Landscape-based cumulative effects models for predicting stream response to mountaintop mining in multistressor Appalachian watersheds

We conducted a survey of 170 streams distributed throughout the mountaintop-mining region of West Virginia (USA) and linked stream data to a temporally consistent and comprehensive land-cover data set. We then applied a generalized linear modeling framework and constructed cumulative effects models capable of predicting in-stream response to future surface-mine development within the context of other landuse activities. Predictive models provided precise estimates of specific conductance (model R2 ≤ 0.77 and cross-validated R2 ≤ 0.74), Se (0.74 and 0.70), and benthic macroinvertebrate community composition (0.72 and 0.65). Deletion tests supported the conclusion that stream degradation across the region is the result of complex, but predictable, additive and interactive effects of surface mining, underground mining, and residential development. Furthermore, we found that as stressors other than surface mining are factored out completely, the surface-mining level that results in exceedance of the 300 µS/cm conductivity benchmark increased from 4.4% in the presence of other stressors to 16.6% when only surface mining was present. Last, extrapolating model results to all unsampled stream segments in the region (n = 26,135), we predicted high levels of chemical (33%) and biological (67%) impairment to streams on the current landscape. Of this total impairment, however, <25% could be attributed to surface mining alone. These results underscore the importance of multistressor landuse models for reliable predictions of stream conditions, and the difficulty of interpreting correlations between surface mining and stream impairment without fully accounting for other landuse activities.