2025/02/13 by Brody Sandel, Cory Merow, Josep M. Serra‐Diaz +1 · 1 voice · 16 citations
Agricultural and Biological Sciences · Environmental Science · Mathematics · #Biology #Disequilibrium #Distribution (mathematics) #Ecology #Ecology and Vegetation Dynamics Studies #Environmental science #Geography #Mathematics #Plant and animal studies #Species Distribution and Climate Change
paper · doi:10.1111/1365-2745.70009
published in Journal of Ecology 113(4), 782-794 (Wiley)
openalex publication_date 2025/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
Abstract Environmental conditions are dynamic, and plants respond to those dynamics on multiple time scales. Disequilibrium occurs when a response occurs more slowly than the driving environmental changes. We review evidence regarding disequilibrium in plant distributions, including their responses to paleoclimate changes, recent climate change and new species introductions. There is strong evidence that plant species distributions are often in some disequilibrium with their environmental conditions. This disequilibrium poses a challenge when projecting future species distributions using species distribution models (SDMs). Classically, SDMs assume that the set of species occurrences is an unbiased sample of the suitable environmental conditions. However, a species in disequilibrium with the environment may have higher‐than‐expected occurrence probabilities (e.g. due to extinction debts) or lower‐than‐expected occurrence probabilities (e.g. due to dispersal limitation) in different areas. If unaccounted for, this will lead to biased estimates of the environmental suitability. We review methods for avoiding such biases in SDMs, ranging from simple thinning of the occurrence dataset to complex dynamic and process‐based models. Such models require large data inputs, natural history knowledge and technical expertise, so implementing them can be challenging. Despite this, we advocate for their increased use, since process‐based models provide the best potential to account for biases in model training data and to then represent the dynamics of species occupancy as ranges shift. Synthesis . Occurrence records for a species are often in disequilibrium with climate. SDMs trained on such data will produce biased estimates of a species' niche unless this disequilibrium is addressed in the modelling. A range of tools, spanning a wide gradient of complexity and realism, can resolve this bias.