Counting the unmarked: Estimating animal population using count data
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Counting the unmarked: Estimating animal population using count data |
| 2. | Creator | Author's name, affiliation, country | Sanjay Gubbi; Nature Conservation Foundation; India |
| 2. | Creator | Author's name, affiliation, country | Srikanth Seshadri; Sahaj Software Solutions; India |
| 2. | Creator | Author's name, affiliation, country | Vijaya Kumara; Associate Professor and Chairman, Department of Wildlife Management, Kuvempu University, Shankaraghatta - 577451, India; India |
| 2. | Creator | Author's name, affiliation, country | Suresh Chandra K; Former Professor (Statistics), University of Madras; India |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Abundance estimation, camera-trapping, count data, density, ungulates, unmarked species |
| 4. | Description | 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. |
| 5. | Publisher | Organizing agency, location | Coordinamento SIBA - Università del Salento |
| 6. | Contributor | Sponsor(s) | Kaplan Graduate Fellowship and Panthera |
| 7. | Date | (YYYY-MM-DD) | 2019-11-20 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | http://siba-ese.unisalento.it/index.php/ejasa/article/view/18583 |
| 10. | Identifier | Digital Object Identifier | 10.1285/i20705948v12n3p604 |
| 11. | Source | Publication/conference title; vol., no. (year) | Electronic Journal of Applied Statistical Analysis; Vol 12, No 3 (2019): Electronic Journal of Applied Statistical Analysis (Special Issue Data Analytics and Statistical Inference) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions | Authors who publish with EJASA agree to the Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License. |