Integrated population modeling to assess demographic variation and contributions to population growth for endangered whooping cranes

Wilson, Scott; Gil-Weir, Karine C.; Clark, Robert G.; Robertson, Gregory J.; Bidwell, Mark T.

doi:10.1016/j.biocon.2016.02.022

Cited by 37 publications

(26 citation statements)

References 31 publications

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“…Kéry and Schaub () present an approach to estimate the correlation between annual estimates of λ and vital rates in an IPM (e.g., Wilson et al. ), and Weegman et al. () calculated asymptotic growth rates in an IPM to investigate population declines in White‐fronted Geese ( Anser albifrons ).…”

Section: Discussionmentioning

confidence: 99%

“…We specifically developed an IPM to conduct sensitivity analysis by deriving asymptotic growth rates and elasticities in a single, comprehensive model, which required simple algebra calculation for a 2 × 2 matrix (see Caswell 2001). Kéry and Schaub (2012) present an approach to estimate the correlation between annual estimates of λ and vital rates in an IPM (e.g., Wilson et al 2016), andWeegman et al (2016) calculated asymptotic growth rates in an IPM to investigate population declines in White-fronted Geese (Anser albifrons). However, to our knowledge, this is the first time traditional LSA methods have been combined with Bayesian IPMs to understand the relative importance of vital rates in affecting λ.…”

Section: Discussionmentioning

confidence: 99%

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Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling

Eacker

Lukacs

Proffitt

et al. 2017

Ecological Applications

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To successfully respond to changing habitat, climate or harvest, managers need to identify the most effective strategies to reverse population trends of declining species and/or manage harvest of game species. A classic approach in conservation biology for the last two decades has been the use of matrix population models to determine the most important vital rates affecting population growth rate (λ), that is, sensitivity. Ecologists quickly realized the critical role of environmental variability in vital rates affecting λ by developing approaches such as life-stage simulation analysis (LSA) that account for both sensitivity and variability of a vital rate. These LSA methods used matrix-population modeling and Monte Carlo simulation methods, but faced challenges in integrating data from different sources, disentangling process and sampling variation, and in their flexibility. Here, we developed a Bayesian integrated population model (IPM) for two populations of a large herbivore, elk (Cervus canadensis) in Montana, USA. We then extended the IPM to evaluate sensitivity in a Bayesian framework. We integrated known-fate survival data from radio-marked adults and juveniles, fecundity data, and population counts in a hierarchical population model that explicitly accounted for process and sampling variance. Next, we tested the prevailing paradigm in large herbivore population ecology that juvenile survival of neonates <90 d old drives λ using our Bayesian LSA approach. In contrast to the prevailing paradigm in large herbivore ecology, we found that adult female survival explained more of the variation in λ than elk calf survival, and that summer and winter elk calf survival periods were nearly equivalent in importance for λ. Our Bayesian IPM improved precision of our vital rate estimates and highlighted discrepancies between count and vital rate data that could refine population monitoring, demonstrating that combining sensitivity analysis with population modeling in a Bayesian framework can provide multiple advantages. Our Bayesian LSA framework will provide a useful approach to addressing conservation challenges across a variety of species and data types.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling

Eacker

Lukacs

Proffitt

et al. 2017

Ecological Applications

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“…, Wilson et al. ). Capture–recapture data are collected by following marked (naturally or with tags) individuals through time, which allows for explicit estimation of population vital rates, and is arguably the most informative approach for tracking populations (Lebreton et al.…”

Section: Introductionmentioning

confidence: 99%

Integrating count and detection–nondetection data to model population dynamics

Zipkin

Rossman

Yackulic

et al. 2017

Ecology

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There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture-recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection-nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection-nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection-nondetection data (1995-2014) with newly collected count data (2015-2016) from a growing population of Barred Owl (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.

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“…The range of issues under consideration is very extensive. The main issue is the demographic development of the territories [6][7][8]. This is covered in the works of T.R.…”

Section: Overview Of Research Problemsmentioning

confidence: 99%