Modelling distribution and abundance with presence‐only data

Abstract

Summary1. Presence-only data, for which there is no information on locations where the species is absent, are common in both animal and plant studies. In many situations, these may be the only data available on a species. We need effective ways to use these data to explore species distribution or species use of habitat. 2. Many analytical approaches have been used to model presence-only data, some inappropriately. We provide a synthesis and critique of statistical methods currently in use to both estimate and evaluate these models, and discuss the critical importance of study design in models where only presence can be identified 3. Profile or envelope methods exist to characterize environmental covariates that describe the locations where organisms are found. Predictions from profile approaches are generally coarse, but may be useful when species records, environmental predictors and biological understanding are scarce. 4. Alternatively, one can build models to contrast environmental attributes associated with known locations with a sample of random landscape locations, termed either 'pseudo-absences' or 'available'. Great care needs to be taken when selecting random landscape locations, because the way in which they are selected determines the modelling techniques that can be applied. 5. Regression-based models can provide predictions of the relative likelihood of occurrence, and in some situations predictions of the probability of occurrence. The logistic model is frequently applied, but can rarely be used directly to estimate these models; instead, case-control or logistic discrimination should be used depending on the sample design. 6. Cross-validation can be used to evaluate model performance and to assess how effectively the model reflects a quantity proportional to the probability of occurrence. However, more research is needed to develop a single measure or statistic that summarizes model performance for presence-only data. 7. Synthesis and applications. A number of statistical procedures are available to explore patterns in presence-only data; the choice among them depends on the quality of the presence-only data. Presence-only records can provide insight into the vulnerability, historical distribution and conservation status of species. Models developed using these data can inform management. Our caveat is that researchers must be mindful of study design and the biases inherent in presence data, and be cautious in the interpretation of model predictions.

Spatial distribution in Norwegian lemming Lemmus lemmus in relation to the phase of the cycle

“…We used data both on presences, i.e., when at least one individual was caught in the capture session on the entire line, square or station, and absence, i.e., when no individual was caught in the capture session on the entire line, square, or station. Several studies of distribution lack absence data and were thus forced to use presence-only modeling (Pearce and Boyce 2006) with a use of "pseudo-absence". Here, to model the summer distribution of lemmings, we had access to both the presence and absence that we call pseudo-absence (as real true-absence is hard to achieve in the field).…”

Modeling of Wildlife-Associated Zoonoses: Applications and Caveats

“…Broadly, ENM approaches can be divided into two major groups: presence-only and presence-absence models. In presence-only models (Pearce and Boyce 2006), species occurrence data are provided by the researcher, and absence data (commonly referred to as ''background data'') are randomly drawn from the larger study area (Stockwell and Peters 1999), or user-defined subsets of the background (e.g., Phillips et al 2009). In presence-absence models (Brotons et al 2004), the user defines the locations on the landscape where the species is known to be present and absent.…”

Using Relative Capacity to Measure Habitat Suitability

“…Second, this map is used to define a smaller-scale survey of species density in various habitat classes, providing data for the relative capacity analysis. The computation of HS maps on the basis of occurrence data-either presence/absence or presence only-is a mature field, and numerous HS modeling methods have been devised and tested (Guisan and zimmermann, 2000;Pearce and boyce, 2006;Peterson, 2006;Soberón, 2007). It is comparatively much easier to collect occurrence data than abundance/density data over broad, heterogeneous areas.…”

“…One of the main outputs of these models is the predictive HS map, which can be used to delineate the spatial distribution of a species, to predict potential range of an invader or a disease, to delineate protected areas for an endangered species, or to map biodiversity hotspots (Guisan and Thuiller, 2005;Peter-son, 2006). The field of HS modeling has developed quickly during the last 15 years, generating a large number of methods and applications (Guisan and zimmermann, 2000;Pearce and boyce, 2006). but what does HS represent exactly?…”