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1.
Lookingbill  Todd  Urban  Dean 《Landscape Ecology》2004,19(4):417-433
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2.
The results of predictive vegetation models are often presented spatially as GIS-derived surfaces of vegetation attributes across a landscape or region, but spatial information is rarely included in the model itself. Geographically weighted regression (GWR), which extends the traditional regression framework by allowing regression coefficients to vary for individual locations (‘spatial non-stationarity’), is one method of utilizing spatial information to improve the predictive power of such models. In this paper, we compare the ability of GWR, a local model, with that of ordinary least-squares (OLS) regression, a global model, to predict patterns of montane ponderosa pine (Pinus ponderosa) basal area in Saguaro National Park, AZ, USA on the basis of variables related to topography (elevation, slope steepness, aspect) and fire history (fire frequency, time since fire). The localized regression coefficients exhibited significant non-stationarity for four of the five environmental variables, and the GWR model consequently described the vegetation-environment data significantly better, even after accounting for differences in model complexity. GWR also reduced observed spatial autocorrelation of the model residuals. When applied to independent data locations not used in model development, basal areas predicted by GWR had a closer fit to observed values with lower residuals than those from the optimal OLS regression model. GWR also provided insights into fine-scale controls of ponderosa pine pattern that were missed by the global model. For example, the relationship between ponderosa pine basal area and aspect, which was obscured in the OLS regression model due to non-stationarity, was clearly demonstrated by the GWR model. We thus see GWR as a valuable complement to the many other global methods currently in use for predictive vegetation modeling.  相似文献   

3.
Current methods of vegetation analysis often assume species response to environmental gradients is homogeneously monotonic and unimodal. Such an approach can lead to unsatisfactory results, particularly when vegetation pattern is governed by compensatory relationships that yield similar outcomes for various environmental settings. In this paper we investigate the advantages of using classification tree models (CART) to test specific hypotheses of environmental variables effecting dominant vegetation pattern in the Piedmont. This method is free of distributional assumptions and is useful for data structures that contain non-linear relationships and higher-order interactions. We also compare the predictive accuracy of CART models with a parametric generalized linear model (GLM) to determine the relative strength of each predictive approach. For each method, hardwood and pine vegetation is modeled using explanatory topographic and edaphic variables selected based on historic reconstructions of patterns of land use. These include soil quality, potential soil moisture, topographic position, and slope angle. Predictive accuracy was assessed on independent validation data sets. The CART models produced the more accurate predictions, while also emphasizing alternative environmental settings for each vegetation type. For example, relic hardwood stands were found on steep slopes, highly plastic soils, or hydric bottomlands – alternatives not well captured by the homogeneous GLM. Our results illustrate the potential utility of this flexible modeling approach in capturing the heterogeneous patterns typical of many ecological datasets.  相似文献   

4.
Topography strongly affects the distribution of insolation in the terrain. Patterns of incoming solar radiation affect energy and water balances within a landscape, resulting in changes in vegetation attributes. Unlike other regions, in seasonally dry tropical forest areas the potential contribution of topography-related environmental heterogeneity to β-diversity is unclear. In Mt. Cerro Verde (Oaxaca), S. Mexico, we: (1) modelled potential energy income for N- and S-facing slopes based on a digital elevation model, (2) examined the response of vegetation structure to slope aspect and altitude and (3) related variations in plant diversity to topography-related heterogeneity. Vegetation survey and modelling of potential energy income (SOLEI-32 model) were based on 30 plots equally distributed among three altitudinal belts defined for each slope of the mountain; combining the three altitudinal belts and the two slopes produced six environmental groups, represented by five vegetation plots each. Potential energy income was about 20% larger on the S than on the N slope (9,735 versus 8,138 MJ/m2), but it did not vary with altitude. In addition, the temporal behaviour of potential energy income throughout the year differed greatly between slopes. Vegetation structure did not show significant changes linked to the environmental gradients analysed, but altitude and aspect did affect β-diversity. We argue that the classic model of slope aspect effect on vegetation needs reconsideration for tropical landscapes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorised users.  相似文献   

5.
Forest canopy phenology is an important constraint on annual water and carbon budgets, and responds to regional interannual climate variation. In steep terrain, there are complex spatial variations in phenology due to topographic influences on microclimate, community composition, and available soil moisture. In this study, we investigate spatial patterns of phenology in humid temperate forest as a function of topography. Moderate-resolution imaging spectro-radiometer (MODIS) vegetation indices are used to derive local patterns of topography-mediated vegetation phenology using a simple post-processing analysis and a non-linear model fitting. Elevation has the most explanatory power for all phenological variables with a strong linear relationship with mid-day of greenup period, following temperatures lapse rates. However, all other phenological variables show quadratic associations with elevation, reflecting an interaction between topoclimatic patterns of temperature and water availability. Radiation proxies also have significant explanatory power for all phenological variables. Though hillslope position cannot be adequately resolved at the MODIS spatial resolution (250 m) to discern impacts of local drainage conditions, extended periods of greenup/senescence are found to occur in wet years. These findings are strongly supported by previous field measurements at different topographic positions within the study area. The capability of detecting topography-mediated local phenology offers the potential to detect vegetation responses to climate change in mountainous terrain. In addition, the large, local variability of meteorological and edaphic conditions in steep terrain provides a unique opportunity to develop an understanding of canopy response to the interaction of climate and landscape conditions.  相似文献   

6.
Modeling vegetation pattern using digital terrain data   总被引：10，自引：0，他引：10  
Using a geographic information system (GIS), digital maps of environmental variables including geology, topography and calculated clear-sky solar radiation, were weighted and overlaid to predict the distribution of coast live oak (Ouercus agrifolia) forest in a 72 km2 region near Lompoc, California. The predicted distribution of oak forest was overlaid on a map of actual oak forest distribution produced from remotely sensed data, and residuals were analyzed to distinguish prediction errors due to alteration of the vegetation cover from those due to defects of the statistical predictive model and due to cartographic errors. Vegetation pattern in the study area was associated most strongly with geologic substrate. Vegetation pattern was also significantly associated with slope, exposure and calculated monthlysolar radiation. The proportion of observed oak forest occurring on predicted oak forest sites was 40% overall, but varied substantially between substrates and also depended strongly on forest patch size, with a much higher rate of success for larger forest patches. Only 21% of predicted oak forest sites supported oak forest, and proportions of observed vegetation on predicted oak forest sites varied significantly between substrates. The non-random patterns of disagreement between maps of predicted and observed forest indicated additional variables that could be included to improve the predictive model, as well as the possible magnitude of forest loss due to disturbances in different parts of the landscape.  相似文献   

7.
An objective method for inductively modelling the distribution of mountain land units using GIS managed topographic variables is presented. The landscape of a small high mountain catchment in the Spanish Pyrenees, covered with grassland, was classified in ten land units by hierarchical agglomerative clustering, using a sample of 194 random plots, in which classes of vegetation, soils and landforms were defined. Additionally, seven layers of topographic variables (altitude, slope angle, aspect, solar radiation, topographic wetness index, specific catchment area, and regolith thickness) were created from a Digital Elevation Model. The affinity of each land unit to the topographic variables was calculated using Binary Discriminant Analysis (BDA), after dichotomising the latter around their mean values. Then, the distribution of each land unit was predicted by boolean operations combining step by step distributions for the seven topographic variables ordered, for each unit, after the absolute values of the Haberman’s residuals in BDA. The predicted distributions were tested (χ2) against that of the observed sampling plots. From the original ten land units, the distributions of eight of them were successfully predicted (four are related to the slope sequence, two reflect the water accumulation in the soil, and two respond to geomorphic processes) while the remaining two had to be rejected. Part of the catchment (39%) was not assigned to any land unit, probably because more distributed variables accounting for snow distribution are necessary.  相似文献   

8.
A model of arctic tundra vegetation derived from topographic gradients   总被引：10，自引：0，他引：10  
We present a topographically-derived vegetation model (TVM) that predicts the landscape patterns of arctic vegetation types in the foothills of the Brooks Range in northern Alaska. In the Arctic there is a strong relationship between water and plant structure and function and TVM is based on the relationships between vegetation types and slope (tan ) and discharge (), two independent variables that can be easily derived from digital terrain data. Both slope and discharge relate to hydrological similarity within a landscape: slope determines the gravitational hydrological gradient and hence influences flow velocity, whereas discharge patterns are computed based on upslope area and quantify lateral flow amount. TVM was developed and parameterized based on vegetation data from a small 2.2 km2 watershed and its application was tested in a larger 22km2 region. For the watershed, TVM performed quite well, having a high spatial resolution and a goodness-of-fit ranging from 71–78%, depending on the functions used. For the larger region, the strength of the vegetation types predictions drops somewhat to between 56–59%. We discuss the various sources of error and limitations of the model for purposes of extrapolation.  相似文献   

9.
We formulated and tested models of relationships among determinants of vegetation cover in two agroforested landscapes of eastern North America (Haut Saint-Laurent, Quebec, Canada) that differed by the spatial arrangement of their geomorphic features and intensity of agricultural activities. Our landscape model compared the woody plots of each landscape in terms of the relative influence of environmental attributes, land use history (1958 – 1997), and spatial context (i.e., proximity of similar or contrasting land cover). Our vegetation model evaluated the relative contribution of the same sets of variables to the distributions of herbs, trees, and shrubs. Relationships were assessed using partial Mantel tests and path analyses. Significant environmental and contextual differences were found between the vegetation plots of the two landscapes, but disturbance history was similar. Our vegetation model confirms the dominant effect of historical factors on vegetation patterns. Whereas land-use history overrides environmental and contextual control for trees, herbaceous and shrub species are more sensitive to environmental conditions. Context is determinant only for understory species in older, less-disturbed plots. Results are discussed in relevance to vegetation dynamics in a landscape perspective that integrates interactions between environmental and human influences.  相似文献   

10.
Species distributions are influenced by many processes operating over varying spatial scales. The development of species distribution models (SDMs), also known as ecological niche models, has afforded the opportunity to predict the distributions of diverse taxa across broad geographic areas and identify variables that are potentially important in regulating these distributions. However, the integration of site-specific habitat data with broad scale climate and landcover data has received limited attention in an SDM framework. We investigate whether SDMs developed with breeding pond, landcover, and climate variables can accurately predict the distributions of nine pond-breeding amphibians in eastern Missouri, USA. Additionally we investigate the relative influences of each environmental variable on the distribution predictions for each study species, and whether the most influential variables are shared among multiple taxa. Boosted regression tree (BRT) SDMs were developed for each species with 38 abiotic and biotic environmental variables, including data from the breeding ponds, surrounding landcover, and climate. To test the models, field surveys were performed in 2007 and 2008 at 103 ponds for nine amphibian species. BRT models developed with breeding pond, landcover, and climate data accurately predicted the occurrences of six of nine species across the study area. Furthermore, the presence of each species was best predicted by a unique combination of environmental variables. Results also suggest that landcover and climate factors may be more influential for species near the edge of their geographic ranges, while local breeding pond factors may be more important for species nearer to the center of their ranges.  相似文献   

11.
A simple, straightforward, cartographic modelling technique is presented for measuring relations between environmental characteristics and rare species distribution patterns. This approach is corroborated by digitizing rare bird distribution data for Tanzania and statistically analyzing these patterns in relation to geographic and environmental variables. Of the available natural resource data for Africa, only the vegetation and soils data appeared accurate enough to represent regional natural resource distribution patterns. Available data for Tanzania at the regional scale is not currently precise or comprehensive enough to analyze ongoing dynamic ecological processes.Statistical relations, associated with a study quadrangle within Tanzania, are documented for these parameters. Final confirmation of the accuracy of predictions about rare species diversity patterns will ensue from future field observations. When confirmed, this methodology can be used for setting conservation priorities in biologically little known regions of the world.  相似文献   

12.
Bolstad  Paul V.  Swank  Wayne  Vose  James 《Landscape Ecology》1998,13(5):271-283
Vegetation in mountainous regions responds to small-scale variation in terrain, largely due to effects on both temperature and soil moisture. However there are few studies of quantitative, terrain-based methods for predicting vegetation composition. This study investigated relationships between forest composition, elevation, and a derived index of terrain shape, and evaluates methods for predicting forest composition. Trees were measured on 406 permanent plots within the boundaries of the Coweeta Hydrologic Lab, located in the Southern Appalachian Mountains of western North Carolina, USA. All plots were in control watersheds, without human or major natural disturbance since 1923. Plots were 0.08 ha and arrayed on transects, with approximately 380 meters between parallel transects. Breast-height diameters were measured on all trees. Elevation and terrain shape (cove, ridge, sideslope) were estimated for each plot. Density (trees/ha) and basal area were summarized by species and by forest type (cove, xeric oak-pine, northern hardwoods, and mixed deciduous). Plot data were combined with a digital elevation data (DEM), and a derived index of terrain shape at two sampling resolutions: 30 m (US Geological Survey), and 80 m (Defense Mapping Agency) sources. Vegetation maps were produced using each of four different methods: 1) linear regression with and without log transformations against elevation and terrain variables combined with cartographic overlay, 2) kriging, 3) co-kriging, and 4) a mosaic diagram. Predicted vegetation was compared to known vegetation at each of 77 independent, withheld data points, and an error matrix was determined for each mapping method.We observed strong relationships between some species and elevation and/or terrain shape. Cove and xeric oak/pine species basal areas were positively and negatively related to concave landscape locations, respectively, while species typically found in the mixed deciduous and northern hardwood types were not. Most northern hardwood species occurred more frequently and at higher basal areas as elevation increased, while most other species did not respond to elevation. The regression and mosaic diagram mapping approaches had significantly higher mapping accuracies than kriging and co-kriging. There were significant effects of DEM resolution on map accuracy, with maps based on 30 m DEM data significantly more accurate than those based on 80 m data. Taken together, these results indicate that both the mapping method and terrain data resolution significantly affect the resultant vegetation maps, even when using relatively high resolution data. Landscape or regional models based on 100 m or lower resolution terrain data may significantly under-represent terrain-related variation in vegetation composition.  相似文献   

13.
Local planning in mountain areas requires spatial information on site factors such as vegetation that is commonly lacking in rugged terrain. This study demonstrates a procedure for the efficient acquisition of a vegetation map using topographic attributes and nominal vegetation data sampled in the field. Topographic attributes were derived from a digital elevation model (DEM) and nominal vegetation data were reduced to normalised scores by detrended correspondence analysis (DCA). The procedure for mapping vegetation types addressed the relations between DCA scores and topographic attributes, spatial correlation of DCA scores and classification of predicted DCA scores based on a cluster analysis of DCA scores at observation locations. The modelled vegetation classes corresponded with the impression obtained in the field. We also showed that the final result is rather sensitive to which samples are included in the analysis. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

14.
Information on vegetation-related land cover change and the principle drivers is critical for environmental management and assessment of desertification processes in arid environments. In this study, we investigated patch-level based changes in vegetation and other major land cover types in lower Tarim River drainage area in Xinjiang, West China, and examined the impacts of environmental factors on those changes. Patterns of land cover change were analyzed for the time sequence of 1987–1999–2004 based on satellite-derived land classification maps, and their relationships with environmental factors were determined using Redundancy Analysis (RDA). Environmental variables used in the analysis included altitude, slope, aspect, patch shape index (fractal dimension), patch area, distance to water body, distance to settlements, and distance to main roads. We found that during the study period, 26% of the land experienced cover changes, much of which were the types from the natural riparian and upland vegetation to other land covers. The natural riparian and upland vegetation patches were transformed mostly to desert and some to farmlands, indicating expanding desertification processes of the region. A significant fraction of the natural riparian and upland vegetation experienced a phase of alkalinity before becoming desert, suggesting that drought is not the exclusive environmental driver of desertification in the study area. Overall, only a small proportion of the variance in vegetation-related land cover change is explainable by environmental variables included in this study, especially during 1987–1999, indicating that patch-level based vegetation change in this region is partly attributable to environmental perturbations. The apparent transformation from the natural riparian and upland vegetation to desert indicates an on-going process of desertification in the region.  相似文献   

15.
A fractal model of vegetation complexity in Alaska   总被引：2，自引：0，他引：2  
A methodology using fractals to measure vegetation complexity in three regions of Alaska is presented. Subjective, binomial (0 = simple, 1 = complex) classifications of the complexity of mapped vegetation polygon patterns within continuous forest inventory plots measured in the regions were made by interpreters of aerial photographs. The fractal dimensions of the vegetation patterns within the plots then were estimated. Subsequently, the subjective classifications of the photo-interpreted plots were regressed against fractal dimension by using logistic regression.Assessment of interobserver agreement among the aerial photo interpreters, by using estimated unweighted Kappa coefficients, indicated substantial classification agreement among observers.Examination of general versus regional applicability of the logistic models provided strong support for applicability of a single model to all three regions. The logistic model provides numerical identification of the division between simple and complex patterns. Possible applications beyond the needs of the study are discussed.  相似文献   

16.
Ecological theory predicts a positive influence of local-, landscape-, and regional-scale spatial environmental heterogeneity on local species richness. Therefore, knowing how heterogeneity measured at a variety of scales relates to local species richness has important implications for conservation of biological diversity. We took a statistical modeling approach to determine which metrics of heterogeneity measured at which scales were useful predictors of local species richness, and whether the heterogeneity-local richness relationship was always positive. Local plant species richness data came from 400-m2 vegetation plots in North and South Carolina, USA. At each of four scales from within plots to across regions, we used either GIS or field data to calculate measures of heterogeneity from abiotic environmental variables, vegetation productivity data, and land cover classifications. Among all predictors at all scales, we found that no measure of heterogeneity was a better predictor of local richness than mean pH within plots. However, at scales larger than within plots, measures of heterogeneity were correlated most strongly with local richness, and each of the three classes of variables we used had a distinct scale at which it performed better than the others. These results highlight the fact that ecological processes occurring across multiple scales influence local species richness differently. In addition, relationships between heterogeneity and richness were usually, though not always, positive, underscoring the importance of processes that occur at a variety of scales to local biodiversity conservation and management.  相似文献   

17.
Tardigrade communities are affected by micro and macro-environmental conditions but only micro-environmental variables, and altitudinal gradients have been studied. We review previous reports of altitudinal effects and evaluate the influence by interacting macro- (climate, soils, biome, and others) and micro-environmental (vegetation, moss and leaf litter) factors on tardigrade assemblages at the Sierra de Guadarrama mountain range (Iberian Central System Mountains, Spain). Terrestrial tardigrade assemblages were sampled using standard cores to collect leaf litter and mosses growing on rocks. General Linear Models were used to examine relationships between Tardigrada species richness and abundance, and macro- and micro-environmental variables (altitude, habitat characteristics, local habitat structure and dominant leaf litter type, and two bioclimatic classifications). Variation partitioning techniques were used to separate the effects of altitude and habitat variation, and to quantify the independent influences of climate and soil, vegetation structure and dominant type of leaf litter. Altitude shows a unimodal relationship with tardigrade species richness, although its effect independent of habitat variation is negligible. The best predictors for species richness were bioclimatic classifications. Separate and combined effects of macro-environmental gradients (soil and climate), vegetation structure and leaf litter type are important determinants of richness. A model including both macro- and micro-environmental variables explained nearly 60% of tardigrade species richness in micro-scale plots. Abundance was significantly related only to soil composition and leaf litter type. Tardigrade abundance was not explained by macro-environmental gradients analysed here, despite a significant correlation between abundance and richness.  相似文献   

18.
Models to predict the breeding distribution of three species of birds in north-east England are described. The models use readily available data from the ornithological literature on the habitat preferences and life-history characteristics of the birds, together with satellite (land cover) and physiographic data. These data are linked via Bayesian decision-rules, and model predictions calculated at the landscape scale using a raster-based geographic Information System. Log-linear regressions of the predicted suitability of the landscape for the birds with observed sets of nest records were statistically significant for all three species. The robustness of the models to the effects of nonindependence of predictor (habitat) variables on Bayesian predictions was investigated using a perturbation method, which gave minor improvements to the accuracy of the predictions. The value of this modelling approach as a method of utilising published autoecological data to predict the landscape distribution of birds is discussed.  相似文献   

19.
Calvete  C.  Estrada  R.  Angulo  E.  Cabezas-Ruiz  S. 《Landscape Ecology》2004,19(5):531-542
Populations of European wild rabbit (Oryctolagus cuniculus) have been decreasing since the 1950s. Changes in agricultural practices have been suggested as reasons for their decline in Mediterranean landscapes. We evaluated the environmental variables affecting rabbit distribution in a semiarid agricultural landscape of Northeastern Spain. Sampling was performed in 147 sites randomly distributed across Zaragoza province. At each site, data were recorded in five 100 m segments along a 1 km transect, following ecotones between crops and natural-vegetation areas. A rabbit abundance index was estimated from latrine count, pellet density and number of plots with pellets. In addition to environmental variables that have been shown to be related to rabbit abundance in other habitats, as climate, soil hardness and topography of the site, we measured landscape components related to agricultural use, such as structure of natural vegetation in remaining areas non-devoted to agricultural use and distances to different types of crops and to ecotone between crop and natural vegetation. Our results showed that rabbit abundance was positively correlated to yearly mean temperature, February and May mean rainfall, and negatively correlated to September and November mean rainfall, hardness of soil, and site topography. In relation to agricultural use, rabbit abundance was positively correlated to the scrub structure of natural-vegetation areas and negatively correlated to distance to edge between cultivated unirrigated cereal crops (wheat or barley) and yearly resting cereal crops. Rabbit abundance increased only when the edge between alternate cereal crops was less than 50 m from the ecotone between crops and natural vegetation.This revised version was published online in May 2005 with corrections to the Cover Date.  相似文献   

20.
Through its control on soil moisture patterns, topography’s role in influencing forest composition is widely recognized. This study addresses shortcomings in traditional moisture indices by employing a water balance approach, incorporating topographic and edaphic variability to assess fine-scale moisture demand and moisture availability. Using GIS and readily available data, evapotranspiration and moisture stress are modeled at a fine spatial scale at two study areas in the US (Ohio and North Carolina). Model results are compared to field-based soil moisture measurements throughout the growing season. A strong topographic pattern of moisture utilization and demand is uncovered, with highest rates of evapotranspiration found on south-facing slopes, followed by ridges, valleys, and north-facing slopes. South-facing slopes and ridges also experience highest moisture deficit. Overall higher rates of evapotranspiration are observed at the Ohio site, though deficit is slightly lower. Based on a comparison between modeled and measured soil moisture, utilization and recharge trends were captured well in terms of both magnitude and timing. Topographically controlled drainage patterns appear to have little influence on soil moisture patterns during the growing season. In addition to its ability to accurately capture patterns of soil moisture in both high-relief and moderate-relief environments, a water balance approach offers numerous advantages over traditional moisture indices. It assesses moisture availability and utilization in absolute terms, using readily available data and widely used GIS software. Results are directly comparable across sites, and although output is created at a fine-scale, the method is applicable for larger geographic areas. Since it incorporates topography, available water capacity, and climatic variables, the model is able to directly assess the potential response of vegetation to climate change.  相似文献   

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