Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory

Austin, M. P.; Belbin, Lee; Meyers, Jacqui; Doherty, Michael; Luoto, Miska

doi:10.1016/j.ecolmodel.2006.05.023

Cited by 178 publications

(173 citation statements)

References 48 publications

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“…As with typical statistical measures, we are interested in both the variance and the bias of our estimates of system performance. An excellent summary of the necessity and use of artificial data in the context of species distribution modelling can be found in Austin et al (2006). Rather than just comparing the apparent optimality of any of the methods, synthetic data also allows us to ask questions concerning to what extent the methods do achieve their goals with respect to their objective functions or on externally specified goals, such as carbon sequestration.…”

Section: Importance Of Synthetic Datamentioning

confidence: 99%

When do conservation planning methods deliver? Quantifying the consequences of uncertainty

Langford

Gordon

Bastin

2009

Ecological Informatics

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The rapid global loss of biodiversity has led to a proliferation of systematic conservation planning methods. In spite of their utility and mathematical sophistication, these methods only provide approximate solutions to real-world problems where there is uncertainty and temporal change. The consequences of errors in these solutions are seldom characterized or addressed. We propose a conceptual structure for exploring the consequences of input uncertainty and oversimplified approximations to real-world processes for any conservation planning tool or strategy. We then present a computational framework based on this structure to quantitatively model species representation and persistence outcomes across a range of uncertainties. These include factors such as land costs, landscape structure, species composition and distribution, and temporal changes in habitat. We demonstrate the utility of the framework using several reserve selection methods including simple rules of thumb and more sophisticated tools such as Marxan and Zonation. We present new results showing how outcomes can be strongly affected by variation in problem characteristics that are seldom compared across multiple studies. These characteristics include number of species prioritized, distribution of species richness and rarity, and uncertainties in the amount and quality of habitat patches. We also demonstrate how the framework allows comparisons between conservation planning strategies and their response to error under a range of conditions. Using the approach presented here will improve conservation outcomes and resource allocation by making it easier to predict and quantify the consequences of many different uncertainties and assumptions simultaneously. Our results show that without more rigorously generalizable results, it is very difficult to predict the amount of error in any conservation plan. These results imply the need for standard practice to include evaluating the effects of multiple real-world complications on the behavior of any conservation planning method.

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Section: Importance Of Synthetic Datamentioning

confidence: 99%

When do conservation planning methods deliver? Quantifying the consequences of uncertainty

Langford

Gordon

Bastin

2009

Ecological Informatics

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“…Discussions of some models, especially of plants, acknowledge that the environmental responses are typically Gaussian or asymmetric (Austin et al 2006; figure 1) and incorporate these forms (or let them vary, see Thuiller et al 2005). However, many animal models make no mention of the theoretical assumptions on which they are based (Austin 2007).…”

Section: Habitat Alteration (A) Habitat Alteration and Changing Climatesmentioning

confidence: 99%

“…(a) Modelling species responses Incorporating the form of the environmental response can improve the realism of models that seek to understand the responses of species to a changing environment (Helmuth et al 2005;Austin et al 2006). Moreover, the ways in which environmental variation affects species distributions may be more complicated than simple models of changes in means and extremes suggest.…”

Section: Climate Changementioning

confidence: 99%

Macrophysiology for a changing world

2008

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The Millennium Ecosystem Assessment (MA) has identified climate change, habitat destruction, invasive species, overexploitation and pollution as the major drivers of biodiversity loss and sources of concern for human well-being. Understanding how these drivers operate and interact and how they might be mitigated are among the most pressing questions facing humanity. Here, we show how macrophysiology-the investigation of variation in physiological traits over large geographical, temporal and phylogenetic scalescan contribute significantly to answering these questions. We do so by demonstrating, for each of the MA drivers, how a macrophysiological approach can or has helped elucidate the impacts of these drivers and their interactions. Moreover, we illustrate that a large-scale physiological perspective can provide insights into previously unrecognized threats to diversity, such as the erosion of physiological variation and stress tolerance, which are a consequence of the removal of large species and individuals from the biosphere. In so doing we demonstrate that environmental physiologists have much to offer the scientific quest to resolve major environmental problems.

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“…To test and compare the domain of application of these methods, I used a virtual-species approach as it allows for a variety of computer experiments and provides a framework for objectively evaluating results Austin et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

“…This approach replaces time series by a snapshot density survey over a sufficiently large area and provides a rapid way to evaluate potential population sizes. I explore three analytical methods, with controls, and test their sensitivity to various sampling and biological conditions by applying them to simulated virtual species Austin et al, 2006). conditions can maintain at equilibrium (begon et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Using Relative Capacity to Measure Habitat Suitability

Hirzel¹

2008

Israel Journal of Ecology & Evolution

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Habitat suitability (HS) modeling relates a species' potential presence/absence to a set of environmental variables. because of stochastic or demographic population fluctuations, relating abundance to environment is difficult and generally requires time-series of population-density data. Here, I propose an approach to compute relative capacity of a set of habitat classes. I defined relative capacity of a habitat class as being proportional to its carrying capacity, and replaced time-series of density measures with spatial replicates. A hypothetical environment was first divided into several habitat classes and population densities were measured in a sample of these habitats. Three methods of computing relative capacity were compared: (1) maximum density per habitat class, (2) coefficients of a Generalized Linear Model (GLM) of the habitat densities, and (3) slopes of isodars computed for all pairs of habitat classes. Accuracy of these methods was evaluated using spatially-explicit demographic simulations. I investigated biological assumptions (species' sensitivity, reactivity and selectivity, regional stochasticity) and sampling design (sample size, distance between pairs of habitat classes, number of habitat classes, uncertainty over abundance). GLM and maximum-density methods provided accurate results when at least five site-pairs were available. Isodars outperformed other methods in landscapes with many habitat classes, but are best suited to large or noise-free data sets. Optimal performance was reached with more than 50 site-pairs and five habitat classes.

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Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory

Cited by 178 publications

References 48 publications

When do conservation planning methods deliver? Quantifying the consequences of uncertainty

When do conservation planning methods deliver? Quantifying the consequences of uncertainty

Macrophysiology for a changing world

Using Relative Capacity to Measure Habitat Suitability

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