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Species distribution models (SDMs) have become a common tool in studies of species–environment relationships but can be negatively affected by positional uncertainty of underlying species occurrence data. Previous work has documented the effect of positional uncertainty on model predictive performance, but its consequences for inference about species–environment relationships remain largely unknown. Here we use over 12 000 combinations of virtual and real environmental variables and virtual species, as well as a real case study, to investigate how accurately SDMs can recover species–environment relationships after applying known positional errors to species occurrence data. We explored a range of environmental predictors with various spatial heterogeneity, species’ niche widths, sample sizes and magnitudes of positional error. Positional uncertainty decreased predictive model performance for all modeled scenarios. The absolute and relative importance of environmental predictors and the shape of species–environmental relationships co-varied with a level of positional uncertainty. These differences were much weaker than those observed for overall model performance, especially for homogenous predictor variables. This suggests that, at least for the example species and conditions analyzed, the negative consequences of positional uncertainty on model performance did not extend as strongly to the ecological interpretability of the models. Although the findings are encouraging for practitioners using SDMs to reveal generative mechanisms based on spatially uncertain data, they suggest greater consequences for applications utilizing distributions predicted from SDMs using positionally uncertain data, such as conservation prioritization and biodiversity monitoring., This research was funded by the Technological grant agency of the Czech Republic (grant no. SS02030018 DivLand) and by OP RDE Improvement in Quality of the Internal Grant Scheme at CZU, reg. no. CZ.02.2.69/0.0/0.0/19_073/0016944 (grant no. 43/2021). In addition, this paper was made possible by generous support from the Fulbright-Masaryk program sponsored by US and Czech governments, which provided Lukáš Gábor with the opportunity to conduct research at Yale University.

This project is related to the manuscript "A global biogeographic regionalization of the benthic ocean", by Aurore A. Maureaud, Gabriel Reygondeau, Kate Ingenloff, Jessiva G. Vigneron, Les Watling, Kevin Winner, Walter Jetz.

Conserving and managing biodiversity in the face of ongoing global change requires sufficient evidence to assess status and trends of species distributions. Here, we propose novel indicators of biodiversity data coverage and sampling effectiveness and analyze national trajectories in closing spatiotemporal knowledge gaps for terrestrial vertebrates (1950 to 2019). Despite a rapid rise in data coverage, particularly in the last 2 decades, strong geographic and taxonomic biases persist. For some taxa and regions, a tremendous growth in records failed to directly translate into newfound knowledge due to a sharp decline in sampling effectiveness. However, we found that a nation’s coverage was stronger for species for which it holds greater stewardship. As countries under the post-2020 Global Biodiversity Framework renew their commitments to an improved, rigorous biodiversity knowledge base, our findings highlight opportunities for international collaboration to close critical information gaps., Conserving and managing biodiversity in the face of ongoing global change requires sufficient evidence to assess status and trends of species distributions. This study analyzes national trajectories in closing spatiotemporal knowledge gaps for terrestrial vertebrates (1950-2019) based on novel indicators of data coverage and sampling effectiveness.

Climate change induced phenological shifts in primary productivity result in trophic mismatches for many organisms1–4, with broad implications for ecosystem structure and function. For birds that have a synchronized timing of migration with resource availability, the likelihood that trophic mismatches may generate a phenological response in migration timing increases with climate change5. Despite the importance of a holistic understanding of such systems at large spatial and temporal scales, particularly given a rapidly changing climate, analyses are few, primarily because of limitations in the access to appropriate data. Here we use 24 years of remotely sensed data collected by weather surveillance radar to quantify the response of a nocturnal avian migration system within the contiguous United States to changes in temperature. The average peak migration timing advanced in spring and autumn, and these changes were generally more rapid at higher latitudes. During spring and autumn, warmer seasons were predictive of earlier peak migration dates. Decadal changes in surface temperatures predicted spring changes in migratory timing, with greater warming related to earlier arrivals. This study represents one of the first system-wide examinations during two seasons and comprises measures from hundreds of species that describe migratory timing across a continent. Our findings provide evidence of spatially dynamic phenological shifts that result from climate change. Climate change affects the timing of bird migration, which can lead to mismatch with resource availability. Migration occurred earlier in spring and autumn in the United States during the past 24 years; warming led to later arrival in the western Unites States and earlier arrival in the rest of the country.

Monitoring avian migration within subarctic regions of the globe poses logistical challenges. Populations in these regions often encounter the most rapid effects of changing climates, and these seasonally productive areas are especially important in supporting bird populations—emphasizing the need for monitoring tools and strategies. To this end, we leverage the untapped potential of weather surveillance radar data to quantify active migration through the airspaces of Alaska. We use over 400 000 NEXRAD radar scans from seven stations across the state between 1995 and 2018 (86% of samples derived from 2013 to 2018) to measure spring and autumn migration intensity, phenology and directionality. A large bow-shaped terrestrial migratory system spanning the southern two-thirds of the state was identified, with birds generally moving along a northwest–southeast diagonal axis east of the 150th meridian, and along a northeast–southwest axis west of this meridian. Spring peak migration ranged from 3 May to 30 May and between, 18 August and 12 September during the autumn, with timing across stations predicted by longitude, rather than latitude. Across all stations, the intensity of migration was greatest during the autumn as compared to spring, highlighting the opportunity to measure seasonal indices of net breeding productivity for this important system as additional years of radar measurements are amassed.

The quantification of Hutchison’s n-dimensional hypervolume has enabled substantial progress in community ecology, species niche analysis and beyond. While non-parametric methods for quantifying and comparing hypervolumes are popular, they do not support a partitioning of the different components and drivers of hypervolume variation. Here, we propose as alternative the use of multivariate normal distributions for the assessment and comparison of niche hypervolumes and introduce this as the multivariate-normal hypervolume (MVNH) framework.The framework provides parametric measures of the size and dissimilarity of niche hypervolumes, each of which can be partitioned into biologically interpretable components. Specifically, We use 1) the determinant of the covariance matrix (i.e. the generalized variance) of a MVNH as a measure of total niche size, which can be partitioned into the components of univariate niche variances and a correlation component; and 2) the Bhattacharyya distance between two MVNHs as a measure of niche dissimilarity, which can be partitioned into the components of Mahalanobis distance between hypervolume centroids and the determinant ratio which measures hypervolume size difference.We use empirical examples of community- and species-level analysis to demonstrate the new insights provided by these metrics. We show that the newly proposed framework enables us to quantify the relative contributions of different hypervolume components and to identify the drivers of functional diversity and environmental niche variation.Our approach overcomes several operational and computational limitations of non-parametric methods and provides a framework that offers both unification and granularity in the assessment of niche volumes and differences, which has wide implications for understanding niche evolution, niche shifts and expansion during biotic invasions etc.

Conserving and managing biodiversity in the face of ongoing global change requires sufficient evidence to assess status and trends of species distributions. Here we analyze national trajectories in closing spatiotemporal knowledge gaps for terrestrial vertebrates (1950-2019) based on novel indicators of data coverage and sampling effectiveness. Despite a rapid rise in data coverage, particularly in the last two decades, strong geographic and taxonomic biases persist. For some taxa and regions, a tremendous growth in records failed to directly translate into newfound knowledge due to a sharp decline in sampling effectiveness. But nation’s coverage is stronger for species they hold greater stewardship for. As countries under the post-2020 Global Biodiversity Framework renew their commitments to an improved, rigorous biodiversity knowledge base, our findings highlight opportunities for international collaboration to close critical information gaps.

Large networks of weather radars are comprehensive instruments for studying bird migration. For example, the US WSR-88D network covers the entire continental US and has archived data since the 1990s. The data can quantify both broad and fine-scale bird movements to address a range of migration ecology questions. However, the problem of automatically discriminating precipitation from biology has significantly limited the ability to conduct biological analyses with historical radar data. We develop MistNet, a deep convolutional neural network to discriminate precipitation from biology in radar scans. Unlike prior machine learning approaches, MistNet makes fine-scaled predictions and can collect biological information from radar scans that also contain precipitation. MistNet is based on neural networks for images, and includes several architecture components tailored to the unique characteristics of radar data. To avoid a massive human labelling effort, we train MistNet using abundant noisy labels obtained from dual polarization radar data. In historical and contemporary WSR-88D data, MistNet identifies at least 95.9% of all biomass with a false discovery rate of 1.3%. Dual polarization training data and our radar-specific architecture components are effective. By retaining biomass that co-occurs with precipitation in a single radar scan, MistNet retains 15% more biomass than traditional whole-scan approaches to screening. MistNet is fully automated and can be applied to datasets of millions of radar scans to produce fine-grained predictions that enable a range of applications, from continent-scale mapping to local analysis of airspace usage. Radar ornithology is advancing rapidly and leading to significant discoveries about continent-scale patterns of bird movements. General-purpose and empirically validated methods to quantify biological signals in radar data are essential to the future development of this field. MistNet can enable large-scale, long-term, and reproducible measurements of whole migration systems.

Applications of remote sensing data to monitor bird migration usher a new understanding of magnitude and extent of movements across entire flyways. Millions of birds move through the western USA, yet this region is understudied as a migratory corridor. Characterizing movements in the Pacific Flyway offers a unique opportunity to study complementary patterns to those recently highlighted in the Atlantic and Central Flyways. We use weather surveillance radar data from spring and autumn (1995–2018) to examine migrants' behaviours in relation to winds in the Pacific Flyway. Overall, spring migrants tended to drift on winds, but less so at northern latitudes and farther inland from the Pacific coastline. Relationships between winds and autumn flight behaviours were less striking, with no latitudinal or coastal dependencies. Differences in the preferred direction of movement (PDM) and wind direction predicted drift patterns during spring and autumn, with increased drift when wind direction and PDM differences were high. We also observed greater total flight activity through the Pacific Flyway during the spring when compared with the autumn. Such complex relationships among birds’ flight strategies, winds and seasonality highlight the variation within a migration system. Characterizations at these scales complement our understanding of strategies to clarify aerial animal movements.

1. Migration is a common strategy used by birds that breed in seasonal environments, and multiple environmental and biological factors determine the timing of migration. How these factors operate in combination during autumn migration, which is considered to be under weaker time constraints relative to spring migration, is not clear. 2. Here, we examine the patterns and determinants of migration timing for nocturnal migrants during autumn migration in the north-eastern USA using nocturnal reflectivity data from 12 weather surveillance radar stations and modelled diurnal probability of occurrence for 142 species of nocturnal migrants. We first model the capacity of seasonal atmospheric conditions (wind and precipitation) and ecological productivity (vegetation greenness) to predict autumn migration intensity. We then test predictions, formulated under optimal migration theory, on how migration timing should be related to assemblage-level estimates of body size and total migration distance within the context of dietary guild (insectivore and omnivore) and level of dietary plasticity during autumn migration. 3. Our results indicate seasonal declines in ecological productivity delineate the beginning and end of peak migration, whose intensity is best predicted by the velocity of winds at migration altitudes. Insectivorous migrants departed earlier in the season and, consistent with our predictions, large-bodied and long-distance insectivorous migrants departed the earliest. Contrary to our predictions, large-bodied and some long-distance omnivorous migrants departed later in the season, patterns that were replicated in part by insectivorous migrants that displayed dietary plasticity during autumn migration. 4. Our findings indicate migration timing in the region is dictated by optimality strategies, modified based on the breadth and flexibility of migrant's foraging diets, with declining ecological productivity defining possible resource thresholds during which migration occurs when winds at migration altitudes are mild. These observations provide the basis to assess how avian migration strategies may be affected by adjustments in seasonal patterns of atmospheric circulation and ecological productivity that may occur under global climate change.

Predictive models are used in many fields to characterize relationships between the attributes of an instance and its classification. While these models can provide valuable support to decision-making, they can be challenging to understand and evaluate. While they provide predicted classifications, they do not generally include indications of confidence in those predictions. Typical quality measures for predictive models are the percentage of predictions which are made correctly. These measures can give some insight into how often the model is correct, but provide little help in understanding under what conditions the model performs well (or poorly). We present a framework for improving understanding of predictive models based on the methods of both machine learning and data visualization. We demonstrate this framework on models that use attributes about individuals in a census data set to predict other attributes of those individuals.

Billions of birds migrate at night over North America each year. However, few studies have described the phenology of these movements, such as magnitudes, directions, and speeds, for more than one migration season and at regional scales. In this study, we characterize density, direction, and speed of nocturnally migrating birds using data from 13 weather surveillance radars in the autumns of 2010 and 2011 in the northeastern USA. After screening radar data to remove precipitation, we applied a recently developed algorithm for characterizing velocity profiles with previously developed methods to document bird migration. Many hourly radar scans contained windborne "contamination," and these scans also exhibited generally low overall reflectivities. Hourly scans dominated by birds showed nightly and seasonal patterns that differed markedly from those of low reflectivity scans. Bird migration occurred during many nights, but a smaller number of nights with large movements of birds defined regional nocturnal migration. Densities varied by date, time, and location but peaked in the second and third deciles of night during the autumn period when the most birds were migrating. Migration track (the direction to which birds moved) shifted within nights from south-southwesterly to southwesterly during the seasonal migration peaks; this shift was not consistent with a similar shift in wind direction. Migration speeds varied within nights, although not closely with wind speed. Airspeeds increased during the night; groundspeeds were highest between the second and third deciles of night, when the greatest density of birds was migrating. Airspeeds and groundspeeds increased during the fall season, although groundspeeds fluctuated considerably with prevailing winds. Significant positive correlations characterized relationships among bird densities at southern coastal radar stations and northern inland radar stations. The quantitative descriptions of broadscale nocturnal migration patterns presented here will be essential for biological and conservation applications. These descriptions help to define migration phenology in time and space, fill knowledge gaps in avian annual cycles, and are useful for monitoring long-term population trends of migrants. Furthermore, these descriptions will aid in assessing potential risks to migrants, particularly from structures with which birds collide and artificial lighting that disorients migrants.