An improved understanding of ungulate population dynamics using count data: Insights from western Montana.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
06
05
2019
accepted:
27
11
2019
entrez:
24
12
2019
pubmed:
24
12
2019
medline:
16
4
2020
Statut:
epublish
Résumé
Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.
Identifiants
pubmed: 31869366
doi: 10.1371/journal.pone.0226492
pii: PONE-D-19-12790
pmc: PMC6927647
doi:
Banques de données
Dryad
['10.5061/dryad.34tmpg4g4']
Types de publication
Journal Article
Observational Study
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0226492Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Ecol Appl. 2010 Sep;20(6):1753-65
pubmed: 20945773
Ecol Appl. 2006 Feb;16(1):74-86
pubmed: 16705962
Trends Ecol Evol. 1998 Feb 1;13(2):58-63
pubmed: 21238201
Philos Trans R Soc Lond B Biol Sci. 2014 Apr 14;369(1643):20130196
pubmed: 24733951
Oecologia. 2003 Nov;137(3):370-6
pubmed: 12955491
Proc Biol Sci. 2016 Jun 29;283(1833):
pubmed: 27335416
Ecology. 2013 Jun;94(6):1245-56
pubmed: 23923485
Biometrics. 2003 Dec;59(4):778-85
pubmed: 14969455
Conserv Biol. 2011 Apr;25(2):356-64
pubmed: 21166714
Evolution. 2016 Dec;70(12):2909-2914
pubmed: 27813056
Proc Natl Acad Sci U S A. 1998 Jan 6;95(1):213-8
pubmed: 9419355
J Anim Ecol. 2011 Nov;80(6):1246-57
pubmed: 21615401
Br J Math Stat Psychol. 2013 Feb;66(1):8-38
pubmed: 22364575
PeerJ. 2014 Oct 09;2:e616
pubmed: 25320683
Oecologia. 1998 Oct;116(4):489-500
pubmed: 28307518
PLoS One. 2018 Mar 29;13(3):e0194566
pubmed: 29596430
Ecol Appl. 2017 Jun;27(4):1280-1293
pubmed: 28188660