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.

Unraveling a century of global change impacts on winter bird distributions in the eastern United States.

One of the most pressing questions in ecology and conservation centers on disentangling the relative impacts of concurrent global change drivers, climate and land-use/land-cover (LULC), on biodiversity. Yet studies that evaluate the effects of both drivers on species' winter distributions remain scarce, hampering our ability to develop full-annual-cycle conservation strategies. Additionally, understanding how groups of species differentially respond to climate versus LULC change is vital for efforts to enhance bird community resilience to future environmental change. We analyzed long-term changes in winter occurrence of 89 species across nine bird groups over a 90-year period within the eastern United States using Audubon Christmas Bird Count (CBC) data. We estimated variation in occurrence probability of each group as a function of spatial and temporal variation in winter climate (minimum temperature, cumulative precipitation) and LULC (proportion of group-specific and anthropogenic habitats within CBC circle). We reveal that spatial variation in bird occurrence probability was consistently explained by climate across all nine species groups. Conversely, LULC change explained more than twice the temporal variation (i.e., decadal changes) in bird occurrence probability than climate change on average across groups. This pattern was largely driven by habitat-constrained species (e.g., grassland birds, waterbirds), whereas decadal changes in occurrence probabilities of habitat-unconstrained species (e.g., forest passerines, mixed habitat birds) were equally explained by both climate and LULC changes over the last century. We conclude that climate has generally governed the winter occurrence of avifauna in space and time, while LULC change has played a pivotal role in driving distributional dynamics of species with limited and declining habitat availability. Effective land management will be critical for improving species' resilience to climate change, especially during a season of relative resource scarcity and critical energetic trade-offs.

Changes in climate drive recent monarch butterfly dynamics.

Declines in the abundance and diversity of insects pose a substantial threat to terrestrial ecosystems worldwide. Yet, identifying the causes of these declines has proved difficult, even for well-studied species like monarch butterflies, whose eastern North American population has decreased markedly over the last three decades. Three hypotheses have been proposed to explain the changes observed in the eastern monarch population: loss of milkweed host plants from increased herbicide use, mortality during autumn migration and/or early-winter resettlement and changes in breeding-season climate. Here, we use a hierarchical modelling approach, combining data from >18,000 systematic surveys to evaluate support for each of these hypotheses over a 25-yr period. Between 2004 and 2018, breeding-season weather was nearly seven times more important than other factors in explaining variation in summer population size, which was positively associated with the size of the subsequent overwintering population. Although data limitations prevent definitive evaluation of the factors governing population size between 1994 and 2003 (the period of the steepest monarch decline coinciding with a widespread increase in herbicide use), breeding-season weather was similarly identified as an important driver of monarch population size. If observed changes in spring and summer climate continue, portions of the current breeding range may become inhospitable for monarchs. Our results highlight the increasingly important contribution of a changing climate to insect declines.

Interrelated impacts of climate and land-use change on a widespread waterbird.

Together climate and land-use change play a crucial role in determining species distribution and abundance, but measuring the simultaneous impacts of these processes on current and future population trajectories is challenging due to time lags, interactive effects and data limitations. Most approaches that relate multiple global change drivers to population changes have been based on occurrence or count data alone. We leveraged three long-term (1995-2019) datasets to develop a coupled integrated population model-Bayesian population viability analysis (IPM-BPVA) to project future survival and reproductive success for common loons Gavia immer in northern Wisconsin, USA, by explicitly linking vital rates to changes in climate and land use. The winter North Atlantic Oscillation (NAO), a broad-scale climate index, immediately preceding the breeding season and annual changes in developed land cover within breeding areas both had strongly negative influences on adult survival. Local summer rainfall was negatively related to fecundity, though this relationship was mediated by a lagged interaction with the winter NAO, suggesting a compensatory population-level response to climate variability. We compared population viability under 12 future scenarios of annual land-use change, precipitation and NAO conditions. Under all scenarios, the loon population was expected to decline, yet the steepest declines were projected under positive NAO trends, as anticipated with ongoing climate change. Thus, loons breeding in the northern United States are likely to remain affected by climatic processes occurring thousands of miles away in the North Atlantic during the non-breeding period of the annual cycle. Our results reveal that climate and land-use changes are differentially contributing to loon population declines along the southern edge of their breeding range and will continue to do so despite natural compensatory responses. We also demonstrate that concurrent analysis of multiple data types facilitates deeper understanding of the ecological implications of anthropogenic-induced change occurring at multiple spatial scales. Our modelling approach can be used to project demographic responses of populations to varying environmental conditions while accounting for multiple sources of uncertainty, an increasingly pressing need in the face of unprecedented global change.

Effects of stewardship on protected area effectiveness for coastal birds.

Evaluation of protected area effectiveness is critical for conservation of biodiversity. Protected areas that prioritize biodiversity conservation are, optimally, located and managed in ways that support relatively large and stable or increasing wildlife populations. Yet evaluating conservation efficacy remains a challenging endeavor. We used an extensive community science data set, eBird, to evaluate the efficacy of protected areas for birds across the Gulf of Mexico and Atlantic coasts of the United States. We modeled trends (2007-2018) for 12 vulnerable waterbirds that use coastal areas during breeding or wintering. We compared two types of protected areas-sites where conservation organizations implemented active stewardship or management or both to reduce human disturbance (hereafter stewardship sites) and local, state, federal, and private protected areas managed to maintain natural land cover (hereafter protected areas)-as well as unprotected areas. We evaluated differences in trends between stewardship, protected, and unprotected areas across the Gulf and Atlantic coasts as a whole. Similar to a background sample, stewardship was known to occur at stewardship sites, but unknown at protected and unprotected areas. Four of 12 target species-Black Skimmer (Rynchops niger), Brown Pelican (Pelecanus occidentalis), Least Tern (Sternula antillarum), and Piping Plover (Charadrius melodus)-had more positive trends (two to 34 times greater) at stewardship sites than protected areas. Furthermore, five target species showed more positive trends at sites with stewardship programs than unprotected sites during at least one season, whereas seven species showed more positive trends at protected than unprotected areas. No species had more negative trends at stewardship sites than unprotected areas, and two species had more negative trends at protected than unprotected areas. Anthropogenic disturbance is a serious threat to coastal birds, and our findings demonstrate that stewardship to reduce its negative impacts helps ensure conservation of vulnerable waterbirds.

La evaluación de la efectividad de las áreas protegidas es de suma importancia para la conservación de la biodiversidad. Las áreas protegidas que priorizan la conservación de la biodiversidad están, de manera óptima, ubicadas y manejadas de maneras que permiten el mantenimiento de poblaciones silvestres relativamente grandes y estables o en incremento. Aun así, la evaluación de la eficacia de la conservación todavía es un esfuerzo desafiante. Usamos un conjunto extensivo de datos de ciencia comunitaria, eBird, para evaluar la eficacia de las áreas protegidas a lo largo de las costas del Golfo de México y del Atlántico en los Estados Unidos. Modelamos las tendencias poblacionales (2007-2018) para doce aves acuáticas vulnerables que usan las áreas costeras durante la temporada de reproducción o de hibernación. Comparamos dos tipos de áreas protegidas - sitios en donde las organizaciones de conservación implementaron una gestión o manejo activo o ambos para reducir la perturbación humana (de ahora en adelante sitios de gestión) y las áreas protegidas locales, estatales, federales y privadas manejadas para mantener la cobertura natural del suelo (de ahora en adelante áreas protegidas) - así como las áreas desprotegidas. Evaluamos las diferencias en las tendencias entre las áreas de gestión, las protegidas y las desprotegidas a lo largo de las cosas del Golfo y del Atlántico como un todo. Similar a una muestra de fondo, se supo que la gestión ocurría en los sitios de gestión, pero no se sabía si ocurría en las áreas protegidas y desprotegidas. Cuatro de las doce especies analizadas - Rhynchops niger, Pelecanus occidentalis, Sternula antillarum y Charadrius melodus - tuvieron tendencias más positivas (2-34 veces mayor) en los sitios de gestión que en las áreas protegidas. Además, cinco especies mostraron más tendencias positivas en los sitios con programas de gestión que en los sitios desprotegidos al menos durante una temporada, mientras que siete especies mostraron más tendencias positivas en sitios protegidos que en las áreas desprotegidas. protegidas que en las desprotegidas. Ninguna especie tuvo más tendencias negativas en los sitios de gestión que en las áreas desprotegidas y dos especies tuvieron más tendencias negativas en las áreas protegidas que en las desprotegidas. La perturbación antropogénica es una amenaza seria para las aves costeras y nuestros hallazgos demuestran que la gestión para reducir sus impactos negativos ayuda a asegurar la conservación de aves acuáticas vulnerables.

Community science validates climate suitability projections from ecological niche modeling.

Climate change poses an intensifying threat to many bird species and projections of future climate suitability provide insight into how species may shift their distributions in response. Climate suitability is characterized using ecological niche models (ENMs), which correlate species occurrence data with current environmental covariates and project future distributions using the modeled relationships together with climate predictions. Despite their widespread adoption, ENMs rely on several assumptions that are rarely validated in situ and can be highly sensitive to modeling decisions, precluding their reliability in conservation decision-making. Using data from a novel, large-scale community science program, we developed dynamic occupancy models to validate near-term climate suitability projections for bluebirds and nuthatches in summer and winter. We estimated occupancy, colonization, and extinction dynamics across species' ranges in the United States in relation to projected climate suitability in the 2020s, and used a Gibbs variable selection approach to quantify evidence of species-climate relationships. We also included a Bird Conservation Region strata-level random effect to examine among-strata variation in occupancy that may be attributable to land-use and ecoregional differences. Across species and seasons, we found strong evidence that initial occupancy and colonization were positively related to 2020 climate suitability, illustrating an independent validation of projections from ENMs across a large geographic area. Random strata effects revealed that occupancy probabilities were generally higher than average in core areas and lower than average in peripheral areas of species' ranges, and served as a first step in identifying spatial patterns of occupancy from these community science data. Our findings lend much-needed support to the use of ENM projections for addressing questions about potential climate-induced changes in species' occupancy dynamics. More broadly, our work highlights the value of community scientist observations for ground-truthing projections from statistical models and for refining our understanding of the processes shaping species' distributions under a changing climate.

Multiscale seasonal factors drive the size of winter monarch colonies.

Monarch butterflies in eastern North America have declined by 84% on Mexican wintering grounds since the observed peak in 1996. However, coarse-scale population indices from northern US breeding grounds do not show a consistent downward trend. This discrepancy has led to speculation that autumn migration may be a critical limiting period. We address this hypothesis by examining the role of multiscale processes impacting monarchs during autumn, assessed using arrival abundances at all known winter colony sites over a 12-y period (2004-2015). We quantified effects of continental-scale (climate, landscape greenness, and disease) and local-scale (colony habitat quality) drivers of spatiotemporal trends in winter colony sizes. We also included effects of peak summer and migratory population indices. Our results demonstrate that higher summer abundance on northern breeding grounds led to larger winter colonies as did greener autumns, a proxy for increased nectar availability in southern US floral corridors. Colony sizes were also positively correlated with the amount of local dense forest cover and whether they were located within the Monarch Butterfly Biosphere Reserve, but were not influenced by disease rates. Although we demonstrate a demographic link between summer and fine-scale winter population sizes, we also reveal that conditions experienced during, and at the culmination of, autumn migration impact annual dynamics. Monarchs face a growing threat if floral resources and winter habitat availability diminish under climate change. Our study tackles a long-standing gap in the monarch's annual cycle and highlights the importance of evaluating migratory conditions to understand mechanisms governing long-term population trends.

Disentangling data discrepancies with integrated population models.

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963-2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

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.

Dynamic N-occupancy models: estimating demographic rates and local abundance from detection-nondetection data.

Occupancy modeling is a widely used analytical technique for assessing species distributions and range dynamics. However, occupancy analyses frequently ignore variation in abundance of occupied sites, even though site abundances affect many of the parameters being estimated (e.g., extinction, colonization, detection probability). We introduce a new model ("dynamic N-occupancy") capable of providing accurate estimates of local abundance, population gains (reproduction/immigration), and apparent survival probabilities while accounting for imperfect detection using only detection/nondetection data. Our model utilizes heterogeneity in detection based on variations in site abundances to estimate latent demographic rates via a dynamic N-mixture modeling framework. We validate our model using simulations across a wide range of values and examine the data requirements, including the number of years and survey sites needed, for unbiased and precise estimation of parameters. We apply our model to estimate spatiotemporal heterogeneity in abundances of barred owls (Strix varia) within a recently invaded region in Oregon (USA). Estimates of apparent survival and population gains are consistent with those from a nearby radio-tracking study and elucidate how barred owl abundances have increased dramatically over time. The dynamic N-occupancy model greatly improves inferences on individual-level population processes from occupancy data by explicitly modeling the latent population structure.

Factors influencing breeding success, ovarian cyclicity, and cub survival in zoo-managed tigers (Panthera tigris).

Understanding factors that influence reproduction and offspring survival in zoo populations is critical for management of threatened and endangered species. Examination of long-term data (1989-2011) compiled from the Association of Zoos and Aquarium's zoo-managed tiger breeding program provides the basis for a more thorough understanding of reproduction and scientifically based decisions for effective population management in this endangered felid. Biological and management-related factors that could influence tiger breeding success and cub survival were evaluated using logistic mixed models. Breeding success improved with female age until approximately age five, then declined thereafter. Experienced female breeders had greater breeding success than inexperienced females. Litter size was most predictive of cub survival, with average-sized litters (3-4 cubs) experiencing the highest proportional survival. Management-related factors, such as whether the breeding institution had a recent tiger litter and whether both animals were already located at the same institution, also influenced breeding success and cub survival. These results highlight the importance of institutional husbandry experience and the need to retain knowledge through staff turnovers to achieve optimal reproductive success. Using fecal estrogen data, frequency of ovarian cyclicity and mean cycle length did not differ by female age or parity; thus, lack of cyclicity and/or increased cycle duration are not likely explanations for declining breeding success with age. These results provide valuable reproductive information that should improve scientific management of zoo-based tiger populations.