Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions

Decoupling both local and global abundance from global range size, challenging the abundance-occupancy relationship in birds

In macroecology, a classic empirical observation has been positive relationships between local abundance and species' range, known as the abundance-occupancy relationships (AORs). The existence of this empirical relationship has informed both theory development and applied questions. Notably, the spatial neutral model of biodiversity predicts AORs. Yet, based on the largest known meta-analysis of 16,562,995 correlations from ~3 billion bird observations, this relationship was indistinguishable from zero. Further, in a phylogenetic comparative analysis, species range had no predictive power over the global mean abundance of 7464 bird species. We suggest that publication and confirmation biases may have created AORs, an illusion of a 'universal' pattern. This nullification highlights the need for ecologists to instigate a credibility revolution like psychology, where many classic phenomena have been nullified.

Improving national-scale breeding bird surveys with integrated distance sampling

Bird population estimation over broad spatial and temporal scales is a key objective in ornithology. To date, bird ecologists mainly relied on standard point counts where the number of detected individuals is interpreted as either the true abundance or proportionally related to it. However, providing accurate estimates of species abundance requires modelling the observation process with temporally replicated data, which is not always possible with the increasing use of ever-bigger datasets from citizen science programs. Data integration methods allow combining temporally replicated sampling at coarser spatial grains with data collected over larger spatial extents. Here, we developed an Integrated distance sampling (IDS) to combine national structured and semi-structured citizen-based bird surveys in France to estimate species abundances using observation distances and accounting for availability, i.e. the probability of individuals being detectable during a given sampling visit. While our simulation study showed an overall increase in the accuracy of estimated parameters for both ecological and observation processes, without significant biases, our case study suggests that such model improvements will depend on specific sampling scenarios. Integrated models represent a promising tool for ecological science, permitting the joint use of large unstructured datasets with scale-restricted structured surveys.

Using a large citizen science dataset to uncover diverse patterns of elevational migration in Himalayan birds

Among montane birds, elevational migration is a well-described phenomenon. Yet, apart from mountain ranges in the Americas, there is little information on the large-scale patterns and extent of elevational migration. Using a large citizen science dataset (eBird), we determine the elevational ranges of 377 Himalayan bird species in their breeding and non-breeding periods. Based on the position of species' seasonal elevation ranges, we describe five elevational migration patterns that broadly include post-breeding upslope and downslope migration. Most high-elevation breeders (65-75%) were downslope migrants, which were further subdivided into four distinct patterns: 'displace' (complete downslope), 'shift' (partial downslope), 'expand' (lower limit expansion) and 'contract' (upper limit contraction). We find significant intraspecific variation in migration patterns across the Himalayas, possibly determined by local biotic and abiotic conditions. Specialized dietary guilds like invertivores were more likely to show shift or displace migration, potentially tracking seasonally fluctuating food resources, while generalists like omnivores and human commensals were more likely to be resident. Territorial birds were largely non-migratory, most likely to retain high-quality breeding territories. As mountains are a bounded domain with limited combinations of species' seasonal elevation ranges, the patterns we describe here are useful for understanding elevational migration globally.

The structure of the end-Cretaceous dinosaur fossil record in North America

Whether non-avian dinosaurs were in decline prior to their extinction 66 million years ago remains a contentious topic. This uncertainty arises from spatiotemporal sampling inconsistency and data absence, which cause challenges in distinguishing between genuine biological trends and sampling artifacts. Consequently, there is an inherent interest in better quantifying the quality of the data and concomitant biases of the dinosaur fossil record. To elucidate the structure of this record and the nature of the biases impacting it, we integrate paleoclimatic, geographic, and fossil data within a Bayesian occupancy modeling framework to simultaneously estimate the probability of dinosaurs occupying and being detected in sites across North America throughout the latest Cretaceous for the first time. We find that apparent declines in occupancy generated from the raw fossil record do not match modeled occupancy probability, which generally remained stable throughout the latest Cretaceous. Instead, they coincide with decreased probability of detecting dinosaur occurrences, despite high overall sampling during this interval. By incorporating model covariates, we additionally reveal that detection probability is directly and significantly influenced by the available area of geological outcrop and modern land cover. Our findings offer evidence that traditional comparisons of diversity estimates between time intervals are likely inaccurate due to underlying structural issues in the geological record operating at both local and regional scales. This study underscores the utility of occupancy modeling as a novel approach in paleobiology for quantifying the impact of heterogeneous sampling on the available fossil record.

Where the wild bees are: Birds improve indicators of bee richness

Widespread declines in wild bee populations necessitate urgent action, but insufficient data exist to guide conservation efforts. Addressing this data deficit, we investigated the relative performance of environmental and/or taxon-based indicators to predict wild bee richness in the eastern and central U.S. Our methodology leveraged publicly available data on bees (SCAN and GBIF data repositories), birds (eBird participatory science project) and land cover data (USDA Cropland Data Layer). We used a Bayesian variable selection algorithm to select variables that best predicted species richness of bees using two datasets: a semi-structured dataset covering a wide geographical and temporal range and a structured dataset covering a focused extent with a standardized protocol. We demonstrate that birds add value to land cover data as indicators of wild bee species richness across broad geographies, particularly when using semi-structured data. These improvements likely stem from the demonstrated sensitivity of birds to conditions thought to impact bees but that are missed by remotely sensed environmental data. Importantly, this enables estimation of bee richness in places that don't have direct observations of bees. In the case of wild bees specifically, we suggest that bird and land cover data, when combined, serve as useful indicators to guide monitoring and conservation priorities until the quality and quantity of bee data improve.

Seasonal Bird Migration Could Explain Regional Synchronicity and Amplification in Human West Nile Virus Case Numbers

West Nile virus (WNV) is a zoonotic virus with a mosquito-avian transmission cycle having occasional spillover to mammals. A network analysis of annual log-transformed WNV case numbers (2003-2022) generated four spatially and temporally coherent clusters among 48 U.S. states and six Canadian provinces. Cluster 1 and Cluster 3 were the largest groups corresponding to the Central Flyway and the closely associated Eastern Flyway (with an east-coast subset). Cluster 2 and Cluster 4 corresponded with less-well defined segments of a distinctly different Western Flyway. Thus, clustering can be explained by migratory pathways of terrestrial birds. We investigated avian involvement in the spread of WNV from potential sources in the southern U.S. Analyses revealed consistent patterns in log-transformed case numbers of human WNV. This study highlights the significant role of migratory birds in shaping the spatiotemporal patterns of WNV incidence across North America. However, the observed variability in incidence also likely reflects the interplay of other factors including local environmental conditions, mosquito populations, and regional variations in both migratory and non-migratory bird populations.

Seasonal dynamics of range expansion in South American thrushes

BACKGROUND

Species ranges are shaped by a variety of ecological and environmental factors that are inherently dynamic, fluctuating in response to climatic, biotic, and anthropogenic influences. Dispersal plays a key role in range shifts, allowing species to adapt to changing habitats and exploit new regions. In this study, we examined the dispersal processes of four thrush species (Turdus amaurochalinus, T. chiguanco, T. falcklandii and T. rufiventris) that have expanded their ranges in recent decades, with a focus on the interaction between spread and seasonal movements.

METHODS

We collected eBird records from 2003 to 2023 to create heat maps that illustrate changes in densities of reported occurrences between seasons and over the years. We also evaluated how bioclimatic and land cover variables influenced the observed variations.

RESULTS

The four thrush species have shown significant range expansions, with initially distinct seasonal distributions, which became increasingly similar over time, leading to significant overlap in their breeding and non-breeding habitats. Temperature and precipitation associated with the presence of the four species varied over time and between seasons. Additionally, all four species exhibited shifts in habitat selection, both seasonally and across years.

CONCLUSION

The changes of range are likely driven by a combination of climate and land-use change, and resource availability, which also influence seasonal dispersal patterns. At the same time, thrushes perform very well in urban environments, which offer stable resources and may contribute to their reduction in seasonal movements. Overall, these findings highlight the dynamic nature of thrush species' range shifts and their adaptation to environmental changes.

The accuracy of capture per unit effort in predicting density of a cryptic snake was more sensitive to reductions in spatial than temporal coverage

A critical component of monitoring wildlife populations is understanding changes in population size or abundance. However, for most populations a complete census is not possible; thus, trends or abundance need to be estimated through alternative means, such as indexes. An important aspect of using indexes, such as capture per unit effort (CPUE), is validating them as accurate or precise predictors of population trends or abundance. We completed such analyses using data collected from visual surveys and trapping for brown treesnakes (Boiga irregularis) within a 5-ha enclosure that was undergoing a continuous population decline. During a ~ 6-year period, we censused and marked the snake population to fully enumerated the population, with new individuals resulting from births and removals resulting only from mortality (natural or experimental). From trapping and visual surveys, we were also able to calculate CPUE as a function of trap nights or km surveyed and used regressions to forecast snake density (snakes/km) in the enclosure from CPUE. We also rarefied the true dataset to measure whether reductions in sampling intensity, either temporally or spatially, affected the accuracy or precision in predicting snake density from CPUE. We found that trap CPUE demonstrated no statistical relationship to density based on our study methods. CPUE during visual surveys did predict actual density, with sufficient spatial and temporal sampling intensity. CPUE from visual surveys was relatively robust against reductions in temporal sampling when spatial intensity remained high. However, reductions in the spatial area covered to less than 50% of the enclosure rapidly reduced the accuracy and precision in using CPUE to forecast density. Our results indicate that visual surveys are a relatively accurate measure of true density for brown treesnakes, given sufficient spatial sampling effort. The spatial area of coverage required for CPUE to accurately predict changes in abundance was, however, intense with > 50% of the spatial area required to be sampled on a given sampling night. Our results indicate that CPUE is only reliable as an index of abundance or population trends for cryptic snakes, if sampling effort covers most of the landscape over which populations are being estimated.

Exploring the impact of land use on bird diversity in high-density urban areas using explainable machine learning models

Amid rapid urbanization, land use shifts in cities globally have profound effects on ecosystems and biodiversity. Birds, as a crucial component of urban biodiversity, are highly sensitive to environmental changes and often serve as indicator species for biodiversity. This study, using Shenzhen as a case study, integrates machine learning techniques with spatial statistical methods. Firstly, a multi-layer perceptron (MLP) model was employed to globally simulate bird richness based on citizen science data. Subsequently, a geographic weighted random forest (GW-RF) model was used to construct the complex relationship between bird diversity and land use. Additionally, SHAP analysis evaluates the effects of urban factors and development patterns on bird diversity. The findings reveal that anthropogenic disturbances and habitat factors significantly influence bird diversity. Furthermore, the impact of land landscape patterns on bird diversity exhibits notable spatial heterogeneity, with landscape patterns within ecological spaces and developed land showing marked differences in their effects on bird diversity. The study's findings clarify the intricate effects of urbanization on bird diversity, pinpointing specific ecological conservation areas. It underscores the importance of ecological conservation in guiding urban development, advocating for strategic restoration to bolster urban sustainability and optimize land use for the protection of ecological diversity.

Treating gaps and biases in biodiversity data as a missing data problem

Big biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large-scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long-term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow.

Extreme migratory connectivity and apparent mirroring of non-breeding grounds conditions in a severely declining breeding population of an Afro-Palearctic migratory bird

Understanding the distribution of breeding populations of migratory animals in the non-breeding period (migratory connectivity) is important for understanding their response to environmental change. High connectivity (low non-breeding population dispersion) may lower resilience to climate change and increase vulnerability to habitat loss within their range. Very high levels of connectivity are reportedly rare, but this conclusion may be limited by methodology. Using multiple tracking methods, we demonstrate extremely high connectivity in a strongly declining, peripheral breeding population of a long-distance migrant, the Common Nightingale in the UK. Non-breeding population dispersion is lower than for previously tracked populations of this and other species and likely lower than can usually be detected by light-level geolocation, the main tracking method for small bodied species. Extremely low levels of population mixing were also detected, so any impacts on this population on the non-breeding grounds are unlikely to be shared with more distant breeding populations, corresponding to the observed patterns of European population change. According to a species distribution model using independent field data, this population's non-breeding grounds had lower suitability than others and likely declined before the period we were able to assess. These results support hypotheses that climatic and habitat-related deterioration of non-breeding grounds contributes to population declines in peripheral and high-connectivity breeding populations of long-distance migrants, including the one studied here.

A Faroese perspective on decoding life for sustainable use of nature and protection of biodiversity

Biodiversity is under pressure, mainly due to human activities and climate change. At the international policy level, it is now recognised that genetic diversity is an important part of biodiversity. The availability of high-quality reference genomes gives the best basis for using genetics and genetic diversity towards the global aims of (1) the protection of species, biodiversity, and nature, and (2) the management of biodiversity for achieving sustainable harvesting of nature. Protecting biodiversity is a global responsibility, also resting on small nations, like the Faroe Islands. Being in the middle of the North Atlantic Ocean and having large fisheries activity, the nation has a particular responsibility towards maritime matters. We here provide the reasoning behind the Genome Atlas of Faroese Ecology (Gen@FarE), a project based on our participation in the European Reference Genome Atlas consortium (ERGA). Gen@FarE has three major aims: (1) To acquire high-quality genomes of all eukaryotic species in the Faroe Islands and Faroese waters. (2) To establish population genetics for species of commercial or ecological interest. (3) To establish an information databank for all Faroese species, combined with a citizen science registration database, making it possible for the public to participate in acquiring and maintaining the overview of Faroese species in both terrestrial and marine environments. Altogether, we believe that this will enhance the society's interest in and awareness of biodiversity, thereby protecting the foundations of our lives. Furthermore, the combination of a wide and highly competent ERGA umbrella and more targeted national projects will help fulfil the formal and moral responsibilities that all nations, also those with limited resources, have in protecting biodiversity and achieving sustainability in harvesting from nature.

The potential for species distribution models to distinguish source populations from sinks

While species distribution models (SDM) are frequently used to predict species occurrences to help inform conservation management, there is limited evidence evaluating whether habitat suitability can reliably predict intrinsic growth rates or distinguish source populations from sinks. Filling this knowledge gap is critical for conservation science, as applications of SDMs for management purposes ultimately depend on these typically unobserved population or metapopulation dynamics. Using linear regression, we associated previously published population level estimates of intrinsic growth and abundance derived from a Bayesian analysis of mark-recapture data for 17 bird species found in the contiguous United States with SDM habitat suitability estimates fitted here to opportunistic data for these same species. We then used the area under the ROC curve (AUC) to measure how well SDMs can distinguish populations categorized as sources and sinks. We built SDMs using two different approaches, boosted regression trees (BRT) and generalized linear models (GLM), and compared their source/sink predictive performance. Each SDM was built with presence points obtained from eBird (a web-available database) and 10 environmental variables previously selected to model intrinsic growth rates and abundance for these species. We show that SDMs built with opportunistic data are poor predictors of species demography in general; both BRT and GLM explained very little spatial variation of intrinsic growth rate and population abundance (median R2 across 17 species was close to 0.1 for both SDM methods). SDMs, however, estimated higher suitability for source populations as compared to sinks. Out of 13 species which had both source and sink populations, both BRT and GLM had AUC values greater than 0.7 for 7 species when discriminating between sources and sinks. Habitat suitability have the potential to be a useful measure to indicate a population's ability to sustain itself as a source population; however more research on a diverse set of taxa is essential to fully explore this potential. This interpretation of habitat suitability can be particularly useful for conservation practice, and identification of explicit cases of when and how SDMs fail to match population demography can be informative for advancing ecological theory.

Toward a comparative framework for studies of altitudinal migration

The study and importance of altitudinal migration has attracted increasing interest among zoologists. Altitudinal migrants are taxonomically widespread and move across altitudinal gradients as partial or complete migrants, subjecting them to a wide array of environments and ecological interactions. Here, we present a brief synthesis of recent developments in the field and suggest future directions toward a more taxonomically inclusive comparative framework for the study of altitudinal migration. Our framework centers on a working definition of altitudinal migration that hinges on its biological relevance, which is scale-dependent and related to fitness outcomes. We discuss linguistic nuances of altitudinal movements and provide concrete steps to compare altitudinal migration phenomena across traditionally disparate study systems. Together, our comparative framework outlines a "phenotypic space" that contextualizes the biotic and abiotic interactions encountered by altitudinal migrants from divergent lineages and biomes. We also summarize new opportunities, methods, and challenges for the ongoing study of altitudinal migration. A persistent, primary challenge is characterizing the taxonomic extent of altitudinal migration within and among species. Fortunately, a host of new methods have been developed to help researchers assess the taxonomic prevalence of altitudinal migration-each with their own advantages and disadvantages. An improved comparative framework will allow researchers that study disparate disciplines and taxonomic groups to better communicate and to test hypotheses regarding the evolutionary and ecological drivers underlying variation in altitudinal migration among populations and species.

A multispecies hierarchical model to integrate count and distance-sampling data

Integrated community models-an emerging framework in which multiple data sources for multiple species are analyzed simultaneously-offer opportunities to expand inferences beyond the single-species and single-data-source approaches common in ecology. We developed a novel integrated community model that combines distance sampling and single-visit count data; within the model, information is shared among data sources (via a joint likelihood) and species (via a random-effects structure) to estimate abundance patterns across a community. Parameters relating to abundance are shared between data sources, and the model can specify either shared or separate observation processes for each data source. Simulations demonstrated that the model provided unbiased estimates of abundance and detection parameters even when detection probabilities varied between the data types. The integrated community model also provided more accurate and more precise parameter estimates than alternative single-species and single-data-source models in many instances. We applied the model to a community of 11 herbivore species in the Masai Mara National Reserve, Kenya, and found considerable interspecific variation in response to local wildlife management practices: Five species showed higher abundances in a region with passive conservation enforcement (median across species: 4.5× higher), three species showed higher abundances in a region with active conservation enforcement (median: 3.9× higher), and the remaining three species showed no abundance differences between the two regions. Furthermore, the community average of abundance was slightly higher in the region with active conservation enforcement but not definitively so (posterior mean: higher by 0.20 animals; 95% credible interval: 1.43 fewer animals, 1.86 more animals). Our integrated community modeling framework has the potential to expand the scope of inference over space, time, and levels of biological organization, but practitioners should carefully evaluate whether model assumptions are met in their systems and whether data integration is valuable for their applications.

Ecological niche contributes to the persistence of the western × glaucous-winged gull hybrid zone

Hybrid zones occur in nature when populations with limited reproductive barriers overlap in space. Many hybrid zones persist over time, and different models have been proposed to explain how selection can maintain hybrid zone stability. More empirical studies are needed to elucidate the role of ecological adaptation in maintaining stable hybrid zones. Here, we investigated the role of exogenous factors in maintaining a hybrid zone between western gulls (Larus occidentalis) and glaucous-winged gulls (L. glaucescens). We used ecological niche models (ENMs) and niche similarity tests to quantify and examine the ecological niches of western gulls, glaucous-winged gulls, and their hybrids. We found evidence of niche divergence between all three groups. Our results support the bounded superiority model, providing further evidence that exogenous selection favoring hybrids may be an important factor in maintaining this stable hybrid zone.

Supplemental structured surveys and pre-existing detection models improve fine-scale density and population estimation with opportunistic community science data

Density and population estimates aid in conservation and stakeholder communication. While free and broadly available community science data can effectively inform species distribution models, they often lack the information necessary to estimate imperfect detection and area sampled, thus limiting their use in fine-scale density modeling. We used structured distance-sampling surveys to model detection probability and calculate survey-specific detection offsets in community science models. We estimated density and population for 16 songbird species under three frameworks: (1) a fixed framework that assumes perfect detection within a specified survey radius, (2) an independent framework that calculates offsets from an independent source, and (3) a calibration framework that calculates offsets from supplemental surveys. Within the calibration framework, we examined the effects of calibration dataset size and data pooling. Estimates of density and population size were consistently biased low in the fixed framework. The independent and calibration frameworks produced reliable estimates for some species, but biased estimates for others, indicating discrepancies in detection probability between structured and community science surveys. The calibration framework produced reliable population estimates with as few as 10 calibration surveys with positive detections. Data pooling dramatically decreased bias. This study provides conservationists and managers with a cost-effective method of estimating density and population.

The power of citizen science to advance fungal conservation

Fungal conservation is gaining momentum globally, but many challenges remain. To advance further, more data are needed on fungal diversity across space and time. Fundamental information regarding population sizes, trends, and geographic ranges is also critical to accurately assess the extinction risk of individual species. However, obtaining these data is particularly difficult for fungi due to their immense diversity, complex and problematic taxonomy, and cryptic nature. This paper explores how citizen science (CS) projects can be lever-aged to advance fungal conservation efforts. We present several examples of past and ongoing CS-based projects to record and monitor fungal diversity. These include projects that are part of broad collecting schemes, those that provide participants with targeted sampling methods, and those whereby participants collect environmental samples from which fungi can be obtained. We also examine challenges and solutions for how such projects can capture fungal diversity, estimate species absences, broaden participation, improve data curation, and translate resulting data into actionable conservation measures. Finally, we close the paper with a call for professional mycologists to engage with amateurs and local communities, presenting a framework to determine whether a given project would likely benefit from participation by citizen scientists.

LIES of omission: complex observation processes in ecology

Advances in statistics mean that it is now possible to tackle increasingly sophisticated observation processes. The intricacies and ambitious scale of modern data collection techniques mean that this is now essential. Methodological research to make inference about the biological process while accounting for the observation process has expanded dramatically, but solutions are often presented in field-specific terms, limiting our ability to identify commonalities between methods. We suggest a typology of observation processes that could improve translation between fields and aid methodological synthesis. We propose the LIES framework (defining observation processes in terms of issues of Latency, Identifiability, Effort and Scale) and illustrate its use with both simple examples and more complex case studies.

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Urban park qualities driving visitors mental well-being and wildlife conservation in a Neotropical megacity

Green infrastructure has been widely recognized for the benefits to human health and biodiversity conservation. However, knowledge of the qualities and requirements of such spaces and structures for the effective delivery of the range of ecosystem services expected is still limited, as well as the identification of trade-offs between services. In this study, we apply the One Health approach in the context of green spaces to investigate how urban park characteristics affect human mental health and wildlife support outcomes and identify synergies and trade-offs between these dimensions. Here we show that perceived restorativeness of park users varies significantly across sites and is mainly affected by safety and naturalness perceptions. In turn, these perceptions are driven by objective indicators of quality, such as maintenance of facilities and vegetation structure, and subjective estimations of biodiversity levels. The presence of water bodies benefited both mental health and wildlife. However, high tree canopy coverage provided greater restoration potential whereas a certain level of habitat heterogeneity was important to support a wider range of bird species requirements. To reconcile human and wildlife needs in green spaces, cities should strategically implement a heterogeneous green infrastructure network that considers trade-offs and maximizes synergies between these dimensions.

Species Richness and Composition of Forest Birds in Urban Parks and Reserves of Buenos Aires City, Argentina

Urbanization is among the main factors of ecosystem transformation and threats to global biodiversity. Urban green spaces provide multiple services, being important for biodiversity and human well-being. However, the relationship between green spaces and forest birds has been scarcely studied in the Global South. In this work, we used citizen science data (eBird) to assess the variation in the species richness and composition of forest birds in two types of public urban green spaces characterized by different vegetation composition and management: parks and reserves. In general, reserves had more native and unmanaged vegetation than parks. We selected parks and reserves located in the coastal area of the Metropolitan Area of Buenos Aires, Argentina. Sampling effort was considered as the number of checklists for each site. The database allowed information to be extracted from 12 sites and 33 species. The most common species were the Green-barred Woodpecker (Colaptes melanochloros), the Narrow-billed Woodcreeper (Lepidocolaptes angustirostris), and the White-crested Tyrannulet (Serpophaga subcristata). Bird species richness was higher in reserves than in parks and was positively related to sampling effort. The forest bird species composition varied according to the type of green area and sampling effort. Species composition showed a significant nestedness, with the least rich sites being a subset of species from the richest sites. Reserves and sites with the highest sampling effort concentrated all species. The results obtained show the importance of urban reserves in the conservation of forest birds.

Farmland biodiversity monitoring through citizen science: A review of existing approaches and future opportunities

Biodiversity monitoring in agricultural landscapes is important for assessing the effects of both land use change and activities that influence farmland biodiversity. Despite a considerable increase in citizen science approaches to biodiversity monitoring in recent decades, their potential in farmland-specific contexts has not been systematically examined. This paper therefore provides a comprehensive review of existing citizen science approaches involving biodiversity monitoring on farmland. Using three complementary methods, we identify a range of programmes at least partially covering farmland. From these, we develop a typology of eight programme types, reflecting distinctions in types of data collected and nature of volunteer involvement, and highlight their respective strengths and limitations. While all eight types can make substantial contributions to farmland biodiversity monitoring, there is considerable scope for their further development-particularly through increased engagement of farmers, for whom receiving feedback on the effects of their own practices could help facilitate adaptive management.

Migratory singers dynamically overlap the signal space of a breeding warbler community

Migratory species inhabit many communities along their migratory routes. Across taxa, these species repeatedly move into and out of communities, interacting with each other and locally breeding species and competing for resources and niche space. However, their influence is rarely considered in analyses of ecological processes within the communities they temporarily occupy. Here, we explore the impact of migratory species on a breeding community using the framework of acoustic signal space, a limited resource in which sounds of species within communities co-exist. Migrating New World warblers (Parulidae, hereafter referred to as migrant species) often sing during refueling stops in areas and at times during which locally breeding warbler species (hereafter breeding species) are singing to establish territories and attract mates. We used eBird data to determine the co-occurrence of 19 migrant and 11 breeding warbler species across spring migration in SW Michigan, generated a signal space from song recordings of these species, and examined patterns of signaling overlap experienced by breeding species as migrants moved through the community. Migrant species were present for two-thirds of the breeding season of local species, including periods when breeding species established territories and attracted mates. Signaling niche overlap experienced by individual breeding species was idiosyncratic and varied over time, yet niche overlap between migrant and breeding species occurred more commonly than between breeding species or between migrant species. Nevertheless, the proportion of niche overlap between migrant and breeding warblers was similar to overlap among breeding species. Our findings showed that singing by migrant species overlapped the signals of many breeding species, suggesting that migrants could have unexplored impacts on communication in breeding species, potentially affecting song detection and song evolution. Our study contributes to a growing body of research documenting the impacts of migratory species on communities and ecosystems.

Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network

Phenological responses to climate change frequently vary among trophic levels, which can result in increasing asynchrony between the peak energy requirements of consumers and the availability of resources. Migratory birds use multiple habitats with seasonal food resources along migration flyways. Spatially heterogeneous climate change could cause the phenology of food availability along the migration flyway to become desynchronized. Such heterogeneous shifts in food phenology could pose a challenge to migratory birds by reducing their opportunity for food availability along the migration path and consequently influencing their survival and reproduction. We develop a novel graph-based approach to quantify this problem and deploy it to evaluate the condition of the heterogeneous shifts in vegetation phenology for 16 migratory herbivorous waterfowl species in Asia. We show that climate change-induced heterogeneous shifts in vegetation phenology could cause a 12% loss of migration network integrity on average across all study species. Species that winter at relatively lower latitudes are subjected to a higher loss of integrity in their migration network. These findings highlight the susceptibility of migratory species to climate change. Our proposed methodological framework could be applied to migratory species in general to yield an accurate assessment of the exposure under climate change and help to identify actions for biodiversity conservation in the face of climate-related risks.

Ecological niche contributes to the persistence of the Western x Glaucous-winged Gull hybrid zone

Hybrid zones occur in nature when populations with limited reproductive barriers overlap in space. Many hybrid zones persist over time, and different models have been proposed to explain how selection can maintain hybrid zone stability. More empirical studies are needed to elucidate the role of ecological adaptation in maintaining stable hybrid zones. Here, we investigated the role of exogenous factors in maintaining a hybrid zone between western gulls (Larus occidentalis) and glaucous-winged gulls (L. glaucescens). We used ecological niche models (ENMs) and niche similarity tests to quantify and examine the ecological niches of western gulls, glaucous-winged gulls, and their hybrids. We found evidence of niche divergence between all three groups. Our results best support the bounded superiority model, providing further evidence that exogenous selection favoring hybrids may be an important factor in maintaining this stable hybrid zone.

Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity

Data deficiencies among rare or cryptic species preclude assessment of community-level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government-funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well-structured, design-based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single-species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging 'integrated community modelling' framework that combines both data integration and community modelling to improve inferences on species- and community-level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community-level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula-based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community-level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents

Effective solutions to conserve biodiversity require accurate community- and species-level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to provide such information in robust ways. Herein we employ a Deep-Reasoning Network implementation of the Deep Multivariate Probit Model (DMVP-DRNets), an end-to-end deep neural network framework, to exploit large observational and environmental data sets together and estimate landscape-scale species diversity and composition at continental extents. We present results from a novel year-round analysis of North American avifauna using data from over nine million eBird checklists and 72 environmental covariates. We highlight the utility of our information by identifying critical areas of high species diversity for a single group of conservation concern, the North American wood warblers, while capturing spatiotemporal variation in species' environmental associations and interspecific interactions. In so doing, we demonstrate the type of accurate, high-resolution information on biodiversity that deep learning approaches such as DMVP-DRNets can provide and that is needed to inform ecological research and conservation decision-making at multiple scales.

Spatial and seasonal variation in thermal sensitivity within North American bird species

Responses of wildlife to climate change are typically quantified at the species level, but physiological evidence suggests significant intraspecific variation in thermal sensitivity given adaptation to local environments and plasticity required to adjust to seasonal environments. Spatial and temporal variation in thermal responses may carry important implications for climate change vulnerability; for instance, sensitivity to extreme weather may increase in specific regions or seasons. Here, we leverage high-resolution observational data from eBird to understand regional and seasonal variation in thermal sensitivity for 21 bird species. Across their ranges, most birds demonstrated regional and seasonal variation in both thermal peak and range, or the temperature and range of temperatures when observations peaked. Some birds demonstrated constant thermal peaks or ranges across their geographical distributions, while others varied according to local and current environmental conditions. Across species, birds typically demonstrated either geographical or seasonal adaptation to climate. Local adaptation and phenotypic plasticity are likely important but neglected aspects of organismal responses to climate change.

Region-wide retreats from lower elevations of range-restricted birds across the Northern Andes

Local studies show upslope shifts in the distribution of tropical birds in response to warming temperatures. Unanswered is whether these upward shifts occur regionally across many species. We considered a nearly 2000-km length of the Northern Andes, where deforestation, temperature, and extreme weather events have increased during the past decades. Range-restricted bird species are particularly vulnerable to such events and occur in exceptionally high numbers in this region. Using abundant crowd-sourced data from the Cornell Lab of Ornithology database, eBird, and the Global Biodiversity Information Facility, we documented distributions of nearly 200 such species. We examined whether species shifted their elevational ranges over time by comparing observed versus expected occurrences below a low elevational threshold and above a high elevational threshold for 2 periods: before and after 2005. We predicted fewer observations at lower elevations (those below the threshold) and more at upper elevations (those above the threshold) after 2005. We also tested for deforestation effects at lower elevations within each species' distribution ranges. We compared relative forest loss with the differences between observed and expected occurrences across the elevational range. Species' retreats from lower elevations were ubiquitous and involved a 23-40% decline in prevalence at the lowest elevations. Increases at higher elevations were not consistent. The retreats occurred across a broad spectrum of species, from predominantly lowland to predominantly highland. Because deforestation showed no relationship with species retreats, we contend that a warming climate is the most parsimonious explanation for such shifts.

Opportunities and challenges for monitoring a recolonizing large herbivore using citizen science

Monitoring is a prerequisite for evidence-based wildlife management and conservation planning, yet conventional monitoring approaches are often ineffective for species occurring at low densities. However, some species such as large mammals are often observed by lay people and this information can be leveraged through citizen science monitoring schemes. To ensure that such wildlife monitoring efforts provide robust inferences, assessing the quantity, quality, and potential biases of citizen science data is crucial. For Eurasian moose (Alces alces), a species currently recolonizing north-eastern Germany and occurring in very low numbers, we applied three citizen science tools: a mail/email report system, a smartphone application, and a webpage. Among these monitoring tools, the mail/email report system yielded the greatest number of moose reports in absolute and in standardized (corrected for time effort) terms. The reported moose were predominantly identified as single, adult, male individuals, and reports occurred mostly during late summer. Overlaying citizen science data with independently generated habitat suitability and connectivity maps showed that members of the public detected moose in suitable habitats but not necessarily in movement corridors. Also, moose detections were often recorded near roads, suggestive of spatial bias in the sampling effort. Our results suggest that citizen science-based data collection can be facilitated by brief, intuitive digital reporting systems. However, inference from the resulting data can be limited due to unquantified and possibly biased sampling effort. To overcome these challenges, we offer specific recommendations such as more structured monitoring efforts involving the public in areas likely to be roamed by moose for improving quantity, quality, and analysis of citizen science-based data for making robust inferences.

Smaller Australian raptors have greater urban tolerance

Urbanisation is occurring around the world at a rapid rate and is generally associated with negative impacts on biodiversity at local, regional, and global scales. Examining the behavioural response profiles of wildlife to urbanisation helps differentiate between species that do or do not show adaptive responses to changing landscapes and hence are more or less likely to persist in such environments. Species-specific responses to urbanisation are poorly understood in the Southern Hemisphere compared to the Northern Hemisphere, where most of the published literature is focussed. This is also true for raptors, despite their high diversity and comparably high conservation concern in the Southern Hemisphere, and their critical role within ecosystems as bioindicators of environmental health. Here, we explore this knowledge gap using community science data sourced from eBird to investigate the urban tolerance of 24 Australian raptor species at a continental scale. We integrated eBird data with a global continuous measure of urbanisation, artificial light at night (ALAN), to derive an urban tolerance index, ranking species from positive to negative responses according to their tolerance of urban environments. We then gathered trait data from the published literature to assess whether certain traits (body mass, nest substrate, habitat type, feeding guild, and migratory status) were associated with urban tolerance. Body size was negatively associated with urban tolerance, as smaller raptors had greater urban tolerance than larger raptors. Out of the 24 species analysed, 13 species showed tolerance profiles for urban environments (positive response), and 11 species showed avoidance profiles for urban environments (negative response). The results of this study provide impetus to conserve native habitat and improve urban conditions for larger-bodied raptor species to conserve Australian raptor diversity in an increasingly urbanised world.

Not all who wander are lost: Trail bias in community science

The exponential growth and interest in community science programs is producing staggering amounts of biodiversity data across broad temporal and spatial scales. Large community science datasets such as iNaturalist and eBird are allowing ecologists and conservation biologists to answer novel questions that were not possible before. However, the opportunistic nature of many of these enormous datasets leads to biases. Spatial bias is a common problem, where observations are biased towards points of access like roads and trails. iNaturalist-a popular biodiversity community science platform-exhibits strong spatial biases, but it is unclear how these biases affect the quality of biodiversity data collected. Thus, we tested whether fine-scale spatial bias due to sampling from trails affects taxonomic richness estimates. We compared timed transects with experienced iNaturalist observers on and off trails in British Columbia, Canada. Using generalized linear mixed models, we found higher overall taxonomic richness on trails than off trails. In addition, we found more exotic as well as native taxa on trails than off trails. There was no difference between on and off trail observations for species that are rarely observed. Thus, fine-scale spatial bias from trails does not reduce the quality of biodiversity measurements, a promising result for those interested in using iNaturalist data for research and conservation management.

Investigating the potential of social media and citizen science data to track changes in species' distributions

How to best track species as they rapidly alter their distributions in response to climate change has become a key scientific priority. Information on species distributions is derived from biological records, which tend to be primarily sourced from traditional recording schemes, but increasingly also by citizen science initiatives and social media platforms, with biological recording having become more accessible to the general public. To date, however, our understanding of the respective potential of social media and citizen science to complement the information gathered by traditional recording schemes remains limited, particularly when it comes to tracking species on the move with climate change. To address this gap, we investigated how species occurrence observations vary between different sources and to what extent traditional, citizen science, and social media records are complementary, using the Banded Demoiselle (Calopteryx splendens) in Britain as a case study. Banded Demoiselle occurrences were extracted from citizen science initiatives (iRecord and iNaturalist) and social media platforms (Facebook, Flickr, and Twitter), and compared with traditional records primarily sourced from the British Dragonfly Society. Our results showed that species presence maps differ between record types, with 61% of the citizen science, 58% of the traditional, and 49% of the social media observations being unique to that data type. Banded Demoiselle habitat suitability maps differed most according to traditional and social media projections, with traditional and citizen science being the most consistent. We conclude that (i) social media records provide insights into the Banded Demoiselle distribution and habitat preference that are different from, and complementary to, the insights gathered from traditional recording schemes and citizen science initiatives; (ii) predicted habitat suitability maps that ignore information from social media records can substantially underestimate (by over 3500 km2 in the case of the Banded Demoiselle) potential suitable habitat availability.

Traits shaping urban tolerance in birds differ around the world

As human density increases, biodiversity must increasingly co-exist with urbanization or face local extinction. Tolerance of urban areas has been linked to numerous functional traits, yet few globally consistent patterns have emerged to explain variation in urban tolerance, which stymies attempts at a generalizable predictive framework. Here, we calculate an Urban Association Index (UAI) for 3,768 bird species in 137 cities across all permanently inhabited continents. We then assess how this UAI varies as a function of ten species-specific traits and further test whether the strength of trait relationships vary as a function of three city-specific variables. Of the ten species traits, nine were significantly associated with urban tolerance. Urban-associated species tend to be smaller, less territorial, have greater dispersal ability, broader dietary and habitat niches, larger clutch sizes, greater longevity, and lower elevational limits. Only bill shape showed no global association with urban tolerance. Additionally, the strength of several trait relationships varied across cities as a function of latitude and/or human population density. For example, the associations of body mass and diet breadth were more pronounced at higher latitudes, while the associations of territoriality and longevity were reduced in cities with higher population density. Thus, the importance of trait filters in birds varies predictably across cities, indicating biogeographic variation in selection for urban tolerance that could explain prior challenges in the search for global patterns. A globally informed framework that predicts urban tolerance will be integral to conservation as increasing proportions of the world's biodiversity are impacted by urbanization.

The role of climate change and niche shifts in divergent range dynamics of a sister-species pair

Species ranges are set by limitations in factors including climate tolerances, habitat use, and dispersal abilities. Understanding the factors governing species range dynamics remains a challenge that is ever more important in our rapidly changing world. Species ranges can shift if environmental changes affect available habitat, or if the niche or habitat connectivity of a species changes. We tested how changes in habitat availability, niche, or habitat connectivity could contribute to divergent range dynamics in a sister-species pair. The great-tailed grackle (Quiscalus mexicanus) has expanded its range northward from Texas to Nebraska in the past 40 years, while its closest relative, the boattailed grackle (Quiscalus major), has remained tied to the coasts of the Atlantic Ocean and the Gulf of Mexico as well as the interior of Florida. We created species distribution and connectivity models trained on citizen science data from 1970-1979 and 2010-2019 to determine how the availability of habitat, the types of habitat occupied, and range-wide connectivity have changed for both species. We found that the two species occupy distinct habitats and that the great-tailed grackle has shifted to occupy a larger breadth of urban, arid environments farther from natural water sources. Meanwhile, the boattailed grackle has remained limited to warm, wet, coastal environments. We found no evidence that changes in habitat connectivity affected the ranges of either species. Overall, our results suggest that the great-tailed grackle has shifted its realized niche as part of its rapid range expansion, while the range dynamics of the boat-tailed grackle may be shaped more by climate change. The expansion in habitats occupied by the great-tailed grackle is consistent with observations that species with high behavioral flexibility can rapidly expand their geographic range by using human-altered habitat. This investigation identifies how opposite responses to anthropogenic change could drive divergent range dynamics, elucidating the factors that have and will continue to shape species ranges.

Climate-related range shifts in Arctic-breeding shorebirds

Aim

To test whether the occupancy of shorebirds has changed in the eastern Canadian Arctic, and whether these changes could indicate that shorebird distributions are shifting in response to long-term climate change.

Location

Foxe Basin and Rasmussen Lowlands, Nunavut, Canada.

Methods

We used a unique set of observations, made 25 years apart, using general linear models to test if there was a relationship between changes in shorebird species' occupancy and their species temperature Index, a simple version of a species climate envelope.

Results

Changes in occupancy and density varied widely across species, with some increasing and some decreasing. This is despite that overall population trends are known to be negative for all of these species based on surveys during migration. The changes in occupancy that we observed were positively related to the species temperature index, such that the warmer-breeding species appear to be moving into these regions, while colder-breeding species appear to be shifting out of the regions, likely northward.

Main Conclusions

Our results suggest that we should be concerned about declining breeding habitat availability for bird species whose current breeding ranges are centered on higher and colder latitudes.

Integrating habitat-masked range maps with quantifications of prevalence to estimate area of occupancy in IUCN assessments

Estimates of species geographic ranges constitute critical input for biodiversity assessments, including those for the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species. Area of occupancy (AOO) is one metric that IUCN uses to quantify a species' range, but data limitations typically lead to either under- or overestimates (and unnecessarily wide bounds of uncertainty). Fortunately, existing methods in which range maps and land-cover data are used to estimate the area currently holding habitat for a species can be extended to yield an unbiased range of plausible estimates for AOO. Doing so requires estimating the proportion of sites (currently containing habitat) that a species occupies within its range (i.e., prevalence). Multiplying a quantification of habitat area by prevalence yields an estimate of what the species inhabits (i.e., AOO). For species with intense sampling at many sites, presence-absence data sets or occupancy modeling allow calculation of prevalence. For other species, primary biodiversity data (records of a species' presence at a point in space and time) from citizen-science initiatives and research collections of natural history museums and herbaria could be used. In such cases, estimates of sample prevalence should be corrected by dividing by the species' detectability. To estimate detectability from these data sources, extensions of inventory-completeness analyses merit development. With investments to increase the quality and availability of online biodiversity data, consideration of prevalence should lead to tighter and more realistic bounds of AOO for many taxonomic groups and geographic regions. By leading to more realistic and representative characterizations of biodiversity, integrating maps of current habitat with estimates of prevalence should empower conservation practitioners and decision makers and thus guide actions and policy worldwide.

Challenges and opportunities for using natural history collections to estimate insect population trends

Natural history collections (NHC) provide a wealth of information that can be used to understand the impacts of global change on biodiversity. As such, there is growing interest in using NHC data to estimate changes in species' distributions and abundance trends over historic time horizons when contemporary survey data are limited or unavailable. However, museum specimens were not collected with the purpose of estimating population trends and thus can exhibit spatiotemporal and collector-specific biases that can impose severe limitations to using NHC data for evaluating population trajectories. Here we review the challenges associated with using museum records to track long-term insect population trends, including spatiotemporal biases in sampling effort and sparse temporal coverage within and across years. We highlight recent methodological advancements that aim to overcome these challenges and discuss emerging research opportunities. Specifically, we examine the potential of integrating museum records and other contemporary data sources (e.g. collected via structured, designed surveys and opportunistic citizen science programs) in a unified analytical framework that accounts for the sampling biases associated with each data source. The emerging field of integrated modelling provides a promising framework for leveraging the wealth of collections data to accurately estimate long-term trends of insect populations and identify cases where that is not possible using existing data sources.

Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds

Estimating absolute and relative abundance of wildlife populations is critical to addressing ecological questions and conservation needs, yet obtaining reliable estimates can be challenging because surveys are often limited spatially or temporally. Community science (i.e., citizen science) provides opportunities for semi-structured data collected by the public (e.g., eBird) to improve capacity of relative abundance estimation by complementing structured survey data collected by trained observers (e.g., North American breeding bird survey [BBS]). We developed two state-space models to estimate relative abundance and population trends: one using BBS data and the other jointly analyzing BBS and eBird data. We applied these models to seven bird species with diverse life history characteristics. Joint analysis of eBird and BBS data improved precision of mean and year-specific relative abundance estimates for all species, but the BBS-only model produced more precise trend estimates compared to the joint model for most species. The relative abundance estimates of the joint model were particularly more precise than the BBS-only estimates in areas where species detectability was low resulting from either low BBS survey effort or low abundance. These results suggest that community science data can be a valuable resource for cost-effective improvement in wildlife abundance estimation.

The correlation between eBird community science and weather surveillance radar-based estimates of migration phenology

Aim

Measuring avian migration can prove challenging given the spatial scope and the diversity of species involved. No one monitoring technique provides all the pertinent measures needed to capture this macroscale phenomenon - emphasizing the need for data integration. Migration phenology is a key metric characterizing large-scale migration dynamics and has been successfully quantified using weather surveillance radar (WSR) data and community science observations. Separately, both platforms have their limitations and measure different aspects of bird migration. We sought to make a formal comparison of the migration phenology estimates derived from WSR and eBird data - of which we predict a positive correlation.

Location

Contiguous United States.

Time period

2002-2018.

Major taxa studied

Migratory birds.

Methods

We estimated spring and autumn migration phenology at 143 WSR stations aggregated over a 17-year period (2002-2018), which we contrast with eBird-based estimates of spring and autumn migration phenology for 293 nocturnally migrating bird species at the 143 WSR stations. We compared phenology metrics derived from all species and WSR stations combined, for species in three taxonomic orders (Anseriformes, Charadriiformes and Passeriformes), and for WSR stations in three North American migration flyways (western, central and eastern).

Results

We found positive correlations between WSR and eBird-based estimates of migration phenology and differences in the strength of correlations among taxonomic orders and migration flyways. The correlations were stronger during spring migration, for Passeriformes, and generally for WSR stations in the eastern flyway. Autumn migration showed weaker correlation, and in Anseriformes correlations were weakest overall. Lastly, eBird-based estimates slightly preceded those derived from WSR in the spring, but trailed WSR in the autumn, suggesting that the two data sources measure different components of migration phenology.

Main conclusions

We highlight the complementarity of these two approaches, but also reveal strong taxonomic and geographic differences in the relationships between the platforms.

Mapping breeding bird species richness at management-relevant resolutions across the United States

Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life-history-based traits across the conterminous United States at three resolutions (0.5, 2.5, and 5 km) and quantify the tradeoff between resolution and accuracy and, hence, relevance for management of the resulting biodiversity maps. We summarized 18 years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percentage variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using ~24 predictor variables based on percentage variance explained, symmetric mean absolute percentage error, and root mean square error values. However, our 2.5-km-resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

Citizen science reveals waterfowl responses to extreme winter weather

Global climate change is increasing the frequency and severity of extreme climatic events (ECEs) which may be especially detrimental during late-winter when many species are surviving on scarce resources. However, monitoring animal populations relative to ECEs is logistically challenging. Crowd-sourced datasets may provide opportunity to monitor species' responses to short-term chance phenomena such as ECEs. We used 14 years of eBird-a global citizen science initiative-to examine distribution changes for seven wintering waterfowl species across North America in response to recent extreme winter polar vortex disruptions. To validate inferences from eBird, we compared eBird distribution changes against locational data from 362 GPS-tagged Mallards (Anas platyrhynchos) in the Mississippi Flyway. Distributional shifts between eBird and GPS-tagged Mallards were similar following an ECE in February 2021. In general, the ECE affected continental waterfowl population distributions; however, responses were variable across species and flyways. Waterfowl distributions tended to stay near wintering latitudes or moved north at lesser distances compared with non-ECE years, suggesting preparedness for spring migration was a stronger "pull" than extreme weather was a "push" pressure. Surprisingly, larger-bodied waterfowl with grubbing foraging strategies (i.e., geese) delayed their northward range shift during ECE years, whereas smaller-bodied ducks were less affected. Lastly, wetland obligate species shifted southward during ECE years. Collectively, these results suggest specialized foraging strategies likely related to resource limitations, but not body size, necessitate movement from extreme late-winter weather in waterfowl. Our results demonstrate eBird's potential to monitor population-level effects of weather events, especially severe ECEs. eBird and other crowd-sourced datasets can be valuable to identify species which are adaptable or vulnerable to ECEs and thus, begin to inform conservation policy and management to combat negative effects of global climate change.

Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models—especially N-mixtures with beta-binomial detection submodels—were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models.

Identification of roadkill hotspots and the factors affecting wombat vehicle collisions using the citizen science tool, WomSAT

Roads directly impact wildlife through vehicle collisions. In Australia only a few studies have examined the impact of environmental characteristics on wombat roadkill. We analysed wombat roadkills reported into WomSAT, a website and application where citizen scientists can upload sightings of wombats, to map wombat roadkill across their distribution. We used Maxent software to identify the main factors influencing wombat roadkill hotspots. A total of 2391 wombat deaths on roads were reported by 567 citizen scientists from 2015 to 2019. More wombat roadkill deaths occurred in winter, with most unaffected by sarcoptic mange. The average daily solar exposure, distance to populated areas, precipitation of warmest quarter (bio 18), temperature seasonality (bio 4), and precipitation seasonality (bio 15) were identified as the main factors influencing wombat roadkill. The most roadkills reported per kilometre (3.7/km) occurred on Old Bega Road and Steeple Flat Road, in the New South Wales southern tablelands. Reduced reporting corresponded with a reduced number of citizen scientists continuing to report sightings over time, reflecting the limitations of the data collected by citizen scientists. However, data should continue to be collected by citizen scientists as it is an easy and low-cost method of collecting data over large areas, and can provide information to managers to support conservation efforts directed at reducing roadkill. Our study emphasises the need to continue to engage and reward citizen scientists for their efforts.