Multiscale ecological assessment of remote sensing images

In landscape ecology, the importance of map extent and resolution on the value of landscape indices is widely discussed, but the information content of the map, mostly derived from remote sensing images, is not. In this study, we sought (1) to understand the influence of changes in maps’ spatial and spectral resolution of agricultural landscape elements, taking hedgerow networks as a case study, and (2) to explore how predictions of species distribution might be affected by maps’ resolutions, taking two carabid species as a case study. To do so, we compared maps from different remote sensors, derived two landscape characterization variables from the maps related to patterns known to drive ecological processes, and analyzed their predictive power on biological data distribution to assess the information content of these maps. The results show that (1) the use of several methods, including landscape metrics, was useful to assess map validity; (2) the spatial resolution of satellite images is not the only important factor; changes in spectral resolution significantly alter maps; (3) the relevant definition of “hedgerow” to construct functional maps is species and process specific; thus the different maps are not either good or bad, but rather provide complementary information; (4) the more a species responds to network structure and over small areas, the less the different maps can be substitutable one to another.     

Modeling scale-dependent landscape pattern,dispersal, and connectivity from the perspective of the organism

Understanding the impacts of habitat fragmentation on dispersal is an important issue in landscape and conservation ecology. Here I examine the effects of fine- to broad-scale patterns in landscape structure on dispersal success of organisms with differing life-history traits. An individual-based model was used to simulate dispersal of amphibian-like species whose movements were driven by land cover and moisture conditions. To systematically control spatial pattern, a landscape model was created by merging simulated land cover maps with synthetic topographic surfaces. Landscapes varied in topographic roughness and spatial contagion in agriculture and urban land cover. Simulations included three different species types that varied in their maximum potential dispersal distances by 1-, 2-, or 4-fold. Two sets of simulations addressed effects of varying aspects of landscape structure on dispersal success. In the first set of simulations, which incorporated variable distances between breeding patches, dispersal success was lowest for all species types when anthropogenic cover was patchily distributed. In the second set, with interpatch distances held constant as landscape composition varied, dispersal success decreased as anthropogenic cover became spatially contagious. Both sets revealed strong main effects of species characteristics, interpatch distances and landscape composition on dispersal success; furthermore, scale-dependent patterns in land cover and moisture gradients had a stronger effect on longer- than shorter-ranging species types. Taken together, these simulations suggest that heuristic conservation strategies could potentially be developed based on important but limited life history information.     

Integrating landscape ecology and conservation physiology

The need to understand how anthropogenic landscape alteration affects fauna populations has never been more pressing. The importance of developing an understanding of the processes behind local extinction is widely acknowledged, but inference from spatial patterns of fauna distribution continues to dominate. However, this approach is limited in its ability to generate strong predictions about future distributions and local extinctions, especially when population-level responses to landscape alteration are subject to long time lags. We review the potential for indices of physiological stress and condition to contribute to understanding of how landscape pattern affects species persistence. Such measures can indicate habitat quality from the perspective of the individual animal, and can reveal environmental stressors before their negative consequences begin to manifest at a population level. Spatial patterns of chronic stress may therefore yield valuable insight into how landscape alteration influences species. We propose that the emerging disciplines of conservation physiology and macrophysiology have much to offer spatial ecology, and have great potential to reveal the physiological pathways through which habitat alteration affects fauna populations and their persistence in fragmented landscapes.     

Historical range of variability in eastern Cascades forests,Washington, USA

The historical range of variability (HRV) has been suggested as a coarse filter approach to maintain ecosystem sustainability and resiliency. The historical range of variability in forest age structure for the central eastern Cascade Range in Washington State, USA was developed from historical fire return intervals and the manner in which fire acted as both cyclic and stochastic processes. The proportions of seven forest structural stages calculated through these processes were applied to the area of each forest series within the central eastern Cascades landscape. Early successional forest stages were more common in high elevation forest than low elevation forest. The historical proportion of old growth and late successional forest varied from 38 to 63 percent of the forested landscape. These process-based estimates are consistent with those developed from forest structural information. HRV is a valuable planning tool for ecosystem conservation purposes, but must be applied to real landscapes with consideration of both temporal and spatial scale. This revised version was published online in July 2006 with corrections to the Cover Date.     

Landscape attributes and life history variability shape genetic structure of trout populations in a stream network

Spatial and temporal landscape patterns have long been recognized to influence biological processes, but these processes often operate at scales that are difficult to study by conventional means. Inferences from genetic markers can overcome some of these limitations. We used a landscape genetics approach to test hypotheses concerning landscape processes influencing the demography of Lahontan cutthroat trout in a complex stream network in the Great Basin desert of the western US. Predictions were tested with population- and individual-based analyses of microsatellite DNA variation, reflecting patterns of dispersal, population stability, and local effective population sizes. Complementary genetic inferences suggested samples from migratory corridors housed a mixture of fish from tributaries, as predicted based on assumed migratory life histories in those habitats. Also as predicted, populations presumed to have greater proportions of migratory fish or from physically connected, large, or high quality habitats had higher genetic variability and reduced genetic differentiation from other populations. Populations thought to contain largely non-migratory individuals generally showed the opposite pattern, suggesting behavioral isolation. Estimated effective sizes were small, and we identified significant and severe genetic bottlenecks in several populations that were isolated, recently founded, or that inhabit streams that desiccate frequently. Overall, this work suggested that Lahontan cutthroat trout populations in stream networks are affected by a combination of landscape and metapopulation processes. Results also demonstrated that genetic patterns can reveal unexpected processes, even within a system that is well studied from a conventional ecological perspective.     

Perceived land use patterns and landscape values

Land use patterns and land form are important sources of information that contribute to the formation of landscape perceptions and values. This paper discusses three concepts of human-landscape relationships: the human as an agent of biological and physical impacts on the landscape; the human as a static receiver and processor of information from the landscape; and the human as an active participant in the landscape —thinking, feeling and acting — a transactional concept. A model of the transactional concept and of human perception and response is presented along with a conjectural example of human-landscape transactions. Three empirical research projects are presented to illustrate varying relationships between and among humans and landscapes. Variations in human experiences, needs and desires, personal utility functions for the use of the landscape, and socio-cultural contexts are suggested as mediating variables on perceived values and human responses. The importance of landscape values information to planning and management activities is discussed.     

Landscape ecology of <Emphasis Type="Italic">Phragmites australis</Emphasis> invasion in networks of linear wetlands

The interaction between landscape structure and spatial patterns of plant invasion has been little addressed by ecologists despite the new insights it can provide. Because of their spatial configuration as highly connected networks, linear wetlands such as roadside or agricultural ditches, can serve as corridors facilitating invasion at the landscape scale, but species dynamics in these important habitats are not well known. We conducted a landscape scale analysis of Phragmites australis invasion patterns (1985–2002 and 1987–2002) in two periurban areas of southern Québec (Canada) focusing on the interaction between the network of linear wetlands and the adjacent land-uses. Results show that, at the beginning of the reference period, the two landscapes were relatively non-invaded and populations occurred mostly in roadside habitats which then served as invasion foci into other parts of the landscape. The intrinsic rates of increase of P. australis populations in linear anthropogenic habitats were generally higher than those reported for natural wetlands. Riparian habitats along streams and rivers were little invaded compared to anthropogenic linear wetlands, except when they intersected transportation rights-of-way. Bivariate spatial point pattern analysis of colonization events using both Euclidian and network distances generally showed spatial dependence (association) to source populations. An autologistic regression model that included landscape and edaphic variables selected transportation rights-of-way as the best predictor of P. australis occurrence patterns in one of the landscapes. Given the high invasion rates observed, managers of linear wetlands should carefully monitor expansion patterns especially when roads intersect landscapes of conservation or economic value.     

Can landscape indices predict ecological processes consistently?

The ecological interpretation of landscape patterns is one of the major objectives in landscape ecology. Both landscape patterns and ecological processes need to be quantified before statistical relationships between these variables can be examined. Landscape indices provide quantitative information about landscape pattern. Response variables or process rates quantify the outcome of ecological processes (e.g., dispersal success for landscape connectivity or Morisita's index for the spatial distribution of individuals). While the principal potential of this approach has been demonstrated in several studies, the robustness of the statistical relationships against variations in landscape structure or against variations of the ecological process itself has never been explicitly investigated. This paper investigates the consistency of correlations between a set of landscape indices (calculated with Fragstats) and three response variables from a simulated dispersal process across heterogeneous landscapes (cell immigration, dispersal success and search time) against variation in three experimental treatments (control variables): habitat amount, habitat fragmentation and dispersal behavior. I found strong correlations between some landscape indices and all three response variables. However, 68% of the statistical relationships were highly inconsistent and sometimes ambiguous for different landscape structures and for differences in dispersal behavior. Correlations between one landscape index and one response variable could range from highly positive to highly negative when derived from different spatial patterns. I furthermore compared correlation coefficients obtained from artificially generated (neutral) landscape models with those obtained from Landsat TM images. Both landscape representations produced equally strong and weak statistical relationships between landscape indices and response variables. This result supports the use of neutral landscape models in theoretical analyses of pattern-process relationships.     

Spatial patterning of fuels and fire hazard across a central U.S. deciduous forest region

Information describing spatial and temporal variability of forest fuel conditions is essential to assessing overall fire hazard and risk. Limited information exists describing spatial characteristics of fuels in the eastern deciduous forest region, particularly in dry oak-dominated regions that historically burned relatively frequently. From an extensive fuels survey of unmanaged forest lands (1,446 plots) we described fuel loadings and spatial patterns of fine and coarse fuels. We attempted to explain the variability in fuel loading of each time-lag fuel class using landscape and seasonal variables through a multiple regression modeling approach. Size class distributions of woody fuels were generally homogeneous across the region except in the glaciated portions of Illinois where loadings appeared lower. Temporally, litter depths progressively decreased from leaffall (November). A fire hazard model that combined seasonal changes in litter depth and fuel moisture content depicted the degree of regional spatial variability during the transition between extreme dry and wet conditions. In the future, fire hazard indices could be paired with ignition probabilities in order to assess spatio-temporal variability of fire risk within the region.     

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.     

Do bird spatial distribution patterns reflect population trends in changing landscapes?

Strong relations between population trends and spatial distribution have been suggested at the regional scale: declining species should have more fragmented distributions because decline causes range retractions towards optimal habitats, whereas increasing species should have more aggregated distributions, because colonization processes are constrained by distance. Most analyses of the effects of land use changes on animal populations are diachronic studies of population dynamics or synchronic studies of species habitat selection. Few studies take simultaneously into account temporal changes in habitat distribution and changes in species spatial distribution. We applied the above rationale to the landscape scale and analysed how population declines, increases or stability, as diagnosed in a long term study, correlate with population connectivity or fragmentation at that scale. We used data on changes in faunal distribution and information on temporal changes in the vegetation in a Mediterranean area that had been subjected to land abandonment. We found that species declining at the landscape scale had retracting fragmented distributions and that expanding species had expanding continuous distributions. However, for the latter, we suggest that the factors involved are related to landscape structure and not to dispersal mediated meta-population processes, which are of little relevance at this local scale. We also show that even species that are numerically stable can show fragmentation of their distribution and major spatial distribution shifts in response to land use changes, especially in species that have low occurrence levels or that are associated with transitory habitats such as heterogeneous shrublands (e.g. Sylvia melanocephala). Studying the spatial structure of species distribution patterns at the landscape scale may provide information about population declines and increases both at the regional and the landscape scale and can improve our understanding of short-term risks of local extinction.     

Deep time: the emerging role of archaeology in landscape ecology

Given the goals of landscape ecology, information from archaeological sites provides a useful source of evidence regarding cultural practices, anthropogenic change, local conditions, and distributions of organisms at a variety of scales across both space and time. Due to the time depth available from the archaeological record, long-term processes can be studied and issues of land use legacies, human influence on landscape heterogeneity, and system histories can be addressed. Archaeological data can produce a diachronic record of past population size, population structure, biogeography, age-at-death, and migration patterns, useful for making ecosystem and wildlife management decisions. Researchers can use archaeological knowledge to differentiate between native and alien taxa, inform restoration plans, identify sustainable harvesting practices, account for modern distributions of taxa, predict future biogeographic changes, and elucidate the interplay of long- and short-term ecological processes.     

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.     

Generating confidence intervals for composition-based landscape indexes

Many landscape indexes with ecological relevance have been proposed, including diversity indexes, dominance, fractal dimension, and patch size distribution. Classified land cover data in a geographic information system (GIS) are frequently used to calculate these indexes. However, a lack of methods for quantifying uncertainty in these measures makes it difficult to test hypothesized relations among landscape indexes and ecological processes. One source of uncertainty in landscape indexes is classification error in land cover data, which can be reported in the form of an error matrix. Some researchers have used error matrices to adjust extent estimates derived from classified land cover data. Because landscape diversity indexes depend only on landscape composition – the extent of each cover in a landscape – adjusted extent estimates may be used to calculate diversity indexes. We used a bootstrap procedure to extend this approach and generate confidence intervals for diversity indexes. Bootstrapping is a technique that allows one to estimate sample variability by resampling from the empirical probability distribution defined by a single sample. Using the empirical distribution defined by an error matrix, we generated a bootstrap sample of error matrixes. The sample of error matrixes was used to generate a sample of adjusted diversity indexes from which estimated confidence intervals for the diversity indexes were calculated. We also note that present methods for accuracy assessment are not sufficient for quantifying the uncertainty in landscape indexes that are sensitive to the size, shape, and spatial arrangement of patches. More information about the spatial structure of error is needed to calculate uncertainty for these indexes. Alternative approaches should be considered, including combining traditional accuracy assessments with other probability data generated during the classification procedure.     

Multiscale sources of variation in ecological variables: modeling spatial dispersion,elaborating sampling designs

Detection of structured spatial variation and identification of spatial scales are important aspects of ecological studies. Spatial structures can correspond to physical features of the environment or to intrinsic characteristics of ecological processes and phenomena. Spatial variability has been approached through several techniques such as classical analysis of variance, or the calculation of fractal dimensions, correlograms or variograms. Under certain assumptions, these techniques are all closely related to one another and represent equivalent tools to characterize spatial structures.Our perception of ecological variables and processes depends on the scale at which variables are measured. We propose simple nested sampling designs enabling the detection of a wide range of spatial structures that show the relationships among nested spatial scales. When it is known that the phenomenon under study is structured as a nested series of spatial scales, this provides useful information to estimate suitable sampling intervals, which are essential to establish the relationships between spatial patterns and ecological phenomena. The use of nested sampling designs helps in choosing the most suitable solutions to reduce the amount of random variation resulting from a survey. These designs are obtained by increasing the sampling intensity to detect a wider spectrum of frequencies, or by revisiting the sampling technique to select more representative sampling units.     

Combining farmers’ decision rules and landscape stochastic regularities for landscape modelling

Landscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called “landscape agronomy”, aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.