The effect of microplastics (MP) exposure on the chironomid species Chironomus riparius Meigen, 1804 was investigated using the OECD sediment and water toxicity test. Chironomid larvae were exposed ...to an environmentally relevant low microplastics concentration (LC), a high microplastics concentration (HC) and a control (C). The LC was 0.007 g m−2 on the water surface + 2 g m−3 in the water column + 8 g m−2 in the sediment, and the HC was 10 X higher than this for each exposure. The size of the majority of the manufactured microplastic pellets varied between 20 and 100 μm. The MP mixture consisted of: polyethylene-terephtalate (PET), polystyrene (PS), polyvinyl-chloride (PVC) and polyamide (PA) in a ratio of 45%: 15%: 20%: 20%, respectively, for the sediment exposure; 100% polyethylene for the water column exposure; and 50% polyethylene: 50% polypropylene for the water surface exposure. Different endpoints were monitored, including morphological changes in the mandibles and mentums of 4th instar larvae, morphological changes in the wings, mortality, emergence ratio, and developmental time. A geometric morphometric analysis showed a tendency toward widening of the wings, elongation of the mentums and changing the shape of the mandibles in specimens exposed to both concentrations of microplastics. The development time of C. riparius was significantly prolonged by the MP treatment: 13.8 ± 0.5; 14.4 ± 0.6; and 15.3 ± 0.4 days (mean ± SD) in the C, LC, and HC, respectively. This study indicates that even environmentally relevant concentrations of MP mixture have a negative influence on C. riparius, especially at the larval stage.
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•Exposure to a mixture of microplastics postponed developmental time of C. riparius.•The average body mass and average body length increased.•The right female wings showed differences in the wing shape in treatments.•The mouthparts deformities of the 4th instar larvae were observed in treatments.•Low concentration of MP mixture had negative influence on C. riparius.
Developmental time of C. riparius was prolonged in treatments. Mandibles showed a tendency to widen; the medium tooth and inner lateral teeth of mentums to shorten.
As complex mosaics of lotic, lentic, and terrestrial habitats, intermittent rivers and ephemeral streams (IRES) support high biodiversity. Despite their ecological importance, IRES are poorly ...represented in routine monitoring programs, but recent recognition of their considerable—and increasing—spatiotemporal extent is motivating efforts to better represent IRES in ecological status assessments. We examine response patterns of aquatic macroinvertebrate communities and taxa to flow intermittence (FI) across three European climatic regions. We used self-organizing map (SOM) to ordinate and classify sampling sites based on community structure in regions with continental, Mediterranean and oceanic climates. The SOM passively introduced FI, quantified as the mean annual % flow, and visualized its variability across classified communities, revealing a clear association between community structure and FI in all regions. Indicator species analysis identified taxa indicative of low, intermediate and high FI. In the continental region, the amphipod Niphargus was indicative of high FI and was associated with groundwater-fed IRES, whereas indicators of Mediterranean IRES comprised Odonata, Coleoptera and Heteroptera taxa, which favor lentic conditions. In the oceanic region, taxa indicative of relatively high FI included leuctrid stoneflies and a limnephilid caddisfly, likely reflecting the colonization of IRES by aerial adults from nearby perennial reaches. The Diptera families Chironomidae and Simuliidae showed contrasting FI preferences among regions, reflecting environmental heterogeneity between regions and the coarse taxonomic resolution to which these organisms were identified. These region-specific community and taxon responses of aquatic biota to FI highlight the need to adapt standard biotic indices to enable effective ecological status assessments in IRES.
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•Macroinvertebrates indicative of flow intermittence are identified in three regions.•The amphipod Niphargus was indicative of intermittence in a groundwater-fed river.•Lentic taxa were indicators of intermittent rivers with persistent isolated pools.•Insects with adult flight characterized intermittent reaches near perennial waters.•Species-level identification could enable identification of more indicator taxa.
Due to ubiquitous distribution of taxa, relatively low-cost and efficient sampling procedure, and known responses to environmental gradients, macroinvertebrate indicators are often a central ...component of biological monitoring of freshwater resources. This study examined establishing a baseline reference of benthic macroinvertebrate indicators in a biomonitoring approach as a means for monitoring the freshwater ponds of Sable Island National Park Reserve (SINPR), Canada. We compared water quality parameters monitored from 2015 to 2019 to a biomonitoring approach deployed in May, June, and August of 2019. A total of 27 taxa were recorded from the 30,226 specimens collected, with highest abundances of Corixidae, Amphipoda, Oligochaeta, and chironomid species
Polypedilum bicrenatum
. We found significant variability of community structure between different months of sampling (
p
= 0.001) and between ponds (
p
< 0.0001). A high correlation was found between dissolved organic carbon, sulfate, and the diversity of macroinvertebrate indicators, while conductivity, ammonia, and calcium were found to be correlated with species richness. While we found that water chemistry parameters exhibited spatial and temporal differences, the diversity of macroinvertebrate indicators is likely to be a more resilient metric for comparison between ponds. Further, our findings demonstrate that biomonitoring can be effective in systems with a low number of small, shallow, freshwater pond ecosystems. As our study deployed a high-resolution identification of biological indicators, we were able to establish a baseline reference for future monitoring as well as identify specific associations between pond water quality and biological assemblages that can be used as a context for the management of SINPR’s freshwater resources. Continued monitoring of these ecosystems in future years will help to understand long-term environmental changes on the island.
A detailed understanding of microplastics (MPs) behaviour in freshwater ecosystems is crucial for a proper ecological assessment. This includes the identification of significant transport pathways ...and net accumulation zones, considering their inherent, and already proven influence on aquatic ecosystems. Bioavailability of toxic agents is significantly influenced by macroinvertebrates' behaviour, such as bioturbation and burrowing, and their prior exposure history. This study investigates the effect of bioturbation activity of Chironomus riparius Meigen, 1804 on the vertical transfer of polyethylene MPs ex-situ. The experimental setup exposes larvae to a scenario of 10× the environmentally relevant high concentration of MPs (80 g m−2). Bioturbation activity was estimated using sediment profile imaging with luminophore tracers. This study demonstrated that spherical MPs are vertically transferred in the sediment due to the bioturbation activity of C. riparius larvae and that their presence influences the intensity of the bioturbation activity over time. The present findings provide a noteworthy contribution to the understanding of the relationship between ecosystem engineers and the dispersion and accumulation of MPs within freshwater ecosystems.
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•Microplastics are vertically transferred by Chironomus riparius larvae bioturbation.•Microplastic size is different after being ingested by Chironomus riparius.•The presence of microplastics has significant effect on Chironomus riparius bioturbation activity.
The analysis of community structure in studies of freshwater ecology often requires the application of dimensionality reduction to process multivariate data. A high number of dimensions (number of ...taxa/environmental parameters × number of samples), nonlinear relationships, outliers, and high variability usually hinder the visualization and interpretation of multivariate datasets. Here, we proposed a new statistical design using Uniform Manifold Approximation and Projection (UMAP), and community partitioning using Louvain algorithms, to ordinate and classify the structure of aquatic biota in two-dimensional space. We present this approach with a demonstration of five previously published datasets for diatoms, macrophytes, chironomids (larval and subfossil), and fish. Principal Component Analysis (PCA) and Ward's clustering were also used to assess the comparability of the UMAP approach compared to traditional approaches for ordination and classification. The ordination of sampling sites in 2-dimensional space showed a much denser, and easier to interpret, grouping using the UMAP approach in comparison to PCA. The classification of community structure using the Louvain algorithm in UMAP ordinal space showed a high classification strength for data with a high number of dimensions than the cluster patterns obtained with the use of a Ward's algorithm in PCA. Environmental gradients, presented via heat maps, were overlayed with the ordination patterns of aquatic communities, confirming that the ordinations obtained by UMAP were ecologically meaningful. This is the first study that has applied a UMAP approach with classification using Louvain algorithms on ecological datasets. We show that the performance of local and global structures, as well as the number of clusters determined by the algorithm, make this approach more powerful than traditional approaches.
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•Uniform Manifold Approximation and Projection (UMAP) is an ordination technique.•We proposed a new data analysis approach based on UMAP and Louvain algorithms.•Different community data were ordinated and classified using the new approach.•The UMAP provides the ecologically meaningful interpretation of the results obtained.•The results indicate that UMAP is more powerful than the traditional approaches.
The ubiquitous presence of microplastics (MP) in aquatic ecosystems can affect organisms and communities in multiple ways. While MP research on aquatic organisms has primarily focused on marine ...ecosystems and laboratory experiments, the community-level effects of MP in freshwaters, especially in lakes, are poorly understood. To examine the impact of MP on freshwater lake ecosystems, we conducted the first in situ community-level mesocosm experiment testing the effects of MP on a model food web with zooplankton as main herbivores, odonate larvae as predators, and chironomid larvae as detritivores for seven weeks. The mesocosms were exposed to a mixture of the most abundant MP polymers found in freshwaters, added at two different concentrations in a single pulse to the water surface, water column and sediment. Water column MP concentrations declined sharply during the first two weeks of the experiment. Contrary to expectations, MP ingestion by zooplankton was low and limited mainly to large-bodied Daphnia, causing a decrease in biomass. Biomass of the other zooplankton taxa did not decrease. Presence of MP in the faecal pellets of odonate larvae that fed on zooplankton was indicative of a trophic transfer of MP. The results demonstrated that MP ingestion varies predictably with MP size, as well as body size and feeding preference of the organism, which can be used to predict the rates of transfer and further effects of MP on freshwater food webs. For chironomids, MP had only a low, short-term impact on emergence patterns while their wing morphology was significantly changed. Overall, the impact of MP exposure on the experimental food web and cross-ecosystem biomass transfer was lower than expected, but the experiment provided the first in situ observation of MP transfer to terrestrial ecosystems by emerging chironomids.
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•Impacts of microplastic exposure on the food web were lower than hypothesized.•Zooplankton microplastic ingestion was low, and mostly by large-bodied Daphnia.•Microplastics were trophically transferred to odonate larvae.•Exposure to microplastics altered wing morphology in chironomids.•The first in-situ transfer of microplastics to terrestrial ecosystems was recorded.
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•Identification of non-biting midges is time-consuming and requires expertise.•Convolutional Neural Networks was used to automate the identification process.•An automated identifier ...was built to identify chironomid species, genera, and subfamilies.•CNN models and humans relied on same morphological characteristics for identification.•The identifier based on deep learning classified taxa with extremely high accuracy.
Morphological species identification is often a difficult, expensive, and time-consuming process which hinders the ability for reliable biomonitoring of aquatic ecosystems. An alternative approach is to automate the whole process, accelerating the identification process. Here, we demonstrate an automatic machine-based identification approach for non-biting midges (Diptera: Chironomidae) using Convolutional Neural Networks (CNNs) as a means of increasing taxonomic resolution of biomonitoring data at a minimal cost. Chironomidae were used to build the automatic identifier, as a family of insects that are abundant and ecologically important, yet difficult and time-consuming to accurately identify. The approach was tested with 10 morphologically very similar species from the same genus or subfamilies, comprising 1846 specimens from the South Morava river basin, Serbia. Three CNN models were built utilizing either species, genus, or subfamily data. After training the artificial neural network, images that the network had not seen during the training phase achieved an accuracy of 99.5% for species-level identification, while at the genus and subfamily level all images were correctly assigned (100% accuracy). Gradient-weighted Class Activation Mapping (Grad-CAM) visualized the mentum, ventromental plates, mandibles, submentum, and postoccipital margin to be morphologically important features for CNN classification. Thus, the CNN approach was a highly accurate solution for chironomid identification of aquatic macroinvertebrates opening a new avenue for implementation of artificial intelligence and deep learning methodology in the biomonitoring world. This approach also provides a means to overcome the gap in bioassessment for developing countries where widespread use techniques for routine monitoring are currently limited.
•The shape of chironomid mandibles can be used for bioassessment.•Deep-learning approaches successfully classify mandibles of chironomids.•Computer algorithms can detect and accurately identify a ...functional feeding group.•The mandibular gula and joint were found to be specific to a functional feeding group.•Automated trait-based methods could enhance the use of chironomids in bioassessment.
The identification of functional feeding traits in aquatic macroinvertebrates often requires a morphology-based identification of species, which is important for trait-based methods of biological assessment. The extent of functional homogenization is compared along scales of impairment, where trait-based information is used as an input in models that examine degradation pathways. However, trait-based information is not always readily available for all groups of aquatic insects, especially for species diverse families, such as chironomids (Diptera: Chironomidae). Taxonomic challenges and ambiguous traits complicate the use of chironomid larvae in trait-based bioassessment. Here, we examine the use of geometric morphometric analysis (GMA), deep learning (Convolutional Neural Networks), and computer vision (deep CNN) applied to the mouthparts (mandibles) of chironomid larvae as a proxy for identifying the relationship between the functional morphology and food acquisition behaviour. We determined the variability in morphology of mandibles for 23 taxa of chironomid larvae from different genera, subfamilies, and their Functional Feeding Group (FFG). Analysis using GMA showed that the five different FFGs examined had different mandibular traits that significantly varied in shape and size. A deep CNN model was then built that was able to classify the 23 taxa into their respective FFG automatically with 92.31 % accuracy. A gradient-weighted Class Activation Mapping (Grad-CAM) algorithm found that the most important part of mandibles for classification were the gula and mandibular joint. We introduced three additional species to the deep CNN models to test whether automatic classification would directly and automatically identify traits of the specimens independently from taxonomic identification. The deep CNN process avoids issues surrounding both taxonomic identification and previous knowledge of a specific taxon’s feeding trait, and in all cases the model classified taxa correctly based on their mandibular traits. The use of deep learning approaches could substantially enhance the use of trait-based approaches and increase the reliability and use of chironomids in bioassessment.
Deep learning techniques have recently found application in biodiversity research. Mayflies (Ephemeroptera), stoneflies (Plecoptera) and caddisflies (Trichoptera), often abbreviated as EPT, are ...frequently used for freshwater biomonitoring due to their large numbers and sensitivity to environmental changes. However, the morphological identification of EPT species is a challenging but fundamental task. Morphological identification of these freshwater insects is therefore not only extremely time-consuming and costly, but also often leads to misjudgments or generates datasets with low taxonomic resolution. Here, we investigated the application of deep learning to increase the efficiency and taxonomic resolution of biomonitoring programs. Our database contains 90 EPT taxa (genus or species level), with the number of images per category ranging from 21 to 300 (16,650 in total). Upon completion of training, a CNN (Convolutional Neural Network) model was created, capable of automatically classifying these taxa into their appropriate taxonomic categories with an accuracy of 98.7 %. Our model achieved a perfect classification rate of 100 % for 68 of the taxa in our dataset. We achieved noteworthy classification accuracy with morphologically closely related taxa within the training data (e.g., species of the genus Baetis, Hydropsyche, Perla). Gradient-weighted Class Activation Mapping (Grad-CAM) visualized the morphological features responsible for the classification of the treated species in the CNN models. Within Ephemeroptera, the head was the most important feature, while the thorax and abdomen were equally important for the classification of Plecoptera taxa. For the order Trichoptera, the head and thorax were almost equally important. Our database is recognized as the most extensive aquatic insect database, notably distinguished by its wealth of included categories (taxa). Our approach can help solve long-standing challenges in biodiversity research and address pressing issues in monitoring programs by saving time in sample identification.
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•EPT taxa indicate ecosystem health, but identification challenges hinder their use.•We use deep learning to improve biomonitoring efficiency and taxonomic resolution.•Convolutional Neural Networks achieved 98.7 % accuracy in classifying 90 EPT taxa.•This approach may replace conventional morphological identification methods.
