The DIEM framework was demonstrated through a case study.

(D) A broad definition of habitat threat was used. The focus of this study was on the natural environment so, habitat threats were defined as being anthropogenic activities or natural events that put natural habitats at risk.

(I) When investigating, spatial data with the finest resolution and the most recent data were used. Identifying the types of threats in an area requires spatial data for each potential threat. Available data sets include land use/cover maps, wildfire frequency maps, invasive species sighting maps, etc. These data are difficult to find at times. There are also threats that are not straightforward, such as climate change, war, and population pressure. Threats such as these require extensive research. The characteristics of these threats can vary based on region.

(E) Threat characteristics such as the maximum distance a threat can impact (D_max) and threat weight were estimated. The D_max value can be, for instance, as low as 0.5 km in Portugal ^[8] and as high as 10 km in China ^[9]. Using an average of every value may not be accurate. Using values from regions that are most like the area of interest is advised. The ideal approach is to thoroughly study the chosen region if resources are available.

(M) Weights were used to create overall threat level maps. The method for creating the maps was similar to the equation derived from the literature ^[4][10], which is as follows:

where f_i is the threat frequency of each threat, and α_i is the threat severity or weight of each threat. The equation has f_i as the threat index value of individual threats. This does not change the results for the four threats where the index was modeled on the basis of presence or absence. Frequency is 1 for each grid cell where the threat occurs, and 0 otherwise. The population-pressure index map had values in the range between 0 and 1, so frequency in this map was a fraction in some cases and did not represent the actual population, but rather a relative value. An alternative to this method is to use the equation derived from Deffense ^[11], which is as follows:

where F_i is the threat factor score, which is understood as the threat index of individual threats. The rationale for not using this method was that there were grid cells in each threat map that had a value of 0. When maps are multiplied together, the resulting map would be 0. The habitat threat index equation derived from Nie, Yang, and Huang ^[12] could also be used. It is as follows:

where N_c is the number of grid cells in a land use type; N_l is the number of land uses in the area, and α_ij is the threat severity of each threat for each land-use type. This equation is used to calculate the HTI for each land use. The threat severity in this equation also needs to account for the sensitivity of land uses to each threat. The advantage of using this equation is that it is not affected by differences in spatial data resolution. The equation can be used with tabular results, and the only spatial data needed are the land use/cover dataset. However, this method requires many more steps than the previous two equations when it comes to general watershed analysis, so it was not used here.

A case study was done on the Choctawhatchee River Watershed. Within the watershed, the natural habitats are open water, barren land, deciduous, coniferous, and mixed forests, shrub lands, grasslands, and both wooded and herbaceous wetlands. Agriculture was the threat that occurs most often, but it had a relatively low average threat weight. Urbanization was the second-most frequent threat, and it had the highest average weight. Mining was the next most frequent, followed by power plants, and both had the same average weight. Population pressure had the lowest average weight and generally occurred where there was development. The greatest threats occurred in the same general areas between maps. Areas with the highest threat value were developed, with the highest values being where urbanization and mining or power plants overlapped. There were a few grid cells on the map that included population pressure where agriculture, population, and mining or power plants overlapped, which yielded a high threat value, but these grid cells were not numerous enough to be recognized by the eye.

Since agriculture and urbanization contributed to the overall threat level the most, management strategies to mitigate the impact of these practices should be considered. These management strategies include agricultural best management practices such as reducing water use and diversion, environmental functional zoning to protect vulnerable habitats from urban growth, and formal mechanisms such as policies created in response to landscape threat assessment models ^[13][14].