Effects of a large-scale bioretention installation on the species composition of an urban bird community as determined by passive acoustic monitoring.

As urbanization accelerates worldwide, municipalities are attempting to construct new green spaces within their borders. The perceived ecological value of these places is frequently tied to their ability to attract urban wildlife, such as birds, which can easily be observed and enjoyed. As one strategy, stormwater is now frequently managed with green infrastructure: planted areas that retain and treat stormwater rather than merely directing it to surface waters. While these practices have the potential to provide habitat for urban wildlife, the ecological effects of these systems are largely unknown. To assess whether one green infrastructure project increases habitat value, we used passive acoustic monitoring to survey urban bird communities in and near a large green infrastructure project in Columbus, Ohio (USA). Bird communities near bioretention cells (rain gardens) were compared to those at nearby lawns and remnant or restored natural areas. We found that recently installed bioretention cells tended to support more omnivores, lower-canopy foraging species, and species from a higher diversity of feeding guilds than did nearby lawn control sites. We were unable to detect effects of nearby bioretention installations on bird species richness at other sites. The observed differences in species richness were fairly small, and we urge caution when anticipating the habitat value of bioretention cells, at least for bird species. However, the results that we observed suggest that bioretention cells could have a more positive impact on bird communities in different contexts or using different design strategies. The bioretention cells surveyed in this study were small and only planted in grasses and forbs, potentially limiting their ability to offer complex habitat. They were also relatively young, and future work is needed to determine their long-term effect on avian communities and biodiversity of other taxa.

Source contribution to phosphorus loads from the Maumee River watershed to Lake Erie.

Coastal eutrophication is a leading cause of degraded water quality around the world. Identifying the sources and their relative contributions to impaired downstream water quality is an important step in developing management plans to address water quality concerns. Recent mass-balance studies of Total Phosphorus (TP) loads of the Maumee River watershed highlight the considerable phosphorus contributions of non-point sources, including agricultural sources, degrading regional downstream water quality. This analysis builds upon these mass-balance studies by using the Soil and Water Assessment Tool to simulate the movement of phosphorus from manure, inorganic fertilizer, point sources, and soil sources, and respective loads of TP and Dissolved Reactive Phosphorus (DRP). This yields a more explicit estimation of source contribution from the watershed. Model simulations indicate that inorganic fertilizers contribute a greater proportion of TP (45% compared to 8%) and DRP (58% compared to 12%) discharged from the watershed than manure sources in the March-July period, the season driving harmful algal blooms. Although inorganic fertilizers contributed a greater mass of TP and DRP than manure sources, the two sources had similar average delivery fractions of TP (2.7% for inorganic fertilizers vs. 3.0% for manure sources) as well as DRP (0.7% for inorganic fertilizers vs. 1.2% for manure sources). Point sources contributed similar proportions of TP (5%) and DRP (12%) discharged in March-July as manure sources. Soil sources of phosphorus contributed over 40% of the March-July TP load and 20% of the March-July DRP load from the watershed to Lake Erie. Reductions of manures and inorganic fertilizers corresponded to a greater proportion of phosphorus delivered from soil sources of phosphorus, indicating that legacy phosphorus in soils may need to be a focus of management efforts to reach nutrient load reduction goals. In agricultural watersheds aground the world, including the Maumee River watershed, upstream nutrient management should not focus solely on an individual nutrient source; rather a comprehensive approach involving numerous sources should be undertaken.

Evaluating management options to reduce Lake Erie algal blooms using an ensemble of watershed models.

Reducing harmful algal blooms in Lake Erie, situated between the United States and Canada, requires implementing best management practices to decrease nutrient loading from upstream sources. Bi-national water quality targets have been set for total and dissolved phosphorus loads, with the ultimate goal of reaching these targets in 9-out-of-10 years. Row crop agriculture dominates the land use in the Western Lake Erie Basin thus requiring efforts to mitigate nutrient loads from agricultural systems. To determine the types and extent of agricultural management practices needed to reach the water quality goals, we used five independently developed Soil and Water Assessment Tool models to evaluate the effects of 18 management scenarios over a 10-year period on nutrient export. Guidance from a stakeholder group was provided throughout the project, and resulted in improved data, development of realistic scenarios, and expanded outreach. Subsurface placement of phosphorus fertilizers, cover crops, riparian buffers, and wetlands were among the most effective management options. But, only in one realistic scenario did a majority (3/5) of the models predict that the total phosphorus loading target would be met in 9-out-of-10 years. Further, the dissolved phosphorus loading target was predicted to meet the 9-out-of-10-year goal by only one model and only in three scenarios. In all scenarios evaluated, the 9-out-of-10-year goal was not met based on the average of model predictions. Ensemble modeling revealed general agreement about the effects of several practices although some scenarios resulted in a wide range of uncertainty. Overall, our results demonstrate that there are multiple pathways to approach the established water quality goals, but greater adoption rates of practices than those tested here will likely be needed to attain the management targets.

Uncertainty in critical source area predictions from watershed-scale hydrologic models.

Watershed-scale hydrologic models are frequently used to inform conservation and restoration efforts by identifying critical source areas (CSAs; alternatively 'hotspots'), defined as areas that export relatively greater quantities of nutrients and sediment. The CSAs can then be prioritized or 'targeted' for conservation and restoration to ensure efficient use of limited resources. However, CSA simulations from watershed-scale hydrologic models may be uncertain and it is critical that the extent and implications of this uncertainty be conveyed to stakeholders and decision makers. We used an ensemble of four independently developed Soil and Water Assessment Tool (SWAT) models and a SPAtially Referenced Regression On Watershed attributes (SPARROW) model to simulate CSA locations for flow, phosphorus, nitrogen, and sediment within the ~17,000-km2 Maumee River watershed at the HUC-12 scale. We then assessed uncertainty in CSA simulations determined as the variation in CSA locations across the models. Our application of an ensemble of models - differing with respect to inputs, structure, and parameterization - facilitated an improved accounting of CSA prediction uncertainty. We found that the models agreed on the location of a subset of CSAs, and that these locations may be targeted with relative confidence. However, models more often disagreed on CSA locations. On average, only 16%-46% of HUC-12 subwatersheds simulated as a CSA by one model were also simulated as a CSA by a different model. Our work shows that simulated CSA locations are highly uncertain and may vary substantially across models. Hence, while models may be useful in informing conservation and restoration planning, their application to identify CSA locations would benefit from comprehensive uncertainty analyses to avoid inefficient use of limited resources.

The seasonality of nutrients and sediment in residential stormwater runoff: Implications for nutrient-sensitive waters.

The discharge of excess nutrients to surface waters causes eutrophication, resulting in algal blooms, hypoxia, degraded water quality, reduced and contaminated fisheries, threats to potable water supplies, and decreases in tourism, cultural activities, and coastal economies. An understanding of the contribution of urban runoff to eutrophication is needed to inform management strategies. More broadly, the seasonality in nutrient concentrations and loads in urban runoff needs further analysis since algal blooms and hypoxia are seasonal in nature. This study quantifies the variation of nutrients and sediment in stormwater runoff across seasons from four urban residential sewersheds located in Columbus, Ohio, USA. An average of 62 runoff events at each sewershed were sampled using automated samplers during stormflow and analyzed for nutrients and total suspended solids (TSS). Spring total nitrogen concentrations had a significantly (p < 0.05) higher median concentration (2.19 mg/L) than fall (1.55 mg/L) and summer (1.50 mg/L). Total phosphorus concentrations were significantly higher in spring (0.22 mg/L) and fall (0.23 mg/L) than summer (0.15 mg/L). TSS concentrations were significantly higher in the spring (74.5 mg/L) and summer (56.5 mg/L) than the fall (34.0 mg/L). In contrast, seasonal loading differences for nutrients or sediment were rare because runoff volume varied in such a way as to offset significant concentration differences and significant seasonality in rainfall intensity. Annual pollutant loadings were similar in magnitude to other residential and even some agricultural runoff studies. Although nutrient loads are the key indicator for determining algal biomass, nutrient concentrations are important for real-time algal growth. Future research efforts should be focused not only on understanding how seasonal urban concentrations and loads impact coastal eutrophication, but also developing improved watershed management focused on critical periods. Improved designs for stormwater control measures need to account for seasonality in pollutant discharge.

Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people.

With the explosion of "big data," digital repositories of texts and images are growing rapidly. These datasets present new opportunities for psychological research, but they require new methodologies before researchers can use these datasets to yield insights into human cognition. We present a new method that allows psychological researchers to take advantage of text and image databases: a procedure for measuring human categorical representations over large datasets of items, such as arbitrary words or pictures. We call this method discrete Markov chain Monte Carlo with people (d-MCMCP). We illustrate our method by evaluating the following categories over datasets: emotions as represented by facial images, moral concepts as represented by relevant words, and seasons as represented by images drawn from large online databases. Three experiments demonstrate that d-MCMCP is powerful and flexible enough to work with complex, naturalistic stimuli drawn from large online databases.

psiTurk: An open-source framework for conducting replicable behavioral experiments online.

Online data collection has begun to revolutionize the behavioral sciences. However, conducting carefully controlled behavioral experiments online introduces a number of new of technical and scientific challenges. The project described in this paper, psiTurk, is an open-source platform which helps researchers develop experiment designs which can be conducted over the Internet. The tool primarily interfaces with Amazon's Mechanical Turk, a popular crowd-sourcing labor market. This paper describes the basic architecture of the system and introduces new users to the overall goals. psiTurk aims to reduce the technical hurdles for researchers developing online experiments while improving the transparency and collaborative nature of the behavioral sciences.

Influence of seasonal fluctuation and loading rates on microbial and chemical indicators during semi-continuous anaerobic digestion.

Minimal attention is paid towards the performance of the 40 million small-scale digesters which frequently operate at psychrophilic temperatures. Understanding the levels of microbial and chemical indicators at various loading rates and temperatures is useful for improving treatment efficiency and management strategies for small-scale digesters. In this study, semi-continuous anaerobic digesters were operated in replicate at four different loading rates (control, 0.3, 0.8 and 1.3 kg VS/m(3)/day) and housed in an environment that simulated seasonal change (27.5°C,10°C and 27.5°C). The results illustrate that class B quality biosolids were generated for all treatments as per guidelines from the United States Environmental Protection Agency (USEPA). The simulated seasonal change did not influence Escherichia coli or faecal coliform levels, while it did appear to have an effect upon levels of Enterococci. Reduced loading rates led to a more stable environment (in terms of pH, levels of volatile fatty acids (VFAs) and total inorganic carbonate (TIC)) as well as lower levels of indicator bacteria, but generated slightly lower biogas volumes (high--53.23 L vs. low--53.19 L) over the course of the study. The results provide important data to improve the performance of small-scale psychrophilic digesters, specifically by reducing loading rates to prevent souring during winter months.

Urban stormwater green infrastructure: Evaluating the public health service role of bioretention using microbial source tracking and bacterial community analyses.

Bioretention cells (BRCs) control stormwater flow on-site during precipitation, reducing runoff and improving water quality through chemical, physical, and biological processes. While BRCs are effective in these aspects, they provide habitats for wildlife and may face microbial hazards from fecal shedding, posing a potential threat to human health and the nearby environment. However, limited knowledge exists regarding the ability to control microbial hazards (e.g., beyond using typical indicator bacteria) through stormwater biofiltration. Therefore, the purpose of this study is to characterize changes in the bacterial community of urban stormwater undergoing bioretention treatment, with the goal of assessing the public health implications of these green infrastructure solutions. Samples from BRC inflow and outflow in Columbus, Ohio, were collected post-heavy storms from October 2021 to March 2022. Conventional culture-based E. coli monitoring and microbial source tracking (MST) were conducted to identify major fecal contamination extent and its sources (i.e., human, canine, avian, and ruminant). Droplet digital polymerase chain reaction (ddPCR) was utilized to quantify the level of host-associated fecal contamination in addition to three antibiotic resistant genes (ARGs): tetracycline resistance gene (tetQ), sulfonamide resistance gene (sul1), and Klebsiella pneumoniae carbapenemase resistance gene (blaKPC). Subsequently, 16S rRNA gene sequencing was conducted to characterize bacterial community differences between stormwater BRC inflow and outflow. Untreated urban stormwater reflects anthropogenic contamination, suggesting it as a potential source of contamination to waterbodies and urban environments. When comparing inlet and outlet BRC samples, urban stormwater treated via biofiltration did not increase microbial hazards, and changes in bacterial taxa and alpha diversity were negligible. Beta diversity results reveal a significant shift in bacterial community structure, while simultaneously enhancing the water quality (i.e., reduction of metals, total suspended solids, total nitrogen) of urban stormwater. Significant correlations were found between the bacterial community diversity of urban stormwater with fecal contamination (e.g. dog) and ARG (sul1), rainfall intensity, and water quality (hardness, total phosphorous). The study concludes that bioretention technology can sustainably maintain urban microbial water quality without posing additional public health risks, making it a viable green infrastructure solution for heavy rainfall events exacerbated by climate change.

Longitudinal Analysis of Urban Stormwater Microbiome and Resistome from Watersheds with and without Green Infrastructure using Long-Read Sequencing.

Since stormwater conveys a variety of contaminants into water bodies, green infrastructure (GI) is increasingly being adopted as an on-site treatment solution in addition to controlling peak flows. The purpose of this study was to identify differences in microbial water quality of stormwater in watersheds retrofitted with GI vs. those without GI. Considering stormwater is recently recognized as a contributor to the antibiotic resistance (AR) threat, another goal of this study was to characterize changes in the microbiome and collection of AR genes (resistome) of urban stormwater with season, rainfall characteristics, and fecal contamination. MinION long-read sequencing was used to analyze stormwater microbiome and resistome from watersheds with and without GI in Columbus, Ohio, United States, over 18 months. We characterized fecal contamination in stormwater via culturing Escherichia coli and with molecular microbial source tracking (MST) to identify sources of fecal contamination. Overall, season and storm event (rainfall) characteristics had the strongest relationships with changes in the stormwater microbiome and resistome. We found no significant differences in microbial water quality or the microbiome of stormwater in watersheds with and without GI implemented. However, there were differences between the communities of microorganisms hosting antibiotic resistance genes (ARGs) in stormwater from watersheds with and without GI, indicating the potential sensitivity of AR bacteria to treatment. Stormwater was contaminated with high concentrations of human-associated fecal bacterial genes, and the ARG host bacterial community had considerable similarities to human feces/wastewater. We also identified 15 potential pathogens hosting ARGs in these stormwater resistome, including vancomycin-resistant Enterococcus faecium (VRE) and multidrug-resistant Pseudomonas aeruginosa. In summary, urban stormwater is highly contaminated and has a great potential to spread AR and microbial hazards to nearby environments. This study presents the most comprehensive analysis of stormwater microbiome and resistome to date, which is crucial to understanding the potential microbial risk from this matrix. This information can be used to guide future public health policy, stormwater reuse programs, and urban runoff treatment initiatives.

Data on the Human Versus artificial intelligence process management experiment.

Human subject experiments are performed to evaluate the influence of artificial intelligence (AI) process management on human design teams solving a complex engineering problem and compare that to the influence of human process management. Participants are grouped into teams of five individuals and asked to generate a drone fleet and plan routes to deliver parcels to a given customer market. The teams are placed under the guidance of either a human or an AI external process manager. Halfway through the experiment, the customer market is changed unexpectedly, requiring teams to adjust their strategy. During the experiment, participants can create, evaluate, share their drone designs and delivery routes, and communicate with their team through a text chat tool using a collaborative research platform called HyForm. The research platform collects step-by-step logs of the actions made by and communication amongst participants in both the design team's roles and the process managers. This article presents the data sets collected for 171 participants assigned to 31 design teams, 15 teams under the guidance of an AI agent (5 participants), and 16 teams under the guidance of a human manager (6 participants). These data sets can be used for data-driven design, behavioral analyses, sequence-based analyses, and natural language processing.

Effects of organic loading rate on reactor performance and archaeal community structure in mesophilic anaerobic digesters treating municipal sewage sludge.

In this study, the organic loading rate (OLR) of a high-solids anaerobic digestion (HSAD) system was increased from 3.4 to 5.0 gVS L(-1) day(-1) and reactor stability, performance and microbial community structure were determined. Laboratory simulations (3.5 L) of the full-scale process (500 dry ton year(-1)) were conducted using continuously stirred-tank mesophilic reactors. OLRs of 3.4 gVS L(-1)day(-1) (equal to the full-scale HSAD), 4.0, 4.5 and 5.0 gVS L(-1)day(-1) were evaluated. Biochemical parameters and archaeal community dynamics were measured over 42 days of steady state operation. Results showed that increasing OLR increased the amount of organic matter conversion and resulted in higher organic matter removal and volumetric methane (CH4) production (VMP) rates. The highest volatile solids (VS) removal and VMP results of 54 ± 2% and 1.4 ± 0.1 L CH4 L(-1)day(-1) were observed for 5.0 gVS L(-1) day(-1). The efficiency of reactor conversion of organic matter to CH(4) was found to be similar in all the treatments with an average value of 0.57 ± 0.07 LCH(4) gVS(-1) (removed). 16S rRNA gene terminal restriction fragment polymorphism (T-RFLP) analyses revealed that archaeal TRFs remained stable during the experiment accounting for an average relative abundance (RA) of 81 ± 1%. Archaea consistent with multiple terminal restriction fragments (TRFs) included members of the Euryarchaeota and Crenarchaeota phyla, including acetoclastic and hydrogenotrophic groups. In conclusion, this laboratory-scale study suggests that performance and stability as well as the archaeal community structure in this HSAD system was unaffected by increasing the OLR by nearly 50% and that this increase resulted in a similar increase in the amount of CH(4) gas generated.

Evaluating the efficacy of targeting options for conservation practice adoption on watershed-scale phosphorus reductions.

Conservation identities of farmers in the Maumee River watershed, derived from farmer surveys, were embedded into a SWAT watershed model. This was done to improve the representation of the heterogeneity among farmers in the decision-making process related to the adoption of conservation practices. Modeled farm operations, created with near field-level Hydrologic Response Units (HRUs) within the SWAT model, were assigned a modeled primary operator. Modeled primary operators held unique conservation identities driven by their spatial location within the watershed. Five pathways of targeting the adoption of subsurface placement of phosphorus and buffer strips to HRUs within the watershed were assessed. Targeting pathways included targeting by HRU-level phosphorus losses, conservation identity of model operators, a hybrid approach combining HRU-level phosphorus losses and conservation identity of the model primary operator managing the HRU, and a proxy measure for random placement throughout the watershed. Targeting the placement of subsurface phosphorus application to all agricultural HRUs resulted in the greatest reduction in total phosphorus losses (32%) versus buffer strips (23%). For both conservation practices, targeting by HRU-level total phosphorus losses resulted in the most efficient rate of phosphorus reduction as measured by the ratio of phosphorus reduction to conservation practice adoption rates. The hybrid targeting approach closely resembled targeting by phosphorus losses, indicating near optimal results can be obtained even when constraining adoption by farmer characteristics. These results indicate that by developing management strategies based on a combination of field-level information and human-operator characteristics, a more efficient use of limited resources can be used while achieving near-maximal environmental benefits as compared to managing environmental outcomes solely based on field-level information.

Simulating internal watershed processes using multiple SWAT models.

The need for effective water quality models to help guide management and policy, and extend monitoring information, is at the forefront of recent discussions related to watershed management. These models are often calibrated and validated at the basin outlet, which ensures that models are capable of evaluating basin scale hydrology and water quality. However, there is a need to understand where these models succeed or fail with respect to internal process representation, as these watershed-scale models are used to inform management practices and mitigation strategies upstream. We evaluated an ensemble of models-each calibrated to in-stream observations at the basin outlet-against discharge and nutrient observations at the farm field scale to determine the extent to which these models capture field-scale dynamics. While all models performed well at the watershed outlet, upstream performance varied. Models tended to over-predict discharge through surface runoff and subsurface drainage, while under-predicting phosphorus loading through subsurface drainage and nitrogen loading through surface runoff. Our study suggests that while models may be applied to predict impacts of management at the basin scale, care should be taken in applying the models to evaluate field-scale management and processes in the absence of data that can be incorporated at that scale, even with the use of multiple models.

Residential urban stormwater runoff: A comprehensive profile of microbiome and antibiotic resistance.

Non-point stormwater runoff is a major contamination source of receiving waterbodies. Heightened incidence of waterborne disease outbreaks related to recreational use and source water contamination is associated with extreme rainfall events. Such extreme events are predicted to increase in some regions due to climate change. Consequently, municipal separate storm sewer systems (MS4s) conveying pathogens to receiving waters are a growing public health concern. In addition, the spread of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria in various environmental matrices, including urban runoff, is an emerging threat. The resistome and microbiota profile of MS4 discharges has yet to be fully characterized. To address this knowledge gap, we first analyzed the relationship between rainfall depth and intensity and E. coli densities (fecal indicator) in stormwater from four MS4 outflows in Columbus, Ohio, USA during the spring and summer of 2017. Microbial source tracking (MST) was conducted to examine major fecal contamination sources in the study sewersheds. A subset of samples was analyzed for microbial and resistome profiles using a metagenomic approach. The results showed a significant positive relationship between outflow E. coli density and rainfall intensity. MST results indicate prevalent fecal contamination from ruminant populations in the study sites (91% positive among the samples tested). Protobacteria and Actinobacteria were two dominant bacteria at a phylum level. A diverse array of ARGs and potentially pathogenic bacteria (e.g. Salmonella enterica Typhimurium), fungi (e.g. Scedosporium apiospermum), and protists (e.g. Acanthamoeba palestinensis) were found in urban stormwater outflows that discharge into adjacent streams. The most prevalent ARGs among samples were ß-lactam resistance genes and the most predominant virulence genes within bacterial community were related with Staphylococcus aureus. A comprehensive contamination profile indicates a need for sustainable strategies to manage urban stormwater runoff amid increasingly intense rainfall events to protect public and environmental health.

Projecting the effects of agricultural conservation practices on stream fish communities in a changing climate.

How anticipated climate change might affect long-term outcomes of present-day agricultural conservation practices remains a key uncertainty that could benefit water quality and biodiversity conservation planning. To explore this issue, we forecasted how the stream fish communities in the Western Lake Erie Basin (WLEB) would respond to increasing amounts of agricultural conservation practice (ACP) implementation under two IPCC future greenhouse gas emission scenarios (RCP4.5: moderate reductions; RCP8.5: business-as-usual conditions) during 2020-2065. We used output from 19 General Circulation Models to drive linked agricultural land use (APEX), watershed hydrology (SWAT), and stream fish distribution (boosted regression tree) models, subsequently analyzing how projected changes in habitat would influence fish community composition and functional trait diversity. Our models predicted both positive and negative effects of climate change and ACP implementation on WLEB stream fishes. For most species, climate and ACPs influenced species in the same direction, with climate effects outweighing those of ACP implementation. Functional trait analysis helped clarify the varied responses among species, indicating that more extreme climate change would reduce available habitat for large-bodied, cool-water species with equilibrium life-histories, many of which also are of importance to recreational fishing (e.g., northern pike, smallmouth bass). By contrast, available habitat for warm-water, benthic species with more periodic or opportunistic life-histories (e.g., northern hogsucker, greater redhorse, greenside darter) was predicted to increase. Further, ACP implementation was projected to hasten these shifts, suggesting that efforts to improve water quality could come with costs to other ecosystem services (e.g., recreational fishing opportunities). Collectively, our findings demonstrate the need to consider biological outcomes when developing strategies to mitigate water quality impairment and highlight the value of physical-biological modeling approaches to agricultural and biological conservation planning in a changing climate.

The hydrologic model as a source of nutrient loading uncertainty in a future climate.

Hydrologic models are applied increasingly with climate projections to provide insights into future hydrologic conditions. However, both hydrologic models and climate models can produce a wide range of predictions based on model inputs, assumptions, and structure. To characterize a range of future predictions, it is common to use multiple climate models to drive hydrologic models, yet it is less common to also use a suite of hydrologic models. It is also common for hydrologic models to report riverine discharge and assume that nutrient loading will follow similar patterns, but this may not be the case. In this study, we characterized uncertainty from both climate models and hydrologic models in predicting riverine discharge and nutrient loading. Six climate models drawn from the Coupled Model Intercomparison Project Phase 5 ensemble were used to drive five independently developed and calibrated Soil and Water Assessment Tool models to assess hydrology and nutrient loadings for mid-century (2046-2065) in the Maumee River Watershed,the largest watershedsdraining to the Laurentian Great Lakes. Under those conditions, there was no clear agreement on the direction of change in future nutrient loadings or discharge. Analysis of variance demonstrated that variation among climate models was the dominant source of uncertainty in predicting future total discharge, tile discharge (i.e. subsurface drainage), evapotranspiration, and total nitrogen loading, while hydrologic models were the main source of uncertainty in predicted surface runoff and phosphorus loadings. This innovative study quantifies the importance of hydrologic model in the prediction of riverine nutrient loadings under a future climate.

Lacandon Maya ecosystem management: sustainable design for subsistence and environmental restoration.

Indigenous groups have designed and managed their ecosystems for generations, resulting in biodiversity protection while producing for their family's needs. Here we describe the agroecosystem of the Lacandon Maya, an indigenous group who live in Chiapas, Mexico. The Lacandon practice a form of swidden agriculture that conserves the surrounding rain forest ecosystem while cycling the majority of their land through five successional stages. These stages include an herbaceous stage, two shrub stages, and two forest stages. A portion of their land is kept in primary forest. This study presents the Lacandon traditional ecological knowledge (TEK) for agroforestry and quantitatively describes the plant community and the associated soil ecology of each successional stage. Also documented is the knowledge of the Lacandon regarding the immediate use of plant species and plant species useful for soil fertility enhancement. Woody plant diversity increases during the successional stages of the Lacandon system, and by the beginning of the first forest stage, the diversity is similar to that of the primary forest. In all stages, Lacandon use 60% of the available plant species for food, medicine, and raw materials. Approximately 45% of the woody plant species present in each fallow stage were thought by the Lacandon to enhance soil fertility. Total soil nitrogen and soil organic matter increased with successional stage and with time from intentional burn. Nutrient and soil nematode dynamics in shrub stages related to the presence of introduced and managed plants, indicating engineered soil enhancement by the Lacandon. The effects on biodiversity and soil ecology coupled with productivity for agricultural subsistence indicate that Lacandon TEK may offer tools for environmental conservation that would provide for a family's basic needs while maintaining a biodiverse rain forest ecosystem. Tools such as these may offer options for regional restoration and conservation efforts such as the Mesoamerican Biological Corridor in Mexico and Central America, where attainment of environmental goals must include methods to provide resources to local inhabitants.

Waste treatment and biogas quality in small-scale agricultural digesters.

Seven low-cost digesters in Costa Rica were studied to determine the potential of these systems to treat animal wastewater and produce renewable energy. The effluent water has a significantly lower oxygen demand (COD decreased from 2,968 mg/L to 472 mg/L) and higher dissolved nutrient concentration (NH(4)-N increased by 78.3% to 82.2mg/L) than the influent water, which increases the usefulness of the effluent as an organic fertilizer and decreases its organic loading on surface waters. On average, methane constituted 66% of the produced biogas, which is consistent with industrial digesters. Through principle component analysis, COD, turbidity, NH(4)-N, TKN, and pH were determined to be the most useful parameters to characterize wastewater. The results suggest that the systems have the ability to withstand fluctuations in the influent water quality. This study revealed that small-scale agricultural digesters can produce methane at concentrations useful for cooking, while improving the quality of the livestock wastewater.