N-mixture model-based estimate of relative abundance of sloth bear (

Sankarshan Chaudhuri, Rajasekar Rajaraman, Sankar Kalyanasundaram, Sambandam Sathyakumar, Ramesh Krishnamurthy

Abstract read

In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Loners Have It: Insights on Breeding and Non-Breeding Individuals of Sloth Bears in Central India.Ecology and evolution · 2026
Article
N-mixture models for population estimation: Application in spotted lanternfly egg mass survey.Current research in insect science · 2024
Review
Factors affecting the occupancy of sloth bear and its detection probability in Parsa-Koshi Complex, Nepal.Ecology and evolution · 2023
Article

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sankarshan ChaudhuriDepartment of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India.

Rajasekar RajaramanDepartment of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India.

Sankar KalyanasundaramSalim Ali Centre for Ornithology and Natural History, Coimbatore, Tamil Nadu, India.

Sambandam SathyakumarDepartment of Endangered Species Management, Wildlife Institute of India, Dehradun, Uttarakhand, India.ORCID 0000-0003-2027-4706

Ramesh KrishnamurthyDepartment of Landscape Level Planning and Management, Wildlife Institute of India, Dehradun, Uttarakhand, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reliable estimation of abundance is a prerequisite for a species' conservation planning in human-dominated landscapes, especially if the species is elusive and involved in conflicts. As a means of population estimation, the importance of camera traps has been recognized globally, although estimating the abundance of unmarked, cryptic species has always been a challenge to conservation biologists. This study explores the use of the N-mixture model with three probability distributions,

Indexed as

SlothsUrsidaeAnimalsForestsHumansIndiaPoisson DistributionCamera trapHuman-dominated landscapeN-mixture modelRelative abundanceSloth bear

Identifiers

PMID36523470

DOI10.7717/peerj.13649

PMCPMC9745790

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.