Mosquito control actions affect chironomid diversity in temporary wetlands of the Upper Rhine Valley

The Upper Rhine Valley, a “hotspot of biodiversity” in Germany, has been treated with the biocide Bacillus thuringiensis var. israelensis (Bti) for mosquito control for decades. Previous studies discovered Bti nontarget effects in terms of severe chironomid abundance reductions. In this study, we investigated the impact of Bti on species level and addressed the community composition of the nontarget family Chironomidae by use of community metabarcoding. Chironomid emergence data were collected in three mosquito‐control relevant wetland types in the Upper Rhine Valley. For all three sites the chironomid species composition, based on operational taxonomic units (OTUs), was different to varying degrees in the Bti‐treated samples versus control samples, ranging from a significant 63% OTU reduction to an OTU replacement. We assumed that predatory chironomids are less prone to Bti than filter feeders, as the latter feed on floating particles leading to direct ingestion of Bti. However, a comparable percentage of predators and filter feeders (63% and 65%, respectively) was reduced in the Bti samples, suggesting that the feeding strategy is not the main driver for Bti sensitivity in chironomids. Finally, our data was compared to a three‐year‐old data set, indicating possible chironomid community recovery due to species recolonization a few years after the last Bti application. Considering the currently discussed worldwide insect decline we recommend a rethinking of the usage of the biocide Bti, and to prevent its ongoing application especially in nature protection reserves to enhance ecological resilience and to prevent boosting the current biodiversity loss.     

Four decades of functional community change reveals gradual trends and low interlinkage across trophic groups in a large marine ecosystem

The rate at which biological diversity is altered on both land and in the sea, makes temporal community development a critical and fundamental part of understanding global change. With advancements in trait‐based approaches, the focus on the impact of temporal change has shifted towards its potential effects on the functioning of the ecosystems. Our mechanistic understanding of and ability to predict community change is still impeded by the lack of knowledge in long‐term functional dynamics that span several trophic levels. To address this, we assessed species richness and multiple dimensions of functional diversity and dynamics of two interacting key organism groups in the marine food web: fish and zoobenthos. We utilized unique time series‐data spanning four decades, from three environmentally distinct coastal areas in the Baltic Sea, and assembled trait information on six traits per organism group covering aspects of feeding, living habit, reproduction and life history. We identified gradual long‐term trends, rather than abrupt changes in functional diversity (trait richness, evenness, dispersion) trait turnover, and overall multi‐trait community composition. The linkage between fish and zoobenthic functional community change, in terms of correlation in long‐term trends, was weak, with timing of changes being area and trophic group specific. Developments of fish and zoobenthos traits, particularly size (increase in small size for both groups) and feeding habits (e.g. increase in generalist feeding for fish and scavenging or predation for zoobenthos), suggest changes in trophic pathways. We summarize our findings by highlighting three key aspects for understanding functional change across trophic groups: (a) decoupling of species from trait richness, (b) decoupling of richness from density and (c) determining of turnover and multi‐trait dynamics. We therefore argue for quantifying change in multiple functional measures to help assessments of biodiversity change move beyond taxonomy and single trophic groups.     

Combining information from multiple bone turnover markers as diagnostic indices for osteoporosis using support vector machines

Context: Osteoporosis (OP) is a progressive systemic bone disease. Dual-energy X-ray absorptiometry (DXA) is routinely employed and is considered the gold standard method for the diagnosis of OP.

Objective: We aimed to investigate the potential use of combined information from multiple bone turnover markers (BTMs) as a clinical diagnostic tool for OP.

Materials and methods: A total of 9053 Chinese postmenopausal women (2464 primary OP patients and 6589 healthy controls) were recruited. Serum levels of six common BTMs, including BAP, BSP, CTX, OPG, OST and sRANKL were assayed. Models based on support vector machine (SVM) were constructed to explore the efficiency of different combinations of multiple BTMs for OP diagnosis.

Results: Increasing the number of BTMs used in generating the models increased the predictive power of the SVM models for determining the disease status of study subjects. The highest kappa coefficient for the model with one BTM (BAP) compared to DXA was 0.7783. The full model incorporating all six BTMs resulted in a high kappa coefficient of 0.9786.

Conclusion: Our findings showed that although single BTMs were not sufficient for OP diagnosis, appropriate combinations of multiple BTMs incorporated into the SVM models showed almost perfect agreement with the DXA.     

Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long‐term data assimilation

It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model‐data fusion framework and systematically analyzed the SSA‐induced biases using time‐series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83‐fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA‐induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young‐aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long‐term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C‐climate feedback.