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Context: Untangling relationships between landscape patterns shaped by human stressors and related response of fish communities is important for identifying biodiversity patterns and conservation targets, yet in large rivers this knowledge is extremely limited. Objectives: Our study focuses on how human stressors within a riparian landscape zone, including both riparian land use and in-channel stressors, explained the fish community structure in a large river. Methods: We studied fish community patterns along the upstream-downstream gradient of the Yangtze River, China. The curve estimation was used to test correlations between fish metrics and the distance from the estuary. We linked human stressors to fish metrics by multivariate generalized linear models. Results: We collected a total of 63 freshwater fish species from 6,147 specimens. Limnophilic species had the highest richness, represented by 30 specie. The predominant riparian land uses in the studied reaches were cropland (65.3% ± 13.1%) and urban land (19.7% ± 13.6%). There were strong negative correlations between riparian land use (e.g., urban land) and in-channel stressors (e.g., shoreline modification, navigation, and fishing pressure) and fish assemblages, especially limnophilic fish abundance, biomass, and richness. Conclusions: These results demonstrate influences of both riparian land use and in-channel stressors on fish communities, and highlight the use of landscape descriptors as a valuable approach to assess linkages between human pressures and fish diversity in large river systems. Management recommendations include: establishing or rehabilitating riparian buffers, improving commercial navigation management, implementing shoreline protection measures, and reinforcing fishing laws and regulations. [ABSTRACT FROM AUTHOR]

Ecosystem-based management of the Laurentian Great Lakes, which spans both the United States and Canada, is hampered by the lack of consistent binational watersheds for the entire Basin. Using comparable data sources and consistent methods, we developed spatially equivalent watershed boundaries for the binational extent of the Basin to create the Great Lakes Hydrography Dataset ( GLHD). The GLHD consists of 5,589 watersheds for the entire Basin, covering a total area of approximately 547,967 km2, or about twice the 247,003 km2 surface water area of the Great Lakes. The GLHD improves upon existing watershed efforts by delineating watersheds for the entire Basin using consistent methods; enhancing the precision of watershed delineation using recently developed flow direction grids that have been hydrologically enforced and vetted by provincial and federal water resource agencies; and increasing the accuracy of watershed boundaries by enforcing embayments, delineating watersheds on islands, and delineating watersheds for all tributaries draining to connecting channels. In addition, the GLHD is packaged in a publically available geodatabase that includes synthetic stream networks, reach catchments, watershed boundaries, a broad set of attribute data for each tributary, and metadata documenting methodology. The GLHD provides a common set of watersheds and associated hydrography data for the Basin that will enhance binational efforts to protect and restore the Great Lakes. [ABSTRACT FROM AUTHOR]

Land use change and other human disturbances have significant impacts on physicochemical and biological conditions of stream systems. Meanwhile, linking these disturbances with hydrology and water quality conditions is challenged due to the lack of high-resolution datasets and the selection of modeling techniques that can adequately deal with the complex and nonlinear relationships of natural systems. This study addresses the above concerns by employing a watershed model to obtain stream flow and water quality data and fill a critical gap in data collection. The data were then used to estimate fish index of biological integrity (IBI) within the Saginaw Bay basin in Michigan. Three methods were used in connecting hydrology and water quality variables to fish measures including stepwise linear regression, partial least squares regression, and fuzzy logic. The IBI predictive model developed using fuzzy logic showed the best performance with the R = 0.48. The variables that identified as most correlated to IBI were average annual flow, average annual organic phosphorus, average seasonal nitrite, average seasonal nitrate, and stream gradient. Next, the predictions were extended to pre-settlement (mid-1800s) land use and climate conditions. Results showed overall significantly higher IBI scores under the pre-settlement land use scenario for the entire watershed. However, at the fish sampling locations, there was no significant difference in IBI. Results also showed that including historical climate data have strong influences on stream flow and water quality measures that interactively affect stream health; therefore, should be considered in developing baseline ecological conditions. [ABSTRACT FROM AUTHOR]

The impacts of differences in watershed land uses, and differences in seasonality on benthic macroinvertebrate communities, were evaluated in 12 stream sites within the Xitiaoxi River watershed, China, from April 2009 to January 2010. The composition of macroinvertebrate community differed significantly among three land use types. Forested sites were characterized by high taxa richness, diversity and the benthic-index of biotic integrity (B-IBI), while farmland and urban disturbed stream sites presented contrary patterns. The percentage of urban land use, conductivity, dissolved oxygen, ammonia nitrogen and total phosphorus were the major drivers for the variations. The land use related water quality stress gradients of the four sampling seasons were determined by means of four independent Principal Component Analyses. The responses of macroinvertebrate community metrics, to anthropogenic stressors, were explored using Spearman Rank Correlation analyses. All the selected metrics, including total numbers of taxa, numbers of Ephemeroptera, Plecoptera and Trichoptera taxa, percentage of non-insect abundance, percentage of scrapers abundance, Pielou's evenness index, Simpson diversity index, and the Benthic Index of Biotic Integrity were correlated significantly with environmental gradients (PC1) in autumn. In other seasons such correlations were less pronounced. Our results imply that autumn is the optimal time to sample macroinvertebrate communities, and to conduct water quality biomonitoring in this subtropical watershed. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

essment of lake impairment status and identification of threats’ type and source is essential for protection of intact, enhancement of modified, and restoration of impaired lakes. For regions in which large numbers of lakes occur, such assessment has usually been done for only small fractions of lakes due to resource and time limitation. This study describes a process for assessing lake impairment status and identifying which human disturbances have the greatest impact on each lake for all lakes that are 2 ha or larger in the state of Michigan using readily available, georeferenced natural and human disturbance databases. In-lake indicators of impairment are available for only a small subset of lakes in Michigan. Using statistical relationships between the in-lake indicators and landscape natural and human-induced measures from the subset lakes, we assessed the likely human impairment condition of lakes for which in-lake indicator data were unavailable using landscape natural and human disturbance measures. Approximately 92% of lakes in Michigan were identified as being least to marginally impacted and about 8% were moderately to heavily impacted by landscape human disturbances. Among lakes that were heavily impacted, more inline lakes (92%) were impacted by human disturbances than disconnected (6%) or headwater lakes (2%). More small lakes were impacted than medium to large lakes. For inline lakes, 90% of the heavily impacted lakes were less than 40 ha, 10% were between 40 and 405 ha, and 1% was greater than 405 ha. For disconnected and headwater lakes, all of the heavily impacted lakes were less than 40 ha. Among the anthropogenic disturbances that contributed the most to lake disturbance index scores, nutrient yields and farm animal density affected the highest number of lakes, agricultural land use affected a moderate number of lakes, and point-source pollution and road measures affected least number of lakes. Our process for assessing lake condition represents a significant advantage over other routinely used methods. It permits the evaluation of lake condition across large regions and yields an overall disturbance index that is a physicochemical and biological indicator weighted sum of multiple disturbance factors. The robustness of our approach can be improved with increased availability of high-resolution disturbance datasets. [ABSTRACT FROM AUTHOR]

Abstract: Biological assessments of river conditions are increasingly conducted at regional and continental scales that match the extent of large-scale river management efforts. Multimetric indices composed of biological community indicators are commonly used to assess ecological condition and indices have recently been applied in large regions. Methods for large-scale multimetric index creation emphasize repeatability, comparability across regions, and objective selection of candidate metrics. Here we used an extensive fish dataset to create a large pool of fish community metrics which were screened to create multimetric indices (MMIs) in eight ecoregions covering the conterminous U.S. Candidate metrics were tested for metric range, corrected for natural gradients using boosted regression trees, and then tested for repeatability and sensitivity to landscape disturbance. Temporally stable and repeatable metrics were then evaluated for redundancy and used to compose MMIs for each region. Our MMIs were significantly correlated to independently developed MMIs, accurately reproducing prior index values with moderate to high precision and little bias. Our study demonstrates the utility of boosted regression tree models for correcting metric values for natural abiotic gradients and shows that the order of screening tests has a potentially important influence on metric selection. The resultant regional indices and component metrics provide a basis for assessing condition and testing hypotheses about landscape influences on aquatic ecosystems at a national scale in the US. [Copyright &y& Elsevier]

Abstract: Anthropogenic activities such as agricultural practices can have large effects on the ecological components and overall health of stream ecosystems. Therefore, having a better understanding of those effects and relationships allows for better design of mitigating strategies. The objectives of this study were to identify influential stream variables that correlate with macroinvertebrate indices using biophysical and statistical models. The models developed were later used to evaluate the impact of three agricultural management practices on stream integrity. Our study began with the development of a high-resolution watershed model for the Saginaw River watershed in Michigan for generating in-stream water quality and quantity data at stream reaches with biological sampling data. These in-stream data were then used to explain macroinvertebrate measures of stream health including family index of biological integrity (FamilyIBI), Hilsenhoff biotic index (HBI), and the number of Ephemeroptera, Plecoptera , and Trichoptera taxa (EPTtaxa). Two methods (stepwise linear regression and adaptive neuro-fuzzy inference systems (ANFIS)) were evaluated for developing predictive models for macroinvertebrate measures. The ANFIS method performed the best on average and the final models displayed the highest R2 and lowest mean squared error (MSE) for FamilyIBI (R2 =0.50, MSE=29.80), HBI (R2 =0.57, MSE=0.20), and EPTtaxa (R2 =0.54, MSE=6.60). Results suggest that nutrient concentrations have the strongest influence on all three macroinvertebrate measures. Consistently, average annual organic nitrogen showed the most significant association with EPTtaxa and HBI. Meanwhile, the best model for FamilyIBI included average annual ammonium and average seasonal organic phosphorus. The ANFIS models were then used in conjunction with the Soil and Water Assessment Tool to forecast and assess the potential effects of different best management practices (no-till, residual management, and native grass) on stream integrity. Based on the model predictions, native grass resulted in the largest improvement for all macroinvertebrate measures. [Copyright &y& Elsevier]