Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an ...important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.
Migration is a common strategy used by birds that breed in seasonal environments. Selection for greater migration efficiency is likely to be stronger for terrestrial species whose migration ...strategies require non-stop transoceanic crossings. If multiple species use the same transoceanic flyway, then we expect the migration strategies of these species to converge geographically towards the most optimal solution. We test this by examining population-level migration trajectories within the Western Hemisphere for 118 migratory species using occurrence information from eBird. Geographical convergence of migration strategies was evident within specific terrestrial regions where geomorphological features such as mountains or isthmuses constrained overland migration. Convergence was also evident for transoceanic migrants that crossed the Gulf of Mexico or Atlantic Ocean. Here, annual population-level movements were characterized by clockwise looped trajectories, which resulted in faster but more circuitous journeys in the spring and more direct journeys in the autumn. These findings suggest that the unique constraints and requirements associated with transoceanic migration have promoted the spatial convergence of migration strategies. The combination of seasonal atmospheric and environmental conditions that has facilitated the use of similar broad-scale migration strategies may be especially prone to disruption under climate and land-use change.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
This article addresses the automatic detection of vocal, nocturnally migrating birds from a network of acoustic sensors. Thus far, owing to the lack of annotated continuous recordings, existing ...methods had been benchmarked in a binary classification setting (presence vs. absence). Instead, with the aim of comparing them in event detection, we release BirdVox-full-night, a dataset of 62 hours of audio comprising 35402 flight calls of nocturnally migrating birds, as recorded from 6 sensors. We find a large performance gap between energy-based detection functions and data-driven machine listening. The best model is a deep convolutional neural network trained with data augmentation. We correlate recall with the density of flight calls over time and frequency and identify the main causes of false alarm.
Migration is a common strategy used by birds that breed in seasonal environments. The patterns and determinants of migration routes, however, remain poorly understood. Recent empirical analyses have ...demonstrated that the locations of two North America migration flyways (eastern and western) shift seasonally, reflecting the influence of looped migration strategies. For the eastern but not western flyway, seasonal variation in atmospheric circulation has been identified as an explanation. Here, we test an alternative explanation based on the phenology of ecological productivity, which may be of greater relevance in western North America, where phenology is more broadly dictated by elevation. Migrants in the western flyway selected lower-elevation spring routes that were wetter, greener and more productive, and higher-elevation autumn routes that were less green and less productive, but probably more direct. Migrants in the eastern flyway showed little season variation but maintained associations with maximum regional greenness. Our findings suggest the annual phenology of ecological productivity is associated with en route timing in both flyways, and the spring phenology of ecological productivity contributes to the use of looped strategies in the western flyway. This fine-tuned spatial synchronization may be disrupted when changing climate induces a mismatch between food availability and needs.
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Data from well-designed experiments provide the strongest evidence of causation in biodiversity studies. However, for many species the collection of these data is not scalable to the spatial and ...temporal extents required to understand patterns at the population level. Only data collected from citizen science projects can gather sufficient quantities of data, but data collected from volunteers are inherently noisy and heterogeneous. Here we describe a ‘Big Data’ approach to improve the data quality in eBird, a global citizen science project that gathers bird observations. First, eBird’s data submission design ensures that all data meet high standards of completeness and accuracy. Second, we take a ‘sensor calibration’ approach to measure individual variation in eBird participant’s ability to detect and identify birds. Third, we use species distribution models to fill in data gaps. Finally, we provide examples of novel analyses exploring population-level patterns in bird distributions.
eBird is a citizen‐science project that takes advantage of the human observational capacity to identify birds to species, and uses these observations to accurately represent patterns of bird ...occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a human/computer learning network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both and thereby continually improves the effectiveness of the net‐ work as a whole. In this article we explore how human/computer learning networks can leverage the contributions of human observers and process their contributed data with artificial intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.
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CEKLJ, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In the face of global environmental change, the importance of protected areas in biological management and conservation is expected to grow. Birds have played an important role as biological ...indicators of the effectiveness of protected areas, but with little consideration given to where species occur outside the breeding season. We estimated weekly probability of occurrence for 308 bird species throughout the year within protected areas in the western contiguous USA using eBird occurrence data for the combined period 2004 to 2011. We classified species based on their annual patterns of occurrence on lands having intermediate conservation mandates (GAP status 2 and 3) administered by the Bureau of Land Management (BLM) and the United States Forest Service (USFS). We identified species having consistent annual association with one agency, and species whose associations across the annual cycle switched between agencies. BLM and USFS GAP status 2 and 3 lands contained low to moderate proportions of species occurrences, with proportions highest for species that occurred year-round or only during the summer. We identified two groups of species whose annual movements resulted in changes in stewardship responsibilities: (1) year-round species that occurred on USFS lands during the breeding season and BLM lands during the nonbreeding season; and (2) summer species that occurred on USFS lands during the breeding season and BLM lands during spring and autumn migration. Species that switched agencies had broad distributions, bred on high-elevation USFS lands, were not more likely to be identified as species of special management concern, and migrated short (year-round species) to long distances (summer species). Our findings suggest cooperative efforts that address the requirements of short-distance migratory species on GAP status 2 lands (
n
= 20 species) and GAP status 3 lands (
n
= 24) and long-distance migratory species on GAP status 2 lands (
n
= 9) would likely benefit their populations. Such efforts may prove especially relevant for species whose seasonal movements result in associations with different environments containing contrasting global change processes and management mandates.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
Abstract
Increasing public engagement in volunteer science1, either through data collection2 or processing3, is both raising public awareness of science and gathering useful information for ...scientists. While the payoffs of citizen science4 are potentially large, achieving them requires new approaches to data management and analysis that can only result from strong cross-disciplinary collaborations. This is especially true in ecology and conservation biology, where historically the understanding of species’ responses to environmental change has been constrained by the limited spatial5 or temporal scale6 of available data. Here we describe collaborative research in ecology, computer science, and statistics to generate essential information for conservation management of North American birds: accurate dynamic bird distributions models based on habitat associations across much of North America. Unique is our ability to describe the broad-scale dynamics of seasonal bird distributions and the associated seasonal patterns of habitat use. Our source of bird distribution data is eBird7, an online bird checklist program that currently gathers more than 74,000 checklists monthly from a large network of contributors. Our results were made possible through a data intensive scientific workflow8 that includes analytical methods merged from the fields of machine learning and statistics. We believe that this novel approach of data collection, synthesis, analysis, and visualization will serve as a hallmark for future research initiatives, with broad applicability across many scientific domains.
Summary
1. Species monitoring is an essential component of assessing conservation status, predicting effects of habitat change and establishing management and conservation priorities. The pervasive ...access to the Internet has led to the development of several extensive monitoring projects that engage massive networks of volunteers who provide observations following relatively unstructured protocols. However, the value of these data is largely unknown. 2. We develop a novel cross‐data validation method for measuring the value of survey data from one source (e.g. an Internet checklist program) relative to a second, benchmark data source. The method fits a model to the data of interest and validates the model using benchmark data, allowing us to isolate the training data's information content from its biases. We also define a data efficiency ratio to quantify the relative efficiency of the data sources. 3. We apply our cross‐data validation method to quantify the value of data collected in eBird – a western hemisphere, year‐round citizen science bird checklist project – relative to data from the highly standardized North American Breeding Bird Survey (BBS). The results show that eBird data contain information similar in quality to that in BBS data, while the information per BBS datum is higher. 4. We suggest that these methods have more general use in evaluating the suitability of sources of data for addressing specific questions for taxa of interest.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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