Informing management with monitoring data: the value of Bayesian forecasting

Abstract

Abstract. Inventory and Monitoring Programs in the National Park Service (NPS) provide informationneeded to support wise planning, management, and decision making. Mathematical and statistical models play a critical role in this process by integrating data from multiple sources in a way that is honest about uncertainty. We show the utility of Bayesian hierarchical models for supporting decisions on managing natural resources of national parks. These models can assimilate monitoring data to provide true forecasts, resulting in probabilistic predictions of future states of park ecosystems accompanied by rigorous estimates of uncertainty. We discuss a novel approach for communicating these forecasts to decision makers who need to evaluate the probability that NPS goals will be met given different management actions, including the null model of no action. We illustrate how this approach has been used successfully to inform decisions on the elk (Cervus elaphus nelsoni) population management in Rocky Mountain National Park based on 47 yr of monitoring data. Forecasts from a discrete time, stage-structured population model assimilated with annual census and sex and age classifications are being used annually to help park managers decide on actions needed to meet goals for elk and vegetation. In particular, park managers were able to determine the probability that the elk population would fall within a desired population range, which led to both population reduction actions and no action depending on the year of interest. Moreover, this approach allowed multiple survey methodologies from the last 47 years to be incorporated into a single model with associated estimates of uncertainty. Models like this one are especially useful for adaptive management where continuous improvement in models and data results in long-term improvement in the wisdom of policy and management.

Managing individual nests promotes population recovery of a top predator

“…To do so, we developed Integrated Population Models (IPM) capable of combining disparate data sources into a joint likelihood that estimates real and latent parameters at multiple biological levels, providing realistic and robust assessments of population dynamics and model uncertainty (Abadi, Gimenez, Arlettaz, & Schaub, ; Schaub & Abadi, ). Since their recent development, IPM have been used to evaluate ongoing threats to rare species (Tenan, Adrover, Muñoz Navarro, Sergio, & Tavecchia, ), assess population viability (Oppel et al., ), improve inference from conservation monitoring (Tempel, Peery, & Gutiérrez, ), guide management planning (Coates et al., ), and evaluate population‐level management (Ketz, Johnson, Monello, & Hobbs, ). Our approach combines observations of nest occupancy and reproductive output to estimate demographic parameters at the individual‐ and population‐level.…”

“…Since their recent development, IPM have been used to evaluate ongoing threats to rare species (Tenan, Adrover, Muñoz Navarro, Sergio, & Tavecchia, 2012), assess population viability (Oppel et al, 2014), improve inference from conservation monitoring (Tempel, Peery, & Gutiérrez, 2014), guide management planning (Coates et al, 2016), and evaluate population-level management (Ketz, Johnson, Monello, & Hobbs, 2016). Our approach combines observations of nest occupancy and reproductive output to estimate demographic parameters at the individual-and population-level.…”

A hierarchical Bayesian approach for handling missing classification data

“…Stage-or age-specific survival probabilities obtained from marked populations (Challenger & Schwarz, 2009;Kendall, 2004) are used in structured matrix population models (Caswell, 2001;Skalski, Ryding, & Millspaugh, 2005) and integrated population models (Besbeas, Freeman, Morgan, & Catchpole, 2004;Schaub & Abadi, 2011;Zipkin & Saunders, 2018) to determine population growth rates, and are compromised when life stages and characteristics are difficult to observe (Zipkin & Saunders, 2018). Ketz, Johnson, Monello, and Hobbs (2016) used classification data of elk in Rocky Mountain National Park in an age-structured integrated population model to obtain demographic parameters when mark-recapture data were unavailable and ignored partial observations that may have influenced model outcomes, which in turn may influence the choice to cull animals to prevent overabundance.…”

Ecological monitoring and evidence‐based decision‐making in America's National Parks: highlights of the Special Feature

“…The NPS is entrusted with stewarding a rather astounding array of natural resources, that, as reflected here, often span very broad geographic (e.g., Miller et al 2016, Rodhouse et al 2016) and temporal scales (e.g., Ketz et al 2016, Paulsen et al 2016, Roland et al 2016, and multiple levels of ecological complexity (e.g., Coletti et al 2016, Fakhraei et al 2016. The NPS is entrusted with stewarding a rather astounding array of natural resources, that, as reflected here, often span very broad geographic (e.g., Miller et al 2016, Rodhouse et al 2016) and temporal scales (e.g., Ketz et al 2016, Paulsen et al 2016, Roland et al 2016, and multiple levels of ecological complexity (e.g., Coletti et al 2016, Fakhraei et al 2016.…”