Statistical population reconstruction of moose (Alces alces) in northeastern Minnesota using integrated population models.

Autor: Severud WJ; Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America., Berg SS; Department of Computer and Information Sciences, University of St. Thomas, Saint Paul, Minnesota, United States of America., Ernst CA; Department of Mathematics, University of St. Thomas, Saint Paul, Minnesota, United States of America., DelGiudice GD; Forest Wildlife Populations and Research Group, Minnesota Department of Natural Resources, Forest Lake, Minnesota, United States of America., Moore SA; Department of Biology and Environment, Grand Portage Band of Lake Superior Chippewa, Grand Portage, Minnesota, United States of America., Windels SK; Voyageurs National Park, International Falls, Minnesota, United States of America., Moen RA; Center for Water and the Environment, University of Minnesota, Duluth, Minnesota, United States of America., Isaac EJ; Department of Biology and Environment, Grand Portage Band of Lake Superior Chippewa, Grand Portage, Minnesota, United States of America., Wolf TM; Department of Veterinary Population Medicine, University of Minnesota, Saint Paul, Minnesota, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Sep 27; Vol. 17 (9), pp. e0270615. Date of Electronic Publication: 2022 Sep 27 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0270615
Abstrakt: Given recent and abrupt declines in the abundance of moose (Alces alces) throughout parts of Minnesota and elsewhere in North America, accurately estimating statewide population trends and demographic parameters is a high priority for their continued management and conservation. Statistical population reconstruction using integrated population models provides a flexible framework for combining information from multiple studies to produce robust estimates of population abundance, recruitment, and survival. We used this framework to combine aerial survey data and survival data from telemetry studies to recreate trends and demographics of moose in northeastern Minnesota, USA, from 2005 to 2020. Statistical population reconstruction confirmed the sharp decline in abundance from an estimated 7,841 (90% CI = 6,702-8,933) in 2009 to 3,386 (90% CI = 2,681-4,243) animals in 2013, but also indicated that abundance has remained relatively stable since then, except for a slight decline to 3,163 (90% CI = 2,403-3,718) in 2020. Subsequent stochastic projection of the population from 2021 to 2030 suggests that this modest decline will continue for the next 10 years. Both annual adult survival and per-capita recruitment (number of calves that survived to 1 year per adult female alive during the previous year) decreased substantially in years 2005 and 2019, from 0.902 (SE = 0.043) to 0.689 (SE = 0.061) and from 0.386 (SE = 0.030) to 0.303 (SE = 0.051), respectively. Sensitivity analysis revealed that moose abundance was more sensitive to fluctuations in adult survival than recruitment; thus, we conclude that the steep decline in 2013 was driven primarily by decreasing adult survival. Our analysis demonstrates the potential utility of using statistical population reconstruction to monitor moose population trends and to identify population declines more quickly. Future studies should focus on providing better estimates of per-capita recruitment, using pregnancy rates and calf survival, which can then be incorporated into reconstruction models to help improve estimates of population change through time.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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