Improving assessment accuracy for lake biological condition by classifying lakes with diatom typology, varying metrics and modeling multimetric indices

A harmonized dataset of sediment diatoms from hundreds of lakes in the northeastern United States

Sediment diatoms are widely used to track environmental histories of lakes and their watersheds, but merging datasets generated by different researchers for further large-scale studies is challenging because of taxonomic discrepancies caused by rapidly evolving diatom nomenclature and taxonomic concepts. We collated five datasets of lake sediment diatoms from the Northeastern USA using a harmonization process which included updating synonyms, tracking the identity of inconsistently identified taxa, and grouping those that could not be resolved taxonomically. Each harmonization step led to an increase in variation explained by environmental variables and a parallel reduction of variation attributable to taxonomic inconsistency. To maximize future use of the data and underlying specimens we provide the original and harmonized counts for 1327 core samples from 607 lakes, name translation schemes, sample metadata, specimen museum locations, and the Northeast Lakes Voucher Flora, which is a set of light microscope images grouped into 1154 morphological operational taxonomic units. Post-hoc harmonization enables data quality control when other approaches (e.g., upfront management of taxonomic consistency) are not possible.

Improving biological condition assessment accuracy by multimetric index approach with microalgae in streams and lakes

Multimetric index (MMI) approach is a broadly used in ecological assessment because it can integrate information of various kinds of ecologically related metrics of freshwater ecosystems and provide an easily understandable score for purpose of further evaluation and managements. Accounting for natural variation and disentangling covariation between natural environmental factors and human disturbance factors are imperative for an accurate assessment. Lots of progress has been made recently on the aforementioned two aspects. Three approaches, a priori classification of sites by regions or typologies, site-specific modeling of expected reference condition and varying metrics in site groups, have been tested in lakes and streams to improve assessment accuracy. All existed studies support that site-specific modeling can efficiently account for natural variation and generate a MMI with good performance. However, until now, no strong evidence has shown that diatom/blue-algae typologies are better than regionalization frameworks on accounting for natural variation either in lakes or in streams. To separate the natural variation explained by site specific modeling from that of varying metrics is necessary for a thorough and accurate evaluation on the valuableness of site-grouping by typologies. Different performance of varying metrics among site groups of streams and lakes was most probably caused by the lack of representativeness of diatom metrics on biological condition rather than the complex multi-stressor gradients in streams and rivers. A recent study showed that blue-green algae enhanced performance of diatom-based MMI on defining lake condition under high level of human disturbance. On the other hand, with more and more extensive and intensive use of statistics techniques in developing MMI, we also discussed some statistical challenges faced by scientists in field of ecological assessment, especially on setting significance level of a statistical test and multiple comparison issue in MMI performance comparison.

Blue-green algae enhanced performance of diatom-based multimetric index on defining lake condition under high level of human disturbance

Degradation of lake conditions could result from many stressors generated by human disturbance. Accurately defining lake ecological condition by multimetric index (MMI) method is of great importance for tracking source of stressors and lake management. For algal assessment, seldom have structural and functional attributes of soft-bodied planktonic algae metrics, one important dimension of biological condition, been used to develop MMI in conjunction with diatom metrics. Another thing is that some researchers found MMI method does not perform well in mid- and high-disturbed lakes. To test the aforementioned questions, we used data sets of the 2007 National Lake Assessment project of the USEPA to develop MMIs with and without using soft-bodied planktonic algae metrics for plains and lowlands area (PLNLOW, high disturbed region of the US) and across the conterminous US. Compared to site groups modeled by single diatom assemblages, we found integrating soft-bodied planktonic algae metric (especially blue-green algae metric) into developing MMIs can significantly improve performance of MMI in PLNLOW region. The separation powers of MMIs of five level III ecoregions, developed by incorporating blue-green algae metric, are consistently higher than those developed by single diatom assemblages (p-value = 0.029). However, when blue-green algae metric was applied to develop MMI along with diatom metrics in the national scale assessment, performances of MMIs are similar to that developed by diatom metrics (0.14 < p-value < 0.86). Different performance of MMIs developed by integrating blue-green algae metric at different spatial scales indicated the usefulness of blue-green algae metric in ecological assessment in mid- and high- disturbed lakes and a tiered approach for using diatom and blue-green algae metric in ecological assessment. We suggest using blue-green algae metric in combined with diatom metric to develop MMI when lakes are mid- and high-disturbed, while a routine diatom assessment would be enough for minimally disturbed sites.

Bottom-up and top-down effects on phytoplankton communities in two freshwater lakes

The relative importance of bottom-up versus top-down effects in aquatic ecosystems remains a longstanding and ongoing controversy. To investigate these effects on phytoplankton communities in freshwater lakes, phytoplankton and zooplankton were sampled, and physical-chemical variables were measured during spring and summer in two important freshwater lakes in northern China: Nansi Lake and Dongping Lake. The redundancy analysis results showed that phytoplankton density and biomass were regulated by physical-chemical variables (bottom-up effects) and predation (top-down effects) together, and the former was more prominent in both lakes. However, the correlation analysis indicated that the top-down effects of zooplankton on phytoplankton were not significant in spring and summer in both lakes, while the bottom-up regulation of physical-chemical variables on phytoplankton had different patterns in the two lakes. In Nansi Lake, the bottom-up effects of physical-chemical variables on phytoplankton were weaker in summer than that in spring due to the abundant nutrients in summer. In Dongping Lake, the bottom-up effects of physical-chemical on phytoplankton were significant both in spring and summer, and the dominant bottom-up control factor shifted from total nitrogen in spring to total phosphorus in summer, with an increased ratio of nitrogen to phosphorus due to changes in limiting factors. In the two studied lakes, with fish culture, the bottom-up effects of phytoplankton on zooplankton were more important than the top-down effects of zooplankton on phytoplankton. These results demonstrate the interactions between phytoplankton and zooplankton and highlight the importance of phytoplankton regulation in freshwater lakes, which has implications for the effective management of freshwater lake ecosystems.

Biological Health Assessments of Lotic Waters by Biotic Integrity Indices and their Relations to Water Chemistry

Biological health assessment (BHA) has developed as an imperative gauge in efficient management of freshwater resources and fish assemblages. The principal objective of this research is directed at the development and application of a new index under the umbrella of the famous index of biological integrity (IBI). Further, we intended to carry out comparative assessments of the new index with two existing indices and testified to their rational in Geum River watershed, which is the third largest river water basin in South Korea. We analyzed the biotic integrity of 149 different study sites in the streams and rivers of Geum River watershed, perusing fish assemblages and water quality data. The results revealed the newer index, i.e., multi-metric (mm) IBI 06 (mmIBI 06 metrics), as comparatively more efficient than previously used indices, i.e., mmIBI 08 and mmIBI 11. Furthermore, the linear regression and correlational analyses indicated harmonic relation of mmIBI 06 with mmIBI 08 (R2 = 0.85) and mmIBI 11 (R2 = 0.87). Nonetheless, linear regression modeling discovered a very strong positive relation between mmIBI 08 and mmIBI 11 (R2 = 0.91), thereby implying previously used indices demonstrate better approximation. In significant contrast to both old indices, the newer index exhibited improved facility to better classify the study sites between the assortments of ‘excellent to very poor’ compared to old indices elucidated one-step lower, i.e., from ‘excellent to poor’. For instance, the newer index categorized 60 sites as ‘very poor’, requiring immediate attention owing to biological degradation. Additionally, the newer index endures grander ability to indicate sites requiring immediate management or restoration attention with a plausible site classification, especially in relation to the invasive alien species (IAS). The water chemistry was mainly influenced by rampant anthropogenic actions compounded by intensive monsoon precipitation that may relinquish highly suitable grounds for invasive alien fish species. This may eventually lead to severe biological degradation and successive deterioration of habitat by the IAS. In conclusion, the newer index endures ample capacity to indicate the fish community disturbances in rivers and streams. Further, correlation, linear regression, as well as principal component analysis (PCA) analyses on biotic indices and water chemistry showed higher approximations. Therefore, our newer index would be a valuable BHA tool to classify and elucidate the streams and rivers by indicating sites necessitating preferred attention and restoration measures.

Advancing evaluation of bioassessment methods: A reply to Liu and Cao

A series of three papers was written about the development of multimetric indices (MMIs) using diatoms in rivers, streams and lakes for transcontinental surveys conducted by the United States Environmental Protection Agency. Stevenson et al. (2013) used the surface sediment diatom data from the 2007 National Lake Assessment to develop national scale site specific models for MMIs to account for natural variation in condition among sites. Liu and Stevenson (2017) also used the 2007 lakes data to evaluate performance of MMIs by grouping sites by ecoregions or typologies (naturally similar types of lakes defined by similarity in diatom species composition) with site specific metric models (SSMMs) that adjust metrics for natural variability among sites. Tang et al. (2016) used benthic diatom data from the 2008-2009 National River and Stream Assessment to develop SSMMs and MMIs by ecoregion and typology. All three studies showed that SSMMs improved performance of diatom MMIs by accounting for natural variation among sites. None of the studies provided consistent evidence that grouping sites by typologies produced better MMI performance than grouping sites by ecoregions. Liu and Cao (2018) criticized the Tang et al. (2016) paper for using means and standard errors to evaluate relative performance of MMI calculation methods at the site group scale, however, their criticism is incorrect. Actually, Tang et al. (2016) only used means to summarize and report relative performance of MMI calculation methods in the body of the paper. Tang et al. (2016) appropriately used non-parametric rank sum approaches to evaluate the probability that the multiple MMI calculations for separate site groups were the same for ecoregion (n = 9) and typology (n = 7) site groups. Liu and Stevenson (2017) used this same non-parametric approach for tests of lake diatom MMIs. Liu and Cao's (2018) concerns can be addressed by distinguishing between the goals and methods used for testing and evaluation of MMI calculation methods at the national and site-group scales. Tang et al. (2016) did not aggregate data across site groups to test MMI performance at the national scale because they were following standard EPA methods that develop separate MMIs for each site group. In conclusion, Liu and Cao (2018) misunderstood the MMI evaluation in Tang et al. (2016) and added no new information to this body of work, because all the concerns they raised were discussed in Liu and Stevenson (2017).