S. Gubbi, Srikanth R. Seshadri, V. Kumara, K. SureshChandra
{"title":"Counting the unmarked: Estimating animal population using count data","authors":"S. Gubbi, Srikanth R. Seshadri, V. Kumara, K. SureshChandra","doi":"10.1285/I20705948V12N3P604","DOIUrl":null,"url":null,"abstract":"Understanding population parameters are important tools for wildlife management, and one of the key objectives of the ecological research. Motion sensor cameras are a widely used tool to estimate abundance and densities of species that are identifiable based on the natural markings on their bodies. Though camera trapping provides information such as count data, on species that are not individually identifiable, estimating population size using conventional capture-recapture methodologies is not possible hindering estimating population information of several wildlife species. However, recent methodologies help use camera trapping data to bridge this gap. Here we extend the model of Chandler and Royle (2013), with suitable modifications, and used camera trap detection data to estimate abundance and density of eight wild-prey, and five domestic prey species of leopards ( Panthera pardus fusca ). In this context, a new procedure has been proposed, based on grouping of the count data, which is useful in cases of large encounters. The current model should apply widely to a range of other unmarked wildlife species such as dholes, lions, golden jackal, Indian fox, ratel, to name a few, that could help understand prey-predator relationships, competition, trophic interactions, species interactions and other similar ecological questions. The methodology could also reduce costs, and maximise the utilisation of existing camera trapping data. The methodology helps understanding population parameters of several endangered, unmarked species to draw up conservation strategies whose estimates are currently largely based on educational guess.","PeriodicalId":44770,"journal":{"name":"Electronic Journal of Applied Statistical Analysis","volume":"12 1","pages":"604-618"},"PeriodicalIF":0.6000,"publicationDate":"2019-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1285/I20705948V12N3P604","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Journal of Applied Statistical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1285/I20705948V12N3P604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 1
Abstract
Understanding population parameters are important tools for wildlife management, and one of the key objectives of the ecological research. Motion sensor cameras are a widely used tool to estimate abundance and densities of species that are identifiable based on the natural markings on their bodies. Though camera trapping provides information such as count data, on species that are not individually identifiable, estimating population size using conventional capture-recapture methodologies is not possible hindering estimating population information of several wildlife species. However, recent methodologies help use camera trapping data to bridge this gap. Here we extend the model of Chandler and Royle (2013), with suitable modifications, and used camera trap detection data to estimate abundance and density of eight wild-prey, and five domestic prey species of leopards ( Panthera pardus fusca ). In this context, a new procedure has been proposed, based on grouping of the count data, which is useful in cases of large encounters. The current model should apply widely to a range of other unmarked wildlife species such as dholes, lions, golden jackal, Indian fox, ratel, to name a few, that could help understand prey-predator relationships, competition, trophic interactions, species interactions and other similar ecological questions. The methodology could also reduce costs, and maximise the utilisation of existing camera trapping data. The methodology helps understanding population parameters of several endangered, unmarked species to draw up conservation strategies whose estimates are currently largely based on educational guess.