The ODMAP protocol: a new tool for standardized reporting that could revolutionize species distribution modeling

Fitzpatrick, Matthew C.; Lachmuth, Susanne; Haydt, Natalie T.

doi:10.1111/ecog.05700

Cited by 19 publications

(14 citation statements)

References 15 publications

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“…We report all additional modelling details in Appendix , Table S1.2 and provide an ODMAP Protocol in Appendix (following Zurell et al, 2020) for standardised and reproducible model documentation (Fitzpatrick et al, 2021). Unless otherwise noted, all these and subsequent data processing and analyses were conducted with R v. 4.0.2 and 4.1.1 (R Core Team, 2021).…”

Section: Methodsmentioning

confidence: 99%

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

Chardon

Nabe‐Nielsen

Assmann

et al. 2022

Diversity and Distributions

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Aim Improving species distribution models (SDMs) and species abundance models (SAMs) of woody shrubs is critical for predicting biodiversity changes in the Arctic, which is experiencing especially high warming rates. Yet, it remains relatively unexplored if SDMs and SAMs can explain local scale patterns. We aim to identify predictor differences for the distribution versus abundance of two widespread Arctic shrub species with high resolution models and to compare validation approaches to assess the models’ predictive abilities. Location Nuup Kangerlua (NK) and Kangerluarsunnguaq (K), two adjacent fjords in Southwest Greenland. Methods We conducted two separate field surveys in either fjord to construct high resolution (~90 m) SDMs and SAMs for Betula nana and Salix glauca, analysing the predictive influences of local scale climate, topography and soil moisture indicators. We then alternatively trained and validated models in either NK or K fjord and compared these results with the common split‐sample validation approach. Finally, we assessed if including local scale biotic predictors improves SAM performance. Results Temperature extremes and precipitation best predicted the distributions of both species, whereas insolation and soil moisture indicators best predicted abundances. Compared to split‐sample validation, both SDM and SAM performance was substantially reduced with separate survey validation. Regardless of validation approach, models performed poor to moderately well, and including local scale biotic parameters improved SAM performance. Main conclusions Substantial differences in model performance between validation approaches highlight the usefulness of using a separate survey for validating model predictive performance. We discuss various factors that might have caused poor model performance, such as not capturing all relevant predictors or enough local scale heterogeneity in predictor or response variables. We emphasise the need to include predictors relevant at the spatial scale of study, such as local scale biotic interactions, for improved predictions at high spatial resolution.

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Section: Methodsmentioning

confidence: 99%

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

Chardon

Nabe‐Nielsen

Assmann

et al. 2022

Diversity and Distributions

View full text Add to dashboard Cite

show abstract

“…To further enhance transparency and reproducibility, especially within the context of complex modeling workflows, it would be valuable for the connectivity modeling community to develop and adopt a detailed, standardized reporting protocol, as has recently been done for species distribution modeling (SDM) with the ODMAP reporting standard (Zurell et al 2020, Fitzpatrick et al 2021). Notably, many connectivity modeling studies build SDMs as part of the resistance layer generation (Fig.…”

Section: Discussionmentioning

confidence: 99%

Four steps to strengthen connectivity modeling

Riordan‐Short

Pither

2023

Ecography

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Maintaining and restoring ecological connectivity is considered a global imperative to help reverse the decline of biodiversity. To be successful, practitioners need to be guided by connectivity modeling research that is rigorous and reliable for the task at hand. However, the methods and workflows within this rapidly growing field are diverse and few have been carefully scrutinized. We propose four steps that should be consistently undertaken in connectivity modeling studies in order to improve rigour and utility: 1) describe the type of connectivity being modeled, 2) assess the uncertainty and sensitivity of model parameters, 3) validate the model outputs, ideally with independent data and 4) make non‐sensitive raw data and code openly available to enhance computational reproducibility. We reviewed the literature to determine the extent to which studies included these four steps. We focused on studies that generated novel landscape connectivity outputs using circuit theory and restricted our assessment to studies concerning terrestrial mammals. Among 181 studies meeting our search criteria, 39% communicated the type of connectivity being modeled, 18% conducted some form of sensitivity or uncertainty analysis (or both), 18% of studies attempted to validate their connectivity model outputs and only 7% used fully independent data to do so. Lastly, 13% of the studies made all raw data available, 2% provided all required code and only two studies provided both. Our findings highlight a clear need and opportunity to improve the reliability, reproducibility and utility of connectivity modeling research. We provide a checklist that researchers can consult and include with outputs. This will help practitioners make more informed decisions and ensure limited resources for connectivity conservation and restoration are allocated appropriately.

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“…Further, this package gives users the tools to determine the effects of their analytical decisions through sensitivity tests. SDM post‐processing toolboxes such as changeRangeR are an important step for measuring and monitoring biodiversity and represent key extensions for SDM methodology and associated documentation and metadata protocols such as ODMAP (Fitzpatrick et al, 2021; Zurell, Franklin, et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

Galante

Triguero

Paz

et al. 2022

Conservat Sci and Prac

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Conservation planning and decision-making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species-and community-level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions.changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by

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The ODMAP protocol: a new tool for standardized reporting that could revolutionize species distribution modeling

Cited by 19 publications

References 15 publications

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

Four steps to strengthen connectivity modeling

changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

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