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  1. The conversion of forest to agriculture is considered one of the greatest threats to avian biodiversity, yet how species respond to habitat modification throughout the annual cycle remains unknown. We examined whether forest bird associations with agricultural habitats vary throughout the year, and if species traits influence these relationships. Using data from the eBird community‐science program, we investigated associations between agriculturally‐modified land cover and the occurrence of 238 forest bird species based on three sets of avian traits: migratory strategy, dietary guild, and foraging strategy. We found that the influence of agriculturally‐modified land cover on species distributions varied widely across periods and trait groups but highlighting several broad findings. First, migratory species showed strong seasonal differences in their response to agricultural land cover while resident species did not. Second, there was a migratory strategy by season interaction; Neotropical migrants were most negatively influenced by agricultural land cover during the breeding period while short‐distance migrants were most negatively influenced during the non‐breeding period. Third, regardless of season, some dietary (e.g. insectivores) and foraging guilds (e.g. bark foragers) consistently responded more negatively to agricultural land cover than others (e.g. omnivores and ground foragers, respectively). Fourth, there were greater differences among dietary guilds in their responses to agricultural land cover during the breeding period than during the non‐breeding period, perhaps reflecting how different habitat and ecological requirements enhance the susceptibility of some guilds during reproduction. These results suggest that management efforts across the annual cycle may be oversimplified and thus ineffective when based on broad ecological generalisations that are static in space and time.

     
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    Free, publicly-accessible full text available September 1, 2024
  2. Abstract Aim

    A unique risk faced by nocturnally migrating birds is the disorienting influence of artificial light at night (ALAN). ALAN originates from anthropogenic activities that can generate other forms of environmental pollution, including the emission of fine particulate matter (PM2.5). PM2.5concentrations can display strong seasonal variation whose origin can be natural or anthropogenic. How this variation affects seasonal associations with ALAN and PM2.5for nocturnally migrating bird populations has not been explored.

    Location

    Western Hemisphere.

    Time Period

    2021

    Major Taxa Studied

    Nocturnally migrating passerine (NMP) bird species.

    Methods

    We combined monthly estimates of PM2.5and ALAN with weekly estimates of relative abundance for 164 NMP species derived using observations from eBird. We identified groups of species with similar associations with monthly PM2.5. We summarized their shared environmental, geographical, and ecological attributes.

    Results

    PM2.5was lowest in North America, especially at higher latitudes during the boreal winter. PM2.5was highest in the Amazon Basin, especially during the dry season (August–October). ALAN was highest within eastern North America, especially during the boreal winter. For NMP species, PM2.5associations reached their lowest levels during the breeding season (<10 μg/m3) and highest levels during the nonbreeding season, especially for long‐distance migrants that winter in Central and South America (~20 μg/m3). Species that migrate through Central America in the spring encountered similarly high PM2.5concentrations. ALAN associations reached their highest levels for species that migrate (~12 nW/cm2/sr) or spend the nonbreeding season (~15 nW/cm2/sr) in eastern North America.

    Main Conclusions

    We did not find evidence that the disorienting influence of ALAN enhances PM2.5exposure during stopover in the spring and autumn for NMP species. Rather, our findings suggest biomass burning in the Neotropics is exposing NMP species to consistently elevated PM2.5concentrations for an extended period of their annual life cycles.

     
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  3. Abstract

    As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.

     
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  4. Sheard, Catherine (Ed.)
  5. Summary

    Bird species’ migratory patterns have typically been studied through individual observations and historical records. In recent years, the eBird citizen science project, which solicits observations from thousands of bird watchers around the world, has opened the door for a data-driven approach to understanding the large-scale geographical movements. Here, we focus on the North American tree swallow (Tachycineta bicolor) occurrence patterns throughout the eastern USA. Migratory departure dates for this species are widely believed by both ornithologists and casual observers to vary substantially across years, but the reasons for this are largely unknown. In this work, we present evidence that maximum daily temperature is predictive of tree swallow occurrence. Because it is generally understood that species occurrence is a function of many complex, high order interactions between ecological covariates, we utilize the flexible modelling approach that is offered by random forests. Making use of recent asymptotic results, we provide formal hypothesis tests for predictive significance of various covariates and also develop and implement a permutation-based approach for formally assessing interannual variations by treating the prediction surfaces that are generated by random forests as functional data. Each of these tests suggest that maximum daily temperature is important in predicting migration patterns.

     
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