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Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change

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Machine Learning for Ecology and Sustainable Natural Resource Management

Abstract

The field of machine learning has grown exponentially over the past decade, helped by faster PCs with more memory, and resulting in development of a plethora of techniques based on ensemble methods. In this chapter, I explore techniques relevant to macroscale ecological niche modelling in a regression context. I evaluate the challenges while predicting suitable habitats under future climates, and address issues related to high dimensional data like variance-bias tradeoffs, overfitting and nonlinearity. To illustrate, I choose a generalist tree species, the white oak (Quercus alba) in the eastern United States, and model its current and future-climate abundances as a multi-response blend of relative and absolute dominance and density. A novel multi-model ensemble approach is developed, using techniques of randomized decision trees and stochastic gradient boosting. I assess model performance, prediction confidence, and predictor importance in a multi-response, multi-model framework and discuss its relevance for tree species management under climate change as well as its limitations and caveats.

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Acknowledgements

The author would like to thank Louis Iverson for his comprehensive review and also two anonymous reviewers for their valuable suggestions. Thanks to the Northern Research Station, USDA Forest Service, for funding.

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Authors and Affiliations

  1. Research Ecologist, USDA Forest Service, Northern Research Station, Delaware, OH, USA

    Anantha M. Prasad

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  1. Anantha M. Prasad
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Correspondence to Anantha M. Prasad .

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Editors and Affiliations

  1. Black Bawks Data Science Ltd, Fort Augustus, Scotland, New Zealand

    Grant Humphries

  2. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

  3. Institute of Arctic Biology, University of Alaska - Fairbanks, FAIRBANKS, AK, USA

    Falk Huettmann

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Prasad, A.M. (2018). Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_6

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