An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics

Detecting changes in population trends depends on the accuracy of estimated mean population growth rates and thus the quality of input data. However, monitoring wildlife populations poses economic and logistic challenges especially in complex and remote habitats. Declines in wildlife populations can...

Full description

Bibliographic Details
Published in:Frontiers in Ecology and Evolution
Main Authors: Infantes, Eduardo, Carroll, Daire, Silva, Willian T. A. F., Härkönen, Tero, Edwards, Scott V., Harding, Karin C.
Other Authors: Svenska Forskningsrådet Formas, Naturvårdsverket, Carl Tryggers Stiftelse för Vetenskaplig Forskning
Format: Article in Journal/Newspaper
Language:unknown
Published: Frontiers Media SA 2022
Subjects:
Online Access:http://dx.doi.org/10.3389/fevo.2022.905309
https://www.frontiersin.org/articles/10.3389/fevo.2022.905309/full
id crfrontiers:10.3389/fevo.2022.905309
record_format openpolar
spelling crfrontiers:10.3389/fevo.2022.905309 2024-09-30T14:36:12+00:00 An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics Infantes, Eduardo Carroll, Daire Silva, Willian T. A. F. Härkönen, Tero Edwards, Scott V. Harding, Karin C. Svenska Forskningsrådet Formas Naturvårdsverket Carl Tryggers Stiftelse för Vetenskaplig Forskning 2022 http://dx.doi.org/10.3389/fevo.2022.905309 https://www.frontiersin.org/articles/10.3389/fevo.2022.905309/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Ecology and Evolution volume 10 ISSN 2296-701X journal-article 2022 crfrontiers https://doi.org/10.3389/fevo.2022.905309 2024-09-03T04:04:33Z Detecting changes in population trends depends on the accuracy of estimated mean population growth rates and thus the quality of input data. However, monitoring wildlife populations poses economic and logistic challenges especially in complex and remote habitats. Declines in wildlife populations can remain undetected for years unless effective monitoring techniques are developed, guiding appropriate management actions. We developed an automated survey workflow using unmanned aerial vehicles (drones) to quantify the number and size of individual animals, using the well-studied Scandinavian harbour seal ( Phoca vitulina ) as a model species. We compared ground-based counts using telescopes with manual flights, using a zoom photo/video, and pre-programmed flights producing orthomosaic photo maps. We used machine learning to identify and count both pups and older seals and we present a new method for measuring body size automatically. We evaluate the population’s reproductive success using drone data, historical counts and predictions from a Leslie matrix population model. The most accurate and time-efficient results were achieved by performing pre-programmed flights where individual seals are identified by machine learning and their body sizes are measured automatically. The accuracy of the machine learning detector was 95–97% and the classification error was 4.6 ± 2.9 for pups and 3.1 ± 2.1 for older seals during good light conditions. There was a clear distinction between the body sizes of pups and older seals during breeding time. We estimated 320 pups in the breeding season 2021 with the drone, which is well beyond the expected number, based on historical data on pup production. The new high quality data from the drone survey confirms earlier indications of a deteriorating reproductive rate in this important harbour seal colony. We show that aerial drones and machine learning are powerful tools for monitoring wildlife in inaccessible areas which can be used to assess annual recruitment and seasonal variations ... Article in Journal/Newspaper harbour seal Phoca vitulina Frontiers (Publisher) Frontiers in Ecology and Evolution 10
institution Open Polar
collection Frontiers (Publisher)
op_collection_id crfrontiers
language unknown
description Detecting changes in population trends depends on the accuracy of estimated mean population growth rates and thus the quality of input data. However, monitoring wildlife populations poses economic and logistic challenges especially in complex and remote habitats. Declines in wildlife populations can remain undetected for years unless effective monitoring techniques are developed, guiding appropriate management actions. We developed an automated survey workflow using unmanned aerial vehicles (drones) to quantify the number and size of individual animals, using the well-studied Scandinavian harbour seal ( Phoca vitulina ) as a model species. We compared ground-based counts using telescopes with manual flights, using a zoom photo/video, and pre-programmed flights producing orthomosaic photo maps. We used machine learning to identify and count both pups and older seals and we present a new method for measuring body size automatically. We evaluate the population’s reproductive success using drone data, historical counts and predictions from a Leslie matrix population model. The most accurate and time-efficient results were achieved by performing pre-programmed flights where individual seals are identified by machine learning and their body sizes are measured automatically. The accuracy of the machine learning detector was 95–97% and the classification error was 4.6 ± 2.9 for pups and 3.1 ± 2.1 for older seals during good light conditions. There was a clear distinction between the body sizes of pups and older seals during breeding time. We estimated 320 pups in the breeding season 2021 with the drone, which is well beyond the expected number, based on historical data on pup production. The new high quality data from the drone survey confirms earlier indications of a deteriorating reproductive rate in this important harbour seal colony. We show that aerial drones and machine learning are powerful tools for monitoring wildlife in inaccessible areas which can be used to assess annual recruitment and seasonal variations ...
author2 Svenska Forskningsrådet Formas
Naturvårdsverket
Carl Tryggers Stiftelse för Vetenskaplig Forskning
format Article in Journal/Newspaper
author Infantes, Eduardo
Carroll, Daire
Silva, Willian T. A. F.
Härkönen, Tero
Edwards, Scott V.
Harding, Karin C.
spellingShingle Infantes, Eduardo
Carroll, Daire
Silva, Willian T. A. F.
Härkönen, Tero
Edwards, Scott V.
Harding, Karin C.
An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics
author_facet Infantes, Eduardo
Carroll, Daire
Silva, Willian T. A. F.
Härkönen, Tero
Edwards, Scott V.
Harding, Karin C.
author_sort Infantes, Eduardo
title An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics
title_short An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics
title_full An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics
title_fullStr An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics
title_full_unstemmed An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics
title_sort automated work-flow for pinniped surveys: a new tool for monitoring population dynamics
publisher Frontiers Media SA
publishDate 2022
url http://dx.doi.org/10.3389/fevo.2022.905309
https://www.frontiersin.org/articles/10.3389/fevo.2022.905309/full
genre harbour seal
Phoca vitulina
genre_facet harbour seal
Phoca vitulina
op_source Frontiers in Ecology and Evolution
volume 10
ISSN 2296-701X
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3389/fevo.2022.905309
container_title Frontiers in Ecology and Evolution
container_volume 10
_version_ 1811639330639183872