Reconstructing long‐term human impacts on plant communities: an ecological approach based on lake sediment DNA

Abstract

Paleoenvironmental studies are essential to understand biodiversity changes over long timescales and to assess the relative importance of anthropogenic and environmental factors. Sedimentary ancient DNA (sedaDNA) is an emerging tool in the field of paleoecology and has proven to be a complementary approach to the use of pollen and macroremains for investigating past community changes. SedaDNA-based reconstructions of ancient environments often rely on indicator taxa or expert knowledge, but quantitative ecological analyses might provide more objective information. Here, we analysed sedaDNA to investigate plant community trajectories in the catchment of a high-elevation lake in the Alps over the last 6400 years. We combined data on past and present plant species assemblages along with sedimentological and geochemical records to assess the relative impact of human activities through pastoralism, and abiotic factors (temperature and soil evolution). Over the last 6400 years, we identified significant variation in plant communities, mostly related to soil evolution and pastoral activities. An abrupt vegetational change corresponding to the establishment of an agropastoral landscape was detected during the Late Holocene, approximately 4500 years ago, with the replacement of mountain forests and tall-herb communities by heathlands and grazed lands. Our results highlight the importance of anthropogenic activities in mountain areas for the long-term evolution of local plant assemblages. SedaDNA data, associated with other paleoenvironmental proxies and present plant assemblages, appear to be a relevant tool for reconstruction of plant cover history. Their integration, in conjunction with classical tools, offers interesting perspectives for a better understanding of long-term ecosystem dynamics under the influence of human-induced and environmental drivers.

Environmental DNA metabarcoding: Transforming how we survey animal and plant communities

“…No studies to date have estimated the sources of eDNA in surface water from a lake's catchment and related it to the diversity locally occurring in the lake. However, ancient DNA from sediment cores in lakes (sedaDNA) has been used to determine historical plant (e.g., Pansu, Giguet-Covex, Ficetola, et al 2015;Parducci et al, 2013) and livestock communities (Giguet-Covex et al, 2014), thus indicating that lakes do receive DNA from species in their catchments which can be incorporated into their sediments. For a more extensive review of sedaDNA being used to reconstruct past ecosystems, see Pedersen et al (2015) and Brown and Blois (2016).…”

Robust Soil Quality Index for Tropical Soils Influenced by Agricultural Activities

“…The minimum content of organic matter was 0.11% and a maximum of 3.94%. This low organic matter content can be attributed to the low amount of organic materials applied to the soil and to the complete removal of the biomass in the field, as it was observed by Pansu et al [12]. On the other hand, Lal & Stewart [3] suggest that continuous and intensive cultivation practices can explain the deterioration of the soil aggregates, combined with the low return of plant biomass to the soil in cultivated land.…”

“…Total annual precipitation varies between 600 and 800 mm. The vegetation present in the municipality varies: tropical deciduous forest is the dominant vegetation, but mangrove, xerophyte, scrub, oak forest, conifers and riparian vegetation could also be found [11] [12].…”

Proper experimental design requires randomization/balancing of molecular ecology experiments

“…Potential contamination and false detection biases were controlled for by following the recommendations of Giguet-Covex et al, 2014;Pansu et al, 2015) were run with GNU "parallel" when possible (Tange, 2011 (Hill, 1973) to estimate the effects of potential laboratory biases and biological factors of interests. The first three Hill numbers correspond to species richness (H1), the exponent of Shannon diversity (H2), and the inverse of the Simpson diversity (H3).…”