Evidence map›Paper›PMID 28428848›Full record

ArticleEcology and evolution2017

Modeling activity patterns of wildlife using time-series analysis.

Jindong Zhang, Vanessa Hull, Zhiyun Ouyang, Liang He, Thomas Connor, Hongbo Yang, Jinyan Huang, Shiqiang Zhou, Zejun Zhang, Caiquan Zhou and 2 more

Abstract read
In one paragraph

Article in Ecology and evolution, 2017. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

12 citing papers in PubMed.

  1. Review
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  11. Giant panda foraging and movement patterns in response to bamboo shoot growth.Environmental science and pollution research international · 2018
    Article
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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

12 authors.

Jindong ZhangKey Laboratory of Southwest China Wildlife Resources Conservation China West Normal University Ministry of Education Nanchong, Sichuan 637009 China.ORCID 0000-0002-6073-6883
Vanessa HullCenter for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI 48823 USA.
Zhiyun OuyangState Key Laboratory of Urban and Regional Ecology Research Center for Eco-environmental Sciences Chinese Academy of Sciences Beijing 100085 China.
Liang HeNational Meteorological Center Beijing 100081 China.
Thomas ConnorCenter for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI 48823 USA.
Hongbo YangCenter for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI 48823 USA.
Jinyan HuangConservation and Research Center for the Giant Panda (CCRCGP) Wolong Nature Reserve Sichuan 623006 China.
Shiqiang ZhouConservation and Research Center for the Giant Panda (CCRCGP) Wolong Nature Reserve Sichuan 623006 China.
Zejun ZhangKey Laboratory of Southwest China Wildlife Resources Conservation China West Normal University Ministry of Education Nanchong, Sichuan 637009 China.
Caiquan ZhouKey Laboratory of Southwest China Wildlife Resources Conservation China West Normal University Ministry of Education Nanchong, Sichuan 637009 China.
Hemin ZhangConservation and Research Center for the Giant Panda (CCRCGP) Wolong Nature Reserve Sichuan 623006 China.
Jianguo LiuCenter for Systems Integration and Sustainability Department of Fisheries and Wildlife Michigan State University East Lansing MI 48823 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency-based time-series analysis, with high-resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda (

Indexed as

animal behaviorexternal and internal influencesgiant panda (Ailuropoda melanoleuca)GPS collartime‐series analysis

Identifiers

PMID28428848
PMCPMC5395454

What OpenQuestion holds

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Registered trials

None linked

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.