遥测和空间调查数据的联合推断。

Ecology Pub Date : 2024-10-30 DOI:10.1002/ecy.4457
Paul G Blackwell, Jason Matthiopoulos
{"title":"遥测和空间调查数据的联合推断。","authors":"Paul G Blackwell, Jason Matthiopoulos","doi":"10.1002/ecy.4457","DOIUrl":null,"url":null,"abstract":"<p><p>Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, \"spatial survey\" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.</p>","PeriodicalId":93986,"journal":{"name":"Ecology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint inference for telemetry and spatial survey data.\",\"authors\":\"Paul G Blackwell, Jason Matthiopoulos\",\"doi\":\"10.1002/ecy.4457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, \\\"spatial survey\\\" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.</p>\",\"PeriodicalId\":93986,\"journal\":{\"name\":\"Ecology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/ecy.4457\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/ecy.4457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

数据整合,即对来自不同观测平台的数据进行联合统计分析,对于空间生态学等对数据要求极高的学科来说至关重要。通过联合分析从同一基础过程中获得的集合数据类型,可以提高物种分布和物种与栖息地关联模型的精度和准确性。然而,由于遥测数据和空间调查数据所需的分析方法根本不同,这两种数据的整合一直难以实现。在这里,"空间调查 "指的是记录空间位置且没有时间结构的调查,例如线或点横断面,但不包括捕获-重捕或遥测。步骤选择函数(SSF--遥测的典型框架)和生境选择函数(HSF--空间调查的默认方法)不仅在规格上不同,而且在结果上也不同。通过施加微观机制(动物运动)必须正确放大到宏观出现(种群分布)的约束,可以写出 SSF 和 HSF 之间的关系,从而使用这两种数据集得出联合似然。我们利用最大似然法实现了这一方法,通过系统模拟探讨了其估算精度,并试图为野外工作分配得出广泛的指导原则。我们发现,遥测数据和调查数据在空间覆盖范围和分辨率上的互补性往往会使联合分析的推断结果明显优于单独使用其中一种数据的分析结果。两种方法之间的最佳分配(或在只能选择一种方法的情况下对两种方法的选择)取决于所花费的总体努力和环境异质性模式。在对两种方法的相对成本进行广泛推断时,我们发现综合分析通常具有更高的精确度。我们的方法论工作展示了如何利用传统计算方法,在新颖的联合似然函数下整合遥测和空间调查数据分析。我们的模拟实验表明,即使这两种方法的相对成本有利于部署一种实地方法而不是另一种,它们的联合使用在很大程度上也是可取的。因此,对空间生态学中使用的两种关键方法进行联合分析不仅是可能的,而且计算效率更高,统计功能更强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint inference for telemetry and spatial survey data.

Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data-hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species-habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, "spatial survey" denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture-recapture or telemetry. Step selection functions (SSFs-the canonical framework for telemetry) and habitat selection functions (HSFs-the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信