Why the Stall? Using metabolomics to define the lack of upstream movement of invasive bigheaded carp in the Illinois River

Bigheaded Carp have spread throughout the Mississippi River basin since the 1970s. Little has stopped the spread as carp have the ability to pass through locks and dams, and they are currently approaching the Great Lakes. However, the location of the leading edge in the Illinois River has stalled for over a decade, even though there is no barrier preventing further advancement towards the Great Lakes. Defining why carp are not moving towards the Great Lakes is important for predicting why they might advance in the future. The aim of this study was to test the hypothesis that anthropogenic contaminants in the Illinois River may be playing a role in preventing further upstream movement of Bigheaded Carp. Ninety three livers were collected from carp at several locations between May and October of 2018. Liver samples were analyzed using gas chromatography-mass spectrometry in a targeted metabolite profiling approach. Livers from carp at the leading edge had differences in energy use and metabolism, and suppression of protective mechanisms relative to downstream fish; differences were consistent across time. This body of work provides evidence that water quality is linked to carp movement in the Illinois River. As water quality in this region continues to improve, consideration of this impact on carp spread is essential to protect the Great Lakes.

GRUPOS FUNCIONAIS FITOPLANCTÔNICOS EVIDENCIAM DIFERENÇAS AMBIENTAIS EM UMA PLANÍCIE DE INUNDAÇÃO TEMPERADA

Floodplain environments have high biodiversity and provide many ecosystem services maintained by the flood pulses. The phytoplankton is essential to the functioning of these ecosystems, acting upon primary productivity and biogeochemical cycles. We evaluated phytoplankton in a river-lake flood system (Illinois River floodplain-USA) during a hydrological cycle and compared the taxonomic (species) and functional (morphologic-based functional groups – MBFG) approaches. As expected, greater species richness was registered in the river and higher biovolume in the lake, as well as the predominance of different MBFGs in each environment. Furthermore, seasonality drove richness and biovolume temporal variation due to the higher water levels during spring. The MBFG IV (i. e. without specialized traits), V (phytoflagellates) and VI (diatoms) were more important for richness and biovolume in both environments. We reinforce the fundamental role of the hydrodynamics characteristics, with higher phytoplankton biovolume values in the lake. Using MBFG resulted in a better explanation to the phytoplankton-environment relationship. Constant water column mixture and high turbidity selected species with traits (e.g. small size, presence of silica) specifically adapted to these conditions.

Implementation of an Award-Winning Invasive Fish Recognition and Separation System

The state of Michigan, U.S.A., was awarded USD 1 million in March 2018 for the Great Lakes Invasive Carp Challenge. The challenge sought new and novel technologies to function independently of or in conjunction with those fish deterrents already in place to prevent the movement of invasive carp species into the Great Lakes from the Illinois River through the Chicago Area Waterway System (CAWS). Our team proposed an environmentally friendly, low-cost, vision-based fish recognition and separation system. The proposed solution won fourth place in the challenge out of 353 participants from 27 countries. The proposed solution includes an underwater imaging system that captures the fish images for processing, fish species recognition algorithm that identify invasive carp species, and a mechanical system that guides the fish movement and restrains invasive fish species for removal. We used our evolutionary learning-based algorithm to recognize fish species, which is considered the most challenging task of this solution. The algorithm was tested with a fish dataset consisted of four invasive and four non-invasive fish species. It achieved a remarkable 1.58% error rate, which is more than adequate for the proposed system, and required only a small number of images for training. This paper details the design of this unique solution and the implementation and testing that were accomplished since the challenge.

Reversing the Chicago River

This chapter looks at the Chicago River and its effect on the lakefront. The chapter investigates how efforts to combat pollution in the river had the effect of cleaning up the water along the lakefront. It demonstrates how the lakefront ceased to be seen as a conduit for commerce — or as a repository for garbage and waste — and came to be regarded as an environmental and recreational amenity. The chapter then shifts to discuss the Chicago River's expansion. It details the obvious implications of the plan to reverse the river, sending sewage through the new canal into the Des Plaines River (whence to the Illinois River), for Illinois communities downstream from Chicago, such as Joliet and Peoria. The chapter also talks about the creation of the sanitary district at the close of the nineteenth century. It illustrates how the opening of the canal and the river's reversal produced an equally dramatic improvement in the quality of the water in the lake bordering Chicago. Ultimately, the chapter evaluates the challenges on the “Chicago diversion” of the waters of Lake Michigan and an endless stream of attempts to resolve the water diversion controversy.

Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S.

Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is “Satisfactory”. This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.