DOI: 10.5061/dryad.7wm37pw54
title: Data and code to compare acoustic ARU and point count data for shorebird species presence across Alaska's North Slope

description: The rapid decline of global bird populations demands effective monitoring
 approaches, particularly for shorebirds, which are experiencing alarming
 population decreases. Most shorebird surveys rely on traditional
 visual survey methods, but face significant challenges in remote locations
 like the Arctic, where high costs and logistical constraints limit survey
 effort and duration. We compared visual area search surveys and
 acoustic recording units (ARUs) for monitoring shorebird species in two
 large regions of Alaska's Arctic Coastal Plain in 2022-2023. We
 deployed ARUs at 129 sites (54 in Arctic National Wildlife Refuge, ANWR,
 75 in Teshekpuk Lake Special Area, TESH; ~11,000-18,000 hours of acoustic
 recordings) where visual surveys were also conducted (~83-113 hours in
 total), and examined differences in species richness, encounter rates, and
 predicted distribution patterns for 12 shorebird species or species
 groups. ARUs detected 50% higher species richness in ANWR and 33%
 at TESH (approximately 2 additional species per plot); the odds of
 encountering a species were more than twice as high with ARUs compared to
 visual surveys (95%CI = 1.64-5.57). Species accumulation curves
 demonstrated that ARUs required fewer plots to detect the maximum number
 of species. Habitat models based on ARU data produced similar predicted
 distributions to visual survey data but enabled modeling for additional
 species due to higher encounter rates. Different survey methods
 showed consistent spatial patterns between sites, with both methods
 detecting higher species richness and encounter rates in TESH than in
 ANWR. Our findings demonstrate that ARUs can effectively monitor
 shorebird communities in the Arctic, offering advantages in temporal
 coverage and synchronous data collection across large spatial extents. The
 performance of ARUs for detecting most shorebird species over long periods
 supports their integration into conservation monitoring programs,
 particularly in remote regions where traditional monitoring approaches are
 challenging to implement and sustain.
description: # Data and code to compare acoustic ARU and point count data for shorebird
 species presence across Alaska's North Slope Dataset DOI:
 [10.5061/dryad.7wm37pw54](10.5061/dryad.7wm37pw54) ## Description of the
 data and file structure This dataset contains counts and presence/absence
 data for acoustic and visual detections of 12 shorebird species at plots
 across Alaska’s Arctic Coastal Plain in June-August of 2022-2023 as part
 of the Program for Regional and International Shorebird Monitoring
 (PRISM). Acoustic data was collected using autonomous recording devices
 (Audiomoth recorders). Specifics of data collection (exact plot locations
 and dates) can be found in the manuscript associated with this dataset.
 This dataset also includes code and data related to the comparison of
 these two data collection methods (acoustic and visual) including the
 creation of species accumulation curves and species distribution models.
 Models included environmental data extracted from online sources; the
 specifics of these extractions is described in detail in the manuscript
 associated with this dataset. ### Files and variables #### File: code.zip
 **Description:** This zip file contains all code required to reproduce the
 analyses and figures in the manuscript associated with this dataset. These
 analyses were completed using the Spyder IDE (v5.5.1) and Python 3.11, or
 in R software version 4.2.3. Please note that TLSA referred to in all
 scripts is Teshekpuk Lake Special Area, or TESH in the manuscript
 associated with this code. The zip file includes the following:
 **-map_SorensonDice_similarity.py :** Python script to calculate and plot
 Sorenson-Dice similarity as color on maps (Fig. 2a in the corresponding
 manuscript), and calculate and compare species' richness (Table 1 in
 the corresponding manuscript). This code requires input * an ARU counts
 file for one of the sites (allCounts.csv) - a metadata file describing
 which ARUs are on which plots (*acoustic_monitoring_*.csv) * a visual
 counts file (*rapid_survey_data.csv) - verification data on species
 presence/absence (pres_verif.csv) **-plot_figureS4-5.py:** Code to produce
 Figure S4-5 in the manuscript associated with this dataset. This script
 requires as input: * an ARU counts file for one of the sites
 (allCounts.csv) - a metadata file describing which ARUs are on which plots
 (*acoustic_monitoring_*.csv) * a visual counts file
 (rapid_survey_data.csv) **ER_and_speciesAccum.R:** Code to reproduce
 figure 2b-c and all encounter rate calculations (including odds ratios and
 Figure S3) in the manuscript associated with this dataset. The encounter
 rate code blocks require as input allSpecPres.csv files from each site,
 which contain relevant info for both visual and ARU detections. The
 species accumulation code block requires accumData.csv files from each
 site. **all_SDMs_code.R:** Species distribution modelling code for
 manuscript associated with this dataset. This includes all models used to
 create Figure 3 and Figures S1-2. This script requires as input final
 environmental variables (elevation_merged.tif, waterdens_2.tif,
 landcov.grd, distance.tif) as well as allSpecPres.csv files for the
 associated site. #### File: data.zip **Description:** This zip file
 contains all data required to reproduce the analyses and figures in the
 manuscript associated with this dataset. Environmental variable data were
 extracted and compiled from online sources and using QGIS and R software
 as described in detail in the manuscript. Only final versions of these
 data used for species distribution models are uploaded here. Please note
 that TLSA referred to in all cases is Teshekpuk Lake Special Area, or TESH
 in the manuscript associated with this code. Species codes throughout the
 zip file are as follows: american golden-plover (amgp), bar-tailed godwit
 (barg), black-bellied plover (bbpl), dunlin (dunl), long-billed dowitcher
 (lbdo), least sandpiper (lesa), pectoral sandpiper (pesa), red-necked and
 red phalaropes (phalarope), semipalmated sandpiper (sesa), stilt sandpiper
 (stsa), western sandpiper (wesa), wilson's snipe (wisn). The zip file
 includes the following: **acoustic_monitoring_anwr.csv:** CSV of acoustic
 metadata for the Arctic National Wildlife Refuge (ANWR). Fields include *
 site- data recording site - plot- plot identity for recording *
 recorderID- acoustic ID for ARU at that plot - lat- latitude of the plot
 location * long- longitude of the plot location
 **acoustic_monitoring_tlsa.csv:** Same as acoustic_monitoring_anwr.csv but
 for Teshekpuk Lake Special Area data. **ANWR_2022_accumData.csv,
 ANWR_2022_PRISM_accumData.csv:** Presence/absence data for each species
 and survey plot in the Arctic National Wildlife Refuge in 2022. Each row
 represents a different survey plot. Cells have a '1' if the
 species was present and a '0' otherwise. The PRISM csv has
 presence/absence from visual survey data; the other csv has
 presence/absence based on ARU survey data. **TLSA_2023_accumData.csv,
 TLSA_2023_PRISM_accumData.csv:** same as ANWR versions but for survey
 plots in the Teshekpuk Lake Special Area in 2023.
 **ANWR_2022_allCounts.csv:** Counts of species' detections in each
 acoustic data file from the Arctic National Wildlife Refuge in 2022.
 Contains the following fields: * ARU name: unique ARU identifier from
 which the file originates - filename: name of the acoustic data file *
 species: 4-letter species code - counts: number of detections of that
 species in that data file **TLSA_2023_allCounts.csv:** Same as the ANWR
 version but for the Teshekpuk Lake Special Area in 2023.
 **ANWR_allSpecPres.csv:** Species presence from visual and ARU data from
 the Arctic National Wildlife Refuge in 2022. Fields are as follows: *
 plot: ARU name from the survey plot in question - lat: plot latitude *
 lon: plot longitude - col_PRISM: Presence or absence in visual data *
 spec: species 4-letter code - colARU_0: Presence or absence in ARU data
 from full summer * colARU_1: Presence or absence in ARU data from before
 July 5 (data used in rest of manuscript associated with this dataset) -
 colARU_2: Presence or absence in ARU data from only June 1 - June 16 *
 colARU_3: Presence or absence in ARU data from only the day of the visual
 survey **TLSA_allSpecPres.csv:** Same as for the Arctic National Wildlife
 Refuge, but for the Teshekpuk Lake Special Area
 **ANWR_PRISM_data_2022_rapid_survey_data.csv:** Raw visual survey data
 from survey plots in the Arctic National Wildlife Refuge. Fields include:
 * plot_id: Unique identifier of the survey plot - date: survey date *
 year: survey year - species_code: 4-letter species code * flyover: number
 of individuals of that species that flew over the plot during surveying -
 total_numb_individuals: total number of individuals of that species
 observed on plot, including breeding and non-breeding individuals as well
 as flyovers **TLSA_PRISM_data_2023_rapid_survey_data.csv:** Same as for
 the Arctic National Wildlife Refuge, but for the Teshekpuk Lake Special
 Area in 2023. **pres_verif.csv:** Verification data for ARU presence
 across all plots used in the manuscript associated with this dataset.
 Fields include the following: * site: Site of plot, either ANWR or TLSA -
 dep: ARU unique identifier for the ARU deployed at each plot at each site
 * amgp, etc: all other field are 4-letter species codes. Values in each
 cell in these fields are 'y' for species verified present,
 'n' for species verified absent. **env:** folder containing all
 final environmental data used in the species distribution models in the
 manuscript associated with this dataset. More information on the
 derivation of these variables can be found in the manuscript. This folder
 includes: **better_coast:** Shapefile and associated files for coastline
 data used in maps of species distribution models. This data was accessed
 using the Global Self-consistent, Hierarchical, High-resolution Geography
 Database
 ([https://www.soest.hawaii.edu/pwessel/gshhg/](https://www.soest.hawaii.edu/pwessel/gshhg/)) **distance.tif:** Distance to the coast for 1kmx1km grid cells covering Alaska's Arctic Coastal Plain **elevation_merged.tif:** Elevation for 1kmx1km grid cells covering Alaska's Arctic Coastal Plain **landcov:** Gridfile and associated files for landcover class data in 1kmx1km grid cells covering Alaska's Arctic Coastal Plain **temp2022.tif, temp2023.tif:** Average June temperature (in 2022 and 2023) for 1kmx1km grid cells covering Alaska's Arctic Coastal Plain **waterbod_lowres:** Shapefile and associated files for water body edges, used in mapping species distribution models **waterdens_2.tif:** Density of waterbodies in a 10 km radius of each 1kmx1km grid cell covering Alaska's Arctic Coastal Plain ## Code/software Code is described in the above section on files and variables (under code.zip)