DOI: 10.5061/dryad.k0p2ngf6p
title: Sensitivity of multiple vital rates for ruffed grouse in the upper Great Lakes region

description: Effective management of wildlife requires a full understanding of
 population dynamics and knowledge of potential drivers that influence
 population growth.  The Ruffed Grouse (Bonasa umbellus) is a
 popular upland game bird widely distributed across the northern United
 States and Canada that has experienced population declines within portions
 of its range in response to forest maturation and habitat loss. 
 Although the species has been extensively studied, few efforts have been
 made to synthesize demographic data into a sensitivity analysis to guide
 management actions.  We reviewed the literature and compiled
 Ruffed Grouse vital rates from 14 field studies conducted across four
 decades (1982−2018) within the Upper Great Lakes region of Michigan,
 Minnesota, and Wisconsin, USA.  We parameterized a deterministic
 matrix model to evaluate population dynamics and conducted sensitivity
 analyses to identify vital rates projected to have the greatest influence
 on the finite rate of population change (λ).  Our modeling effort
 projected a stable but highly variable annual rate of population change (λ
 = 1.01; 95% CI = 0.88–1.14) for Ruffed Grouse in the Upper Great Lakes
 region.  Stochastic rates of population change derived from
 spring drumming surveys (λ = 1.01; 95% CI = 0.61–1.45) and Christmas Bird
 Count surveys (λ = 0.99; 95% CI = 0.62–1.76) of the corresponding regional
 population provided validation of stable trends over the same time period
 as our demographic model.  Prospective elasticities and
 variance-scaled sensitivities suggested λ would be greatly influenced by
 components of reproductive performance: nesting success, chick survival,
 and post-fledging juvenile survival.  Retrospective analysis
 indicated that much of the overall variability in λ and annual
 productivity was also attributed to annual variation in nesting
 success.  Management of this species has often focused on fall
 and overwinter survival, but population projection models provided little
 evidence that survival was the predominant factor affecting population
 growth of Ruffed Grouse in this region.  A suite of confounding
 factors and demographic processes that drive population trends can differ
 significantly across a species’ range.  In the Upper Great Lakes
 region, management efforts aimed at maximizing reproductive success would
 likely have the greatest potential influence on Ruffed Grouse population
 growth.  Other types of systematic, regional survey data can also
 be useful for validating population trends derived from demographic
 modeling studies.
description: We reviewed documents for estimates of Ruffed Grouse vital rates
 from field studies located in Michigan, Minnesota, and Wisconsin.  We
 searched wildlife ecology literature for vital rate data on Ruffed Grouse
 populations by searching primary (Ruffed Grouse, grouse,
 <i>Bonasa</i>, and <i>Bonasa umbellus</i>) and
 secondary (nesting, nest survival, clutch size, chick survival, and adult
 survival) keywords in electronic databases including Biological Abstracts,
 Google Scholar, JSTOR, Web of Science, and Wildlife and Ecology Studies
 Worldwide.  We also checked original references from previous demographic
 and life-history summaries to ensure studies were not overlooked.  We
 reviewed references and literature included in published peer-reviewed
 journal articles, graduate theses and dissertations, and agency reports
 with relevant vital rate data spanning a four-decade time period during
 the years 1982–2018.  We restricted our compilation of literature to this
 timeframe in an attempt to evaluate present-day Ruffed Grouse populations
 and to ensure that our modeling effort could account for potential
 stochastic processes that may occur across multiple 10-year population
 cycles.  In total, we synthesized available data from 14 demographic field
 research studies located in the Upper Great Lakes region of northern
 Michigan, Minnesota, and Wisconsin.
description: Population models and sensitivity analyses were built and
 evaluated in Program R using the package POPBIO.  Computer code is
 provided.