{"title":"利用高斯过程回归从时间序列数据中量化和探索状态依赖的生态相互作用。","authors":"Taiju Yukihira, Yutaka Osada, Michio Kondoh","doi":"10.1098/rsif.2025.0154","DOIUrl":null,"url":null,"abstract":"<p><p>Ecological interactions in natural communities are often highly nonlinear; that is, interaction strengths can fluctuate temporally depending on community states. An effective and reliable tool to infer state-dependent interactions from empirical data is crucial to ecological studies. Here, we propose a novel non-parametric inference method based on Gaussian process regression to quantify interaction strengths from nonlinear time series data. We introduce the method by extending the Gaussian process empirical dynamic modelling (GP-EDM) approach in ecology. To confirm its applicability, we investigated the performance of the proposed method, using both synthetic and real-time series data. The results highlight that the proposed method possesses several distinct features. First, throughout performance comparison with existing methods (S-map and regularized S-map), the proposed method achieves higher inference accuracy for noisy time series data. Second, the proposed method analytically accounts for the dependence of interaction strengths on community states. This enables us to locally evaluate state-dependent changes in interaction strengths by exploring hypothetical community states. Moreover, because the posterior function is derived analytically, the proposed method can easily evaluate the inference uncertainty (e.g. credible interval), resulting in more reliable inference outcomes. The proposed method provides a basis for addressing state dependence in analyses of species interactions.</p>","PeriodicalId":17488,"journal":{"name":"Journal of The Royal Society Interface","volume":"22 228","pages":"20250154"},"PeriodicalIF":3.5000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12308230/pdf/","citationCount":"0","resultStr":"{\"title\":\"Quantifying and exploring state-dependent ecological interactions from time series data using Gaussian process regression.\",\"authors\":\"Taiju Yukihira, Yutaka Osada, Michio Kondoh\",\"doi\":\"10.1098/rsif.2025.0154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Ecological interactions in natural communities are often highly nonlinear; that is, interaction strengths can fluctuate temporally depending on community states. An effective and reliable tool to infer state-dependent interactions from empirical data is crucial to ecological studies. Here, we propose a novel non-parametric inference method based on Gaussian process regression to quantify interaction strengths from nonlinear time series data. We introduce the method by extending the Gaussian process empirical dynamic modelling (GP-EDM) approach in ecology. To confirm its applicability, we investigated the performance of the proposed method, using both synthetic and real-time series data. The results highlight that the proposed method possesses several distinct features. First, throughout performance comparison with existing methods (S-map and regularized S-map), the proposed method achieves higher inference accuracy for noisy time series data. Second, the proposed method analytically accounts for the dependence of interaction strengths on community states. This enables us to locally evaluate state-dependent changes in interaction strengths by exploring hypothetical community states. Moreover, because the posterior function is derived analytically, the proposed method can easily evaluate the inference uncertainty (e.g. credible interval), resulting in more reliable inference outcomes. The proposed method provides a basis for addressing state dependence in analyses of species interactions.</p>\",\"PeriodicalId\":17488,\"journal\":{\"name\":\"Journal of The Royal Society Interface\",\"volume\":\"22 228\",\"pages\":\"20250154\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12308230/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of The Royal Society Interface\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1098/rsif.2025.0154\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Royal Society Interface","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1098/rsif.2025.0154","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Quantifying and exploring state-dependent ecological interactions from time series data using Gaussian process regression.
Ecological interactions in natural communities are often highly nonlinear; that is, interaction strengths can fluctuate temporally depending on community states. An effective and reliable tool to infer state-dependent interactions from empirical data is crucial to ecological studies. Here, we propose a novel non-parametric inference method based on Gaussian process regression to quantify interaction strengths from nonlinear time series data. We introduce the method by extending the Gaussian process empirical dynamic modelling (GP-EDM) approach in ecology. To confirm its applicability, we investigated the performance of the proposed method, using both synthetic and real-time series data. The results highlight that the proposed method possesses several distinct features. First, throughout performance comparison with existing methods (S-map and regularized S-map), the proposed method achieves higher inference accuracy for noisy time series data. Second, the proposed method analytically accounts for the dependence of interaction strengths on community states. This enables us to locally evaluate state-dependent changes in interaction strengths by exploring hypothetical community states. Moreover, because the posterior function is derived analytically, the proposed method can easily evaluate the inference uncertainty (e.g. credible interval), resulting in more reliable inference outcomes. The proposed method provides a basis for addressing state dependence in analyses of species interactions.
期刊介绍:
J. R. Soc. Interface welcomes articles of high quality research at the interface of the physical and life sciences. It provides a high-quality forum to publish rapidly and interact across this boundary in two main ways: J. R. Soc. Interface publishes research applying chemistry, engineering, materials science, mathematics and physics to the biological and medical sciences; it also highlights discoveries in the life sciences of relevance to the physical sciences. Both sides of the interface are considered equally and it is one of the only journals to cover this exciting new territory. J. R. Soc. Interface welcomes contributions on a diverse range of topics, including but not limited to; biocomplexity, bioengineering, bioinformatics, biomaterials, biomechanics, bionanoscience, biophysics, chemical biology, computer science (as applied to the life sciences), medical physics, synthetic biology, systems biology, theoretical biology and tissue engineering.