Distribution models for riparian landbirds and waterbirds in the Sacramento-San Joaquin Delta
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».