A taxonomy-free diatom eDNA-based technique for assessing lake trophic level using lake sediments.

Anthropogenic eutrophication is one of the most pressing issues facing lakes globally. Our ability to manage lake eutrophication is hampered by the limited spatial and temporal extents of monitoring records, stemming from the time-consuming and expensive nature of physiochemical and biological monitoring. Diatom-based biomonitoring presents an alternative to traditional eutrophication monitoring, yet it is restricted by the high degree of taxonomic expertise required. Environmental DNA metabarcoding, while providing a promising substitute for diatom community enumeration, is plagued by inadequate taxonomic coverage of reference databases and methodological bias, limiting its use for biomonitoring. Here we show that taxonomy-free diatom-biomonitoring, in which environmental DNA metabarcoding data is utilised but not assigned to specific taxonomic classes, presents an accurate, fast, and relatively automated alternative to taxonomically assigned eutrophication biomonitoring. Our taxonomy-free index accounted for 85% of trophic level variability across 89 lakes and had the lowest average prediction error of the three approaches tested. By not relying on taxonomic identification or metabarcoding reference databases, taxonomy-free biomonitoring maintains diatom diversity that is lost in taxonomic assignment using molecular approaches. Furthermore, by utilising lake sediments, the approach outlined here presents a time-integrated estimation of lake trophic level and thus does not require time-consuming seasonal sampling. Taxonomy-free biomonitoring addresses the limitations of traditional physicochemical eutrophication monitoring and taxonomic biomonitoring alternatives and can be used to extend the spatial and temporal extents of eutrophication monitoring.

Resolving 500 Years of Anthropogenic Impacts in a Mesotrophic Lake: Nutrients Outweigh Other Drivers of Lake Change.

Interactions among multiple stressors, legacies of past perturbations, and the lack of historical information make it difficult to determine the influence of individual anthropogenic impacts on lakes and separate them from natural ecosystem variability. In the present study, we coupled paleolimnological approaches, historical data, and ecological experiments to disentangle the impacts of multiple long-term stressors on lake ecosystem structure and function. We found that the lake structure and function remained resistant to the impacts of catchment deforestation and erosion, and the introduction of several exotic fish species. Changes in ecosystem structure and function were consistent, with nutrient enrichment being the primary driver of change. Significant and sustained changes in the lake diatom community structure (and their nutrient requirements), bacterial community function, and paleolimnological proxies of ecosystem function coincided with nitrogen and phosphorus fertilizers in the catchment. The results highlight that the effects of increased nutrient inputs are much stronger than the influence of other, potentially significant, drivers of ecosystem change, and that the degree of nutrient impact can be underestimated by environmental monitoring due to its diffuse and accumulative nature. Delineating the effects of multiple anthropogenic drivers requires long-term records of both impacts and lake ecosystem change across multiple trophic levels.

Paleo-diatom records reveal ecological change not detected using traditional measures of lake eutrophication.

Lakes provide crucial ecosystem services and harbour unique and rich biodiversity, yet despite decades of research and management focus, cultural eutrophication remains a predominant threat to their health. Our ability to manage lake eutrophication is restricted by the lack of long-term monitoring records. To circumvent this, we developed a bio-indicator approach for inferring trophic level from lake diatom communities and applied this to sediment cores from two lakes experiencing eutrophication stress. Diatom indicators strongly predicted observed trophic levels, and when applied to sediment cores, diatom predicted trophic level reconstructions were consistent with monitoring data and land-use histories. However, there were significant recent shifts in diatom communities not captured by the diatom-based index or monitoring data, suggesting that conventional trophic level indices obscure important ecological change. New approaches, such as the one in this study, are critical to detect early changes in water quality and prevent the decline of lake ecosystems worldwide.

A bacterial index to estimate lake trophic level: National scale validation.

Lakes and their catchments have been subjected to centuries to millennia of exploitation by humans. Efficient monitoring methods are required to promote proactive protection and management. Traditional monitoring is time consuming and expensive, which limits the number of lakes monitored. Lake surface sediments provide a temporally integrated representation of environmental conditions and contain high microbial biomass. Based on these attributes, we hypothesized that bacteria associated with lake trophic states could be identified and used to develop an index that would not be confounded by non-nutrient stressor gradients. Metabarcoding (16S rRNA gene) was used to assess bacterial communities present in surface sediments from 259 non-saline lakes in New Zealand encompassing a range of trophic states from alpine microtrophic lakes to lowland hypertrophic lakes. A subset of lakes (n = 96) with monitoring data was used to identify indicator amplicon sequence variants (ASVs) associated with different trophic states. A total of 10,888 indicator taxa were identified and used to develop a Sediment Bacterial Trophic Index (SBTI), which signficantly correlated (r2 = 0.842, P < 0.001) with the Trophic Lake Index. The SBTI was then derived for the remaining 163 lakes, providing new knowledge of the trophic state of these unmonitored lakes. This new, robust DNA-based tool provides a rapid and cost-effective method that will allow a greater number of lakes to be monitored and more effectively managed in New Zealand and globally. The SBTI could also be applied in a paleolimnological context to investigate changes in trophic status over centuries to millennia.