The predictive performance of process-explicit range change models remains largely untested

David E. Uribe-Rivera*, Gurutzeta Guillera-Arroita, Saras M. Windecker, Patricio Pliscoff, Brendan A. Wintle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Ecological models used to forecast range change (range change models; RCM) have recently diversified to account for a greater number of ecological and observational processes in pursuit of more accurate and realistic predictions. Theory suggests that process-explicit RCMs should generate more robust forecasts, particularly under novel environmental conditions. RCMs accounting for processes are generally more complex and data hungry, and so, require extra effort to build. Thus, it is necessary to understand when the effort of building a more realistic model is likely to generate more reliable forecasts. Here, we review the literature to explore whether process-explicit models have been tested through benchmarking their temporal predictive performance (i.e. their predictive performance when transferred in time) and model transferability (i.e. their ability to keep their predictive performance when transferred to generate predictions into a different time) against simpler models, and highlight the gaps between the rapid development of process-explicit RCMs and the testing of their potential improvements. We found that, out of five ecological processes (dispersal, demography, physiology, evolution, species interactions) and two observational processes (sampling bias, imperfect detection) that may influence reliability of forecasts, only the effects of dispersal, demography and imperfect detection have been benchmarked using temporally-independent datasets. Only nine out of twenty-nine process-explicit model types have been tested to assess whether accounting for processes improves temporal predictive performance. We found no benchmarks assessing model transferability. We discuss potential reasons for the lack of empirical validation of process-explicit models. Considering these findings, we propose an expanded research agenda to properly test the performance of process-explicit RCMs, and highlight some opportunities to fill the gaps by suggesting models to be benchmarked using existing historical datasets.

Original languageEnglish
Article numbere06048
JournalEcography
Volume2023
Issue number4
DOIs
StatePublished - Apr 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos.

Keywords

  • ecological forecast
  • model transferability
  • predictive performance
  • process-explicit models
  • range shift
  • species distribution models

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