Dear MARMAM community,
Further to an earlier post about this in the second half of 2019, my co-authors and I are thrilled to announce the publication of our new open access paper in Methods in Ecology and Evolution: Bouchet PJ, Miller DL, Roberts JJ, Mannocci L, Harris CM, Thomas L (2020). dsmextra: Extrapolation assessment tools for density surface models. https://besjournals.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.13469 The paper describes the dsmextra R package, a toolkit for assessing extrapolation in ecological models — with an emphasis (and worked examples) on abundance models of cetacean populations. The latest version (v1.1.2) of the package can be downloaded freely from https://github.com/densitymodelling/dsmextra Abstract (1) Forecasting the responses of biodiversity to global change has never been more important. However, many ecologists faced with limited sample sizes and shoestring budgets often resort to extrapolating predictive models beyond the range of their data to support management actions in data‐deficient contexts. This can lead to error‐prone inference that has the potential to misdirect conservation interventions and undermine decision‐making. Despite the perils associated with extrapolation, little guidance exists on the best way to identify it when it occurs, leaving users questioning how much credence they should place in model outputs. To address this, we present dsmextra, a new R package for measuring, summarizing and visualizing extrapolation in multivariate environmental space. (2) dsmextra automates the process of conducting quantitative, spatially explicit assessments of extrapolation on the basis of two established metrics: the Extrapolation Detection (ExDet) tool and the percentage of data nearby (%N). The package provides user‐friendly functions to (a) calculate these metrics, (b) create tabular and graphical summaries, (c) explore combinations of covariate sets as a means of informing covariate selection and (d) produce visual displays in the form of interactive html maps. (3) dsmextra implements a model‐agnostic approach to extrapolation detection that is applicable across taxonomic groups, modelling techniques and datasets. We present a case study fitting a density surface model to visual detections of pantropical spotted dolphins Stenella attenuata in the Gulf of Mexico. (4) Predictive modelling seeks to deliver actionable information about the states and trajectories of ecological systems, yet model performance can be strongly impaired out of sample. By assessing conditions under which models are likely to fail or succeed in extrapolating, ecologists may gain a better understanding of biological patterns and their underlying drivers. Critical to this is a concerted effort to standardize best practice in model evaluation, with an emphasis on extrapolative capacity. This work forms an output of the DenMod project, a collaborative partnership between the University of St Andrews, Duke University, and NOAA Fisheries, and supported through funding by the U.S. Navy’s Living Marine Resources programme. More information on project aims and outputs can be found at: https://synergy.st-andrews.ac.uk/denmod/ Kind regards, Phil Bouchet Postdoctoral Research Fellow Centre for Research into Ecological & Environmental Modelling (CREEM) University of St Andrews St Andrews, Scotland
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