Local adaptation is of fundamental importance for populations to cope with fast, human-mediated environmental changes. In the past, analyses of local adaptation were restricted to few model species. Nowadays, due to the increased affordability of high-throughput sequencing, local adaptation can be studied much easier by searching for patterns of positive selection using genomic data. In the present study, we analysed effects of wastewater treatment plant and ore mining effluents on stream invertebrate populations. The two different anthropogenic stressors have impacted on stream ecosystems over different time scales and with different potencies. As target organisms we selected two macroinvertebrate species with different life histories and dispersal capacities: the caddisfly Glossosoma conformis and the flatworm Dugesia gonocephala. We applied a genome-wide genetic marker technique, termed ddRAD (double digest restriction site associated DNA) sequencing, to identify local adaptation. Ten and 18% of all loci were identified as candidate loci for local adaptation in D. gonocephala and G. conformis, respectively. However, after stringent re-evaluation of the genomic data, strong evidence for local adaptation remained only for one population of the flatworm D. gonocephala affected by high copper concentration from ore mining. One of the corresponding candidate loci is arnt, a gene associated with the response to xenobiotics and potentially involved in metal detoxification. Our results support the hypotheses that local adaptation is more likely to play a central role in environments impacted by a stronger stressor for a longer time and that it is more likely to occur in species with lower migration rates. However, these findings have to be interpreted cautiously, as several confounding factors may have limited the possibility to detect local adaptation. Our study highlights how genomic tools can be used to study the adaptability and thus resistance of natural populations to changing environments and we discuss prospects and limitations of the methods.
Keywords: Metal mining; Population genomics; Positive selection; Wastewater treatment plants.
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