Microbial response to heavy metal-polluted soils:: Community analysis from phospholipid-linked fatty acids and ester-linked fatty acids extracts

Heavy metal pollution of soil is of concern for human health and ecosystem function. The soil microbial community should be a sensitive indicator of metal contamination effects on bioavailability and biogeochemical processes. Simple methods are needed to determine the degree of in situ pollution and effectiveness of remediating metal-contaminated soils. Currently, phospholipid-linked fatty acids (PLFAs) are preferred for microbial profiling but this method is time consuming, whereas direct soil extraction and transesterification of total ester-linked fatty acids (ELFAs) is attractive because of its simplicity. The 1998 mining acid-metal spill of > 4000 ha in the Guadiamar watershed (southwestern Spain) provided a unique opportunity to study these two microbial lipid profiling methods. Replicated treatments were set up as nonpolluted, heavy metal polluted and reclaimed, and polluted soils. Inferences from whole community-diversity analysis and correlations of individual fatty acids with metals suggested Cu, Cd, and Zn were the most important in affecting microbial community structure, along with pH. The microbial stress marker, monounsaturated fatty acids, was significantly lower for reclaimed and polluted soil over nonpolluted soils for both PLFA and ELFA extraction. Another stress marker, the monounsaturated to saturated fatty acids ratio, only showed this for the PLFA. The general fungal marker (18:2 omega 6c), the arbuscule mycorrhizae marker (16:1 omega 5c), and iso- and anteiso-branched PLFAs (Gram positive bacteria) were suppressed with increasing pollution whereas 17:0cy (Gram negative bacteria) increased with metal pollution. For both extraction methods, richness and diversity were greater in nonpolluted soils and lowest in polluted soils. The ELFA method was sensitive for reflecting metal pollution on microbial communities and could be suitable for routine use in ecological monitoring and risk assessment programs because of its simplicity and reproducibility.