Benthic Macroinvertebrate Index Research Articles

Spatial Analysis of Aquatic Ecological Health under Future Climate Change Using Extreme Gradient Boosting Tree (XGBoost) and SWAT

Climate change not only affects the water resource system but also has a great impact on the aquatic ecosystem, which is complexly linked to various organic and inorganic matter. It is difficult to simulate the current aquatic ecosystem and predict the future system due to the immensity and complexity of aquatic ecosystems; however, a spatial analysis of future aquatic ecological health is necessary if we are to adapt and take action against future climate change. In this study, we evaluated the aquatic ecological health of the Han River basin under the future climate change RCP4.5 and RCP8.5 scenarios using three indices: fish assessment index (FAI), trophic diatom index (TDI), and benthic macroinvertebrate index (BMI). For this, we developed the SWAT-XGBoost linkage algorithm, and the algorithm accuracy for the FAI, TDI, and BMI was 89.3~95.2%. In the case of the FAI and BMI assessment of aquatic ecological health, the upstream Han River was classified as a hot spot. In the case of the TDI, the downstream area of the Han River was classified as a cold spot. However, as the current TDI downstream was classified as grades D and E, continuous management is needed.

Estimation of aquatic ecosystem health using deep neural network with nonlinear data mapping

Estimation of aquatic ecosystem health indices can assist in reducing the burden of time-consuming, labor-intensive, and cost-effective fieldwork for the sustainable evaluation of freshwater ecosystem status. In this study, we developed a deep neural network to estimate the trophic diatom index (TDI), benthic macroinvertebrate index (BMI), and fish assessment index (FAI) using water quality and hydraulic and hydrological data. A convolutional neural network (CNN) model was built to estimate health indices. In addition, an autoencoder was adopted to produce manifold features that were used as inputs for the CNN model. Conventional machine learning models, including artificial neural networks, support vector machines, random forests, and extreme gradient boosting, have been developed to estimate the TDI, BMI, and FAI. The results showed that the CNN with an autoencoder exhibited the best performance, with validation accuracies of Nash Sutcliffe Efficiency (NSE) and root mean squared error (RMSE) values of 0.592 and 17.249 for TDI, 0.669 and 12.282 for BMI, and 0.638 and 13.897 for FAI, respectively. The autoencoder enhanced the nonlinear feature learning of the time series and static input data, which contributed to improving the CNN feature extraction for accurate estimation of aquatic ecosystem health indices compared to other data-driven approaches. Therefore, deep learning techniques can be used to investigate aquatic ecosystem health by successfully reflecting the quantitative and qualitative features of health indices.

Ecological health assessment of the Yellow River mainstem based on benthic macroinvertebrate index of biological integrity

Ecological health assessment of the Yellow River mainstem based on benthic macroinvertebrate index of biological integrity

Analysis of environmental factors in sediment based on benthic macroinvertebrates in streams, Korea

AbstractThis research summarizes the data on benthic macroinvertebrates collected in the 15 streams during spring (June) and fall (September–October) of 2022. The objectives of this study were (i) to evaluate the quality of surface waters, sediments, and benthic macroinvertebrates; and (ii) to compare the relationship with the environmental variables in water, sediments and benthic macroinvertebrates that inhabit 15 streams. A total of 114 taxa were collected. The assemblages from the 15 sites were dominated by Chironomidae and Tubificidae, especially Chironomidae constituted 45.0% density occurrence. The low grades of Benthic Macroinvertebrate Index (BMI) were calculated in 10 sites with disturbed (“poor” or “very poor”). Low BMI sites mostly showed Tubificidae or Chironomidae dominant characteristics. The biological indices, BMI, % Tubificidae, and % Chironomidae, were significantly correlated with DO and conductivity. DO was also significantly correlated with all the benthic macroinvertebrate factors and diversity except percent Tubificidae. The heavy metal parameters were correlated mostly with species number, BMI, and diversity index. The objectives of this study examine the diversity of benthic macroinvertebrate in the 15 streams and investigate the relative importance of environmental quality for benthic macroinvertebrate distribution.

Evaluation of Water Quality by Benthic Macroinvertebrate at the Jukjeon Stream in Korea

This work was studied to investigate the water quality of the Jukjeon stream in Korea based on benthic macroinvertebrates. Several ecological score of benthic macroinvertebrate community were used in the estimation of stream from each of the three stations of the sub-watersheds. During the study periods, the results showed a total of 619 individual benthic macroinvertebrates were collected, including 23 families and 44 species. The first dominant species was Culicini sp. and second was Baetis fuscatus. Ecological score of benthic macroinvertebrate community (ESB) was varied from 61 (St. D) to 71 (St. A) with a mean of 66.5. Water quality at the Jukjeon Stream was grade I. The values of benthic macroinvertebrate index (BMI) at St. A and St. B were 60.4 and 53.4, respectively. Water quality at the two stations was grade C (moderate) on the based BMI. The values of BMI at St. C and St. D were 45.5 and 39.6, respectively. Water quality at the two stations was grade D (bad). The Shannon diversity index (H') of diversity was varied from 3.76 (St. A) to 4.18 (St. B) with a mean of 4.05. The rank percentages of species based on individual abundance (RI) were 4.84 (St. A), 5.66 (St. B), 6.31 (St. C), and 5.97 (St. D). Evenness indices (J) were varied from 0.79 (St. C) to 0.84 (St. B). The values of EPT (Ephemeroptera, Plecoptera and Tricoptera) at St. A, St. B, St. C, and St. D were 0.56, 0.48, 0.29, and 0.17 with a mean of 0.37, respectively. The EPT group accounted for 37% of the total species and 53% of the total individuals. The diversity and richness indices were lower in St. A than in the other stations. In this study, rapid assessment of water quality by macroinvertebrate indicators involves effort and reduced cost in natural stream evaluation.

Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA

Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.

Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

Changes in hydrological characteristics and increases in various pollutant loadings due to rapid climate change and urbanization have a significant impact on the deterioration of aquatic ecosystem health (AEH). Therefore, it is important to effectively evaluate the AEH in advance and establish appropriate strategic plans. Recently, machine learning (ML) models have been widely used to solve hydrological and environmental problems in various fields. However, in general, collecting sufficient data for ML training is time-consuming and labor-intensive. Especially in classification problems, data imbalance can lead to erroneous prediction results of ML models. In this study, we proposed a method to solve the data imbalance problem through data augmentation based on Wasserstein Generative Adversarial Network (WGAN) and to efficiently predict the grades (from A to E grades) of AEH indices (i.e., Benthic Macroinvertebrate Index (BMI), Trophic Diatom Index (TDI), Fish Assessment Index (FAI)) through the ML models. Raw datasets for the AEH indices composed of various physicochemical factors (i.e., WT, DO, BOD5, SS, TN, TP, and Flow) and AEH grades were built and augmented through the WGAN. The performance of each ML model was evaluated through a 10-fold cross-validation (CV), and the performances of the ML models trained on the raw and WGAN-based training sets were compared and analyzed through AEH grade prediction on the test sets. The results showed that the ML models trained on the WGAN-based training set had an average F1-score for grades of each AEH index of 0.9 or greater for the test set, which was superior to the models trained on the raw training set (fewer data compared to other datasets) only. Through the above results, it was confirmed that by using the dataset augmented through WGAN, the ML model can yield better AEH grade predictive performance compared to the model trained on limited datasets; this approach reduces the effort needed for actual data collection from rivers which requires enormous time and cost. In the future, the results of this study can be used as basic data to construct big data of aquatic ecosystems, needed to efficiently evaluate and predict AEH in rivers based on the ML models.

Species Composition of Benthic Macroinvertebrate and Water Evaluation at the Baekyeok Stream in Korea

This study was conducted to investigate water quality evaluation using benthic macroinvertebrates at a mountain torrential river (Baekyeok Stream in South Korea). All individuals collected from the protocols of the USEPA Rapid Bioassessment Protocol III (RBP III) for benthic macroinvertebrates. In total, 841 individuals belonging to 37 species of 27 families were collected from the Baekyeok Stream. Arthropoda exhibited greatest species diversity with 26 species identified, followed by Mollusca (5 species) and Annelida (5 species). Platyhelminthes accounted for only one family for four seasons within the four studied areas. Total ecological score of benthic macroinvertebrate community (TESB) was varied from 79 (upper region, St. A) to 56 (low region, St. D) with a mean of 70.8. Benthic macroinvertebrate index (BMI) was varied from 47.130 (St. D) to 62.354 (St. A) with a mean of 53.158. Classification of saprobity based on Shannon-Weaver s diversity (H ) was varied from 2.189 to 2.802 with a mean of 2.723. Thus, water quality evaluation was β -mesosaprobic at all stations according to saprobic value. In conclusion, the water quality scores of upper region at Baekyeok Stream was slightly better than those of low region. Judging only the distribution of benthic macroinvertebrates, the water quality of Baekyeok Stream was not so good.

Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Multiple studies have been conducted to identify the complex and diverse relationships between stream ecosystems and land cover. However, these studies did not consider spatial dependency inherent from the systemic structure of streams. Therefore, the present study aimed to analyze the relationship between green/urban areas and topographical variables with biological indicators using regression tree analysis, which considered spatial autocorrelation at two different scales. The results of the principal components analysis suggested that the topographical variables exhibited the highest weights among all components, including biological indicators. Moran′s I values verified spatial autocorrelation of biological indicators; additionally, trophic diatom index, benthic macroinvertebrate index, and fish assessment index values were greater than 0.7. The results of spatial autocorrelation analysis suggested that a significant spatial dependency existed between environmental and biological indicators. Regression tree analysis was conducted for each indicator to compensate for the occurrence of autocorrelation; subsequently, the slope in riparian areas was the first criterion of differentiation for biological condition datasets in all regression trees. These findings suggest that considering spatial autocorrelation for statistical analyses of stream ecosystems, riparian proximity, and topographical characteristics for land use planning around the streams is essential to maintain the healthy biological conditions of streams.

Evaluating the Relationships between Riparian Land Cover Characteristics and Biological Integrity of Streams Using Random Forest Algorithms.

The relationships between land cover characteristics in riparian areas and the biological integrity of rivers and streams are critical in riparian area management decision-making. This study aims to evaluate such relationships using the Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI), Fish Assessment Index (FAI), and random forest regression, which can capture nonlinear and complex relationships with limited training datasets. Our results indicate that the proportions of land cover types in riparian areas, including urban, agricultural, and forested areas, have greater impacts on the biological communities in streams than those offered by land cover spatial patterns. The proportion of forests in riparian areas has the greatest influence on the biological integrity of streams. Partial dependence plots indicate that the biological integrity of streams gradually improves until the proportion of riparian forest areas reach about 60%; it rapidly decreases until riparian urban areas reach 25%, and declines significantly when the riparian agricultural area ranges from 20% to 40%. Overall, this study highlights the importance of riparian forests in the planning, restoration, and management of streams, and suggests that partial dependence plots may serve to provide insightful quantitative criteria for defining specific objectives that managers and decision-makers can use to improve stream conditions.

Linking Altered Flow Regimes to Biological Condition: an Example Using Benthic Macroinvertebrates in Small Streams of the Chesapeake Bay Watershed

Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.

Predictive biological indices for algae populations in diverse stream environments

Predictive biological indices have transformed the bioassessment landscape by allowing universal indices to be applicable across diverse environments. The successful development of a predictive benthic macroinvertebrate index for California wadeable streams helped to demonstrate the power of these tools in complex geographic settings. However, previous efforts to develop predictive algal indices for California were limited by poor performance and were ultimately unsuccessful. For this study, we leveraged a robust statewide dataset to develop two different types of predictive algal indices for California wadeable streams: an index of observed-to-expected taxa (O/E) to measure taxonomic completeness and a multimetric index (MMI) to evaluate ecological structure. We developed multiple versions of each index, including one for diatoms, one for soft-bodied algae, and a hybrid index using both assemblages. We evaluated index performance using a series of screening criteria for precision, accuracy, responsiveness, and regional bias. We found that final index performance varied among all assemblages: the best performing O/E index was a diatom-only index, whereas the predictive diatom and hybrid MMIs out-performed all other indices with excellent responsiveness and precision. We found that in comparison to benthic macroinvertebrates, algal communities were characterized by high beta diversity across reference sites and low average species richness per site, resulting in disparate algal populations that were challenging to model with predictive approaches, particularly for soft-bodied algae assemblages. While all O/E indices were considered to have weak performance, the predictive diatom and hybrid MMIs are accurate, responsive, and precise indices that will provide a powerful assessment of biological condition for statewide applications.

The Relationships between Benthic Macroinvertebrate and Environmental Factors in Iancheon and Bukcheon Streams, Korea

In this study, we investigated the relationships between benthic macroinvertebrate assemblages and various environmental factors in Iancheon (NIA) and Bukcheon (NBC) streams, Korea. We collected benthic macroinvertebrates and 33 environmental factors in April 2017 at 9 sites (5 sites in NIA and 4 sites in NBC). We identified 93 species (5 phyla, 9 classes, 16 orders, and 53 families) and 69 species (5 phyla, 9 classes, 17 orders, and 47 families) in NIA and NBC streams, respectively. Considering benthic macroinvertebrate index (BMI), NIA (88.2) and NBC (80.2) streams were in “very good” status. Upstream areas showed the highest scores, 95.5 (NIA1) and 94.2 (NBC1), whereas BMI score was the lowest in downstream areas of both streams, especially in NBC4 (51.0 “bad” status). Cluster analysis and non-metric multidimensional scaling analysis represented the differences of benthic macroinvertebrate assemblages according to spatial and anthropogenic gradients. Our findings provide reference data and highlight the need for the continued monitoring to maintain the good status and manage macroinvertebrate diversity in these two streams, in Sangju-si, Korea.

An Estimated Structural Equation Model to Assess the Effects of Land Use on Water Quality and Benthic Macroinvertebrates in Streams of the Nam-Han River System, South Korea.

The extent of anthropogenic land use in watersheds determines the amount of pollutants discharged to streams. This indirectly and directly affects stream water quality and biological health. Most studies have therefore focused on ways to reduce non-point pollution sources to streams from the surrounding land use in watersheds. However, the mechanistic pathways between land use and the deterioration of stream water quality and biological assemblages remain unclear. This study estimated a structural equation model (SEM) representing the impact of agricultural and urban land use on water quality and the benthic macroinvertebrate index (BMI) using IBM AMOS in the Nam-Han river systems, South Korea. The estimated SEM showed that the percent of urban and agricultural land in the watersheds significantly affected both the water quality and the BMI of the streams. Specifically, a higher percent of urban land use had directly increased the biochemical oxygen demand (BOD) and total phosphorus (TP), and deteriorated the BMI of streams. Similarly, higher proportions of agricultural land use had also directly increased the BOD, total nitrogen (TN), and total phosphorus (TP) concentrations, and lowered the BMI of streams. In addition, it was observed that the percent of urban and agricultural land use had indirectly deteriorated the BMI through increased BOD. However, we were not able to observe any significant indirect effect of the percent of urban and agricultural land use through increased nutrients including TN and TP. These results indicate that increased urban and agricultural land use in the watersheds had directly and indirectly affected the physicochemical characteristics and benthic macroinvertebrate communities in streams. Our findings emphasize the need to develop more elaborate environmental management and restoration strategies to improve the water quality and biological status of streams.

Multivariate-statistics based selection of a benthic macroinvertebrate index for assessing water quality in the Paute River basin (Ecuador)

Abstract Multivariate statistics -Soft Independent Modelling of Class Analogies (SIMCA), Principal Components Analysis (PCA), Multiple Regression (MR)- were used to search for key biotic and water quality (WQ) variables within a dataset/matrix collected over a five-year period in the Paute River Basin (Ecuador). Benthic macroinvertebrates and 27 descriptive physical, chemical, microbiological, hydrological and geomorphological variables were collected from 64 monitoring sites across the basin. Nine macroinvertebrate biotic indices were calculated. The SIMCA method was applied to find the most accurate biotic index that best discriminated among less polluted (C1), moderately polluted (C2) and highly polluted (C3) sites. A cross-validation scheme was applied to evaluate the performance of the modelling process. Within the PCA that was further refined using a Kruskal-Wallis test, the key WQ variables that mostly contributed to the macroinvertebrate-based WQ classification were identified. The results showed that the Elmidae-Plecoptera-Trichoptera (ElmPT) index was the most accurate biotic classifier. Riparian vegetation and streambed heterogeneity were the best predictors of the C1 class, while the concentration of faecal coliforms, pH, temperature and dissolved oxygen, best predicted the C3 class. The reduction of the field monitored parameters could help designing more cost-effective but equally accurate future WQ monitoring schemes in the basin.

Large scale faecal (spraint) counts indicate the population status of endangered Eurasian otters (Lutra lutra)

Abstract The validity of using spraint (otter faeces) density for population monitoring has been debated for more than 30 years. In this study, we investigated endangered Eurasian otter (Lutra lutra) spraint occurrence and densities at large scales (over 23,800 km2, a quarter of South Korea) over three years (2014–2016). To clarify the spatial heterogeneity of spraint density and count distributions, we applied the global Morans’ I test and hot spot analysis. We also constructed models with 30 environmental factors (six landscape, eight anthropogenic, 13 aquatic health indices, one prey abundance, and two meteorological factors) using generalized linear mixed models with repeated measurements. Our geographical analysis showed regional clusters of otters extending over distances of more than 80 km. The most parsimonious model, a zero-inflated negative binomial model, indicated that our otter spraint counts were significantly positively related to the benthic macro-invertebrate index and precipitation and negatively related to proportion of home range covered by water. In addition, this model showed that absence probabilities of otter spraint were significantly positively related to human populations and negatively related to the number of fish species and altitude. The best explanatory model suggests that our count data was highly related to otter population status, and also affected by anthropogenic disturbance.

Aquatic ecosystem health assessment of a typical sub-basin of the Liao River based on entropy weights and a fuzzy comprehensive evaluation method

River ecosystem health assessments provide the foundation for river ecological protection and integrated management. To evaluate the aquatic ecosystem health of the Fan River basin, benthic macroinvertebrate indices (the Multimeric Macroinvertebrates Index Flanders (MMIF) and Family Biotic Index (FBI)), a habitat index (the river habitat quality Index (RHQI)) and a water quality index (the Improved Water Pollution Index (IWPI)) were selected. The entropy weighting method was used to calculate the RHQI and IWPI. A fuzzy comprehensive evaluation method was used to evaluate the aquatic ecosystem health. The evaluation results indicated that the aquatic ecosystem health of the Fan River basin was better in 2018 than in 2011, which respectively belonged to the ends of the 11th and 12th Five-Year Plans of the Major Science and Technology Programs for Water Pollution Control and Treatment in China. The proportions of sampling stations with good, moderate and poor grades in 2011 were 50.0%, 40.0% and 10.0%, respectively, and in 2018, the proportions of stations with excellent, good and moderate grades were 20.0%, 50.0% and 30.0%, respectively. A correlation analysis showed that the RHQI was significantly correlated with the MMIF, FBI and IWPI. The riparian land use pattern was an important factor that influenced changes in the aquatic ecosystem health grade. Of the water quality parameters, total phosphorous (TP) and potassium bichromate index (COD) were the main factors that affected the characteristics of benthic macroinvertebrates and the aquatic ecosystem health.

Macroinvertebrate indices versus microbial fecal pollution characteristics for water quality monitoring reveals contrasting results for an Ethiopian river

Abstract Awash River is one of the major surface water sources used by millions of people in the central Highlands of Ethiopia. However, numerous pollution sources exert significant pressure on the river. Different approaches for assessing the status of water quality exist, but few studies compared the performance of distinct methods. Therefore, this study aims to evaluate the consistency of fecal indicator bacteria for environmental health assessment of rivers by comparing them to assessments of physicochemical tests as well as newly developed macroinvertebrate indices. Physicochemical, biological (macroinvertebrates) and microbiological (Escherichia coli and Enterococci) parameters were assessed at five sites along the upper Awash River. For E. coli and Enterococci moderate to strong fecal pollution levels, ranging from 7.9 × 102 to 7.6 × 103 cfu/100 ml and 7.6 × 102 to 1.1 × 104 cfu/100 ml, were observed, respectively. The concentrations of both fecal indicator bacteria exceeded the standards set by the European Union and the World Health Organization for safe recreational water. Hence, all sites were categorized as poor for swimming and recreation. In contrast, three African benthic macroinvertebrate indices (South African Scoring System 5, Tanzanian River Scoring System, Ethiopian Biotic Score) indicated a natural or good water quality with slight ecological degradation at the upstream sites, and a moderate to poor ecological status at the downstream sites. While macroinvertebrate communities were able to reflect anthropogenic disturbances, mainly caused by different land uses, fecal indicator bacteria, most likely driven by the high pressure of extensive livestock fecal emission and overgrazing in the whole catchment, did not. This study underpins the necessity of combining different indicator systems to analyze human pressures in Africa in a holistic way, which can serve as a basis for management and sustainable use of fundamental resources such as water from freshwater ecosystems.

Evaluation of Watershed Scale Aquatic Ecosystem Health by SWAT Modeling and Random Forest Technique

In this study, we evaluated the aquatic ecosystem health (AEH) with five grades (A; very good to E; very poor) of FAI (Fish Assessment Index), TDI (Trophic Diatom Index), and BMI (Benthic Macroinvertebrate Index) using the results of SWAT (Soil and Water Assessment Tool) stream water temperature (WT) and quality (T-N, T-P, NH4, NO3, and PO4). By applying Random Forest, one of the machine learning algorithms for classification analysis, each AEH index was trained and graded from the SWAT results. For Han river watershed (34,418 km2) in South Korea, the 8 years (2008~2015) observed AEH data of Spring and Fall periods at 86 locations from NAEMP (National Aquatic Ecological Monitoring Program) were used. The AEH was separately trained for Spring (FAIs, TDIs, and BMIs) and Fall (FAIa, TDIa, and BMIa), and the AEH results of Random Forest with SWAT (WT, T-N, T-P, NH4, NO3, and PO4) as input variables showed the accuracy of 0.42, 0.48, 0.62, 0.45, 0.4, and 0.58, respectively. The reason for low accuracy was from the weak strength of the individual trees and high correlation between the trees composing the Random Forest due to the data imbalance. The AEH distribution results showed that the number of Grade A of total FAI, TDI, and BMI were 84, 0, and 158 respectively and they were mostly located at the upstream watersheds. The number of Grade E of total FAI, TDI, and BMI were 4, 50, and 13 and they were shown at downstream watersheds.

MEASURING LOTIC ECOSYSTEM RESPONSES TO NUTRIENTS: A Mismatch that Limits the Synthesis and Application of Experimental Studies to Management.

MEASURING LOTIC ECOSYSTEM RESPONSES TO NUTRIENTS: A Mismatch that Limits the Synthesis and Application of Experimental Studies to Management.