DOI: 10.5061/dryad.c59zw3rm8
title: Borrego Springs groundwater dependent ecosystem identification

description: The 2014 Sustainable Groundwater Management Act (SGMA) mandates that all
 beneficial users of groundwater, including environmental users such as
 Groundwater Dependent Ecosystems (GDEs), be considered in Groundwater
 Sustainability Plans (GSPs) with management strategies to avoid
 undesirable outcomes given continued groundwater extraction. The GDE
 Project addressed substantial data gaps which led to the exclusion of the
 mesquite bosque near the Borrego Sink as a Groundwater Dependent Ecosystem
 (GDE) in the Borrego Springs Subbasin Groundwater Management Plan (GMP).
 Through multiple lines of evidence—including field measurements, advanced
 sensor technologies, and remote sensing datasets—this study confirms that
 the mesquite bosque is connected to groundwater and functions as a
 beneficial user of groundwater.
description: # Borrego Springs Groundwater Dependent Ecosystem Investigation Dataset
 DOI: [10.5061/dryad.c59zw3rm8](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](mailto:thuxman@uci.edu) Co-investigators: Laurel
 Brigham, UC Irvine, [brighaml@uci.edu](mailto:brighaml@uci.edu); Nicole
 Fiore, UC Irvine, [nmfiore@uci.edu](mailto:nmfiore@uci.edu) Contact person
 for questions: Dr. Travis Huxman, UC Irvine,
 [thuxman@uci.edu](mailto: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). | Acronym | Name | | :------ | :---------------------------------------------------- | | BLM | Bureau of Land Management  | | BS | Borrego Springs | | CBC | Christmas Bird Count, Audubon Society | | CDF | California Department of Forestry and Fire Protection | | CDFW | California Department of Fish and Wildlife | | CDL | Clark Dry Lake | | CESA | California Endangered Species Act | | CNDDB | California Natural Diversity Database | | CRPR | California Rare Plant Rank | | ESA | Endangered Species Act, Federal | | GDE | Groundwater Dependent Ecosystem | | IUCN | International Union for Conservation of Nature | | SDNHM | San Diego Natural History Museum | | UCI | University of California Irvine | | UNK | Unknown | | USFWS | United States Fish and Wildlife Service | | Source | Date Range | | :------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | iNat  | 2009 through February 2025, including the UCI Mesquite Bosque GDE Capstone Project iNat Group from 11/01/2023-01/30/2025 and the 2023 GDE BioBlitz iNat Group on 11/01/2023. | | iNat (Plants) | Species verified on the SDNHM checklists as of February 2025, including species observed by the 2023 GDE BioBlitz iNat Group and the UCI Mesquite Bosque GDE Capstone Project iNat Group (dates above).  | | eBird | January 2015-January 2025 | | SDNHM | 03/09/2023-04/15/2024 | | CBC | 2014 & 2017 at both locations (CBC's "Clark Lake", "North Mesquite", and "South Mesquite" areas).  | | Note: | The iNat source includes data from the UCI Mesquite Bosque GDE Capstone Project iNat Group ([https://www.inaturalist.org/projects/uci-mesquite-bosque-gde-capstone-project](https://www.inaturalist.org/projects/uci-mesquite-bosque-gde-capstone-project)) and from the 2023 GDE BioBlitz iNat Group ([https://www.inaturalist.org/projects/2023-gde-bioblitz](https://www.inaturalist.org/projects/2023-gde-bioblitz)). | ##### 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. | Observation Method | Date Range | | :----------------- | :-------------------- | | Camera Trap | 05/31/2023-12/11/2024 | | Bird Point Count | 12/16/2023-01/26/2025 | | Bird Incidental | 12/16/2023-01/26/2025 | | Beat Sheet  | 01/24/2025-01/26/2025 | | Light Trap | 01/24/2025-01/26/2026 | ##### 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](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/](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](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/](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](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)