Data and code from: First insights into the movement patterns of maned wolf with sarcoptic mange
Data files
Aug 03, 2026 version files 572.56 KB
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01_data_cleaning.R
2.46 KB
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01_function_ctmm_remove_outliers.R
1.26 KB
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02_function_ctmm_seasonal.R
2.81 KB
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02_home_range_function_ctmm.R
1.18 KB
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03_overlap_ctmm.R
2.05 KB
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03_resist_avg_jags.R
735 B
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04_jags_tssf.R
924 B
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04_lulc_prop_TEHS.R
9.59 KB
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05_prep_data_to_run_TEHS.R
10.64 KB
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06_run_TEHS_models.R
9.58 KB
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07_inspect_TEHS_results.R
4.61 KB
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gps_maned_wolf.rds
141.25 KB
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gps_no_outliers_final.csv
376.80 KB
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README.md
8.66 KB
Abstract
This dataset contains GPS telemetry data of two female maned wolves (Chrysocyon brachyurus) in Southeastern Brazil. One female was clinically diagnosed with mange infestation (symptomatic), and the other was healthy. The dataset was compiled to investigate how female maned wolf movement patterns are influenced by their health status. This is the first study to describe how the largest canid in South America responds to sarcoptic mange infestation. This dataset and the associated code allow researchers to reproduce the analyses conducted by Meiga et al., including home range, home range overlap, distance moved, habitat selection, and time spent in different habitats according to health status.
This Dryad data package contains the analytical scripts, documentation, and the minimum input data required to reproduce the numbered analytical workflow associated with the article First insights into the movement patterns of maned wolves with sarcoptic mange.
The package includes:
- GPS telemetry data used in the analyses.
- Numbered R scripts for data cleaning, home-range estimation, home-range overlap, and Time-Explicit Habitat Selection analyses.
- Helper functions required by the analytical scripts.
The analytical scripts use short, project-relative paths. Because the files are hosted at the top level of the Dryad deposit, users must recreate the directory structure expected by the scripts before running the workflow.
Directory structure data
All files in this Dryad data package are hosted at the top level of the deposit.
Data files
gps_maned_wolf.rds: Raw GPS telemetry data read by01_data_cleaning.R.gps_no_outliers_final.csv: GPS telemetry data after outlier removal, used by02_home_range_function_ctmm.Rand04_lulc_prop_TEHS.R.
Analytical scripts
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01_data_cleaning.R: Reads the raw GPS telemetry data, removes locations identified as outliers, and creates:data/processed/gps_no_outliers_final.csv -
02_home_range_function_ctmm.R: Uses the cleaned GPS data to fit movement models and estimate home ranges with thectmmpackage. -
03_overlap_ctmm.R: Calculates home-range overlap between the two monitored individuals. -
04_lulc_prop_TEHS.R: Extracts and summarizes land use and land cover information used in the Time-Explicit Habitat Selection analyses. -
05_prep_data_to_run_TEHS.R: Prepares the movement and land-use and land-cover data for the Time-Explicit Habitat Selection models. -
06_run_TEHS_models.R: Fits the Time-Explicit Habitat Selection models using JAGS. -
07_inspect_TEHS_results.R: Reads, summarizes, and inspects the results produced by the Time-Explicit Habitat Selection models.Run the scripts in numerical order. Each script writes the derived files required by the following step, so
output/is created locally as the workflow progresses. Derived files in this directory are not included in the Dryad deposit because they can be regenerated by running the workflow.
Helper-function scripts
- The following scripts contain functions called by the main analytical workflow.
01_function_ctmm_remove_outliers.R: Helper functions used by01_data_cleaning.Rto identify and remove GPS-location outliers.02_function_ctmm_seasonal.R: Helper functions used by02_home_range_function_ctmm.Rto fit movement models and estimate home ranges.03_resist_avg_jags.R: Helper functions used by06_run_TEHS_models.Rto fit and summarize the JAGS habitat-selection models.04_jags_tssf.R: Additional JAGS functions and model specifications used by06_run_TEHS_models.R.
Files and variables
File: gps_maned_wolf.rds
Description: An R data frame containing 3,414 GPS telemetry records from two GPS-collared maned wolves (Chrysocyon brachyurus) and 15 variables.
Columns contain:
| Variable | Definition |
|---|---|
animal_id |
Unique identifier of the GPS-collared individual. |
timestamp |
Date and time of the GPS telemetry record, in UTC. |
acquisition_start_time |
Date and time when the GPS-location acquisition attempt started, in UTC. |
gps_fix_time |
Date and time when the GPS location fix was obtained, in UTC. |
gps_fix_attempt |
Outcome/status of the GPS fix attempt. |
gps_latitude |
Geographic latitude of the GPS location, in decimal degrees (WGS 84). |
gps_longitude |
Geographic longitude of the GPS location, in decimal degrees (WGS 84). |
gps_utm_zone |
UTM zone reported by the GPS collar. |
gps_utm_northing |
UTM northing reported by the GPS collar. |
gps_utm_easting |
UTM easting reported by the GPS collar. |
receive_time |
Date and time when the telemetry record was received, in UTC. |
repetition_count |
Number of repeated transmissions associated with the telemetry record. |
predeployment_data |
Indicates whether the record was collected before collar deployment (Yes or No). |
x_5880 |
Projected X coordinate, in metres, using SIRGAS 2000 / Brazil Polyconic (EPSG:5880). |
y_5880 |
Projected Y coordinate, in metres, using SIRGAS 2000 / Brazil Polyconic (EPSG:5880). |
File: gps_no_outliers_final.csv
Description: A CSV file containing 3,376 GPS telemetry records from two GPS-collared maned wolves after outlier removal. The file contains 9 variables, and column names were standardized for compatibility with functions in the ctmm R package. This is the final dataset used in all subsequent movement analyses.
Columns contain:
| Variable | Definition |
|---|---|
timestamp |
Date and time of the retained GPS location, in UTC. |
longitude |
GPS longitude, in decimal degrees. |
latitude |
GPS latitude, in decimal degrees. |
t |
Timestamp expressed as seconds since 1 January 1970 (Unix time). |
class |
GPS-fix classification indicating the fix type and availability of horizontal and vertical precision information. |
HDOP |
Horizontal dilution of precision, a unitless GPS-location quality metric. |
z |
GPS-reported altitude associated with the location, in metres. |
VDOP |
Vertical dilution of precision, a unitless GPS-location quality metric. |
track |
Unique identifier of the focal maned wolf. |
MapBiomas land use and land cover raster
data/raster/mapbiomas_10m_sp_2023-0000000000-0000000000.tif: required by scripts 04 and 05, but intentionally not uploaded in this Dryad deposit because it is distributed separately by MapBiomas and is subject to the Mapbiomas terms of use. The data is available on MapBiomas page. Download the 2023, 10-m LULC raster for São Paulo from https://mapbiomas.org/ and save it using this exact path and filename before running those scripts.
Requirements for a full analytical rerun
The complete sequence from scripts 01 through 07 was validated from a clean output/ directory with R 4.6.1 and JAGS 4.3.1. Install JAGS at the system level, then ensure the following R packages are available: ctmm, lubridate, move2, tidyverse, terra, sf, jagsUI, and rjags.
install.packages(c(
"ctmm", "lubridate", "move2", "tidyverse", "terra", "sf",
"jagsUI", "rjags"
))
Instructions for reproducing the analyses
Download all files from the Dryad data package. Before running the workflow, recreate the directory structure expected by the scripts:
- Move
gps_maned_wolf.rdstodata/raw/gps_maned_wolf.rds. - Move
gps_no_outliers_final.csvtodata/processed/gps_no_outliers_final.csv. - Move the four helper-function scripts to the
accessory_functions/directory. - Create the
data/raster/directory and save the MapBiomas raster there using the filename specified below.
The numbered main analytical scripts should remain in the project root directory.
Annotated Code
This document presents the complete analytical workflow with explanatory notes, collapsible code blocks, and the final figures. Access the rendered HTML page at https://manedwolf.anameiga.com/.
The active and navigable version of the analytical materials is also maintained through the associated GitHub repository (https://github.com/anayoko/maned-wolf-mange-movement). The files included in this Dryad deposit represent the archived version associated with the published study.
Animal data collection
Two adult female maned wolves were monitored simultaneously for 12 months at the Itirapina Ecological Station in São Paulo, Brazil. The animals were tracked using GPS collars (TGW-4470-4 Iridium) programmed to record locations at three-hour intervals. Both females underwent veterinary clinical examinations to assess signs of sarcoptic mange.
Land use and land cover data
Land use and land cover information was obtained from the 2023 MapBiomas Collection 10 at 10-m spatial resolution, available on MapBiomas (https://brasil.mapbiomas.org/en/). The classes included in the analyses were savanna, forest, forest plantation, crops, mosaic of uses, and grassland. Grassland was used as the baseline category in the habitat selection model.
Data processing and movement analyses
GPS locations were organized and processed in R. Movement metrics were estimated using amt R package. Home range estimates were obtained using autocorrelated kernel density estimation implemented in the ctmm package. Habitat use was evaluated using a time-explicit habitat selection model.
The complete analytical workflow, including explanatory notes, annotaded code blocks, software information, and the resulting figures, is available as a rendered Quarto HTML document at https://manedwolf.anameiga.com/.
The analytical scripts are also publicy available through the associated GitHub repository https://github.com/anayoko/maned-wolf-mange-movement.