Urgent need for conservation and restoration measures to improve landscape connectivity.

Neglect of ecological knowledge is a limiting factor in the use of statistical modelling to predict species distribution. Three components are needed for statistical modelling, an ecological model concerning the ecological theory to be used or tested, a data model concerning the collection and measurement of the data, and a statistical model concerning the statistical theory and methods used. This component framework is reviewed with emphasis on ecological theory. The expected shape of a species response curve to an environmental gradient is a central assumption on which agreement has yet to be reached. The nature of the environmental predictors whether indirect variables, e.g. latitude that have no physiological impact on plants, or direct variables, e.g. temperature also influence the type of response expected. Straight-line relationships between organisms and environment are often used uncritically. Many users of canonical correlation analysis use linear (straight-line) functions to relate ordination axes to variables such as slope and aspect though this is not a necessary part of the method. Some statisticians have used straight lines for species/environment relationships without testing, when evaluating new statistical procedures. Assumptions used in one component often conflict with those in another component. Statistical models can be used to explore ecological theory. Skewed species response curves predominate contrary to the symmetric unimodal curves assumed by some statistical methods. Improvements in statistical modelling can be achieved based on ecological concepts. Examples include incorporating interspecific competition from dominant species; more proximal predictors based on water balance models and spatial autocorrelation procedures to accommodate non-equilibrium vegetation. #

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

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Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.

Abstract. We review patterns of plant species richness with respect to variables related to resource availability and variables that have direct physiological impact on plant growth or resource availability. This review suggests that there are a variety of patterns of species richness along environmental gradients reported in the literature. However, part of this diversity may be explained by the different types and lengths of gradients studied, and by the limited analysis applied to the data. To advance in understanding species richness patterns along environmental gradients, we emphasise the importance of: (1) using variables that are related to the growth of plants (latitudinal and altitudinal gradients have no direct process impact on plant growth); (2) using multivariate gradients, not single variables; (3) comparing patterns for different life forms; and (4) testing for different shapes in the species richness response (not only linear) and for interaction between variables.

A method is described, using Generalized Linear Modelling, for determining the qualitative environmental realized niche (QERN) of plant species. Five species of Eucalyptus growing in south-eastern Australia are used as examples. The frequency of each species occurrence in a data set comprising 6080 observations over an area of ::::::40 000 km 2 is modelled as a function offour environmental variables: mean annual rainfall, mean annual temperature, a solar radiation index, and rock type. The position and shape of the response of a species to these four variables is the species' QERN. The effect of rock type on the distribution of each species was found to be highly significant. To reduce model complexity, the data were stratified on rock type, and models for each species were generated for the two common rock types, granites and soft sediments. Whilst the model-fitting procedure generally is the same, each species' response demanded some modifications of the procedure. The continuous variates, mean annual temperature, mean annual rainfall, and solar radiation index, were categorized so that the magnitude of the model coefficients from each category would indicate the shape of each species' response. Continuous functions were then fitted to the categorical response shapes. Of the 10 models (5 species and 2 rock types) 5 revealed a skewed response to one or more of the environmental variables, one was linear, and two were bell shaped. Two more models revealed complex response shapes, suggesting that the environmental variables were inappropriate or insufficient to model those species' realized niches.The results provide evidence for asymmetric responses of species to environmental variables contrary to the bell-shaped symmetrical responses commonly assumed in ecological theory. Models of species' realized niches are necessary in community ecology to describe the role of environment, prior to studies on the role of competition in determining community composition. Limitations of the models, both statistical and ecological, are considered, with suggestions for improvements.

Summary1. Biogeographical models of species' distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management). 2. Most models still largely ignore or inappropriately take into account important features of species' distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species' predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues. 3. We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity. 4. Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species' distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species' distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.