Disturbances and gap dynamics in a semiarid grassland: A landscape-level approach

We developed a spatially-explicit gap dynamics simulation model to evaluate the effects of disturbances at the scale of a landscape for a semiarid grassland in northcentral Colorado, USA. The model simulates the establishment, growth, and death of individual plants on a small plot through time at an annual time step. Long-term successional dynamics on individual plots (single gaps) and on a landscape composed of a grid of plots were evaluated. Landscapes were simulated as either a collection of independent plots or as a collection of interacting plots where processes on one plot were influenced by processes on adjacent plots. Because we were interested in the recovery of the dominant plant species, the perennial grass blue grama (Bouteloua gracilis (H.B.K.) Lag. ex Griffiths) after disturbances, we focused on scale-dependent processes, such as seed dispersal, that are important to the recruitment of individuals of B. gracilis. The type of simulated landscape was important to the recovery time of B. gracilis after a disturbance. Landscapes composed of independent plots recovered more rapidly following a disturbance than landscapes composed of interacting plots in which the recovery time was dependent on the spatial scale of the disturbance.     

Effects of changing spatial scale on the analysis of landscape pattern

The purpose of this study was to observe the effects of changing the grain (the first level of spatial resolution possible with a given data set) and extent (the total area of the study) of landscape data on observed spatial patterns and to identify some general rules for comparing measures obtained at different scales. Simple random maps, maps with contagion (i.e., clusters of the same land cover type), and actual landscape data from USGS land use (LUDA) data maps were used in the analyses. Landscape patterns were compared using indices measuring diversity (H), dominance (D) and contagion (C). Rare land cover types were lost as grain became coarser. This loss could be predicted analytically for random maps with two land cover types, and it was observed in actual landscapes as grain was increased experimentally. However, the rate of loss was influenced by the spatial pattern. Land cover types that were clumped disappeared slowly or were retained with increasing grain, whereas cover types that were dispersed were lost rapidly. The diversity index decreased linearly with increasing grain size, but dominance and contagion did not show a linear relationship. The indices D and C increased with increasing extent, but H exhibited a variable response. The indices were sensitive to the number (m) of cover types observed in the data set and the fraction of the landscape occupied by each cover type (P _k); both m and P _kvaried with grain and extent. Qualitative and quantitative changes in measurements across spatial scales will differ depending on how scale is defined. Characterizing the relationships between ecological measurements and the grain or extent of the data may make it possible to predict or correct for the loss of information with changes in spatial scale.     

Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida,USA

The spatial distribution of soil carbon (C) is controlled by ecological processes that evolve and interact over a range of spatial scales across the landscape. The relationships between hydrologic and biotic processes and soil C patterns and spatial behavior are still poorly understood. Our objectives were to (i) identify the appropriate spatial scale to observe soil total C (TC) in a subtropical landscape with pronounced hydrologic and biotic variation, and (ii) investigate the spatial behavior and relationships between TC and ecological landscape variables which aggregate various hydrologic and biotic processes. The study was conducted in Florida, USA, characterized by extreme hydrologic (poorly to excessively drained soils), and vegetation/land use gradients ranging from natural uplands and wetlands to intensively managed forest, agricultural, and urban systems. We used semivariogram and landscape indices to compare the spatial dependence structures of TC and 19 ecological landscape variables, identifying similarities and establishing pattern–process relationships. Soil, hydrologic, and biotic ecological variables mirrored the spatial behavior of TC at fine (few kilometers), and coarse (hundreds of kilometers) spatial scales. Specifically, soil available water capacity resembled the spatial dependence structure of TC at escalating scales, supporting a multi-scale soil hydrology-soil C process–pattern relationship in Florida. Our findings suggest two appropriate scales to observe TC, one at a short range (autocorrelation range of 5.6 km), representing local soil-landscape variation, and another at a longer range (119 km), accounting for regional variation. Moreover, our results provide further guidance to measure ecological variables influencing C dynamics.     

Spatial and temporal analysis of landscape patterns

A variety of ecological questions now require the study of large regions and the understanding of spatial heterogeneity. Methods for spatial-temporal analyses are becoming increasingly important for ecological studies. A grid cell based spatial analysis program (SPAN) is described and results of landscape pattern analysis using SPAN are presentedd. Several ecological topics in which geographic information systems (GIS) can play an important role (landscape pattern analysis, neutral models of pattern and process, and extrapolation across spatial scales) are reviewed. To study the relationship between observed landscape patterns and ecological processes, a neutral model approach is recommended. For example, the expected pattern (i.e., neutral model) of the spread of disturbance across a landscape can be generated and then tested using actual landscape data that are stored in a GIS. Observed spatial or temporal patterns in ecological data may also be influenced by scale. Creating a spatial data base frequently requires integrating data at different scales. Spatial is shown to influence landscape pattern analyses, but extrapolation of data across spatial scales may be possible if the grain and extent of the data are specified. The continued development and testing of new methods for spatial-temporal analysis will contribute to a general understanding of landscape dynamics.     

Selection of scale for Everglades landscape models

This article addresses the problem of determining the optimal “Model Grain” or spatial resolution (scale) for landscape modeling in the Everglades. Selecting an appropriate scale for landscape modeling is a critical task that is necessary before using spatial data for model development. How the landscape is viewed in a simulation model is dependent on the scale (cell size) in which it is created. Given that different processes usually have different rates of fluctuations (frequencies), the question of selection of an appropriate modeling scale is a difficult one and most relevant to developing spatial ecosystem models. The question of choosing the appropriate scale for modeling is addressed using the landscape indices (e.g., cover fraction, diversity index, fractal dimension, and transition probabilities) recently developed for quantifying overall characteristics of spatial patterns. A vegetation map of an Everglades impoundment area developed from SPOT satellite data was used in the analyses. The data from this original 20 × 20 m data set was spatially aggregated to a 40 × 40 m resolution and incremented by 40 meters on up to 1000 × 1000 m (i.e., 40, 80, 120, 160 … 1000) scale. The primary focus was on the loss of information and the variation of spatial indices as a function of broadening “Model Grain” or scale. Cover fraction and diversity indices with broadening scale indicate important features, such as tree islands and brush mixture communities in the landscape, nearly disappear at or beyond the 700 m scale. The fractal analyses indicate that the area perimeter relationship changes quite rapidly after about 100 m scale. These results and others reported in the paper should be useful for setting appropriate objectives and expectations for Everglades landscape models built to varying spatial scales.     

The Importance of Spatial Scale in Habitat Models: Capercaillie in the Swiss Alps

The role of scale in ecology is widely recognized as being of vital importance for understanding ecological patterns and processes. The capercaillie (Tetrao urogallus) is a forest grouse species with large spatial requirements and highly specialized habitat preferences. Habitat models at the forest stand scale can only partly explain capercaillie occurrence, and some studies at the landscape scale have emphasized the role of large-scale effects. We hypothesized that both the ability of single variables and multivariate models to explain capercaillie occurrence would vary with the spatial scale of the analysis. To test this hypothesis, we varied the grain size of our analysis from 1 to just over 1100 hectares and built univariate and multivariate habitat suitability models for capercaillie in the Swiss Alps. The variance explained by the univariate models was found to vary among the predictors and with spatial scale. Within the multivariate models, the best single-scale model (using all predictor variables at the same scale) worked at a scale equivalent to a small annual home range. The multi-scale model, in which each predictor variable was entered at the scale at which it had performed best in the univariate model, did slightly better than the best single-scale model. Our results confirm that habitat variables should be included at different spatial scales when species-habitat relationships are investigated.     

Hierarchical relationships between landscape structure and temperature in a managed forest landscape

Management may influence abiotic environments differently across time and spatial scale, greatly influencing perceptions of fragmentation of the landscape. It is vital to consider a priori the spatial scales that are most relevant to an investigation, and to reflect on the influence that scale may have on conclusions. While the importance of scale in understanding ecological patterns and processes has been widely recognized, few researchers have investigated how the relationships between pattern and process change across spatial and temporal scales. We used wavelet analysis to examine the multiscale structure of surface and soil temperature, measured every 5 m across a 3820 m transect within a national forest in northern Wisconsin. Temperature functioned as an indicator – or end product – of processes associated with energy budget dynamics, such as radiative inputs, evapotranspiration and convective losses across the landscape. We hoped to determine whether functional relationships between landscape structure and temperature could be generalized, by examining patterns and relationships at multiple spatial scales and time periods during the day. The pattern of temperature varied between surface and soil temperature and among daily time periods. Wavelet variances indicated that no single scale dominated the pattern in temperature at any time, though values were highest at finest scales and at midday. Using general linear models, we explained 38% to 60% of the variation in temperature along the transect. Broad categorical variables describing the vegetation patch in which a point was located and the closest vegetation patch of a different type (landscape context) were important in models of both surface and soil temperature across time periods. Variables associated with slope and microtopography were more commonly incorporated into models explaining variation in soil temperature, whereas variables associated with vegetation or ground cover explained more variation in surface temperature. We examined correlations between wavelet transforms of temperature and vegetation (i.e., structural) pattern to determine whether these associations occurred at predictable scales or were consistent across time. Correlations between transforms characteristically had two peaks; one at finer scales of 100 to 150 m and one at broader scales of ^>300 m. These scales differed among times of day and between surface and soil temperatures. Our results indicate that temperature structure is distinct from vegetation structure and is spatially and temporally dynamic. There did not appear to be any single scale at which it was more relevant to study temperature or this pattern-process relationship, although the strongest relationships between vegetation structure and temperature occurred within a predictable range of scales. Forest managers and conservation biologists must recognize the dynamic relationship between temperature and structure across landscapes and incorporate the landscape elements created by temperature-structure interactions into management decisions.     

Post-storm surveys reveal large-scale spatial patterns and influences of site factors,forest structure and diversity in endemic bark-beetle populations

The storm that struck France on december 26^th and 28^th 1999 felled 140 million m³ of timber and had a high economic, social and landscape impact. This event offered the opportunity to study large-scale patterns in populations of forest insect pests that would benefit from the abundant breeding material. A large-scale survey was carried out in France in 2000 to sample the most frequently observed species developing on spruce (Ips typographus, Pityogene schalcographus) and pine (Tomicus piniperda, Ips sexdentatus) in 898 locations distributed throughout wind-damaged areas. The local abundance of each species scored on a 0 to 5 scale was analysed using geostatistical estimators to explore the extent and intensity of spatial autocorrelation, and was related to site, stand, and neighbourhood landscape metrics of the forest cover (in particular the interconnection with broadleaf forest patches) found within dispersal distance. All species but I. sexdentatus, which was much less abundant, displayed large-scale spatial dependence and regional variations in abundance. Lower infestation levels per tree (windfalls and standing trees) were observed in stands with a high proportion of wind-damaged trees, which was interpreted as the result of beetles distributing themselves among the available breeding material. More infestations were observed in wind-broken trees as compared to wind-felled trees. More importantly, populations showed significant relationships with the structure of coniferous stands (in particular with the number of coniferous patches). T. piniperda population levels were negatively correlated to the amount of coniferous edge shared with broadleaf forest patches, possibly because of the disruptive effect of non-host volatiles on host-finding processes at the landscape-scale. The differences observed between species regarding patterns and relationships to site, stand, and forest cover characteristics are discussed in relation to the ecological characteristics of each species.     

Is bird incidence in Atlantic forest fragments influenced by landscape patterns at multiple scales?

The degree to which habitat fragmentation affects bird incidence is species specific and may depend on varying spatial scales. Selecting the correct scale of measurement is essential to appropriately assess the effects of habitat fragmentation on bird occurrence. Our objective was to determine which spatial scale of landscape measurement best describes the incidence of three bird species (Pyriglena leucoptera, Xiphorhynchus fuscus and Chiroxiphia caudata) in the fragmented Brazilian Atlantic forest and test if multi-scalar models perform better than single-scalar ones. Bird incidence was assessed in 80 forest fragments. The surrounding landscape structure was described with four indices measured at four spatial scales (400-, 600-, 800- and 1,000-m buffers around the sample points). The explanatory power of each scale in predicting bird incidence was assessed using logistic regression, bootstrapped with 1,000 repetitions. The best results varied between species (1,000-m radius for P. leucoptera; 800-m for X. fuscus and 600-m for C. caudata), probably due to their distinct feeding habits and foraging strategies. Multi-scale models always resulted in better predictions than single-scale models, suggesting that different aspects of the landscape structure are related to different ecological processes influencing bird incidence. In particular, our results suggest that local extinction and (re)colonisation processes might simultaneously act at different scales. Thus, single-scale models may not be good enough to properly describe complex pattern–process relationships. Selecting variables at multiple ecologically relevant scales is a reasonable procedure to optimise the accuracy of species incidence models.     

Environmental,historical, and contextual determinants of vegetation cover: a landscape perspective

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.     

A comparison of quantitative methods for examining landscape pattern and scale

Ecologists have long recognized the importance of spatial and temporal patterns that characterize heterogeneity in landscapes. However, despite the realization that inferences about ecological phenomena are scale dependent, little attention has been paid to determining appropriate scales of measurement (e.g., plot or grain size) in studies of landscape dynamics or ecosystem change. This paper compares the results from three data sets using several quantitative methods available for characterizing landscape heterogeneity and/or for determining scale of measurement. Methods evaluated include tests of non-randomness, estimation of patch size, spectral analysis, fractals, variance ratio analysis, and correlation analysis. The results showed that no one method provides consistently good estimates of scale. Thus, sampling strategies for landscape studies should be derived from estimates of patch size and/or scale of pattern obtained from more than one of these methods.     

Old-growth forest landscape transitions from pre-European settlement to present

We conducted a multi-temporal spatial analysis of forest cover for a 9600 ha landscape in northern Wisconsin, U.S.A., using data from pre-European settlement (1860s), post-settlement (1931), and current (1989) periods. Using GIS we have shown forest landscape changes and trajectories that have been generally described in aggregate for the norther Great Lake States region. We created the pre-European settlement map from the witness tree data of the original federal General Land Office survey notes. The 1931 cover was produced from the Wisconsin Land Economic Inventory, and the 1989 cover map was based on color infrared photography. We used GIS to analyze 1) land area occupied by different forest types at different dates, 2) temporal transitions between dates and their driving proceses, and 3) successional trajectories with landforms and spatial associations of forest types. Over the 120 year period, forest cover has changed from a landscape dominated by old-growth hemlock (Tsuga canadensis) and hardwood forests (Acer saccharum, Betula alleghaniensis) to largely second-growth hardwoods and conifers. The former dominant hemlock is largely eliminated from the landscape. From 1860 to 1931, large-scale disturbances associated with logging were the dominant processes on the landscape. Early successional forest types covered much of the landscape by the 1930s. From 1931 to 1989, succession was the dominant process driving forest transitions as forest types succeeded to a diverse group of upland hardwood and conifer forest types. If successional trajectories continue, a more homogeneous landscape may develop comprised of both a northern hardwood type dominated by sugar maple, and a boreal conifer/hardwood forest.     

Multi-scale factors and long-term responses of Chihuahuan Desert grasses to drought

Factors with variation at broad (e.g., climate) and fine scales (e.g., soil texture) that influence local processes at the plant scale (e.g., competition) have often been used to infer controls on spatial patterns and temporal trends in vegetation. However, these factors can be insufficient to explain spatial and temporal variation in grass cover for arid and semiarid grasslands during an extreme drought that promotes woody plant encroachment. Transport of materials among patches may also be important to this variation. We used long-term cover data (1915–2001) combined with recently collected field data and spatial databases from a site in the northern Chihuahuan Desert to assess temporal trends in cover and the relative importance of factors at three scales (plant, patch, landscape unit) in explaining spatial variation in grass cover. We examined cover of five important grass species from two topographic positions before, during, and after the extreme drought of the 1950s. Our results show that dynamics before, during, and after the drought varied by species rather than by topographic position. Different factors were related to cover of each species in each time period. Factors at the landscape unit scale (rainfall, stocking rate) were related to grass cover in the pre- and post-drought periods whereas only the plant-scale factor of soil texture was significantly related to cover of two upland species during the drought. Patch-scale factors associated with the redistribution of water (microtopography) were important for different species in the pre- and post-drought period. Another patch-scale factor, distance from historic shrub populations, was important to the persistence of the dominant grass in uplands (Bouteloua eriopoda) through time. Our results suggest the importance of local processes during the drought, and transport processes before and after the drought with different relationships for different species. Disentangling the relative importance of factors at different spatial scales to spatial patterns and long-term trends in grass cover can provide new insights into the key processes driving these historic patterns, and can be used to improve forecasts of vegetation change in arid and semiarid areas.     

Transmutation and functional representation of heterogeneous landscapes

Models of local small-scale ecological processes can be used to describe related processes at larger spatial scales if the influences of increased scale and heterogeneity are carefully considered. In this paper we consider the changes in the functional representation of an ecological process that can occur as one moves from a local small-scale model to a model of the aggregate expression of that process for a larger spatial extent. We call these changes spatial transmutation. We specifically examine landscape heterogeneity as a cause of transmutation. Spatial transmutation as a consequence of landscape heterogeneity is a source of error in the prediction of aggregate landscape behavior from smaller scale models. However, we also demonstrate a procedure for taking advantage of spatial transmutation to develop appropriately scaled landscape functions. First a mathematical function describing the process of interest as a local function of local variables is defined. The spatial heterogeneity of the local variables is described by their statistical distribution in the landscape. The aggregate landscape expression of the local process is then predicted by calculating the expected value of the local function, explicitly integrating landscape heterogeneity. Monte Carlo simulation is used to repeat the local-to-landscape extrapolation for a variety of landscape patterns. Finally, the extrapolated landscape results are regressed on landscape variables to define response functions that explain a useful fraction of the total variation in landscape behavior. The response functions are hypotheses about the functional representation of the local process at the larger spatial scale.     

Landscape structure influences continental distribution of hantavirus in deer mice

We hypothesized that landscape structure affects movement of individuals through the landscape, which affects the rate and pattern of disease transmission. Based on this hypothesis, we predicted a relationship between landscape structure and disease incidence in spatially structured populations. We tested this prediction for hantavirus incidence in deer mice (Penomysens moniculatus), using a novel index of habitat fragmentation for transect data. A series of four stepwise logistic regression analyses were conducted on serological and ecological data from 2837 mice from 101 sites across Canada. The significant variables, ranked in decreasing order of size of their effect on virus incidence were: human buildings, landscape composition (amount of deer mouse habitat in the 1-km radius landscape surrounding each site), landscape configuration (fragmentation of deer mouse habitat in the 1-km radius landscape surrounding each site), mean annual temperature, and seasonal variation. Our results suggest that epidemiological models should consider not only the demographic structure of the host population, but its spatial structure as well, as inferred from landscape structure. Landscape structure can have a greater effect on the pattern of distribution of a virus in its host population than other ecological variables such as climate and seasonal change. The usefulness of landscape data in epidemiological models depends on the use of the appropriate spatial scale, which can be determined empirically. Epidemiological models with a spatially structured host population can benefit from the explicit consideration of landscape structure.     

Spatial aspects of metapopulation survival – from model results to rules of thumb for landscape management

The role of spatial configuration for metapopulation survival is analyzed by using a stochastic metapopulation model. This model reveals conditions which must be satisfied by the species' ecology and the landscape settings before a metapopulation can persist over a long term. Taking this as a basis, initial rules of thumb for landscape management are deduced. The following results are highlighted: (1) the critical correlation length d_c of the extinction processes determines a spatial scale of the metapopulation dynamics. (2) Only species with a dispersal range d_r above the correlation length d_c are able to benefit from landscape management at all. (3) A certain metapopulation can only persist over a long term if no patch is inside the range of correlation of another one. (4) There is a hierarchy of importance in the characteristics of a spatial configuration (scale and type) and, hence, in the scopes of landscape management. To conclude, some general consequences for supporting species survival by management are discussed. Some prospects concerning the use of models for decision support in landscape planning are discussed.     

Influence of landscape structure and stand age on species density and biomass of a tropical dry forest across spatial scales

Three central related issues in ecology are to identify spatial variation of ecological processes, to understand the relative influence of environmental and spatial variables, and to investigate the response of environmental variables at different spatial scales. These issues are particularly important for tropical dry forests, which have been comparatively less studied and are more threatened than other terrestrial ecosystems. This study aims to characterize relationships between community structure and landscape configuration and habitat type (stand age) considering different spatial scales for a tropical dry forest in Yucatan. Species density and above ground biomass were calculated from 276 sampling sites, while land cover classes were obtained from multi-spectral classification of a Spot 5 satellite imagery. Species density and biomass were related to stand age, landscape metrics of patch types (area, edge, shape, similarity and contrast) and principal coordinate of neighbor matrices (PCNM) variables using regression analysis. PCNM analysis was performed to interpret results in terms of spatial scales as well as to decompose variation into spatial, stand age and landscape structure components. Stand age was the most important variable for biomass, whereas landscape structure and spatial dependence had a comparable or even stronger influence on species density than stand age. At the very broad scale (8,000–10,500 m), stand age contributed most to biomass and landscape structure to species density. At the broad scale (2,000–8,000 m), stand age was the most important variable predicting both species density and biomass. Our results shed light on which landscape configurations could enhance plant diversity and above ground biomass.     

Analysis of fine-scale spatial pattern of a grassland from remotely-sensed imagery and field collected data

An important practical problem in the analysis of spatial pattern in ecological systems is that requires spatially-intensive data, with both fine resolution and large extent. Such information is often difficult to obtain from field-measured variables. Digital imagery can offer a valuable, alternative source of information in the analysis of ecological pattern. In the present paper, we use remotely-sensed imagery to provide a link between field-based information and spatially-explicit modeling of ecological processes. We analyzed one digitized color infrared aerial photograph of a serpentine grassland to develop a detailed digital map of land cover categories (31.24 m × 50.04 m of extent and 135 mm of resolution), and an image of vegetation index (proportional to the amount of green biomass cover in the field). We conducted a variogram analysis of the spatial pattern of both field-measured (microtopography, soil depth) and image-derived (land cover map, vegetation index, gopher disturbance) landscape variables, and used a statistical simulation method to produce random realizations of the image of vegetation index based upon our characterization of its spatial structure. The analysis revealed strong relationships in the spatial distribution of the ecological variables (e.g., gopher mounds and perennial grasses are found primarily on deeper soils) and a non-fractal nested spatial pattern in the distribution of green biomass as measured by the vegetation index. The spatial pattern of the vegetation index was composed of three basic components: an exponential trend from 0 m to 4 m, which is related to local ecological processes, a linear trend at broader scales, which is related to a general change in topography across the study site, and a superimposed periodic structure, which is related to the regular spacing of deeper soils within the study site. Simulations of the image of vegetation index confirmed our interpretation of the variograms. The simulations also illustrated the limits of statistical analysis and interpolations based solely on the semivariogram, because they cannot adequately characterize spatial discontinuities.