Dataset DOI: 10.5061/dryad.69p8cz9g9

Description of the data and file structure

This dataset includes 5 years of camera trap data ranging from Jan 1, 2017 to Dec 31, 2022. Species were identified in camera trap photos by community scientists that maintained the cameras, through crowdsourcing on the Zooniverse platform (SnapshotWisconsin.org), and uncertain species IDs were confirmed by expert observers.

Five geospatial variables were used to measure the degree of human impact across the landscape in Wisconsin: nighttime light (NTL, Li et al. 2020), human population density (GPW, CIESIN 2018, Doxsey-Whitfield et al. 2015), access to cities (Weiss et al. 2018), impacting sound (Mennitt et al. 2013), and global human modification of terrestrial systems (GHM, Kennedy et al. 2019).

Precise location data of camera traps has been removed for sake of privacy.

Files and variables

File: Diel_Observations.csv

Description: This .csv file contains the rho and p values for the Spearman correlation performed on each species for the geospatial variables. It also contains the average percent nocturnality for each species, as well as the total number of observations for each species throughout the campaign.

Variables

• Species: Species observed
• number_obs: Total number of observations per species
• nocturnality: Average percent nocturnality (decimal * 100) per species
• NTL_rho: rho value for the Spearman correlation of NTL
• NTL_p: p value for the Spearman correlation of NTL
• GHM_rho: rho value for the Spearman correlation of GHM
• GHM_p: p value for the Spearman correlation of GHM
• PopDActive_rho: rho value for the Spearman correlation of population density
• PopDActive_p: p value for the Spearman correlation of population density
• A2C_rho: rho value for the Spearman correlation of access to cities
• A2C_p: p value for the Spearman correlation of access to cities
• impSound_rho: rho value for the Spearman correlation of impacting sound
• impSound_p: p value for the Spearman correlation of impacting sound

File: detections_data.csv

Description: Data for each observation, corrected for duplicate observations.

Variables

• X: Unique identifier for each observation

• SPECIES: Species observed

  COUNT: Number of target species within the observation

• DETECTION_DATETIME: Date and time of detection

• COUNTY_NAME: County where camera trap is located

• DATE: Full date of detection

• YEAR: Year of detection

• MONTH: Month of detection

• TOTAL_ACTIVE_DAYS: Total time camera trap was active before detection

• WI_Anthropocene_GPW_populationDensity: Global population density at location of detection in km^2

• WI_Anthropocene_Li_et_al_NTL_2013: Night light levels at location of detection in Defence Meteorological Satellite Program (DMSP) digital dumber (DN) value

• WI_Anthropocene_NASA_SEDAC_nsGHM: Cumulative global human modification at location of detection

• WI_Anthropocene_NPS_impSound: Impacting sound level at location of detection, L50, dBA

• WI_Anthropocene_Weiss_et_al_A2C: Travel time to city at location of detection

Code/software

Data was processed in R Studio version 2025.05.1+513. Packages used included suncalc, lubridate, ggplot2, geosphere, dplyr, tidyr, ggstatsplot, corrplot, sf, readr, and stringr.

Access information

Data was derived from the following sources:

• Population density: https://doi.org/10.7927/H4F47M65
• NTL: https://doi.org/10.6084/m9.figshare.9828827.v2
• Impacting sound: https://irma.nps.gov/DataStore/Reference/Profile/2217356
• Access to cities: https://doi.org/10.1038/nature25181
• GHM: https://doi.org/10.1111/gcb.14549

References cited for dataset

Center For International Earth Science Information Network-CIESIN-Columbia University. (2018). Gridded Population of the World, Version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision UN WPP Country Totals, Revision 11 (Version 4.11) [Data set]. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4F47M65

Li, Xuecao, Yuyu Zhou, Min Zhao, and Xia Zhao. 2020. “A Harmonized Global Nighttime Light Dataset 1992–2018.” Scientific Data 7 (1): 168. https://doi.org/10.1038/s41597-020-0510-y.

Kennedy, Christina M., James R. Oakleaf, David M. Theobald, Sharon Baruch-Mordo, and Joseph Kiesecker. 2019. “Managing the Middle: A Shift in Conservation Priorities Based on the Global Human Modification Gradient.” Global Change Biology 25 (3): 811–26. https://doi.org/10.1111/gcb.14549.

Mennitt, Dan, Kurt Fristrup, Lisa Nelson, and Megan McKenna. 2013. “Mapping the Extent of Noise on a National Scale Using Geospatial Models.” The Journal of the Acoustical Society of America 134 (5_Supplement): 4159. https://doi.org/10.1121/1.4831244.

Townsend, P. A., Clare, J. D., Liu, N., Stenglein, J. L., Anhalt‐Depies, C., Van Deelen, T. R., ... & Zuckerberg, B. (2021). Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management. Ecological Applications, 31(8), e02436

Weiss, D. J., A. Nelson, H. S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, et al. 2018. “A Global Map of Travel Time to Cities to Assess Inequalities in Accessibility in 2015.” Nature 553 (7688): 333–36. https://doi.org/10.1038/nature25181.