Dataset DOI: 10.5061/dryad.2rbnzs853

1. Dataset overview

This data package contains field-survey data, camera-trap deployment information, camera-trap activity records, environmental covariate datasets, and R scripts used to investigate spatial and temporal niche relationships between Himalayan Goral (Naemorhedus goral) and Barking Deer (Muntiacus muntjak) in Uttarkashi District, Western Himalaya, India.

The package supports three main components of the study:

1. description of species occurrence and camera-trap sampling locations;
2. analysis of diel activity patterns and temporal niche overlap; and
3. analysis of environmental/ecological niche relationships between Himalayan Goral and Barking Deer.

The public dataset contains 12 files:

Field and camera-trap data

• Sign__survey_final.xlsx
• Camera_trap_deployment.xlsx

Temporal activity data

• Year_data.csv
• Winter_season.csv
• Summer_season.csv

Environmental/niche data

• Goral_winter_final.csv
• Goral_summer_final.csv
• Barking_deer_summer_final.csv
• BD_winter.csv

R analysis scripts

• Activity_overlapcode.R
• watson_two_sample_test.R
• Niche_overlap_humboldt.R

The R scripts were originally uploaded as .txt files and were subsequently renamed with the .R extension so that their file type correctly reflects their contents.

2. Note on filenames used inside the R scripts

During the original analyses, several working filenames were used that differ from the final filenames used in the archived Dryad package. These names refer to analytical working copies rather than additional datasets.

Users attempting to reproduce the analysis should therefore use the archived filename equivalents given below.

Filename appearing in original code

Archived/public dataset filename

Description

• Year.csv / Year_data.csv

  Combined summer and winter activity dataset

• GOS_ENV.csv / Goral_summer_final.csv

  Himalayan Goral summer environmental dataset

• Goral_winter.csv or equivalent winter Goral working file / Goral_winter_final.csv

  Himalayan Goral winter environmental dataset

• Barking Deer summer working file, where used / Barking_deer_summer_final.csv

  Barking Deer summer environmental dataset

• Summer.csv or equivalent / Summer_season.csv

  Summer activity dataset

• Winter.csv or equivalent / Winter_season.csv

  Winter activity dataset

• Barking deer records / BD_winter.csv

  Barking Deer winter environmental dataset

3. Directory structure and preparation before running the code

The simplest way to reproduce the analyses is to download all files from Dryad and place them in a single local directory.

For example:

Goral_BarkingDeer_Dryad/

|-- README.md
|-- Camera_trap_deployment.xlsx
|-- Sign_survey_final.xlsx
|-- Year_data.csv
|-- Winter_season.csv
|-- Summer_season.csv
|-- Goral_winter_final.csv
|-- Goral_summer_final.csv
|-- Barking_deer_summer_final.csv
|-- Activity_overlapcode.R
|-- watson_two_sample_test.R
|-- Niche_overlap_humboldt.R

The R working directory should be set to the folder containing these files before running the scripts.

For example:

setwd("path/to/Goral_BarkingDeer_Dryad")

Alternatively, users may open an RStudio Project located in the downloaded dataset directory and use relative file paths.

Because some archived filenames contain spaces, filenames must be entered exactly as archived unless users rename the files and modify the corresponding read.csv() statements in the scripts.

For example:

goral.summer <- read.csv("Goral_summer_final.csv")

Users should not alter species names, coordinate fields, environmental-variable names, or time variables unless the corresponding script is also modified.

 4. Analytical workflow

The relationship among the data files and scripts is summarized below.

FIELD DATA
|
|-- Sign_survey_final.xlsx
|      |
|      --> species occurrence/location information
|
|-- Camera_trap_deployment.xlsx
|--> camera-station locations and altitude

CAMERA-TRAP DETECTIONS
|
|-- Winter_season.csv
|-- Summer_season.csv
|       |
|       --> Year_data.csv
|           (combined annual/pooled activity dataset)
|
|       --> Activity_overlapcode.R
|                |
|                --> activity density estimates
|                --> diel activity plots
|                --> temporal overlap estimates
|
|       --> watson_two_sample_test.R
|                |
|                --> circular comparison of species activity distributions

 ENVIRONMENTAL / NICHE DATA
|
|-- Goral_winter final.csv
|-- Goral_summer final.csv
|-- Barking_deer_summer_final.csv
|       |
|       --> Niche_overlap_humboldt.R
|                |
|                --> environmental niche characterization
|                --> niche overlap / equivalency / background analyses,
|                    as implemented in the archived script

Year_data.csv is already the combined dataset generated from summer and winter activity records. Users should therefore not append Summer_season.csv and Winter_season.csv to Year_data.csv again, as this would duplicate observations.

 5. Recommended order for reproducing the analyses

The scripts are independent analytical scripts rather than a single automated pipeline. They can be run separately according to the analysis required.

For complete reproduction, the recommended order is:

Step 1 – Download and organize files

Download all files and place them in the same working directory.

Step 2 – Inspect temporal datasets

The following datasets contain camera-trap activity records:

Winter_season.csv
Summer_season.csv
Year_data.csv
Year_data.csv represents the combined annual dataset.

Step 3 – Run temporal activity overlap analysis

Run:

Activity_overlapcode.R

The script reads species-specific detection times, converts time information to the format required for circular/activity analysis, estimates activity distributions, and quantifies temporal overlap between Himalayan Goral and Barking Deer.

The analysis can be performed separately for:

• winter;
• summer; and
• pooled/annual data.

The appropriate input should therefore be selected from:

Winter_season.csv
Summer_season.csv
Year_data.csv

Step 4 – Run statistical comparison of activity distributions

Run:

watson_two_sample_test.R

This script statistically evaluates differences between the circular distributions of activity times of Himalayan Goral and Barking Deer using the Watson two-sample test.

It uses the same underlying activity data as the temporal-overlap analysis.

Step 5 – Run environmental niche analysis

Run:

Niche_overlap_humboldt.R

The principal environmental input files supplied with the archived dataset are:

Goral_winter final.csv
Goral_summer final.csv
Barking_deer_summer_final.csv

These files contain species records together with values extracted from environmental variables.

The script performs the environmental/ecological niche analyses implemented in the associated study.

Users should note the filename crosswalk described in Section 2 because some input names appearing in the original working code differ from the names in the archived dataset.

 6. Description of individual data files

6.1 Sign_survey_final.xlsx

This file contains field sign-survey records for Himalayan Goral and Barking Deer.

Each row represents a species sign or field observation recorded during field surveys.

Variables

Sr.no
Sequential record number assigned to each observation.

Lat
Latitude associated with the field observation in decimal degrees.

long
Longitude associated with the field observation in decimal degrees.

species
Species associated with the observation.

Species represented are:

• Himalayan Goral – Naemorhedus goral
• Barking Deer – Muntiacus muntjak

Coordinates are referenced to WGS84.

For Himalayan Goral, publicly released coordinates should be interpreted according to the sensitive-species generalization procedure described in Section 10.

 6.2 Camera_trap_deployment.xlsx

This file contains information for camera-trap stations established during the study.

Variables

Sr. no
Sequential identifier assigned to each camera-trap station.

Lat.
Latitude of the camera-trap deployment location in decimal degrees.

Long.
Longitude of the camera-trap deployment location in decimal degrees.

Altitude(m)
Elevation of the camera-trap station above mean sea level, expressed in metres.

Coordinates use the WGS84 geographic coordinate system.

 7. Temporal activity datasets

7.1 Year_data.csv

This file contains  camera-trap activity records used for temporal activity analyses.

It combines records from:

• Winter_season.csv: 472 records
• Summer_season.csv: 172 records

The combined dataset contains:

• 486 Barking Deer activity records
• 158 Himalayan Goral activity records

Each row represents a species camera-trap activity/detection record.

Variables

Species
Species associated with the camera-trap detection.

Records correspond to:

• Himalayan Goral
• Barking Deer

Time
Time associated with the camera-trap detection.

This variable was used to characterize diel activity patterns, construct circular activity distributions, and quantify temporal overlap between the two species.

Season
Season during which the detection was obtained.

Categories are:

• Winter
• Summer

Year_data.csv was used for pooled or annual activity analyses.

 7.2 Winter_season.csv

This file contains activity records obtained during the winter sampling period.

Each row represents a species-specific camera-trap activity record used to characterize winter diel activity patterns and evaluate temporal overlap between Himalayan Goral and Barking Deer.

Principal variables

Species
Species associated with each activity record.

Time
Time associated with each camera-trap detection.

The time information was transformed within the R workflow into the circular/time representation required for activity-density and temporal-overlap analysis.

Where other fields occur, their interpretation follows the corresponding definitions for Year_data.csv.

 7.3 Summer_season.csv

This file contains  activity records obtained during the summer sampling period.

Each row represents a species-specific camera-trap activity record used to characterize summer diel activity patterns and temporal niche overlap.

Principal variables

Species
Species associated with the camera-trap detection.

Time
Time associated with the camera-trap detection.

Time information was used in temporal activity-density and circular-distribution analyses.

Where other fields occur, their interpretation follows the corresponding definitions for Year_data.csv.

 8. Environmental datasets

The environmental datasets contain species observations together with environmental covariate values extracted at or associated with the analysed locations.

The principal environmental variables belong to climatic, terrain, land-cover, hydrological, and anthropogenic categories.

8.1 Bioclimatic variables

Bioclimatic variables were obtained from WorldClim v2.1.

Bio1 – Annual Mean Temperature
Annual mean temperature.

Unit: °C, subject to the scaling convention of the downloaded WorldClim layer used during processing.

Bio12 – Annual Precipitation
Annual precipitation.

Unit: millimetres.

Bio14 – Precipitation of Driest Month
Precipitation during the driest month.

Unit: millimetres.

 8.2 Terrain variables

Aspect
Terrain orientation derived from elevation information.

Typical range: 0–360 degrees.

Rugged
Terrain-ruggedness variable representing local variation in terrain configuration.

The variable was derived from elevation/terrain information and used as a relative measure of landscape ruggedness.

 8.3 Land-cover variables

Grassland
Variable representing grassland habitat or grassland cover associated with the analysed location.

ENLF
Evergreen Needle Leaf Forest variable representing evergreen needle-leaved forest cover.

Mixed
Variable representing mixed forest or mixed vegetation.

SGWST
Small Grass With Scattered Trees; land-cover variable representing grass-dominated habitat containing scattered trees.

 8.4 Anthropogenic variables

Human
Human Influence Index used to characterize anthropogenic influence on the landscape.

Source: Socioeconomic Data and Applications Center (SEDAC), Human Influence Index.

Spatial resolution: approximately 1 km.

The index ranges from:

0 = lowest/no recorded direct human influence

64 = highest direct anthropogenic influence

Source information:

https://sedac.ciesin.columbia.edu/

Road
Distance to the nearest road or road-related spatial feature used in the analysis.

The variable represents Euclidean distance to roads, expressed in metres where calculated from projected spatial layers.

Higher values therefore generally indicate locations farther from roads.

8.5 Hydrological variable

water
Distance to the nearest mapped water body or water-related spatial feature.

The variable represents Euclidean distance to water, expressed in metres where calculated from projected spatial layers.

Higher values therefore generally represent greater distance from water.

9. Individual environmental files

9.1 Goral_winter final.csv

This file contains Himalayan Goral records together with environmental covariates used for winter-season ecological/environmental niche analysis.

Each row represents a Himalayan Goral record and the environmental values associated with the corresponding analysed location.

Variables include geographic coordinates and the following environmental predictors:

• Grassland
• ENLF
• Aspect
• Human
• Mixed
• Rugged
• SGWST
• Bio12
• Bio14
• water
• Bio1
• Road

Coordinate fields

Latitude / coordinate field
Latitude in decimal degrees.

Longitude / coordinate field
Longitude in decimal degrees.

Publicly released Himalayan Goral coordinates have been spatially generalized for conservation purposes, as described in Section 10.

Environmental variables

Grassland
Grassland habitat variable associated with the analysed location.

ENLF
Evergreen Needle Leaf Forest variable.

Aspect
Terrain aspect representing slope orientation.

Human
Human Influence Index describing anthropogenic influence.

Mixed
Mixed-forest or mixed-vegetation habitat variable.

Rugged
Terrain-ruggedness variable.

SGWST
Small Grass With Scattered Trees habitat variable.

Bio12
Annual precipitation, in millimetres.

Bio14
Precipitation of the driest month, in millimetres.

water
Distance to water or water-related spatial feature.

Bio1
Annual mean temperature.

Road
Distance to road or road-related spatial feature.

 9.2 Goral_summer final.csv

This file contains Himalayan Goral records and associated environmental covariates used for summer-season environmental/ecological niche analysis.

Each row represents a Himalayan Goral record together with associated geographic and environmental information.

The environmental variables have the same definitions as those described for Goral_winter final.csv.

The publicly released Himalayan Goral geographic coordinates have been generalized to reduce the risk of disclosure of conservation-sensitive occurrence locations.

 9.3 Barking_deer_summer_final.csv

This file contains Barking Deer records and associated environmental covariates used for summer-season environmental/ecological niche analysis.

Each row represents a Barking Deer record together with environmental values associated with the corresponding analysed location.

The environmental variables describe habitat, terrain, climatic, hydrological, and anthropogenic conditions.

Variables shared with the Himalayan Goral environmental datasets have the same meanings and units described above.

9.4 BD_winter.csv
This file contains winter-season Barking Deer records together with associated environmental covariates used in the environmental niche analysis. The variables include geographic coordinates and environmental predictors such as Grassland, ENLF, Aspect, Human, Mixed, Rugged, SGWST, Bio12, Bio14, water, Bio1, Road, and Slope. Variables shared with the other environmental datasets follow the same definitions described previously.

 10. Geographic coordinate system

Unless otherwise specified, geographic coordinates are expressed as latitude and longitude in decimal degrees using:

World Geodetic System 1984

WGS84

EPSG:4326

Distance-based environmental variables were calculated from spatial data in an appropriate projected coordinate framework during GIS processing before being associated with species locations.

 11. Generalization of sensitive Himalayan Goral locations

Himalayan Goral (Naemorhedus goral) is treated as a conservation-sensitive species in this public data release.

To reduce the risk associated with releasing precise occurrence locations, geographic coordinates associated with Himalayan Goral observations were spatially generalized before public release.

Latitude and longitude values for Himalayan Goral observations were rounded to 0.1 decimal degrees.

Consequently, coordinates contained in the public dataset represent generalized locations and should not be interpreted as the precise sites at which Himalayan Goral individuals, signs, or camera-trap detections occurred.

The original higher-precision coordinates are retained separately by the data custodians and are not included in the publicly accessible data package.

Associated fields were also reviewed to reduce the possibility of reconstructing sensitive localities from ancillary information.

For biodiversity-data standards that support a dataGeneralizations field, the appropriate statement is:

Geographic coordinates associated with Himalayan Goral (Naemorhedus goral) occurrences were rounded to 0.1 decimal degrees before public release to reduce disclosure of conservation-sensitive occurrence locations. Public coordinates therefore represent generalized rather than exact observation locations.

Users must not attempt to reverse-engineer or reconstruct the original precise occurrence locations.

12. Description of R scripts

12.1 Activity_overlapcode.R

Purpose

This script was used to characterize diel activity patterns and quantify temporal activity overlap between Himalayan Goral and Barking Deer.

Inputs

Depending on whether annual or season-specific activity is being analysed, the script uses:

Year_data.csv

Winter_season.csv

Summer_season.csv

A legacy reference to:

Year.csv

should be interpreted as:

Year_data.csv

Main workflow

The script:

1. imports camera-trap activity/detection times;
2. separates detections by species;
3. converts clock time into the circular/radian format required by the analytical functions;
4. estimates species-specific diel activity-density distributions;
5. calculates the coefficient of temporal overlap between Himalayan Goral and Barking Deer; and
6. produces graphical representations of species activity distributions and their overlap.

The script can be used separately with winter, summer, or combined annual activity data.

Outputs

The script returns analytical estimates and graphical outputs within the R session, including species activity-density curves and temporal-overlap estimates.

Where the script contains graphical device or file-writing commands, outputs are written to the current R working directory.

 12.2 watson_two_sample_test.R

Purpose

This script was used to statistically compare the temporal activity distributions of Himalayan Goral and Barking Deer.

Inputs

The input consists of species-specific activity times derived from:

Year_data.csv

Winter_season.csv

Summer_season.csv

depending on whether annual, winter, or summer activity distributions are being tested.

Analysis

Activity time is circular data because 00:00 and 24:00 represent the same point in the daily cycle.

The Watson two-sample test was therefore used to assess whether the circular distributions of activity times differed significantly between Himalayan Goral and Barking Deer.

Outputs

The script produces the Watson two-sample test statistic and associated significance information in the R console/session.

 12.3 Niche_overlap_humboldt.R

Purpose

This script was used to investigate environmental/ecological niche relationships between Himalayan Goral and Barking Deer.

Archived environmental inputs

The environmental datasets provided with the public package are:

Goral_winter final.csv

Goral_summer final.csv

Barking_deer_summer_final.csv

Some filenames appearing in the original working script differ from the final archived names.

In particular:

GOS_ENV.csv

corresponds to:

Goral_summer final.csv

Users should follow the filename crosswalk in Section 2 when reproducing the analysis.

Analysis

The script uses environmental information associated with species records to characterize and compare environmental niches of Himalayan Goral and Barking Deer.

The workflow includes the niche-comparison procedures implemented in the archived script, including niche overlap and associated niche comparison tests where specified in the code.

Important limitation

The original working code also contains a reference to:

BD_winter.csv

A separate Barking Deer winter environmental dataset with that filename is not part of the current public package.

Users should follow the guidance provided in Section 2 before attempting to execute any portion of the script requiring that object.

13. Software environment

All statistical analyses were conducted in R version 3.5.1. The analyses were performed during 2024.

The original sessionInfo() output from the analytical computer was not retained. Therefore, while the R version is known exactly, the complete historical package-session metadata cannot be reconstructed verbatim. To facilitate reproducibility, the package versions below represent a reconstructed software environment compatible with R 3.5.1 and with the analytical workflow used in this study.

Software/package

Version used/recommended for reconstruction

Purpose in the analysis

R

3.5.1

Statistical computing environment

humboldt

1.0.0.0420121

Environmental niche overlap, niche equivalency and background similarity analyses

sp

2.1-4

Spatial data classes and spatial-data handling

raster

3.6-26

Raster environmental data handling and extraction

ggplot2

3.5.1

Data visualization and graphical outputs

circular

0.5-0

Representation and analysis of circular/time-of-day data

CircStats

0.2-6

Circular statistical tests, including comparison of activity distributions

suntools

1.0.0

Solar-time related functions required by temporal activity analyses

readxl

1.3.1

Import of Microsoft Excel field and camera-trap datasets

overlap

0.3.9

Estimation and plotting of coefficients of temporal activity overlap

The capitalization of R package names should be retained as follows when loading packages:

library(humboldt)

library(sp)

library(raster)

library(ggplot2)

library(circular)

library(CircStats)

library(suntools)

library(readxl)

library(overlap)

The software environment can therefore be described as:

R version 3.5.1

Principal R packages:

humboldt 1.0.0.0420121

sp 2.1-4

raster 3.6-26

ggplot2 3.5.1

circular 0.5-0

CircStats 0.2-6

suntools 1.0.0

readxl 1.3.1

overlap 0.3.9

Because the original package-level sessionInfo() record was not retained, these package versions should be regarded as the reconstructed computational environment used to facilitate reuse of the archived scripts rather than as a verbatim copy of the original R session.

Users attempting to reproduce the analyses on current versions of R should be aware that some spatial packages and dependencies used by the original humboldt workflow have subsequently been updated, superseded, or archived. Consequently, use of the historical R 3.5.1-compatible environment may provide greater consistency with the original analytical workflow.

14. Relationship of packages to analytical scripts

14.1 Activity_overlapcode.R

This script performs the temporal activity-overlap analysis using camera-trap detection times.

The principal packages used for this component are:

library(overlap)

library(circular)

library(CircStats)

library(suntools)

library(ggplot2)

The overlap package is used to estimate coefficients of overlap between the diel activity distributions of Himalayan Goral and Barking Deer. Detection times are converted to an appropriate circular representation prior to estimation and plotting of activity-density functions.

Depending on the analysis being reproduced, the input data are:

Year_data.csv

Winter_season.csv

Summer_season.csv

Year_data.csv contains the combined annual dataset and should not be combined again with the summer and winter files.

14.2 watson_two_sample_test.R

This script statistically compares the temporal activity distributions of Himalayan Goral and Barking Deer using circular statistics.

The principal packages used are:

library(circular)

library(CircStats)

The analysis uses species-specific detection times derived from:

Year_data.csv

Winter_season.csv

Summer_season.csv

depending on whether pooled, winter, or summer activity patterns are being compared.

14.3 Niche_overlap_humboldt.R

This script performs the environmental niche-overlap analyses.

The principal packages used are:

library(humboldt)

library(sp)

library(raster)

library(ggplot2)

The environmental input datasets included in the archive are:

Goral_winter final.csv

Goral_summer final.csv

Barking_deer_summer_final.csv

BD_winter.csv

These datasets contain the species records and associated environmental covariates required for the environmental niche analyses.

The humboldt package is used for environmental-space niche analyses, including the niche-overlap and associated niche-comparison procedures implemented in the archived script.

15. Reproducing the software environment

Before running the analyses, users should place the relevant data files and R scripts within a common working directory and set that directory in R.

For example:

setwd("path/to/downloaded/Dryad/dataset")

The required libraries should then be loaded before executing the corresponding analytical script.

For example:

library(humboldt)

library(sp)

library(raster)

library(ggplot2)

library(circular)

library(CircStats)

library(suntools)

library(readxl)

library(overlap)

The scripts may subsequently be executed using:

source("Activity_overlapcode.R")

source("watson_two_sample_test.R")

source("Niche_overlap_humboldt.R")

Users installing packages under a modern R version may obtain versions that differ substantially from those used in the original analytical environment. For maximum reproducibility, archived versions corresponding to the software environment described above should be used where possible.

16. Package dependencies

The precise package list should be read from the library() and/or require() statements in each archived script.

The analysis requires packages providing functionality for the following operations:

Temporal overlap analysis

Functions for:

• kernel density estimation of activity times;
• temporal activity overlap;
• plotting activity distributions.

Circular statistical analysis

Functions for:

• conversion and handling of circular data;
• Watson two-sample testing.

Environmental niche analysis

Functions for:

• multivariate environmental niche characterization;
• niche overlap calculations;
• niche equivalency and/or background comparisons implemented in the script;
• manipulation of environmental data; and
• generation of associated plots.

To recreate the final software environment on another machine, users should install the packages identified in the scripts and verify versions using sessionInfo().

 17. Relationship between raw, processed, and analytical files

The files in this package represent different levels of the analytical workflow.

Field/location information

Sign_survey_final.xlsx

Camera_trap_deployment.xlsx

contain field observations and sampling-site information.

Temporal analytical data

Winter_season.csv

Summer_season.csv

contain season-specific activity records.

Year_data.csv

is a combined dataset containing both seasons and should therefore be regarded as a derived/combined analytical input rather than an independent third sampling dataset.

Environmental analytical data

Goral_winter final.csv

Goral_summer final.csv

Barking_deer_summer_final.csv

contain environmental covariates associated with species locations and were prepared as inputs for environmental niche comparison.

These are analytical tables rather than original raster environmental layers.

R scripts

Activity_overlapcode.R

watson_two_sample_test.R

Niche_overlap_humboldt.R

implement the principal temporal and environmental niche analyses.

 16. Reproducing the analyses

A user wishing to reproduce the analyses should:

1. download the complete Dryad package;
2. place all files in one directory;
3. start R or RStudio;
4. set the R working directory to the downloaded folder;
5. install packages called using library() or require() in the relevant script;
6. check the filename crosswalk in Section 2;
7. ensure Year.csv, if present in the code, is replaced by Year_data.csv;
8. ensure GOS_ENV.csv, if present in the code, is replaced by Goral_summer final.csv;
9. select Winter_season.csv, Summer_season.csv, or Year_data.csv according to the temporal analysis being reproduced;
10. run Activity_overlapcode.R for temporal-overlap analysis;
11. run watson_two_sample_test.R for statistical comparison of circular activity distributions;
12. run Niche_overlap_humboldt.R for environmental niche analyses; and
13. note the limitation associated with any remaining reference to BD_winter.csv.

Scripts can be executed in R using:

source("Activity_overlapcode.R")

source("watson_two_sample_test.R")

source("Niche_overlap_humboldt.R")

provided their input filenames have first been reconciled with the archived filenames described above.

 18. File naming and reproducibility

The file names listed in this README correspond to the public Dryad package.

Because the original analysis was developed using working filenames, some file-import commands inside the scripts may use historical names.

These differences represent filename changes rather than separate data sources except for the explicitly noted BD_winter.csv reference.

Users may reproduce the analyses by either:

1. editing the read.csv() statement in the script to use the archived filename; or
2. making a local copy of the corresponding archived file using the historical filename.

Editing the script is recommended because it maintains a clear relationship with the archived data.

 19. Interpretation of spatial information

Geographic coordinates in the public data package should be interpreted with consideration of the sensitive-species protection described above.

In particular, generalized Himalayan Goral coordinates are intended to permit broad spatial interpretation and data transparency while preventing disclosure of precise observation sites.

Generalized coordinates should not be used to infer:

• precise individual locations;
• precise locations of field signs;
• exact camera-trap detections;
• exact habitat-use sites; or
• fine-scale movement pathways.

Analyses requiring precise site-level Himalayan Goral coordinates cannot be reproduced directly from the generalized public coordinates.

 20. Data-use considerations

Users of these data should:

• acknowledge that sensitive Himalayan Goral coordinates have been spatially generalized;
• avoid attempting to reconstruct precise sensitive-species locations;
• retain the original definitions of species and environmental variables when reusing the data;
• recognize Year_data.csv as the combined summer–winter activity dataset;
• consult the filename crosswalk when running archived scripts;
• cite the associated publication and Dryad dataset when using these data; and
• consult the accompanying R scripts when reproducing analyses.

 21. Data sources

Environmental variables were obtained or derived from the following general data sources:

WorldClim v2.1
Bioclimatic variables including Bio1, Bio12, and Bio14.

SEDAC Human Influence Index
Human Influence Index used as an anthropogenic-pressure variable.

https://sedac.ciesin.columbia.edu/

Digital Elevation Model
Used to derive terrain characteristics including aspect and ruggedness.

Land-cover spatial data
Used to derive Grassland, Evergreen Needle Leaf Forest, Mixed Forest, and Small Grass With Scattered Trees variables.

Road and hydrological spatial layers
Used to derive Euclidean-distance variables representing proximity to roads and water.

The archived CSV files contain values extracted from or derived from these spatial layers. The original raster layers themselves are not part of this Dryad package.

22. Units and interpretation summary

Variable

Interpretation

Unit / scale

Lat / Latitude

Geographic latitude

Decimal degrees, WGS84

Long / Longitude

Geographic longitude

Decimal degrees, WGS84

Altitude(m)

Elevation above mean sea level

metres

Time

Camera-trap detection time

clock time; transformed to circular time in R

Season

Sampling season

Winter / Summer

Bio1

Annual mean temperature

°C or WorldClim native scaled value

Bio12

Annual precipitation

mm

Bio14

Precipitation of driest month

mm

Aspect

Terrain orientation

degrees, 0–360

Rugged

Terrain ruggedness

relative/unitless terrain metric

Human

Human Influence Index

0–64

Grassland

Grassland habitat/cover variable

value from processed environmental layer

ENLF

Evergreen Needle Leaf Forest

value from processed environmental layer

Mixed

Mixed forest/vegetation

value from processed environmental layer

SGWST

Small Grass With Scattered Trees

value from processed environmental layer

water

Euclidean distance to water

metres where calculated in projected coordinates

Road

Euclidean distance to road

metres where calculated in projected coordinates

 23. Known limitations affecting reuse

Users should be aware of the following limitations.

1. Himalayan Goral coordinates in the public release have been generalized to 0.1 decimal degrees and therefore cannot reproduce analyses requiring the original precise occurrence coordinates.
2. Some R scripts retain historical input filenames that differ from the archived filenames. The filename crosswalk in Section 2 should be followed.
3. Year_data.csv is already the combined version of Summer_season.csv and Winter_season.csv; combining the three files would duplicate data.
4. A legacy input named BD_winter.csv is referenced in the original working code but is not included as a separate file in the present public package. Any part of a script requiring this file cannot be reproduced unless the corresponding dataset is provided or the analytical code is revised appropriately.
5. The environmental CSV files contain covariate values used in the analyses; the original environmental raster layers are not included in the archived package.
6. Exact R and package-version information should be taken from the computational environment used for the final analysis using sessionInfo(). Versions should not be inferred where the historical environment was not preserved.

 24. Contact and data responsibility

Questions regarding interpretation of the data, sensitive-species coordinate generalization, or analytical workflow should be directed to the authors/data custodians identified in the Dryad dataset record.

The precise Himalayan Goral coordinates are intentionally excluded from the publicly accessible dataset for conservation reasons.

 25. Reproducibility statement

The public archive contains the field-data tables, activity datasets, environmental analytical tables, and R scripts used for the principal spatio-temporal niche analyses described in the study.

The README documents:

• the role of each archived file;
• relationships among files;
• the recommended order of analysis;
• input files required by individual scripts;
• historical versus archived filenames;
• working-directory requirements;
• interpretation and units of dataset variables;
• sensitive-species data generalization;
• software and package requirements; and
• known limitations to complete reproduction.

Users should use the archived filenames shown in this README when reproducing the analyses and should consult the comments within individual R scripts for analysis-specific options and parameters.