Sinchan Ghosh, A. Banerjee, Soumalya Mukhopadhyay, S. Bhattacharya, S. Ray
{"title":"Predicting the probability of avian reproductive success and its components at a nesting site","authors":"Sinchan Ghosh, A. Banerjee, Soumalya Mukhopadhyay, S. Bhattacharya, S. Ray","doi":"10.21203/rs.3.rs-1313546/v1","DOIUrl":null,"url":null,"abstract":"\n Avian reproduction has three chronological components: nesting, mating, hatching, and fledging. Predicting the probability of individual components helps to identify the period of reproduction that needs the most aid, increasing the conservation efficiency. This prediction requires identification of biotic, abiotic, and sociological variables of a bird’s environment responsible for these componentwise success probabilities. There is also no standard methodology to estimate these probability values separately. This study estimates the absolute success probability of each component, identifies correlated environmental predictors and gives a modeling framework to accurately predict the success probabilities using Merops Philippines as a test bed. The result using surveyed data and proposed methodology indicates the corridor between nesting and mating is most vulnerable to the environment. Social structure is the key to all reproductive components but nesting. Both biotic and abiotic factors are crucial determinants of nesting success. Mating, hatching, and fledging success depend more on biotic factors than abiotic ones. Linear modeling frameworks are helpful to explore which types of environment are a better determinant of the success of a reproductive component. Artificial neural networking is more useful to predict the successes of a new site. Although developed using Merops philippinus data, the proposed methodology and modeling framework are also applicable for other birds.","PeriodicalId":178797,"journal":{"name":"Ecol. Informatics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecol. Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21203/rs.3.rs-1313546/v1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Avian reproduction has three chronological components: nesting, mating, hatching, and fledging. Predicting the probability of individual components helps to identify the period of reproduction that needs the most aid, increasing the conservation efficiency. This prediction requires identification of biotic, abiotic, and sociological variables of a bird’s environment responsible for these componentwise success probabilities. There is also no standard methodology to estimate these probability values separately. This study estimates the absolute success probability of each component, identifies correlated environmental predictors and gives a modeling framework to accurately predict the success probabilities using Merops Philippines as a test bed. The result using surveyed data and proposed methodology indicates the corridor between nesting and mating is most vulnerable to the environment. Social structure is the key to all reproductive components but nesting. Both biotic and abiotic factors are crucial determinants of nesting success. Mating, hatching, and fledging success depend more on biotic factors than abiotic ones. Linear modeling frameworks are helpful to explore which types of environment are a better determinant of the success of a reproductive component. Artificial neural networking is more useful to predict the successes of a new site. Although developed using Merops philippinus data, the proposed methodology and modeling framework are also applicable for other birds.