DOI: 10.5061/dryad.34tmpg4g4
title: An improved understanding of ungulate population dynamics using count data: insights from western Montana

description: 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.
description: This file is the .rds file format, i.e., it is a list of all the
 separate data objects required for the model.  The list has 14 pieces (in
 order): 1.  counts (matrix: hunting district = rows,
 years = columns) = survey counts of all elk. 2. 
 classified (matrix: hunting district = rows, years = columns) = number
 classified out of total counts. 3.  class counts
 (array: class = dimension 1 (calves, adult females, adult males), hunting
 district = dimension 2, years = dimension 3) = number of animals in each
 class. 4.  calf.harvest (matrix: hunting district =
 rows, years = columns) = estimated number of calves harvested.
 5.  antlerless.harvest (matrix: hunting district = rows, years =
 columns) = estimated number of adult females harvested.
 6.  antler.harvest (matrix: hunting district = rows, years =
 columns) = estimated number of adult males harvested.
 7-14.  covariates (matrix: hunting district = rows, years =
 columns) = covariates used in analysis.