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Machine Learning for Ecology and Sustainable Natural Resource Management pp 3–26Cite as

Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective

Abstract

The application of machine learning algorithms in ecology has surged forward in the past two decades. More and more, we are seeing innovative and interesting uses of these sophisticated algorithms which are driving inference and understanding in natural resource management. The concept behind machine learning is to provide data to a computer and allow the machine to ‘learn’ the patterns in those data. These learned relationships are applied and analysed in a variety of ways from clustering to prediction. In ecology and natural resource management, these methods are not well taught in classroom settings, which is leading to a major disconnect between ecologists and the latest analytical techniques. In this chapter, we introduce machine learning with a focus on ecological and natural resource management applications. We provide definitions and a list of a few key algorithms that are becoming commonplace in the analysis of wildlife data. We further introduce a few broad concepts (i.e., data sharing, metadata and citizen science) in the sphere of ecological sciences. The ideas presented here will help us to better understand how to apply machine learning for conservation, management, and academic studies. These examples can also be used to teach the next generation of scientists how to best use machine learning algorithms in their own work. Our broader goals here, and elsewhere in this book, is to promote a holistic understanding of our planet through algorithms that can handle many interacting covariates that represent how the world really works.

Keywords

  • Machine learning
  • Data mining
  • Ecology
  • Open access data
  • Algorithms

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Fig. 1.1.1
Fig. 1.2.1

Notes

  1. 1.

    This work was carried out with the Global Primate Network in Nepal, namely Ganga Ram Regmi, Madan Krishna Suwal, Dikpal Krishna Karmacharya, Kamal Kandel and Sonam Tashi Lama.

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Acknowledgements

We would like to thank all the proponents of machine learning algorithms and their use in ecology. The list of people to thank for the discussions in this chapter is too extensive, but without them, our work would not be possible. Special thanks to V Morera and M Garcia Reyes for reviewing this chapter and providing insightful comments.

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Authors and Affiliations

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

    Grant R. W. Humphries

  2. EWHALE Lab, Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks, Fairbanks, AK, USA

    Falk Huettmann

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  1. Grant R. W. Humphries
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  2. Falk Huettmann
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Correspondence to Grant R. W. Humphries .

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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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Humphries, G.R.W., Huettmann, F. (2018). Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective. 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_1

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