Insect Community Diversity Tracks Degradation and Recovery of a Wastewater Assimilation Marsh in Southeast Louisiana

Kaur

et al. 2020

Preprint

Maternally inherited endosymbionts are one of the most abundant bacteria infecting arthropods and show extensive horizontal transfer. Such widespread distribution and extensive recombination among these endosymbionts could be an outcome of horizontal transfer as for such genetic exchanges to occur their hosts should come in contact. One such level of biological organization where different hosts can do that is the ecological community. Despite various studies focusing on known model species and specific ecological interactions among hosts, reports on community wide endosymbiont data are rare. To better understand endosymbiont spread, we investigated the incidence, diversity, extent of horizontal transfer and recombination of three such endosymbionts (Wolbachia, Cardinium and Arsenophonus) in a specific soil arthropod community. Wolbachia strain characterization was done using multiple genes whereas single 16S rRNA gene was used for Cardinium and Arsenophonus. Amongst 3509 individual host arthropods belonging to 390 morphospecies, 12.05% were infected with Wolbachia, 2.82% with Cardinium and 2.05% with Arsenophonus. Phylogenetic incongruence between host and endosymbiont indicated extensive horizontal transfer of endosymbionts within this community. Three cases of recombination between Wolbachia supergroups and eight incidences of within supergroup genetic exchange were also found. Statistical tests of similarity indicated supergroup A Wolbachia and Cardinium to show a pattern consistent with rapid horizontal transfer within the community. However same tests done for super group B Wolbachia and Arsenophonus did not show similar patterns. We highlight the importance of extensive community wide studies for a better understanding of the spread of endosymbionts across global arthropod communities.

Section: Resultssupporting

confidence: 90%

Are ecological communities the seat of endosymbiont horizontal transfer and diversification? A case study with soil arthropod community.

Kaur

et al. 2020

Preprint

“…The study confirmed the role of chironomids in bioassessment. Chironomid larvae are useful bioindicators of water quality such as pH and nutrients [41][42][43]. The most abundant chironomid taxa in the peat pool habitat, pelophilous Chironomus sp.…”

Section: Discussionmentioning

confidence: 99%

Environmental Drivers of Macroinvertebrate Assemblages within Peat Pool Habitat-Implication for Bioassessment

Tarkowska‐Kukuryk

2021

Water

Macroinvertebrates are a crucial component of wetland trophic webs. Many taxa are used as bioindicators of ecosystem change. However, relationships between macroinvertebrates and the environmental factors in peat pool habitats are still not well recognized. The present study shows the results of long-term studies during the years 2010–2020, on the responses of macroinvertebrates to the changes of environmental variables in a peat pool habitat formed as a result of peat exploitation on continental raised bog. The RDA analysis significantly explained 87.3% of the variance in macroinvertebrates abundances. Assemblages of most taxa (Anisoptera, Chironomidae, Ceratopogonidae, Coleoptera, Hydrachnidia and Tabanidae) showed a strong relationship with N-NO3 and pH. Moreover, densities of Chaoboridae larvae were explained by chlorophyll-a concentrations. Dominant taxon, Chironomidae, constituted from 48% to 87% of total faunal density. The highest proportions showed larvae of Psectrocladius sp. (gr. sordidellus) and Chironomus sp. Changes in the composition of macroinvertebrate fauna over a period of 10 years reflect the eutrophication process of the peat pool and deterioration of habitat conditions.

Natural Resource Modeling

“…Wetlands are areas where the presence of water determines or influences most, if not all, of an area's biogeochemistry. Wetland plays very important roles in the environment, principally water purification (Saggaï, Ainouche, Nelson, Cattin, & EI Amrani, 2017;Weller & Bossart, 2017), flood control (Dube et al, 2017;James, Capon, & Quinn, 2015), shoreline stability (Davies-Vollum & West, 2015;Patrick, Weller, & Ryder, 2016), and so forth. Wetland is also considered as the most biologically diverse of all ecosystems, serving as home to thousands of animals and plants (Brandt, Petersen, Grossman, Allen, & Benzing, 2015;Sirami, Jacobs, & Cumming, 2013).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

Lin

et al. 2019

In real-world wetland vegetation morphology (WVM) detection, large scene variations such as those due to landform, vegetation, sunlight, weather, and sky, as well as camera parameter settings such as focal length and shooting angle, require systematic and complicated artificial intelligence technology to accurately discriminate inter and intra-class wetland objections. To deal with these challenges, we introduced a deep-level discriminative model based on convolutional neural networks (CNN) for classifying the images of DongZhai Harbor intertidal, Lashi Lake alpine, Yancheng coastal and Zoige plateau wetlands in China. A 96-dimensional convolution operation with kernel sizes of 11 × 11 × 3 first applied to the resized 227 × 227 WVM input pictures to acquire the effective morphologic features. The perceptron layers of the rectified linear unit and the batch normalization were used in the middle layer to achieve the better gradient propagation property during the training process. The WVM features were down-sampled by the pooling networks to reduce the neuron dimensions. The fully connected layer linked to the output of the convolutional and pooling layers to obtain the high-level WVM species information for the final WVM classification purpose. The deep-level CNN-based method was compared with the traditional shallow-level feature-designed algorithms of conditional maximum entropy regression, multilayer perceptron and support vector machine. The deep-level algorithm showed the superior performance for detecting the WVM species, which provided a superior alternative routine for accurate artificial intelligence classification of WVM in the ecological engineering application. Recommendations for Resource Managers• This proposed artificial intelligence model can be used to effectively help people understand the macroscopic scene changes of ecological elements from the wetland vegetation morphology images. • The detection results show the robustness of the discriminative model, for it is not affected by the complex natural factors such as landform, vegetation, sunlight, weather, and sky, as well as human factors such as camera parameter settings of focal length and shooting angle to some extent. • The deep-level model based on the cascade convolution framework can be used to extract more distinguishing detailed ecological features from the original wetland vegetation morphology images compared with the shallow-level algorithms using a single convolutional layer. K E Y W O R D S classification, convolutional neural network, ecological engineering, vegetation morphology, wetland