{"title":"一个消费者,两种资源模式下的成功偏见社会学习","authors":"Talia Borofsky, Marcus W. Feldman","doi":"10.1016/j.tpb.2022.05.004","DOIUrl":null,"url":null,"abstract":"<div><p>Previous analyses have predicted that social learning should not evolve in a predator–prey system. Here we examine whether success-biased social learning, by which social learners copy successful demonstrators, allows social learning by foragers to evolve. We construct a one-predator, two-prey system in which foragers must learn how to feed on depletable prey populations in an environment where foraging information can be difficult to obtain individually. We analyze two models in which social learning is success-biased: in the first, individual learning does not depend on the resource dynamics, and in the second model it depends on the relative frequency of the resource. Unlike previous results, we find that social learning does not cause predators to over-harvest one type of prey over the other. Furthermore, increasing the probability of social learning increases the probability of learning a successful foraging behavior, especially when individually learned information tends to be inaccurate. Whereas social learning does not evolve among individual learners in the first model, the assumption of resource-dependent learning in the second model allows a mutant with an increased probability of social learning to spread through the forager population.</p></div>","PeriodicalId":49437,"journal":{"name":"Theoretical Population Biology","volume":"146 ","pages":"Pages 29-35"},"PeriodicalIF":1.2000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0040580922000399/pdfft?md5=1e643e802c1c00f54a9fffea93d1d957&pid=1-s2.0-S0040580922000399-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Success-biased social learning in a one-consumer, two-resource model\",\"authors\":\"Talia Borofsky, Marcus W. Feldman\",\"doi\":\"10.1016/j.tpb.2022.05.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Previous analyses have predicted that social learning should not evolve in a predator–prey system. Here we examine whether success-biased social learning, by which social learners copy successful demonstrators, allows social learning by foragers to evolve. We construct a one-predator, two-prey system in which foragers must learn how to feed on depletable prey populations in an environment where foraging information can be difficult to obtain individually. We analyze two models in which social learning is success-biased: in the first, individual learning does not depend on the resource dynamics, and in the second model it depends on the relative frequency of the resource. Unlike previous results, we find that social learning does not cause predators to over-harvest one type of prey over the other. Furthermore, increasing the probability of social learning increases the probability of learning a successful foraging behavior, especially when individually learned information tends to be inaccurate. Whereas social learning does not evolve among individual learners in the first model, the assumption of resource-dependent learning in the second model allows a mutant with an increased probability of social learning to spread through the forager population.</p></div>\",\"PeriodicalId\":49437,\"journal\":{\"name\":\"Theoretical Population Biology\",\"volume\":\"146 \",\"pages\":\"Pages 29-35\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0040580922000399/pdfft?md5=1e643e802c1c00f54a9fffea93d1d957&pid=1-s2.0-S0040580922000399-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Theoretical Population Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0040580922000399\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical Population Biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0040580922000399","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECOLOGY","Score":null,"Total":0}
Success-biased social learning in a one-consumer, two-resource model
Previous analyses have predicted that social learning should not evolve in a predator–prey system. Here we examine whether success-biased social learning, by which social learners copy successful demonstrators, allows social learning by foragers to evolve. We construct a one-predator, two-prey system in which foragers must learn how to feed on depletable prey populations in an environment where foraging information can be difficult to obtain individually. We analyze two models in which social learning is success-biased: in the first, individual learning does not depend on the resource dynamics, and in the second model it depends on the relative frequency of the resource. Unlike previous results, we find that social learning does not cause predators to over-harvest one type of prey over the other. Furthermore, increasing the probability of social learning increases the probability of learning a successful foraging behavior, especially when individually learned information tends to be inaccurate. Whereas social learning does not evolve among individual learners in the first model, the assumption of resource-dependent learning in the second model allows a mutant with an increased probability of social learning to spread through the forager population.
期刊介绍:
An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena.
Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.