Boosting rare benthic macroinvertebrates taxa identification with one-class classification

Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task.Comment: 5 pages, 1 figure, 2 table

SPECIES IDENTIFICATION FOR AQUATIC BIOMONITORING USING DEEP RESIDUAL CNN AND TRANSFER LEARNING

Aquatic insects and other benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, an expensive and time-consuming process of species identification represents one of the key obstacles for reliable biomonitoring of aquatic ecosystems. In this paper, we proposed a deep learning (DL) based method for species identification that we evaluated on several available public datasets (FIN-Benthic, STONEFLY9, and EPT29) along with our Chironomidae dataset (CHIRO10). The proposed method relies on three DL techniques used to improve robustness when training is done on a relatively small dataset: transfer learning, data augmentation, and feature dropout. We applied transfer learning by employing ResNet-50 deep convolutional neural network (CNN) pretrained on ImageNet 2012 dataset. The results show significant improvement compared to original contributions and confirms that there is a considerable gain when there are multiple images per specimen

Taxonomic and trait-based responses of the orders Ephemeroptera, Plecoptera, Odonata, And Trichoptera (EPOT) to sediment stress in the Tsitsa River and its tributaries, Eastern Cape, South Africa

Increased urbanization and industrialisation due to human population growth and associated high demand for food have led to widespread disturbances of freshwater ecosystems and associated resources. A widely recognised consequence of these disturbances is the excessive delivery of sediments into the freshwater ecosystems, which severely affects the functioning and integrity of these systems.. The major water quality impairment in the Tsitsa River and its tributaries, situated in the Mzimvubu catchment in the Eastern Cape Province of South Africa, is known to be excessive sediment input. In this study, the application of macroinvertebrates taxonomic-based and trait-based approaches was used to assess the responses and vulnerability of Ephemeroptera, Plecoptera, Odonata and Trichoptera (EPOT) species to settled and suspended sediments stress in eight selected sampling sites in the Tsitsa River and its tributaries. The eight selected sites were Site 1 (Tsitsa upstream), Site 2 (Tsitsa downstream), Site 3 (Qurana tributary), Site 4 (Pot River upstream), Site 5 (Pot River downstream), Site 6 (Little Pot River), Site 7 (Millstream upstream) and Site 8 (Millstream downstream). The methods used in this study involved the analysis of water physico-chemical variables as well as sediment characteristics, derivation of five EPOT metrics, EPOT species-level taxonomic analysis, individual EPOT trait analysis and the development of a novel trait-based approach using a combination of traits. The sampling of EPOT taxa was done using the SASS5 protocols. Identification of EPOT was done to genus/species level and all data were subjected to relevant statistical analysis. The results of ecological categories derived for the physico-chemical variables generally indicated the ecological categories A and B, which was indicative of good water quality conditions. The result of sediment particle analysis revealed four distinct site groups: site group 1 (Tsitsa River upstream and Qurana tributary), site group 2 (Tsitsa River downstream and Millstream upstream), site group 3 (Pot River, both upstream and downstream, and Millstream downstream) and site group 4 (Little Pot River). The species-level taxonomic analysis of EPOT revealed that site group 1 was the most sediment-influenced sites whereas site group 4 was the least sediment-influenced. Species such as Paragopmhus sp., Aeshna sp. and Baetis sp. were considered sediment-tolerant with strong positive association with site group 1. The novel trait-based approach developed in this study proved useful in predicting the responses of EPOT species to sediment stress, and further discriminated between the study sites. The approach was used to group EPOT species into four vulnerability classes. The result showed that filter feeding EPOT species that have filamentous gills, preferring stone biotopes and feeding on detritus (FPOM) were mostly classified as highly vulnerable to sediment stress and indicated no significant association with the highly sediment-influenced site group 1. The TBA largely corresponded well to the predictions made with the relative abundance of the vulnerable class decreasing in the sediment-influenced sites compared to the tolerant and highly tolerant classes. Overall, the study revealed the importance of the complementary use of taxonomic and trait-based approaches to biomonitoring

Riverine Ecosystem Management: Science for Governing Towards a Sustainable Future

This open access book surveys the frontier of scientific river research and provides examples to guide management towards a sustainable future of riverine ecosystems. Principal structures and functions of the biogeosphere of rivers are explained; key threats are identified, and effective solutions for restoration and mitigation are provided. Rivers are among the most threatened ecosystems of the world. They increasingly suffer from pollution, water abstraction, river channelisation and damming. Fundamental knowledge of ecosystem structure and function is necessary to understand how human acitivities interfere with natural processes and which interventions are feasible to rectify this. Modern water legislation strives for sustainable water resource management and protection of important habitats and species. However, decision makers would benefit from more profound understanding of ecosystem degradation processes and of innovative methodologies and tools for efficient mitigation and restoration. The book provides best-practice examples of sustainable river management from on-site studies, European-wide analyses and case studies from other parts of the world. This book will be of interest to researchers in the field of aquatic ecology, river system functioning, conservation and restoration, to postgraduate students, to institutions involved in water management, and to water related industries