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Using citizen data in a population model to estimate population size of moose (Alces alces)

Kalén, Christer and Andren, Henrik and Månsson, Johan and Sand, Håkan (2022). Using citizen data in a population model to estimate population size of moose (Alces alces). Ecological Modelling. 471 , 110066
[Research article]

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Abstract

Long-term and wide-ranging citizen science programs provide a unique opportunity to monitor wildlife populations and trends through time while encouraging stakeholder participation, engagement, and trust. Hunter observations is such a program that in Sweden is used on a regular basis to monitor population trends of moose. However, hunter observations are not reliable to determine the actual population size. We developed a mechanistic moose population model that integrated citizen science data and used it at various geographical scales to estimate moose population size between 2012 and 2020. A sensitivity analysis, specifically performed for recruitment, adult sex ratio and calf sex ratio, showed that the simulated population size was most sensitive for variation in recruitment. According to the results, Sweden had a total moose population of -311 000 (+/- 4%) individuals pre-hunt and -228 000 (+/- 4%) post-hunt in 2020. The post-hunt moose abundance has decreased nationwide with 15%, from 0.72 to 0.61 moose per km2 during the 2012 - 2020 period. The present post-hunt moose density was estimated at 0.39, 0.78, 0.84 and 0.54 per km2 for the regions northernmost, northern, central and southern Sweden, respectively. The simulation model can be used for strategic and operative management at various geographical scales and is publicly available. By integrating citizen data with a mechanistic population model, a new low-cost method of estimating population size and relevant population dynamics was established.

Authors/Creators:Kalén, Christer and Andren, Henrik and Månsson, Johan and Sand, Håkan
Title:Using citizen data in a population model to estimate population size of moose (Alces alces)
Series Name/Journal:Ecological Modelling
Year of publishing :2022
Volume:471
Article number:110066
Number of Pages:18
Publisher:ELSEVIER
ISSN:0304-3800
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Ecology
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 405 Other Agricultural Sciences > Fish and Wildlife Management
Keywords:Alces alces, Moose management, Simulation model, Sensitivity analysis, Population model, Citizen data
URN:NBN:urn:nbn:se:slu:epsilon-p-118576
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-118576
Additional ID:
Type of IDID
DOI10.1016/j.ecolmodel.2022.110066
Web of Science (WoS)000831078500002
ID Code:28633
Faculty:S - Faculty of Forest Sciences
NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Ecology
(S) > Dept. of Ecology
Deposited By: SLUpub Connector
Deposited On:29 Aug 2022 13:28
Metadata Last Modified:29 Aug 2022 13:31

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