» Articles » PMID: 26640661

Unmanned Aircraft Systems Complement Biologging in Spatial Ecology Studies

Overview
Journal Ecol Evol
Date 2015 Dec 8
PMID 26640661
Citations 10
Authors
Affiliations
Soon will be listed here.
Abstract

The knowledge about the spatial ecology and distribution of organisms is important for both basic and applied science. Biologging is one of the most popular methods for obtaining information about spatial distribution of animals, but requires capturing the animals and is often limited by costs and data retrieval. Unmanned Aircraft Systems (UAS) have proven their efficacy for wildlife surveillance and habitat monitoring, but their potential contribution to the prediction of animal distribution patterns and abundance has not been thoroughly evaluated. In this study, we assess the usefulness of UAS overflights to (1) get data to model the distribution of free-ranging cattle for a comparison with results obtained from biologged (GPS-GSM collared) cattle and (2) predict species densities for a comparison with actual density in a protected area. UAS and biologging derived data models provided similar distribution patterns. Predictions from the UAS model overestimated cattle densities, which may be associated with higher aggregated distributions of this species. Overall, while the particular researcher interests and species characteristics will influence the method of choice for each study, we demonstrate here that UAS constitute a noninvasive methodology able to provide accurate spatial data useful for ecological research, wildlife management and rangeland planning.

Citing Articles

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.

Klingstrom T, Zonabend Konig E, Zwane A Brief Funct Genomics. 2024; 24.

PMID: 39158344 PMC: 11735752. DOI: 10.1093/bfgp/elae032.


High-precision tracking and positioning for monitoring Holstein cattle.

Luo W, Zhang G, Yuan Q, Zhao Y, Chen H, Zhou J PLoS One. 2024; 19(5):e0302277.

PMID: 38743665 PMC: 11093326. DOI: 10.1371/journal.pone.0302277.


Positioning Methods and the Use of Location and Activity Data in Forests.

Keefe R, Wempe A, Becker R, Zimbelman E, Nagler E, Gilbert S Forests. 2023; 10(5).

PMID: 37180360 PMC: 10174273. DOI: 10.3390/f10050458.


A practical approach with drones, smartphones, and tracking tags for potential real-time animal tracking.

Mesquita G, Mulero-Pazmany M, Wich S, Rodriguez-Teijeiro J Curr Zool. 2023; 69(2):208-214.

PMID: 37091991 PMC: 10120989. DOI: 10.1093/cz/zoac029.


Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study.

Mesquita G, Rodriguez-Teijeiro J, Wich S, Mulero-Pazmany M Curr Zool. 2021; 67(2):157-163.

PMID: 33854533 PMC: 8026149. DOI: 10.1093/cz/zoaa038.


References
1.
Rutz C, Hays G . New frontiers in biologging science. Biol Lett. 2009; 5(3):289-92. PMC: 2679933. DOI: 10.1098/rsbl.2009.0089. View

2.
SEBER G . A review of estimating animal abundance. Biometrics. 1986; 42(2):267-92. View

3.
Vermeulen C, Lejeune P, Lisein J, Sawadogo P, Bouche P . Unmanned aerial survey of elephants. PLoS One. 2013; 8(2):e54700. PMC: 3566131. DOI: 10.1371/journal.pone.0054700. View

4.
Barasona J, Mulero-Pazmany M, Acevedo P, Negro J, Torres M, Gortazar C . Unmanned aircraft systems for studying spatial abundance of ungulates: relevance to spatial epidemiology. PLoS One. 2015; 9(12):e115608. PMC: 4281124. DOI: 10.1371/journal.pone.0115608. View

5.
Mulero-Pazmany M, Stolper R, van Essen L, Negro J, Sassen T . Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS One. 2014; 9(1):e83873. PMC: 3885534. DOI: 10.1371/journal.pone.0083873. View