A genoscape-network model for conservation prioritization in a migratory bird

Using community science to map western monarch butterflies (Danaus plexippus) in spring

Migratory animals follow seasonal cycles comprising linked phases often with different habitat requirements and demographic processes. Conservation of migratory species therefore must consider the full seasonal cycle to identify points limiting population viability. For western monarch butterflies, which have experienced significant declines, early spring is considered a critical period in the annual population cycle. However, records of western monarchs in early spring, when overall abundance is lowest, have historically been extremely limited. We used a community science initiative, the Western Monarch Mystery Challenge, to collect data on monarch distribution throughout the western United States between February 14th and April 22nd over 3 years. Using data from the Western Monarch Mystery Challenge and iNaturalist, we identified potential breeding habitat for western monarchs in early spring that spanned a large geographic area and several ecoregions. We observed monarchs in early spring that likely eclosed in the current year, suggesting that population expansion from overwintering sites reflects both movement and population growth. The number of records of western monarchs from early spring was higher during the Mystery Challenge (33.0/year) than earlier years (5.1/year). This study demonstrates the potential for and limitations of community science to increase our understanding of species at points in the life cycle when they are rare.

Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)

Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low-coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine-scale population structure in a widespread songbird, the American Redstart (Setophaga ruticilla), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population-specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate-Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.

Genetic identification of avian samples recovered from solar energy installations

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or "feather spots"-known to be of avian origin but not readily assignable to species via morphology-may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

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 data sets 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 postbreeding 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 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 to 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.

Demographic responses to climate-driven variation in habitat quality across the annual cycle of a migratory bird species

The demography and dynamics of migratory bird populations depend on patterns of movement and habitat quality across the annual cycle. We leveraged archival GPS‐tagging data, climate data, remote‐sensed vegetation data, and bird‐banding data to better understand the dynamics of black‐headed grosbeak (Pheucticus melanocephalus) populations in two breeding regions, the coast and Central Valley of California (Coastal California) and the Sierra Nevada mountain range (Sierra Nevada), over 28 years (1992–2019). Drought conditions across the annual cycle and rainfall timing on the molting grounds influenced seasonal habitat characteristics, including vegetation greenness and phenology (maturity dates). We developed a novel integrated population model with population state informed by adult capture data, recruitment rates informed by age‐specific capture data and climate covariates, and survival rates informed by adult capture–mark–recapture data and climate covariates. Population size was relatively variable among years for Coastal California, where numbers of recruits and survivors were positively correlated, and years of population increase were largely driven by recruitment. In the Sierra Nevada, population size was more consistent and showed stronger evidence of population regulation (numbers of recruits and survivors negatively correlated). Neither region showed evidence of long‐term population trend. We found only weak support for most climate–demographic rate relationships. However, recruitment rates for the Coastal California region were higher when rainfall was relatively early on the molting grounds and when wintering grounds were relatively cool and wet. We suggest that our approach of integrating movement, climate, and demographic data within a novel modeling framework can provide a useful method for better understanding the dynamics of broadly distributed migratory species.