1. Investigating the environmental cause of global wilderness and species richness distributions Crewenna Dymond, School of Geography, University of Leeds, UK Abstract The environmental factors which affect biodiversity, specifically species richness, and wilderness quality were investigated at the global scale using national species richness data (Groombridge 1994) and a continuous wilderness quality grid (WCMC/Lesslie 2000). At a high wilderness quality (category 3 and those above) a combination of climate, elevation and population explained one fifth of the wilderness distribution, whilst at low quality (category 15 and those above) latitude, elevation and population explain 37.9% of the distribution. Latitude and climate explained nearly half of the variation in mammalian species richness, whilst climate alone explained 16.7% of the variation in the distribution of flowering plants. It was found that high elevation and latitude were key to the distribution of high wilderness quality and the conifer and cycad group were also determined by these characteristics. The most important determinants of species richness were found to be low latitude and ‘good’ climate with precipitation and temperature being most influential. Understanding the factors defining patterns of wilderness today will help plan for their protection on a large scale. Appreciating how the same factors effect the distribution of species richness will aid in conservation of biodiversity, particularly that in protected wilderness or that requiring pristine habitat. This research is part of a Ph.D. to investigate species richness and wilderness interactions at multiple scales, including a study in Tongass National Forest, Southeast Alaska. Aim There has been considerable research into the causes of gradients in species richness, for example latitude (Rohde 1992) and water energy dynamics (O’Brien 1998 ) are just two of the proposed theories. In the field of wilderness science there has been little attempt to integrate biodiversity into wilderness investigations, in spite of biophysical naturalness being frequently used as an attribute of wilderness in inventory or identification procedures. However, it is possible to identify factors that could be simultaneously responsible for the distribution of species richness and wilderness (see Figure 2). It is the aim of this research to investigate how far these factors are responsible for the distributions of wilderness and species richness observed in the mid 1990’s. Additionally, using the same data it is possible to determine whether biodiversity and wilderness contribute to the distributions of one another. Method 1) From published global databases on climate, altitude, latitude and population summary information were calculated. These variables include mean, minimum, maximum and range and for human population, density and rural population density were calculated. Species richness data, for mammals, birds, flowering plants and conifers and cycads (seed bearing plants) are at the national scale, in order to factor out the effect of area, these data were regressed against the log 10 ( area) of the country and the residual values used in further analysis. These residuals indicate how much more than expected the richness of a country is, assuming a linear relationship between species and area on a logarithmic scale. 2) Principal Component Analysis (PCA) was used to reduce the number of variables and to ensure independence. The new PCA axes are a product of the summary variables for each factor. For example, the new latitude axis is the product of the distributions of mean, minimum, maximum and range in latitude in each country (see Figure 3). 3) Multiple regression models were built to test the contribution of each new axis to the distribution of species richness of major taxa (mammals, birds, flowering plants and conifers and cycads) and of a series of wilderness quality proportions. A backwards stepwise procedure leaves only those factors that fulfil the entry requirements of the model in the final step and whose contribution is statistically significant. Research Sponsored by NERC GT04/98/130, Congress attendance sponsored by Anglo American Corporation, organized by the Wilderness Trust. Email - C.Dymond@geog.leeds.ac.uk. References Groombridge, Brian (Ed); 1994, Global Biodiversity Data Sourcebook, WCMC Biodiversity Series, WCMC, Cambridge O’Brien, Eileen, M; 1998, Water-energy-dynamics, climate and prediction of woody plant species richness: and interim general model, Journal of Biogeography, 25, 379-398 Rohde, Klaus; 1992, Latitudinal gradients in species richness: the search for the primary cause, Oikos, 65, 514-527 World Conservation and Monitoring Centre; 2000, Global Continuous Wilderness Grid, personal communication, May 2000 Lesslie, Rob;2000, Creation of the Global Continuous Wilderness Grid, personal communication, June 2000 Using PCA to summarize variation Figure 1. Global wilderness quality continuum, at a resolution of 0.5 decimal degrees, each cell has a wilderness quality from high (22 - green) to low (1 - pink) (WCMC 2000; Lesslie 2000). High quality wilderness (category 3) was found to contribute to the species richness of mammals (a further 5.9% Adj. R 2 ) and flowering plants (4.8%). This was a negative contribution, meaning that low species richness was important for wilderness. Conifers and cycad richness added between 7.0% and 17.6% to the success of the low quality wilderness models, again this was a negative contribution indicating that low numbers of this group are associated with low quality wilderness. The top PCA graph for latitude shows the ordination of each country based on the four summary latitude variables of mean, minimum, maximum and range. The table of eigenvalues and variance below demonstrates that Axis 1 (x) is better at explaining the variation within these data than axis 2 (y). The second chart indicates how well mean latitude correlates with the new Axis 1 (r = 0.887). This process was repeated for each of the environment factors. Temperature, precipitation and evapo-transpiration (AET, PET and deficit) were incorporated into the same PCA axis (climate) as these are closely related. Population density and rural population density were calculated and incorporated into a single population axis. Figure 3. Example of PCA graph of latitude to derive a new axis explaining variation within summary variables BIODIVERSITY WILDERNESS Latitude Altitude Evapotranspiration Precipitation Temperature Population RemotenessNaturalness Solar energy Soil Aspect Figure 2. Factors identified as important for the distribution of wilderness and biodiversity, in purple, while additional factors that define each individually are marked in blue. Research supervisors: Dr. S. Carver and Dr. O. Phillips Results The stepwise regression procedure revealed that different factors are responsible for the distribution of each group and some are better explained than others. For example, AdjR 2 = 16.6% B coefficients Latitude = -0.455 Elevation = 0.410 p = 0.01 N = 119 AdjR 2 = 42.0% B coefficients Climate = -0.529 Latitude = 0.767 p = 0.00 N = 137 Figure 4 shows that climate and latitude positively contribute to the variation in the distribution of mammal species richness and explain 42%. For the conifer and cycad group latitude and elevation are most explanatory but only account for 16.6% of the variation. The B coefficients confirm that conifers and cycads are positively effected by elevation and prefer high latitudes. Figure 4. Probability plots of residuals for mammals and conifer/cycad groups from multiple linear regression Figure 5. Change in Adjusted R 2 values with reduction in wilderness quality (1 = high; 21 = low) Discussion Results indicate that there is a fluctuation in the ability of the models to explain the variation in species richness and wilderness quality. For mammals low latitude and ‘good’ climate (high precipitation and constant warm temperatures) were important determinants. For conifers and cycads, high latitudes and elevation were found to be contributory. High wilderness quality is determined by a combination of all factors, reflecting the variation in locations in which wilderness currently persists. However it was determined that high latitudes and high elevation were particularly important. The negative contribution of conifer and cycad species richness to the distribution of low quality wilderness indicates that this group may also be dependent on environments with wilderness characteristics. A difference in the environmental factors that determine the species richness of different groups has been found, whilst wilderness quality appears to respond to the same conditions. Further research is needed to determine how far these findings are true at smaller scales. There is also a fluctuation in the ability of the regression models to explain wilderness quality. This can be seen in Figure 5 where the highest Adjusted R 2 is found for the mid-range wilderness quality categories; 37.9% of the variation is explained for Category 15. For high wilderness quality all of the dependent factors explain some of the variation in the distribution of wilderness (climate, latitude, elevation and population). For categories 7 and 11, at mid-quality, latitude is not longer considered important and at low qualities (19 and 21), only elevation is contributory.