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

物种分布模型(SDMS)是生态学和自然资源管理中广泛使用的工具。SDMS是通过将对物种的观察(例如，是否存在)与环境特征相关联来建立的。一旦建成，它们就可以用来预测一个物种在新地点出现的可能性有多大，或者被解释为理解物种为什么生活在他们生活的地方。物种和环境数据的空间方面对经常用于建立SDMS的机器学习方法提出了挑战，该奖项侧重于其中的两个挑战。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，一些数据通常从模型构建中拿出来。然后，该模型预测未见数据的能力被用作其质量的衡量标准。然而，对于空间数据，随机选择数据进行保留可能会导致质量估计中的乐观偏差。该奖项将支持对模型质量评估方法的研究，这些方法考虑到数据的空间特征，以便对模型质量进行公正的估计。当提供给SDM的数据来自社区科学项目收集的越来越多的存储库时，第二个挑战就出现了。报告不足是生物多样性调查中的普遍现象(因为人们通常不能在观察期间观察到所有物种的所有个体)，社区科学也不例外。通过在同一地点进行多次观测并估计发现物种的概率，可以纠正漏报带来的错误，但社区科学计划往往不是这样构建的。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报告不足的错误。除了这些科学目标，该奖项还将支持对研究生、本科生和大学预科学生的教育和推广，包括制作一套基准数据集，一门新的计算机科学入门课程，以及STEM俱乐部和夏令营的模块。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。考虑到训练和测试折叠之间的领域适应的预期交叉验证框架将通过考虑空间自相关性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，以获得对物种在全球变化下的表现的估计。此外，它还将定义并提出一种新型空间集群问题的解决方案：创建旨在满足后续建模阶段的假设的空间建模抽象。在由SDMS提供信息的科学和管理问题中，这将提高纠正观测错误的能力，并转化为更好的栖息地模型。这些方法将不仅适用于SDM中的激励性应用，还将适用于各种空间域。该教育计划将在生态学和计算机科学之间架起桥梁，同时借鉴最佳教育实践来改善招收和留住未得到充分服务的学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.