Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition.

Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.

Mt. Apo Biotic Index (MABI): a macroinvertebrate-based multimetric index for assessing stream biotic integrity of wadeable streams within a geothermal production field in Mindanao, Philippines.

Monitoring the ecological integrity of streams is a challenge, especially in the tropics, which experience high rates of degradation. Multimetric scoring systems have been widely used in other countries in evaluating current stream conditions; however, it has never been done in the Philippines. This study focuses on the development of a benthic macroinvertebrate-based multimetric index for the overall assessment of streams in Mt. Apo, Mindanao, Philippines. The index was used to develop existing physicochemical and biological data obtained during 2010 to 2015 surveys from 15 monitoring sites within the Mt. Apo Geothermal Project (MAGP). Metrics related to benthic macroinvertebrate abundance, richness, composition, functional habit groups, functional feeding groups, and pollution tolerance were screened for their range, temporal stability, sensitivity, discrimination efficiency (DE), redundancy, and responsiveness to anthropogenic impacts. The resulting multimetric index, the Mt. Apo Biotic Index (MABI), is computed as the sum of the individual metric scores after metric transformation using the discrete scoring method DRQ1 (D = discrete, R = reference, Q1 = 25th percentile) of the six core metrics: (1) number of Coleoptera individuals (abundance), (2) number of taxa (richness); (3) [%] Coleoptera taxa (composition), (4) number of sprawler individuals (functional habit group), (5) [%] collector-filterer taxa (functional feeding group), and (6) the Biological Monitoring Working Party Thai version (BMWP-Thai; pollution tolerance). MABI scores were classified into five condition ratings of stream biotic integrity: very poor (6 to 10), poor (11 to 15), fair (16 to 20), good (21 to 25), and excellent (26 to 30). The study demonstrated that the resulting pilot index may provide useful information that will benefit policymakers and resource managers in formulating more comprehensive stream management approaches and conservation plans for priority sites in the region.

Evaluating the impacts of small cascade hydropower from a perspective of stream health that integrates eco-environmental and hydrological values.

With small cascade hydropower projects (SCHPs) increasingly employed in small and medium rivers, methods to assess changes in health status within the stream system have become essential to river ecological environment management. In this study, we used a cloud based fuzzy evaluation method to synthetically diagnose the health status of a stream, both as a whole and its parts (hydrological regime, riparian landscape, aquatic community, water quality, and social demand), under the impacts of SCHPs. The results indicated that: (1) average maximum and minimum flows decreased by 20% and 10% respectively, since SCHPs were implemented. Furthermore, the 38% increase in low flow frequency indicated that SCHPs might amplify droughts, the opposite of large hydropower projects which have been shown to alleviate drought; (2) implementation of SCHPs enhanced heterogeneity and fragmentation in riparian landscapes and decreased diversity of riparian vegetation, and dominant species were more likely to emerge on the upstream side of dam; (3) diversity of phytoplankton, zooplankton, and benthic animals decreased by 14%, 4%, and 16%, respectively, during high-impact period (HIP); and fish species decreased by 26% with a shift from rapid flow adapted to lentic and slow flow adapted species; and (4) the stream still exhibited a healthy state during HIP, but the degree of certainty belonging to "healthy" decreased from 0.279 to 0.192, indicating that the stream health was nearing a deteriorated state. This evaluation model clarified imperceptible and fuzzy changes in stream health which will be helpful in follow-up management decisions.

Agricultural Land-Use Legacy, The Invasive Alga Didymosphenia geminata and Invertebrate Communities in Upland Streams with Natural Flow Regimes.

The integrity of freshwater ecosystems worldwide is under threat from agriculture and invasive species. Past agricultural activity can have persistent effects on aquatic diversity even decades after restoration, and the spread of invasive species is increasingly difficult to prevent due to globalisation. In the South Island of New Zealand, the invasive diatom Didymosphenia geminata (Didymo) causes nuisance blooms in streams. The impact of Didymo on stream invertebrate communities in upland streams with natural flow regimes remains poorly understood. We investigated the relationships between legacy effects of agriculture, Didymo and benthic invertebrate communities at 55 stream sites in Mahu Whenua, a 530 km2 conservation area comprising four former New Zealand high-country farms. The farms were destocked of sheep 4-9 years before stream sampling started. Kick-netting was used to collect macroinvertebrates from 7-23 streams within each farm to provide a land-use legacy gradient. Moreover, samples from 16 sites with clearly visible Didymo mats covering most of the stream bed (indicating high biomass and a dominant role in the biofilm) were compared with 39 sites without such Didymo mats. Total invertebrate taxon richness and EPT richness (taxon richness of larval mayflies, stoneflies and caddisflies) were lower in the stream catchments destocked most recently. When Didymo was present, relative EPT abundance was lower than when Didymo was absent, and Deleatidium mayflies decreased whereas midges and oligochaetes increased. These results highlight the need to look at past land-use practices when restoring high-country streams after agricultural impacts. They also show that Didymo can have negative effects on invertebrate communities in upland streams with natural flow regimes, a stream type previously overlooked in studies on this invasive diatom.

The influence of land use in a highly modified catchment: Investigating the importance of scale in riverine health assessment.

Riverine landscapes are studied at varying scales, investigating the complex cause-effect pathways between rivers and their physical, chemical and biological attributes. Policy development, management and planning are often formulated and applied at the regional or catchment scale, however the ecological evidence required to inform at this scale is typically collected from the much smaller scale. This research was aimed at determining if patterns in diatom and macroinvertebrate community composition can be attributed to a specific/single land use in a catchment with multiple land uses. The impacts of forest, macadamia, grazing, sugar cane and urban land uses in the Richmond River Catchment of Northern NSW, Australia were investigated at 20 micro-catchment scale sites. A total of 124 diatom species from 43 genera, along with 92 families and three sub-families of macroinvertebrates, were collected and used to calculate the Richmond River Diatom Index (RRDI), AUSRIVAS and SIGNAL2 scores. Statistical analyses showed distinct groupings of land use categories providing evidence of cause-effect pathways attributed to individual land uses. The RRDI, AUSRIVAS and SIGNAL2 scores all showed distinctions between land use categories, though they were clearer in the RRDI. The RRDI indicated that the grazing sites had the poorest health of the land use categories, followed by sugar cane and urban while the macadamia and forest sites were relatively healthy. Signal 2 scores showed similar trends to the RRDI, while the AURIVAS scores did not present clear trends, particularly in the edge habitat of macadamia land use sites. The results indicated that riparian vegetation and instream habitat play an important role in attenuating inputs and that rehabilitation efforts could potentially improve water quality at a micro-catchment scale and subsequently, result in river health improvement at the catchment scale. The research collected at this micro-catchment scale presents new evidence that further informs and affects decisions made at the catchment scale, where policy and planning is developed and implemented.

Development and evaluation of a comprehensive drought index.

Droughts are known as the world's costliest natural disasters impacting a variety of sectors. Despite their wide range of impacts, no universal drought definition has been defined. The goal of this study is to define a universal drought index that considers drought impacts on meteorological, agricultural, hydrological, and stream health categories. Additionally, predictive drought models are developed to capture both categorical (meteorological, hydrological, and agricultural) and overall impacts of drought. In order to achieve these goals, thirteen commonly used drought indices were aggregated to develop a universal drought index named MASH. The thirteen drought indices consist of four drought indices from each meteorological, hydrological, and agricultural categories, and one from the stream health category. Cluster analysis was performed to find the three closest indices in each category. Then the closest drought indices were averaged in each category to create the categorical drought score. Finally, the categorical drought scores were simply averaged to develop the MASH drought index. In order to develop predictive drought models for each category and MASH, the ReliefF algorithm was used to rank 90 variables and select the best variable set. Using the best variable set, the adaptive neuro-fuzzy inference system (ANFIS) was used to develop drought predictive models and their accuracy was examined using the 10-fold cross validation technique. The models' predictabilities ranged from R2 = 0.75 for MASH to R2 = 0.98 for the hydrological drought model. The results of this study can help managers to better position resources to cope with drought by reducing drought impacts on different sectors.

Optimization of bioenergy crop selection and placement based on a stream health indicator using an evolutionary algorithm.

The emission of greenhouse gases continues to amplify the impacts of global climate change. This has led to the increased focus on using renewable energy sources, such as biofuels, due to their lower impact on the environment. However, the production of biofuels can still have negative impacts on water resources. This study introduces a new strategy to optimize bioenergy landscapes while improving stream health for the region. To accomplish this, several hydrological models including the Soil and Water Assessment Tool, Hydrologic Integrity Tool, and Adaptive Neruro Fuzzy Inference System, were linked to develop stream health predictor models. These models are capable of estimating stream health scores based on the Index of Biological Integrity. The coupling of the aforementioned models was used to guide a genetic algorithm to design watershed-scale bioenergy landscapes. Thirteen bioenergy managements were considered based on the high probability of adaptation by farmers in the study area. Results from two thousand runs identified an optimum bioenergy crops placement that maximized the stream health for the Flint River Watershed in Michigan. The final overall stream health score was 50.93, which was improved from the current stream health score of 48.19. This was shown to be a significant improvement at the 1% significant level. For this final bioenergy landscape the most often used management was miscanthus (27.07%), followed by corn-soybean-rye (19.00%), corn stover-soybean (18.09%), and corn-soybean (16.43%). The technique introduced in this study can be successfully modified for use in different regions and can be used by stakeholders and decision makers to develop bioenergy landscapes that maximize stream health in the area of interest.

Impact of treated effluent discharges on fish communities: Evaluating the effects of pollution on fish distribution, abundance and environmental integrity.

Domestic effluent discharges change water quality and habitat conditions in urban watersheds, though less known about how these impact fish communities. This work assessed the impact of chronic wastewater pollution on biotic and abiotic factors in six urban streams in Patagonia. Stream hydrological features, water quality conditions and fish communities were analyzed during a one-year period. The oxygen saturation and water velocity showed significant differences between up- and downstream locations of wastewater treatment plants (WWTPs). Chemical parameters revealed an eutrophication process downstream of the WWTP input, with increased biological oxygen demanding (BOD), nitrogen, ammonium, soluble reactive phosphorus, and chlorophyll a concentrations, indicating nutrient enrichment that can lead to a potential for algal growth. The study highlighted significant differences in fish abundance, density, and biomass between reaches upstream (Control) and downstream (Impacted) of the WWTP discharges, suggesting a detrimental impact on fish communities. While juveniles, females and males of the native Catfish (Hatcheria macraei) preferred pristine zones, juveniles and males of the native Perch (Percichthys spp.) displayed preferences for areas with higher nutrient levels. Exotic species like Rainbow Trout (Oncorhynchus mykiss) (juveniles, females and males) preferred low-nutrient, high-quality habitats, while juveniles, females and males of Brown Trout (Salmo trutta) were found near the WWTP facilities. Although some previous studies have suggested that nutrient enrichment might benefit fish, our findings highlight the negative impacts on fish abundance and distribution due to WWTPs. Fish species appear to demonstrate certain degrees of tolerance to pollution, with larger individuals displaying greater tolerance. Although the pollution levels may did not result in an irreversible collapse of the system, the absence of fish in the stream with the highest pollution level would indicate an ongoing environmental deterioration. Anthropogenic activities, especially municipal effluent discharge, exacerbate environmental degradation and demand specific management actions to maintain ecosystem integrity.

Techniques to improve ecological interpretability of black-box machine learning models.

Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous U.S., as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate-response relationships. Additionally interaction effects were quantified according to interaction strength (IAS) and Friedman's H2 statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests.

Incidence and Determinants of Health Care-Associated Blood Stream Infection at a Neonatal Intensive Care Unit in Ujjain, India: A Prospective Cohort Study.

Very little is known about laboratory-confirmed blood stream infections (LCBIs) in neonatal intensive care units (NICUs) in resource-limited settings. The aim of this cohort study was to determine the incidence, risk factors, and causative agents of LCBIs in a level-2 NICU in India. The diagnosis of LCBIs was established using the Centre for Disease Control, USA criteria. A predesigned questionnaire containing risk factors associated with LCBIs was filled-in. A total of 150 neonates (43% preterm) were included in the study. The overall incidence of LCBIs was 31%. The independent risk factors for LCBIs were: preterm neonates (relative risk (RR) 2.23), duration of NICU stay more than 14 days (RR 1.75), chorioamnionitis in the mother (RR 3.18), premature rupture of membrane in mothers (RR 2.32), neonate born through meconium-stained amniotic fluid (RR 2.32), malpresentation (RR 3.05), endotracheal intubation (RR 3.41), umbilical catheterization (RR 4.18), and ventilator-associated pneumonia (RR 3.17). The initiation of minimal enteral nutrition was protective from LCBIs (RR 0.22). The predominant causative organisms were gram-negative pathogens (58%). The results of the present study can be used to design and implement antibiotic stewardship policy and introduce interventions to reduce LCBIs in resource-limited settings.

Ecohydrological modeling for large-scale environmental impact assessment.

Ecohydrological models are frequently used to assess the biological integrity of unsampled streams. These models vary in complexity and scale, and their utility depends on their final application. Tradeoffs are usually made in model scale, where large-scale models are useful for determining broad impacts of human activities on biological conditions, and regional-scale (e.g. watershed or ecoregion) models provide stakeholders greater detail at the individual stream reach level. Given these tradeoffs, the objective of this study was to develop large-scale stream health models with reach level accuracy similar to regional-scale models thereby allowing for impacts assessments and improved decision-making capabilities. To accomplish this, four measures of biological integrity (Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT), Family Index of Biotic Integrity (FIBI), Hilsenhoff Biotic Index (HBI), and fish Index of Biotic Integrity (IBI)) were modeled based on four thermal classes (cold, cold-transitional, cool, and warm) of streams that broadly dictate the distribution of aquatic biota in Michigan. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow and water quality in seven watersheds and the Hydrologic Index Tool was used to calculate 171 ecologically relevant flow regime variables. Unique variables were selected for each thermal class using a Bayesian variable selection method. The variables were then used in development of adaptive neuro-fuzzy inference systems (ANFIS) models of EPT, FIBI, HBI, and IBI. ANFIS model accuracy improved when accounting for stream thermal class rather than developing a global model.