Distribution models for riparian landbirds and waterbirds in the Sacramento-San Joaquin Delta
Bibliographic record
Abstract
SUMMARYDistribution models for 9 riparian landbird species and 6 groups of waterbird species in the Sacramento-San Joaquin River Delta of California. DESCRIPTIONThese predictive models were developed to relate the probability of species or group presence as a function of the surrounding landscape, facilitating predictions of species presence or absence over the entire landscape. Each .RData object is structured as a list containing individual model objects of class `gbm` for each species or group. Models were developed using Boosted Regression Trees, implemented in R using the R packages `dismo` (Hijmans et al. 2021) and `gbm` (Greenwell et al. 2020). Models were developed from pre-existing bird survey data, including 2,547 surveys for riparian landbirds conducted at 716 unique locations throughout the Central Valley of California during the breeding season (May and June), 2011–2019, and 7,820 surveys for waterbirds conducted at 504 unique locations in the Delta during the fall (July 15–November 15) and winter (November 17–March 5) seasons, 2013–14 and 2014–15. Waterbird models were developed for each of the fall and winter seasons, with 46 species grouped into 6 distinct groups defined by similar habitat requirements, foraging style, and diet. These models were used to predict the distribution of each species and group across a baseline Delta landscape (representing land cover in 2018), and these predictions were used to identify Priority Bird Conservation Areas in the Delta. In addition, the models were used to predict distributions for alternative scenarios of future landscape change, and to evaluate the net change from the baseline distributions in the total area of suitable habitat. These models are required for evaluating the change in Biodiversity Support benefits using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial scenario analyses or analyzing new scenarios. For additional details about the development and applications of these data, please see: Dybala KE, et al. (2025) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. San Francisco Estuary and Watershed Science 23(2). doi:10.15447/sfews.2025v23iss2art2. Available from: https://escholarship.org/uc/item/388196xc Dybala KE, et al. (2023) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. San Francisco Estuary and Watershed Science 21(3). doi:10.15447/sfews.2023v21iss3art4. Available from: https://escholarship.org/uc/item/5r53m1k6 Dybala KE (2023) DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta. R package version 1.0.0. doi:10.5281/zenodo.7718620. https://pointblue.github.io/DeltaMultipleBenefits Literature Cited: Greenwell B, Boehmke B, Cunningham J, Developers G (2020). gbm: Generalized Boosted Regression Models. R package version 2.1.8. https://CRAN.R-project.org/package=gbm Hijmans RJ, Phillips S, Leathwick J, Elith J (2021). dismo: Species Distribution Modeling. R package version 1.3-5. https://CRAN.R-project.org/package=dismo FUNDING STATEMENTThese data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife. POINT OF CONTACTKristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org SUGGESTED CITATIONDybala KE, Sesser KA, Reiter ME, Shuford WD, Golet GH, Hickey CM, Gardali T. 2023. Distribution models for riparian landbirds and waterbirds in the Sacramento-San Joaquin Delta. doi: 10.5281/zenodo.7531945 DATA DISTRIBUTIONZenodo. (https://doi.org/10.5281/zenodo.7531945) PROGRESSComplete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision. UPDATE FREQUENCYNot Planned DATEThese models were developed 2019-2022, based on bird survey data collected 2011-2019. FIELD DEFINITIONSN/A ABBREVIATION DEFINITIONS BRT_models_riparianlandbirds.RData: NUWO: Nuttall's Woodpecker (Picoides nuttallii) ATFL: Ash-throated Flycatcher (Myiarchus cinerascens) BHGR: Black-headed Grosbeak (Pheucticus melanocephalus) LAZB: Lazuli Bunting (Passerina amoena) COYE: Common Yellowthroat (Geothlypis trichas) YEWA: Yellow Warbler (Setophaga petechia) SPTO: Spotted Towhee (Pipilo maculatus) SOSP: Song Sparrow (Melospiza melodia) YBCH: Yellow-breasted Chat (Icteria virens) BRT_models_waterbirds.RData: geese: Geese Greater White-fronted Goose (Anser albifrons) Snow Goose (Anser caerulescens) Ross's Goose (Anser rossii) Cackling Goose (Branta hutchinsii) Canada Goose (Branta canadensis) dblr: Dabbling ducks, including: Wood Duck (Aix sponsa) Gadwall (Mareca strepera) American Wigeon (Mareca americana) Mallard (Anas platyrhynchos) Blue-winged Teal (Spatula discors) Cinnamon Teal (Spatula cyanoptera) Northern Shoveler (Spatula clypeata) Northern Pintail (Anas acuta) Green-winged Teal (Anas carolinensis) divduck: Diving ducks (Note: this model was only developed for the winter season) Canvasback (Aythya valisineria) Ring-necked Duck (Aythya collaris) Lesser Scaup (Aythya affinis) Bufflehead (Bucephala albeola) Common Goldeneye (Bucephala clangula) Hooded Merganser (Lophodytes cucullatus) Common Merganser (Mergus merganser) Ruddy Duck (Oxyura jamaicensis) crane: Cranes Greater Sandhill Crane (Antigone canadensis tabida) Lesser Sandhill Crane (Antigone canadensis canadensis) shore: Shorebirds Western Sandpiper (Calidris mauri) Least Sandpiper (Calidris minutilla) Dunlin (Calidris alpina) Black-necked Stilt (Himantopus mexicanus) American Avocet (Recurvirostra americana) Greater Yellowlegs (Tringa melanoleuca) Lesser Yellowlegs (Tringa flavipes) Long-billed Dowitcher (Limnodromus scolopaceus) Short-billed Dowitcher (Limnodromus griseus) Wilson's Snipe (Gallinago delicata) cicon: Herons/Egrets (Ciconiiformes) Great Blue Heron (Ardea herodias) Great Egret (Ardea alba) Snowy Egret (Egretta thula) Cattle Egret (Bubulcus ibis) Green Heron (Butorides virescens) Black-crowned Night-Heron (Nycticorax nycticorax) ACCESS & USE CONSTRAINTSCC-by-4.0 (https://creativecommons.org/licenses/by/4.0/) KEYWORDS Themes: birds, landbirds, songbirds, waterbirds, waterfowl, shorebirds, distribution, habitat Place: Sacramento-San Joaquin River Delta, Central Valley, California
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".