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Abstract Effective solutions to conserve biodiversity require accurate community‐ and species‐level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to provide such information in robust ways. Herein we employ a Deep‐Reasoning Network implementation of the Deep Multivariate Probit Model (DMVP‐DRNets), an end‐to‐end deep neural network framework, to exploit large observational and environmental data sets together and estimate landscape‐scale species diversity and composition at continental extents. We present results from a novel year‐round analysis of North American avifauna using data from over nine million eBird checklists and 72 environmental covariates. We highlight the utility of our information by identifying critical areas of high species diversity for a single group of conservation concern, the North American wood warblers, while capturing spatiotemporal variation in species' environmental associations and interspecific interactions. In so doing, we demonstrate the type of accurate, high‐resolution information on biodiversity that deep learning approaches such as DMVP‐DRNets can provide and that is needed to inform ecological research and conservation decision‐making at multiple scales.
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Embedding Conjugate Gradient in Learning Random Walks for Landscape Connectivity Modeling in Conservation
Models capturing parameterized random walks on graphs have been widely adopted in wildlife conservation to study species dispersal as a function of landscape features. Learning the probabilistic model empowers ecologists to understand animal responses to conservation strategies. By exploiting the connection between random walks and simple electric networks, we show that learning a random walk model can be reduced to finding the optimal graph Laplacian for a circuit. We propose a moment matching strategy that correlates the model’s hitting and commuting times with those observed empirically. To find the best Laplacian, we propose a neural network capable of back-propagating gradients through the matrix inverse in an end-to-end fashion. We developed a scalable method called CGInv which back-propagates the gradients through a neural network encoding each layer as a conjugate gradient iteration. To demonstrate its effectiveness, we apply our computational framework to applications in landscape connectivity modeling. Our experiments successfully demonstrate that our framework effectively and efficiently recovers the ground-truth configurations.
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- Award ID(s):
- 1763108
- PAR ID:
- 10189271
- Date Published:
- Journal Name:
- Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)
- Page Range / eLocation ID:
- 4338-4344
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.24963/ijcai.2020/598
