Evidence map›Paper›PMID 39294790›Full record

ArticleEnvironmental evidence2023

Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map.

Jared A Elmore, Emma A Schultz, Landon R Jones, Kristine O Evans, Sathishkumar Samiappan, Morgan B Pfeiffer, Bradley F Blackwell, Raymond B Iglay

Abstract read
In one paragraph

Article in Environmental evidence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

8 authors.

Jared A ElmoreDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA. jae8908@gmail.com.ORCID http://orcid.org/0000-0002-5980-1561
Emma A SchultzDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA.
Landon R JonesDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA.
Kristine O EvansDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA.
Sathishkumar SamiappanGeosystems Research Institute, Mississippi State University, Mississippi State, MS, 39762, USA.
Morgan B PfeifferU.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH, USA.
Bradley F BlackwellU.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH, USA.
Raymond B IglayDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA.

Funding

National Wildlife Research Center AP20WSNWRC00C010National Wildlife Research Center AP20WSNWRC00C026National Wildlife Research Center AP21WSNWRC00C013National Wildlife Research Center AP21WSNWRC00C014
6 · The paper itself

Abstract

backgroundSmall unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals.

methodsWe investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions. REVIEW

findingsWe found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review.

conclusionsOur systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images.

Indexed as

CountMonitorRemotely piloted aircraftRPAUAVUncrewed vehicle systemUnmanned aerial vehicleUnmanned aircraft systemUVSWildlife

Identifiers

PMID39294790
PMCPMC11378819

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.