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DNA metabarcoding from the ethanol used to store macroinvertebrate bulk samples is a convenient methodological option in molecular biodiversity assessment and biomonitoring of aquatic ecosystems, as it preserves specimens and reduces problems associated with sample sorting. However, this method may be affected by errors and biases, which need to be thoroughly quantified before it can be mainstreamed into biomonitoring programmes. Here, we used 80 unsorted macroinvertebrate samples collected in Portugal under a Water Framework Directive monitoring programme, to compare community diversity and taxonomic composition metrics estimated through morphotaxonomy versus metabarcoding from storage ethanol using three markers (COI‐M19BR2, 16S‐Inse01 and 18S‐Euka02) and a multimarker approach. A preliminary in silico analysis showed that the three markers were adequate for the target taxa, with detection failures related primarily to the lack of adequate barcodes in public databases. Metabarcoding of ethanol samples retrieved far less taxa per site (alpha diversity) than morphotaxonomy, albeit with smaller differences for COI‐M19BR2 and the multimarker approach, while estimates of taxa turnover (beta diversity) among sites were similar across methods. Using generalized linear mixed models, we found that after controlling for differences in read coverage across samples, the probability of detection of a taxon was positively related to its proportional abundance, and negatively so to the presence of heavily sclerotized exoskeleton (e.g., Coleoptera). Overall, using our experimental protocol with different template dilutions, the COI marker showed the best performance, but we recommend the use of a multimarker approach to detect a wider range of taxa in freshwater macroinvertebrate samples. Further methodological development and optimization efforts are needed to reduce biases associated with body armouring and rarity in some macroinvertebrate taxa. [ABSTRACT FROM AUTHOR]

Reservoirs are ecosystems that provide important services and reserves of aquatic biodiversity, especially in arid and semiarid regions. However, they are also highly vulnerable to pollution, water abstraction and land use change. Here, we aimed to develop a "dirty‐water" predictive model that could be used to create recovery scenarios and analyze the effect of measures on the biological quality of reservoirs. Accordingly, we constructed a machine learning predictive model that was trained with environmental and macroinvertebrate community data from 129 sites, sampled in six reservoirs in Northeast Brazil. Two different rehabilitation scenarios were simulated (D1 = lower improvement, 25% change; and D2 = higher improvement, 75% change), and three initial levels of disturbance were considered based on PCA analyses: least disturbed, moderately disturbed and severely disturbed. The effects were analyzed in terms of changes in expected taxa (E), observed/expected taxon ratio (OE), biotic and trophic status indices and the spatial distribution of sensitive taxa. The simulations resulted in significant improvements (p <.001) for all disturbance levels and indicators analyzed. Furthermore, rehabilitation measures in artificial systems, such as reservoirs, could result in a higher biodiversity, biological quality and water quality, also in the least disturbed sites. Thus, this approach has the potential to provide information on management and conservation measures, since the simultaneous predictions of many taxa can be used, including: mapping the distribution of species; monitor the population dynamics of species at risk of extinction; and determining the best rehabilitation measures to improve disturbed ecosystems, allowing the determination of the most cost‐effective restoration measures. [ABSTRACT FROM AUTHOR]

Thermal springs provide extreme ecological conditions for aquatic communities owing to their high water temperature and particular water chemistry. The thermal springs and their connected watercourses provide a thermal laboratory by offering a wide range of temperatures within short spatial distances. To date, the information on how the biodiversity of these springs is related to water temperature or chemistry is limited. We studied the effects of water temperature on diatom community diversity and structure with the objective to supply a baseline for the conservation of thermal springs. We sampled 31 sites of 16 thermal springs across a temperature gradient between 18.5 and 63.0°C in the north-western Iberian Peninsula and related diatom richness and structure to water temperature. A total of 124 diatom species were identified in springs with water temperature between 18.5 and 42.4°C. Community diversity decreased with increasing temperature and the highest species richness was found at temperatures ≤ 25°C. Three diatom assemblages were defined, discriminated by temperature ranges (≤25°C, 25–35°C and ≥35°C). Water temperature was found to be an important driver of diatom community composition in the thermal systems studied. Temperature affects diatom distribution decreasing diversity with the increase in global water temperature. [ABSTRACT FROM AUTHOR]