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Doug P. Armstrong

Publications and source records attributed to Doug P. Armstrong.

7 recordsLinked to original sources
USGS2025

Simulating demography, monitoring, and management decisions to evaluate adaptive management strategies for endangered species

Adaptive management (AM) remains underused in conservation, partly because optimization-based approaches require real-world problems to be substantially simplified. We present an approach to AM based in management strategy evaluation, a method used largely in fisheries. Managers define objectives and nominate alternative adaptive strategies, whose future performance is simulated by integrating ecological, learning and decision processes. We applied this approach to conservation of hihi (Notiomystis cincta) across Aotearoa-New Zealand. For multiple extant and prospective hihi populations, we jointly simulated demographic trends, monitoring, estimation, and decisions including translocations and supplementary feeding. Results confirmed that food supplementation assisted recovery, but was more intensive and expensive. Over 20 years, actively pursuing learning, e.g., by removing food from populations, provided little benefit. Recovery group members supported continuing current management or increasing priority on existing populations before reintroducing new populations. Our method can complement formal optimization-based approaches and improve AM uptake, particularly for programs involving many complex and coordinated decisions.

Conservation Letters↗
USGS2023

Thirteen years of turtle capture–mark–recapture in a small urban pond complex in Louisiana, USA

Turtles are one of the most imperiled vertebrate groups in the world. With habitat destruction unabated in many places, urban and suburban greenspaces may serve as refugia for turtles, at least those species able to tolerate heavily altered landscapes. In south-central Louisiana, we have conducted a turtle capture–mark–recapture effort in two ponds in an urban greenspace for 13 yr to understand species composition, survival, and individual growth rates. We had 574 total captures of 251 individuals of five species from 2009–2021, with Trachemys scripta elegans (Red-Eared Sliders) and Sternotherus odoratus (Eastern Musk Turtles) being the most common. Apparent annual survival for T. scripta (0.79) was similar to estimates reported in other studies in altered habitats, whereas apparent annual survival for S. odoratus (0.89) was slightly or much higher than other published studies. Growth rates of T. scripta were comparable to other studies and showed both sexes have similar rates of growth until maturity, which is earlier and at a smaller size in males. The two ponds showed marked differences in captures by size, with significantly more juvenile T. scripta captured in the pond with more vegetation, depth, and a softer bottom. Most T. scripta (78.5%) that were recaptured came from the same pond from which they were originally captured. The basic demographic data gained in this study can serve as a starting point for broader questions on urbanization effects and as a comparison to more natural populations.

Louisiana↗
USGS2016

Adaptive management for improving species conservation across the captive-wild spectrum

Conservation of endangered species increasingly envisages complex strategies that integrate captive and wild management actions. Management decisions in this context must be made in the face of uncertainty, often with limited capacity to collect information. Adaptive management (AM) combines management and monitoring, with the aim of updating knowledge and improving decision-making over time. We provide a guide for managers who may realize the potential of AM, but are unsure where to start. The urgent need for iterative management decisions, the existence of uncertainty, and the opportunity for learning offered by often highly-controlled captive environments create favorable conditions for AM. However, experiments and monitoring may be complicated by small sample sizes, and the ability to control the system, including stochasticity and observability, may be limited toward the wild end of the spectrum. We illustrate the key steps to implementing AM in threatened species management using four case studies, including the management of captive programs for cheetah (Acinonyx jubatus) and whooping cranes (Grus americana), of a translocation protocol for Arizona cliffroses Purshia subintegra and of ongoing supplementary feeding of reintroduced hihi (Notiomystis cincta) populations. For each case study, we explain (1) how to clarify whether the decision can be improved by learning (i.e. it is iterative and complicated by uncertainty) and what the management objectives are; (2) how to articulate uncertainty via alternative, testable hypotheses such as competing models or parameter distributions; (3) how to formally define how additional information can be collected and incorporated in future management decisions.

Biological Conservation↗
USGS2016

Demographic modeling for reintroduction decision-making

In this chapter we consider the construction and use of population models to support reintroduction decision making. We begin by reviewing the decision-analytic process, also known as structured decision making. The material on structured decision making builds on the chapter by Chauvenet et al. (This Volume) who focus their attention on the objective setting step of structured decision making. In a decision-analytic setting, modeling cannot commence before objective setting, because the purpose of models is to provide predictions about how different management alternatives will influence the attainment of management objectives. After the introductory section on structured decision making, we turn our attention to the construction and use of population models. We discuss the steps in modeling, including structuring and planning models, obtaining parameter estimates from data, filling information gaps using expert judgment, constructing models, and using models to evaluate action alternatives. We close the chapter with a discussion of research and development needs that we believe will advance the use of population models to support the effective management of reintroduced populations.

Book chapter↗
USGS2015

When do we need more data? A primer on calculating the value of information for applied ecologists

Applied ecologists continually advocate further research, under the assumption that obtaining more information will lead to better decisions. Value of information (VoI) analysis can be used to quantify how additional information may improve management outcomes: despite its potential, this method is still underused in environmental decision-making. We provide a primer on how to calculate the VoI and assess whether reducing uncertainty will change a decision. Our aim is to facilitate the application of VoI by managers who are not familiar with decision-analytic principles and notation, by increasing the technical accessibility of the tool. Calculating the VoI requires explicit formulation of management objectives and actions. Uncertainty must be clearly structured and its effects on management outcomes evaluated. We present two measures of the VoI. The expected value of perfect information is a calculation of the expected improvement in management outcomes that would result from access to perfect knowledge. The expected value of sample information calculates the improvement in outcomes expected by collecting a given sample of new data. We guide readers through the calculation of VoI using two case studies: (i) testing for disease when managing a frog species and (ii) learning about demographic rates for the reintroduction of an endangered turtle. We illustrate the use of Bayesian updating to incorporate new information. The VoI depends on our current knowledge, the quality of the information collected and the expected outcomes of the available management actions. Collecting information can require significant investments of resources; VoI analysis assists managers in deciding whether these investments are justified.

Methods in Ecology and Evolution↗
USGS2013

Demographics of reintroduced populations: estimation, modeling, and decision analysis

Reintroduction can be necessary for recovering populations of threatened species. However, the success of reintroduction efforts has been poorer than many biologists and managers would hope. To increase the benefits gained from reintroduction, management decision making should be couched within formal decision-analytic frameworks. Decision analysis is a structured process for informing decision making that recognizes that all decisions have a set of components—objectives, alternative management actions, predictive models, and optimization methods—that can be decomposed, analyzed, and recomposed to facilitate optimal, transparent decisions. Because the outcome of interest in reintroduction efforts is typically population viability or related metrics, models used in decision analysis efforts for reintroductions will need to include population models. In this special section of the Journal of Wildlife Management, we highlight examples of the construction and use of models for informing management decisions in reintroduced populations. In this introductory contribution, we review concepts in decision analysis, population modeling for analysis of decisions in reintroduction settings, and future directions. Increased use of formal decision analysis, including adaptive management, has great potential to inform reintroduction efforts. Adopting these practices will require close collaboration among managers, decision analysts, population modelers, and field biologists.

Journal of Wildlife Management↗