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Modern Applied Statistics With S

Summary
1In recent years the use of species distribution models by ecologists and conservation managers has increased considerably, along with an awareness of the need to provide accuracy assessment for predictions of such models. The kappa statistic is the most widely used measure for the performance of models generating presence–absence predictions, but several studies have criticized it for being inherently dependent on prevalence, and argued that this dependency introduces statistical artefacts to estimates of predictive accuracy. This criticism has been supported recently by computer simulations showing that kappa responds to the prevalence of the modelled species in a unimodal fashion.
2In this paper we provide a theoretical explanation for the observed dependence of kappa on prevalence, and introduce into ecology an alternative measure of accuracy, the true skill statistic (TSS), which corrects for this dependence while still keeping all the advantages of kappa. We also compare the responses of kappa and TSS to prevalence using empirical data, by modelling distribution patterns of 128 species of woody plant in Israel.
3The theoretical analysis shows that kappa responds in a unimodal fashion to variation in prevalence and that the level of prevalence that maximizes kappa depends on the ratio between sensitivity (the proportion of correctly predicted presences) and specificity (the proportion of correctly predicted absences). In contrast, TSS is independent of prevalence.
4When the two measures of accuracy were compared using empirical data, kappa showed a unimodal response to prevalence, in agreement with the theoretical analysis. TSS showed a decreasing linear response to prevalence, a result we interpret as reflecting true ecological phenomena rather than a statistical artefact. This interpretation is supported by the fact that a similar pattern was found for the area under the ROC curve, a measure known to be independent of prevalence.
5Synthesis and applications. Our results provide theoretical and empirical evidence that kappa, one of the most widely used measures of model performance in ecology, has serious limitations that make it unsuitable for such applications. The alternative we suggest, TSS, compensates for the shortcomings of kappa while keeping all of its advantages. We therefore recommend the TSS as a simple and intuitive measure for the performance of species distribution models when predictions are expressed as presence–absence maps.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2664.2006.01214.x

Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)

Leu-M

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.

/pdf/predicting-species-distributions-for-conservation-decisions-2ycxvy2131.pdf

Predicting species distributions for conservation decisions

https://www.whoi.edu/cms/files/Ecological_Modelling_2007_Austin_53560.pdf

Species distribution models and ecological theory: A critical assessment and some possible new approaches

Predictive modelling of species geographical distributions isa thriving ecological and biogeographical discipline. Majoradvances in its conceptual foundation and applications havetaken place recently, as well as the delineation of the outstandingchallenges still to be met (Araujo & Guisan, 2006; Guisan

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1472-4642.2007.00356.x

The application of predictive modelling of species distribution to biodiversity conservation

http://www.ebd.csic.es/ricardo/publi/Seoane_etal_2004a.pdf

Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Habitat-suitability modelling to assess the effects of land-use changes on Dupont’s lark Chersophilus duponti: A case study in the Layna Important Bird Area

http://www.lmcarrascal.eu/pdf/ecmod05.pdf

Species-specific traits associated to prediction errors in bird habitat suitability modelling

The construction of predictive models of species distribution for conservation and regional plan- ning can be facilitated by automatic procedures employed for the selection and transformation of predictors. It has been claimed, however, that empirical predictive models benefit from the inclusion of expert opinion at different stages of the model-building process. This is a time-consuming task that is limited by the availability of experts and difficult to standardize. Automated procedures for predictor selection based on statistical criteria are faster and easier to integrate into a geographic information system and may render highly explanatory models that fit the data with which they were built. But these models do not necessarily predict independent observations well and cannot be used to extrapolate to other areas. On the contrary, supervised models may include more frequently causal relationships, and therefore may more accurately predict new observations and extrapolate better to other areas. We built predictive models for the presence/absence of 10 bird species in two areas of Andalusia (southwestern Spain) to compare three different procedures for predictor selection ranging from a completely unsupervised to a fully supervised method. We evaluated models in three ways: (1) with the same data used to build the models, (2) with a different evaluation data set, and (3) with data from a different geographic area. The increase in the degree of expert input during model construction resulted in a significant decrease of model predictive ability when evaluated with an independent data set, and did not improve the predictive ability of the model when transferred to a new area. Unsupervised models had a greater tendency to overfit the building data, but this did not negatively affect model predictive ability or transferability to a new area. Incorporating expert opinion in the model-building process neither rendered better models as measured by their predictive ability nor resulted in models that were better suited to other regions. Therefore, unsupervised fitting procedures seem to be an adequate and cost-effective way to proceed when the aim is to generate potential distribution maps of species in a regional context.

/pdf/effect-of-expert-opinion-on-the-predictive-ability-of-518n9perxt.pdf

Javier Seoane

Papers

The application of predictive modelling of species distribution to biodiversity conservation

Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Habitat-suitability modelling to assess the effects of land-use changes on Dupont’s lark Chersophilus duponti: A case study in the Layna Important Bird Area

Species-specific traits associated to prediction errors in bird habitat suitability modelling

Effect of Expert Opinion on the Predictive Ability of Environmental Models of Bird Distribution