Error and uncertainty in habitat models

Abstract

Summary1. Species distribution models (habitat models) relate the occurrence or abundance of a species to environmental and/or geographical predictors that then allow predictions to be mapped across an entire region. These models are used in a range of policy settings such as managing greenhouse gases, biosecurity threats and conservation planning. Prediction errors are almost ubiquitous in habitat models. An understanding of the source, magnitude and pattern of these errors is essential if the models are to be used transparently in decision making. 2. This study considered the sources of errors in habitat models. It divided them into two main classes, error resulting from data deficiencies and error introduced by the specification of the model. Common and important data errors included missing covariates, and samples of species' occurrences that were small, biased or lack absences. These affected the types of models that could be developed and the probable errors that would occur. Almost all models had missing covariates, and this introduced significant spatial correlation in the errors of the analysis. 3. A challenging aspect of modelling is that species' distributions are affected by processes operating in both environmental and geographical space. We differentiated between global (aspatial) and local (spatial) errors, and discussed how they arise and what can be done to alleviate their effects. 4. Synthesis and applications. This study brings together statistical and ecological thinking to consider the appropriate techniques for habitat modelling. Ecological theory suggests models capable of defining optima, while allowing for interactions between variables. Statistical considerations, including impacts of data errors, suggest models that deal with multimodality and discontinuity in response surfaces. Models are typically simple approximations of the true probability surface. We suggest the use of flexible regression techniques, and explain what makes such methods superior for ecological modelling. The most robust modelling approaches are likely to be those in which care is taken to match the model with knowledge of ecology, and in which each is allowed to inform the other.

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

“…Data quality encompasses concepts of accuracy, precision, and uncertainty, and can be of different types including spatial, thematic, and temporal. The different components of data quality have been reviewed in a number of publications (e.g., Jager and King, 2004;Barry and Elith, 2006;Moudrý and Šímová, 2012;Levin et al, 2014), and I thus focus here on their impacts on habitat maps in a conservation context.…”

Modeling distribution and abundance of multiple species: Different pooling strategies produce similar results

“…Variables were chosen based on previous studies of avian distribution in boreal Canada , augmented to include those we expected would relate to waterfowl distribution and abundance at national extent (Appendix A). Variables were chosen or derived with ecological mechanisms in mind (Mac Nally 2000, Barry andElith 2006, Wenger andOlden 2012), constrained by availability and coverage. Some variables in the original set of variables were highly correlated, such as monthly mean temperatures and precipitation (Cumming and Le Blanc 2010).…”

Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

“…To predict the fish assemblages, presence-absence data of 77 common indicator species with prevalence higher than 6% were compiled and used in the model procedure (see Table S2 for details). This handling method could reduce the errors associated with excessively small sample sizes and increase the predictive accuracy (Stockwell and Peterson, 2002;Barry and Elith, 2006). While SR per lake (total number of fish species in one lake) was used for predicting fish species richness.…”