Environmental filtering of macroinvertebrate traits influences ecosystem functioning in a large river floodplain

Article describes how the biodiversity–ecosystem function hypothesis postulates that higher biodiversity is correlated with faster ecosystem process rates and increased ecosystem stability in fluctuating environments. The authors examined linkages among floodplain wetland habitats, invertebrate communities and their associated traits, and ecosystem function across 60 sites within the floodplain wetlands of the lower Wolastoq, Saint John River, New Brunswick

Replicate DNA metabarcoding can discriminate seasonal and spatial abundance shifts in river macroinvertebrate assemblages

Metabarcoding is capable of delivering consistent and accurate fine-resolution biodiversity data, and offers great promise for improving aspects of environmental assessment and research. Even so, many ecologists are keen to make further inferences about species’ abundances and the number of sequence reads has proven to be a poor proxy for abundance. The conservative interpretation has been to treat metabarcoding data as presence/absence, and although such data are less rich, occurrence and abundance are only different expressions of the same phenomenon. Interestingly if we assume the probability of detecting individuals is constant, it should be possible to use changes in the frequency of detection to infer changes in the underlying abundance. We tested the possibility that changes in the abundance structure of benthic macroinvertebrate communities could be recovered using replicated metabarcoding.We conducted 5 monthly surveys from Jun-Nov 2019 at the Catamaran Brook, a small tributary of the Little Southwest Miramichi River in New Brunswick, Canada. Each survey collected 30 benthic samples divided between control and treatment cages that excluded predatory fish. A further 6 samples were taken for traditional microscopic identification and counting.Analysis of the metabarcoding data demonstrated that we could recover plausible changes in abundance from occurrence data, including significant responses to both seasonal dynamics and the experimental exclusion of predators. The microscopy samples merely confirmed that count data are highly stochastic, and therefore while specific estimates of expected abundance from our model are highly uncertain, they capture those differences we could validate. In summary, while we confirmed that occurrence data are more robust for routine bioassessment, it is possible to recover fine-resolution changes in abundance that can inform ecological studies using metabarcoding

Functional traits link anthropogenic impact and disturbance regimes driving ecosystem function in a floodplain wetland complex

Floodplains are disturbance-driven ecosystems with high spatial and temporal habitat diversity, making them both highly productive and hosts to high biodiversity. The unpredictable timing of flood and drought years creates a mosaic of habitat patches at different stages of succession, while water level fluctuation directly influences macrophyte community dynamics, and thus habitat structure. This habitat complexity and diversity of disturbance regimes makes floodplains an ideal ecosystem in which to examine the links between biodiversity, traits and ecosystem function. With up to 90% of floodplains in North America and Europe altered to the point of functional extinction, it is particularly imperative to study and conserve those that remain intact, such as the Lower Saint John River and its associated floodplain, including the Grand Lake Meadows and Portobello Creek wetland complex. Despite the rise in trait-based science, taxonomic resolution has imposed limitations, especially in wetland and floodplain ecosystems where communities are vastly understudied compared to their riverine counterparts. Compared to traditional biomonitoring, DNA-based biomonitoring from high-throughput genomics sequencing methods is powerful in that it can reliably characterize community composition in unprecedented detail, allowing us to assess how disturbance and environmental filters interact with invertebrate traits and ecosystem function. Using structural equation analysis, we take a whole ecosystem approach to examine ecosystem health across a floodplain disturbance gradient. We focus chiefly on how anthropogenic alteration within watersheds affects downstream floodplain wetlands, how the resulting patch diversity shapes communities and, finally, how those communities influence ecosystem function through trait diversity metrics. We also examine and compare which traits are associated with crucial ecosystem gradients

Assessing the Health Information Source Perceptions of Tweens Using Card-Sorting Exercises

As young people are increasingly turning to the Internet to meet their information needs, it is imperative to investigate their perceptions regarding various potential sources of health information. A series of card-sorting exercises were administered to new participants in an after-school programme (HackHealth) to find out which sources of health information these greater Washington DC metro area middle school students would turn to, which they would not and their reasons behind these judgements. The findings revealed that participants were very aware of the importance of trustworthiness when looking for health information and they valued both professional expertise based on formal education and expertise born of personal experience with a particular health condition. However, they also valued convenience, ease and speed, and sometimes sacrificed information quality. Some important implications of these findings for healthcare and information professionals are identified and suggestions for future research in this area are offered

Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems:Results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop

A workshop was held in Wageningen, The Netherlands, in September 2017 to collate data and literature on three aquatic ecosystem types (agricultural drainage ditches, urban floodplains, and urban estuaries), and develop a general framework for the assessment of multiple stressors on the structure and functioning of these systems. An assessment framework considering multiple stressors is crucial for our understanding of ecosystem responses within a multiply stressed environment, and to inform appropriate environmental management strategies. The framework consists of two components: (i) problem identification and (ii) impact assessment. Both assessments together proceed through the following steps: 1) ecosystem selection; 2) identification of stressors and quantification of their intensity; 3) identification of receptors or sensitive groups for each stressor; 4) identification of stressor-response relationships and their potential interactions; 5) construction of an ecological model that includes relevant functional groups and endpoints; 6) prediction of impacts of multiple stressors, 7) confirmation of these predictions with experimental and monitoring data, and 8) potential adjustment of the ecological model. Steps 7 and 8 allow the assessment to be adaptive and can be repeated until a satisfactory match between model predictions and experimental and monitoring data has been obtained. This paper is the preface of the MAEGA (Making Aquatic Ecosystems Great Again) special section that includes three associated papers which are also published in this volume, which present applications of the framework for each of the three aquatic systems

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables