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Supporting Online Material for
Changes in Climatic Water Balance Drive Downhill Shifts in Plant Species’ Optimum Elevations
Shawn M. Crimmins, Solomon Z. Dobrowski,* Jonathan A. Greenberg, John T. Abatzoglou, Alison R. Mynsberge
*To whom correspondence should be addressed. E-mail:
Published 21 January 2011, Science 331 324 (2010) DOI: 10.1126/science.1199040
This PDF file includes
Materials and Methods Figs. S1 to S4 Tables S1 to S3 References
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SUPPORTING ONLINE MATERIAL
Materials and Methods
Historic vegetation plot data
Historical species presence and absence data were drawn from 13,746 vegetation plots
measured as part of the Wieslander Vegetation Type Map (VTM) Project (Fig. S2). Individual
VTM plots comprise complete vascular plant inventories of 800 m2 in forests and 400 m2 in
other vegetation types, from which we extracted species presence/absence data. The VTM
project was originally designed to map the vegetative communities of California, with plot data
collected in the 1930s (S1, S2). VTM plots were established in the majority of the state’s natural
vegetative areas excluding the desert regions in the southern portion of the state (Sonoran and
Mojave deserts) and the agricultural region of the central valley. Original VTM data have been
digitized and georeferenced as part of the VTM Digitization Project and are considered accurate
within 200 m (S3).
Modern vegetation plot data
We compiled contemporary data on plant species presence and absence from a variety of
sources, primarily federal and state land management agencies and university researchers, into a
dataset of 33,596 vegetation plots throughout our study area, sampled primarily from 2000–
2005. This represents a span of approximately 75 years between time periods. Data collection
protocols varied among sources, ranging from fixed area plots to cluster designs (such as USFS
FIA). Sizes ranged from small (400 m2) fixed area plots to larger patch based measurements.
However, most plots were similar in size to those from the historic data (800 m2). We extracted
presence and absence data only for species that were taxonomically distinguishable in both time
periods to ensure accurate and consistent species identification. At each historical and modern
plot location we extracted relevant environmental parameters including elevation, mean annual
temperature, and annual climatic water deficit (see below).
Study area
We defined our study area as the ecoregions (S4) within the state that contained adequate
spatial coverage of long-term weather stations and vegetation surveys from both time periods
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(Fig. S2, Fig. 1). This included the Northwest, Cascade Ranges, Sierra Nevada, and Central
Western ecoregions and encompassed an area of 177,000 km2 (Fig. S2). This region had
abundant survey plots (>10,000) in each time period and contained numerous long-term climate
stations. This area covered the majority of the mountainous regions in the state north of 35°
latitude.
Climate data
We used gridded (800 m) monthly normal (i.e. monthly average) climate data developed by
the Parameter-elevation Regression on Independent Slopes Model (PRISM) (S5). Monthly
normals for climatic parameters were averaged across 30 year historical (1905–1935) and
modern (1975–2005) time frames, providing mean climatic values for the previous 3 decades
prior to vegetation data collection (see above). Climate surfaces were downscaled to a resolution
of 400 m using dynamic lapse rate estimates (S6). Downscaling of climate surfaces was
conducted because of the strong influence of physiography on air temperature in the region (S7)
and previous research demonstrating that small-scale factors can influence the distribution of
sensitive species (S8). Clear sky radiation was modeled for the study area at a 400 m resolution.
Potential evapotranspiration (PET) was calculated via the Penman-Monteith method using
downscaled climate data, radiation maps, and wind maps from the National Renewable Energy
Laboratory (S9). Climatic water deficit was calculated as the annual difference between PET
and precipitation.
We created a continuous surface of 20th century change in deficit by subtracting modern
values from historical values (Fig. S1). Based on this surface, our study area experienced an
average decrease in climatic water deficit of 88 mm (Fig. S1). As a means of verifying our
interpolated climate surfaces we used monthly climate data from 33 long-term National Weather
Service cooperative observer network (COOP) stations within our study area that have
undergone screening for quality control and climate inhomogeneities (S10) to estimate change in
climatic water deficit. Of these stations, 30 exhibited decreases in climatic water deficit (Fig. 1
in text) and yielded an average overall decrease in climatic water deficit of 100.0 mm (95% CI
63.3–136.7). The close agreement between our interpolated climate surfaces and station level
measurements demonstrate that most of our study region experienced an increase in water
availability during the 20th century (Fig. S1, Fig 2 in text). Further, linear trends from weather
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stations showing significant increases in other climatic parameters including mean annual
temperature (+0.068°C/decade), annual precipitation (+22.7 mm/decade), and potential
evapotranspiration (+3.3 mm/decade) correspond closely to estimates derived from interpolated
climate surfaces.
Bias removal
Because of the opportunistic approach with which the vegetation data were collected, there
was potential for geographic and/or environmental bias between our samples. To account for
this we used a subsampling procedure in which we randomly selected a balanced number of plots
from each time period from 20 equal-width bins based on climatic water deficit. We similarly
subsampled the original datasets in 20 equal-width bins based on elevation and again by mean
annual temperature using 1°C width bins (S11). Results were similar across subsampled datasets
regardless of the binning variable, indicating a minimal effect of sampling bias (Table S2). We
present results here from our subset based on deficit. This procedure left us with a balanced
sample of 8,747 survey plots in each time period (Fig. S2).
Statistical analyses
Because species range limits (e.g. maximum and minimum elevations) are highly dependent
on sampling effort and may not represent population-level responses (S12) to climate change, we
chose to quantify species’ optimums. We estimated the position of each species along climatic
(mean annual temperature, climatic water deficit) and geographic (elevation) gradients in each
time period using Gaussian response curves (S13). Using logistic regression with a quadratic
function we modeled species’ optimums from the plot level presence/absence data (S14). The
optimum of a Gaussian response function is defined as the location along the gradient with the
highest probability of occurrence. Optimum elevations have recently been used to describe
altitudinal shifts in plant species distributions (S11, S15). We only included species for which
the logistic model exhibited a unimodal response in each time period and provided significant (P
< 0.05) improvement over a linear model using a residual deviance test. We estimated 95%
confidence intervals around optimums using a profile deviance approach (S14). We developed
models only for species that had adequate representation (≥ 50 presences) in each time period
after the subsampling procedure described above. In addition to calculating optimums, we also
estimated the tolerance of each species (S14). Tolerance is an estimate of the width of the
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Gaussian response function representing the range of conditions in which a species can occur,
and as such is used to identify overall range contractions or expansions (S11).
We assessed changes in optimums and tolerance between time periods using paired t-tests
where each species was considered a sampling unit. We examined the effects of species traits on
shifts in optimum and tolerance using a Kruskal-Wallis test (S16). Species traits examined
included lifeform (tree, shrub), physiognomy (evergreen, deciduous), dispersal mechanism
(wind, animal, gravity, ballistic), and level of fire adaptation (low, medium, high) (S17). We
excluded herbaceous species from our comparisons of lifeform and physiognomy due to limited
sample size (n = 3). We compared the proportion of species shifting their optimums upward or
downward by fitting 95% confidence intervals on the binomial proportion and examining overlap
with a null hypothesis of a proportion of 0.5.
Expected elevations shifts
We projected altitudinal shifts in water deficit resulting from regional-scale changes in
temperature and precipitation. Aggregated monthly climate data from the 33 meteorological
stations within the study area were used to estimate the regional-scale climatic water deficit for a
1920-1949 reference period. A sensitivity analysis examined the expected elevation shift
required to maintain the original climatic water deficit given changes in temperature (±3º C,
increments of 0.2º C) and precipitation (± 25%, increments of 2%). While changes in
temperature and precipitation with respect to elevation are highly variable both geographically
and temporally (S7, S18), at macroscales we prescribed a fixed lapse rate of 5º C/km (S7) and
fixed orographic precipitation rate of 100%/km (S19). For the given matrix of climatic changes
and fixed lapse and orographic precipitation rates we estimated the elevation shift required to
maintain deficit calculated for the reference period.
Figure S3 illustrates the elevation sensitivity to changes in temperature and precipitation
relative to the reference period. Changes in temperature and precipitation between recent
decades (1976-2005) and an earlier sampling period (1920-1949) were derived for each of the 33
stations and for a regional average. These values were then superimposed on the sensitivity plot
to indicate estimated change in elevation required to maintain climatic water deficit (Fig. S3). At
the macroscale level the sensitivity analysis suggests a downward elevation shift of 85 m (±21 m,
95% CI). Our sensitivity analysis suggests heightened sensitivity to regional changes in
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precipitation rather than temperature (Fig. S3). The sensitivity of changes in temperature and
precipitation vary as a function of local climatology whereby changes in precipitation are more
influential in wet areas, whereas changes in temperature and their influence on PET are
respectively stronger in warmer arid environments. Although the sensitivity appears linear, the
potential evapotranspiration response to warming is nonlinear.
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Figures
Figure S1: Interpolated change in mean annual temperature (A), annual potential evapotranspiration (B), annual precipitation (C), and climatic water deficit (D) between historical (1905–1935) and modern (1975–2005) time periods in California, USA.
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Figure S2: Distribution of historical (ca. 1935) and modern (ca. 2005) survey plots in California within our study region. Uniform gray areas represent ecoregions not included in our analysis. Note: The figure of modern plot locations does not depict 3,740 plots from the U.S Forest Service Forest Inventory and Analysis (FIA) dataset collected on National Forest lands (highlighted in yellow). In California, the FIA vegetation inventory plots consist of the systematic P2 sampling grid as well as a variable intensity systematic grid (mean plot spacing = 1 plot/19 km2) for the intensification plots across the National Forests. Data from these plots were used in the analysis but are not depicted in this figure due to legal reasons.
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Figure S3: Estimated change in elevation (m) corresponding to changes in regional temperature and precipitation values (1920-1949 reference period). The hollow circle ( ) represents the elevation change required to match regional temperature and precipitation changes between the 1976-2005 period and the 1920-1949 reference period assuming static climatic conditions; individual station trends are shown by x’s.
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Figure S4: Elevation (A), climatic water deficit (B), and mean annual temperature (°C) tolerance between historical (circa 1935) and modern (circa 2005) time periods. Each point represents a single species. Insets represent number of species with increasing (above horizontal line) or decreasing (below horizontal line) tolerance.
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Table S1. Magnitude of shift in optimum elevation (m), climatic water deficit (mm), and mean annual temperature (°C) during the 20th century (ca. 1935 to ca. 2005) in California. Cells colored in grey indicate significant (non-overlapping 95% confidence intervals between time periods) shifts in optimums. Empty cells represent species for which shifts could not be calculated due to a lack of unimodal response curves in at least one time period. Species Elevation Deficit Temperature Abies concolor -25.1 -99.3 -0.030 Abies magnifica 147.0 378.4 -1.150 Adenostoma fasciculatum -12.9 -126.6 0.395 Aesculus californica -433.6 -78.4 2.288 Amelanchier alnifolia -276.8 - 1.529 Artemisia californica - 134.1 3.535 Arctostaphylos glauca 41.4 - 0.429 Arbutus menziesii 234.3 - -0.332 Arctostaphylos nevadensis -197.0 - -0.146 Artemisia tridentata -253.7 - 1.738 Arctostaphylos viscida -100.8 - -0.875 Baccharis pilularis - -311.9 0.667 Calocedrus decurrens -80.0 -382.4 0.111 Ceanothus cordulatus 214.4 93.2 -0.763 Ceanothus cuneatus -129.9 - -3.875 Ceanothus integerrimus -160.6 75.3 0.752 Cercocarpus ledifolius -711.7 435.3 3.910 Cercocarpus montanus -76.0 - 2.098 Ceanothus prostratus -20.6 - 0.205 Ceanothus velutinus -270.7 - 1.334 Chrysolepis chrysophylla -439.7 - 0.742 Chamaebatia foliolosa 23.7 -20.6 0.441 Chrysolepis sempervirens 211.2 392.1 -1.473 Corylus cornuta -276.4 56.1 0.485 Cornus nuttallii -285.3 50.6 0.666 Eriodictyon californicum -61.5 -335.4 -3.523 Eriogonum fasciculatum 61.4 - - Ericameria linearifolia -59.9 -307.0 0.796 Ericameria nauseosa -313.9 - 3.243 Frangula californica - - 1.454 Fraxinus dipetala -100.4 -549.0 0.265 Garrya fremontii -154.4 -189.0 1.924 Heteromeles arbutifolia -53.1 120.6 0.241 Hesperoyucca whipplei -130.6 - - Juniperus californica -151.5 -708.2 -1.677 Juniperus occidentalis -745.1 - 3.141
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Lithocarpus densiflorus -492.7 - 1.122 Lotus scoparius - 269.4 0.369 Monardella odoratissima - 349.5 - Pinus albicaulis - 858.2 - Pinus attenuata 284.2 - -0.948 Pinus jeffreyi 86.1 - -0.242 Pinus lambertiana -176.8 -156.7 0.364 Pinus monticola -416.3 -170.2 0.700 Pinus ponderosa 70.8 118.4 -0.961 Pinus sabiniana -119.7 -302.1 - Prunus emarginata -36.7 - 0.069 Pseudotsuga menziesii 239.0 - -1.349 Pteridium aquilinum 744.2 -108.8 -0.751 Purshia tridentata -316.3 -274.9 1.724 Quercus agrifolia - -220.2 0.060 Quercus berberidifolia 667.7 - -2.289 Quercus chrysolepis -94.9 -162.3 0.072 Quercus douglasii -30.4 -204.4 1.506 Quercus durata -156.8 -53.8 0.184 Quercus garryana -31.0 618.0 -1.391 Quercus kelloggii -37.0 -178.7 -0.008 Quercus lobata - -323.3 0.460 Quercus vacciniifolia -93.5 - 0.294 Quercus wislizeni - -94.0 - Rhamnus crocea 483.6 483.9 -0.752 Rhamnus ilicifolia -232.3 -356.7 3.427 Rhus trilobata 160.2 - -3.809 Ribes roezlii 247.0 737.3 -0.835 Salvia leucophylla 293.6 201.4 - Symphoricarpos mollis -690.7 -777.9 2.561 Toxicodendron diversilobum 162.7 -567.3 -1.041 Tsuga mertensiana -255.1 -134.5 2.850 Umbellularia californica -558.2 - 0.549 Vaccinium ovatum -186.8 - 0.777 Vaccinium parvifolium -206.9 - 0.508 Vulpia myuros -13.7 787.3 1.426 Wyethia mollis -352.6 522.6 1.142
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Table S2. Magnitude of shift in optimum (Shift), statistical significance of shift based on paired t-test (P-value), and number of species with increasing/decreasing shifts in optimums (n+/n-) after subsampling based on one of three environmental variables. Shifts in each variable were recalculated after each subsample. Similarity of results across subsampling variables indicates minimal bias between vegetation surveys from historical (ca. 1935) and modern (ca. 2005) time periods. Elevation (m) Deficit (mm) Temperature (°C) Subsample Shift P-value n+/n- Shift P-value n+/n- Shift P-value n+/n-
Deficit -88.2 0.016 18/46 -11.1 0.843 19/27 0.370 0.067 44/22Elevation -148.4 <0.001 14/44 -46.3 0.599 15/20 0.336 0.071 39/23Temperature -93.7 0.005 16/41 -51.4 0.453 12/24 0.318 0.063 42/18
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Table S3: Effects of species traits on shifts in optimum elevation. Degrees of freedom (df) and P-values based on nonparametric Kruskal-Wallis test. Herbaceous species (n = 3) were not included in analyses of lifeform or physiognomy effects. Species traits were: lifeform (tree, shrub), physiognomy (evergreen, deciduous), dispersal mechanism (wind, animal, gravity, ballistic), and level of fire adaptation (low, medium, high) (S17). Species trait n df P
Lifeform 61 1 0.6598
Physiognomy 61 1 0.1805
Dispersal mechanism 64 3 0.9173
Fire adaptation 64 2 0.4475
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