Standards for distribution models in biodiversity assessments

Araújo, Miguel B.; Anderson, Robert P.; Barbosa, A. Márcia; Beale, Colin M.; Dormann, Carsten F.; Early, Regan; García, Raquel; Guisan, Antoine; Maiorano, Luigi; Naimi, Babak; O’Hara, Robert B.; Zimmermann, Niklaus E.; Rahbek, Carsten

doi:10.1126/sciadv.aat4858

Cited by 771 publications

(777 citation statements)

References 154 publications

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“…Poweshiek skipperling using Maxent software and following a set of best practice methods to select the best-fit model that uses the fewest predictor variables and minimizes correlation among these predictors (Yost et al 2008;Araújo et al 2019). For each species, we first constructed models containing all bioclimatic, geophysical, and landsurface predictor layers, plus the ecological variables described above.…”

Section: Model Selection We Modeled Distribution Of the Rpbb Andmentioning

confidence: 99%

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

Richardson¹,

Bang

Budreski³

et al. 2019

Integr Envir Assess & Manag

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Characterizing potential spatial overlap between federally threatened and endangered (“listed”) species distributions and registered pesticide use patterns is important for accurate risk assessment of threatened and endangered species. Because accurate range information for such rare species is often limited and agricultural pesticide use patterns are dynamic, simple spatial co‐occurrence methods may overestimate or underestimate overlap and result in decisions that benefit neither listed species nor the regulatory process. Here, we demonstrate a new method of co‐occurrence analysis that employs probability theory to estimate spatial distribution of rare species populations and areas of pesticide use to determine the likelihood of potential exposure. Specifically, we 1) describe a probabilistic method to estimate pesticide use based on crop production patterns; 2) construct species distribution models for 2 listed insect species whose ranges were previously incompletely described, the rusty‐patched bumble bee (Bombus affinis) and the Poweshiek skipperling (Oarisma poweshiek); and 3) develop a probabilistic co‐occurrence methodology and assessment framework. Using the principles of the Bayes' theorem, we constructed probabilistic spatial models of pesticide use areas by integrating information from land‐cover spatial data, agriculture statistics, and remote‐sensing data. We used maximum entropy methods to build species distribution models for 2 listed insects based on species collection and observation records and predictor variables relevant to the species' biogeography and natural history. We further developed novel methods for refinement of these models at spatial scales relevant to US Fish and Wildlife Service (FWS) regulatory priorities (e.g., critical habitat areas). Integrating both probabilistic assessments and focusing on USFWS priority management areas, we demonstrate that spatial overlap (i.e., potential for exposure) is not deterministic but instead a function of both species distribution and land use patterns. Our work serves as a framework to enhance the accuracy and efficiency of threatened and endangered species assessments using a data‐driven likelihood analysis of species co‐occurrence. Integr Environ Assess Manag 2019;00:1–12. © 2019 SETAC

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Section: Model Selection We Modeled Distribution Of the Rpbb Andmentioning

confidence: 99%

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

Richardson¹,

Bang

Budreski³

et al. 2019

Integr Envir Assess & Manag

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“…Species distribution models (SDMs) are a common tool used to study these relationships. Data quality (both for species occurrences and environmental variables) is currently considered a major factor limiting SDM accuracy (Araújo et al 2019) and demonstrating, quantifying and understanding the consequences of these errors is therefore critical. All applications of SDMs, however, assume that species occurrence data are largely free of spatial error.…”

Section: Introductionmentioning

confidence: 99%

The effect of positional error on fine scale species distribution models increases for specialist species

et al. 2019

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Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models' performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.

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“…Suboptimal methodologies will become more immediately transparent and requests for metadata information will encourage researchers to conduct more comprehensive analyses and supply information that is vital for their peers to understand, evaluate and use their work. Araújo et al () recently proposed a set of best practices and reporting standards for the use of species distribution models in biodiversity models; our metadata standards and tools reflect these same ideals. For example, best practices can be established by defining a family of the entities required for biodiversity assessments (e.g., a new biodiversity family).…”

Section: Discussionmentioning

confidence: 99%

Species' range model metadata standards: RMMS

Merow

Maitner

Owens

et al. 2019

Global Ecol Biogeogr

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Aim The geographic range and ecological niche of species are widely used concepts in ecology, evolution and conservation and many modelling approaches have been developed to quantify each. Niche and distribution modelling methods require a litany of design choices; differences among subdisciplines have created communication barriers that increase isolation of scientific advances. As a result, understanding and reproducing the work of others is difficult, if not impossible. It is often challenging to evaluate whether a model has been built appropriately for its intended application or subsequent reuse. Here, we propose a standardized model metadata framework that enables researchers to understand and evaluate modelling decisions while making models fully citable and reproducible. Such reproducibility is critical for both scientific and policy reports, while international standardization enables better comparison between different scenarios and research groups. Innovation Range modelling metadata (RMMS) address three challenges: they (a) are designed for convenience to encourage use, (b) accommodate a wide variety of applications, and (c) are extensible to allow the research community to steer them as needed. RMMS are based on a metadata dictionary that specifies a hierarchical structure to catalogue different aspects of the range modelling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to modify and extend. To facilitate use, we have developed an R package, rangeModelMetaData, to build templates, automatically fill values from common modelling objects, check for inconsistencies with standards, and suggest values. Main conclusions Range Modelling Metadata tools foster cross‐disciplinary advances in biogeography, conservation and allied disciplines by improving evaluation, model sharing, model searching, comparisons and reproducibility among studies. Our initially proposed standards here are designed to be modified and extended to evolve with research trends and needs.

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Standards for distribution models in biodiversity assessments

Abstract: Biodiversity assessments use a variety of data and models. We propose best-practice standards for studies in these assessments.

Cited by 771 publications

References 154 publications

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

A Probabilistic Co‐Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns

The effect of positional error on fine scale species distribution models increases for specialist species

Species' range model metadata standards: RMMS

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