Abstract
Addressing current global challenges such as biodiversity loss, global change, and increasing demands for ecosystem services requires improved ecological prediction. Recent increases in data availability, process understanding, and computing power are fostering quantitative approaches in ecology. However, flexible methodological frameworks are needed to utilize these developments towards improved ecological prediction. Deep learning is a rapidly evolving branch of machine learning, yet has received only little attention in ecology to date. It refers to the training of deep neural networks (DNNs), i.e. artificial neural networks consisting of many layers and a large number of neurons. We here provide a reproducible example (including code and data) of designing, training, and applying DNNs for ecological prediction. Using bark beetle outbreaks in conifer-dominated forests as an example, we show that DNNs are well able to predict both short-term infestation risk at the local scale and long-term outbreak dynamics at the landscape level. We furthermore highlight that DNNs have better overall performance than more conventional approaches to predicting bark beetle outbreak dynamics. We conclude that DNNs have high potential to form the backbone of a comprehensive disturbance forecasting system. More broadly, we argue for an increased utilization of the predictive power of DNNs for a wide range of ecological problems.
Highlights
Ecology is a relatively young discipline, and many of its theoretical foundations are less than a century old (Real and Brown, 1991)
We did not find a single study employing deep learning published in ecological flagship journals such as Ecology, Journal of Ecology, Ecology Letters, BioScience, Ecological Applications, Journal of Applied Ecology, Diversity and Distributions, or Global Ecology and Biogeography
Deep learning has the potential to become a powerful tool for ecologists (Reichstein et al, 2019), especially as the field moves towards a more quantitative and predictive approach (Clark et al, 2001; Evans et al, 2012)
Summary
Ecology is a relatively young discipline, and many of its theoretical foundations are less than a century old (Real and Brown, 1991). Ecology has matured considerably as a scientific field, which is inter alia reflected by a strong increase in the application of ecological knowledge, data, and methods (e.g., Shea and Chesson, 2002), as well as a recent push towards predictive ecology (Clark et al, 2001; Evans et al, 2012; Dietze et al, 2018). Ecological prediction broadly describes the process of putting ecological knowledge, data, and methods to use for making testable, quantitative estimates about future states of an ecosystem (Luo et al, 2011). The increasing focus on prediction is motivated, amongst other things, by the growing realization that ecology is central to addressing a number of the most pressing challenges faced by humanity in the 21st century, such as to mitigate the impacts of climate change and halt biodiversity loss (Mouquet et al, 2015).
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