Table of Contents

General

Update: July 2020
What are eBird Science Products?
Can I contribute data for the Status and Trends Analysis?

Abundance

How is relative abundance defined?
Interpreting the Abundance Maps
How is “Percentage of total population in region” calculated?

Range

How are species’ ranges defined?
Interpreting the Range Maps
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

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 the 2016 calendar year?
How do the maps match what we know about each species’ biology?

References

 

General

Update: July 2020

The abundance and range updates are complete! This update includes data products for 610 North American breeding birds, describing each species’ abundance and range for calendar year 2018. Updated trend maps are coming soon.

What are eBird Science Products?

The eBird Status and Trend products provide basic ecological information for over 600 species in North America, 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 analyses are used to predict 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.

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?

Previously, we defined relative abundance as the count of individuals of a given species that we expect a single skilled eBirder to record if they conducted a hypothetical checklist traveling one kilometer over one hour at the optimal time of day for detection of that species. Standardization to a one hour, one kilometer checklist is conceptually simple, but this fixed amount of search effort translates into different detection rates for different regions, seasons, and species. We now optimize search effort and user behavior, specific for the given region, season, and species, in order to maximize detection rates. Therefore, relative abundance predictions correspond to sending an expert eBirder to each pixel on the map, starting at the optimal time of day, while expending the effort necessary to maximize detection of the species.

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 2.96km × 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.

Interpreting the Abundance Maps

The Abundance Maps show the species’ seasonally averaged relative abundance with each season stacked upon one-another. The sequence of seasons from top to bottom are:

Top layer: Year-round (purple) > Breeding (red) > Non-breeding (blue) > Pre-breeding migration (green) > Post-breeding migration (yellow): Lowest layer.

When the breeding and non-breeding seasons overlap this is shown as year-round. In most species, the two migration seasons have at least 60% overlap and are combined into one “migration” season (yellow); for an exception, see the Rufous Hummingbird example under “Interpreting the Range Maps”. Below is a guide to specific aspects of this Eastern Phoebe example map.

For this Eastern Phoebe abundance map refer to the original image to see the legend. Dark gray indicates “no prediction” areas (1, 2), such as northern Canada, eastern Honduras and Nicaragua. Areas shown in light grey indicate where abundance is predicted to be zero (3), such as Nevada or Honduras. The most intense red indicates high breeding season abundance (e.g., New York) (4). Paler red in Nova Scotia (5) indicates lower breeding season abundance; areas at the edge of the range consistently show lower abundance, such as eastern Colorado (6). Core winter areas, such as Florida (7), show high non-breeding season abundance, while areas of central and southern Mexico, such as Veracruz (8), reflect lower non-breeding season abundance. Eastern Phoebe occurs year-round in much of the southern United States, occurring at high abundance in Piedmont areas such as the Atlanta area of Georgia (9) and lower abundance in areas such as west central Texas (10). Eastern Phoebe occurs only as a migrant, both spring and fall, in areas shown in yellow (11), such as northwest Texas.

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

For each species and stationary 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 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 five 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, and expending the effort necessary to maximize detection of the species for each day of the week, and detecting the species on at least five percent of the checklists within a week. Each species’ range was estimated for all 52 weeks of the year at 2.96km × 2.96.km grid cell locations across North America. To create easy-to-read range boundaries, the 2.96km grid data were aggregated to an 8.89km grid and spatially smoothed. Both the smoothed, aggregated seasonal boundaries and the raw 2.96km grid cell boundaries are provided for download.

Interpreting the Range Maps

The Range Maps show the species’ seasonal range boundaries with each season stacked upon one-another. The sequence of seasons from top to bottom are:

Top layer: Year-round (purple) > Breeding (red) > Non-breeding (blue) > Pre-breeding migration (green) > Post-breeding migration (yellow): Lowest layer.

When the breeding and non-breeding seasons overlap this is shown as year-round. In most species, the two migration seasons have at least 60% overlap and are combined into one “migration” season (yellow);  but in the Rufous Hummingbird example in this section the breeding and non-breeding seasons have less than 60% overlap and have been shown separately. Below is a guide to specific aspects of this Rufous Hummingbird range map.

For this Rufous Hummingbird range map refer to the original image to see the legend. Range boundaries are defined by the transitions between areas where a skilled eBirder could expect to detect the species on 5 out of 100 checklists in a week and where they would not. No prediction zones, shown as dark grey, such as northern Canada (1) and eastern Honduras (2)  are areas where there was not enough information to define the seasonal range. Areas of light gray (3), such as Iowa and the Yucatan Peninsula, indicate areas are outside of the range of Rufous Hummingbird. Breeding range is shown in red (4), such as British Columbia; non-breeding range is shown in blue (5), such as Jalisco. Note for Rufous Hummingbird, isolated non-breeding occurrence in cities and towns along the Gulf coast may be shown (6). The pre-breeding migration range appears in green (7), such as in California, and the post-breeding migration range appears as yellow (8), such as in Colorado; the separation of the migratory ranges indicates that the migratory range was at least 40% distinct between seasons.

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 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 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 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.

How is “Days of occupation in region” calculated?

Days of occupation in 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 (shown in the first row). AUC measures a model’s ability to discriminate between locations where species was detected and locations where the species was not detected. 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

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

The eBird checklists that were used to generate the Status and Trends Products are referred to as “qualifying checklists.” Checklists need to meet a number of conditions to be qualifying checklists:

  • Submitted as of 15 January 2019
  • Observation dates from 1 January 2014 through 15 January 2019
  • In 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, stationary, or area protocols (i.e., not incidental or historical protocol)
  • If traveling checklists, were not longer than 10 kilometers
  • If area count checklists, did not cover more than 5626 hectares
  • 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 this link 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. For example, some bird species, such as Mourning Doves, are often seen in low elevation croplands, whereas other species, such as Clark’s Nutcracker, may often be seen in higher elevation forests. To match species to certain habitats, it is important to include good information on habitats in the analysis.

The analyses for the Status and Trend products use 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 1.5km 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. 2017). Land cover was described using the MCD12Q1 dataset from NASA, using the FAO-Land Cover Classification System, which includes classes for land cover, land use, and hydrology by year. Water cover, by year, was described by the MOD44W v006 dataset from NASA and was segmented into inland, coastal, and ocean water using the global shoreline vector (Sayre et al. 2019). To describe tidal mudflats, we used a high resolution intertidal change dataset (Murrary 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. Finally, 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.

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 North American 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 non-breeding 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 North America.

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 there are two separate colors of gray. Areas of light gray indicate the species is absent (or very rarely occurs). Areas of dark gray indicate that we don’t know. The paler 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 at least 50 qualifying checklists within the region and a 30-day period. The darker gray refers to areas of “no predictions” where there was insufficient data to assess whether the species was present or absent. That is, there were fewer than 50 qualifying checklists within the region and a 30-day period.

Why are models available only for the Western Hemisphere?

The Status and Trend products were limited to the Western hemisphere because it currently has the highest density of eBird data. In future releases we will expand the Status and Trend products to include more species across more regions around the globe.

Why are some islands areas of “No Prediction”?

Some islands have insufficient data for us to predict whether a species is present or absent (see above in What is the difference between “modeled area” and “no prediction”?). In the 2019 version of the eBird Status and Trends, we used a new island dataset (Sayre et al. 2019) that enabled 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:

  • There is sufficient eBird data to capture patterns of species’ occurrence and abundance, and,
  • 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 birds.

Therefore, error rates generally increase in regions when one or both of these 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, Black Vulture records from Western Mexico were extrapolated to Baja California where the species has virtually never been recorded. 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 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:

  • For other species, the absence of range or abundance maps for a season indicates that 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”.

Can I download the data?

The following products are currently available for download:

  • Raster data for the 2.96km weekly relative abundance and occurrence estimates for all species are now hosted by Amazon Web Services Open Data program at https://registry.opendata.aws/ebirdst/. We also provide the R package ebirdst to help access, manipulate, and analyze these data.
    • If you are looking to use the data primarily in GIS software, the R package is still the easiest way to download the data. See the Data Access section of the package documentation. Install the R package, load the package, and use the ebirdst_download() function, which will tell you where the data has been downloaded. From there, you can use the GeoTIFF files in any software you like. Alternatively, you can download directly from the Amazon S3 bucket (arn:aws:s3:::ebirdst-data). There is a CSV file in the root of the bucket that describes all the species and run names. Accessing in this way requires either the AWS CLI (e.g., aws s3 cp s3://ebirdst-data/abetow-ERD2018-EBIRD_SCIENCE-20191109-a5cf4cb2/results/tifs/abetow-ERD2018-EBIRD_SCIENCE-20191109-a5cf4cb2_hr_2018_abundance_median.tif .) or direct access to the URL endpoint (e.g., https://ebirdst-data.s3-us-west-2.amazonaws.com/abetow-ERD2018-EBIRD_SCIENCE-20191109-a5cf4cb2/results/tifs/abetow-ERD2018-EBIRD_SCIENCE-20191109-a5cf4cb2_hr_2018_abundance_median.tif).
  • Spatial data for range boundaries 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.

All downloads are available through or on 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, B. Petersen, C. Wood, I. Davies, B. Sullivan, M. Iliff, S. Kelling. 2019. eBird Status and Trends, Version: November 2019. 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 2.96km grid data were aggregated to an 8.89km 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 the 2016 calendar year?

The 2016 data products, and the ebirdst R package developed to work with them, are still available. The Installation section of the package README describes how to install a previous version of the package, which maintains functionality for working with older versions, including access for downloading them. The release notes describe which calendar year of predictions correspond to which package version.

Read this if you are thinking of comparing the 2018 and 2016 estimates: There have been a number of important changes between the 2016 and 2018 estimates. These changes were made in order 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 2018-2016 comparisons. For this reason, we do not suggest using 2016 and 2018 for direct comparisons.

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 kept. 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 what you are seeing in the maps on each species page online and in the downloadable data is not only accurate according to the predictive performance metrics described above, but also accurate in terms of species biology according to expert reviewers.

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.

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