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67
Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecol
, 2002
"... Landscape ecology has made tremendous progress in recent decades, but as a rapidly developing discipline it is faced with new problems and challenges. To identify the key issues and research priorities in landscape ecology, a special session entitled “Top 10 List for Landscape Ecology in the 21st Ce ..."
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Cited by 71 (7 self)
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Landscape ecology has made tremendous progress in recent decades, but as a rapidly developing discipline it is faced with new problems and challenges. To identify the key issues and research priorities in landscape ecology, a special session entitled “Top 10 List for Landscape Ecology in the 21st Century ” was organized at the 16th
A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications, Ecological Modeling
, 2002
"... and applications ..."
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Effects of changing scale on landscape pattern analysis: scaling relations
, 2004
"... Landscape pattern is spatially correlated and scale-dependent. Thus, understanding landscape structure and func-tioning requires multiscale information, and scaling functions are the most precise and concise way of quantify-ing multiscale characteristics explicitly. The major objective of this study ..."
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Cited by 63 (8 self)
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Landscape pattern is spatially correlated and scale-dependent. Thus, understanding landscape structure and func-tioning requires multiscale information, and scaling functions are the most precise and concise way of quantify-ing multiscale characteristics explicitly. The major objective of this study was to explore if there are any scaling relations for landscape pattern when it is measured over a range of scales grain size and extent. The results showed that the responses of landscape metrics to changing scale fell into two categories when computed at the class level i.e., for individual land cover types: simple scaling functions and unpredictable behavior. Similarly, three categories were found at the landscape level, with the third being staircase pattern, in a previous study when all land cover types were combined together. In general, scaling relations were more variable at the class level than at the landscape level, and more consistent and predictable with changing grain size than with chang-ing extent at both levels. Considering that the landscapes under study were quite diverse in terms of both com-position and configuration, these results seem robust. This study highlights the need for multiscale analysis in order to adequately characterize and monitor landscape heterogeneity, and provides insights into the scaling of landscape patterns.
A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological modelling 168
, 2003
"... Natural complexity can best be explored using spatial analysis tools based on concepts of landscape as process continuums that can be partially decomposed into objects or patches. We introduce a five-step methodology based on multi-scale segmentation and object relationship modelling. Hierarchical p ..."
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Cited by 51 (2 self)
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Natural complexity can best be explored using spatial analysis tools based on concepts of landscape as process continuums that can be partially decomposed into objects or patches. We introduce a five-step methodology based on multi-scale segmentation and object relationship modelling. Hierarchical patch dynamics (HPD) is adopted as the theoretical framework to address issues of heterogeneity, scale, connectivity and quasi-equilibriums in landscapes. Remote sensing has emerged as the most useful data source for characterizing land use/land cover but a vast majority of applications rely on basic image processing concepts developed in the 1970s: one spatial scale, per-pixel classification of a multi-scale spectral feature space. We argue that this methodology does not make sufficient use of spatial concepts of neighbourhood, proximity or homogeneity. In contrast, the authors demonstrate in this article the utility of the HPD framework as a theoretical basis for landscape analysis in two different projects using alternative image processing methodologies, which try to overcome the ‘pixel-centred ’ view. The first project focuses on habitat mapping using a high dimension multi-scale GIS database. Focal patches are derived through aggregating automatically generated landscape segments using sub-patch information including dominant tree crown densities and species. The second project uses fractal-based segmentation to produce multiple candidate segmented agricultural scenes, and then develops a decision framework to choose the combination of segmentation levels best suited to identifying shrub encroachment. The challenge and flexibility of the multi-scale segmentation/object relationship modelling approach lies
Multiscale Analysis of Landscape Heterogeneity: Scale Variance and Pattern Metrics
, 2000
"... A major goal of landscape ecology is to understand the formation, dynamics, and maintenance of spatial heterogeneity. Spatial heterogeneity is the most fundamental characteristic of all landscapes, and scale multiplicity is inherent in spatial heterogeneity. Thus, multiscale analysis is imperative f ..."
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Cited by 47 (8 self)
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A major goal of landscape ecology is to understand the formation, dynamics, and maintenance of spatial heterogeneity. Spatial heterogeneity is the most fundamental characteristic of all landscapes, and scale multiplicity is inherent in spatial heterogeneity. Thus, multiscale analysis is imperative for understanding the structure, function and dynamics of landscapes. Although a number of methods have been used for multiscale analysis in landscape ecology since the 1980s, the effectiveness of many of them, including some commonly used ones, is not clear or questionable. In this paper, we discuss two approaches to multiscale analysis of landscape heterogeneity: the direct and indirect approaches. We will focus on scale variance and semivariance methods in the first approach and 17 landscape metrics in the second. The results show that scale variance is potentially a powerful method to detect and describe multiple-scale structures of landscapes, while semivariance analysis may often fail to do so especially if landscape variability is dominant at broad scales over fine scales. Landscape metrics respond to changing grain size rather differently, and these changes are reflective of the modifiable areal unit problem as well as multiple-scale structures in landscape pattern. Interestingly, some metrics (e.g., the number of patches, patch density, total edge, edge density, mean patch size, patch size coefficient of variation) exhibit consistent, predictable patterns over a wide range of grain sizes, whereas others (e.g., patch diversity, contagion, landscape fractal dimension) have nonlinear response curves. The two approaches to multiple-scale analysis are complementary, and their pros and cons still need to be further investigated systematically.
Empirical patterns of the effects of changing scale on landscape metrics
, 2002
"... While ecologists are well aware that spatial heterogeneity is scale-dependent, a general understanding of scaling relationships of spatial pattern is still lacking. One way to improve this understanding is to systematically ex-amine how pattern indices change with scale in real landscapes of differ ..."
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Cited by 38 (3 self)
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While ecologists are well aware that spatial heterogeneity is scale-dependent, a general understanding of scaling relationships of spatial pattern is still lacking. One way to improve this understanding is to systematically ex-amine how pattern indices change with scale in real landscapes of different kinds. This study, therefore, was designed to investigate how a suite of commonly used landscape metrics respond to changing grain size, extent, and the direction of analysis (or sampling) using several different landscapes in North America. Our results showed that the responses of the 19 landscape metrics fell into three general categories: Type I metrics showed predictable responses with changing scale, and their scaling relations could be represented by simple scaling equations (linear, power-law, or logarithmic functions); Type II metrics exhibited staircase-like responses that were less predictable; and Type III metrics behaved erratically in response to changing scale, suggesting no con-sistent scaling relations. In general, the effect of changing grain size was more predictable than that of changing extent. Type I metrics represent those landscape features that can be readily and accurately extrapolated or in-terpolated across spatial scales, whereas Type II and III metrics represent those that require more explicit con-sideration of idiosyncratic details for successful scaling. To adequately quantify spatial heterogeneity, the metric-scalograms (the response curves of metrics to changing scale), instead of single-scale measures, seem necessary.
Use and misuse of landscape indices
, 2004
"... Landscape ecology has generated much excitement in the past two decades. One reason was that it brought spatial analysis and modeling to the forefront of ecological research. However, high expectations for landscape analysis to improve our understanding and prediction of ecological processes have la ..."
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Cited by 36 (1 self)
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Landscape ecology has generated much excitement in the past two decades. One reason was that it brought spatial analysis and modeling to the forefront of ecological research. However, high expectations for landscape analysis to improve our understanding and prediction of ecological processes have largely been unfulfilled. We identified three kinds of critical issues: conceptual flaws in landscape pattern analysis, inherent limitations of landscape indices, and improper use of pattern indices. For example, many landscape analyses treat quantitative description of spatial pattern as an end itself and fail to explore relationships between pattern and process. Landscape indices and map data are sometimes used without testing their ecological relevance, which may not only confound interpretation of results, but also lead to meaningless results. In addition, correlation analysis with indices is impeded by the lack of data because of difficulties in large-scale experimentation and by complicated behavior of indices because of their varying responses to changes in scale and spatial pattern. These problems represent significant challenges to landscape pattern analysis, especially in terms of relating pattern to process. In this perspective paper, we examine the underlying problems of these challenges and offer some solutions.
A multiscale framework for landscape analysis: Object-specific analysis and upscaling.
- Landscape Ecology,
, 2001
"... Abstract Landscapes are complex systems that require a multiscale approach to fully understand, manage, and predict their behavior. Remote sensing technologies represent the primary data source for landscape analysis, but suffer from the modifiable areal unit problem (MAUP). To reduce the effects o ..."
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Cited by 33 (4 self)
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Abstract Landscapes are complex systems that require a multiscale approach to fully understand, manage, and predict their behavior. Remote sensing technologies represent the primary data source for landscape analysis, but suffer from the modifiable areal unit problem (MAUP). To reduce the effects of MAUP when using remote sensing data for multiscale analysis we present a novel analytical and upscaling framework based on the spatial influence of the dominant objects composing a scene. By considering landscapes as hierarchical in nature, we theorize how a multiscale extension of this object-specific framework may assist in automatically defining critical landscape thresholds, domains of scale, ecotone boundaries, and the grain and extent at which scale-dependent ecological models could be developed and applied through scale.
Disturbance and climate effects on carbon stocks and fluxes across western
- Oregon USA,” Global Change Biol
, 2004
"... We used a spatially nested hierarchy of field and remote-sensing observations and a process model, Biome-BGC, to produce a carbon budget for the forested region of Oregon, and to determine the relative influence of differences in climate and disturbance ..."
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Cited by 26 (5 self)
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We used a spatially nested hierarchy of field and remote-sensing observations and a process model, Biome-BGC, to produce a carbon budget for the forested region of Oregon, and to determine the relative influence of differences in climate and disturbance
Physical Review A
, 1998
"... Ion-signaling and transduction networks are central to fungal development and virulence because they regulate gene expression, filamentation, host association, and invasion, pathogen stress response and survival. Dysregulation of ion homeostasis rapidly mediates cell death, forming the mechanistic b ..."
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Cited by 15 (3 self)
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Ion-signaling and transduction networks are central to fungal development and virulence because they regulate gene expression, filamentation, host association, and invasion, pathogen stress response and survival. Dysregulation of ion homeostasis rapidly mediates cell death, forming the mechanistic basis by which a growing number of amphipathic but structurally unrelated compounds elicit antifungal activity. Included in this group is carvacrol, a terpenoid phenol that is a prominent component of oregano and other plant essential oils. Carvacrol triggers an early dose-dependent Ca 2+ burst and long lasting pH changes in the model yeast Saccharomyces cerevisiae. The distinct phases of ionic transients and a robust transcriptional response that overlaps with Ca 2+ stress and nutrient starvation point to specific signaling events elicited by plant terpenoid phenols, rather than a non-specific lesion of the membrane, as was previously considered. We discuss the potential use of plant essential oils and other agents that disrupt ion-signaling pathways as chemosensitizers to augment conventional antifungal therapy, and to convert fungistatic drugs with strong safety profiles into fungicides.
