Dataset DOI: 10.5061/dryad.c59zw3rm8

Description of the data and file structure

GENERAL INFORMATION

-------------------------------------------------

Dataset Title: Borrego Springs groundwater dependent ecosystem identification

Principal investigator: Dr. Travis Huxman, UC Irvine, thuxman@uci.edu

Co-investigators: Laurel Brigham, UC Irvine, brighaml@uci.edu; Nicole Fiore, UC Irvine, nmfiore@uci.edu

Contact person for questions: Dr. Travis Huxman, UC Irvine, thuxman@uci.edu

README version date: June 13, 2025

Date of data collection: 2023 - 2025

Information about geographic location of data collection: Borrego Springs, California (San Diego County)

Keywords used to describe the data topic: Borrego Springs Subbasin, Mesquite Bosque Habitat, Groundwater Dependent Ecosystems

Language information: These datasets are in English.

Information about funding sources that supported the collection of the data: This project was funded by the Prop 68 California Department of Water Resources Sustainable Groundwater Management Grant Program, administered by the Borrego Water District.

SHARING AND ACCESSING INFORMATION

-------------------------------------------------

Licenses or restrictions placed on the data: no licenses or restrictions are placed on this data.

METHODOLOGY AND DATA SPECIFIC INFORMATION

-------------------------------------------------

--Image Classification--

Image Classification products

Areas used for image classification

BS_classification_area.zip

Description: The area across which image classification of mesquite around the Borrego Sink were classified

cdl_classification_area.zip

Description: The area across which image classification of mesquite near Clark Dry Lake were classified

Training samples used for image classification

Training_samples_CDL.zip

Description: Training samples used to train the model in ArcGIS for classifying mesquite near Clark Dry Lake

Training_samples_BS.zip

Description: Training samples used to train the model in ArcGIS for classifying mesquite around the Borrego Sink

Image classification produced

2023_Mesquite_Classification_CDL_v021524.zip

Description: Image classification of mesquite near Clark Dry Lake

2023_Mesquite_Classification_BS_v71524.zip

Description: Image classification of mesquite near Borrego Springs

Habitat map produced

2023_Mesquite_Bosque_Map_BS_v12723.zip

Description: Map of mesquite bosque habitat near the Borrego Sink produced using the image classification

2023_Mesquite_Bosque_Map_CDL_v12723.zip

Description: Map of mesquite bosque habitat near Clark Dry Lake produced using the image classification

Image Classification methods

To develop more accurate and contemporary mapping of potential GDE we first conducted image classification of aerial images from 2016 to identify live mesquite trees (Neltuma odorata [formerly Prosopis glandulosa]) in the Borrego Springs Subbasin near the Borrego Sink. To identify the coverage of mesquite within our study area we used object-based supervised classification in ArcGIS Pro (v. 3.1.0) with the Support Vector Machine (SVM) as our supervised classification approach. We used the default settings for SVM within ArcGIS. We classified 0.7 m resolution National Agriculture Imagery Program imagery (NAIP) visualized in the near infrared as this provided greater contrast between the mesquite and perennial shrubs. The NAIP imagery came from 22 and 23 April 2016 and was mosaicked in Google Earth Engine. This year was selected because it was the closest year to SGMA implementation (2015) that contained high quality imagery when plants were active. 

We conducted the supervised image classification for our two primary sites separately. In the Borrego Springs Subbasin, we used the Palm Canyon Drive and Borrego Valley Road as our north and west bounds, respectively. To the south and east, we used the extent of mesquite within the Borrego Springs Subbasin as our bounds. At Clark Dry Lake, we included the expanse between the feet of the two mountain ranges bounding the lake to the east and west. Training samples took the form of polygons.

After classification, classes other than Live Mesquite were reclassified as Barren in order to simplify validation, as only the Live Mesquite category was of interest. For validation, we used 100 random assessment points per category (equal stratification for Live Mesquite and Barren) for each primary site).

The image classification was used alongside on the ground field observations to redraw the boundaries of mesquite bosque in the Borrego Sink area and Clark Dry Lake. To redraw the boundaries of the mesquite bosque we first selected only the polygons produced during image classification with an area greater than 5 m2 in order to minimize the presence of shrubs which may have been inaccurately classified. We next created a 5 m buffer around the resultant polygons. Then we aggregated the resultant polygons that were within 10 m from each other to include only polygons with a minimum size of 400 m2 after aggregation and a minimum hole size of 1000 m2. The buffering and aggregating steps were done to ensure we mapped a mesquite bosque ecosystem rather than individual, isolated mesquite trees. Next, to produce polygons with a simplified shape, we simplified the polygons with the “Retain Critical Bends (Wang-Müller)” simplification algorithm with a 25 m simplification tolerance and a minimum area of 1000 m2. We then eliminated polygon holes smaller than 50,000 m2 and used the dissolve tool to merge all overlapping polygons. Next, we aggregated polygons within 50 m of each other in order to better capture the mesquite bosque habitat, which includes interstitial space and associated understory vegetation in addition to live mesquite trees. Finally, as the habitat map methods may have excluded isolated individual trees, we ensured that any mesquite trees that were identified in the live mesquite tree map were also included in the habitat map.

--Isotopes--

File: all_soils_isotopes_coords.csv

Description: We sampled soils for soil water isotopic composition in order to better understand mesquite water source. Missing data: NA

Variables

• Collection_Date: Date the data were collected
• Year: Year of data collection
• Campaign_Month: Month of the sampling campaign (April, May, August)
• Site: Site 1-5
• Item: describes the type of sample
• Identifier: unique identifier for the soil sample
• Latitude: latitude of the collected sample
• Longitude: longitude of the collected sample
• Depth_cm: depth of the collected sample
• D2H: hydrogen isotopic composition of extracted water (unit: permille)
• D18O: oxygen isotopic composition of extracted water (unit: permille)
• Quality_Control_Notes: notes from the isotope processing lab

File: Nov23_WY_well_isotopes_coords_all.csv

Description: West Yost (a consulting firm) sampled several wells in the Borrego Springs monitoring network for isotopic composition in order to better understand mesquite water source. Missing data: NA

Variables

• Collection_Window: Date window during which the data were collected
• Year: Year of data collection
• Campaign_Month: Month of the sampling campaign (November)
• Collected_By: organization that collected the samples
• State_Well_Number: the CA state well number for the sampled wells
• Identifier: unique well identifier
• Item: describes the type of sample
• D2H: hydrogen isotopic composition of sampled water (unit: permille)
• D18O: oxygen isotopic composition of sampled water (unit: permille)
• Quality_Control_Notes: notes from the isotope processing lab

File: all_isotopes_well_creek.csv

Description: We sampled wells and Coyote Creek for water isotopic composition in order to better understand mesquite water source. Missing data: NA

Variables

• Collection_Date: Date the data were collected
• Year: Year of data collection
• Campaign_Month: Month of the sampling campaign (April, May, August)
• Site: Sites 5-7, 12, 14 
• Item: describes the type of sample
• State_Well_Number: CA state well number
• Identifier: unique identifier for the well or Coyote Creek sample
• Replicate: replicate number for each identifier
• Latitude: latitude of the collected sample
• Longitude: longitude of the collected sample
• D2H: hydrogen isotopic composition of sampled water (unit: permille)
• D18O: oxygen isotopic composition of sampled water (unit: permille)
• Quality_Control_Notes: notes from the isotope processing lab

File: all_trees_isotopes_coords.csv

Description: We sampled mesquite trees and creosote and saltbush shrubs for water isotopic composition in order to better understand mesquite water source. Missing data: NA

Variables

• Collection_Date: Date the data were collected
• Year: Year of data collection
• Campaign_Month: Month of the sampling campaign (April, May, August, November)
• Site: Sites 1-15
• Item: describes the type of sample
• Identifier: unique identifier for the tree or shrub
• Latitude: latitude of each tree or shrub
• Longitude: longitude of each tree or shrub
• Species: species of tree or shrub
• D2H: hydrogen isotopic composition of extracted water (unit: permille)
• D18O: oxygen isotopic composition of extracted water (unit: permille)
• Quality_Control_Notes: notes from the isotope processing lab

Isotope methods

To assess the source of water present in plant leaves, we collected twigs, soil, and groundwater samples in 2023 and 2024. In 2023, we collected twigs from 12 mesquite trees across Sites 1 through 5 for a total of 60 trees (Figure 2.9). In 2024 we sampled the same 60 mesquite trees plus three co-located creosote shrubs (Larrea tridentata) from Sites 1, 2, 3, and 5, and three co-located saltbush shrubs (Atriplex lentiformis) at Site 4. There was not sufficient creosote present at Site 4 for sampling. The creosote and saltbush are comparatively shallow-rooted species that are not expected to directly access groundwater and these species serve as a comparison to the mesquite trees. In 2024 we also collected an additional six trees at Site 1 and five trees at Site 3, and established Sites 6 through 15 across which sampled an additional 55 mesquite trees for a total of 66 new trees to increase our spatial representation (Figure 2.10, Table 2.3). Twigs were collected in 2023 on 10 through 12 April, 31 May and 1 June, 15 and 16 August, 1 through 3 November and in 2024 on 24 and 25 April, 20 and 21 May, and 14 and 15 August. 

To assess surface soil water as a water source for sampled plants, we sampled soils at one location at each of the five sites at the following depth ranges: 0-10 cm, 10-40 cm, 40-70 cm, 70-100 cm, 100-150 cm. In 2023, soil samples were collected from Sites 1 through 5 on 10 through 12 April 2023 and at the primary sites on 31 May and 1 June 2023. In 2024, soil samples were collected at Sites 1 through 5 on 24 and 25 April, 20 and 21 May, and 14 and 15 August. 

To assess groundwater as a water source for sampled plants, we collected samples from a well on State Parks land near Clarks Dry Lake (State Well ID: 10S07E07C001S) and had samples collected by West Yost throughout Borrego Springs (Figure 2.11). The well in Anza-Borrego State Park near Clark Dry Lake (State Well ID: 10S07E07C001S) was sampled in 2023 on 11 April and 31 May and in 2024 on 24 and 25 April, 20 and 21 May, and 14 and 15 August while West Yost sampled seven wells between 12 and 16 November 2023. During the May 2024 sampling campaign we also sampled five wells at the Wastewater Treatment Plant, one well on private property near the Borrego Sink, and took three water samples from Coyote Creek. We sampled rainfall from storms on 22 December 2023, 24 January 2024, and 14 February 2025.

Sample collection for isotopic analysis

Plant water

Mature mesquite and creosote twigs with fully expanded leaves were selected from sunlit branches near the outer canopy. Twigs were cut approximately in 1–2 cm (0.39–0.79 in) lengths, with a maximum thickness of 1.2 cm (0.47 in) diameter. To minimize the effects of evaporation of water from the twigs, vials were quickly filled with cut twigs and capped with minimal headspace. Vials were then sealed with parafilm and were refrigerated until analysis. 

Soil surface water

Soils were collected within two times the approximate diameter at breast height of tagged mesquite trees. Soil cores were augered using an 8 cm (3.15 in) diameter and 10 cm (3.94 in) tall manual auger. To minimize the effects of evaporation of water from the soil, jars were quickly filled and capped with minimal headspace, sealed with parafilm, and refrigerated until analysis. 

Groundwater

We used a bailer to sample the well near Clark Dry Lake and fill one dram glass vials which were quickly filled and capped with minimal headspace, sealed with parafilm, and refrigerated until analysis. West Yost collected samples from both non-pumping wells (i.e., monitoring wells) and active pumping wells (i.e., private wells). For non-pumping wells, a portable pump is lowered slowly down the well, positioning the intake at the predetermined selected sampling depth. For active pumping wells, samples were taken from the designated sampling outlet. The location of this outlet varies by well.

Analysis of water isotopes in field samples

Water isotopes were analyzed by the University of Wyoming Stable Isotope Facility. Water samples were analyzed for their δ¹⁸O and δ²H isotopic composition using a Thermo Scientific Delta V Plus isotope ratio mass spectrometer coupled to a Thermo Flash HT high-temperature conversion elemental analyzer (TC/EA) via a ConFlo IV open split interface at the University of Wyoming Stable Isotope Facility. Samples were introduced into the TC/EA via a Thermo AI 1310 liquid autosampler. The TC/EA converted water molecules into CO and H₂ gases at 1420°C. These gases were separated chromatographically and introduced into the mass spectrometer for isotopic analysis. Quality assurance and quality control (QA/QC) procedures, including the use of reference materials and statistical analysis, were employed to ensure data accuracy and precision.

--Water Potential--

Water potential products

WaterPotential_GDE_2023_24.csv

Description: We measured predawn and midday water potential of mesquite and creosote to assess water availability and water stress. Missing data: NA

Variables

• Collection_Date: Date the data were collected
• Year: Year of data collection
• Campaign_Month: Month of the sampling campaign (April, May, August)
• Site: 1 (Borrego Springs) or 5 (Clark Dry Lake)
• Identifier: Unique ID for each tree or shrub
• Latitude: latitude of each tree or shrub
• Longitude: longitude of each tree or shrub
• Species: species of tree or shrub
• Time_of_Day: midday or predawn
• Replicate1_mPa: first replicate twig (unit: megapascals)
• Replicate2_mPa: second replicate twig (unit: megapascals)
• Replicate3_mPa: third replicate twig (unit: megapascals)
• Notes: notes from data collection

Water potential methods

To assess water availability and water stress of mesquite we assessed predawn and midday water potential on 24 mesquite trees in 2023 and 24 mesquite trees and six creosote shrubs in 2024. These mesquite and creosote were located at Sites 1 and 5, our primary Borrego Sink and Clark Dry Lake sites, respectively. Plants were sampled in 2023 on 10 through 12 April, 31 May and 1 June, 15 and 16 August and in 2024 on 24 and 25 April, 20 and 21 May, and 14 and 15 August. 

We collected three twigs per tree using the protocol described by Rodriguez-Dominguez et al. (2022). Briefly, we collected a twig containing several leaves, placed it into a plastic bag which was nested within a larger plastic bag containing a moist paper towel, and then placed it into a cooler such that the bag did not touch the ice packs. Midday water potential was assessed between 11:15 am and 1:15 pm and predawn water potential was assessed between 3:00 and 5:30 am. In the lab, we used a Scholander-style pressure bomb (PMS Instrument Company, Corvallis, OR, USA) to determine water potential, noting the pressure at the first sign of water.

--Live and Dead Mesquite Cover--

Live and Dead Mesquite Cover Products

File: GDE_crosshair_dryad_2023.csv

Description: Live and dead mesquite cover at two study sites collected in April 2023. Missing data: NA

Variables

• Date: Date the data were collected
• Site: Either 1 (Borrego Springs) or 5 (Clark Dry Lake)
• Identifier: unique ID for each crosshair transect center point
• Crosshair_Center_Latitude: Latitude of the crosshair center point
• Crosshair_Center_Longitude: Longitude of the crosshair center point
• LiveMesquite: the number of live mesquite encountered
• DeadStanding: the number of dead standing mesquite encountered
• DeadDown: the number of dead down mesquite encountered

Live and Dead Mesquite Cover Methods

To assess the cover of live and dead mesquite trees, two crosshair transects composed of four 25 m belt transects (2 m wide) were randomly placed within mesquite bosque at each of the two primary sites (Figure 3.10). The center of the crosshair point was located in the field using GPS, and each of the belt transects were walked with a 2 m dowel for 25 m in each cardinal direction. Live, dead, and standing dead mesquite that intersected the 2 m dowel were counted between 12 and 14 April 2023. 

--ET Sensors--

ET Sensor Products

GDE_ET_Sensor_Data.zip

• Metadata_GDE_ET_sensor_Data.xlsx
• Site_1_58868_LI710all_2025-04-30T09-12.csv
• Site_4_58869_LI710all_2025-04-30T10-48.csv
• Site2_58866_LI710all_2025-04-30T16-49_comb.csv
• Site5_58867_LI710all_2025-04-30T11-28.csv

ET Sensor Methods

Please see the metadata file (Metadata_GDE_ET_sensor_Data.xlsx) for detailed dataset file overviews.

--Remote Sensing--

Remote Sensing Products

GDE_Remote_Sensing_Products.zip

• Metadata_GDE_Remote_Sensing_Products.xlsx
• 1984_2015_NDVIChange_MKtau.zip
• 2024_DepthToGroundwater_ftbgs.zip
• 2024_GDEBehavior.zip
• 2024_MesquiteBosqueProductivity.zip
• SGMA_NDVIChange_MKtau.zip

Remote Sensing Methods

Please see the metadata file (Metadata_GDE_Remote_Sensing_Products.xlsx) for detailed dataset file overviews.

--Species inventory and monitoring plan--

File: 2009-2025_Mesquite_Bosque_Species_Inventory_-_ParticipatoryScienceSDNHM.csv

**Description: **a comma-delimited file containing the Participatory Science and SDNHM data subset for the 2009-2025 Mesquite Bosque Species Inventory. It includes species documented in Borrego Springs and Clark Dry Lake Mesquite Bosques by eBird, iNaturalist, Christmas Bird Count, and San Diego Natural History Museum (SDNHM).

Variables

• Source: participatory science platform (iNat, eBird), Borrego Springs Christmas Bird Count (CBC), San Diego Natural History Museum (SDNHM) 
• Method of Observation: how the observation was made (e.g., photo, reported)
• Location: either BS (Borrego Springs) or CDL (Clark Dry Lake)
• Common Name: species common name
• Scientific Name: species scientific name
• Taxa: type of taxa (birds, mammals, reptiles, invertebrates)Status: conservation status
• List/Organization: where the conservation status came from
• Comments: optional notes

File: 2023-2025_Mesquite_Bosque_Species_Inventory_-_UCIObservations.csv

**Description: ** a comma-delimited file containing the UCI Observation data subset for the 2009-2025 Mesquite Bosque Species Inventory. It includes species documented in Borrego Springs and Clark Dry Lake Mesquite Bosques by camera traps, avian point count, invertebrate beat sheet, and invertebrate light trap surveys.

Variables

• Observation Method: survey type
• Location: either BS (Borrego Springs) or CDL (Clark Dry Lake)
• Common Name: species common name
• Scientific Name: species scientific name
• Taxa: type of taxa (birds, mammals, reptiles, invertebrates)
• Status: conservation status
• List/Organization
• Species Observation Exclusive to UCI: yes or no, was this observation only detected through the current study's work or was it found in the participatory science dataset
• Comments: optional notes

File: 2023-2025_CSV_Camera_Processing.csv

Description: a comma-delimited file containing data from camera traps. Data are from May 2023 to December 2024.

Variables

• Camera Start Date: date the camera was deployed in the format YYYY-MM-DD
• Folder Name: unique identifier for each deployment; notes camera date and camera name
• Camera End Date: date the camera deployment ended in the format YYYY-MM-DD
• CameraName: unique camera ID denoting bosque and camera number
• Location Code: unique ID for each camera placement
• Image: number of image as it appears in the relevant camera folder
• Species: common name of the observed species
• SpeciesCode: 4-letter alpha code for species observed
• # of Individuals: how many individuals of that species were observed
• Date: date the image was taken in the format YYYYMMDD
• Time: time the image was taken in 24-hour format
• Image Quality/Presentation Use: scale for rating image quality; 1 = good, clear image that can be used for presentations; 2= okay, animal may be partially obscured or blurry; 3 = not good, animal is hard to see or too blurry to identify
• Notes: optional notes on the observation
• DaysActive: additional column for later ease of analysis

File: Wildlife_Camera_Trap.qmd

Description: A Quarto document for analysing camera trap data. Uses Rstudio version 4.3.2 and the vegan package version 2.6-10

File: Bird_Survey_Data.csv

Description: a comma-delimited file containing total counts for the avian point count surveys from December 2023 to May 2025.

Variables

• Date: Date survey was conducted
• Month/Year: Date including Month and Year
• Time: Start time of the survey
• Surveyors: Surveyors who surveyed that day
• Site: Survey site ID
• Point: What survey point within the survey site
• Temp_F: Temperature in Fahrenheit
• Cloud_Cover: Percentage of cloud cover in the sky
• Wind_Speed: Wind speed in MPH
• Survey_Point: What survey point within the survey site
• Species: 4 letter banding code for bird observed
• Distance_m: distance of how far the bird was observed from you
• Notes: optional, additional survey notes
• Resident: Indicate whether bird is a year-round resident or is only migrating through or seasonally

File: Bird_Survey_R_data.qmd

Description: A Quarto document for analyzing the avian point count survey data using R Statistical Software v4.5.0. Uses the vegan and lmertest packages.

File: GDE_eBird.qmd

Description: A Quarto document for filtering data from eBird using R Statistical Software v.4.5.0. Uses the auk package.

References

===========

Kuznetsova A, Brockhoff PB, Christensen RHB (2017). “lmerTest Package: Tests in Linear

Mixed Effects Models.”Journal of Statistical Software, 82(13), 1-26.

doi:10.18637/jss.v082.i13 https://doi.org/10.18637/jss.v082.i13.

Matthew Strimas-Mackey, Eliot Miller, and Wesley Hochachka (2023). auk: eBird Data

Extraction and Processing with AWK. R package version 0.7.0.

https://cornelllabofornithology.github.io/auk/

Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin P, O'Hara R, Solymos P,

Stevens M, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D,

Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista H, FitzJohn R, Friendly M,

Furneaux B, Hannigan G, Hill M, Lahti L, McGlinn D, Ouellette M, Ribeiro Cunha E,

Smith T, Stier A, Ter Braak C, Weedon J (2024). _vegan: Community Ecology Package_.

R package version 2.6-8, https://CRAN.R-project.org/package=vegan.

R Core Team (2025). _R: A Language and Environment for Statistical Computing_. R

Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Rodriguez-Dominguez, C. M., Forner, A., Martorell, S., Choat, B., Lopez, R., Peters, J. M. R., 

Pfautsch, S., Mayr, S., Carins-Murphy, M. R., McAdam, S. A. M., Richardson, F., 

Diaz-Espejo, A., Hernandez-Santana, V., Menezes-Silva, P. E., Torres-Ruiz, J. M., Batz, T. A., & 

Sack, L. (2022). Leaf water potential measurements using the pressure chamber: Synthetic 

testing of assumptions towards best practices for precision and accuracy. *Plant Cell and *

Environment, 45(7), 2037–2061. https://doi.org/10.1111/pce.14330

Access information

Other publicly accessible locations of the data:

• NA

Data was derived from the following sources:

• National Agriculture Imagery Program
• Sentinel-2 Satellite Data
• The USGS/NASA Landsat Program
• The USGS 3D Elevation Program (3DEP)