Table of Contents

General

Update: July 2021
What are eBird Science Products?
Can I contribute data for the Status and Trends Analysis?

Abundance

How is relative abundance defined?
How is “Percentage of total population in region” calculated?

Range

How are species’ ranges defined?
How is “Percentage of region occupied” calculated?
How is “Percentage of range in region” calculated?
How is “Days of occupation in region” calculated?

Appendix

What are “Predictive performance metrics”?

Technical

What Status modeling methods were used?
Which eBird data were used to generate the Status and Trends Products?
What environmental data were used for the Status and Trend products?
How are seasons defined for each species? Why are there gaps between seasons?
Why are pre-breeding and post-breeding migrations sometimes separated?
What is the difference between “modeled area” and “no prediction”?
Why are models available only for the Western Hemisphere?
Why are some islands areas of “No Prediction”
What are Bird Conservation Regions and how have they been defined?
Some of the maps have errors? Why does this happen?
Why do some products show as “Unavailable” or cannot be clicked on?
Can I download the data?
How can I find out more about the data analysis used to generate these products?
Why does it look like some species disappear during some parts of the year (e.g., shorebirds)?
How do we ensure that the eBird data are accurate?
What happened to the eBird Status and Trends Data Products for previous years?
How do the maps match what we know about each species’ biology?
How do I interpret the habitat regional charts?

References

 

General

Updated: July 2021

The data behind the Status products are now available for download. These data include estimates of species ranges, abundances, and environmental associations for over 800 species. We provide an R package, ebirdst, to help access, manipulate, and analyze these data. Use of the R package requires an access key tied to your eBird username, which you can get by reading and agreeing to our updated Terms of Use at https://ebird.org/st/request. For details on how to access the data see Can I download the data?

What are eBird Science Products?

The eBird Status and Trend products provide basic ecological information for over 800 species globally, describing their ranges, abundances, environmental associations, and population trends. To generate these products, we use statistical and machine learning analyses designed to combine eBird data with a range of environmental data. The result is a prediction of the occurrence and abundance of species across the Western hemisphere at weekly intervals. These predictions are the building block of the Status and Trends products and are summarized in several ways to produce the different products.

There are three, currently available, major products for eBird Status and Trends:

  1. Abundance animations represent weekly relative abundances, revealing movements of a population throughout the year.
  2. Seasonal abundance maps indicate the average relative abundance of a species in each season of their annual cycle.
  3. Range maps show species seasonal range boundaries, similar to traditional range maps.
  4. Habitat regional charts show the types of habitat species are associated with every week of the year for states, provinces, and Bird Conservation Regions where they occur within the Western Hemisphere.

Different products require different volumes of data and the most data-intensive products such as trend maps are only available for some of the species and seasons. We only present products for species and seasons that have passed analytical and expert quality review tests.

Can I contribute data for the Status and Trends Analysis?

Yes, any eBirder can contribute! These analyses were only possible thanks to the eBird submissions of hundreds of thousands of eBird users. Our ability to update and improve the Status and Trends products in the future continues to depend on the contributions of eBirders like you!

If you submitted checklists that meet all the requirements answered under “Which eBird data were used to generate the Status and Trends Products?“, then you have already contributed data to the Status and Trends! Any future checklists you submit that meet these requirements will automatically be included in analyses for the updated Status and Trends Products.

Remember, any observation is useful, whether it is from today or your field notebooks from 15 years ago. Whether it is from a hotspot with amazing birds, or a place with few species – all checklists are valuable. To ensure your eBirding checklists are most useful to scientific efforts like this, you can make your checklists:

  • Complete checklists (i.e., record all species you were able to identify),
  • Provide a count or estimate of the number of individuals for each species
  • Use one of these protocols: traveling or stationary count.
  • Provide information on the start time, duration, number of observers, and distance traveled (The eBird mobile App now does many of these automatically with the new tracks)
  • Provide documentation of unusual sightings with descriptions or photos.

See our article on how to make your eBird checklists more valuable.

Abundance

How is relative abundance defined?

Relative abundance is the count of individuals of a given species detected by an expert eBirder at the optimal time of day, while expending the effort necessary to maximize detection of the species. Relative abundance predictions have been optimized for search effort and user skill, specific for the given region, season, and species, in order to maximize detection rates.

For each species, relative abundance was estimated for all 52 weeks of the year across a regular spatial grid with a density of one location per 2.96 km × 2.96 km grid cell across the Western Hemisphere. Estimates at each location and date were made based on the local habitat, elevation, and topography at that location.

Because detecting birds in the environment can be difficult, we know that there are always some individual birds that are missed by eBirders. For this reason, we refer to the quantity estimated as a relative measure of abundance. Although the relative abundance estimates will underestimate the true abundance, they do provide a standardized index that can be used to compare abundance in different regions. For example, if relative abundance is 10 in one area and 5 in another area, then we would estimate abundance is twice as high in the first area, even if we’re not sure of the actual number of individuals in the area.

See featured examples of relative abundance

 

How is “Percentage of total population in region” calculated?

For each species and season, we summed the relative abundance estimates across the selected region and then divided it by the sum of the relative abundance estimates across the entire seasonal range. The result is presented as a percentage.

This will be a reasonable estimate if the whole population is within the “modeled area.” For this reason, the reporting of this value was contingent on the majority of a species’ known seasonal range being within the modeled area. If the percentage of total population value is missing, but the season was mapped in other products, it is because an expert reviewer believed that a significant portion of the species’ seasonal range was not included, but that the distribution within the modeled area was correct.

Note that since the state/province and Bird Conservation Regions (BCR) boundaries do not have offshore coverage, species with significant abundance offshore (e.g., Brown Pelican) do not include the offshore portion of their population in these calculations.

Range

How are species’ ranges defined?

Species’ ranges were defined as the areas where the species is expected to occur on at least ten percent of the predicted hypothetical checklists in a given week. This is equivalent to a single skilled eBirder starting at the optimal time of day, expending the effort necessary to maximize detection of the species for each day of the week, and detecting the species on at least ten percent of the checklists within a week. Each species’ range was estimated for all 52 weeks of the year at 2.96 km × 2.96 km grid cell locations across the Western Hemisphere. To create easy-to-read range boundaries, the 2.96 km grid data were aggregated to an 8.89 km grid and spatially smoothed. Both the smoothed, aggregated seasonal boundaries and the raw 2.96 km grid cell boundaries are available for download.

See featured examples of eBird Status and Trends range maps

How is “Percentage of region occupied” calculated?

Percentage of region occupied is calculated as the percent of the selected region that is covered by the range of the species. Note that since the state/province and Bird Conservation Regions (BCR) boundaries do not have offshore coverage, species with significant water-based abundance values (e.g., Brown Pelican) do not include the offshore portion of their population in these calculations.

How is “Percentage of range in region” calculated?

Percentage of range in a region is calculated as the fraction of a species’ total North American range that falls within the selected region.

This will be a reasonable estimate if the whole population is within the “modeled area”. For this reason, the reporting of this value was contingent on the majority of a species’ known seasonal range being within the modeled area. If the percentage of total population value is missing, but the season was mapped in other products, it is because an expert reviewer believed that a significant portion of the species’ seasonal range was not included, but that the distribution within the modeled area was correct.

Note that since the state/province and BCR boundaries do not have offshore coverage, species with significant abundance offshore (e.g., Brown Pelican) do not include the offshore portion of their population in these calculations.

See featured example of regional statistics

How is “Days of occupation in region” calculated?

Days of occupation in a region is the number of days that a species is present in the selected region. A species is defined to be present in a region when at least 5% of the region was within the species range during the given season.

Appendix

What are “Predictive performance metrics”?

To assess and communicate the quality of the model estimates, we evaluated several metrics that describe the model’s ability to predict the observed patterns of species occupancy and abundance. These metrics are made available primarily for interested researchers.

To quantify the performance of the range estimates we used the Area Under the Curve (AUC) and Kappa statistics to describe the models’ ability to correctly classify occupied and unoccupied sites. AUC measures a model’s ability to discriminate between locations where species are and are not likely to occur. Technically, it is the probability that the model will rank a randomly chosen positive observation (species detected) higher than a randomly chosen negative one (species not detected). Cohen’s Kappa statistic was designed to measure the same metric, but taking into account the background prevalence. To quantify the quality of the relative occupancy predictions we also evaluated AUC and Kappa (shown in the second row). To quantify the quality of the abundance estimates we computed Spearman’s Rank Correlation (SRC) and the percent Poisson Deviance Explained (P-DE) (Shown in the third row). SRC measures how well the abundance estimates rank the observed abundances and the P-DE measures the correspondence between the magnitude of the estimated counts and observed counts. More details about predictive performance metrics and how they were calculated and interpreted can be found in Fink et al. (2019).

Technical

What Status modeling methods were used?

The bird observation data that are the backbone of eBird Status and Trends Data Products and ShorebirdViz models are eBird checklists. The total dataset consists of 36.1 million eBird checklists (sample size) from 11 million unique locations collected from 2006 through 2020 across the hemisphere.

To generate estimates of relative abundance the ecological data science team created state-of-the art statistical and machine learning models (Fink et al. 2019). The models include three classes of predictor variables that account for variation in ecological and citizen science data. The predictors include: (1) five search-effort variables and 12 hourly weather variables to account for variation that affects how well a birder can detect a species in a region if it is present, (2) three variables to account for variation during different time periods, and (3) sixty environmental descriptors from remote sensing data to capture associations of birds with a variety of landscapes across the hemisphere. The search-effort variables are: (1) the time spent searching for birds, (2) whether the observer was stationary or traveling, (3) the distance traveled during the search, (4) the number of people in the search party, and (5) a standardized measurement to account for differences in behavior among eBirders (Kelling et al. 2015; Johnston et al. 2018). The 12 hourly weather variables come from the Copernicus ECMWF hourly reanalysis product (Hersbach et al. 2018) and help account for both an observer’s availability to detect species and how easy a species is to detect. The observation time of the day is used to account for variation in bird behavior throughout the day. The day of the year (1-366) and year on which the search was conducted are used to capture intra- and inter-annual variation. To describe the local landscape where eBirders went birding, variables describing elevation (Becker et al. 2009), topography (Amatulli et al. 2017), shorelines (Carroll et al. 2017; Murray et al. 2019), islands (Sayre et al. 2018), land cover, land use, & hydrology (Friedl & Sulla-Menashe 2019), and nighttime lights (Cao et al. 2014) are included in the model. From these variables we generate a suite of predictors that describe the average feature value and how much it varies across the local landscape, defined here as a 3km pixel.

The statistical model aims to generate accurate predictions of each species’ occurrence and abundance while dealing with the inherent challenges of abundance estimation based on citizen science data (Fink et al. 2019). We use a two-step hurdle model based on Random Forests (RFs) (sensu Johnston et. al. 2015) to incorporate the list of predictor variables (above) while accounting for a large number of observations with zero counts of a species. To alleviate site selection biases, common in citizen science data sets, we randomly subsample the training data to balance spatial and temporal coverage. By including search-effort and weather predictors in the RF models, we can control for important sources of variation in detectability when making predictions. We also case-balanced the first step occurrence model (Chen et al. 2004, Robinson et al. 2018) to improve performance for rare and/or hard to detect species and we calibrate occurrence rate predictions to ensure the probabilistic quality of the resulting estimates (Dormann 2020).

To scale-up the relative abundance base model across global-year-round spatio temporal extents while preserving fine-scale information we use a divide-and-recombine strategy based on the Adaptive Spatio-Temporal Exploratory Model (AdaSTEM; Fink et al. 2013, Fink et al. 2014). The AdaSTEM framework creates and trains an ensemble of spatiotemporally overlapping base models that are subsequently recombined based on shared locations and dates. The ensemble is constructed by partitioning the study extent using a randomly located and oriented spatiotemporal grid. Each partition cell is a spatiotemporal block called a stixel. Each stixel defines the spatiotemporal extent for a single base model that is independently trained using data that falls within that stixel. The stixels’ temporal width is set to 28 days and the spatial stixel dimensions were adaptively sized to generate smaller stixels in regions with higher data density, using QuadTrees (Samet 1984), a recursive partitioning algorithm. To generate independent, overlapping base models, the randomized partitioning process is repeated 100 times and in each partition training data is subsampled. Finally, to make ensemble predictions at a given location and date, the 100 overlapping base model predictions are averaged.

The AdaSTEM-based Status workflow generates six data products: estimates of species’ 1) occurrence rates, 2) abundances, 3) ranges, 4) model validation metrics, 5) habitat associations.

References

Amatulli, G., Domisch, S., Tuanmu, M.N., Parmentier, B., Ranipeta, A., Malczyk, J. and Jetz, W., (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific data, 5, p.180040.

Becker, J. J., D. T. Sandwell, W. H. F. Smith, J. Braud, B. Binder, J. Depner, D. Fabre, J. Factor, S. Ingalls, S-H. Kim, R. Ladner, K. Marks, S. Nelson, A. Pharaoh, R. Trimmer, J. Von Rosenberg, G. Wallace, P. Weatherall., Global Bathymetry and Elevation Data at 30 Arc Seconds Resolution: SRTM30_PLUS, Marine Geodesy, 32:4, 355-371, 2009.

Cao, C., F. DeLuccia, X. Xiong, R. Wolfe, F. Weng, Early On-orbit Performance of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) Satellite, IEEE Trans. on Geosci. and Remote Sens., Volume: 52 , Issue: 2, Digital Object Identifier: 10.1109/TGRS.2013.2247768, Publication Year: 2014 , Page(s): 1142 – 1156.

​​Carroll, M.L., DiMiceli, C.M., Wooten, M.R., Hubbard, A.B., Sohlberg, R.A., Townshend, J.R.G (2017). MOD44W MODIS/Terra Land Water Mask Derived from MODIS and SRTM L3 Global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2019-09-06 from https://doi.org/10.5067/MODIS/MOD44W.006

Chen, C., Liaw, A., & Breiman, L. (2004). Using random forest to learn imbalanced data. University of California, Berkeley, 110(1-12), 24.

Dormann, C.F. (2020). Calibration of probability predictions from machine‐learning and statistical models. Global Ecology and Biogeography, 29(4), pp.760-765.

Fink, D., Damoulas, T. and Dave, J., (2013), July. Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13) July 14–18, 2013 in Bellevue, Washington, USA.

Fink, D., Damoulas, T., Bruns, N.E., La Sorte, F.A., Hochachka, W.M., Gomes, C.P. and Kelling, S., (2014). Crowdsourcing meets ecology: hemisphere-wide spatiotemporal species distribution models. AI magazine, 35(2), 19-30.

Fink, D., Auer, T., Johnston, A., Ruiz-Gutierrez, V., Hochachka, W.M., Kelling, S. (2019). Modeling avian full annual cycle distribution and population trends with citizen science data. Ecological Applications 30:e02056. 10.1002/eap.2056

Friedl, M., D. Sulla-Menashe. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006. 2019, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD12Q1.006. Accessed 2019-09-06.

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 02-2021), 10.24381/cds.adbb2d47

Johnston, A., Fink, D., Reynolds, M. D., Hochachka, W. M., Sullivan, B. L., Bruns, N. E., … & Kelling, S. (2015). Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications, 25(7), 1749-1756.

Johnston, A., Fink, D., Hochachka, W.M., and Kelling, S. (2018). Accounting for observer expertise improves ecological inference from citizen science data. Methods in Ecology and Evolution, 9:88-97.

Kelling, S. T., Johnston, A., Hochachka, W.M., Iliff, M., Fink, D., Gerbracht, J. Lagoze, C., La Sorte, F.A., Moore, T., Wiggins, A., Wong, W.K., Wood, C., and Yu, J. (2015). Can observation skills of citizen scientists be estimated using species accumulation curves? PLoS ONE, 10(10):e0139600.

Murray, N.J., Phinn, S.R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M.B., Clinton, N., Thau, D. and Fuller, R.A., (2019). The global distribution and trajectory of tidal flats. Nature, 565(7738), p.222.
Robinson, O. J., V. Ruiz-Gutierrez, and D. Fink. (2018). Correcting for bias in distribution modelling for rare species using citizen science data. Diversity and Distributions 24: 460-472

Samet, H. (1984). The quadtree and related hierarchical data structures. ACM Computing Surveys (CSUR), 16(2), 187-260.

Sayre, R., Noble, S., Hamann, S., Smith, R., Wright, D., Breyer, S., Butler, K., Van Graafeiland, K., Frye, C., Karagulle, D. and Hopkins, D., (2018). A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units. Journal of Operational Oceanography, pp.1-10.

Which eBird data were used to generate the Status and Trends Products?

Checklists used in Status and Trends products must meet the following conditions to be included in analyses.

  • Submitted as of 10 April 2020
  • Observation dates from 1 January 2005 through 10 April 2020
  • Global (most species were run for only the Western Hemisphere)
  • Complete checklists (all bird species detected and identified were included)
  • The primary checklist in a shared checklist
  • Checklists that used the generic traveling or  stationary protocols (i.e., not incidental protocol)
  • If traveling checklists, were not longer than 10 kilometers
  • Not longer in duration than 24 hours
  • Contained information on: start time, duration, protocol, number of observers, and distance traveled.
  • Counts of species were available (i.e., not just ‘present’)

See the eBird Help Center for more information about eBird checklists.

What environmental data were used for the Status and Trend products?

The analyses used to produce the Status and Trend products rely on matching bird observations with characteristics of the local environment. We used data on elevation, topography, and habitat to describe the local landscapes where eBirders searched for birds. Each checklist location is matched to the environmental data within approximately a 1.5 km radius around the location. For elevation and bathymetry, we calculated the mean and standard deviation of each within the checklist radius using the Global Bathymetry and Elevation Digital Elevation Model. For topography the aspect and slope within the checklist radius were calculated as the mean and the standard deviation (Amatulli et al. 2018). Habitat was described using the MCD12Q1 dataset from NASA and the FAO-Land Cover Classification System, which includes classes for land cover, land use, and hydrology by year. Water cover was described by the Aster Global Water Bodies Dataset, covering 2000-2013 at 30 meter spatial resolution, and with ocean, river, and freshwater categories (NASA/METI/AIST/Japan Spacesystems 2019). Islands were identified using the global shoreline dataset (Sayre et al. 2019). To describe tidal mudflats, we used a high resolution intertidal change dataset (Murray et al. 2019). The land cover, water cover, and intertidal datasets were summarized as the percentage of land cover and edge density within the 1.5 km radius of each checklist.  We used the NOAA VIIRS Nighttime Lights composite for 2016 with the cloud mask, outliers removed, and non-lights set to zero (vcm-orm-ntl). The nighttime lights values were calculated as the mean and standard deviation within the checklist radius. Finally, we summarised road density for five types using the GLOBIO Global Roads Inventory Project (GRIP) (Meijer et al. 2018).

How are seasons defined for each species? Why are there gaps between seasons?

Breeding and non-breeding season dates are defined for each species as the weeks when the species’ population does not move. For this reason, these seasons are also described as stationary periods. The dates were defined by experts in the status and distribution of Western Hemisphere birds based on the weekly abundance maps. The selected dates were then checked to make sure that they generally matched expected patterns of phenology for the species.

Migration periods are defined as the periods of movement between the stationary non-breeding and breeding seasons. Note that for many species these migratory periods include not only movement from breeding grounds to non-breeding grounds, but also post-breeding dispersal, molt migration, and other movements. For some species, the transition between stationary and migratory seasons is not clear. Both breeding and nonbreeding ranges are often represented within the migratory seasons since some individuals will have arrived in those areas while other individuals of the species are still migrating. In these cases transitional weeks were excluded to provide the clearest picture of individual seasons. For some species, this resulted in seasons that appear shorter than expected, especially when considered within specific regions.

Season dates are defined specifically to be used with eBird Status and Trends products. These dates should not in general be used to delineate the migration and breeding phenology of species, although in many cases Status and Trends dates may approximate these phenological dates. In addition, the dates used for Status and Trends are distinct from the corresponding seasonal dates defined in Birds of the World.

Why are pre-breeding and post-breeding migrations sometimes separated?

Some species have pre-breeding and post-breeding migration seasons combined into a single migratory season. These species (e.g., Magnolia Warbler, Black-throated Gray Warbler) use fairly similar areas for both their migrations. However, some species such as Rufous Hummingbird use different paths for their two migrations. For these species we split the map to show pre-breeding migration (green) and post-breeding migration (yellow) separately. If at least 40% of the area used for one migration season is not covered by the other migration season, then we show them as distinct colors.

What is the difference between “modeled area” and “no prediction”?

On the relative abundance and range maps the light gray shows the “modeled area,” where there was sufficient data to run a model, but the species was predicted to be absent. Sufficient data required there to be, on average, at least 0.1% spatial coverage of 3 kilometer grid cells within the region for a given week. The dark gray refers to areas of “no predictions” where there was insufficient data to assess whether the species was present or absent. That is, there was less than 0.1% spatial coverage of 3 kilometer grid cells within the region for a given week.

Why are some models available only for the Western Hemisphere?

The Status and Trend products were mostly limited to the Western hemisphere because this region currently has the highest density of eBird data. In 2021, we plan to model all species globally.

Why are some islands areas of “No Prediction”?

Some islands have insufficient data to predict whether a species is present or absent (see above in What is the difference between “modeled area” and “no prediction”?). In eBird Status and Trends, we use an island dataset (Sayre et al. 2019) that enables us to distinguish between islands with and without a particular species. The benefit of this method is that the models can, in effect, distinguish the geographic barriers relevant to islands, constraining species to or excluding species from specific islands. The distributions in the Caribbean for both White-winged Dove and Mourning Dove are good examples of this method in action. However, a consequence is that for species that show variation between islands, each island now needs more information before we can make predictions to it. As a result, many islands that are not frequently eBirded, such as a number of the islands in British Columbia, are often represented as “No Prediction.”

What are Bird Conservation Regions and how have they been defined?

Bird Conservation Regions (BCRs) are regions with similar bird communities, environmental conditions and resource management issues (Sauer et al., 2003). We used the established Bird Conservations Regions for the United States and Canada in accord with the NABCI Bird Conservation Regions, several of which span the border into Mexico. Because many of the Mexican Bird Conservation regions are too small to support the spatial scale of the Status and Trend analysis, we aggregated the Mexican Bird Conservation regions into larger regions in consultation with partners at CONABIO. Since Bird Conservation regions have not been defined in Central America, South America, and the Caribbean we defined regions based on countries, aggregating the smallest countries or groups of countries into units that were large enough to support the spatial scale of our analysis. See the map below for the BCRs that we use, including the customized versions for Mexico, Central America, South America, and the Caribbean.

Note that summaries of the eBird Status and Trends Data Products can be computed across user-defined regions by downloading the data and using the ebirdst R package.

Some of the maps have errors? Why does this happen?

Like any predictive models, the Status and Trend models make errors when predicting species ranges and abundance. When predicting ranges there are two types of errors: predicting species absence in areas that are actually occupied and predicting species presence in areas that are actually unoccupied. There can also be errors in the estimates of relative abundance, with estimates that are higher or lower than the actual counts.

The predictive models used to generate the Status and Trend products account for gaps in eBird data by sharing information from nearby areas. This works well when (1) there is sufficient eBird data to capture patterns of species’ occurrence and abundance and (2) when the environmental data together with the other predictors used in the models do a reasonably good job describing the ecological characteristics that are important to the species.

Error rates generally increase in regions when one or both of the above conditions is not met. First, in regions where the density of checklists is low (e.g. central Canada and the Amazon basin) there is little information to learn patterns of species’ occurrence and abundance. In these areas, incorrect extrapolation can be a risk. For example, species that occur on the coast of Greenland, such as Iceland Gull, are often extrapolated inland, where they likely do not occur, but where there is no data to inform their absence.

Second, error rates also tend to be higher when species’ detection rates are low. Even if there are many checklists in a region, having very few detections of a species limits the amount of information available to characterize the environment that the species is associated with. Third, error rates increase in regions where the environmental data fail to describe important ecological features for each species.

See the link What are the “Predictive performance metrics”? for more information about how we assess the quality of model estimates.

Why do some products show as “Unavailable” or cannot be clicked on?

Species products may be missing for various reasons:

  • The absence of range or abundance maps for a season indicates that the model was poor and expert review indicated that the season should be excluded from visualizations and analysis. See section Some of the maps have errors? Why does this happen? For more information about prediction errors.
  • The “Percentage of total population in region” and “Percentage of range in region” statistics were excluded for species when experts judged that more than 25% of the range occurred outside the “modeled area.”Abundance and range regional stats are unavailable for all species outside of the Western Hemisphere and are unavailable for some species whose ranges are primarily Central or South American.

Can I download the data?

Data Products for the most recent results (estimates for 2019 released in December 2020) are available for download. Access to these data is provided through the R package ebirdst, which also includes tools to manipulate and analyze these data. Use of the R package requires an access key tied to your eBird username, which you can get by filling out the Access Request Form at: https://ebird.org/st/request. Access with this form generates a key to be used with this R package and is provided immediately (as long as commercial use is not requested). Our terms of use have been updated to be more permissive in many cases, particularly academic and research use. When requesting data access, please be sure to carefully read the terms of use and ensure that your intended use is not restricted.

Once an access key has been obtained, consult the package documentation for ebirdst for details on how to download data. The available data includes:

  • Raster data for the 2.96km resolution weekly relative abundance, count, and occurrence estimates, as well as seasonal relative abundance estimates in raster format
  • Information about the modeled relationships between estimated occurrence and the ecological covariates used in the model in the form of predictor importance estimates and partial dependence relationships
  • Information about the statistical performance of the models, which can be estimated either over the entire spatiotemporal extent of the model results, or for a specific region and season

If you are looking to use the data primarily in outside of R, e.g. in GIS software, the R package is still the easiest way to download the data. See the Data Access section of the package documentation for instructions on how to download the data. Use the get_species_path() function to see where the data has been downloaded for a particular species. From there, you can use the GeoTIFF files in any software you like.

In addition to the data available via the R package, the following is available for download directly the the Status and Trends website:

  • Spatial data for range boundaries (for December 2020 release) can be downloaded as Geopackage (GPKG) files. The boundaries are available both as raw (directly from the analysis) and smoothed (as seen in the visualizations) range boundaries. See the Range section below to learn more.
  • All images of the range maps, abundance maps, and predictive performance plots available on the eBird Status and Trends website can be downloaded and used for presentations and display.
  • The complete set of regional abundance and range statistics are available as a CSV file for download.

These products are available through the Download page. All downloadable products can be used for research, presentations, or display on webpages provided they are for non-commercial purposes and properly attributed; see our Recommend Citation below or here.

Recommended citation: Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, C. Wood, I. Davies, M. Iliff, L. Seitz. 2020. eBird Status and Trends, Data Version: 2019; Released: 2020. Cornell Lab of Ornithology, Ithaca, New York. https://doi.org/10.2173/ebirdst.2019

How can I find out more about the data analysis used to generate these products?

To generate the eBird Status and Trend products, we use statistical and machine learning models that combine the bird observation data from eBird with environmental data from NASA. The analytical process is designed to correct for biases and gaps within the eBird data. You can read more about the methods in a number of scientific publications. Fink et al. 2019 provides an overview of analytical methodology used for the Status and Trend products. More details about the modeling strategy used to estimate species’ relative abundance is described in Johnston et al. 2015. Details about the methods used to scale the relative abundance model across large spatial and temporal extents can be found in Fink et al. 2010 and Fink et al. 2013. For a more comprehensive description of the eBird database from a scientific perspective see Sullivan et al. 2014.

Why does it look like some species disappear during some parts of the year (e.g., shorebirds)?

For some species, it may appear as though they are not where you expect them to be, when you expect them to be there. For example, we know Whimbrel occurs along the coast of Chile during the winter, however this may be very difficult to see on the animated maps. For a species that sticks mainly to coasts, its range for much of the year will only be about one pixel wide. The resolution of each pixel is 2.96 x 2.96 km. So, when the map extent spans much of the Western Hemisphere, these very small pixels can be difficult to see.

Using the ebirdst R package, the data you see on these maps may be downloaded and plotted within your area of interest. This makes the pixels appear much clearer when zoomed in as can be seen in the maps shown below. Note that for the range maps, the 2.96 km grid data were aggregated to an 8.89 km grid and spatially smoothed, making the range for these species easier to see.

A full hemisphere view of American Oystercatcher weekly relative abundance for January 4th. It is impossible to see the range in most places at this scale and representation.

Loading the data into QGIS (https://qgis.org/en/site/) after downloading with the ebirdst R package and zooming in to northern coastal Peru, the relative abundance estimates for January 4th become visible, but are still hard to see.

However, if zoomed in even further and placed over Google satellite imagery, it’s possible to see the full 2.96km resolution of the relative abundance estimates and their accurate registration along the narrow strip of coastal habitat suitable for American Oystercatcher.

How do we ensure that the eBird data are accurate?

There are more than 5,000 automated filters that are active during the submission process for every checklist submitted in eBird. The common filters that may be triggered are for species that are rare for a region and/or season and abnormally high counts of a species for a region and/or season. eBird will ask that additional information (description of the bird(s), counting process, pictures, sound recordings, etc.) be provided for these observations. There are also more than 2,000 eBird reviewers worldwide that examine these checklists as they are submitted. These reviewers work to make sure that each rare sighting is validated before the data from each checklist is available to be used in any analyses.

What happened to the eBird Status and Trends Data Products for previous years?

To request access to previous versions of the Data Products, please contact ebird@cornell.edu.

Read this if you are thinking of comparing estimates across years: there have been a number of important changes between yearly estimates. These changes were made to improve the quality and scope of the Data Products, taking advantage of increased data volume and quality as well as methodological improvements. These changes were not designed to facilitate cross-year comparisons. For this reason, we strongly discouraged comparing Status and Trends results between years.

How do the maps match what we know about each species’ biology?

In addition to the quantitative assessment of the Data Products based on the predictive performance metrics, we want to know, qualitatively, how well the abundance and range information match up with each species’ known biology. For this, each species map is reviewed, week-by-week, by species distribution experts. Individual seasons exhibiting serious inaccuracies are rejected. Species distribution experts evaluate the information over the entire range and if most of it is accurate, the species and season combo is retained. Small regions of false positives or false negatives are acceptable (and highlight areas where more data is needed). Any season (for a single season or all seasons for a single species) that is rejected by an expert reviewer is excluded from all Status and Trends Data Products, including the visualizations online and the downloadable data. This way, we know that the maps online and in the downloadable data are not only accurate according to the predictive performance metrics described above, but also accurate in terms of species biology according to expert reviewers.

How do I interpret the habitat regional charts?

The habitat regional charts show the relative strength of association with different habitat types for the selected state, province, or Bird Conservation Region for every week of the year. Colored areas above the zero-line show a positive association with habitat types, whereas colored areas below the zero-line show a negative association with habitat types (e.g. not likely to use). The thickness of each color represents the relative strength of the positive or negative association of the given habitat type. Please see link to What environmental data were used for the Status & Trend products? for more information on the habitat data.

In some species you will notice that the habitat associations become more variable and sometimes drop out completely. This happens at the edge of species’ ranges or at the beginning and end of seasons when relative abundance is low. To help understand when relative abundance is low, the black line over the habitat chart shows the trajectory of the weekly mean of relative abundance scaled between zero and one. Relative abundance is the count of individuals of a given species detected by an expert eBirder at the optimal time of day, while expending the effort necessary to maximize detection of the species.

Mouse over the chart to see the numerical values describing the relative strength of habitat associations for the given week. These values describe the relative importance of the habitat within the region and given week. Habitat types with land cover values less than 1% are not shown, resulting in total contributions that may sum to less than 100%.

For more information on habitat regional charts and to see examples please visit the habitat regional charts homepage.

 

References

Amatulli, G., Domisch, S., Tuanmu, M.N., Parmentier, B., Ranipeta, A., Malczyk, J. and Jetz, W. 2018. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific data, 5, p.180040.

Fink, D., Auer, T., Johnston, A., Ruiz-Gutierrez, V., Hochachka, W.M., Kelling, S. (2019) Modeling avian full annual cycle distribution and population trends with citizen science data. Ecological Applications. 0000):e02056. 10.1002/eap.2056

Fink, D., Damoulas, T., & Dave, J. (2013, July). Adaptive Spatio-Temporal Exploratory Models: Hemisphere-wide species distributions from massively crowdsourced eBird data. In AAAI.

Fink, D., Hochachka, W. M., Zuckerberg, B., Winkler, D. W., Shaby, B., Munson, M. A., … & Kelling, S. (2010). Spatiotemporal exploratory models for broad‐scale survey data. Ecological Applications20(8), 2131-2147.

Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley and X. Huang. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, 2001-2012, Collection 5.1 IGBP Land Cover, Boston University, Boston, MA, USA.

Hansen, M. C., DeFries, R. S., Townshend, J. R. G., & Sohlberg, R. 2000. Global land cover classification at the 1km spatial resolution using a classification tree approach. International Journal of Remote Sensing 21:1331-1364.

IUCN. 2001. IUCN Red List categories and criteria: Version 3.1. Prepared by IUCN Species Survival Commission. World Conservation Union, Gland, Switzerland and Cambridge, United Kingdom. Ii + 30 pp.

Johnston, A., Fink, D., Reynolds, M. D., Hochachka, W. M., Sullivan, B. L., Bruns, N. E., … & Kelling, S. (2015). Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications25(7), 1749-1756.

Meijer, J.R., Huijbegts, M.A.J., Schotten, C.G.J. and Schipper, A.M. (2018): Global patterns of current and future road infrastructure. Environmental Research Letters, 13-064006. Data is available at www.globio.info

Murray N. J., Phinn S. R., DeWitt M., Ferrari R., Johnston R., Lyons M. B., Clinton N., Thau D. & Fuller R. A. (2019) The global distribution and trajectory of tidal flats. Nature. 565:222-225. http://dx.doi.org/10.1038/s41586-018-0805-8

NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global Water Bodies Database V001. 2019, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/ASTER/ASTWBD.001. Accessed 2020-12-14.

Sauer, J.R., Fallon, J.E. & Johnson, R. 2003. Use of North American Breeding Bird Survey data to estimate population change for Bird Conservation Regions. The Journal of Wildlife Management 67:372–389.

Sauer, J. R., D. K. Niven, J. E. Hines, D. J. Ziolkowski, Jr, K. L. Pardieck, J. E. Fallon, and W. A. Link. 2017. The North American Breeding Bird Survey, Results and Analysis 1966 – 2015. Version 2.07.2017 USGS Patuxent Wildlife Research Center, Laurel, MD

Sayre R. et al. (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, S47-S56, DOI: 10.1080/1755876X.2018.1529714

Sullivan, B. L., Aycrigg, J. L., Barry, J. H., Bonney, R. E., Bruns, N., Cooper, C. B., … & Fink, D. (2014). The eBird enterprise: an integrated approach to development and application of citizen science. Biological Conservation169, 31-40.