Geology topics

Alexander Wright

Publications and source records attributed to Alexander Wright.

3 recordsLinked to original sources

USGS2024

Matching decision support modeling frameworks to disease emergence stages and associated management objectives

Wildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre-epidemic, early outbreak, mid-epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats.

Conservation Science and Practice↗

USGS2022

A comparison of monitoring designs to assess wildlife community parameters across spatial scales

Dedicated long-term monitoring at appropriate spatial and temporal scales is necessary to understand biodiversity losses and develop effective conservation plans. Wildlife monitoring is often achieved by obtaining data at a combination of spatial scales, ranging from local to broad, to understand the status, trends, and drivers of individual species or whole communities and their dynamics. However, limited resources for monitoring necessitates tradeoffs in the scope and scale of data collection. Careful consideration of the spatial and temporal allocation of finite sampling effort is crucial for monitoring programs that span multiple spatial scales. Here we evaluate the ability of five monitoring designs - stratified random, weighted effort, indicator unit, rotating panel, and split panel - to recover parameter values that describe the status (occupancy), trends (change in occupancy), and drivers (spatially-varying covariate and an autologistic term) of wildlife communities at two spatial scales. Using an amphibian monitoring program that spans a network of U.S. National Parks as a motivating example, we conducted a simulation study for a regional community occupancy sampling program to compare the monitoring designs across varying levels of sampling effort (ranging from 10 to 50%). We found that the stratified random design outperformed the other designs for most parameters of interest at both scales, and was thus generally preferable in balancing the estimation of status, trends, and drivers across scales. However, we found that other designs had improved performance in specific situations. For example, the rotating panel design performed best at estimating spatial drivers at a regional level. Thus, our results highlight the nuanced scenarios in which various design strategies may be preferred, and offer guidance as to how managers can balance common tradeoffs in large-scale and long-term monitoring programs in terms of the specific knowledge gained. Monitoring designs that improve accuracy in parameter estimates are needed to guide conservation policy and management decisions in the face of broad-scaled environmental challenges, but the preferred design is sensitive to the specific objectives of a monitoring program.

Ecological Applications↗

USGS2020

Moving from decision to action in conservation science

Biodiversity loss is a major threat to the integrity of ecosystems and is projected to worsen, yet the path to successful conservation remains elusive. Decision support frameworks (DSFs) are increasingly applied by resource managers to navigate the complexity, uncertainty, and differing socio-ecological objectives inherent to conservation problems. Most published conservation research that uses DSFs focuses on analytical stages (e.g., identifying an optimal decision), making it difficult to assess and learn from previous examples in a conservation practice context. Here, we (1) evaluate the relationship between the application of decision science and the resulting conservation outcomes, and (2) identify and address existing barriers to the application of DSFs to conservation practice. To do this, we develop a framework for evaluating conservation initiatives using decision science that emphasizes setting attainable goals, building momentum, and obtaining partner buy-in. We apply this framework to a systematic review of amphibian conservation decision support projects, including a follow-up survey of the pertinent conservation practitioners, stakeholders, and scientists. We found that all projects identified optimal solutions to reach stated objectives, but positive conservation outcomes were limited when implementation challenges arose. Further, we identified multiple barriers (e.g., dynamic and hierarchical leadership, scale complexity, limited resource availability) that can inhibit the progression from decision identification to action implementation (i.e., ‘decision-implementation gap’), and to successful conservation outcomes. Based on these results, we provide potential actionable steps and avenues for future development of DSFs to facilitate the transition from decision to action and the realization of conservation successes.

Biological Conservation↗