Figure 1
Map of the 9,586 random points (grey) across North Carolina, USA used to quantify variation in available habitat types, and the 4,295 sites sampled with camera traps (blue). Black lines indicate the three primary ecoregions (from west to east): mountains, piedmont, coastal.
Table 1
Habitat variables considered for the analysis of how representative the data were of the state.
| HABITAT | DESCRIPTION | UNIT | SOURCE |
|---|---|---|---|
| Cover type | Open, forest, developed, or other | (Homer et al. 2015) | |
| Elevation | Elevation | m | USGS |
| Large core forests | Area within a 5 km radius consisting of continuous forest fragment (forest parcels >2 ha) | % | (Homer et al. 2015) |
| Developed | Area within a 5 km radius consisting of developed land use | % | (Homer et al. 2015) |
| Houses | Housing density within 1 km radius of the site | houses/km2 | (Hammer et al. 2004) |
| Roads | Road density within a 250 m radius of site | km/km2 | NCDOT |
| Tree Cover | Tree cover at 30 m pixel resolution | % | (Hansen et al. 2013) |
Figure 2
Distribution of elevational habitats available in North Carolina measured at 9,586 random points across the states. Colors show the 10 categories that each represent ~10% of the variation in available habitat. We used these categories to see if our camera samples were representative of the dimensions of a given habitat type.
Table 2
Covariates used in occupancy models.
| CATEGORY | COVARIATE | DESCRIPTION |
|---|---|---|
| Habitat | Forest cover | % forested in 5 km2 buffer |
| Habitat | Housing density | Average housing density (houses/km) in 5 km2 square buffer |
| Habitat | Contagion index (“Clumpiness”) | The propensity for a 5 km2 square raster pixel of a given land-cover class to be neighboring a different land-cover class |
| Habitat | PRD | Patch richness density. Number of land-cover types per 100 ha in 5 km2 square buffer |
| Site variation | Yard | Categorical predictor of whether the camera was placed within a residential yard |
| Site variation | Richness | Number of species detected at the camera site |
| Nuisance | Precipitation | Precipitation rate averaged over camera deployment period (Mesinger et al. 2006) |
| Nuisance | Temperature | Temperature averaged over camera deployment period (Dee et al. 2011) |
| Nuisance | EVI | Enhanced vegetation index; a measure of greenness at the camera site. |
| Nuisance | Julian | Julian day of the year |
| Nuisance | Detection distance | Furthest distance away that the camera was triggered by a human |
| Nuisance | Bait | Categorical of whether bait was used at the camera site |
| Survey effort | Trap nights | Length (days) of camera trap deployment. Used to control for variation in effort (i.e., catch per unit effort) |
Table 3
Example of results for the representation of the camera trap sampling for one ecoregion (mountains) for one habitat type (tree cover). Columns show number of cameras set in each habitat type by staff, volunteers, and total. Habitat types with <40 camera samples (bold) were judged to be insufficiently sampled. In this example, additional sampling by staff ensured adequate sampling for the 98–100% category but not for the 0–40% category. The proportional availabilities of a habitat categories for that ecoregion are given by the % of random points that fell into that category, which are then summed if they are sampled adequately (>40 pts) or very adequately (>60) to quantify the total % of a given habitat type adequately sampled in a given ecoregion. Additional habitats/ecoregion results are in Appendix 1.
| TREE COVER % | STAFF CAMS | VOLUNTEER CAMS | ALL CAMS | RANDOM % | ADEQUATE SAMPLE (>40) | VERY ADEQUATE SAMPLE (>60) |
|---|---|---|---|---|---|---|
| 0 | 17 | 51 | 68 | 13% | 13% | 13% |
| 0–40 | 10 | 24 | 34 | 4% | 0 | 0 |
| 40–81 | 22 | 78 | 100 | 9% | 9% | 9% |
| 81–94 | 50 | 96 | 146 | 12% | 12% | 12% |
| 94–98 | 33 | 101 | 134 | 12% | 12% | 12% |
| 98–100 | 18 | 26 | 44 | 3% | 3% | 0 |
| 100 | 99 | 348 | 447 | 45% | 45% | 45% |
| Total | 95.6% | 92.2% |
Table 4
Percentage of the area in three ecoregions of the state adequately sampled (>40 sites) by camera traps in the North Carolina’s Candid Critters(NCCC) project across seven habitat dimensions. See Table 3 for an example of how this was estimated for one habitat/ecoregion and Appendix 1 for all results.
| HABITAT COVARIATE | COASTAL | MOUNTAINS | PIEDMONT | AVERAGE |
|---|---|---|---|---|
| Tree cover | 100 | 95.6 | 100 | 98.5 |
| Elevation | 99.7 | 99.9 | 99.7 | 99.8 |
| Large forests | 98.9 | 93.7 | 99.8 | 97.5 |
| Developed | 100 | 100 | 99.6 | 99.9 |
| Houses | 100 | 100 | 99.2 | 99.7 |
| Land use | 100 | 96.3 | 100 | 98.8 |
| Roads | 100 | 100 | 100 | 100 |
| Average | 99.8 | 97.9 | 99.8 | 99.2 |
Figure 3
Graphs showing relative bias (a measure how different the estimates were from the full data set estimate) of occupancy estimates for white-tailed deer and coyote. These were calculated with subsets of the full NCCC dataset for a) three ecoregions, b) four seasons, and c) ecoregion-seasons. Our estimates reached our goal for bias (<0.1) at very small sample sizes for the common deer and after sampling 250–300 sites for the less common coyote across all spatio-temporal divisions. The lack of change in these estimates with increasing sample size also indicates a stable, robust result. Results were similar for estimates of error (Supplemental Figure 1).
Figure 4
Graphs showing the changes in the relative error (relative root mean square error [RRMSE], a measure of how variable the estimates of occupancy were across the replicates) with larger sample size for estimated ecological relationships for agriculture land cover in occupancy models for coyotes and deer. Models were run across regions (top), seasons (middle), and regions-seasons (bottom). Only significant model effects are shown. Error estimates approached our 10% goal more rapidly with more restricted models (i.e., region-season) suggesting spatio-temporal variability in these relationships added variation to the larger-scale models. Full results for changes in error and bias of all covariates are available in Supplemental Figure 2.