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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.
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Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?
Abstract Understanding the ranges of rare and endangered species is central to conserving biodiversity in the Anthropocene. Species distribution models (SDMs) have become a common and powerful tool for analyzing species–environment relationships across geographic space. Although evaluating the distribution of rare species is integral to their conservation, this can be difficult when limited distribution data are available. Community science platforms, such as iNaturalist, have emerged as alternative sources for species occurrence data. Although these observations are often thought to be of lower quality than those of natural history collections, they may have potential for improving SDMs for species with few occurrence records from collections. Here, we investigate the utility of iNaturalist data for developing SDMs for a rare high‐elevation plant,Telesonix jamesii. Because methods for modeling rare species are limited in the literature, five different modeling techniques were considered, including profile methods, statistical models, and machine learning algorithms. The inclusion of iNaturalist data doubled the number of usable records forT. jamesii.We found that a random forest (RF) model using ensemble training data performed the highest of any model (area under curve = 0.98). We then compared the performance of RF models that use only natural history training data and those that use a combination of natural history (herbarium specimens) and iNaturalist training data. All models heavily relied on climate data (mean temperature of driest quarter, and precipitation of the warmest quarter), indicating that this species is under threat as climate continues to change. Validation datasets affected model fits as well. Models using only herbarium data performed slightly poorer when evaluated with cross‐validation than when validated externally with iNaturalist data. This study can serve as a model for future SDM studies of species with similar data limitations.
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- Award ID(s):
- 2102974
- PAR ID:
- 10403825
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 14
- Issue:
- 3
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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