Evidence map›Paper›PMID 36311406›Full record

ArticleEcology and evolution2022

The inappropriate use of time-to-independence biases estimates of activity patterns of free-ranging mammals derived from camera traps.

Christopher Peral, Marietjie Landman, Graham I H Kerley

Abstract read
In one paragraph

Article in Ecology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christopher PeralCentre for African Conservation Ecology Nelson Mandela University Gqeberha South Africa.ORCID https://orcid.org/0000-0003-4739-5452
Marietjie LandmanCentre for African Conservation Ecology Nelson Mandela University Gqeberha South Africa.ORCID https://orcid.org/0000-0002-5500-2121
Graham I H KerleyCentre for African Conservation Ecology Nelson Mandela University Gqeberha South Africa.ORCID https://orcid.org/0000-0003-2702-5200

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measuring and comparing activity patterns provide key insights into the behavioral trade-offs that result in animal activity and their extrinsic and intrinsic drivers. Camera traps are a recently emerged source of data for sampling animal activity used to estimate activity patterns. However, nearly 70% of studies using such data to estimate activity patterns apply a time-to-independence data filter to discard appreciable periods of sampling effort. This treatment of activity as a discrete event emerged from the use of camera trap data to estimate animal abundances, but does not reflect the continuous nature of behavior, and may bias resulting estimates of activity patterns. We used a large, freely available camera trap dataset to test the effects of time to independence on the estimated activity of eight medium- to large-sized African mammals. We show that discarding data through the use of time-to-independence filters causes substantial losses in sample sizes and differences in the estimated activity of species. Activity patterns estimated for herbivore species were more affected by the application of time-to-independence data filters than carnivores, this extending to estimates of potential interactions (activity overlap) between herbivore species. We hypothesize that this pattern could reflect the typically more abundant, social, and patch-specific foraging patterns of herbivores and suggest that this effect may bias estimates of predator-prey interactions. Activity estimates of rare species, with less data available, may be particularly vulnerable to loss of data through the application of time-to-independence data filters. We conclude that the application of time-to-independence data filters in camera trap-based estimates of activity patterns is not valid and should not be used.

Indexed as

behaviorcamera trapsdaily activityoverlappseudoreplicationSerengeti

Identifiers

PMID36311406
PMCPMC9596328

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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.