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Abstract Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no ‘rule of thumb’ for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.
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Predicting Habitat Shifts of High-Alpine Amphibians in the Southern Rocky Mountain Region
Global biodiversity is diminishing rapidly. The number of accepted “background” extinction rates range from 0.1 to 4 species per million per year. However, some authors have noted an increase in extinctions to 150–260 species per million per year since 1500. As the largely anthropogenic-induced global biodiversity crisis progresses, an abundance of taxa are faced with changing environments, decreasing population sizes, and/or extinction. Concerning amphibians, at least 43% of all species are experiencing population declines, with the 2008 Global Amphibian Assessment showing that 32% of all amphibian species are threatened or extinct. Populations of high-alpine amphibians in the southern Rocky Mountain region (i.e., Boreal toads [Anaxyrus boreas boreas], Tiger salamanders [Ambystoma mavortium], and Boreal chorus frogs [Pseudacris maculata]) have swiftly declined in recent decades due to high 649 susceptibility to diseases (e.g., Batrachochytrium dendrobatidis and ranaviruses) and changing environmental conditions. This study will focus on the effects of climate change by examining the habitat of these three amphibious species. A Maximum Entropy (MaxEnt) model is a commonly used machine-learning method of creating SDMs by accounting for the most uniform distribution and the provided predictors. Well-fitted models can be produced for different climatic scenarios by including climatic and environmental factors, or predictors, relevant to the species of interest, appropriately adjusting the regularization parameter and active feature classes for each model, and properly evaluating the models. By adopting this method of SDMs along with a decadal time-series approach, we demonstrate a larger-scale pattern of potential habitat shifts over time in this alpine habitat caused by climate change.
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- Award ID(s):
- 2125921
- PAR ID:
- 10548938
- Publisher / Repository:
- 10th World Congress of Herpetology
- Date Published:
- Format(s):
- Medium: X
- Location:
- Sarawak, Malaysia
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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