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Freshwater turtle populations are susceptible to declines following small increases in the mortality of adults, making it essential to identify and understand potential threats. Freshwater turtles ingest fish hooks associated with recreational angling, and this is likely a problem because hook ingestion is a source of additive mortality for sea turtles. We used a Bayesian-modeling framework, observed rates of hook ingestion by freshwater turtles, and mortality of sea turtles from hook ingestion to examine the probability that a freshwater turtle in a given population ingests a hook and subsequently dies from it. We used the results of these analyses and previously published life-history data to simulate the effects of hook ingestion on population growth for 3 species of freshwater turtle. In our simulation, the probability that an individual turtle ingests a hook and dies as a result was 1.2-11%. Our simulation results suggest that this rate of mortality from hook ingestion is sufficient to cause population declines. We believe we have identified fish-hook ingestion as a serious yet generally overlooked threat to the viability of freshwater turtle populations. Las poblaciones de tortugas de agua dulce son susceptibles a las declinaciones como consecuencia de pequeños incrementos en la mortalidad de los adultos, lo que hace que sea esencial la identificación y el entendimiento de las amenazas potenciales. Las tortugas de agua dulce ingieren anzuelos de pesca asociados con la pesca recreativa y esto es probablemente un problema pues la ingesta de anzuelos es una fuente de mortalidad añadida para las tortugas marinas. Utilizamos un marco de trabajo de modelo Bayesiano, observamos las tasas de ingesta de anzuelos por parte de las tortugas de agua dulce y la mortalidad de las tortugas marinas a partir de la ingesta de anzuelos para examinar la probabilidad de que una tortuga de agua dulce en una población dada ingiera un anzuelo y muera por esto. Utilizamos los resultados de estos análisis y datos de historia de vida publicados previamente para simular los efectos de la ingesta de anzuelos sobre el crecimiento poblacional de tres especies de tortuga de agua dulce. En nuestra simulación, la probabilidad de que una tortuga individual ingiera un anzuelo y muera como resultado fue de 1.2 - 11 %. Los resultados de nuestra simulación sugieren que esta tasa de mortalidad por ingesta de anzuelos es suficiente para ocasionar declinaciones en la población. Creemos que hemos identificado la ingesta de anzuelos como una amenaza seria, aunque ignorada generalmente, para la viabilidad poblacional de las tortugas de agua dulce.

Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias. Innovation We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size. Main conclusions Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non‐detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research.

Aim: To improve the accuracy of inferences on habitat associations and distribution patterns of rare species by combining machine-learning, spatial filtering and resampling to address class imbalance and spatial bias of large volumes of citizen science data. Innovation: Modelling rare species' distributions is a pressing challenge for conservation and applied research. Often, a large number of surveys are required before enough detections occur to model distributions of rare species accurately, resulting in a data set with a high proportion of non-detections (i.e. class imbalance). Citizen science data can provide a cost-effective source of surveys but likely suffer from class imbalance. Citizen science data also suffer from spatial bias, likely from preferential sampling. To correct for class imbalance and spatial bias, we used spatial filtering to under-sample the majority class (non-detection) while maintaining all of the limited information from the minority class (detection). We investigated the use of spatial under-sampling with randomForest models and compared it to common approaches used for imbalanced data, the synthetic minority oversampling technique (SMOTE), weighted random forest and balanced random forest models. Model accuracy was assessed using kappa, Brier score and AUC. We demonstrate the method by evaluating habitat associations and seasonal distribution patterns using citizen science data for a rare species, the tricoloured blackbird (Agelaius tricolor). Main Conclusions: Spatial under-sampling increased the accuracy of each model and outperformed the approach typically used to direct under-sampling in the SMOTE algorithm. Our approach is the first to characterize winter distribution and movement of tricoloured blackbirds. Our results show that tricoloured blackbirds are positively associated with grassland, pasture and wetland habitats, and negatively associated with high elevations or evergreen forests during both winter and breeding seasons. The seasonal differences in distribution indicate that individuals move to the coast during the winter, as suggested by historical accounts.

Species vary in their responses to urban habitat; most species avoid these environments, whereas others tolerate or even thrive in them. To better characterize the extent to which species vary in their responses to urban habitat (from this point forwards “urban tolerance”), we used several methods to quantify these responses at a continental scale across all birds. Using open access community science-derived data from the eBird Status and Trends Products and two different types of high-resolution geospatial data that quantify urbanization of landscapes, we calculated urban tolerance for 432 species with breeding ranges that overlap large cities in Canada or the USA. We developed six different calculations to characterize species-level urban tolerance, allowing us to assess how each species’ relative abundance across their breeding range varied with estimates of urban habitat use and intensity. We assessed correlations among these six indices, then compressed the two best-performing indices into a single principal component (multivariate urban tolerance index) that captured variation in urban tolerance among species. We assessed the accuracy of our single and multivariate urban tolerance indices using 24 test species that have been well characterized for their tolerance or avoidance of the urban habitat, as well as with previously published, independent urban tolerance estimates. Here, we provide this new dataset of species-level urban tolerance estimates that improves upon previous metrics by incorporating continental-scale, continuous estimates that better differentiate species’ tolerance of urban habitat compared with existing, categorical methods. These refined metrics can be used to test hypotheses that link ecological, life history, and behavioral traits to avian urban tolerance. The dataset is licensed as CC-By Attribution 4.0 International. Users must appropriately cite the data paper and dataset if used in publications and scientific presentations.

Aim Information on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, is a fundamental source of ecological knowledge. However, collecting information at relevant scales is often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen science has been signalled as an increasingly important source to fill in data gaps where information is needed to make comprehensive and robust inferences on species distributions. However, there are perceived trade‐offs of combining highly structured, scientific survey data with largely un‐structured, citizen science data. Methods We explore these trade‐offs by applying a simplified approach of filtering citizen science data to resemble structured survey data and analyse both sources of data under a common framework. To accomplish this, we integrated high‐resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality. Results The integration of survey data with the filtered citizen science data resulted in improved inference and increased the extent and accuracy of distribution models on shorebirds for the Central Valley. The structured surveys improved the overall accuracy of ecological inference over models using citizen science data only by increasing the representation of data collected from high‐quality habitats for shorebirds. Main conclusions The practical approach we have shown for data integration can also be used to improve the efficiency of designing biological surveys in the context of larger, citizen science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple method we present can be used to integrate other types of data with more localized efforts, ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.

Understanding migratory connectivity for species of concern is of great importance if we are to implement management aimed at conserving them. New methods are improving our understanding of migration; however, banding (ringing) data is by far the most widely available and accessible movement data for researchers. Here, we use band recovery data for American black ducks ( Anas rubripes ) from 1951–2011 and analyze their movement among seven management regions using a hierarchical Bayesian framework. We showed that black ducks generally exhibit flyway fidelity, and that many black ducks, regardless of breeding region, stopover or overwinter on the Atlantic coast of the United States. We also show that a non-trivial portion of the continental black duck population either does not move at all or moves to the north during the fall migration (they typically move to the south). The results of this analysis will be used in a projection modeling context to evaluate how habitat or harvest management actions in one region would propagate throughout the continental population of black ducks. This analysis may provide a guide for future research and help inform management efforts for black ducks as well as other migratory species.

The COVID-19 pandemic has disrupted field research programs, making conservation and management decision-making more challenging. However, it may be possible to conduct population assessments using integrated models that combine community science data with existing data from structured surveys. We developed a space–time integrated model to characterize spatial and temporal variability in population distribution. We fit our integrated model to 10 years of eBird (2010–2020) and 9 years of aerial survey (2010–2019) Mottled Duck count data to forecast 2020 population size along the western Gulf Coast of Texas and Louisiana. Estimates of Mottled Duck abundance were similar in magnitude to estimates calculated using previous methods but were more precise and showed evidence of a declining population. The spatial distribution for Mottled Ducks each year was characterized by several concentrations of relatively high abundance, although the location of these abundance \"hotspots\" varied over time. Expected abundance was higher for areas with a higher proportion of area covered by marsh habitat. By leveraging large-scale community science data, we were able to conduct a population assessment despite the disruption in structured surveys caused by the pandemic. As participation in community science platforms continues to increase, we anticipate modeling frameworks, like the integrated model we developed here, will become increasingly useful for informing conservation and management decision-making.

Avian population sizes fluctuate and change over vast spatial scales, but the mechanistic underpinnings remain poorly understood. A key question is whether spatial and annual variation in avian population dynamics is driven primarily by variation in breeding season recruitment or by variation in overwinter survival. We present a method using large‐scale volunteer‐collected data from project eBird to develop species‐specific indices of net population change as proxies for survival and recruitment, based on twice‐annual, rangewide snapshots of relative abundance in spring and fall. We demonstrate the use of these indices by examining spatially explicit annual variation in survival and recruitment in two well‐surveyed nonmigratory North American species, Carolina wren Thryothorus ludovicianus and northern cardinal Cardinalis cardinalis. We show that, while interannual variation in both survival and recruitment is slight for northern cardinal, eBird abundance data reveal strong and geographically coherent signals of interannual variation in the overwinter survival of Carolina wren. As predicted, variation in wintertime survival dominates overall interannual population fluctuations of wrens and is correlated with winter temperature and snowfall in the northeastern United States, but not the southern United States. This study demonstrates the potential of participatory science (also known as citizen science) datasets like eBird for inferring variation in demographic rates and introduces a new complementary approach towards illuminating the macrodemography of North American birds at comprehensive continental extents.

Sea-level rise threatens both human communities and vulnerable species within coastal areas. Joint spatial planning can allow conservation and social resiliency goals to work in synergy. We present a case study integrating distribution information of a threatened saltmarsh bird, the eastern black rail ( Laterallus jamaicensis jamaicensis ), with social information to facilitate such joint planning. We constructed a distribution model for the species within an urbanizing coastal region (New Jersey, USA) and integrated this with publicly available parcel and protected area data to summarize ownership patterns. We estimated that c . 0.3–2.8% ( c . 260–2200 ha) of available saltmarsh is occupied by eastern black rail, most of which is publicly owned (79%). Privately owned saltmarsh was spread across nearly 5000 individual parcels, 10% of which contained areas with the highest likelihood of rail presence according to our model (top quartile of predicted occupancy probabilities). Compared with all privately owned saltmarsh, parcels with probable rail habitat were larger (median: 5 versus 2 ha), contained more marsh (87% versus 59%) and were less economically valuable (US $11 200 versus US$ 36 100). Our approach of integrating species distributions with landownership data helps clarify trade-offs and synergies in species conservation and coastal resiliency planning.

Avian migration is one of Earth’s largest processes of biomass transport, involving billions of birds. We estimated continental biomass flows of nocturnal avian migrants across the contiguous United States using a network of 143 weather radars. We show that, relative to biomass leaving in autumn, proportionally more biomass returned in spring across the southern United States than across the northern United States. Neotropical migrants apparently achieved higher survival during the combined migration and non-breeding period, despite an average three- to fourfold longer migration distance, compared with a more northern assemblage of mostly temperate-wintering migrants. Additional mortality expected with longer migration distances was probably offset by high survival in the (sub)tropics. Nearctic–Neotropical migrants relying on a ‘higher survivorship’ life-history strategy may be particularly sensitive to variations in survival on the overwintering grounds, highlighting the need to identify and conserve important non-breeding habitats. Data from 143 weather radars provide an estimate of biomass flows from nocturnal migrating birds across the continental United States, providing a picture of survival rates for species with different life-history and migratory strategies.