Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world
| Autor: | Richard C. Hamelin, Amanda D. Roe, Vivek Srivastava, Verena C. Griess, Melody A. Keena |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Ecology business.industry 010604 marine biology & hydrobiology Principle of maximum entropy Feature selection Biology Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences Environmental niche modelling Biological dispersal Artificial intelligence Sudden oak death Baseline (configuration management) business computer Ecology Evolution Behavior and Systematics |
| Zdroj: | Biological Invasions. 23:297-349 |
| ISSN: | 1573-1464 1387-3547 |
| DOI: | 10.1007/s10530-020-02372-9 |
| Popis: | Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. Maximum entropy modelling (MaxEnt) is a popular choice for SDM, but questions have been raised about how these models are developed. Without biologically informed baseline assumptions, complex default SDM models could be selected, even though alternative settings may be more appropriate. Here we explored the effects of various SDM design strategies on distribution mapping of four forest invasive species (FIS) in Canada. We found that if we ignored the underlying FIS biology such as use of biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions when we developed our SDMs, we obtained complex SDMs (default) that provided an incomplete picture of the potential FIS invasion. We recommend simplifying SDM complexity and including biologically informed assumptions to achieve more accurate dispersal predictions, particularly when projecting FIS spread across time. We strongly encourage SDM users to perform species-specific tuning when modeling FIS distributions with MaxEnt to determine the best SDM design. |
| Databáze: | OpenAIRE |
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