The pervasive influence of island biogeography theory on forest fragmentation research has often led to a misleading conceptualization of landscapes as areas of forest/habitat and 'non-forest/non-habitat' and an overriding focus on processes within forest remnants at the expense of research in the human-modified matrix. The matrix, however, may be neither uniformly unsuitable as habitat nor serve as a fully-absorbing barrier to the dispersal of forest taxa. In this paper, we present a conceptual model that addresses how forest habitat loss and fragmentation affect biodiversity through reduction of the resource base, subdivision of populations, alterations of species interactions and disturbance regimes, modifications of microclimate and increases in the presence of invasive species and human pressures on remnants. While we acknowledge the importance of changes associated with the forest remnants themselves (e.g. decreased forest area and increased isolation of forest patches), we stress that the extent, intensity and permanence of alterations to the matrix will have an overriding influence on area and isolation effects and emphasize the potential roles of the matrix as not only a barrier but also as habitat, source and conduit. Our intention is to argue for shifting the examination of forest fragmentation effects away from a patch-based perspective focused on factors such as patch area and distance metrics to a landscape mosaic perspective that recognizes the importance of gradients in habitat conditions.

Because of the difficulties involved with separating natural fluctuations in climatic variables from possible directional changes related to human activities (e.g., heightened atmospheric CO 2 concentrations related to fossil fuel consumption), some researchers have focused on developing alternative indicators to detect hypothesized climate changes. It has, for example, been suggested that the locations of ecotones, transitions between adjacent ecosystems or biomes, should be monitored. It is assumed that changes in climate, especially increases in atmospheric temperature, will result in shifts in the location (altitude or latitude) of ecotones as plants respond to the newly imposed climatic conditions. In this article, we address the use of two montane ecotones, the alpine tree-line ecotone and the deciduous/Boreal forest ecotone, in monitoring global climatic change. In so doing, we 1) outline the factors that create and maintain each ecotone's position at a given location; 2) assess the projected response of the ecotones to various aspects of global warming; and 3) discuss the usefulness of both ecotones as indicators of global climate change. While it is likely that extended periods of directional climate change would bring about an altitudinal shift in the ranges of montane species and the associated ecotones, we question whether the response at either ecotone will be at a timescale useful for detecting climate change (a few decades) owing to disequilibrium related to upslope edaphic limitations and competitive interactions with established canopy and subcanopy individuals. Further, limitations related to the prediction of the complex and interacting effects of projected changes in temperature, precipitation and site water balance on photosynthetic processes of plant species raise uncertainties about the expected responses of both ecotones.

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Landscape pattern indicators or ‘metrics’ provide simple measures of landscape structure that can be easily calculated with readily available data and software. Unfortunately, the ecological relevance of many metrics (i.e. the relationship between metric values and the real-world ecological processes that they are meant to serve as proxies for) is often unproven and questionable, and concerns are regularly voiced that such metrics fail to capture important aspects of landscape function. In this paper, I provide a review of landscape measures that may better link landscape pattern and function, ranging from approaches that extend existing metrics by incorporating a more functional component (e.g. core area measures, least cost distances) to those rooted in graph, network, and electrical circuit theory. While more ‘functional’ approaches are becoming increasingly popular, the selection of appropriate landscape metrics in many applications involves tradeoffs regarding data requirements, ease of calculation, functional basis, and simplicity of interpretation by a range of specialist and non-specialist stakeholders. Regardless, there continues to be a need for landscape metrics because they are seen by many land managers and stakeholders as simple, intuitive tools for assessing and monitoring changes in landscape pattern and, by extension, the effects on underlying ecological processes. Future needs include: (1) the development of more user-friendly landscape analysis software that can simplify graph-based analyses and visualization; and (2) studies that clarify the strengths and weaknesses of different approaches, including the potential limitations and biases in graph and network-based measures.

Aim Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi-scale framework to account for different origins of SAC, and compared non-spatial models with models that accounted for SAC at multiple levels.Location We modelled the spatial distribution of an invasive forest pathogen, Phytophthora ramorum, in western USA.Methods We applied one conventional statistical method (generalized linear model, GLM) and one nonparametric technique (maximum entropy, Maxent) to a large dataset on P. ramorum occurrence (n = 3787) to develop four types of model that included environmental variables and that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a local scale using autocovariates, or multiple scales using spatial eigenvector mapping. We evaluated model accuracies and amounts of explained spatial structure, and examined the changes in predictive power of the environmental and spatial variables.Results Accounting for different scales of SAC significantly enhanced the predictive capability of iSDMs. Dramatic improvements were observed when fine-scale SAC was included, suggesting that local range-confining processes are important in P. ramorum spread. The importance of environmental variables was relatively consistent across all models, but the explanatory power decreased in spatial models for factors with strong spatial structure. While accounting for SAC reduced the amount of residual autocorrelation for GLM but not for Maxent, it still improved the performance of both approaches, supporting our hypothesis that dispersal and colonization processes are important factors to consider in distribution models of biological invasions.Main conclusions Spatial autocorrelation has become a paradigm in biogeography and ecological modelling. In addition to avoiding the violation of statistical assumptions, accounting for spatial patterns at multiple scales can enhance our understanding of dynamic processes that explain ecological mechanisms of invasion and improve the predictive performance of static iSDMs.