An improved understanding of ungulate population dynamics using count data: Insights from western Montana

Abstract

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.

Integrated Carnivore‐Ungulate Management: A Case Study in West‐Central Montana

“…We found that although elk calf recruitment increased following the mountain lion harvest treatment, the effects of the treatment on recruitment were short-term. Calf recruitment responds to annual variability in winter severity and growing season conditions both in our study area (Paterson et al 2019b) and in general (Garrott et al 2003, Griffin et al 2011, Lukacs et al 2018. However, even after accounting for annual and environmental variation using a population modeling approach, our dataset showed a short-term increase in recruitment following treatment (Fig.…”

“…The elk modeling process applied in this study also offers a novel approach that provides advancements and improvements over traditional methods that have been used to interpret monitoring data. The integrated population model allowed for biological inferences that would otherwise have likely been masked by the lack of statistical power induced by not treating observations as a biological process time series and the variance associated with the observation process (Paterson et al 2019 b ). Detecting the effects of carnivore harvest on ungulate populations is difficult (e.g., National Research Council 1997, Hurley et al 2011), and our approach, which accounted for the process and observations separately, allowed for stronger inference regarding these relationships.…”

“…To assess changes in the population dynamics of elk herds in both study areas, we used an integrated population modeling approach (Paterson et al 2019 b ) that models 2 separate processes: 1) the biological processes of survival, per capita recruitment, and harvest, and 2) the observation process that gives rise to the observed counts and age ratios. We used annual spring count and age‐sex classification data collected from fixed wing aircraft.…”

Regional variability in pregnancy and survival rates of Rocky Mountain bighorn sheep

“…SWE acc represented the effects of winter severity and was estimated as the mean value of cumulative daily winter snow water equivalent across each population’s winter range (https://www.nohrsc.noaa.gov/). Precipitation and NDVI covariates represented the effects of growing season conditions on forage resources and were estimated during the early (May–June) and late summer (July–September) as the mean value across each population’s summer range (Paterson et al 2019). We evaluated one covariate that indexed population‐level predation risk from mountain lions (LION) using two steps.…”

“…gov/). Precipitation and NDVI covariates represented the effects of growing season conditions on forage resources and were estimated during the early (May-June) and late summer (July-September) as the mean value across each population's summer range (Paterson et al 2019). We evaluated one covariate that indexed population-level predation risk from mountain lions (LION) using two steps.…”