Geology topics

Amy L. Scarpignato

Publications and source records attributed to Amy L. Scarpignato.

6 recordsLinked to original sources

USGS2026

The collective application of shorebird tracking data to conservation

Addressing urgent conservation issues, such as the drastic declines of North American migratory birds, requires creative, evidence-based, efficient, and collaborative approaches. The abundance of over 50% of monitored North American shorebird populations has declined by over 50% since 1980. To address these declines, we developed a partnership of scientists and practitioners called the Shorebird Science and Conservation Collective (hereafter the collective ). The collective was founded to translate the combined findings of shorebird tracking data into on-the-ground conservation action. With advice from an advisory group, the collective acts as an intermediary whereby dedicated staff collate and analyze data contributions from scientists to support knowledge requests from conservation practitioners. In its first three years, data contributions from 75 organizations include over 7.1 million shorebird observations forming movement paths of 3420 individuals representing 36 species tracked across the Americas and have informed 18 conservation projects spanning education, land and species management, land conservation, and policy requests. Others engaged in translational science from big data could consider similar knowledge-sharing models that prioritize usable data products, foster collaborative engagement between science experts and practitioners, build focused communities around topics or taxonomic groups, and employ a proof-of-concept phase to develop scalable solutions while making progress toward long-term funding to sustain impact. As the volume of scientific data continues to grow, intermediaries, such as the collective, can be vital liaisons to rapidly integrate and interpret research to support conservation action. Dedicated to the memory of Shiloh Schulte and his conservation achievements for shorebirds.

Conservation Biology↗

USGS2025

Power source, data retrieval method, and attachment type affect success of dorsally mounted tracking tag deployments in 37 species of shorebirds

Animal-borne trackers are commonly used to study bird movements, including in long-distance migrants such as shorebirds. Selecting a tracker and attachment method can be daunting, and methodological advancements often have been made by trial and error and conveyed by word of mouth. We synthesized tracking outcomes across 2745 dorsally mounted trackers on 37 shorebird species around the world. We evaluated how attachment method, power source, data retrieval method, relative tracker mass, and biological traits affected success, where success was defined as whether or not each tag deployment reached its expected tracking duration (i.e. all aspects succeeded for the intended duration of the study: attachment, tracking, data acquisition, and bird survival). We conducted separate analyses for tag deployments with remote data retrieval (‘remote-upload tag deployments') and those that archived data and had to be recovered (‘archival tag deployments'). Among remote-upload tag deployments, those that were a lighter mass relative to the bird, were beyond their first year of production, transmitted data via satellite, or were attached with a leg-loop harness were most often successful at reaching their expected tracking duration. Archival tag deployments were most successful when applied at breeding areas, or when applied to males in any season. Remote-upload tag deployments with solar power, satellite data retrieval, or leg-loop harnesses continued tracking for longer than those with battery power, other types of data retrieval, or glue attachments. However, the majority of tag deployments failed to reach their expected tracking duration (71% of remote-upload, 83% of archival), which could have been due to tracker failure, attachment failure, or bird mortality. Our findings highlight that many tag deployments may fail to meet the goals of a study if tracking duration is crucial. Using our results, we provide guidelines for selecting a tracker and attachment to improve success at meeting study goals.

Journal of Avian Biology↗

USGS2025

Delineating ecologically-distinct groups for annual cycle management of a declining shorebird

1. Patterns of migratory connectivity are increasingly used to understand and manage threats throughout the annual cycle of migratory species. Strong migratory connectivity refers to when individuals from different populations remain spatially separated across the annual cycle, which may expose populations to unique sets of threats and conditions that cause differential population trends. However, the populations or groups used for species’ management are often defined a priori based on expert knowledge and/or management units, which may mask important population segregation and obscure differential population trends and their drivers. 2. We compared three approaches to defining management groups of a declining shorebird, the long-billed curlew ( Numenius americanus ), for annual cycle management: by expert-opinion, according to management flyways, and with unsupervised clustering of satellite tracking data that maximizes the strength of migratory connectivity. 3. Despite the curlews having a continuous breeding range and a pattern of parallel migration, all three approaches identified groups with different population trends, movement behaviours and habitat selection across the annual cycle, suggesting these are meaningful ecological groups. The expert and clustering approaches resulted in similar group structure, strong estimates of migratory connectivity (measured as MC = 0.64 across seasons), movement behaviour and habitat selection; however, the expert approach identified an additional divide between the easternmost grouping, which revealed strongly negative population trends in the group occupying the Chihuahuan desert during the stationary nonbreeding season. In contrast, the flyway delineation resulted in weaker estimates of migratory connectivity, marginal differences in population trends and less between-group differences in movement behaviour and habitat selection. 4. Synthesis and applications . Using measurements of migratory connectivity in concert with expert opinion can define ecologically distinct groups for wildlife management that differ in the environmental conditions they experience across seasons of the annual cycle, which is a key component for understanding and reversing declines of migratory species.

Journal of Applied Ecology↗

USGS2025

Challenges and opportunities for data integration to improve estimation of migratory connectivity

Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type. The R package MigConnectivity was developed to estimate population-specific connectivity and the range-wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long-distance reencounter data, collected from the same or different individuals, to estimate population-specific transition probabilities (estTransition) and the range-wide strength of those transition probabilities (estStrength). We used simulation and real-world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting ( Passerina ciris ), Yellow Warbler ( Setophaga petechia ) and Bald Eagle ( Haliaeetus leucocephalus ), two of which had bidirectional data. We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding-to-nonbreeding or nonbreeding-to-breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season. The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range-wide coverage that has been hard to achieve using single data types. The best-case scenario for data integration is to have data from all regions, especially if the question is range-wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals.

Methods in Ecology and Evolution↗

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↗

USGS2017

Full annual cycle climate change vulnerability assessment for migratory birds

Climate change is a serious challenge faced by all plant and animal species. Climate change vulnerability assessments (CCVAs) are one method to assess risk and are increasingly used as a tool to inform management plans. Migratory animals move across regions and continents during their annual cycles where they are exposed to diverse climatic conditions. Climate change during any period and in any region of the annual cycle could influence survival, reproduction, or the cues used to optimize timing of migration. Therefore, CCVAs for migratory animals best estimate risk when they include climate exposure during the entire annual cycle. We developed a CCVA incorporating the full annual cycle and applied this method to 46 species of migratory birds breeding in the Upper Midwest and Great Lakes (UMGL) region of the United States. Our methodology included background risk, climate change exposure × climate sensitivity, adaptive capacity to climate change, and indirect effects of climate change. We compiled information about migratory connectivity between breeding and stationary non-breeding areas using literature searches and U.S. Geological Survey banding and re-encounter data. Climate change exposure (temperature and moisture) was assessed using UMGL breeding season climate and winter climate from non-breeding regions for each species. Where possible, we focused on non-breeding regions known to be linked through migratory connectivity. We ranked 10 species as highly vulnerable to climate change and two as having low vulnerability. The remaining 34 species were ranked as moderately vulnerable. In general, including non-breeding data provided more robust results that were highly individualistic by species. Two species were found to be highly vulnerable throughout their annual cycle. Projected drying will have the greatest effect during the non-breeding season for species overwintering in Mexico and the Caribbean. Projected temperature increases will have the greatest effect during the breeding season in UMGL as well as during the non-breeding season for species overwintering in South America. We provide a model for adaptive management of migratory animals in the face of projected climate change, including identification of priority species, research needs, and regions within non-breeding ranges for potential conservation partnerships.

Ecological Applications↗