CAREER: Machine Learning Methods for Spatial Data with Applications in Ecology

职业：空间数据的机器学习方法及其在生态学中的应用

• 批准号: 2046678
• 负责人: Rebecca Hutchinson
• 金额: $ 56.4万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046678&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Methods; Spatial

Species distribution models (SDMs) are widely used tools in ecology and natural resource management. SDMs are built by correlating observations of a species (e.g., whether it is present or absent) with environmental features. Once built, they can be used to predict how likely a species is to occur at a new site or interpreted to understand why species live where they do. The spatial aspects of species and environmental data present challenges for the machine learning methods often used to build SDMs, and this award focuses on two of those challenges. First, one must assess the quality of an SDM in order to determine the validity of its predictions and interpretation. To assess quality, some data are often held out from model building. Then, the model’s ability to predict the unseen data are used as a measure of its quality. With spatial data however, randomly selecting data to hold out can lead to optimistic bias in quality estimates. This award will support research into methods for assessing model quality that account for spatial characteristics of the data in order to produce unbiased estimates of model quality. A second challenge arises when the data supplied to an SDM come from the growing repositories collected by community science programs. Under-reporting is a common phenomenon in biodiversity surveys (since one typically cannot observe all individuals of all species during an observation), and community science is no exception. The error introduced by under-reporting can be corrected by conducting multiple observations at the same site and estimating the probability of detecting the species, but community science programs are often not structured this way. This award will support research to create groups of multiple observations after the fact, so that under-reporting error can be accounted for better in this growing data resource. In addition to these scientific aims, this award will support education and outreach to graduate, undergraduate, and pre-college students, including the production of a set of benchmark datasets, a new introductory computer science course, and modules for STEM clubs and camps. The research contributions of this award will enable scientists to build better models of spatial phenomena. The anticipated framework for cross-validation that accounts for domain adaptation between training and test folds will produce better generalization performance estimates by accounting for spatial autocorrelation and admitting target testing distributions. In SDM, this means that researchers will be able to correct for bias induced by autocorrelation and provide climate model projections to obtain estimates of how species will fare under global change. In addition, it will define and propose solutions to a new type of spatial clustering problem: creating spatial modeling abstractions aimed at meeting the assumptions of a subsequent modeling phase. In science and management questions informed by SDMs, this will improve the ability to correct for observational errors and translate to better habitat models. The methods will be applicable beyond the motivating applications in SDM to a variety of spatial domains. The education plan will build bridges between ecology and computer science, while drawing on best educational practices to improve recruitment and retention of underserved students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

物种分布模型是生态学和自然资源管理中广泛使用的工具。SDM是通过将物种的观察（例如，是否存在）与环境特征。一旦建成，它们可以用来预测一个物种在新地点出现的可能性，或者解释为什么物种生活在它们所生活的地方。物种和环境数据的空间方面对通常用于构建SDM的机器学习方法提出了挑战，该奖项重点关注其中两个挑战。首先，必须评估SDM的质量，以确定其预测和解释的有效性。为了评估质量，一些数据往往是从模型构建。然后，模型预测未知数据的能力被用作其质量的度量。然而，对于空间数据，随机选择数据来支持可能会导致质量估计的乐观偏差。该奖项将支持对模型质量评估方法的研究，这些方法考虑了数据的空间特征，以便对模型质量进行无偏估计。当提供给SDM的数据来自社区科学计划收集的不断增长的知识库时，第二个挑战出现了。低报是生物多样性调查中的一个常见现象（因为在一次观察中通常无法观察到所有物种的所有个体），社区科学也不例外。少报所带来的错误可以通过在同一地点进行多次观测并估计发现物种的概率来纠正，但社区科学计划通常不是这样构建的。该奖项将支持在事后创建多个观察组的研究，以便在这个不断增长的数据资源中更好地解释漏报错误。除了这些科学目标外，该奖项还将支持研究生，本科生和大学预科生的教育和推广，包括制作一套基准数据集，新的入门计算机科学课程以及STEM俱乐部和营地的模块。该奖项的研究贡献将使科学家能够建立更好的空间现象模型。考虑训练和测试折叠之间的域适应的交叉验证的预期框架将通过考虑空间自相关和承认目标测试分布来产生更好的泛化性能估计。在SDM中，这意味着研究人员将能够纠正自相关引起的偏差，并提供气候模型预测，以获得物种在全球变化下的估计。此外，它将定义并提出一种新型的空间聚类问题的解决方案：创建空间建模抽象，旨在满足后续建模阶段的假设。在科学和管理问题中，这将提高对观测误差的纠正能力，并转化为更好的栖息地模型。这些方法将适用于超越SDM中的激励应用程序到各种空间域。该教育计划将在生态学和计算机科学之间建立桥梁，同时借鉴最佳教育实践，以改善对服务不足学生的招聘和保留。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data

空间聚类算法在从社区科学数据构建物种分布模型中的作用

• 发表时间: 2021
• 期刊: ICML 2021 Workshop: Tackling Climate Change with Machine Learning
• 作者: Roth, Mark;Hallman, Tyler;Robinson, W. Douglas;Hutchinson, Rebecca A.
• 通讯作者: Hutchinson, Rebecca A.