Spatially explicit densities of wildlife are important for understanding environmental drivers of populations, and density surfaces of intraspecific classes allow exploration of links between demographic ratios and environmental conditions. Although spatially explicit densities and class densities are valuable, conventional design-based estimators remain prevalent when using camera-trapping methods for unmarked populations. We developed a density surface model that utilized camera-trap distance sampling data within a hierarchical generalized additive modelling framework. We estimated density surfaces of intraspecific classes of a common ungulate, white-tailed deer (Odocoileus virginianus), across three large management regions in Indiana, USA. We then extended simple statistical theory to test for differences in two ratios of density. Deer density was influenced by landscape fragmentation, wetlands, and anthropogenic development. We documented class-specific responses of density to availability of concealment cover, and found strong evidence that increased recruitment of young was tied to increased resource availability from anthropogenic agricultural land use. The coefficients of variation of the total density estimates within the three regions we surveyed were 0.11, 0.10, and 0.06. Our strategy extends camera-trap distance sampling, and enables managers to use camera traps to better understand spatial predictors of density. Our density estimates were more precise than previous estimates from camera-trap distance sampling. Population managers can use our methods to detect finer spatiotemporal changes in density or ratios of intraspecific-class densities. Such changes in density can be linked to land use, or to management regimes on habitat and harvest limits of game species.

The attached data sets, code, and other additional files are organized into the following folder structure:

• "Conventional_Camera-Trap_Distance_Sampling_Analysis" contains code and data for conducting a conventional camera-trap-distance-sampling analysis using the same distances measured to white-tailed deer as in Delisle et al. (In Review). This is for comparison purposes between density surface modelling and conventional distance sampling and not directly needed to recreate the density surface model in Delisle et al. (In Review). 
• "Step1_Estimating_Detection_Function" contains data and code needed for estimating the distance-sampling detection functions as in Delisle et al. (In Review). 
• "Step2_Estimating_Activity_Level" contains data and code needed for estimating the activity level of white-tailed deer as seen in Delisle et al. (In Review). 
• "Step3_Fitting_and_Selecting_Density_Surface_Models" contains data and code for fitting several candidate density surface models (some containing factor-smooth interactions) to camera-trap-distance-sampling data and selecting a final model between the fit candidate models. 
• "Step4_Assessing_Extrapolation_in_Prediction_Grid" contains the code and data needed to determine if the covariates associated with each cell in the prediction grids exhibit univariate or combinatorial extrapolation beyond the range of the covariates we actually sampled at camera-trap locations. 
• "Step5_Predictions_of_Density" contains data, code, and other files needed to predict deer density across the prediction grids using the final density surface model. This file also estimates point estimates of density. 
• "Step6_Variance_Estimation" contains data, code, and other files needed to predict the uncertainty of density estimates across the prediction grids. This file also estimates the uncertainty associated with point estimates of density. 
• "Step7_Testing_For_Differences_in_Ratios" contains code and data needed for comparing ratios of deer density in different regional management units of Indiana, USA. 

Delisle, Zackary J, David L Miller, and Robert k Swihart. In Review. “Modeling Density Surfaces of Intraspecific Classes Using Camera-Trap Distance Sampling.” Methods in Ecology and Evolution.

Researchers should cite this work as follows:

• Swihart, R.; Delisle, Z. (2023). Modeling density surfaces of intraspecific classes using camera-trap distance sampling. Purdue University Research Repository. doi:10.4231/RF5H-C895

  BibTex | EndNote

1. abundance
2. deer
3. density surface modelling
4. Forestry and Natural Resources
5. Forestry and Natural Resources (Dept)
6. generalized additive model
7. precision
8. recruitment
9. Remote Sensing
10. ungulate

The data, r code, RData files, and raster files herein were used to complete the analyses presented in Delisle et al. (In Review). 

Delisle, Zackary J, David L Miller, and Robert k Swihart. In Review. “Modeling Density Surfaces of Intraspecific Classes Using Camera-Trap Distance Sampling.” Methods in Ecology and Evolution.

The dataset can be more easily accessed through the ftp protocol as file 10_4231_RF5H-C895.zip on the PURR's FTP server. For instructions how to access the zip file, see https://purr.purdue.edu/kb/projects/access-datasets-using-ftp-client.