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spatialMaxent: Adapting species distribution modeling to spatial data
Conventional practices in species distribution modeling lack predictive power when the spatial structure of data is not taken into account. However, choosing a modeling approach that accounts for overfitting during model training can improve predictive performance on spatially separated test data...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
Philipps-Universität Marburg
2023
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| 主題: | |
| オンライン･アクセス: | PDFフルテキスト |
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| 要約: | Conventional practices in species distribution modeling lack predictive power when
the spatial structure of data is not taken into account. However, choosing a modeling
approach that accounts for overfitting during model training can improve predictive
performance on spatially separated test data, leading to more reliable models.
This study introduces spatialMaxent (https:// github. com/ envima/ spati alMaxent), a
software that combines state-of-the-art spatial modeling techniques with the popular
species distribution modeling software Maxent. It includes forward-variableselection,
forward-feature-selection, and regularization-multiplier tuning based on
spatial cross-validation, which enables addressing overfitting during model training
by considering the impact of spatial dependency in the training data. We assessed
the performance of spatialMaxent using the National Center for Ecological Analysis
and Synthesis dataset, which contains over 200 anonymized species across six regions
worldwide. Our results show that spatialMaxent outperforms both conventional
Maxent and models optimized according to literature recommendations without using
a spatial tuning strategy in 80 percent of the cases. spatialMaxent is user-friendly and
easily accessible to researchers, government authorities, and conservation practitioners.
Therefore, it has the potential to play an important role in addressing pressing
challenges of biodiversity conservation. |
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| 記述事項: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| DOI: | 10.1002/ece3.10635 |