Reliability in Distribution Modeling—A Synthesis and Step-by-Step Guidelines for Improved Practice

Abstract

Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.

Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland

“…Static or seasonal area closures are usually based on historical data on species distribution, thus putting these areas at risk of losing ecological relevance as species' distributions shift with changing climate and human activity [87]. Satellite data could add value to species distribution models, where the major focus is to identify spatial patterns in datasets [88]. In addition, satellite-based data could be useful in developing A systematic monitoring of biophysical changes and trends in the marine environments will be key to understanding the complex dynamics of marine ecosystems, especially bottom-up (primary production) forcing.…”

Predicting Ecosystem Pattern across Landscapes

“…Landscapes of heterogeneous ecosystems are composed of these different ecosystems. Here, each ecosystem manifests at characteristic spatial scales (Massol et al 2011, Soranno et al 2019, Bullock et al 2022 and has characteristic levels of integration (Rowe 1961, Loreau 2020, uniformity (Pickett and Candenasso 1995, Halvorsen et al 2021), and distinction (Dor-haim et al 2019.…”

“…While training data employed in spatial biodiversity modelling are typically obtained from field surveys, predictor (sometimes called environmental, explanatory, or covariate) data are derived from remotely sensed, or modelled, spatial data extending completely across a study region (Bryn et al 2021). These data are required to model ecosystems continuously in space (Figure 1B).…”

“…Outputs of spatial models are often continuous (e.g., probability of occurrence or co-occurrence) and many modelling applications require the identification of suitable thresholds for identifying ecological meaningful levels of relative continuity or discontinuity across space (Zurell et al 2020). For example, users may want to know if the probability of occurrence, for a particular species, is high enough to justify conservation actions (Bryn et al 2021). Thresholds have also been applied for determining whether co-occurrence patterns among groups of species reveal higher order levels of spatially structured biodiversity (e.g., communities -Ferrier and Guisan 2006) or ecological geography (e.g., bioregions -Hill et al 2020).…”

“…Data required for spatial modelling of species and communities are generally well documented (Bryn et al 2021). The extraordinary popularity of these models has also prompted development of new training and predictor data, and investigations of the suitability these data have for biodiversity modelling are ongoing (e.g., Guillera-Arroita et al 2015, Brodie et al 2020).…”