eBird Status and Trends

Fink, Daniel; Auer, Tom; Johnston, Alison; Strimas-Mackey, M; Robinson, Orin J.; Ligocki, S; Hochachka, W; Wood, Christopher S.; Davies, I; Iliff, M; Seitz, L

doi:10.2173/ebirdst.2019

Cited by 49 publications

(55 citation statements)

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“…To do so, the fitted model was used to predict the detected biomass at each location across a 8.4 km × 8.4 km mapping grid covering the study region, day (spaced 7 days apart) and year, based on topographical and land‐cover values at the grid location, and assuming a standard sampling effort: a single observer with a high CCI of four, performing a travelling count starting at 07:00 AM local time, for a duration of 1 hour and distance of 1 km. The mapping grid is the same as the ones used in eBird Status and Trends data products (eBird S&T; Fink, Auer, Johnston, Strimas‐Mackey, et al., 2020). The values of the effort variables were chosen to reduce the underestimation caused by non‐detection, and therefore, bring the predicted values closer to the unknown true values: for instance, observers with high CCI tended to detect species at a higher rate (Johnston et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

Continental‐scale biomass redistribution by migratory birds in response to seasonal variation in productivity

Fink

Sorte

et al. 2022

Global Ecol Biogeogr

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Aim Animal migration is often explained as the result of resource tracking in seasonally dynamic environments. Therefore, resource availability should influence both the distributions of migratory animals and their seasonal abundance. We examined the relationship between primary productivity and the spatio‐temporal distributions of migratory birds to assess the role of energy availability in avian migration. Location North America. Time period Full annual cycle, 2011–2016. Major taxa studied Nocturnally migrating landbirds. Methods We used observations of nocturnally migrating landbirds from the eBird community‐science programme to estimate weekly spatial distributions of total biomass, abundance and species richness. We related these patterns to primary productivity and seasonal productivity surplus estimated using a remotely sensed measure of vegetation greenness. Results All three avian metrics showed positive spatial associations with primary productivity, and this was more pronounced with seasonal productivity surplus. Surprisingly, biomass showed a weaker association than did abundance and richness, despite being a better indicator of energetic requirements. The strength of associations varied across seasons, being the weakest during migration. During spring migration, avian biomass increased ahead of vegetation green‐up in temperate regions, a pattern also previously described for herbivorous waterfowl. In the south‐eastern USA, spring green‐up was instead associated with a net decrease in biomass, and winter biomass greatly exceeded that of summer, highlighting the region as a winter refuge for short‐distance migrants. Main conclusions Although instantaneous energy availability is important in shaping the distribution of migratory birds, the stronger association of productivity with abundance and richness than with biomass suggests the role of additional drivers unrelated to energetic requirements that are nonetheless correlated with productivity. Given recent reports of widespread North American avifaunal declines, including many common species that winter in the south‐eastern USA, understanding how anthropogenic activities are impacting winter bird populations in the region should be a research priority.

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Section: Methodsmentioning

confidence: 99%

Continental‐scale biomass redistribution by migratory birds in response to seasonal variation in productivity

Fink

Sorte

et al. 2022

Global Ecol Biogeogr

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“…For example, the use of eBird abundance estimates by the USFWS to delineate low-risk collision areas could significantly reduce costs for the agency itself, as well as for the potential eagle permittees seeking to develop wind energy projects. As of 2020, the weekly, year-round estimates of eBird relative abundance are being produced at a 2.96 km × 2.96 km resolution on an annual basis, and freely available for download from the Cornell Lab of Ornithology for >800 species, including bald and golden eagles (Fink et al, 2020), including species from Europe and Latin America. Therefore, low-risk zones can be updated on an annual basis by the USFWS at little to no cost to the agency.…”

Section: Impli C Ati On S Of Validating C S Data For P Oli C Ymak Er S: Us F Ws C a S E S Tu Dymentioning

confidence: 99%

A pathway for citizen science data to inform policy: A case study using eBird data for defining low‐risk collision areas for wind energy development

Ruiz‐Gutiérrez

Bjerre

Otto

et al. 2021

Journal of Applied Ecology

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“…We used modelled products from the 2020 release of AdaSTEM, which predicts weekly occurrence and relative abundance for the year 2019 across the Western Hemisphere (Fink, Auer, Johnston, Strimas-Mackey, et al, 2020). We used the AdaSTEM variable abundance_median in our analysis and two AdaSTEM occurrence-based statistics, PI and PD, which we define below, for two environmental descriptors in the model, ALAN and road density.…”

Section: Bird Data and Modelsmentioning

confidence: 99%

The role of artificial light at night and road density in predicting the seasonal occurrence of nocturnally migrating birds

Sorte

Johnston

Rodewald

et al. 2022

Diversity and Distributions

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Aim Artificial light at night (ALAN) and roads are known threats to nocturnally migrating birds. How associations with ALAN and roads are defined in combination for these species at the population level across the full annual cycle has not been explored. Location Western Hemisphere. Methods We estimated range‐wide exposure, predictor importance and the prevalence of positive associations with ALAN and roads at a weekly temporal resolution for 166 nocturnally migrating bird species in three orders: Passeriformes (n = 104), Anseriformes (n = 27) and Charadriiformes (n = 35). We clustered Passeriformes based on the prevalence of positive associations. Results Positive associations with ALAN and roads were more prevalent for Passeriformes during migration when exposure and importance were highest. Positive associations with ALAN and roads were more prevalent for Anseriformes and Charadriiformes during the breeding season when exposure was lowest. Importance was uniform for Anseriformes and highest during migration for Charadriiformes. Our cluster analysis identified three groups of Passeriformes, each having similar associations with ALAN and roads. The first occurred in eastern North America during migration where exposure, prevalence, and importance were highest. The second wintered in Mexico and Central America where exposure, prevalence and importance were highest. The third occurred throughout North America where prevalence was low, and exposure and importance were uniform. The first and second were comprised of dense habitat specialists and long‐distance migrants. The third was comprised of open habitat specialists and short distance migrants. Main conclusions Our findings suggest ALAN and roads pose the greatest risk during migration for Passeriformes and during the breeding season for Anseriformes and Charadriiformes. Our results emphasise the close relationship between ALAN and roads, the diversity of associations dictated by taxonomy, exposure, migration strategy and habitat and the need for more informed and comprehensive mitigation strategies where ALAN and roads are treated as interconnected threats.

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eBird Status and Trends

Cited by 49 publications

References 0 publications

Continental‐scale biomass redistribution by migratory birds in response to seasonal variation in productivity

Continental‐scale biomass redistribution by migratory birds in response to seasonal variation in productivity

A pathway for citizen science data to inform policy: A case study using eBird data for defining low‐risk collision areas for wind energy development

The role of artificial light at night and road density in predicting the seasonal occurrence of nocturnally migrating birds

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