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An Accuracy Assessment of a Spatial
Bioclimatic Model
D.W. ~ c ~ e n n eB.G.
~ ' ,~ a c k e f ,M.F. ~ u t c h i n s o n R.A.
~ , Sims4
Abstract.-Spatially explicit models of forest ecosystems require the
integration of biophysical data at a range of scales. Prediction of biotic
response should be based on spatially explicit models of the driving
environmental processes, in particular those related to climate and
topography. Ongoing research in the forests of Ontario have focused on
the development of suitably scaled digital elevation models and mesoscaled models of climatic averages. These in turn have been used to
spatially extend various biological survey data. In this paper we present
some accuracy assessments of the spatial climate models and subsequent
differences in species' predicted distributions.
INTRODUCTION
Resource managers require reliable spatial information about species'
distributions to help make decisions such as establishing monitoring sites or
protected area networks. How important a particular landscape is to a species often
depends on where else it does or could occur. Spatial information can help provide
context. However considerable effort is required to develop these data. Typical
resource inventories such as aerial photographs or remotely sensed data provide
relatively little information about non-commercial andlor non-overstory species,
so some other type of modelling is required.
Theory would suggest that ecological response, such as a species' distribution,
is a function of a complex myriad of factors including disturbance regimes, human
inputs (eg. land use history), biological regimes (eg. interspecies competition) and
a number of physical determinants. The latter include thermal, radiation, moisture
and nutrient regimes. Data on climate, terrain and soil parent material are the
basic inputs to modelling these regimes across landscapes. If spatial data on the
'
Senior Economist, Canadian Forest Service - Sault Ste. Marie, hturio, Cmvlda
Senior Lecturer, Department of Geography, The Australian National University, Canberra, Australia.
Senior Fellow, Centrefor Resource and Environmental Studer, The Australian National University, Canberra Australia
4
Research Scientist, CaMdian Forat Service - Sault Ste. Marie, Ontario, Canada.
regimes are available and species' responses can be calibrated with the regimes,
then there becomes an enhanced capability to extrapolate information gained from
analysis of field based observations. This type of empirical framework is directly
relevant to the problem of spatially quantifying species' distributions, abundance,
and productivity (eg. Mackey 1994). In this paper, some accuracy issues
associated with a spatial climate model and the problem of extrapolating site data
to quantify a species' (jack pine, Pinus banksiana) potential distribution are
investigated.
A SPATIAL CLIMATE MODEL FOR THE PROVINCE OF ONTARIO
It is well accepted that climate exerts a strong control on both the distribution
and productivity of plant species. However, correlations between various climate
parameters and the ecological response of interest is often poor. One reason for
this is that weather stations are located well away from field survey points and
any estimates of climatic parameters involve some interpolation. Some relatively
new methods for interpolating climate provide opportunities for generating more
reliable spatial estimates of climatic parameters ex situ than previously possible
(see Hutchinson 1995, 1987). Applications of these methods to estimate long term
monthly mean climate values at points of survey and subsequently explain spatial
variation in ecological response have been successful (eg. Nix 1986, Mackey
1994, Yee and Mitchell 1991). The methods make use of partial thin plate
smoothing splines (Eq. 1) to interpolate historical weather station data:
where f is an unknown smooth to be estimated, yjare a set of p known functions
and the p, are a set of unknown parameters which have to be estimated. The x,
generally represent coordinates in two or three dimensional space. The ei are zero
mean random errors. Hutchinson (1995) describes the estimation procedures in
detail. In applications to date, climate surfaces have been developed as a function
of latitude, longitude and elevation (eg. Nix 1986, Mitchell 1991, Mackey et al.
1996) although any number of independent variables are possible in principal (eg.
distance from a major water body). These three parameters are known to be
important determinants of long-term climate. The resultant climate surfaces can
also be mapped relatively easily in a GIs using software which couples the
functions to a Digital Elevation Model (DEM) (see Hutchinson 1993). A DEM is
a regular grid of latitude, longitude and elevation.
The main advantage of the thin plate spline techniques over competing
methods is that they do not require any a priori estimation of the spatial autocovariance structure. In addition, data smoothing can be optimized by minimizing
the generalized cross validation statistic (Hutchinson 1995, Hutchinson and Gessler
1994).
Mackey et al. (1996 in press) used these methods to generate climate surfaces
for the province of Ontario as a function of x, y (latitude and longitude) and z (km
above sea level). Table 1 summarizes the standard errors of the basic surfaces of
monthly mean maximum temperature, monthly mean minimum temperature and
monthly precipitation. Besides mapping the surfaces and secondary indices in a
GIs by coupling the functions to a DEM, estimates of climatic values can be
generated at field survey locations. The latitude, longitude and elevation (x, y and
z) of each survey location are required. Hence, estimates of various climate
variables can be estimated and climateiplant or planthima1 relations can be
subsequently examined. Spatial predictions can be made by linking the statistical
functions to spatial databases of the independent variables.
Table l.-Number of data point. and approximate standard errors of fitted climate
surfaces for Ontario.
Climate Variable
Number of Data Points
monthly mean max. temp.
47 1
monthly mean min .temp.
47 1
Standard Error
monthly precipitation
(old surface)
monthly precipitation
(new surface)
470
(less Ignace)
Climate Estimate Errors Associated With Incorrect Field Survey Locations
Considerable effort has been placed on the collation of existing biological
survey data in Ontario so that these methods can be applied. For example, there
are now over 4100 forest ecosystem classification (FEC) plots in existence
throughout the province (Sims and Uhlig 1992). These data are essentially
vegetation surveys in which numerous characteristics were measured in 10 by 10
metre plots and included understorey and overstory plants, soil properties and
some mensurational attributes. Some of the newer plots used GPS technology to
record location data. If location information was not already recorded, the original
survey records and topographic maps were used to record x, y and z (it is also
possible to use a DEM to derive estimates of elevation if only x and y are
available).
The importance of accurate location information will vary spatially. For
example; steeper climatic gradients will exist where elevation changes are more
abrupt, or near very large water bodies such as the Great Lakes. To gauge the
importance of location information across the province, an analysis of the location
data associated with observations of jack pine was performed. Of the 4100 FEC
plots, 951 contained jack pine. The x,y,z coordinates of each of these were
perturbed in two ways. Figure 1 reports an example where the elevation (z) values
were increased by 50 metres (elevations above sea level in Ontario range from 0
metres near Hudson Bay to over 610 metres northwest of Lake Superior). New
climate values were generated with these increased elevation values and compared
to the original estimates. Results for three climatic values (growing season length,
maximum temperature of the hottest month and precipitation of the coldest
quarter) are shown in figure 1. The original range of values at the 95 1 plots is also
reported in the figure.
In addition to the elevation perturbations, x and y coordinates were
systematically altered. Figure 1 reports the results of increasing x and y by six
minutes. Across the range of the plot locations, the implication in terms of
distance is approximately 13 krn northwest of each "true" geographic plot position.
The changes in elevation and latitudenongitude produced similar changes in
growing season length. The changes in temperature and precipitation were
consistent with the relative scaling of the input data. The differences from the
original values are marginal. The biggest impact was on precipitation in the
coldest quarter. In this case the elevation change caused a general decrease in
values across all sites (from - 1.5% to -5.7% in relation to the original values). The
lat/long shift resulted in a -6.9% to 9.9% change in relation to the original values.
These differences are for the most part within the standard errors reported in table
1. The results illustrate the spatially varying dependency of climate on these 3
variables.
SPATIAL PREDICTIONS OF THE RANGE OF JACK PINE IN
ONTARIO
As mentioned, the ability to generate grids of climatic values and estimates of
climate at points of survey offer opportunities to develop spatial predictions of
animal-plantklimate relations. A variety of approaches are possible depending on
the nature of the biological site data including the use of statistical relationships
(eg. presence of an organism = f (minimum temperature of the coldest month). An
approach that has proven useful when only presence data is available is a grid
matching procedure (Nix, 1986). In this case a climatic profile of the species is
generated, based on observations of where it occurs and the estimates of the
climatic values at each of those locations. This profile is then matched to grids of
those climatic parameters. This is the BIOCLIMlBIOMAP procedure developed
by Nix (1986) and colleagues at the Australian National University (McMahon et
al. 1995).
BIOCLIM makes use of climate surfaces, DEMs and species' location
information to produce bioclimatic profiles. These profiles essentially describe the
climatic conditions (eg. the range of mean annual temperatures, annual
precipitation) sampled by a plot network. BIOMAP takes the BIOCLIM output
a
b
1%diff. = -0.6to 1.5
1
2
3
4
5
Growing season length (range of original values = I62 to 21 1 days)
-0.6 -0.4 -0.2 0.0
0.2
0.4
-0.6 -0.4 -0.2 0.0
0.2
0.4
Maximum temperature of the hottest month (range of original values = 19.8to 26.5'C)
4
% diff. = -6.9to 9.9
800
-12 -8
% diff. = -1.5to -5.7
800
-4
0
4
8
12
-12 -8
-4
0
4
8
12
Precipitation of the coldest quarter (range of original values = 69 to 272 mm)
Figure 1. Differences in three climate variable estimates at 951 FEC plots when
perturbing 1atitudeAongitude +6 minutes (column a) and elevation +50 metres (column b).
The range of differences as a percentage of original values is presented for each case (% dim.
and identifies (flags) grid cells which match the bioclimate profile for the selected
climatic variables. The resulting data can be input into a GIs and interpreted as
a spatial prediction of the climatic domain of the species. In this way, landscapes
can be identified which are climatically suitable for the species. Whether the
species actually occurs will depend on the influence of other environmental
processes.
This approach to predicting the potential domain of a species is relatively
transparent to interpretation. However there are limitations; all of the selected
climatic variables used in the BIOMAP predictions are assumed to have an equal
impact on the species' distribution. Statistical options are limited when the
available data indicate only where a species is present; ie. there are no
observations of where it is absent. BIOCLIM/BIOMAP is a repeatable and useful
method for analyzing these types of survey data and can be easily updated with
new observations. However the spatial prediction is also contingent on the
representativeness of the underlying biological site data and quality of the climate
surfaces.
Figure 2a is a BIOMAP of jack pine based on analysis of 951 province-wide
FEC plots. Future work by the authors will provide a detailed discussion of this
type of analysis for a suite of commercial tree species in Ontario). A set of
bioclimate parameters (eg. annual mean temperature, precipitation in the warmest
and coldest quarters) derived from the monthly mean climate variables were
coupled to a 1-kilometre DEM of the province in order to produce the BIOMAP.
The resulting dark areas represent the "core" climate domain for the species. Grid
cells are identified as a core area if all of their climatic parameter values fall
within 10-90% of the survey data climatic values (ie. the 10-90 percentiles of the
survey climatic parameters are the climatic conditions where most of the site data
occur). The lightest shade of grey indicates those grid cells that fall within the
range of climatic values defined by the survey data, indicating more marginal
conditions.
Noted in figure 2a is a "hole" near Ignace, Ontario, which suggests the area
is outside the climatic domain of jack pine. This outcome has been noticed with
BIOMAP analysis of several plants and animals and has been a source of some
interest given an a priori expectation that the region would be quite suitable to
many of the species. During an investigation of the spatial pattern of the
bioclimatic parameters in that region, it was found that precipitation during the
coldest quarter is significantly drier there than the surrounding area. Figure 3a
shows the precipitation surface for that area in close detail and also identifies the
locations of FEC plots in that part of the province. Clearly there are no FEC plots
in this area. Thus, being an empirical approach, the BIOMAP output is doing what
would be expected; producing a "hole" at those grid cells where at least one of the
climate parameters has values outside the range of the climatic values for known
occurrences of the species. An implication is that the area is outside the range of
jack pine.
Ontario,
intermediate area
(b)
r//
core area
a
yu
i
Figure 2. Climatic domain analysis for jack pine in Ontario using original precipitation
surface (a) and new precipitation surface (b) Map (c) is the difference between (a) and (b).
.
One question is whether the underlying climate surface is robust (see figure
3a). However, the apparent anomaly is a reflection of the input data. It was
determined that one weather station (Ignace), at the centre of the "hole" was
reporting much drier conditions than nearby stations. For example, long-term
November to April precipitation is 138mm compared to 171-212mm for nearby
stations. As well as having a shorter history of record collection than other
stations, further investigations revealed that Canadian Atmospheric Environment
Service experts had several reasons to be suspicious of the quality of data from
this particular station. In fact, this Ignace station is now closed.
Given this, a new precipitation surface was created without the Ignace station
data. Results are shown in figure 3b. The steep gradient associated with
precipitation of the coldest quarter has been smoothed. The standard error of the
monthly precipitation surfaces using data from 470 weather stations ranged from
3.3% to 8.2% compared to 3.2% to 7.6% (see table 1). The implication in terms
of the predicted climatic range of jack pine is shown in figure 2b. The "Ignace
hole" shown in figure 3a is now part of the core climatic range for jack pine. The
difference between figures 2a and 2b is shown in figure 2c. Although there are
some changes across the province, the most significant changes occurred in the
Ignace area where grid cells that were flagged as outside the species range are
now part of its core range.
CONCLUDING COMMENTS
Reliable, quantitative spatial data should ideally be used to help make
assessments about the potential of landscapes to support particular species. Species
distributions must be modelled through integration of various data types and
sensitivity analysis of the results to input values should occur. Empirical models
are generally driven by the input data, in this paper these are the climate and
biological data. The results presented here reiterate one truism well known to
ecological researchers: data quality control should be a paramount concern.
However it is clear that some data quality issues will not arise without extensive
investigation. Prior to the analysis of site data in conjunction with climate, we
were quite satisfied with the Ontario climate model. In this case the quest to
derive spatial information from site-based observations revealed an area of Ontario
that lacked FEC plots and had questionable weather station data.
From a narrower perspective, the analysis summarized in figure 1 suggests
considerable scope to make use of historical field data in Ontario. There are
numerous vegetation and wildlife survey datasets available in Ontario to which
latitude, longitude and elevation could be appended. The analysis of differences
in climate estimates in this paper should allay concerns about moderate errors in
location records.
Figure 3. Precipitation in coldest quarter at the 'Ignace hole' (48:45N--50:00N, 91:15W
--92:45W) with nearby FEC plots overlaid.