531
Landscape Ecology 17: 531–537, 2002. © 2002 Kluwer Academic Publishers. Printed in the Netherlands.
Landscape diversity in a conservation area and commercial and communal rangeland in Xeric Succulent Thicket, South Africa C. Fabricius 1,*, A.R. Palmer 2 and M. Burger 3 1
Department of Environmental Science, Rhodes University, Grahamstown, 6140, South Africa; 2Agricultural Research Council, Range & Forage Institute, Grahamstown, 6140, South Africa; 3Southern African Frog Atlas Project, Avian Demography Unit, University of Cape Town, Rondebosch, 7700, South Africa; *Author for correspondence (e-mail:
[email protected]) Received 13 April 2001; accepted in revised form 7 March 2002
Key words: Arid savanna, Biodiversity, Land use, Landscape ecology, Pixel diversity, Satellite imagery Abstract Nature reserves in Xeric Succulent Thicket of South Africa contain a greater diversity of wildlife and correspondingly a greater diversity of disturbance agents than adjacent, unconserved freehold and communal rangeland. Although more lightly stocked, it is unknown whether protected areas contain a higher diversity of landscape patches (i.e., sub-landscape features such as bush clumps, termitariums, bare patches or animal wallows) which could influence the reflectance value of a single pixel depicting a 20 × 20 m area in a SPOT satellite image, than unconserved land. Our key questions were: How does patch diversity in a nature reserve compare with that on commercial and communal rangeland? Can pixel diversity in a SPOT satellite image be used to quantify these differences? And, is there a correlation between reflectance diversity in a SPOT image and patch diversity on the ground? As a first step, the coefficients of variation (CV) for 10 groups of 12 picture-element (pixel) values of a dry season SPOT satellite image were calculated for two commercial farms and a communal rangeland. The same data were collected on a nature reserve, 50 to 100 m inside the boundary between the reserve and the freehold or communal rangeland. Next, we recorded the variety of 20 × 20 m plots on the ground, also in groups of 12 plots, at the same geographical coordinates as the satellite-based measurements. The means of the satellitebased and ground-based indeces were significantly and positively correlated. In addition, the nature reserve displayed significantly higher pixel CVs than the communal rangeland, and also contained significantly higher ground-based diversity indeces than the freehold, and possibly the communal, rangeland. We postulate that the higher landscape patchiness in the nature reserve is a result of the diversity of disturbances caused by wildlife (especially megaherbivores) coupled with naturally low stocking rates, while the lower diversity in the communally managed rangeland is the result of continuous heavy grazing coupled with intensive fuelwood harvesting. The satellite-based technique is useful for identifying potential sites of high biodiversity, wherein more intensive sampling at a finer scale can be undertaken. It is, however, important to use dry season imagery because of the temporary ‘masking’ effect of ephemerals during the wet season. Introduction The rapid decline of biodiversity in most ecosystems, often as a result of incompatible land use, necessitates urgent studies of the impact of land management on biodiversity (Schulze and Mooney 1994). The Xeric Succulent Thicket of the Eastern Cape province, South Africa, provides a good opportunity to explore
the effect of contrasting land-use treatments on the biotic diversity along a steep environmental gradient, and to study the impacts of land use on fragile ecosystems (sensu Nilsson and Grelsson (1995)). This vegetation type recovers slowly following disturbance and degradation (Palmer (1981, 1990); La Cock et al. 1990; Midgley and Cowling 1993; Stuart-Hill and Aucamp 1993; Ainslie et al. 1994; Kerley et al.
532 1995) and the sustainability of livestock farming and subsistence land use in Xeric Succulent Thicket is seriously questioned (Kerley et al. (1995, 1996)). It is popularly believed that protected areas contain higher levels of biodiversity than unconserved land. This assumption is particularly strong regarding the Xeric Succulent Thicket vegetation type (La Cock et al. 1990; Moolman and Cowling 1994; Kerley et al. 1995); although the assumption seems intuitive, the hypothesis remains untested at all spatial scales. While exploring rangeland degradation patterns in Xeric Succulent Thicket, Tanser and Palmer (1999), for example, recorded a lower satellite-derived diversity index (moving standard deviation index) on conserved than on communally managed rangeland. This increase in pixel diversity was strongest in the wet season, using the red band of Landsat TM data. Tanser and Palmer (1999), however, failed to explore the potential of dry season imagery, and other spectral bands, to discern trends in pixel diversity across contrasting land-use boundaries in the region. During the dry season, and especially at the end of prolonged drought, land use has a marked effect on vegetation structure in Xeric Succulent Thicket (Palmer et al. 1988). Satellite-derived indices would probably be more effective in detecting permanent structural changes in the landscape during the dry than during the wet season. The purpose of this paper is two-fold: (i) to compare the land-element diversity on a nature reserve in semi-arid Xeric Succulent Thicket savanna to that of adjacent commercial and communal rangeland, and (ii) to develop and test broad-scale techniques able to rapidly assess the potential of particular areas to preserve land element diversity (sensu Bell and McShane (1984)). Our key questions are: How does land element diversity in a nature reserve compare with that on commercial and communal rangeland? Can pixel diversity in a SPOT satellite image be used to quantify these differences? And, is there a correlation between reflectance diversity in a SPOT image and land element diversity on the ground? An assessment of landscape diversity must take into account the importance of environmental heterogeneity, disturbance and large-scale land use in influencing and maintaining biodiversity (Schluter and Ricklefs 1993; Pino et al. 2000). After all, landscapes and ecosystems are the basic structural components where animals and plants live, and plant communities are merely ‘phytometers’ which reflect landscape change (Lapin and Barnes 1995). Landscape com-
plexity leads to diverse habitat types and niches for different organisms; more species are able to coexist in a complex landscape than in a landscape where spatial heterogeneity is low since there is a high turnover of species between patches (Tilman 1982; Schluter and Ricklefs 1993). The diversity of land elements in a landscape may provide a potentially useful index of biological diversity (Recher 1969; Forman and Godron 1986), in turn useful for identifying sites of conservation significance for future protected areas, evaluating environmental impacts and monitoring the impacts of land use on biodiversity. Land transformation primarily manifests itself at the landscape scale, thus incorporation of data representing this scale is essential to understanding large-scale changes in ecosystems (Friedman and Zube 1992). A landscape approach to conservation has the added advantage of facilitating the protection of a variety of organisms that remain either undescribed or undetected (Franklin 1993). Satellite-based radiometers can measure the reflectance of various wavelengths of sunlight from landscape features such as vegetation, soil, water and rock outcrops. In the case of SPOT HRV data, a single picture element (or pixel) integrates the reflectance across a 20 × 20 m plot on the ground. Images of structurally homogeneous landscapes, with a small variety of land elements, will contain a smaller variety of pixels than structurally diverse landscapes. This is evident from the greater colour variation displayed on a satellite image of a complex landscape. For example, a uniformly cultivated land is an extreme example of land-element homogeneity, where lack of colour variation on the satellite image is clearly visible.
Study area The study area is the central Great Fish River Valley between Grahamstown and Fort Beaufort in the Eastern Cape Province, South Africa (centred around 33° S, 27° E) (Figure 1). It consists of the 45,000 ha Great Fish River Nature Reserve (here abbreviated NR) and surrounding freehold livestock ranches and communally managed rangeland. Rainfall in this study area is temporally and spatially variable, with an annual mean of 420 mm. The vegetation type was initially called Valley Bushveld (Acocks 1988), but was later re-named to Xeric Succulent Thicket (La Cock et al. 1990; Low
533
Figure 1. The Great Fish River Reserve, Eastern Cape, South Africa.
and Robelo 1996). At the biome level it was originally considered to be part of the savanna biome (Rutherford and Westfall 1986) but, because it is floristically and structurally different from ‘normal’ savannas, it is now classified as the Thicket Biome (Low and Robelo 1996). The natural landscape consists of a matrix of drought-resistant, mainly evergreen, low, dense shrubland, dominated by spinescent woody shrubs and drought-resistant succulents. The vegetation consists of bush clumps of varying sizes, interspersed with barren areas of unpalatable dwarf shrubs, or forbs and grasses during wetter periods. In pristine vegetation the clumps are positioned close together with few barren areas between clumps, but as degradation sets in the barren areas become more conspicuous and the clumps better defined (Fabricius et al. 2002 (in press)). The bush clumps vary in size, from 10 to 30 m in diameter, and are spaced between 5 and 30 m apart.
Six localities were studied: three in the nature reserve (NR1, NR2, NR3), two on commercial farms (CF1, CF2) and one on communal rangeland (CR). CF1 was heavily and continuously grazed by cattle and other livestock at a stocking rate of about 6 ha/LAU (large animal units) until about 1986, and is regarded as degraded by agricultural standards (R. Dempsey, Dept. of Agriculture, Grahamstown, pers. comm.). CF2 is intensively managed according to a short-duration, high-intensity, multi-camp grazing system. The stocking rate (mostly cattle) is about 12 ha/LAU. CR is continuously grazed at a very high stocking rate of less than 2 ha/LAU (Forbes and Trollope 1991) by subsistence pastoralists. All wild mammals larger than hares are entirely absent from the CR because of the high human density of 70 people per km 2 (Ainslie et al. 1994). The CR is severely degraded and has a substantially lower vegetation cover than the NR (Fabricius et al. (in press)). Since the absence of public electricity in the CR area
534 necessitates the use of firewood for heating and cooking, the overuse of firewood poses a threat to the natural resource base, as does overgrazing. The nature reserve was a privately-owned commercial farm until 1976. In 1976 the Andries Vosloo section of the reserve, where sites NR1 and NR2 are situated, was proclaimed. In 1986 the Sam Knott section, which contains site NR3, was added. Since proclamation, the nature reserve has not contained domestic livestock but supports a variety of large herbivores, notably the megaherbivores black rhinoceros (Diceros bicornis), African buffalo (Syncerus caffer) and eland (Taurotagus oryx); since introduction their numbers have increased rapidly. The nature reserve is continuously grazed and browsed by wild animals at a set stocking rate of about 17 ha/LAU.
Methods For comparison, three localities in the nature reserve were paired to localities on adjacent unconserved land (totaling three paired sites). Paired Site I comprised a locality on the nature reserve (NR1) and a locality situated diagonally across the reserve’s boundary, on the more heavily stocked commercial farm (CF1). Paired Site II compared a different locality in the nature reserve (NR2) to a locality on the conservatively stocked commercial farm (CF2). Paired Site III comprised an area in the nature reserve (NR3) and one on communally managed rangeland near the village of Ndwayana (CR). Picture element values were extracted from a SPOT satellite image of each of the localities, so that indeces of pixel diversity could be calculated. Field data were collected at the same localities and at the same scale as the SPOT image, in order to calculate an index of structural diversity related to disturbance, vegetation height and cover. Satellite-derived reflectance diversity Band 1 (0.50–0.59 m) of a dry season SPOT image was geo-referenced to a geographic projection, using GRASS 4.1 (USACERL 1994). Reflectance values were extracted from the image, each representing a 20 × 20 m pixel on the ground. Ten samples of 12 pixels were defined at each site. Reflectance values were used to calculate the coefficient of variation (CV) for each group of 12 pixels. We thus generated 10 CV values for each site being compared. The rationale
was that a greater diversity of land elements (detectable at a scale of 20 × 20 m) in a group would result in a greater variety of reflectance values and a higher CV value, as compared to groups representing a smaller diversity of landscape elements. The ‘distance’ between groups was the equivalent of 50 m on the ground, and the clusters were selected to represent localities 50 to 100 m from the boundary of the nature reserve. Land-element diversity on the ground The diversity of land elements on the ground was recorded at Paired Sites I and II, but not at Paired Site III due to logistic reasons. Ground surveys and the recording of the satellite image took place in winter of the same year. A land element is defined here as a 20 × 20 m plot, while land-element diversity is defined as the variety of such plots which make up the landscape. Land elements in the study area were bush clumps, disturbance patches or bare areas, ranging in size from 10 m to 30 m in diameter. Using GRASS, the coordinates of the 120 groundbased plots (20 × 20 m) were matched to the coordinates of the satellite-derived pixel values. A global positioning system (GPS) was used to locate sample sites to the nearest 5 m for each selected SPOT pixel. The lay-out of the ground-based plots followed the same configuration as the satellite-derived pixel values (i.e., 10 groups, each consisting of 12 groundbased plots). Within each 20 × 20 m plot the following four data attributes were recorded: 1. geographic coordinates 2. average percentage canopy cover of trees and shrubs in five categories: 1–20%, 21–40%, 41– 60%, 61–80%, > 80% 3. average height of trees and shrubs in five height categories: < 0.5 m, 0.5–1 m, > 1–1.5 m, > 1.5–2 m, > 2 m; and 4. presence of natural and anthropogenic disturbance features such as erosion gullies, termitaria, other zoogenic mounds, animal wallows, animal paths, temporary lakes (pans), dams, cultivated lands, roads and tracks. For each of the 10 clusters a crude ground-based land-element diversity index was calculated: D = S c + S h + S d, where
535 S c = number of different vegetation cover classes in the cluster; S h = number of different vegetation height classes in the cluster; S d = number of disturbance features in the cluster. This composite index was more sensitive to changes in land use than any of its individual components, and enabled us to calculate 10 ‘D’ values for each locality. Each D value matched a satellite-derived CV value, both in size and geographical position. Data analysis
Table 1. Comparison between the median coefficient of variation (CV) pixel values and median ground-based diversity indeces in the nature reserve and on unconserved land. NR1-3 = localities in the nature reserve; CF1, CF2 = localities on commercial farms; CR = locality on communal rangeland. The hypothesis was tested that the CV values of localities in the nature reserve and that of localities on unconserved land were similar, using a Wilcoxon pair-wise test. N = 10 pairs at each of three paired sites. Paired Site I
Paired Site II
Paired Site III
NR1
CF1
NR2
NR3
CR
0.04
0.09 0.5 > 0.1
0.10 2.2 0.01
0.06
–
–
Satellite-derived Median 0.07 Z 1.5 P > 0.05 Ground-based Median 7 Z 1.7 P 0.05
CF2
0.08
First, the median satellite-derived CVs in the nature reserve and on unprotected land were compared in a pair-wise manner, using data from Paired Sites I, II and III. Second, the median ground-based D values of the NR and that of unconserved land were compared, also in pair-wise manner, using data from Paired Sites I and II. A non-parametric Wilcoxon test for pair-wise comparison was used to test the hypothesis that the frequency distribution of the values being compared were the same. Thirdly, the correlation between individual ground-based diversity values D and their corresponding satellite derived CVs was calculated, using data from Paired Sites I and II. Fourthly, the correlation between mean ground-based Ds and mean satellite-derived CVs was calculated, using data from the four localities at Paired Sites I and II.
The diversity of 20 × 20 m plots on the ground reflected the same pattern as that displayed by the pixel CV values, although comparison between localities NR3 and CR were not included in this part of the study (Table 1). When respective pairs were compared, the nature reserve showed significantly higher land-element diversity (D values) on the ground at both Paired Site I and Paired Site II (Z = 1.7 and 2.1, respectively; P < 0.05, Wilcoxon pair-wise test).
Results
Relation between pixel CV values and measured land-element diversity
Satellite-derived reflectance diversity The different types of land use influenced the diversity of reflectance values radiated from the 20 × 20 m plots. For paired sites, the median CVs of reflectance values of pixel clusters for each of the localities in the nature reserve were consistently higher than those of corresponding pairs of clusters for unconserved land (Table 1). In the comparison between NR1 and CF1 (Paired Site I), P was less than 0.10 but greater than 0.05 (Z = 1.5), while significant differences existed only at Paired Site III, NR3-CR (Z = 2.2, P = 0.01, Wilcoxon pair-wise test).
5
11 2.1 0.02
10
Land-element diversity on the ground
The strong correlation between the satellite-based CVs and ground-based land-element diversity indices (D) suggests that pixel diversity reflected actual landelement diversity on the ground. The individual D indeces of all samples and their corresponding CV’s were significantly correlated (r = 0.41, df = 38, P < 0.05). For the four localities (NR1, CF1, NR2, CF2), the median satellite-derived CV’s and median D index also correlated (r = 0.95, df = 2, P < 0.05). Unknown D indices could be predicted from known satellite-derived CV values, using the function D = 121 * median CV. When this was done for NR3 and CR the interpolation indicated that the D value for the nature reserve could have been about 1.5 times that of the communal rangeland.
536 Discussion This study indicates that, in a comparison between paired sites, localities in the nature reserve (NR) have greater land-element diversity, as indicated by a higher diversity of reflectance values as well as ground-based diversity indeces, than corresponding paired localities on unprotected land. These differences were particularly pronounced when the nature reserve was compared to communally managed rangeland. This applied to paired localities only: localities which were positioned far apart, such as CF2 and NR1 or CF1 and CR, could not be compared in this way because of probable between-site differences in abiotic factors such as topography, rainfall and substrate. It is postulated that low pixel diversity, corresponding to less land-element diversity, is due to the effect of continuous heavy grazing by domestic livestock on the vegetation of eutrophic clay-rich soils in a semi-arid area. When this is coupled with intensive collection of firewood, e.g., in the communal rangeland, the reduction in land element diversity is even more pronounced. Such land use causes a decrease in the size of bush clumps until they disappear entirely or until they decrease to a size which cannot be detected on the satellite image. The greater land-element diversity in the nature reserve results in a greater diversity of habitats and can be also linked to greater faunal species diversity. For example, foliage-height diversity and bird species diversity have been positively correlated at the regional level (Recher 1969), and rodent diversity has been linked to the complexity of structure in plant communities (cf. Whitford et al. (1978)). To date, various methods have been used to quantify landscape diversity, most of which require GIS software and vectorized data which are measured as polygons. These include affinity analysis (Scheiner 1992), patch richness, dominance measured by the Shannon index (O’Neill et al. 1988), the Simpson index (Baker and Cai 1992), or fractal geometry (Mladenoff et al. 1993). As far as could be ascertained, the present study is the first to use coefficient of variation values in a rasterised image as an index of land element diversity. The technique is simple to apply, given that the appropriate hardware, software and data are available, and makes use of unsophisticated algorithms. In view of the low cost of such a study once the necessary hardware and satellite data have
been purchased the method appears as effective as other, more sophisticated and expensive techniques. It is important to select a meaningful scale of measurement that is appropriate to the goals of the research (Cullinan and Thomas 1992). In this study the resolution of a 20 × 20 m grid seemed appropriate from an ecological point of view since 20 × 20 m plots reflected the approximate size of real patches in the landscape. Also, a 20 × 20 m grid was the best available scale in satellite imagery. The satellitebased technique might be most appropriately applied to landscapes where patches occur at a scale coarser than 20 × 20 m, and less so in landscapes where patches occur at finer scales.
Conclusions The technique described here can be used to identify priority conservation areas for more detailed sampling at the species or higher taxonomic level. The use of the method to monitor landscapes for early signs of land degradation should also be investigated. The method further has the potential to be used at a regional scale, provided that sufficient benchmark sites are available as a basis for comparison. Ephemerals (e.g., forbs and annual grasses) can temporarily ‘mask’ the effect of land degradation on pixel diversity during the wet season. It is therefore important to use images taken during the dry season when studying the effects of land use on land element diversity.
Acknowledgements The study was funded by the Eastern Cape Ministry of Economic Affairs, Environment & Tourism. Phil Hockey, William Baker, André Boshoff and Stephen Henley provided useful comments and ideas. Ash Davenport and Tony Phillips allowed the research to take place on their land. The Ndwayana community leaders gave us access to the communal area.
References Acocks J.P.H. 1988. Veld types of Southern Africa. Memoirs of the Botanical Survey of South Africa, no. 59. Dept. of Agriculture Technical Services, Pretoria, South Africa.
537 Ainslie A., Fox R. and Fabricius C. 1994. Towards policies for feasible and sustainable natural resource use: the mid-Fish River zonal study, Eastern Cape. Final report to the LAPC Natural Resource Management Programme. Rhodes University, Grahamstown, South Africa. Baker W.L. and Cai Y. 1992. The r.le programs for multi-scale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology 7: 291–302. Bell R.H.V. and McShane T. 1984. Landscape classification. In: Bell R.H.V. and McShane-Caluzi E. (eds), Conservation and wildlife management in Africa. US Peace Corps, Washington, DC, USA, pp. 93–106. Cullinan V.I. and Thomas J.M. 1992. A comparison of quantitative methods for examining landscape pattern and scale. Landscape Ecology 7: 211–227. Fabricius C., Burger M. and Hockey P. 2002. Biodiversity in a protected area, and on commercial rangeland and communal land in Xeric Succulent Thicket, South Africa: terrestrial faunal assemblages. Journal of Applied Ecology (in press). Forbes R.G. and Trollope W.S.W. 1991. Veld management in the communal areas of the Ciskei. Journal of the Grassland Society of South Africa 8: 147–152. Forman R.T. and Godron M. 1986. Landscape Ecology. John Wiley & Sons, New York, New York, USA. Franklin J.F. 1993. Preserving biodiversity: species, ecosystems or landscapes? Ecological Applications 3: 202–205. Friedman S.K. and Zube E.H. 1992. Assessing landscape dynamics in a protected area. Environmental Management 16: 363– 370. . Kerley G.I.H., Knight M.H. and De Kock M. 1995. Desertification of subtropical thicket in the Eastern Cape, South Africa: are there alternatives? Environmental Monitoring Assess 37: 211– 230. La Cock G.D., Palmer A.R. and Everard D.A. 1990. Re-assessment of the area and conservation status of Subtropical Transitional Thicket Valley Bushveld in the Eastern Cape, South Africa. S.A. J. Photogram. Rem. Sens. Cartogr. 15: 231–235. Lapin M. and Barnes B.V. 1995. Using the landscape ecosystem approach to assess species and ecosystem diversity. Conservation Biology 9: 1148–1158. Low A.B. and Robelo A.G. (eds) 1996. Vegetation of South Africa, Lesotho and Swaziland. Department of Environmental Affairs and Tourism, Pretoria, South Africa. Midgley J.J. and Cowling R.M. 1993. Regeneration patterns in Cape subtropical transitional thicket: where are all the seedlings? S.A. Journal of Botany 59: 496–499. Mladenoff D.J., White M.A., Pastor J. and Crow T.R. 1993. Comparing Spatial Pattern in Unaltered Old-Growth and Disturbed Forest Landscapes. Ecological Applications 3: 294–306. Moolman H.J. and Cowling R.M. 1994. The impact of elephant and goat grazing on the endemic flora of South African succulent thicket. Biological Conservation 68: 53–63.
Nilsson C. and Grelsson G. 1995. The fragility of ecosystems: a review. Journal of Applied Ecology 32: 677–692. O’Neill R.V., Krummel J.R., Gardner R.H., Sugihara G., Jackson B., DeAngelis D.L. et al. 1988. Indeces of landscape pattern. Landscape Ecology 1: 153–162. Palmer A.R. 1981. A study of the vegetation of the Andries Vosloo Kudu Reserve, Cape Province. MSc Dissertation, Rhodes University, Grahamstown, South Africa. Palmer A.R. 1990. A qualitative model of vegetation history in the Eastern Cape midlands, South Africa. Journal of Biogeography 17: 35–46. Palmer A.R., Crook B.J.S. and Lubke R.A. 1988. Aspects of the vegetation and soil relationships in the Andries Vosloo Kudu Reserve, Cape Province. S.A. Journal of Botany 54: 309–314. Pino J., Roda F., Ribas J. and Pons X. 2000. Landscape structure and bird species richness: Implications for conservation in rural areas between natural parks. Landscape and Urban Planning 49: 35–48. Recher D. 1969. Bird species diversity and habitat diversity in Australia and North America. American Naturalist 103: 75–80. Rutherford M.C. and Westfall R.H. 1986. Biomes of southern Africa – an objective categorization. Memoirs of the Botantical Survey of South Africa, 54. Botanical Research Institute, Dept. of Agriculture, Pretoria, South Africa, 98 pp. Scheiner S.M. 1992. Measuring pattern diversity. Ecology 73: 1860–1867. Schluter D. and Ricklefs R.E. 1993. Species diversity: an introduction to the problem. In: Ricklefs R.E. and Schluter D. (eds), Species diversity in ecological communities. University of Chicago Press, Chicago, Illinois, USA, pp. 1–10. Schulze E.D. and Mooney H.A. (eds) 1994. Biodiversity and Ecosystem Function. Springer-Verlag, Berlin. Stuart-Hill G.C. and Aucamp A.J. 1993. Carrying capacity of the succulent Valley Bushveld of the Eastern Cape. African Journal of Range Forest Science 10: 1–10. Tanser F.C. and Palmer A.R. 1999. The application of a satellitederived landscape diversity index to monitor degradation patterns in the Great Fish River Valley, Eastern Cape Province, South Africa. Journal of Arid Environment 43: 477–484. Tilman D. 1982. Resource competition and community structure. Princeton University Press, Princeton, New Jersey, USA. USACERL 1994. GRASS 4.1 Geographic Resources Analysis Support System. US Corps of Engineers Research Laboratory, Champaign, Illinois, USA. Whitford W.G., Dick-Peddie D., Walters D. and Ludwig J. 1978. Effect of shrub defoliation on grass cover and rodent species in a Chihuahuanan desert ecosystem. Journal of Arid Environment 1: 237–242.
