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

• Main Authors: Kalén, Christer, Andren, Henrik, Månsson, Johan, Sand, Håkan
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Ecology; Fish and Wildlife Management; Alces alces
• Online Access: https://pub.epsilon.slu.se/28633/; https://pub.epsilon.slu.se/28633/1/kalen-s-et-al-20220829.pdf

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.