Journal of Biogeography (J. Biogeogr.) (2006) 33, 1384–1396
ORIGINAL
ARTICLE
Spatial variation of climatic and
non-climatic controls on species
distribution: the range limit
of Tsuga heterophylla
Daniel G. Gavin* and Feng Sheng Hu
Department of Plant Biology, University of
Illinois, Urbana, IL, USA
ABSTRACT
Aim To assess which climatic variables control the distribution of western
hemlock (Tsuga heterophylla), how climatic controls vary over latitude and
between disjunct coastal and interior sub-distributions, and whether non-climatic
factors, such as dispersal limitation and interspecific competition, affect range
limits in areas of low climatic control.
Location North-western North America.
Methods We compared four bioclimatic variables [actual evapotranspiration
(AET), water deficit (DEF), mean temperature of the coldest month (MTCO),
and growing degree-days (GDD5)] with the distribution of T. heterophylla at a
2-km grid cell resolution. The distribution is based on a zonal ecosystem
classification where T. heterophylla is the dominant late-successional species. For
each bioclimatic variable and at each degree of latitude, we calculated the
threshold that best defines the T. heterophylla distribution and assessed the extent
to which T. heterophylla was segregated to one end of the bioclimatic gradient. We
also fitted two forms of multivariate bioclimatic models to predict the
T. heterophylla distribution: a simple threshold model and a complex Gaussian
mixture model. Each model was trained separately on the coastal and interior
distributions, and predicted areas outside of the T. heterophylla distribution
(overprediction) were evaluated with respect to known outlier populations.
Results Actual evapotranspiration was the most accurate predictor across the
T. heterophylla distribution; other variables were important only in certain areas.
There was strong latitudinal variation in the thresholds of all variables except
AET, and the interior distribution had wider bioclimatic thresholds than the
coastal distribution. The coastal distribution was predicted accurately by both
bioclimatic models; areas of overprediction rarely occurred > 10 km from the
observed distribution and generally matched small outlier populations. In
contrast, the interior distribution was poorly predicted by both models; areas of
overprediction occurred up to 140 km from the observed distribution and did
not match outlier populations. The greatest overprediction occurred in Idaho and
Montana in areas supporting species that typically co-exist with T. heterophylla.
*Correspondence: Daniel Gavin, Department of
Geography, University of Oregon, Eugene, OR
97403-1251, USA.
E-mail: dgavin@uoregon.edu
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Main conclusions The high predictive capacity of AET is consistent with this
species’ physiological requirements for a mild and humid climate. Spatial
variation of MTCO, GDD5 and DEF thresholds probably reflects both the
correlation of these variables with AET and ecotypic variation. The level of
overprediction in portions of the interior suggests that T. heterophylla has not
completely expanded into its potential habitat. Tsuga heterophylla became
common in the interior 2000–3500 years ago, compared with > 9000 years ago in
the coastal region. The limited time for dispersal, coupled with frequent fires at
the margins of the distribution and competition with disturbance-adapted
species, may have retarded range expansion in the interior. This study
www.blackwellpublishing.com/jbi
doi:10.1111/j.1365-2699.2006.01509.x
ª 2006 The Authors
Journal compilation ª 2006 Blackwell Publishing Ltd
Climatic and non-climatic controls on T. heterophylla
demonstrates that bioclimatic modelling can help identify various climatic and
non-climatic controls on species distributions.
Keywords
Actual evapotranspiration, bioclimatic envelope model, climate, local adaptation, model evaluation, North America, predictive species models, range
expansion, Tsuga heterophylla.
INTRODUCTION
The distributions of plant species are affected by both climatic
factors (e.g. temperature and water balance) and non-climatic
factors (e.g. biotic interactions: Woodward, 1987; Huston,
2003). Climate affects species distributions directly by imposing physiological constraints on growth and reproduction,
and indirectly by mediating competitive interactions (Shao &
Halpin, 1995; Sykes et al., 1996), although some constraints
may vary spatially because of local adaptation (Case & Taper,
2000). For some species, distribution limits correlate poorly
with climatic gradients, especially at fine spatial resolutions,
suggesting the importance of other controls such as dispersal
limitation, disturbance and competition (Malanson et al.,
1992; Loehle, 1998). The difficulty of distinguishing controls
has led to several critiques that warn against directly mapping
species distributions from climate (Woodward & Beerling,
1997; Davis et al., 1998; Hampe, 2004). However, no studies
have attempted to distinguish the importance of climatic and
non-climatic controls throughout the entire distribution of a
species. A detailed examination of the distribution–climate
correlation, especially in the light of a species’ autecology,
synecology (interactions with commonly associated species),
ecophysiology and historical distribution, may yield insights
on the relative roles of climatic and non-climatic controls.
Western hemlock [Tsuga heterophylla Raf. (Sarg.)], a common late-successional tree in north-western North America,
presents an excellent case for examining the climatic and nonclimatic controls on distribution. On one hand, the distribution
of T. heterophylla should strongly reflect regional climate
because of its physiological requirement for mild and humid
conditions (Packee, 1990). The species requires higher water
potential to maintain cell turgor than most other conifers in the
region (Jackson & Spomer, 1979; Lassoie et al., 1985), and it has
a relatively high tendency for xylem cavitation (Bond &
Kavanagh, 1999). The shallow roots of the species (Bennett
et al., 2002) further exacerbate its vulnerability to water deficits.
On the other hand, dispersal limitation and interspecific
competition may reduce the importance of climatic controls
in some areas more than others. Pollen records indicate the
relatively recent arrival of T. heterophylla in a large disjunct
interior distribution (2000–3500 yr bp: Mehringer, 1996;
Rosenberg et al., 2003) compared with the main coastal
distribution (mostly occupied by 9000 yr bp), thus T. heterophylla had less time to reach its climatically defined distributional limits in the interior than in the coastal region. Unlike in
the coastal distribution, stand-replacing fire is more frequent in
the interior, and T. heterophylla must compete with early
successional tree species (e.g. Pinus contorta, Picea glauca · engelmannii, Betula papyrifera) in post-fire forests. These
factors suggest that T. heterophylla may be subject to stronger
non-climatic controls at its distribution limits in the interior
than in the coastal distribution.
In this study we address spatial variation of climatic controls
on the distribution of T. heterophylla by comparing mapped
bioclimatic variables with its observed distribution. First, for
each individual bioclimatic variable, we assess its predictive
capacity and determine the threshold that best predicts the
T. heterophylla distribution. Spatial variation in the thresholds
of bioclimatic variables may be due to adaptations to local
climates (Rehfeldt et al., 1999), as shown for Tsuga canadensis
(eastern hemlock: Kessell, 1979). We use a base map with a
2-km grid cell resolution to capture the species–climate
relationship in mountainous areas. Second, we use bioclimatic
envelope models to assess how accurately climate can predict
the T. heterophylla distribution (Guisan & Zimmermann, 2000;
Pearson & Dawson, 2003). We focus on areas of prediction
outside of the observed distribution (overprediction) and
assess whether such areas are consistent with potential nonclimatic controls (Svenning & Skov, 2004). If bioclimatic
models are poorly constructed, they may falsely suggest either
weaker or stronger climatic control than actually exists
(underfitting and overfitting, respectively). As our goal is to
assess potential non-climatic controls operating in areas of
overprediction, we use an approach that errs on the side of
overfitting in order to minimize false overprediction. Specifically, we use a complex bioclimatic model with variable
interactions to fit the species–climate relationship in addition
to a simple threshold bioclimatic model. We also model only
the core distribution of T. heterophylla in order not to include
outlying populations that may occur in anomalous local
conditions unrepresentative of the regional climate. Such
populations, if included in constructing a bioclimatic model,
may falsely increase the breadth of the climatic envelope and
lead to false overprediction.
METHODS
Data sources
We defined the observed distribution of T. heterophylla using
zonal ecosystems based on the Biogeoclimatic Zones of British
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http://agdc.usgs.gov/data/projects/hlct/hlct.html
http://gis.ca.gov/catalog/BrowseRecord.epl?id¼708
http://www.gis.state.or.us/data/alphalist.html
http://srmwww.gov.bc.ca/ecology/bei/shiningmtns.html
0.7
0.4
State and province codes are defined in Fig. 1.
US Geological Survey
US Forest Service
Alaska statewide vegetation
California Vegetation
Survey (calveg)
AK: west of 141 W
CA: north-west coast
US Geological Survey
Ecoregions of Oregon
OR: south of 4545¢ N
13.7
Coastal western hemlock,
Northern coastal hemlock,
Pacific silver fir, Interior
transition hemlock, Interior hemlock,
Coastal spruce
Coastal lowlands, Coastal uplands,
Willipa Hills, Volcanics,
Southern coast range,
Mid-coastal sedimentary,
Umpqua Cascades, Western Cascades
Closed spruce and hemlock forest
Polygons selected to best match
Little (1971): red alder, Sitka
spruce–grand fir, some urban area
85.2
British Columbia Ministry of
Sustainable Resource
Management
Broad ecosystem
inventory
AK, BC, WA, MT,
ID: 4545¢ N to 60 N
and E of 141 W
Map units included
Per cent
of zone
Region
Government agency
The degree of segregation of T. heterophylla along each
bioclimatic gradient was quantified with receiver-operating
characteristic analysis (ROC: Swets, 1988). ROC analysis
Data source
Univariate analyses
Table 1 Data sources for the Tsuga heterophylla zonal ecosystem map
Columbia (Meidinger & Pojar, 1991). This classification was
developed from extensive field surveys and aerial photo
interpretation, and eventually mapped using elevation and
aspect rules that vary among regions. Climatic data were not
used in the mapping of zonal ecosystems, although it was
assumed that the maps of zonal ecosystems reflect climatic
gradients (Meidinger & Pojar, 1991). We did not use maps
inclusive of all outlying populations because microclimates or
local topographic effects rather than regional climate may
maintain such populations. Zonal ecosystems are particularly
suitable for describing the core distribution of late-successional
species such as T. heterophylla because zones are defined by the
expected late-successional dominants, even if they are rare due
to disturbance. Another advantage of zonal ecosystems is that
they are defined at finer spatial resolutions than most speciesdistribution data (Little, 1971) and thus are suitable for areas
with steep climatic gradients where coarse resolution data
would not adequately describe the species–climate correlation.
We created the 2-km grid cell resolution map using a mosaic
of previous classifications (Table 1); 98.9% of the zonal
ecosystem was defined by two classification schemes (Broad
Ecosystem Inventory and Ecoregions of Oregon) with a
concordant overlap in northern Oregon. In all, the
T. heterophylla distribution was 302,008 km2 (75,502 2-km
grid cells, Albers equal-area projection), of which 18% occurs
in the interior (Fig. 1). We defined the coastal and interior
regions as the areas that are separated by a line equidistant
between the two sub-distributions and that encompass up to
140 km from the distribution elsewhere. These regions extend
the analysis out to areas with substantially different climatic
regimes (e.g. east of the Rocky Mountains; Fig. 1).
Our analyses used four bioclimatic variables that are
expected to have a strong functional relationship with the
distribution of T. heterophylla: actual evapotranspiration
(AET); mean temperature of the coldest month (MTCO);
water balance deficit (DEF); and growing degree-days on a
5 C base (GDD5). We calculated these four variables (see
Table 2 for methods) from gridded climate normals (1961–90)
obtained from the PRISM climate-mapping project (Daly
et al., 1994). These variables were selected based on the results
of earlier studies. Gavin & Hu (2005) used the same data with a
regression tree analysis to select variables from a list of nine
potential variables. That study found AET to be the most
predictive of the T. heterophylla distribution, followed by
MTCO and GDD5. McKenzie et al. (2003) used general linear
models to select variables to predict conifer species in
Washington State, and found AET, DEF and soil degree-days,
among others, to be the most predictive. Other studies have
shown that AET and DEF are functionally significant predictors of vegetation in general (Stephenson, 1998).
URL (accessed March 2006)
D. G. Gavin and F. S. Hu
Journal of Biogeography 33, 1384–1396
ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd
Climatic and non-climatic controls on T. heterophylla
Figure 1 (a) The Tsuga heterophylla distribution (shaded areas) defined from several
zonal ecosystem or ecoregion classifications
(Table 1). The dashed line is equidistant
from the T. heterophylla distribution between
the coastal and interior distributions, and
140 km from the T. heterophylla distribution
elsewhere. AB ¼ Alberta, AK ¼ Alaska,
BC ¼ British Columbia, CA ¼ California,
ID ¼ Idaho, MT ¼ Montana, NV ¼ Nevada, OR ¼ Oregon, WA ¼ Washington. (b)
Areas in each region with analogous climate
in the opposing region, defined as grid points
with at least one analogue grid point in the
opposing region. A climatic analogue is defined as two grid points with differences in
AET < 25 mm, DEF < 25 mm, MTCO
< 1 C, and GDD5 < 200 degree-days. See
Table 2 for definitions of variables.
Table 2 Bioclimatic variables used in this
study
Bioclimatic variable
Code
Method of calculation
Actual evapotranspiration
AET
Deficit
Mean temperature of
the coldest month
Annual growing degree-days
on a 5 C base
DEF
MTCO
Custom computer program following
Willmott et al. (1985), a modified method
of Thornthwaite & Mather (1955) and
Thornthwaite et al. (1957). Field capacity
was assumed to be 100 mm; day length
followed Forsythe et al. (1995)
Same as above
Calculated directly from mapped
climatic variables
Linear interpolation of monthly normals
to daily values
involves (1) determining the fraction of T. heterophylla grid
points that exceed a threshold (the true positive fraction, TPF)
and the fraction of non-T. heterophylla grid points that exceed
the same threshold (the false positive fraction, FPF), and (2)
plotting TPF as a function of FPF over the full range of
thresholds (an ROC curve). The area under the ROC curve
(AUC) is a measure of the segregation of T. heterophylla grid
points towards one end of the range of values of a bioclimatic
variable. AUC values near 0.5 indicate no segregation of
T. heterophylla grid points to either end of the gradient; values
near 1 indicate strong segregation of T. heterophylla; and values
near 0 also indicate segregation, but in the opposite direction.
We assessed the significance of AUC in contiguous onedegree latitudinal bands by comparing it with a distribution of
GDD5
AUC values computed from a null model of the T. heterophylla
distribution. The null model consisted of: (1) determining the
range of bioclimatic values occupied by T. heterophylla, (2)
selecting such a range at a random location within the entire
range of combined T. heterophylla and non-T. heterophylla grid
points, and (3) randomly selecting grid points from this
segment equal to the number of grid points that occur within
the observed T. heterophylla distribution. We constructed 95%
confidence envelopes of AUC from 2000 realizations of the
null model.
We calculated thresholds of bioclimatic variables from the
5th or 95th percentiles of each variable within the
T. heterophylla distribution in contiguous 1 latitudinal bands.
Thresholds were calculated only where the T. heterophylla
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D. G. Gavin and F. S. Hu
distribution was segregated to one end of the gradient as
determined by a statistically significant AUC. We also calculated overall thresholds for the coastal and interior regions.
Latitudinal trends in AUC and thresholds were determined
with linear regression, and inter-region differences in AUC and
thresholds were assessed qualitatively.
Multivariate bioclimatic models
Model forms and training regions
We applied multivariate bioclimatic models to examine (1)
how closely bioclimatic models can fit the observed
T. heterophylla distribution using a resubstitution test (hereafter termed within-region prediction), and (2) whether
models trained on one region (e.g. coastal region) can predict
the other region (e.g. interior region) and thus how climatic
controls differ between the regions (hereafter termed crossregion prediction). For within-region prediction we used the
GM–SMAP model based on multivariate Gaussian mixture
distributions (described below). This model is inappropriate
for cross-region prediction because the climate space (vectors
of the four variables) differs greatly between the coastal and
interior regions (Fig. 1b). Any model that includes variable
interactions will perform poorly across regions with different
variable interactions due to unrealistic extrapolation (Beerling
et al., 1995; Sykes, 2001). This issue is quite pronounced when
using four variables and their interactions, as in this paper.
Therefore, for cross-region predictions we used a simple
rectilinear envelope (RE) model that predicts the T. heterophylla distribution using coastal and interior-region thresholds
calculated from the univariate analysis (similar to the BIOCLIM model of Beaumont & Hughes, 2002).
Gaussian mixture–sequential maximum a posteriori (GM–
SMAP) segmentation
The GM–SMAP model was demonstrated to have a greater
capacity to predict the T. heterophylla distribution than either
regression trees or smoothed response surfaces in Gavin & Hu
(2005). This model was developed to ‘segment’ multi-spectral
satellite images into land-cover types (Bouman & Shapiro,
1994); here it is used to predict a species distribution using
several bioclimatic variables. First, ‘climatic signatures’ were
computed by fitting GM distributions to the climate in each of
two ‘classes’: the area within and outside the T. heterophylla
distribution. GM distributions were fitted using the program
cluster (Bouman, 1998), which breaks each class into
subclasses using the expectation-maximization (EM) algorithm of Dempster et al. (1977) and computes a multivariate
Gaussian distribution for each subclass. The closest two
subclasses are then merged and the EM algorithm is run
again, and this merging is repeated until the number of
subclasses yields the smallest Rissanen minimum description
length (Bouman, 1998), an information-theoretical index that
penalizes the fit of a model with the number of parameters in
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the model. The result is a response surface that optimally
describes the training data and adapts to steep or gradual
gradients in environmental space.
The training data included the entire T. heterophylla
distribution and every third grid point (on a diagonal pattern)
in the area outside the T. heterophylla distribution. The latter
reduced the number of samples to a more tractable size for
fitting the model; given a high degree of spatial autocorrelation
in the 2-km grid resolution, it should minimally affect the
model’s accuracy. The GM model fitted the climatic variability
using a larger number of subclasses in the coastal region (131
and 134 subclasses for the area within and outside of the
T. heterophylla distribution, respectively) than in the interior
(60 and 104, respectively).
The GM distributions were used to make predictions using
the SMAP algorithm (i.smap function in grass GIS software),
a method that uses predictions at coarse spatial scales to guide
predictions at fine spatial scales (Bouman & Shapiro, 1994).
This method results in more contiguous predictions and
increases the overall accuracy of predictions compared with a
non-spatial method using the same GM distributions
(McCauley & Engel, 1995).
Model evaluation
Predicted vs. observed distributions of T. heterophylla were
compared in each region using the kappa (j) statistic. j ranges
from )1 (the predicted distribution complements the observed
distribution) to 1 (the predicted and observed distributions are
identical), and values close to 0 indicate that the predicted
distribution is not a better fit than that expected by chance
alone. To show the locations of overpredicted areas, we
summarized overprediction for each 1 latitudinal band as a
function of distance to the range limit of T. heterophylla in
10-km distance classes.
We examined whether areas of model overprediction, using
within-region predictions from the GM–SMAP model, coincided with outlier populations of T. heterophylla beyond its
distribution as defined by the zonal ecosystems. We focused on
three areas that varied in the amount of overprediction: the
northern and southern ends of the interior region and the
southern end of the coastal region. In the USA, known
occurrences in vegetation plots were obtained from the
VegBank database (VEGBANK Contributors, 2004) and the
Forest Inventory and Analysis (FIA) database (USDA Forest
Service, 2005). Landowner privacy regulations prevent the US
Government from distributing FIA data associated with precise
plot locations. We therefore obtained from the Forest Service a
polygon map of T. heterophylla presence generalized to a
hexagonal grid (27 km between polygon centres) that was not
subject to the plot-location errors in the publicly available FIA
data. In British Columbia, known occurrences were obtained
from the Biogeoclimatic Ecosystem Classification database
(http://www.for.gov.bc.ca/hre/becweb/index.htm). Overall, we
searched > 46,000 plots for the presence of T. heterophylla in
these three areas.
Journal of Biogeography 33, 1384–1396
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Climatic and non-climatic controls on T. heterophylla
In the interior region, all four bioclimatic variables are
weaker predictors than in the coastal region (Fig. 3). However,
as in the coastal region, AET is the strongest predictor,
although it is insignificant at two out of eight latitudinal bands
(AUC ¼ 0.59–0.84). MTCO is a weak predictor in the south
(AUC ¼ 0.50), and it increases to significant levels in the
north (> 50 N; AUC ¼ 0.68–0.78). DEF is a poor predictor
(AUC ¼ 0.70–0.36), significant only at 47–48 N. GDD5 is
not predictive in the south (AUC ¼ 0.38), but it increases in
strength with latitude and reaches significant levels at the
northern end (AUC ¼ 0.72).
The thresholds of all four bioclimatic variables show that
T. heterophylla has greater climatic tolerances in the interior
region compared with the coastal region (Fig. 2a). The
T. heterophylla distribution extends into lower AET, MTCO
and GDD5 in the interior region than in the coastal region
(380 vs. 419 mm; )10.9 vs. )9.0 C; 579 vs. 650 degree-days).
In addition, the distribution extends into higher DEF in the
interior region (168 mm) than in the coastal region (145 mm).
Threshold values show latitudinal trends for several variables
but not for others (Fig. 2b). In the coastal distribution, the
threshold values have no latitudinal trend for AET, varying
from 343 to 537 mm (linear regression, P ¼ 0.149), but they
decrease with increasing latitude for MTCO (from 6.6 to
RESULTS
Univariate analyses
In the coastal region, AET is the strongest predictor of the
T. heterophylla distribution, with T. heterophylla preferring
sites with higher AET than other locations at the same latitude
(Figs 2 & 3). Similarly, DEF is also a strong predictor over
most of the coastal region, with T. heterophylla preferring
lower DEF than other locations at the same latitude (Figs 2 &
3). The predictive power of AET and DEF, as measured by the
area under the ROC curve (AUC), is significantly greater than
the null model (Fig. 3), but it decreases from a maximum in
the south (AUC ¼ 0.97 and 0.96 for AET and DEF, respectively) to a minimum in the north (AUC ¼ 0.83 and 0.53 for
AET and DEF, respectively). MTCO is also a strong predictor
over most of the coastal region (AUC ¼ 0.83–0.99), with
T. heterophylla preferring higher MTCO at a given latitude, but
this preference is only marginally significant from 45–50 N
(AUC ¼ 0.73–0.84). GDD5 is a moderate predictor
(AUC ¼ 0.61–0.94), with T. heterophylla preferring higher
GDD5 at all latitudes except 45–48 N where T. heterophylla
occurs closer to the low end of the GDD5 gradient
(AUC ¼ 0.26–0.30).
(a)
(b)
AET (mm)
600
400
200
MTCO (°C)
0
10
0
–10
–20
200
0
3000
GDD5 (degree-days)
Figure 2 (a) Distribution of bioclimatic
variables within and outside the Tsuga heterophylla distribution (black and grey box
plots, respectively) in the coastal and interior
regions. Boxes show the middle quantile and
vertical lines extend to the 5th and 95th
percentiles. (b) Box plots of bioclimatic
variable distributions for contiguous 1 latitudinal bands and separately for the coastal
and interior regions. Open circles indicate the
thresholds for T. heterophylla at latitudes
where the T. heterophylla distribution is significantly segregated to one end of the bioclimatic gradient.
DEF (mm)
400
2000
1000
0
tal ior
as nter
I
Region
Co
Journal of Biogeography 33, 1384–1396
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45
50
Latitude (°N)
55
60
50
54
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D. G. Gavin and F. S. Hu
1
Coastal region
AET
Table 3 Kappa (j) statistics for predictions of the Tsuga heterophylla distribution for two bioclimatic models each trained separately on the coastal and interior regions
Interior region
Testing region
0.5
0
Training region
Coastal
Interior
RE
Coastal
Interior
Coastal
Interior
0.674
0.591
0.731
–
0.269
0.140
–
0.532
MTCO
1
Area under the ROC curve (AUC)
Model
GM–SMAP
0.5
The GM–SMAP model cannot be used for cross-region prediction
because of dissimilar climate between regions.
0
DEF
1
0.5
0
GDD5
1
0.5
0
40
50
46
60
Latitude (°N)
50
56
Figure 3 The degree to which Tsuga heterophylla is segregated to
one end of the bioclimatic gradient in 1 latitudinal bands, as
calculated by ROC analysis. Solid line, AUC for the observed
T. heterophylla distribution; dashed lines, show the 95% confidence envelope for null models of randomly generated T. heterophylla distributions. *Slope of trend significant at P < 0.05;
**P < 0.01.
)16.6 C, P < 0.001), DEF (210 to 0 mm, P < 0.001), and
GDD5 (2456–333 degree-days, P < 0.001). Threshold variations in the interior are much smaller for AET (373–459 mm)
and MTCO ()10.0 to )11.8 C), and they show no latitudinal
trends.
Multivariate bioclimatic models
The coastal-trained RE model fits the coastal region much
more accurately than the interior-trained model fits the
interior region (difference in j ¼ 0.534; Table 3; Fig. 4a,b).
The interior RE model overpredicts the area occupied by
T. heterophylla by > 80% of the interior region, and the
overpredictions occur at distances > 100 km from the
species’ actual distribution at several latitude bands (Fig. 5).
Overprediction by the interior model is relatively minor in
the coastal region (e.g. at 55 N). The coastal RE model,
based on narrower thresholds than the interior RE model,
results in a greater j in the interior region (due to less
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overprediction; Table 3). However, it incorrectly delimits
(underpredicts) large areas at the north end of the interior
(red areas on Fig. 4a).
Using GM–SMAP, the coastal-trained model also fits the
coastal region more accurately than the interior-trained model
fits the interior region (difference in j ¼ 0.199; Table 3;
Fig. 4c,d). Areas of overprediction based on the coastal model
rarely extend > 30 km from the coastal T. heterophylla distribution (Fig. 5). In contrast, the interior model overpredicts a
large area (c. 30% of the total predicted area) up to 140 km from
the interior T. heterophylla distribution. Because of climatic
dissimilarity between the two regions (Fig. 1b), we do not
interpret cross-region predictions from the GM–SMAP model.
A finer-resolution examination of the GM–SMAP model in
three areas shows that the predicted T. heterophylla distribution is a close match to the observed distribution, with a few
exceptions (Fig. 6). At the south end of the coastal region, the
model accurately predicts the T. heterophylla distribution in
most areas, and the few areas of overprediction coincide with
outlier T. heterophylla populations detected by vegetation plots
(Fig. 6c). At the north end of the interior region, the model is
unable to predict narrow belts (< 5 km wide) of the
T. heterophylla distribution, and two small areas of overprediction exist to the north (Fig. 6b). Small outlier populations
of T. heterophylla occur up to 130 km from its zonal
distribution, but not near the areas of overprediction. At the
south end of the interior region, the model predicts the
T. heterophylla distribution accurately along its eastern and
western borders, but it overpredicts extensively to the south
and in disjunct patches to the east (Fig. 6a). Although outlier
T. heterophylla populations coincide with some of these
patches of overprediction to the east, few outlier populations
occur to the south.
DISCUSSION
Variation in bioclimatic thresholds
The lack of latitudinal trend and high predictive capacity of
AET suggest that it is a fundamental control on the distribution
of T. heterophylla (Figs 2 & 3). This result is not surprising
because T. heterophylla is known to occur in areas with mild and
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Climatic and non-climatic controls on T. heterophylla
Figure 4 Bioclimatic model predictions of
the Tsuga heterophylla distribution. Predictions from the rectilinear envelope model
using (a) coastal-region thresholds and (b)
interior-region thresholds, and from the
Gaussian mixture-sequential maximum a
posteriori model trained on (c) the coastal
region and (d) the interior region. Crossregion predictions with the GM–SMAP
model (shaded areas) are not reliable due to
dissimilarity of climate between the two
regions (see Methods and Fig. 1b).
humid climates. Other bioclimatic modelling studies of
T. heterophylla or similar species have also identified AET as
the most predictive variable (Shao & Halpin, 1995; Stephenson,
1998; McKenzie et al., 2003). In contrast with these previous
modelling results, however, our data reveal inter-regional
variation in bioclimatic requirements. Specifically, T. heterophylla in the interior extends into areas with AET 39 mm lower
than in the coastal region. In addition, the other three variables
all show greater within-region (latitudinal) variation in thresholds than AET. For MTCO and GDD5, latitudinal variation in
thresholds is quite large (> 10 C and > 2000 degree-days,
respectively).
Spatial variation in the thresholds of some bioclimatic
variables probably reflects their correlation with a functionally
significant variable (e.g. AET). For example, the high MTCO
threshold in the southern portion of the coastal distribution
probably results from the restriction of precipitation to the
winter months in that area. Because of very dry summers,
T. heterophylla must meet its AET requirements by growing at
low elevations where temperatures are high enough for
photosynthesis during a portion of the rainy season. Further
north, precipitation is less restricted to the winter months, thus
T. heterophylla may occur in areas with colder winters
provided there is sufficient summer moisture (one exception
is at 58 N where coastal T. heterophylla extends eastward into
rainshadowed valleys with less summer moisture; Figs 1 & 2).
This rationale also explains latitudinal variation in GDD5,
because GDD5 is moderately correlated with MTCO in the
coastal region (r ¼ 0.85). Similarly, the latitudinal trend in
DEF may be attributed to its relationship with AET. In the
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D. G. Gavin and F. S. Hu
Figure 5 The extent and distance of overpredicted areas from the observed Tsuga
heterophylla distribution for two bioclimatic
models trained on the coastal and interior
regions. Bubble sizes show the proportion of
the overpredicted area in 10-km distance
classes. Solid and grey bubbles refer to coastal
and interior regions, respectively. Note that
the results of GM–SMAP modelling were
analysed only within the region used to train
the model.
Figure 6 Tsuga heterophylla outlier populations from vegetation plots occurring outside the main (zonal) T. heterophylla distribution and
in relation to bioclimatic model predictions of the T. heterophylla distribution. The inset in (a) shows the locations of the three enlarged
areas. The vegetation plots in the Forest Inventory and Analysis (FIA) program of the US Forest Service are generalized to a range limit (red
lines) positioned along the borders of a hexagonal grid (27-km diameter cells) because exact FIA plot locations are protected. Three outlier
FIA plots occur in areas that suggest misidentification of mountain hemlock (Tsuga mertensiana) and remain unverified (dashed lines). The
bioclimatic model predictions are from the GM–SMAP model trained on the interior region (a,b) or the coastal region (c). Elevation is
shown as the grey-scale background.
north, areas with DEF > 20 mm also have very low precipitation (and low AET), which restricts T. heterophylla to very
low DEF within these areas. In the south, T. heterophylla occurs
in areas with very mild winters that are also very dry in the
summer (relatively high DEF). While these co-varying trends
suggest that non-AET variables are often redundant with AET,
maps of areas delimited by individual thresholds show that
1392
there are indeed several areas where only DEF, GDD5 or
MTCO are predictive. For example, DEF is the only limiting
variable in the dry lowland valleys of western Oregon and
Washington (see Fig. S1 in Supplementary Material).
Bioclimatic thresholds may also be affected by local
adaptation causing clines in physiological thresholds (ecotypic
variation). Common-garden experiments provided independ-
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Climatic and non-climatic controls on T. heterophylla
ent evidence of ecotypic variation in growth rate and frost
hardiness (Kuser & Ching, 1980, 1981). Tsuga heterophylla
seeds originating from throughout its distribution, and
germinated in an Oregon shadehouse at ambient temperature,
showed that increasing provenance latitude resulted in
increased survival following an early winter frost, decreased
growth rates, and decreased shoot : root ratios (Kuser &
Ching, 1980). Hannerz et al. (1999) found similar results in
south-western British Columbia. Furthermore, compared with
coastal provenances at the same latitudes, interior provenances
have greater frost hardiness and lower growth rates (Kuser &
Ching, 1980, 1981). The finding of a frost-tolerance cline from
these experiments matches our observed trend in MTCO
thresholds (Fig. 2), but we suggest that the majority of this
trend is attributable to the seasonality of precipitation
mentioned above. One plausible scenario for clinal variation
in MTCO thresholds is that AET provides a fundamental
control on distribution limits (little local adaptation with
respect to AET), but that within the AET threshold there are
local adaptations to seasonal temperature variables. Unfortunately, these common-garden studies did not address moisture
requirements and yielded inconsistent results regarding budburst dates, thus they do not offer direct evidence that clines in
the other three variables result from local adaptation.
The occurrence of variation in bioclimatic thresholds
presents difficulties for bioclimatic modelling (Loehle &
LeBlanc, 1996). If a single AET threshold were sufficiently
predictive throughout the observed distribution of T. heterophylla, predicting past and future distributions would be
limited primarily by the accuracy of palaeo- and projected
climatic data. However, multiple variables and their interactions (the GM–SMAP model) were required to fit the interior
distribution (Fig. 4), suggesting a more complex species–
climate relationship. Complexity such as spatial variation in
thresholds may be modelled by the variable interactions of the
GM–SMAP model. For example, a GDD5 · MTCO interaction may be more predictive than individual variables separately because the threshold of one variable appears correlated
with the other (Fig. 2). The drawback of using multiple
variable interactions is that models may become too specific to
the environmental space of the training data, lowering
accuracy when predicting in different climatic regimes. This
problem is demonstrated by the inability of the GM–SMAP
model, which includes variable interactions, to make crossregion predictions.
Climatic and biotic controls of the T. heterophylla
distribution
Both the RE and GM–SMAP models fit the coastal distribution
rather well, suggesting strong climatic control of T. heterophylla occurrence. Their overprediction within 10 or 20 km of the
T. heterophylla distribution (Fig. 5) probably reflects limitations in the spatial resolution of the climate data compared
with that of the zonal ecosystem data. In valley bottoms, a one
grid point-wide line often represents the T. heterophylla
distribution, which is probably too fine to be captured by
the climatic data. Furthermore, the SMAP algorithm makes
predictions in the context of neighbouring grid points,
effectively smoothing the predictions. We expect that finerscale features such as narrow valleys could be accurately
predicted by downscaling the temperature data using locally
computed lapse rates and a fine-scale (e.g. 500-m) digital
elevation model (Hamann & Wang, 2005). However, increased
accuracy at fine spatial scales would not affect the major,
coarse-scale patterns of overprediction detected in this study.
The interior T. heterophylla distribution is more poorly
constrained by climate than the coastal distribution, regardless
of the bioclimatic variables or form of model used. In fact, the
complex GM–SMAP model (using all variable interactions)
could not predict the interior distribution as accurately as a
single AET threshold predicted the coastal distribution
(Fig. 4d, j ¼ 0.532 vs. Fig. S1, j ¼ 0.553). The weaker
climatic control of the interior distribution is upheld with
further exploratory analyses. First, using latitude-specific
thresholds increases the fit of the interior RE model very little,
reflecting the fact that these thresholds vary only a minor
amount by latitude (Fig. 2). Second, the steeper climatic
gradients in the coastal distribution may have resulted in a
smaller area of overprediction. Indeed the sensitivity of the
area of overprediction to changes in thresholds in the coastal
distribution was c. 50% of that in the interior distribution.
However, this sensitivity difference accounts for < 20% of the
large discrepancy in the overpredicted areas using coastal
vs. interior thresholds (Fig. 5a,b).
The overprediction of a few specific localities distal from the
main interior distribution suggests that T. heterophylla has not
fully expanded into its potential interior range. The most
extensive overprediction occurs in Idaho and north-western
Montana (Fig. 6). Many of these areas are patchy habitat
islands on windward sides of mountain ranges (Fig. 6), and in
Idaho these areas support dense forests with species that
usually co-occur with T. heterophylla. Many of these species
probably have broader climatic envelopes than T. heterophylla,
such as the canopy-dominant trees Thuja plicata (western
redcedar) and Pinus monticola (western white pine). Several
other species have distributions that overlap widely with the
coastal T. heterophylla distribution but are also common south
of the interior T. heterophylla distribution, such as Alnus rubra,
Cornus nutallii, Blechnum spicant and Viola sempervirens
(Daubenmire, 1952, 1975; Johnson, 1968; Habeck, 1978).
Lorain (1988) identified 41 vascular plant species with this
geographical pattern. Daubenmire (1952, 1975) speculated that
this large number of disjunct coastal species in the unglaciated
valleys of northern Idaho (the Clearwater and Lochsa valleys)
indicated the location of a Pleistocene refugium, which is
supported by the phylogeographical patterns of several plant
and animal species (Brunsfeld et al., 2001). However,
T. heterophylla is absent from this refugium and has not been
observed more than 4 km south of its zonal distribution
(c. 4645¢ N, Fig. 6; Habeck, 1978, 1987; Cooper et al., 1991).
It is surprising that T. heterophylla has not colonized moist
Journal of Biogeography 33, 1384–1396
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1393
D. G. Gavin and F. S. Hu
drainages in these areas, as other species with broadly similar
climatic requirements exist in abundance.
Areas of unoccupied potential range in the interior may be
attributable to a combination of dispersal limitation and
interspecific competition. Pollen records (Mehringer, 1996;
Rosenberg et al., 2003) indicate that T. heterophylla arrived in
northern Idaho relatively recently (c. 3000–2000 yr bp). Thus
time may have been a limiting factor for T. heterophylla to
disperse into patchy habitat islands. However, outlying
T. heterophylla populations exist (Fig. 6), and T. heterophylla
trees can produce very large seed crops (106 seeds ha)1) that
disperse effectively for at least hundreds of metres (Clark,
1970). These factors argue against dispersal as a sole constraint
on the modern range of this species. We suggest that late
arrival in the interior, in combination with interspecific
competition, has limited colonization of potential range. The
interior region supports diverse forest communities with a
mixture of species typical of the moist (coastal) and dry
(interior) regions (Cooper et al., 1991), creating a competitive
seedling environment. In addition, fires are frequent at the
southern end of the interior distribution (Rollins et al., 2001),
and in the high light conditions following fire T. heterophylla is
outcompeted by diverse shrub and early successional tree
species (Mueggler, 1965). Thus frequent fire over the past
several thousand years (Brunelle & Whitlock, 2003) could have
prevented or slowed T. heterophylla colonization by limiting
the availability of safe sites (Green, 1983).
The mismatch in potential and occupied distribution of
T. heterophylla should be viewed in terms of climatic change
and local adaptation, which would have resulted in a
continually changing potential distribution over time. Thus
the size of overprediction also changes over time. The onset of
cooler conditions 3100–2500 yr bp (Luckman et al., 1993) may
have expanded the potential distribution of T. heterophylla
south and east in Idaho and Montana, not all of which has
been colonized. Alternatively, local adaptation of interior
populations may have greatly expanded the predicted distribution (sensu Case & Taper, 2000).
In terms of future climatic change, the significance of local
adaptation is unclear (Harte et al., 2004). Climatic change will
probably occur too rapidly for the emergence of new ecotypes,
and will necessitate the reorganization of current ecotypes on
the landscape. Given sufficient climatic change, loss of potential
distribution may cause the extinction of marginal ecotypes
(Davis & Shaw, 2001). This study showed that a large portion of
the interior distribution of T. heterophylla might be vulnerable
to warmer and drier conditions because much of the interior
distribution occurs near its AET threshold (Fig. 2). It is possible,
however, that increased atmospheric CO2 concentrations will
increase water-use efficiency and lower the AET threshold,
complicating our ability to predict distributions in future (and
past) climates (Williams et al., 2001). Regardless, turnover of
dominant forest species in cedar–hemlock forests is likely to be
slow (Habeck, 1978). Such anticipated lags in vegetation change
suggest that non-climatic controls on distribution limits may
become more important in the future.
1394
ACKNOWLEDGEMENTS
This study was supported by a National Science Foundation
Grant and a Packard Fellowship in Science and Engineering.
We thank Elizabeth LaPoint from the USDA Forest Service for
the analysis of the Forest Inventory and Analysis plot data, Will
MacKenzie from the British Columbia Ministry of Forests
for the British Columbia plot data, and Steve Brunsfeld for
discussions on forest zones in Idaho. We are grateful for
comments from two anonymous referees.
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SUPPLEMENTARY MATERIAL
The following supplementary material is available for this
article online from http://www.Blackwell-Synergy.com:
Figure S1 Areas delimited by the thresholds of four bioclimatic variables with respect to the distribution of Tsuga
heterophylla.
BIOSKETCHES
Daniel Gavin is an assistant professor in the Department of
Geography at the University of Oregon. He is interested in
Quaternary palaeoecology, biogeography, fire history and
forest ecology.
Feng Sheng Hu is an associate professor in the Departments
of Plant Biology and Geology at the University of Illinois. He is
interested in climatic change and Quaternary ecology.
Editor: Glen MacDonald
Journal of Biogeography 33, 1384–1396
ª 2006 The Authors. Journal compilation ª 2006 Blackwell Publishing Ltd