Cornell Laboratory of Ornithology, Cornell University, Ithaca, New York, USA.
Department of Computer Science, Cornell University, Ithaca, New York, USA.
Ecology. 2023 Dec;104(12):e4175. doi: 10.1002/ecy.4175. Epub 2023 Oct 23.
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.
有效的生物多样性保护解决方案需要在相关的、可操作的尺度上，并在整个物种分布范围内，提供准确的群落和物种水平的信息。然而，数据和方法上的限制限制了我们以稳健的方式提供此类信息的能力。在这里，我们采用深度推理网络实现的深度多元概率模型（DMVP-DRNets），这是一个端到端的深度神经网络框架，来充分利用大型观测和环境数据集，并估计大陆范围内的景观尺度物种多样性和组成。我们使用来自超过 900 万份 eBird 清单和 72 个环境协变量的数据集，对北美的鸟类进行了一项新颖的全年分析，并展示了结果。我们通过确定一个单一保护关注组（北美林莺）的高物种多样性的关键区域，突出了我们信息的实用性，同时捕捉了物种环境关联和种间相互作用的时空变化。通过这样做，我们展示了深度学习方法（如 DMVP-DRNets）可以提供的关于生物多样性的准确、高分辨率信息的类型，这些信息是在多个尺度上为生态研究和保护决策提供信息所必需的。
