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

William J. Severud, Sergey S. Berg, Connor A. Ernst, Glenn D Del Giudice, Seth A Moore, Steven K Windels, Ron A. Moen, Edmund J. Isaac, Tiffany M Wolf

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere0270615
JournalPloS one
Volume17
Issue number9 September
DOIs
StatePublished - Sep 2022

Bibliographical note

Funding Information:
CAE was financially supported in part by the Center for Applied Mathematics Summer Research Program at the University of Saint Thomas (https://cas.stthomas.edu/departments/areas-of-study/mathematics/center-for-appliedmathematics/). Moose collaring work was funded by Voyageurs National Park (https://www.nps.gov/voya/index.htm; SKW and RAM), the USGS-NPS Natural Resource Preservation Program (PMIS 140435; SKW and RAM), University of Minnesota-Duluth (https://nrri.umn.edu/; SKW and RAM), U.S. Fish and Wildlife Service Tribal Wildlife Grant (https://www.fws.gov/service/tribal-wildlife-grants; SAM), U.S. Environmental Protection Agency Great Lakes Restoration Initiative (https://www.glri.us/node/443, SAM), and the Bureau of Indian Affairs Endangered Species Program (https://www.bia.gov/bia/ots/division-natural-resources/branch-fishwildlife-recreation/endangered-species-program; SAM), Minnesota Zoo Ulysses S. Seal Conservation Fund (https://mnzoo.org/conservation/around-world/ulysses-s-sealconservation-grant-program/; TMW), Indianapolis Zoo Conservation Fund (https://www.indianapoliszoo.com/conservation/field-support/; TMW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2022 Public Library of Science. All rights reserved.

Keywords

  • Animals
  • Deer
  • Female
  • Minnesota/epidemiology
  • North America
  • Pregnancy

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Journal Article

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