Deep learning with citizen science data enables estimation of species diversity and composition at continental extents
Deep learning with citizen science data enables estimation of species diversity and composition at continental extents
复制标题
利用公民科学数据进行深度学习可以估计大陆范围内的物种多样性和组成
DOI：
10.1002/ecy.4175
复制
发表时间：
2023
期刊：
影响因子：
4.8
通讯作者：
Fink, Daniel
中科院分区：
文献类型：
--
作者：
Davis, Courtney L.;Bai, Yiwei;Chen, Di;Robinson, Orin;Ruiz‐Gutierrez, Viviana;Gomes, Carla P.;Fink, Daniel
保护生物多样性的有效解决方案需要在相关、可操作的范围内和整个物种分布范围内提供准确的社区和物种层面的信息。然而，数据和方法的限制限制了我们以强有力的方式提供此类信息的能力。在这里，我们使用深度多变量概率模型(DMVP-DRNets)的深度推理网络实现(DMVP-DRNets)，这是一个端到端的深度神经网络框架，用于同时利用大量的观测和环境数据集，并估计大陆范围内景观尺度的物种多样性和组成。我们介绍了一项新的北美鸟类全年分析的结果，该分析使用了900多万份eBird清单和72个环境协变量的数据。我们强调我们的信息的用处，为单一的保护关注群体--北美林莺--确定物种高度多样性的关键区域，同时捕捉物种环境联系和种间相互作用的时空变化。在这样做的过程中，我们展示了深度学习方法(如DMVP-DRNet)可以提供的关于生物多样性的准确、高分辨率信息，以及在多个尺度上为生态研究和保护决策提供信息所需的信息。
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.