Investigating the Environmental Cause of
Global Wilderness and Species Richness
Distributions
Crewenna Dymond
Steve Carver
Oliver Phillips
Abstract—Environmental factors that affect the distributions of
wilderness character and the species richness of mammals, birds,
flowering plants (angiosperms), and conifers and cycads (seedbearing plants) were investigated at the global scale using national species richness data and a continuous wilderness quality
grid. Principle Component Analysis and Multiple Regression were
used to develop environmental characteristic models that are
straight forward to interpret. High elevation and high latitude
were key to the distribution of wilderness quality, conifers, and
cycads. The most important determinants of species richness,
however, were found to be low latitude and “good” climate (high
precipitation and constant warm temperature). Understanding
factors that influence presence of wilderness today will help plan
for its protection on a large scale. Appreciating how the same
factors affect the distribution of species richness will aid in conservation of biodiversity, particularly that in protected wilderness
requiring pristine habitat.
Introduction ____________________
Wilderness inventories and assessments frequently use
biophysical naturalness as an indicator of wilderness quality. For example, the Australian National Wilderness Inventory (ANWI) describes biophysical naturalness as “the degree to which the natural environment is free from biophysical
disturbance caused by the influence of modern technological
society” (Lesslie and Maslen 1995). However, it is proposed
that biophysical naturalness is not an adequate measure of
biodiversity and, consequently, can underestimate the true
biological value of environments with wilderness qualities.
Furthermore, within the study of wilderness science it is
contended that there is insufficient regard for biodiversity
issues although it is assumed that wilderness offers unique
biological protection opportunities. It is argued that more
accurate measures of biological values should be incorporated into wilderness assessments in addition to the ap-
praisal of biophysical alteration. It is proposed that a reason
for the lack of incorporation is the paucity of available
biodiversity information, particularly at a large scale. Furthermore, this may be exacerbated by a lack of understanding of the contribution of wilderness environments to biodiversity and how certain environments are conducive to the
persistence of wilderness.
A research analysis strategy has been designed to determine the extent to which environmental factors explain
variation in the distribution of species richness and wilderness at the global scale. Also, this research explores whether
species richness makes a contribution to the distribution of
wilderness and vice versa. This research began with analysis at the global scale, and so it was considered important to
determine precisely which environmental factors might be
responsible for the distributions of wilderness and species
richness. There has been considerable research into the
causes of species richness variation, for example, latitudinal gradients (Rohde 1992) and water energy dynamics
(O’Brien 1998), and although theories are still being debated there is at least some consensus about the main causes
in this variation. In the field of wilderness science we have
not attempted to explain the patterns of wilderness distribution, although we may be able to intuitively describe the
characteristics of the environments in which it currently
exists. It is important to distinguish between factors responsible solely for wilderness, such as remoteness, and those
related to species richness, such as soil type, and those that
may serve to affect both. These key variables have been
isolated and are shown in the conceptual model (fig. 1).
Soil
Solar energy
Aspect
BIODIVERSITY
Latitude Altitude Evapotranspiration Precipitation Temperature Population
Crewenna Dymond is a Ph.D. Student, School of Geography, University of
Leeds. Dr. Steve Carver and Dr. Oliver Phillips are her Research Supervisors
and Lecturers at the School of Geography, University of Leeds, Leeds, LS2
9JT, UK. Fax: +44 113 233 3308, E-mail: c.dymond@geog.leeds.ac.uk
In: Watson, Alan; Sproull, Janet, comps. 2003. Science and stewardship
to protect and sustain wilderness values: Seventh World Wilderness Congress symposium; 2001 November 2–8; Port Elizabeth, South Africa. Proc.
RMRS-P-27. Ogden, UT: U.S. Department of Agriculture, Forest Service,
Rocky Mountain Research Station.
USDA Forest Service Proceedings RMRS-P-27. 2003
Naturalness
WILDERNESS
Remoteness
Figure 1—Conceptual model: factors identified as
important for the distribution of biodiversity and
wilderness.
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Dymond, Carver, and Philips
Investigating the Environmental Cause of Global Wilderness and Species Richness Distributions
Global Hypotheses ______________
Using the global biodiversity and wilderness conceptual
interaction model (fig. 1) as a starting point, it was possible
to define a series of research hypotheses and these are
summarized in table 1. These hypotheses have determined
the content of this analysis. However, because it is not clear
which factors are most important, the process has been
inductive, allowing the data to shape the conclusions without being confined by the hypotheses.
Method for Global Analysis _______
Table 2 lists the data used for the global analysis. While
nationwide or even continental databases of species richness doubtless exist, for example the North American
Breeding Bird Survey, such information is rarely available
from a single source for the whole World. The national data
from Groombridge (1994) proved to be the most appropriate source of global species richness data for this analysis.
We considered it important to use taxa that have been
relatively well inventoried, and for this reason mammals
and birds were chosen. However, some indicator of
phytodiversity needed to be incorporated because case studies at smaller scales included tree and plant diversity. As a
result, the flowering plant (angiosperms) and conifer and
cycad (seed-bearing plants) groups were also incorporated
from the Groombridge (1994) data set.
The use of national species richness data necessitates
some consideration of the relationship between species and
area. While there are grounds for presuming a linear relationship between species richness and area on a logarithmic
scale (MacArthur and Wilson 1969), there has also been
considerable debate about nonlinear relationships between
the richness of individual groups and area (for example,
Rahbek 1997). In this work, however, the log10 of species
richness has been regressed against the log10 of country
area, as an independent variable. Thus, we can quantify the
extent that species richness depends on area for these
groups. The residual values produced from the regression
models indicate how far removed from expected each of the
Table 1—The basic research hypotheses generated from the conceptual
model.
Species richness
is high in areas with:
Wilderness quality
is high in area with:
Low latitude
Low altitude
Low water deficit (AET = PET)
High precipitation
Moderate to high temperature
High latitude
High altitude
High water deficit
Low precipitation
Extremes of high and low
temperature
Low human population
Moderate to high population
Table 2—Data type and source for the global analysis of environmental factors influencing the distribution of wilderness and species richness.
Environmental
factors
Data requirements
Data source
Wilderness
Global wilderness inventory
Continuous wilderness grid
Polygon coverage, McCloskey and Spalding (1989);
22 category wilderness grid, WCMC (2000); Lesslie (2000,
personal communication)
Species richness
Global species richness for flora and fauna
Mammals, birds, flowering plants, and conifer and cycads national
species richness data, Groombridge (1994)
Population
National population statistics
1995 mid-year population estimates, Census Bureau of the U.S.
(1995);
Urbanization estimates, United Nations Population Division (1998);
Global Land Cover Characterization, U.S. Geological Survey and
others (2000)
Rural:urban population proportions per country
Urban area per country
Latitude
0.5 decimal degree resolution grid
Created in Arc Grid
Elevation
High resolution global digital elevation model
5-minute resolution DTM5, Skellern (1999) derived from ETOP05,
NGDC (1988)
Climate
Global precipitation grid
Global temperature grid
Global potential evapotranspiration grid
Global actual evapotranspiration grid
Global water deficit
30-minute monthly means, Leemans and Cramer (1991);
30-minute monthly means, Leemans and Cramer (1991);
30-minute monthly means, Ahn and Tateishi (1994a);
30-minute monthly means, Ahn and Tateishi (1994b);
Calculated from Ahn and Tateishi (1994a,b)
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Dymond, Carver, and Philips
Investigating the Environmental Cause of Global Wilderness and Species Richness Distributions
national richness values are. In further analyses, these
residual values have been used as the measure of species
richness.
Only one global wilderness database has been published
(McCloskey and Spalding 1989) (fig. 2). However, its classification criteria are very strict, as clearly seen by the absence
of wilderness in the contiguous 48 States of the United
States, although there are many protected wilderness areas
there. Individual nations that protect wilderness define
allocation criteria that are applicable to their country. For
example, some countries enable cultural issues to be incorporated. This process means that there is a wide difference
between the real nature of wilderness in each of the countries that have protected it. A worldwide wilderness assessment, such as McCloskey and Spalding (1989), is useful
because it applies the same criteria ubiquitously. However,
this inventory does not facilitate the identification of areas
with wilderness qualities that are less remote from human
features. To be able to identify wildernesslike environments
in every country, a “sliding scale” of wilderness, or decreasing levels of wilderness quality, need to be determined. In
conjunction with the World Conservation Monitoring Centre (now UNEP-WCMC), Lesslie (personal communication)
has replicated the procedures for the Australian National
Wilderness Inventory (Lesslie and Maslen 1995) using the
Digital Chart of the World as the major data source (Defense
Mapping Agency 1992). The product is a grid-based continuous wilderness database that defines 22 wilderness quality
classes and can be seen in figure 3. Unlike the ANWI, the
global wilderness grid was not constructed using biophysical
naturalness as one of its four criteria, as there is a lack of
global data of this type.
Data for each of the environmental factors are available
from grid-based maps, commonly at a resolution of 0.5
decimal degrees (dd) or 30-minute interval. The species
richness data are only available nationally, which means
analysis must occur at this scale. To compensate, a series of
summary data have been compiled for each climatic and
geographical factor in order to account for the wide variation
in conditions experienced within the countries. The summary statistics of mean, minimum, maximum, and range
were calculated for each variable. For the population data,
density and rural population density were calculated using
the Global Land Cover Characterization (USGS and others
2000) and rural to urban population proportion data (United
Nations Population Division 1998).
Principal Component Analysis (PCA) was applied to each
of the groups of summary variables to reduce the total
number of variables to be tested. For example, mean, range,
maximum, and minimum elevation data were reduced to a
single axis or factor. This new axis accounts for the variation
found within the four summary statistics. In this way, the
variation is integral to the analysis and not unnecessarily
oversimplified. Because the climatic variables of temperature, precipitation, and evapotranspiration (actual and potential evapotranspiration and water deficit) are closely
related, we decided that they should all be entered into a
PCA model to derive one axis that summarizes all of these
variables. In this way, the assumption of linear regression
that the predictor variables are independent is upheld. The
new axes from the PCA were used in a series of backward
stepwise multiple regression models to investigate the contribution of each environmental factor (independent) on
species richness and wilderness (dependent factors). This
facilitates the analysis of whether species richness directly
affects wilderness and vice versa. Furthermore, the regression coefficients (B) from the models indicate by how much
and in what direction (positive or negative) the independent
factor is influential.
Figure 2—Reconnaissance inventory of the amount of wilderness remaining in the world
(McCloskey and Spalding 1989). Each wilderness polygon must be 400,000 ha (1 million
acres) in size and at least 6 km from features such as roads or other development (reprinted
with permission of AMBIO, the Royal Swedish Academy of Sciences).
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Dymond, Carver, and Philips
Investigating the Environmental Cause of Global Wilderness and Species Richness Distributions
Figure 3—Global grid-based wilderness continuum at a resolution of 0.5 decimal degree
(30 minute) (Lesslie 2000, personal communication). High quality wilderness is in black,
with grey representing low quality wilderness—urban and developed areas (printed here
with kind permission of Rob Lesslie).
Results of Global Analysis ________
Results From Analysis With all Climate
Variables Combined
Table 3 summarizes the results of the regression models
by showing the dependent variable, the predictor variables
remaining after the final step of the model, and the adjusted
2
R , which indicates how much variation is explained by the
predictor variables. The results indicate that climate is
important for the distributions of mammalian, avian, and
angiosperm species richness. Latitude is identified as a
predictor for the pattern of mammalian, avian, and seedbearing plant species richness. We could argue that climate
and latitude are the most important determinants of species
richness, as they are identified most frequently in these
models. The results indicate that the models are much better
at predicting the causes of mammalian and avian species
richness (variation explained is 42.0 percent and 36.2 percent, respectively) than of phytodiversity (flowering plants,
and conifers and cycads, 16.7 percent and 16.6 percent,
respectively). This may be due to the quality of the data
used for these groups, as both the mammal and bird groups
are proportionately better inventoried than the other two
groups.
The normal probability plot of the residuals from the
angiosperm regression revealed that this group does not
share a linear relationship with area, as assumed. Without
this relationship with area, the regression procedure will not
be able to explain this group well. Conifers (seed- bearers),
unlike the other groups, have a particular geographic distribution, with species richness being higher at low latitudes
and very few of their form found in the tropics. For cycad
richness the reverse is true, but there are few species in this
subgroup. The group is therefore biased toward temperate
locations, and again the procedure used here is not good at
dealing with this pattern.
234
For wilderness, an interesting trend in the determinants
emerges through this analysis. At high levels of wilderness
quality, both from the McCloskey and Spalding inventory
(1989) and Lesslie’s continuum (2000), a combination of
climate, elevation, and population are significant predictors. For categories 1 and 2, latitude is also identified as an
important determinant. Consultation of maps of the various
wilderness quality classifications indicates that Lesslie’s
categories 1 and 2 are higher in wilderness quality than
McCloskey and Spalding’s polygon wilderness. The maps
also reveal that quality category 3 most closely approximates the polygon inventory. At this level, latitude ceases to
become important, and predictors of the Lesslie continuum
once again match the McCloskey and Spalding predictors.
Climate, elevation, and population continue to drive the
distribution of the quality categories of 7 and 11. At category 15, the explanatory power of the model peaks at 37.9
percent, and latitude replaces climate as a predictor variable. For the lowest quality wilderness (17–21), elevation is
the only predictor that significantly shapes the distribution, indicating that this also determines the nature and
extent of human activity that is the basis for this evaluation of wilderness.
Results From Analysis With Climate
Variables Included Independently
The use of a single-climate axis does not facilitate the
identification of the individual facets of climate included in
the research hypotheses. In addition to the first model run,
the results of which are shown in table 3, a second run of the
regression models was carried out where each of the climate
variables were entered separately. Instead of combining
temperature, precipitation, and evapotranspiration into one
PCA axis, each of these factors, consisting of their respective
summary variables, were inputted into their own PCA run
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Dymond, Carver, and Philips
Investigating the Environmental Cause of Global Wilderness and Species Richness Distributions
Table 3—Results from the stepwise multiple regression of environment factors against species
richness residuals for four major groups and wilderness from the McCloskey and
Spalding (1989) polygon inventoryb and various wilderness qualities from Lesslie
(2000, personal communication), where Quality 1 is high and Quality 21 is lowc. Each
quality category consists of the proportion of land of that quality, in each country, plus all
that at higher levels (cumulative). LAT = latitude axis, ELEV = elevation axis, CLIM =
climate axis (sum of temperature, precipitation, and evapotranspiration), and POP =
human population axis. + and – symbols indicate whether the predictor contributes
positively or negatively to the dependent variable. aAdjusted R2 equals the amount of
variation explained by the model.
Dependent variable
Predictors in final model
Adjusted R2
Mammal richness
Bird richness
Flowering plant richness
Conifer and cycad richness
LAT–, CLIM+
LAT–, CLIM+, POP+
CLIM+
LAT+, ELEV+
0.420 (42.0%a)
.362
.167
.166
Polygon wildernessb
Quality 1 wildernessc
Quality 2 wilderness
Quality 3 wilderness
Quality 7 wilderness
Quality 11 wilderness
Quality 15 wilderness
Quality 17 wilderness
Quality 19 wilderness
Quality 21 wilderness
CLIM+, ELEV+, POP+
LAT+, CLIM-, ELEV+, POP–
LAT+, CLIM-, ELEV+, POP–
CLIM+, ELEV+, POP–
CLIM+, ELEV+, POP–
CLIM+, ELEV+, POP–
LAT+, ELEV+, POP–
ELEV+
ELEV+
ELEV+
to define three new axes. Although this method does not
ensure that the independent variables are truly independent,
it does facilitate the identification of the individual climatic
variables that are contributory to the distributions of the
dependent species richness and wilderness variables. The
results of this analysis can be found in table 4 in the same
format as table 3. This analysis reveals that the climatic
.371
.121
.187
.206
.297
.363
.379
.210
.174
.153
variables most responsible for the distributions of species
richness are in fact precipitation, and for the plant groups of
flowering plants and conifers and cycads, temperature is
also important. Precipitation is also identified as important
for all of the wilderness categories with the addition of
evapotranspiration, and for wilderness quality categories 11
and 15, temperature also contributes.
Table 4—Results from the stepwise multiple regression of environment factors against species
richness residuals for four major groups and wilderness quality where the climatic
factors have been included individually; these factors are indicated in italics. LAT =
latitude axis, ELEV = elevation axis, POP = human population axis, EVAP =
evapotranspiration axis, PPT = precipitation axis, and TEMP = temperature axis, + and –
symbols indicate whether the predictor contributes positively or negatively to the
dependent variable. aAdjusted R2 equals the amount of variation explained by the
model.
Dependent variable
Predictors in final model
Mammal richness
Bird richness
Flowering plant richness
Conifer and cycad richness
LAT–, PPT+
LAT–, PPT+
TEMP-, PPT+
LAT+, PPT+, TEMP-
Polygon wilderness
Quality 1 wilderness
Quality 2 wilderness
Quality 3 wilderness
Quality 7 wilderness
Quality 11 wilderness
Quality 15 wilderness
Quality 17 wilderness
Quality 19 wilderness
Quality 21 wilderness
ELEV+, EVAP+, PPT-, POP–
ELEV+, EVAP+, PPT-, POP–
ELEV+, EVAP+, PPT-, POP–
ELEV+, EVAP+, PPT-, POP–
ELEV+, EVAP+, PPT-, POP–
ELEV+, EVAP+, PPT+, POP–, TEMP+
ELEV+, EVAP+, PPT-, POP–, TEMP+
ELEV+, EVAP+, PPTELEV+, EVAP+, PPTELEV+, EVAP+, PPT-
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Adjusted R2
0.479 (47.9%)
.381
.284
.284
.446
.189
.274
.313
.400
.527
.523
.283
.233
.207
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Dymond, Carver, and Philips
Investigating the Environmental Cause of Global Wilderness and Species Richness Distributions
Does Wilderness Contribute to the
Distribution of Species Richness
and Vice Versa?
To test whether wilderness quality can actually explain
any of the variation in the distribution of species richness
and vice versa, each must be incorporated as independent
variables into the regression process. To do this, the models
where climate is represented by one independent axis were
rerun with the addition of species richness in the dependent
wilderness models and wilderness in the dependent species
richness models. The results indicate that high quality
wilderness (category 3) does explain a small percentage of
the variation in species richness of the mammal, flowering
plant, and conifer and cycad groups, respectively. An additional 5.9 percent, 4.8 percent, and 2.5 percent of the variation is explained.
We might expect an environmental state, like wilderness,
to influence the patterns of species richness in the same way
that temperature makes a contribution. However, it is
difficult to be sure of any contribution of species richness to
wilderness. It is perhaps only through the analysis of different groups of species that this might become meaningful.
However, the results indicate that for high levels of wilderness quality, mammalian and angiosperm species richness,
in particular, do explain some of the variation in the proportion of wilderness at these qualities. For example, mammalian richness adds 10.9 percent to the success of the model to
predict the variation in the distribution of wilderness quality category 2. The situation is reversed when the conifer and
cycad group is used as an independent predictor of wilderness because it only adds to the explanatory success of
models of low wilderness quality. Here, it contributes a
further 17.6 percent to the success of the wilderness quality
17 category.
Discussion _____________________
The results reveal that different factors are responsible
for the distribution of each of the species groups, and some
groups are better explained than others. For wilderness
quality there is also a fluctuation in the ability of the models
to explain the distributions. As quality changes, so do the
factors that are considered contributory. The second run of
models, where climate variables are entered individually,
reveal that only certain aspects of climate contribute to the
explanatory power of the models. It is hard to ascertain
which of the hypotheses set at the beginning of the research
are appropriate without considering the direction of the
relationships between the predictor and dependent variables. Included in tables 3 and 4 are an indication of whether
each of the predictor variables contribute positively or negatively to the distributions.
The models show that latitude negatively contributes to
the distribution of mammalian and avian species richness;
low latitude is important for these groups. Latitude does not
seem to be important for angiosperm richness, whereas
“good” climate, and in particular high precipitation, is important. The model revealed that temperature negatively
contributed to angiosperm richness, which is unexpected,
but the B coefficient was very low (B = 0.0062). Climate was
236
also important for mammals and birds, and high precipitation was confirmed as important. The pattern is reversed for
the conifer and cycad group; latitude positively contributes
to richness, high elevation, high precipitation, and low
temperatures.
At high levels of wilderness quality (categories 1 and 2),
high elevation, high latitude, low precipitation, and low
population all contribute to variation. By definition, wilderness is devoid of human features and, therefore, of resident
population, so it is to be expected that low population is
important in the models. As wilderness quality decreases,
high elevation and low precipitation continue to be important contributors to the distributions. For models with
climate variables entered together, the lowest wilderness
quality categories (17–21) are only explained by elevation.
We can conclude that mammalian, avian, and angiosperm
richness prefer low latitudes, whereas high wilderness quality is associated with high latitudes. Altitude was not found
to affect the patterns of species richness for these three
groups but was an important predictor variable for all of the
wilderness categories. Evapotranspiration (actual and potential axis) was not found to be influential to the variation
in species richness groups but was repeatedly recognized as
important for wilderness. Further interpretation is needed
here to elucidate the real meaning of the PCA axis that
summarizes this complex interaction. High temperature
was found to influence patterns of angiosperm richness but
not mammalian or avian richness. In general, temperature
was not found to affect the distribution of wilderness. In
contrast to the other species richness groups, the seedbearing plants (conifers and cycads) were positively influenced by latitude and elevation and prefer low temperatures, indicating an affiliation for poor climate. It is possible,
therefore, that optimal conditions for this group are characteristics akin to those for high wilderness quality.
Different environmental characteristics appear to shape
the distribution of wilderness and the species richness of the
groups investigated. At the onset, it was hypothesized that
approximately opposing characteristics were responsible for
patterns of wilderness and species richness, and it has been
shown that to some extent this is true. It has also been found
that wilderness environments contribute in a small way to
the variation in species richness for some groups. Counterintuitively, species richness also plays a part in the distribution of wilderness, but we suggest that this may be more
correlation than causation. The fluctuation in environmental conditions preferred by the different taxa examined is to
be expected and illustrates how important the use of more
than one group is to this investigation. National species
richness data has constrained this work, and it is expected
that should grid-based biodiversity information become
available, it will enhance the accuracy of research of this
kind. A grid-based wilderness continuum has been useful in
improving the quality of global wilderness distribution data.
Further Research _______________
The next step in this research is to identify factors that
influence the relationships between species richness and
wilderness quality at smaller scales. Access to the 1998,
United States Department of Agriculture, Forest Service,
USDA Forest Service Proceedings RMRS-P-27. 2003
Dymond, Carver, and Philips
Investigating the Environmental Cause of Global Wilderness and Species Richness Distributions
Southeast Alaska Inventory, has facilitated analysis of the
effect of distance from human features on species richness
on Prince of Wales Island, Tongass National Forest. More
indepth field data have been collected to investigate the
impact of recreation trails, as a common wilderness impact,
on species richness.
Acknowledgments ______________
This Ph.D. research is sponsored by the Natural Environment Research Council, Grant Number GT04/98/130. Attendance at the 7th World Wilderness Congress by C. Dymond
was kindly sponsored by the Anglo American Corporation
and was organized by the Wilderness Trust. I am extremely
grateful to both for their support. Thanks to my supervisors,
Dr. Steve Carver and Dr. Oliver Phillips, for their editorial
comments.
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