{"title":"利用基于特征的预测模型确定鸟类对自由放养的家猫的脆弱性","authors":"Martin Philippe-Lesaffre, Elsa Bonnaud","doi":"10.1016/j.ecolind.2025.113434","DOIUrl":null,"url":null,"abstract":"<div><div>We developed a method to assess bird species’ vulnerability to predation by free-ranging domestic cats (<em>Felis catus</em>) using prey preference data from citizen science in Italy and the United Kingdom. By combining species traits and geographical range, we trained random forest models to predict prey preferences and identify missing prey species. Our analysis showed that including the geographical range significantly improved model accuracy and reduced prey detectability issues. Cross-validation confirmed that models trained in one country could effectively predict prey preferences in another, allowing for broader application. Shapley additive explanations values analysis revealed that small, generalist birds with a low hand-wing index and large geographical range were most likely to be preyed upon. We used these models to create vulnerability lists for United States bird species, which showed moderate overlap but high consistency with previous studies, highlighting their robustness. These results showed that this method could thus be used to improve our understanding of cat predation and inform targeted conservation strategies, with better citizen science data being crucial for further improvements.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"174 ","pages":"Article 113434"},"PeriodicalIF":7.0000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pinpointing bird species vulnerability to free-ranging domestic cats using trait-based predictive models\",\"authors\":\"Martin Philippe-Lesaffre, Elsa Bonnaud\",\"doi\":\"10.1016/j.ecolind.2025.113434\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We developed a method to assess bird species’ vulnerability to predation by free-ranging domestic cats (<em>Felis catus</em>) using prey preference data from citizen science in Italy and the United Kingdom. By combining species traits and geographical range, we trained random forest models to predict prey preferences and identify missing prey species. Our analysis showed that including the geographical range significantly improved model accuracy and reduced prey detectability issues. Cross-validation confirmed that models trained in one country could effectively predict prey preferences in another, allowing for broader application. Shapley additive explanations values analysis revealed that small, generalist birds with a low hand-wing index and large geographical range were most likely to be preyed upon. We used these models to create vulnerability lists for United States bird species, which showed moderate overlap but high consistency with previous studies, highlighting their robustness. These results showed that this method could thus be used to improve our understanding of cat predation and inform targeted conservation strategies, with better citizen science data being crucial for further improvements.</div></div>\",\"PeriodicalId\":11459,\"journal\":{\"name\":\"Ecological Indicators\",\"volume\":\"174 \",\"pages\":\"Article 113434\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Indicators\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1470160X25003644\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25003644","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Pinpointing bird species vulnerability to free-ranging domestic cats using trait-based predictive models
We developed a method to assess bird species’ vulnerability to predation by free-ranging domestic cats (Felis catus) using prey preference data from citizen science in Italy and the United Kingdom. By combining species traits and geographical range, we trained random forest models to predict prey preferences and identify missing prey species. Our analysis showed that including the geographical range significantly improved model accuracy and reduced prey detectability issues. Cross-validation confirmed that models trained in one country could effectively predict prey preferences in another, allowing for broader application. Shapley additive explanations values analysis revealed that small, generalist birds with a low hand-wing index and large geographical range were most likely to be preyed upon. We used these models to create vulnerability lists for United States bird species, which showed moderate overlap but high consistency with previous studies, highlighting their robustness. These results showed that this method could thus be used to improve our understanding of cat predation and inform targeted conservation strategies, with better citizen science data being crucial for further improvements.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.