Data Version: 2022 (available November 2023)
Citation:
Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, S. Ligocki, O. Robinson, W. Hochachka, L. Jaromczyk, C. Crowley, K. Dunham, A. Stillman, I. Davies, A. Rodewald, V. Ruiz-Gutierrez, C. Wood. 2023. eBird Status and Trends, Data Version: 2022; Released: 2023. Cornell Lab of Ornithology, Ithaca, New York. https://doi.org/10.2173/ebirdst.2022
effort_hrs
and effort_distance_km
having been maximized with partial dependence values separately.Details The foundation of CCI is a predictive model of checklist-level species richness ($S$; i.e. number of species). In updating CCI, changes were made to both the form of the predictive model of $S$ and to the method that attributes variation in richness to particular observers.
Prior to Version 2022, predictive features comprised weather, landcover, habitat diversity, protocol, day of year, and variables that are particular to the observer: observer_id and checklist_number (i.e., index of how many checklists a user has ever submitted from any stixel to eBird; not to be confused with checklist_id). A mixed-effects generalized additive model (GAM) was fit to $S$. This GAM used as predictive features the natural log of checklist_number
, a smooth spline of solar_noon_diff
, and the raw values of all other predictors, with a random effect specification for observer_id
and checklist_number
. The model was used to make predictions $p_{i}$ of $S$ to data representing a “standardized search”, in which all features except observer_id
and checklist_number
were held constant (at the column-wise mean) across observations. CCI was derived from the variation in resulting predictions, and scaled to have mean 0 and variance 1.
$$ CCI_{i} = \(pi - mean(p)\) / sd(p) $$
Version 2022 changed the functional form of the predictive model from a (mostly) linear mixed-effects model to a random forest. Further, it removed observer_id
and checklist_number
from the suite of predictive features; the model is now blind to person-specific effects. Instead, predictions to real data absent any personal information establish conditional expectations of richness given habitat, effort, weather, etc. Each expected value parameterizes a Poisson distribution, which is used to compute the exceedance probability of the actually-observed S
, which is then mapped to a standard-normal quantile. A GAM with a “factor smooth” basis for checklist_number
and observer_id
is applied to smooth the raw values for each observer. CCI currently comprises these smoothed values.
has_evi
that describes whether the covariate was available at a given date and location.has_shoreline
covariate, as the shoreline covariates are not spatially exhaustive, describing whether the covariate was available at a given location.effort_distance_km
and effort_hrs
are set to their 90th quantiles when making predictions to determine the range boundary. Previously these were chosen to maximize the partial dependence (PD) curve. solar_noon_diff
) are now chosen to maximize the abundance partial dependence (PD) constrained to values where the species was detected. Previously they were chosen using the occurrence partial dependence curve and were not constrained to detections.
-CHANGED: When maximizing the prediction value for CCI at a stixel level, the allowable range of values is now 0-2, up from 0-1.85, based on the range of the new version of CCI values overall.effort_distance_km
is now 2 km, to more closely reflect the distribution of checklists and to increase overall signal.Data Version: 2021 (available November 2022)
Citation:
Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, S. Ligocki, O. Robinson, W. Hochachka, L. Jaromczyk, A. Rodewald, C. Wood, I. Davies, A. Spencer. 2022. eBird Status and Trends, Data Version: 2021; Released: 2022. Cornell Lab of Ornithology, Ithaca, New York. https://doi.org/10.2173/ebirdst.2021
Data Version: 2020 (available June 2022)
Citation:
Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. https://doi.org/10.2173/ebirdst.2020
Data Version: 2019 (currently available)
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
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