance on interspecific Dependence on other species Literature and expert opinion.
interactions (facilitation, pollinators,
and seed dispersers) leads to
high vulnerability.
Genetics Low genetic diversity leads to Not scored for any species due to
high vulnerability. insufficient information.
Phenological response Shorter bloom period leads to CNPS’s Rare Plant Inventory bloom-
high vulnerability. period database.
each species was placed into one of four and range overlap ofpredicted future range with
categories (decreasing in vulnerability): (1) Barri- predicted current range using the Maxent algo-
ers completely surround the current distribution, rithm, a statistical model that uses machine
(2) barriers border the current distribution learning (Phillips et al. 2006; Elith and Leathwick
incompletely but will likely impair distributional 2009).
shits, (3) barriers border the current distribution The spatial centroid ofeach CNDDB mapped
incompletely but will be unlikely to impair occurrence record was used. The records of the
CNDDB
distribution shifts, and (4) significant barriers have been carefully curated by the staff
do not exist. For renewable energy, each species of CDFG, yet uncertainty in the exact locations
wasplaced into one offourcategories (decreasing likely remains, contributing an unknown, but
in vulnerability): The likelihood that a species’ likely small, amount of variation to the model
natural history or range may be (1) very likely to results presented here. ACnAother limitation is that
conflict with mitigation-related land-use changes, occurrences outside of are omitted; however,
(2) likely to conflict with land-use changes, (3) visual examination of the distributions of our
likely to benefit from land-use changes, or (4) focal species suggests that only —10% have
very likely to benefit from land-use changes. distributions that may cross into Oregon or Baja
California, Mexico. We used a dataset acquired
CCVIsection C: life history traits (16factors). from WorldClim (Hijmans et al. 2005) compris-
The life history traits were grouped into the ing four climate variables (annual temperature,
following categories: dispersal/movement, sensitiv- annual precipitation, seasonality of temperature,
ity to temperature or moisture, disturbance-depen- and seasonality of precipitation) for current
dence, geologic restriction, interspecific interac- conditions (mean 1950-mean 2000) and for
tions, genetic diversity, and phenology (Table 2). future conditions (called “2080,” but data are
Eachspecieswasscoredbyassessingwhetheritslife means for 2070-2100; Global Circulation Model
history traits would be expected to decrease, [GCM] CGCM3.1, ES A1B) at 1 km2 resolution.
somewhat decrease, neutral, somewhat increase, We fit a Maxent model for current conditions
increase, or greatly increase its vulnerability to and used the resulting model to predict climatic
climatechange.Ifinformationwasnotavailablefor suitability, ranging from 0-1, for both current
a particular factor, it was scored as unknown. and future conditions; background points were
Guidanceonhowtorankeachfactorwasprovided selected randomly. Maxent was run using the
byNatureServe. Ranksfortenfactorsin thisgroup default “auto features” mode, allowing the use of
were required or the vulnerability index returned a linear, quadratic, product, threshold, and hinge
score of“Insufficient Evidence.” features. These continuous surfaces were then
converted to binary (suitable/unsuitable) using a
CCVI Section D: Modeled response (two threshold determined as the value that maximizes
factors). We modeled the change in range size the kappa, a statistical measure ofthe agreement
MADRONO
198 [Vol. 60
Table 3. Climate Change Vulnerability Index Score Descriptions.
Index scores Descriptions
Extremely vulnerable (EV) Abundance and/or range extent within geographical area assessed
extremely likely to substantially decrease or disappear by 2050.
Highly vulnerable (HV) Abundanceand/orrangeextentwithingeographical areaassessedlikelyto
decrease significantly by 2050.
Moderately vulnerable (MV) Abundanceand/orrangeextentwithingeographicalareaassessedlikelyto
decrease by 2050.
Presumed stable (PS) Available evidence does not suggest that abundance and/or range extent
within the geographical area assessed will change (increase/decrease)
substantially by 2050. Actual range boundaries may change.
Increase likely (IL) Available evidence suggests that abundance and/or range extent within
geographical area assessed is likely to increase by 2050.
Insufficient evidence (IE) Available information about a species’ vulnerability is inadequate to
calculate an index score.
between predictions and observations, and the traits for all species (i.e., when the factors were
AUC values were calculated (Cohen 1960; marked as unknown). We also compared the
Jimenez-Valverde 2011). Change in range size CCVI scorewith California RarePlant Rank and
was calculated as the total area predicted as rarity type using two one-wayANOVAs. Finally,
suitable in the future (t2) minus the total area we tested if range size change or range overlap
predicted as suitableinthe present(ti), dividedby was related to California Rare Plant Rank and
total area predicted as suitable at Range rarity type using four one-way ANOVAs.
overlap was calculated as the total area predicted
to be suitable at ti and t2, divided by the total SDM Sensitivity Analysis
area predicted as suitable at ti. The geographic
extent of all models was California. This may For each of the 156 species, we ran 22
overestimaterangelosswhen a species’ new range additional SDMs to estimate the sensitivity of
is predicted to be outside ofCalifornia. range predictions to modeling algorithms and
choice ofpredictor variables. Our 23 models (all
CCVIriskfactorscore. Thenaturalhistory and run in Maxent unless stated otherwise) were: 1)
distributional information for each species was 19 climate variables (bioclim); 2) four climate
entered into the CCVI Excel calculator to obtain variables (described above); 3-14) four climate
scores foreach species. The outputwas one ofsix variables, with different GCM*ES combinations
vulnerability scores: extremely vulnerable (EV), (GCMs included BCCR-BCM2.0, CSIRO-
highly vulnerably (HV), moderately vulnerable Mk3.0, INM-CM3.0, and MIROC3.2 [medres];
(MV), presumed stable (PS), increase likely (IL), ESs included A2, AB, and Bl); 15) 19 climate
and insufficient evidence (IE; Table 3). All variables with soil type; 16) 19 climate variables
vulnerability index scores were calculated with with soil properties (pH, organic matter, and
and without modeled response to climate change clay); 17) fourclimatevariableswith soil type; 18)
(Section D). four climate variables with soil properties (pH,
Statistical analysis of CCVI predictors. To voarrgiaanbilecs,mawtittehr,a acnudstocmliazy)e;d g1e9)ogrfaopuhriccleixmtaetnet
identify which of the factors were most strongly for each species; 20) four climate variables with
associated with the resulting vulnerability scores, an equal number of presences and psuedo-
we evaluated the distribution ofrisk factor scores absences; 21) four climate variables, with Ran-
against the distribution ofCCVI scores across all dom Forest; 22) four climate variables, with
ofourspecies. Factorsthatwerefrequentlyscored random forest, and with acustomized geographic
as increasing or decreasing vulnerability should extent for each species; 23) fourclimate variables,
show up as significant predictors ofthe distribu- with the boosted regression tree model. Soil type
tion of CCVI scores, while factors that were data came from the Geologic Map of California
infrequently scored as increasing or decreasing (Jennings et al. 2010), which we simplified into
vulnerability should not. We converted the CCVI seven “soil types” (gabbro, granite, limestone,
scores and factor ranks to their numeric equiva- sandstone, serpentine, shale, and volcanic) and
ltehnetsf,actthoernsfrreogrmetshseesdetsheectCioCnVs.IWsecodriedvnso.tetaescthfoorf wraesrteeriozebdtatione1dkmf2rroemsoltuhteionN.aStouirlaplropReerstoyurdcaetsa
a relationship of CCVI with dietary versatility, Conservation Service (NRCS) soil survey geo-
genetic variation, or genetic bottlenecks, because graphic database (SSURGO) and rasterized to
no species were scored for those factors. Tests 1 km2. The customized geographic extents were
varied in the number ofspecies included because determined by intersecting the point occurrence
we did not have information on all life history data withtheJepson Ecoregions (Hickman 1993);
2013] ANACKER ET AL.: CLIMATE CHANGE AND RARE PLANTS IN CALIFORNIA 199
Fig. 2. Illustrationoftopographiccomplexity, (a)Eriogonum twisselmanniihadananomalyscoreof—0.61 anda
topographic complexity (standard deviation ofelevation) of27.3. (b) Limosella australishad an anomaly score of
—0.64 and a topographic complexity score of0.51.
foreachspecies, thepredictorrastersurfaceswere used for post-hoc means separation by model
cropped to the shape of the ecoregions that type. We also examined the relationship of
contained the species. anomaly score and CCVI score using linear
To compare the model predictions, we calculated regression. Finally, we tested if the median
an“anomalyscore”foreachspeciesandeachmodel. anomaly score of each species was related to
For a given species, at each known occurrence, an California Rare Plant Rank or rarity type using
anomaly value was calculated as the predicted two one-way ANOVAs.
suitability in the present (ti) subtracted from the
predicted suitability in the future (t2). The mean of Topographic Complexity Analysis
the anomaly values across all occurrences was the
A
“anomaly score” for each species. negative As an index of “topographic complexity,” we
anomaly score meant that suitability in the future calculated the standard deviation of elevation
was predicted to be lower than current suitability. (resolution = 30 X 30 m) within 100 m of each
There are two main advantages of using an occurrence, and took the mean of those values
anomaly score to compare model predictions for per species. Topographic complexity measured at
SDM m
our sensitivity analysis, rather than using 1000 was highly correlated with topographic
the conventional metrics ofchange in range size complexity measured at 100 m (r = 0.94, P <
or range overlap. First, the anomaly score can be 0.001), so just the 100 m buffer was used. A
derived without converting continuous predicted species in a topographically complex landscape
surfaces to binary, the latter of which requires was considered less vulnerable than a species in a
determining a threshold above or below which a topographically homogeneous landscape (Fig. 2).
particular location is considered suitable or We asked if the topographic complexity score
unsuitable. For rare species, it is especially was significantly related to the CCVI score using
difficult to choose a meaningful threshold, given linear regression. We also tested if the topo-
the limitednumber ofpoint occurrences available graphic complexity score was related to Califor-
for evaluation of the prediction errors made at nia Rare Plant Rank orraritytypeusingtwo one-
various threshold values (Hijmans 2012). Second, way ANOVAs.
anomaly scores are based on climate suitability
change only at known occurrences, while range Results
size and overlap consider the entirety ofCalifor-
nia. Most rare plant species occupy only a small Climate Change Vulnerability Index
portion of their range where specific habitat
requirements are met, and managers are most Ofthe 156 species assessed, 99 weredetermined
concerned with how climate suitability will to be vulnerable (extremely vulnerable, highly
change where the species actually occurs. Evalu- vulnerable, or moderately vulnerable) to climate
atingchange in suitability overthe entire range of change and 48 were determined to be stable or
the species requires assumptions about habitat increasing (presumed stable or increase likely).
occupancy and movement that may not be met The distribution of final scores was: extremely
by many rare plant species. Furthermore, using vulnerable (n = 2), highly vulnerable (n = 40),
the anomaly score meant that predictions were moderately vulnerable (n = 57), presumed stable
only necessary for known occurrences, making (n =; 32), increase likely (n = 16), and insufficient
our sensitivity analysis computationally feasible. evidence (n = 9). Piperia yadonii Rand, Morgan
We asked ifthe anomaly score was significant- & Ackerman and MimuluspurpureusA. L. Grant
ly related to the model type, within species, using were the species scored as extremely vulnerable.
a two-way ANOVA. Tukey’s HSD tests were All assessment scores and species attribute data
MADRONO
200 [Vol. 60
Table4. TopFiveMostVulnerable SpeciesasDeterminedbyClimateChangeVulnerabilityIndex
Score. For CCVI, IL = increase likely, PS = presumed stable, MV = moderately vulnerable, HV = highly
vulnerable, EV = extremely vulnerable, and IE = insufficient evidence. CCVI (without D) is the CCVI score
recalculated afterexcluding the species distribution model results. The global (G) and state (S) rankings are from
the California Natural Diversity Data Base; G-rank is the global rank, reflecting global rarity, and S-rank is the
staterank, reflectingstaterarity. Bothindicesrangefrom 1 to5,with 1 beingcriticallyimperiledand5beingsecure.
CaliforniaRarePlantRank 1BisrareorendangeredinCaliforniaandelsewhereand Rank2israreorendangered
in California, butmorecommonelsewhere. Federallistingis the status ofthe species accordingto the Endangered
Species Act. Section D refers to the modeled response ofthe CCVI. CCVI index score definitions follow Table 3.
*Limosella australismay not be native to California.
California
CCVI Rare Plant Federal
Rank Species CCVI (without D) G-rank S-rank Rank listing
Based on CCVI with Section D
Piperiayadonii (Orchidaceae) EV HV 5 2 2 None
21 Mimuluspurpureus (Phrymaceae) EV HV 2 2.2 IB None
3 Calliandra eriophylla (Fabaceae) HV MV 5 2&3 2 None
4 Limosella australis* HV HV 4&5 2 2 None
(Scrophulariaceae)
5 Taraxacum californicum HV MV 2 2 IB Endangered
(Asteraceae)
Based on CCVI without Section D
231 PMOroocnguootlgtoyipnaievaiasbccroinadgmads(oiPnioia(icL(eaAamseit)aecreaacee)ae) MMHVVV EEEVVV 311 311 IIIBBB EEEnnndddaaannngggeeerrreeeddd
4 Symphyotrichum lentum HV EV 2 2 IB None
(Asteraceae)
5 Mimuluspurpureus (Phrymaceae) EV HV 2 2.2 IB None
are reported in Appendix 1. The top five most SDM Sensitivity Analysis
D
vulnerable species, with and without Section
(“modeled response”), are listed in Table 4. The anomaly scores statistically differed by
For the 156 species assessed, the final CCVI species and by model type (species P < 0.001;
score was significantly related to just one of the model typeP < 0.001), where themodelswith the
factors that were used to calculate it: anthropo- most positive anomalies (greatest increase in
genicbarriers (NatureServe factor B2b; r2 = 0.09, suitability) were those made with Maxent that
P < 0.001, n = 147). Anthropogenic barriers included soils information, and the models with
were determined to limit the migration ability of themostnegative anomalies (greatest reductionis
99 of the 156 species, and the presence of suitability) were those made with Random
anthropogenic barriers was significantly related Forest.
to overall climate change vulnerability. Although The anomaly score and the CCVI score were
not significantly related to overall climate change significantly related (r2 = 0.54, P < 0.001), where
vulnerability, two factors were found to increase species scored as vulnerable also had low
vulnerability for the majority of rare plants anomaly scores. This is not surprising, given the
assessed: land use change from human response CCVI score included modeled response. In fact,
to climate change was found to increase vulner- the CCVI score calculated after excluding mod-
ability for 80 species, and narrow temperature eled response was not significantly related to the
tolerance (“historical thermal niche”) was also anomaly score. The anomaly score was not
found to increase vulnerability for 80 species. significantly related to California Rare Plant
Overall climate change vulnerability was not Rank or rarity type.
significantly related to California Rare Plant
Rank. For example, final CCVI scores for IB Topographic Complexity Analysis
species were spread rather evenly as highly
vulnerable (n = 35), moderately vulnerable (n Topographic complexity and the CCVI score
= 49), presumed stable (n = 30), and increase were not significantly related. Topographic com-
likely (n = 14). In addition, overall climate plexity was not significantly related to California
change vulnerability was not related to rarity Rare Plant Rank, but was significantly related to
type. Similarly, neither change in range size nor rarity type (P < 0.001), where habitat specialists
range overlap were significantly related to Cali- occurred in locations with higher topographic
fornia Rare Plant Rank or rarity type. complexity than habitat generalists.
2013] ANACKER ET AL.: CLIMATE CHANGE AND RARE PLANTS IN CALIFORNIA 201
Discussion calculating the overall CCVI score. This is
perhaps unsurprising, given that the indirect
We have three key results: (1) 2/3 of our focal exposure and life-history traits are weighted by
species were scored as vulnerable to climate direct exposure in the calculation of the final
SDM
change, (2) predictions were highly vari- CCVI score (Young et al. 2012). While this
able, and (3) topographic complexity may pro- multiplicative approach makes it difficult to
vide complementary information on climate identify which risk factors are most important
change vulnerability. for a group of species, it is the appropriate
approach for the CCVI, because a species with
Climate Change Vulnerability Index (CCVI) zero exposure to climate change should be
considered invulnerable even if it has traits that
Ninety-nine of our 156 species (63%) were make it sensitive to climate change, and vice
vulnerable to climate change (scored as moder- versa.
ately vulnerable or worse). We present a list of We found the CCVI scores were independent
the top five most vulnerable species (Table 4), an of both California Rare Plant Rank and rarity
annotated species list (Appendix 1), and all the type. This suggests that the rarest species, such as
information we used to make our determinations the California Rare Plant Rank IB species or
(CCVI Excel workbooks and species profiles habitat specialists with small ranges and small
available online; CDFG 2011). We were unable population sizes, are not necessarily the most
to elucidate strong relationships between species vulnerable to climate change. We also found no
characters and vulnerability. No significant relationship of CCVI scores with other species
relationship was found between the CCVI attribute information, including plant life-cycle
vulnerability rank and California Rare Plant duration, plant growth form, and biogeographic
Rank, plant rarity type (Rabinowitz 1981), or affinity (results not shown). This is a desirable
any species life history trait considered. This result, as it suggests that the CCVI scorescontain
suggests that direct exposure to climate change novel information. This is perhaps the greatest
based on projected changes to future temperature strength ofthe CCVI: It represents a framework
and precipitation conditions within a species’ for thinking exclusively about climate change
range was the strongest driver ofvulnerability. vulnerability. In developing the CCVI, a goal of
One vulnerability factor from the Indirect NatureServewas tocreatean indexthatwould be
Exposure Section (landscape configuration) was as independent as possible from existing rarity
significantly (but weakly) related to the CCVI ranking indices. We suggest that the CCVI score
scores: anthropogenic barriers. For 99 ofthe 156 can be viewed alongside of rarity scores (e.g.,
species, the ability to migrate to track shifting California Rare Plant Rank) to identify the most
climate will likely be impeded by man-made vulnerable and rarest species.
barriers. This may reflect the fact that many rare
species are concentrated in coastal areas, where
SDM
population density and associated fragmentation Sensitivity Analysis
AarreeaexatnrdemLeolsyAhniggehle(se).g.(,SttehieneStaanl.F2r0a0n0)c.isCcooasBtaayl Our sensitivity analysis found that SDM
areas support “naturally rare” plant species that predictions were highly variable. The range of
have evolved or survived over time in local anomaly scores per species was very large
refugia because of cool, aseasonal climates and (Fig. 3), reflecting sensitivities to the choice of
a high level of soil heterogeneity. Other rare predictor variables and model algorithm (i.e.,
plants in coastal areas were once more common Maxent, Random Forest, and boosted regression
and have become “anthropogenically rare” due trees). Further, for 90 of the 156 species, the
to high levels of development and habitat loss. direction ofthe anomaly (decreasing suitability or
Whether naturally or anthropogenically rare, if increasing suitability) was even variable (yellow
these species are unable to tolerate new climate bars of Fig. 3). One of the largest sources of
conditions and cannot find refuge from novel, variability in modeled response was the algo-
intolerable climates locally, the likelihood of rithm. In particular, Random Forest always
dispersing to a more favorable, distant location produced the highest anomaly scores. Despite
AUC
is expected to be very low given man-made the high variability in anomaly scores,
barriers. These species are prime candidates for values were uniformly high (mean = 0.988,
assisted migration (McLachlan et al. 2007; median — 0.993, range = 0.921-1.00). These
AUC
Richardson et al. 2009; Vitt et al. 2010). high values suggest that all models are
The general inability to predict the CCVI equally good, despite the fact that the models
vulnerability scores using the indirect exposure make radically different predictions about future
AUC
and life-history traits reflects the high importance climatic suitability. This is unsurprising, as
ofdirect exposure (projected changes to temper- values tend to be inflated for species with narrow
ature and moisture within a species’ range) in distributions, and therefore may not adequately
MADRONO
202 [Vol. 60
Fig. 3. Sensitivity analysis results, by species. The variation in anomaly score across 24 distribution models is
representedwithboxplots, whereopencirclesindicatestatistical outliers. Darkgreyboxes arethose specieswhose
distributions do not cross the horizontal dashed line. Light grey boxes are those species whose distributions do
cross the horizontal dashed line.
capture differences in model success for rare dependent on model algorithm and the choice of
plants (Lobo et al. 2007; Gogol-Prokurat 201 1). predictor variables. None of our species were
The variability in our modeled results is scored as “increase likely” when we excluded the
probably due in large part to working with rare modeled results, despite that approximately half
species, which have few occurrences (the median of the factors could be scored as decreasing
number of occurrences for our species was 35) vulnerability. Thus, the potential positive effects
and many non-climatic factors that determine ofclimate change as measured by the CCVI were
their distributions. This reflects the “rare species driven solely by increases in future range size
modeling paradox,” which says that the species predicted by SDMs. However, SDM results may
“most in need ofpredictive distribution modeling be relatively reliable for predicting where suitable
... are the most difficult to model” (Lomba et al. habitat will be in the future, compared to
2010). In other words, while it is extremely predicting declining suitability (Schwartz 2012).
important to identify the climatic tolerances of While recent advances in modeling techniques
rare species, given ourconservationconcerns, itis (i.e., consensusmodeling, modelingcladesinstead
extremely difficult to make meaningful models of of species, adding process models, factoring out
the climatic ranges based on the limited available spatial autocorrelation in occurrence data) may
information. help create a model that better reflects the niches
The variability of the modeled results is cause ofplants, we expect that a quantitative modeling
for concern because our CCVI scores include approach, in isolation, will never be sufficient to
modeled results fromjust one ofthe 24 different accurately predict the fate of rare species in the
models. When we excluded these modeled results face ofclimate change.
from our CCVI scores, some ofthe CCVI scores
moved up ordown a score level (e.g., from highly Topographic Complexity Analysis
vulnerable to moderately vulnerable or increase
likely to presumed stable; see Table 4 and Fig. 4 Our index oftopographic complexity may help
foran example). In otherwords, the results ofthe create a finer understanding of climate change
single model used in the CCVI have a large vulnerability for our species. For example, 41
impact on the final CCVI score, which is species are ranked as highly vulnerable, but they
worrisome since modeled results are extremely have a range of topographic complexity scores.
