DOI: 10.5061/dryad.69p8cz9g9
title: Use of camera traps to understand Wisconsin mammal diel activity under anthropogenic pressures

description: This data uses Snapshot Wisconsin (Townsend et al. 2021) data and
 geospatial variables pertaining to 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). The aim of using this data is to understand how
 anthropogenic variables influence mammal diel activity in Wisconsin, USA.
 This dataset contains the relevant information for the geospatial
 variables used as well as the camera trap data spatially matched to
 them. 
description: # Use of camera traps to understand Wisconsin mammal diel activity under
 anthropogenic pressures Dataset DOI:
 [10.5061/dryad.69p8cz9g9](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](https://doi.org/10.7927/H4F47M65) *
 NTL:
 [https://doi.org/10.6084/m9.figshare.9828827.v2](https://doi.org/10.6084/m9.figshare.9828827.v2) * Impacting sound: [https://irma.nps.gov/DataStore/Reference/Profile/2217356](https://irma.nps.gov/DataStore/Reference/Profile/2217356) * Access to cities: [https://doi.org/10.1038/nature25181](https://doi.org/10.1038/nature25181) * GHM: [https://doi.org/10.1111/gcb.14549](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](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](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](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](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](https://doi.org/10.1038/nature25181).