Geology topics

Mitch D. Weegman

Publications and source records attributed to Mitch D. Weegman.

5 recordsLinked to original sources

USGS2025

Spring weather influences breeding propensity, the most important productivity component for Arctic-nesting lesser snow geese

Animal reproduction is composed of several stages, which collectively determine overall productivity. Yet, it is not fully understood how different productivity components contribute to population change. To bridge this gap, we leveraged integrated population modelling and transient life-table response experiments, together with population-level data on lesser snow geese ( Anser caerulescens caerulescens ) breeding on Wrangel Island, Russia, from 1970 to 2022. We assessed contributions of breeding propensity, clutch size, nest success, egg survival, hatching success and pre-fledging survival to population change, and tested hypotheses about the effects of environmental drivers and density dependence on different components. Breeding propensity contributed the most to variation in population growth, followed by nest success. These two components were negatively affected by the timing of snow melt. We found no overall deleterious effects of climate change on productivity. Density dependence had a positive effect on multiple productivity components, likely through predator swamping. Our results show the importance of breeding propensity to the population dynamics of this long-lived animal, which is notable because this productivity component is often overlooked. Our results also demonstrate that the effects of environmental conditions and density dependence can differ among animal populations of different sizes, locations and life histories.

Wrangel Island↗

USGS2025

Environmental drivers of productivity explain population patterns of an Arctic-nesting goose across a half-century

Joint estimation of demographic rates and population size has become an essential tool in ecology because it enables evaluating mechanisms for population change and testing hypotheses about drivers of demography in a single modeling framework. This approach provides a comprehensive perspective on population dynamics and how animal populations will respond to global pressures in future years. However, long-term data for such analyses are often limited in quantity and quality. We developed an integrated population model combining data on demography and population size from nine different sources to understand the population ecology of the lesser snow goose ( Anser caerulescens caerulescens ) in the Pacific Flyway in North America from 1970 to 2022. We divided the flyway population into Wrangel Island and Western Arctic subpopulations and assessed demographic mechanisms for population change and environmental and anthropogenic drivers that influenced demography. During 1970–2022, the estimated spring population of snow geese in the Pacific Flyway increased from ~300,000 to ~2,300,000. Short-term changes in population growth rate were primarily driven by changes in productivity in the Western Arctic and productivity and immigration in Wrangel Island. Changes in hunting and natural mortality had less influence on short-term but likely contributed to the pronounced long-term population growth. Early snowmelt positively influenced per capita productivity in both regions, and warm, rainy weather during the non-breeding season was associated with high per capita productivity in the Western Arctic. In the Western Arctic, per capita productivity was negatively associated with population size, and adult natural mortality was positively associated with population size, indicating density-dependent regulation in this subpopulation. In Wrangel Island, warm weather in early fall decreased juvenile natural mortality. Our results demonstrate that per capita productivity and immigration, rather than adult survival, were the primary mechanisms of short-term population change in this long-lived species. Our results also indicate that environmental conditions and density-dependent effects can impact population dynamics more than harvest, even for a long-lived, commonly harvested species. We demonstrate that a warming climate can have multiple effects on demography, emphasizing the importance of assessing a variety of spatial and temporal factors when predicting how populations might respond to large-scale environmental changes. This emphasizes the importance of conservation plans that consider these environmental drivers, although this may complicate direct management of such populations.

Ecological Applications↗

USGS2024

Evidence of longitudinal differences in spring migration strategies of an Arctic-nesting goose

During spring, migratory birds are required to optimally balance energetic costs of migration across heterogeneous landscapes and weather conditions to survive and reproduce successfully. Therefore, an individual's migratory performance may influence reproductive outcomes. Given large-scale changes in land use, climate, and potential carry-over effects, understanding how individuals migrate in relation to breeding outcomes is critical to predicting how future scenarios may affect populations. We used GPS tracking devices on 56 Greater White-fronted Geese ( Anser albifrons ) during four spring migrations to examine whether migration characteristics influenced breeding propensity and breeding outcome. We found a strong longitudinal difference in arrival to the breeding areas (18 days earlier), pre-nesting duration (90.9% longer), and incubation initiation dates (9 days earlier) between western- and eastern-Arctic breeding regions, with contrasting effects on breeding outcomes, but no migration characteristic strongly influenced breeding outcome. We found that breeding region influenced whether an individual likely pursued a capital or income breeding strategy. Where individuals fell along the capital-income breeding continuum was influenced by longitude, revealing geographic effects of life-history strategy among conspecifics. Factors that govern breeding outcomes likely occur primarily upon arrival to breeding areas or are related to individual quality and previous breeding outcome, and may not be directly tied to migratory decision-making across broad scales.

Louisiana, Texas↗

USGS2024

The Amazon Basin’s rivers and lakes support Nearctic-breeding shorebirds during southward migration

Identifying the migration routes and stopover sites used by declining species is critical for developing targeted conservation actions. Long-distance migratory shorebirds are among the groups of birds declining most rapidly, yet we frequently lack detailed knowledge about the routes and stopover sites they use during their hemisphere-spanning migrations. This is especially true for species that migrate through mid-continental regions in the Western Hemisphere. We therefore used satellite transmitters to track 212 individuals of 6 shorebird species during their southward migrations— Pluvialis dominica (American Golden-Plover), Limosa haemastica (Hudsonian Godwit), Tringa flavipes (Lesser Yellowlegs), and Calidris subruficollis (Buff-breasted Sandpiper), C. melanotos (Pectoral Sandpiper), and Bartramia longicauda (Upland Sandpiper)—as they crossed the Amazon Basin of South America, a region from which reports of shorebird numbers are increasing but remain relatively rare. Our results make clear that the Amazon Basin provides stopover habitat for a large number of shorebirds: more than 74% of individuals tracked crossing the Amazon Basin stopped over in the region for an average of 2–14 days, with some spending the entire nonbreeding season there. All species selected stopover sites along the region’s many rivers and lakes, while within stopover sites each species exhibited distinct habitat preferences. The timing of stopovers within sub-basins of the Amazon Basin also coincided with periods of low water, when the muddy, shallow water habitats preferred by most shorebirds are likely plentiful. Together, our results highlight the need for detailed investigations into shorebird abundance and distribution within the Amazon Basin, threats to shorebirds within particular subbasins, and links between shorebird conservation efforts and those targeting the myriad other species that inhabit this dynamic, hyper-diverse region.

Amazon Basin↗

USGS2018

Animal movement models for migratory individuals and groups

Animals often exhibit changes in their behaviour during migration. Telemetry data provide a way to observe geographic position of animals over time, but not necessarily changes in the dynamics of the movement process. Continuous‐time models allow for statistical predictions of the trajectory in the presence of measurement error and during periods when the telemetry device did not record the animal's position. However, continuous‐time models capable of mimicking realistic trajectories with sufficient detail are computationally challenging to fit to large datasets. Furthermore, basic continuous‐time model specifications (e.g. Brownian motion) lack realism in their ability to capture nonstationary dynamics. We present a unified class of animal movement models that are computationally efficient and provide a suite of approaches for accommodating nonstationarity in continuous trajectories due to migration and interactions among individuals. Our approach uses process convolutions to allow for flexibility in the movement process while facilitating implementation and incorporating location uncertainty. We show how to nest convolution models to incorporate interactions among migrating individuals to account for nonstationarity and provide inference about dynamic migratory networks. We demonstrate these approaches in two case studies involving migratory birds. Specifically, we used process convolution models with temporal deformation to account for heterogeneity in individual greater white‐fronted goose migrations in Europe and Iceland, and we used nested process convolutions to model dynamic migratory networks in sandhill cranes in North America. The approach we present accounts for various forms of temporal heterogeneity in animal movement and is not limited to migratory applications. Furthermore, our models rely on well‐established principles for modelling‐dependent data and leverage modern approaches for modelling dynamic networks to help explain animal movement and social interaction.

Methods in Ecology and Evolution↗