Data and code from: Water availability has stronger effects on West Nile virus dynamics in water-limited regions
Data files
Jun 08, 2026 version files 119.73 MB
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analysis_panelmodels.Rmd
10.21 KB
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data.zip
119.57 MB
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figures.Rmd
46.16 KB
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README.md
28.09 KB
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sensitivity_text.Rmd
66.67 KB
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Table_S1.txt
1.84 KB
Abstract
West Nile virus dynamics are shaped by hydrological conditions that influence mosquito habitat and pathogen transmission, but identifying causal relationships is difficult in managed landscapes where irrigation decouples local water conditions from precipitation, complicating climate-disease inference. We address this challenge using a 21-year panel of more than 19 million Culex tarsalis mosquitoes from California's Central Valley, applying fixed-effects panel models to estimate how surface water availability affects mosquito abundance and infection rates while accounting for spatial differences and shared temporal variation. We find that wetter conditions lead to higher mosquito abundance but slightly lower infection rates, suggesting divergent responses of vector population growth and pathogen amplification. These patterns are consistent across multiple hydrological measures, including drought indices, soil moisture, surface water, and evapotranspiration. Effects are strongest in water-limited regions, where hydrological variability is greatest and buffering by snowmelt-fed river systems is weakest. Overall, hydrological conditions exert contrasting effects on key components of West Nile virus dynamics, and these relationships are strongly conditioned by human water management. Our results highlight how irrigation decouples local hydrological conditions from broader climatic variability, underscoring the need for fine-scale hydrological data and panel-based approaches to identify drivers of disease dynamics in human-managed landscapes.
Dataset DOI: 10.5061/dryad.cnp5hqcm6
Please reach out to the corresponding author (ssambado@stanford.edu or sbsambado@gmail.com) if additional guidance is needed to re-create these analyses or figures.
Description of the data and file structure
Files and variables
File: analysis_panelmodels.Rmd
Description: Script to run fixed effects models in the main text for 1) the overall effect of Palmer Drought Severity Index (PDSI), 2) spatial heterogeneity of PDSI, and 3) associations with other hydrological variables. Models are split into abundance (mean mosquitoes per trap night) and infection (West Nile virus minimum infection rate). With the provided data, users should be able to run the entire script to replicate model outputs.
File: figures.Rmd
Description: Script to create the figures in the main and supplemental text. Due to large spatial files and data copyright concerns, the data to run this script is not available, hence why the script is commented out. Users can at see how figures were made and if users want to run the code, they can download the publicly available data and run the script. Information on where to access various spatial files and raw data can be found in Supplementary Table 1. Please reach out to corresponding author (ssambado@stanford.edu) if additional guidance is needed.
File: sensitivity_text.Rmd
Description: Script for sensitivity and robustness analysis of the fixed effect panel models. With the data files from data.zip and the model outputs from ‘analysis_panelmodels.Rmd’, users can recreate the text, tables and figures used in Supplemental Text 4, 5, 6, 7, and 8.
File: data.zip
Description: Unzip this file to have access to data needed for ‘analysis_panel.Rmd' and ‘sensitivity_text.Rmd'. Subfolder derived has the .csv files for the panel models. Subfolder interim contains .csv files needed for figure making.
File: abund_tar_panel_select.csv
Description: File in derived folder for abundance panel regression models.
- county: county name in California (chr)
- cluster_id: identification number of cluster sampling unit (chr)
- year: year of sampling (int)
- month: numeric of calendar month of sampling (int)
- n_traps: total number of unique individual traps within cluster_id sample (int)
- total_mos: total number of mosquitoes collected during sampling period year-month-cluster_id (int)
- total_trap_nights: total nights the traps were deployed during sampling period year-month-cluster_id (int)
- mos_per_trap_night: average number of total mosquitoes collected during a trap night (total_mos/total_trap_nights) (dbl)
- latitude: geographic coordinate of cluster_id centroid (dbl)
- longitude: geographic coordinate of cluster_id centroid (dbl)
- hydro_region: hydrological region the cluster_id is located in, hydrological classifications were done by California Department of Water Resources (chr)
- pdsi_raw: untransformed palmer drought severity index (PDSI), positive numbers reflect wetter landscape conditions whereas negative numbers reflect drier landscape conditions. Typical index ranges from -4 to 4.
- pdsi_lag0: scaled and averaged palmer drought severity index (PDSI) (pdsi_raw) for current sampling period at year-month-cluster_id
- pdsi_lag1: scaled and averaged palmer drought severity index (PDSI) for 1-month prior to sampling period at year-month-cluster_id
- pdsi_lag2: scaled and averaged palmer drought severity index (PDSI) for 2-months prior to sampling period at year-month-cluster_id
- pdsi_lag3: scaled and averaged palmer drought severity index (PDSI) for 3-months prior to sampling period at year-month-cluster_id
- pdsi_lag4: scaled and averaged palmer drought severity index (PDSI) for 4-months prior to sampling period at year-month-cluster_id
- temp_raw: untransformed near-surface air temperature (Celsius)
- temp_lag0: scaled and averaged temperature (temp_raw) for current sampling period at year-month-cluster_id
- temp_lag1: scaled and averaged temperature for 1-months prior to sampling period at year-month-cluster_id
- temp_lag2: scaled and averaged temperature for 2-months prior to sampling period at year-month-cluster_id
- temp_lag3: scaled and averaged temperature for 3-months prior to sampling period at year-month-cluster_id
- temp_lag4: scaled and averaged temperature for 4-months prior to sampling period at year-month-cluster_id
- bird_raw: untransformed bird community competence index, larger positive numbers reflect bird communities that are more likely to amplify West Nile virus transmission. The originial index was calculated in MacDonald et al. 2020 (Ecology). Index values are positive, non-zero values.
- bird_lag0: scaled and averaged bird community competence index (bird_raw) for current sampling period at year-month-cluster_id
- bird_lag1: scaled and averaged bird community competence index (bird_raw) for 1-month prior to sampling period at year-month-cluster_id
- bird_lag2: scaled and averaged bird community competence index (bird_raw) for 2-months prior to sampling period at year-month-cluster_id
- bird_lag3: scaled and averaged bird community competence index (bird_raw) for 3-months prior to sampling period at year-month-cluster_id
- bird_lag4: scaled and averaged bird community competence index (bird_raw) for 4-months prior to sampling period at year-month-cluster_id
- soilmoisture_raw: untransformed near-surface soil moisture (m3/m3). Values are positive and range 0.01-0.4 in our study.
- soilmoisture_lag0: scaled and averaged soil moisture (soilmoisture_raw) for current sampling period at year-month-cluster_id
- soilmoisture_lag1: scaled and averaged soil moisture for 1-month prior to sampling period at year-month-cluster_id
- soilmoisture_lag2: scaled and averaged soil moisture for 2-months prior to sampling period at year-month-cluster_id
- soilmoisture_lag3: scaled and averaged soil moisture for 3-months prior to sampling period at year-month-cluster_id
- soilmoisture_lag4: scaled and averaged soil moisture for 4-months prior to sampling period at year-month-cluster_id
- et_raw: untransformed actual evapotranspiration (mm). Values are positive and range 0.00-148.63 in our study.
- et_lag0: scaled and averaged evapotranspiration (et_raw) for current sampling period at year-month-cluster_id
- et_lag1: scaled and averaged evapotranspiration for 1-month prior to sampling period at year-month-cluster_id
- et_lag2: scaled and averaged evapotranspiration for 2-months prior to sampling period at year-month-cluster_id
- et_lag3: scaled and averaged evapotranspiration for 3-months prior to sampling period at year-month-cluster_id
- et_lag4: scaled and averaged evapotranspiration for 4-months prior to sampling period at year-month-cluster_id
- jrc_5km_raw: untransformed standing surface water (%) represented as percentage of pixels covered by water surrounding within a 5 km buffer zone of our sampling cluster_id. Values are positive and range 0.00-68.67 in our study.
- jrc_5km_lag0: scaled and averaged standing surface water (jrc_5km_raw) for current sampling period at year-month-cluster_id
- jrc_5km_lag1: scaled and averaged standing surface water for 1-month prior to sampling period at year-month-cluster_id
- jrc_5km_lag2: scaled and averaged standing surface water for 2-months prior to sampling period at year-month-cluster_id
- jrc_5km_lag3: scaled and averaged standing surface water for 3-months prior to sampling period at year-month-cluster_id
- jrc_5km_lag4: scaled and averaged standing surface water for 4-months prior to sampling period at year-month-cluster_id
- jrc_10km_raw: untransformed standing surface water (%) represented as percentage of pixels covered by water surrounding within a 10 km buffer zone of our sampling cluster_id. Values are positive and range 0.00-68.67 in our study.
- jrc_10km_lag0: scaled and averaged standing surface water (jrc_10km_raw) for current sampling period at year-month-cluster_id
- jrc_10km_lag1: scaled and averaged standing surface water for 1-month prior to sampling period at year-month-cluster_id
- jrc_10km_lag2: scaled and averaged standing surface water for 2-months prior to sampling period at year-month-cluster_id
- jrc_10km_lag3: scaled and averaged standing surface water for 3-months prior to sampling period at year-month-cluster_id
- jrc_10km_lag4: scaled and averaged standing surface water for 4-months prior to sampling period at year-month-cluster_id
File: wnv_tar_panel_select.csv
Description: File in derived folder for abundance panel regression models for Culex tarsalis mosquitoes.
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county: county name in California (chr)
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cluster_id: identification number of cluster sampling unit (chr)
-
year: year of sampling (int)
-
month: numeric of calendar month of sampling (int)
-
n_traps: total number of unique individual traps within cluster_id sample (int)
-
total_pools: total number of testing pools which contained multiple mosquitoes (int)
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positive_pools: total number of testing pools that tested positive for West Nile virus (int)
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total_mos_tested: total number of mosquitoes tested during sampling period year-month-cluster_id (int)
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WNV_MIR: minimum infection rate of West Nile virus of testing pool ((number of positive pools/number of mosquitoes tested)*1000) (dbl)
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latitude: geographic coordinate of cluster_id centroid (dbl)
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longitude: geographic coordinate of cluster_id centroid (dbl)
-
hydro_region: hydrological region the cluster_id is located in, hydrological classifications were done by California Department of Water Resources (chr)
-
pdsi_raw: untransformed palmer drought severity index (PDSI), positive numbers reflect wetter landscape conditions whereas negative numbers reflect drier landscape conditions. Typical index ranges from -4 to 4.
-
pdsi_lag0: scaled and averaged palmer drought severity index (PDSI) (pdsi_raw) for current sampling period at year-month-cluster_id
-
pdsi_lag1: scaled and averaged palmer drought severity index (PDSI) for 1-month prior to sampling period at year-month-cluster_id
-
pdsi_lag2: scaled and averaged palmer drought severity index (PDSI) for 2-months prior to sampling period at year-month-cluster_id
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pdsi_lag3: scaled and averaged palmer drought severity index (PDSI) for 3-months prior to sampling period at year-month-cluster_id
-
pdsi_lag4: scaled and averaged palmer drought severity index (PDSI) for 4-months prior to sampling period at year-month-cluster_id
-
temp_raw: untransformed near-surface air temperature (Celsius)
-
temp_lag0: scaled and averaged temperature (temp_raw) for current sampling period at year-month-cluster_id
-
temp_lag1: scaled and averaged temperature for 1-months prior to sampling period at year-month-cluster_id
-
temp_lag2: scaled and averaged temperature for 2-months prior to sampling period at year-month-cluster_id
-
temp_lag3: scaled and averaged temperature for 3-months prior to sampling period at year-month-cluster_id
-
temp_lag4: scaled and averaged temperature for 4-months prior to sampling period at year-month-cluster_id
-
bird_raw: untransformed bird community competence index, larger positive numbers reflect bird communities that are more likely to amplify West Nile virus transmission. The originial index was calculated in MacDonald et al. 2020 (Ecology). Index values are positive, non-zero values.
-
bird_lag0: scaled and averaged bird community competence index (bird_raw) for current sampling period at year-month-cluster_id
-
bird_lag1: scaled and averaged bird community competence index (bird_raw) for 1-month prior to sampling period at year-month-cluster_id
-
bird_lag2: scaled and averaged bird community competence index (bird_raw) for 2-months prior to sampling period at year-month-cluster_id
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bird_lag3: scaled and averaged bird community competence index (bird_raw) for 3-months prior to sampling period at year-month-cluster_id
-
bird_lag4: scaled and averaged bird community competence index (bird_raw) for 4-months prior to sampling period at year-month-cluster_id
-
soilmoisture_raw: untransformed near-surface soil moisture (m3/m3). Values are positive and range 0.01-0.4 in our study.
-
soilmoisture_lag0: scaled and averaged soil moisture (soilmoisture_raw) for current sampling period at year-month-cluster_id
-
soilmoisture_lag1: scaled and averaged soil moisture for 1-month prior to sampling period at year-month-cluster_id
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soilmoisture_lag2: scaled and averaged soil moisture for 2-months prior to sampling period at year-month-cluster_id
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soilmoisture_lag3: scaled and averaged soil moisture for 3-months prior to sampling period at year-month-cluster_id
-
soilmoisture_lag4: scaled and averaged soil moisture for 4-months prior to sampling period at year-month-cluster_id
-
et_raw: untransformed actual evapotranspiration (mm). Values are positive and range 0.00-148.63 in our study.
-
et_lag0: scaled and averaged evapotranspiration (et_raw) for current sampling period at year-month-cluster_id
-
et_lag1: scaled and averaged evapotranspiration for 1-month prior to sampling period at year-month-cluster_id
-
et_lag2: scaled and averaged evapotranspiration for 2-months prior to sampling period at year-month-cluster_id
-
et_lag3: scaled and averaged evapotranspiration for 3-months prior to sampling period at year-month-cluster_id
-
et_lag4: scaled and averaged evapotranspiration for 4-months prior to sampling period at year-month-cluster_id
-
jrc_5km_raw: untransformed standing surface water (%) represented as percentage of pixels covered by water surrounding within a 5 km buffer zone of our sampling cluster_id. Values are positive and range 0.00-68.67 in our study.
-
jrc_5km_lag0: scaled and averaged standing surface water (jrc_5km_raw) for current sampling period at year-month-cluster_id
-
jrc_5km_lag1: scaled and averaged standing surface water for 1-month prior to sampling period at year-month-cluster_id
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jrc_5km_lag2: scaled and averaged standing surface water for 2-months prior to sampling period at year-month-cluster_id
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jrc_5km_lag3: scaled and averaged standing surface water for 3-months prior to sampling period at year-month-cluster_id
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jrc_5km_lag4: scaled and averaged standing surface water for 4-months prior to sampling period at year-month-cluster_id
-
jrc_10km_raw: untransformed standing surface water (%) represented as percentage of pixels covered by water surrounding within a 10 km buffer zone of our sampling cluster_id. Values are positive and range 0.00-68.67 in our study.
-
jrc_10km_lag0: scaled and averaged standing surface water (jrc_10km_raw) for current sampling period at year-month-cluster_id
-
jrc_10km_lag1: scaled and averaged standing surface water for 1-month prior to sampling period at year-month-cluster_id
-
jrc_10km_lag2: scaled and averaged standing surface water for 2-months prior to sampling period at year-month-cluster_id
-
jrc_10km_lag3: scaled and averaged standing surface water for 3-months prior to sampling period at year-month-cluster_id
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jrc_10km_lag4: scaled and averaged standing surface water for 4-months prior to sampling period at year-month-cluster_id
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File: abund_tar_panel_cluster.csv
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Description: File in
interimfolder for unfiltered data prior to fitting abundance panel models inanalysis_panelmodels.Rmd. This file does not have covariate data. -
county: county name in California (chr)
-
cluster_id: identification number of cluster sampling unit (chr)
-
surv_year: year of sampling (int)
-
month: numeric of calendar month of sampling (int)
-
n_traps: total number of unique individual traps within cluster_id sample (int)
-
total_mos: total number of mosquitoes collected during sampling period year-month-cluster_id (int)
-
total_trap_nights: total nights the traps were deployed during sampling period year-month-cluster_id (int)
-
mos_per_trap_night: average number of total mosquitoes collected during a trap night (total_mos/total_trap_nights) (dbl)
File: wnv_tar_panel_cluster.csv
Description: File in interim folder for unfiltered data prior to fitting infection panel models in analysis_panelmodels.Rmd. This file does not have covariate data.
- county: county name in California (chr)
- cluster_id: identification number of cluster sampling unit (chr)
- surv_year: year of sampling (int)
- month: numeric of calendar month of sampling (int)
- n_traps: total number of unique individual traps within cluster_id sample (int)
- total_pools: total number of testing pools which contained multiple mosquitoes (int)
- positive_pools: total number of testing pools that tested positive for West Nile virus (int)
- total_mos_tested: total number of mosquitoes tested during sampling period year-month-cluster_id (int)
- WNV_MIR: minimum infection rate of West Nile virus of testing pool ((number of positive pools/number of mosquitoes tested)*1000) (dbl)
File: covariates_scalelag_stationmeta.csv
Description: File in interim folder for unfiltered covariate data prior to merging with model outcome (abundance or infection) datasets for analysis_panelmodels.Rmd. This file does not have outcome data.
- county: county name in California (chr)
- latitude: geographic coordinate of cluster_id centroid (dbl)
- longitude: geographic coordinate of cluster_id centroid (dbl)
- cluster_id: identification number of cluster sampling unit (chr)
- hydro_region: hydrological region the cluster_id is located in, hydrological classifications were done by California Department of Water Resources (chr)
- year: year of sampling (int)
- month: numeric of calendar month of sampling (int)
- pdsi_raw: untransformed palmer drought severity index (PDSI), positive numbers reflect wetter landscape conditions whereas negative numbers reflect drier landscape conditions. Typical index ranges from -4 to 4.
- pdsi_lag0: scaled and averaged palmer drought severity index (PDSI) (pdsi_raw) for current sampling period at year-month-cluster_id
- pdsi_lag1: scaled and averaged palmer drought severity index (PDSI) for 1-month prior to sampling period at year-month-cluster_id
- pdsi_lag2: scaled and averaged palmer drought severity index (PDSI) for 2-months prior to sampling period at year-month-cluster_id
- pdsi_lag3: scaled and averaged palmer drought severity index (PDSI) for 3-months prior to sampling period at year-month-cluster_id
- pdsi_lag4: scaled and averaged palmer drought severity index (PDSI) for 4-months prior to sampling period at year-month-cluster_id
- temp_raw: untransformed near-surface air temperature (Celsius)
- temp_lag0: scaled and averaged temperature (temp_raw) for current sampling period at year-month-cluster_id
- temp_lag1: scaled and averaged temperature for 1-months prior to sampling period at year-month-cluster_id
- temp_lag2: scaled and averaged temperature for 2-months prior to sampling period at year-month-cluster_id
- temp_lag3: scaled and averaged temperature for 3-months prior to sampling period at year-month-cluster_id
- temp_lag4: scaled and averaged temperature for 4-months prior to sampling period at year-month-cluster_id
- bird_raw: untransformed bird community competence index, larger positive numbers reflect bird communities that are more likely to amplify West Nile virus transmission. The originial index was calculated in MacDonald et al. 2020 (Ecology). Index values are positive, non-zero values.
- bird_lag0: scaled and averaged bird community competence index (bird_raw) for current sampling period at year-month-cluster_id
- bird_lag1: scaled and averaged bird community competence index (bird_raw) for 1-month prior to sampling period at year-month-cluster_id
- bird_lag2: scaled and averaged bird community competence index (bird_raw) for 2-months prior to sampling period at year-month-cluster_id
- bird_lag3: scaled and averaged bird community competence index (bird_raw) for 3-months prior to sampling period at year-month-cluster_id
- bird_lag4: scaled and averaged bird community competence index (bird_raw) for 4-months prior to sampling period at year-month-cluster_id
- soilmoisture_raw: untransformed near-surface soil moisture (m3/m3). Values are positive and range 0.01-0.4 in our study.
- soilmoisture_lag0: scaled and averaged soil moisture (soilmoisture_raw) for current sampling period at year-month-cluster_id
- soilmoisture_lag1: scaled and averaged soil moisture for 1-month prior to sampling period at year-month-cluster_id
- soilmoisture_lag2: scaled and averaged soil moisture for 2-months prior to sampling period at year-month-cluster_id
- soilmoisture_lag3: scaled and averaged soil moisture for 3-months prior to sampling period at year-month-cluster_id
- soilmoisture_lag4: scaled and averaged soil moisture for 4-months prior to sampling period at year-month-cluster_id
- et_raw: untransformed actual evapotranspiration (mm). Values are positive and range 0.00-148.63 in our study.
- et_lag0: scaled and averaged evapotranspiration (et_raw) for current sampling period at year-month-cluster_id
- et_lag1: scaled and averaged evapotranspiration for 1-month prior to sampling period at year-month-cluster_id
- et_lag2: scaled and averaged evapotranspiration for 2-months prior to sampling period at year-month-cluster_id
- et_lag3: scaled and averaged evapotranspiration for 3-months prior to sampling period at year-month-cluster_id
- et_lag4: scaled and averaged evapotranspiration for 4-months prior to sampling period at year-month-cluster_id
- jrc_5km_raw: untransformed standing surface water (%) represented as percentage of pixels covered by water surrounding within a 5 km buffer zone of our sampling cluster_id. Values are positive and range 0.00-68.67 in our study.
- jrc_5km_lag0: scaled and averaged standing surface water (jrc_5km_raw) for current sampling period at year-month-cluster_id
- jrc_5km_lag1: scaled and averaged standing surface water for 1-month prior to sampling period at year-month-cluster_id
- jrc_5km_lag2: scaled and averaged standing surface water for 2-months prior to sampling period at year-month-cluster_id
- jrc_5km_lag3: scaled and averaged standing surface water for 3-months prior to sampling period at year-month-cluster_id
- jrc_5km_lag4: scaled and averaged standing surface water for 4-months prior to sampling period at year-month-cluster_id
- jrc_10km_raw: untransformed standing surface water (%) represented as percentage of pixels covered by water surrounding within a 10 km buffer zone of our sampling cluster_id. Values are positive and range 0.00-68.67 in our study.
- jrc_10km_lag0: scaled and averaged standing surface water (jrc_10km_raw) for current sampling period at year-month-cluster_id
- jrc_10km_lag1: scaled and averaged standing surface water for 1-month prior to sampling period at year-month-cluster_id
- jrc_10km_lag2: scaled and averaged standing surface water for 2-months prior to sampling period at year-month-cluster_id
- jrc_10km_lag3: scaled and averaged standing surface water for 3-months prior to sampling period at year-month-cluster_id
- jrc_10km_lag4: scaled and averaged standing surface water for 4-months prior to sampling period at year-month-cluster_id
Code/software
software:
version - R 4.6.0 (2026-04-24)
os - macOS Tahoe 26.4.1
system - aarch64, darwin23
packages:
- broom_1.0.12
- dplyr_1.2.1
- calecopal_0.1.0
- fixest_0.14.1
- forcats_1.0.1
- ggplot2_4.0.3
- ggpubr_0.6.3
- ggspatial_1.1.10
- here_1.0.2
- janitor_2.2.1
- lubridate_1.9.5
- maps_3.4.3
- modelsummary_2.6.0
- paletteer_1.7.0
- patchwork_1.3.2
- purrr_1.2.2
- readr_2.2.0
- scales_1.4.0
- stringr_1.6.0
- terra_1.9-27
- tidyr_1.3.2
- tibble_3.3.1
- tigris_2.2.1
- tidycensus_1.7.5
- tidyverse_2.0.0
- wesanderson_0.3.7
Access information
Table_S1.txt: Information on how to access the raw data used for figures and analysis. With the exception of the mosquito surveillance data provided by CalSurv, all other data are publicly available.
1. California Vector-borne Disease Surveillance System (CalSurv Gateway). Mosquito surveillance data. California Department of Public Health. Data request can be submitted to: https://ca.vectorsurv.org/.
2. Climatologies at High Resolution for the Earth’s Land Surface (CHELSA) v2.1. Temperature data. Associated paper: https://doi.org/10.1038/sdata.2017.122; Data available at: https://www.chelsa-climate.org/models/chelsa.
3. Abatzoglou, J. T. gridMET: Gridded surface meteorological data. Associated paper: https://doi.org/10.1002/joc.3413; Data available at: https://www.climatologylab.org/gridmet.html.
4. Martens, B. et al. GLEAM v4.2b evapotranspiration data. Associated paper: https://doi.org/10.1038/s41597-025-04610-y; Data available at: https://www.gleam.eu/
5. European Space Agency Climate Change Initiative. Soil moisture dataset. Associated paper: https://ieeexplore.ieee.org/document/9165186/; Data available at: https://climate.esa.int/en/projects/soil-moisture/
6. Joint Research Centre (JRC) Surface Water. Global Surface Water dataset. Associated paper: https://doi.org/10.1038/nature20584; Data available at: https://global-surface-water.appspot.com/download.
7. Cornell Lab of Ornithology. eBird Status and Trends data products. Data available at: https://science.ebird.org/en/status-and-trends/data-access. Index produced by MacDonald et al. 2024 https://doi.org/10.1002/ecy.4420.
8. PRISM Climate Group, Oregon State University. PRISM climate data (normals). Associated paper: https://doi.org/10.1002/joc.3413; Data available at: https://prism.oregonstate.edu/normals/.
9. Land IQ. Land use and irrigation mapping data. Data available at: https://www.landiq.com/land-use-mapping.
10. California Department of Water Resources (CDWR). Faunt, C.C. 2012. Alluvial Boundary of California’s Central Valley Survey data. US Geological Survey. Data available at: https://www.sciencebase.gov/catalog/item/6314056bd34e36012efa2bf4.
11. California Department of Water Resources (CDWR). Hydrological Region boundaries. Spatial Data Standard v3.1. 2019. Data available at: https://gis.data.ca.gov/maps/3ce65cc69e0048169396b3610d6256b0/explore?location=36.824706%2C-119.742694%2C6
12. National Hydrography Dataset (NHD). US Geological Survey. Data available at: https://www.usgs.gov/national-hydrography/national-hydrography-dataset.
13. National Wetlands Inventory (NWI). US Fish and Wildlife Service. 2004. Data available at: https://www.fws.gov/program/national-wetlands-inventory.
