Geology topics

Sarah P. Saunders

Publications and source records attributed to Sarah P. Saunders.

6 recordsLinked to original sources

USGS2026

Co-producing coastal bird research on the Gulf Coast through structured decision making (SDM) and constructed value of information (CVoI)

Conservation practitioners face the complex challenge of addressing global biodiversity threats within social-ecological systems with multiple jurisdictions, numerous interest groups, and changing environmental conditions. Bridging the research-to-implementation gap between scientists and managers, which integrates diverse perspectives that represent the variety of considerations impacting management outcomes can help to inform efficient and effective conservation. Avian conservation programs along the U.S. Gulf Coast exemplify these challenges, as management decisions must address diverse threats to declining coastal bird populations across large breeding, wintering, and migratory ranges while facing uncertainty surrounding the efficacy of large-scale strategies. This provides opportunities for improving management through co-produced research with structured decision making (SDM). We present a case study that demonstrates the application of co-production principles and SDM to collaboratively develop high-priority research questions informing effective avian conservation and management. Additionally, we applied a constructed value of information (CVoI) framework to identify and prioritize which uncertainties to reduce and better understand how research questions impact management actions. The integration of SDM’s five-step decision making framework ensured key principles of co-production were met, while co-production enhanced SDM outcomes by incorporating diverse perspectives and knowledge bases. CVoI further allowed participants to collectively determine final high-priority research questions in the face of complexity and uncertainty. Together, these approaches supported co-produced knowledge-generation, strategic hypothesis prioritization, and identification of research questions most likely to improve conservation decisions. This case study from the U.S. Gulf Coast demonstrates how co-produced research through SDM offers a broadly applicable approach for navigating ecological complexity and bridging the research-to-implementation gap in avian conservation.

Gulf Coast↗

USGS2025

Effectiveness of stewardship and management strategies to conserve coastal bird populations in the northern Gulf of Mexico: A literature review

Shorebirds, seabirds, and wading birds (hereafter coastal birds) have experienced considerable losses over the last century and require proactive conservation management to stabilize or grow populations. Habitat loss and/or degradation and human disturbance are among the most urgent threats faced by coastal bird populations. Identifying effective conservation management techniques to mitigate these threats is of great interest in the northern Gulf of Mexico (nGoM), a region that provides important habitat during the entire life cycle of resident birds and an essential breeding, wintering, and stopover site for migratory birds. A suite of 35 coastal birds have been identified as priority species for multi-scale conservation monitoring in this region by the Gulf of Mexico Avian Monitoring Network (GoMAMN). This review focuses on impacts of human disturbance and anthropogenic habitat loss and/or degradation on coastal birds and effectiveness of the management strategies implemented to mitigate them, with the goal of informing nGoM management. Our review found that human disturbance was best alleviated by simultaneously deploying complementary stewardship techniques (e.g., signs, fencing, steward patrols, education and community involvement, and beach closures to humans, dogs, and vehicles). However, the relative efficacy of each individual technique is unclear given that only 13% of human disturbance management studies and 38% of habitat management studies have been conducted in the nGoM region. Given the nature of coastal bird habitat and associated risks from sea level rise and human development, most habitat management studies encouraged strategic applications of beach renourishment, limitations on beach raking, as well as site- and species-specific restoration strategies. Studies demonstrated that successful management of coastal birds in the nGoM combined these approaches, employing complementary and adaptive strategies over extended periods.

Journal of Field Ornithology↗

USGS2022

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter datasets versus pseudo-absence locations during migratory periods, and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and post-breeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations, when compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings, and thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach for integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

Ecological Applications↗

USGS2021

Bridging the research-implementation gap in avian conservation with translational ecology

The recognized gap between research and implementation in avian conservation can be overcome with translational ecology, an intentional approach in which science producers and users from multiple disciplines work collaboratively to co-develop and deliver ecological research that addresses management and conservation issues. Avian conservation naturally lends itself to translational ecology because birds are well studied, typically widespread, often exhibit migratory behaviors transcending geopolitical boundaries, and necessitate coordinated conservation efforts to accommodate resource and habitat needs across the full annual cycle. In this perspective, we highlight several case studies from bird conservation practitioners and the ornithological and conservation social sciences exemplifying the 6 core translational ecology principles introduced in previous studies: collaboration, engagement, commitment, communication, process, and decision-framing. We demonstrate that following translational approaches can lead to improved conservation decision-making and delivery of outcomes via co-development of research and products that are accessible to broader audiences and applicable to specific management decisions (e.g., policy briefs and decision-support tools). We also identify key challenges faced during scientific producer–user engagement, potential tactics for overcoming these challenges, and lessons learned for overcoming the research-implementation gap. Finally, we recommend strategies for building a stronger translational ecology culture to further improve the integration of these principles into avian conservation decisions. By embracing translational ecology, avian conservationists and ornithologists can be well positioned to ensure that future management decisions are scientifically informed and that scientific research is sufficiently relevant to managers. Ultimately, such teamwork can help close the research-implementation gap in the conservation sciences during a time when environmental issues are threatening avian communities and their habitats at exceptional rates and at broadening spatial scales worldwide.

Ornithological Applications↗

USGS2019

Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N ‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N ‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N ‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N ‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.

Ecology and Evolution↗

USGS2017

Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies Danaus plexippus

Quantifying how climate and land use factors drive population dynamics at regional scales is complex because it depends on the extent of spatial and temporal synchrony among local populations, and the integration of population processes throughout a species’ annual cycle. We modeled weekly, site-specific summer abundance (1994–2013) of monarch butterflies Danaus plexippus at sites across Illinois, USA to assess relative associations of monarch abundance with climate and land use variables during the winter, spring, and summer stages of their annual cycle. We developed negative binomial regression models to estimate monarch abundance during recruitment in Illinois as a function of local climate, site-specific crop cover, and county-level herbicide (glyphosate) application. We also incorporated cross-seasonal covariates, including annual abundance of wintering monarchs in Mexico and climate conditions during spring migration and breeding in Texas, USA. We provide the first empirical evidence of a negative association between county-level glyphosate application and local abundance of adult monarchs, particularly in areas of concentrated agriculture. However, this association was only evident during the initial years of the adoption of herbicide-resistant crops (1994–2003). We also found that wetter and, to a lesser degree, cooler springs in Texas were associated with higher summer abundances in Illinois, as were relatively cool local summer temperatures in Illinois. Site-specific abundance of monarchs averaged approximately one fewer per site from 2004–2013 than during the previous decade, suggesting a recent decline in local abundance of monarch butterflies on their summer breeding grounds in Illinois. Our results demonstrate that seasonal climate and land use are associated with trends in adult monarch abundance, and our approach highlights the value of considering fine-resolution temporal fluctuations in population-level responses to environmental conditions when inferring the dynamics of migratory species.

Illinois↗