Reimagining infrastructure for a biodiverse future.

Civil infrastructure will be essential to face the interlinked existential threats of climate change and rising resource demands while ensuring a livable Anthropocene for all. However, conventional infrastructure planning largely neglects the contributions and maintenance of Earth's ecological life support systems, which provide irreplaceable services supporting human well-being. The stability and performance of these services depend on biodiversity, but conventional infrastructure practices, narrowly focused on controlling natural capital, have inadvertently degraded biodiversity while perpetuating social inequities. Here, we envision a new infrastructure paradigm wherein biodiversity and ecosystem services are a central objective of civil engineering. In particular, we reimagine infrastructure practice such that 1) ecosystem integrity and species conservation are explicit objectives from the outset of project planning; 2) infrastructure practices integrate biodiversity into diverse project portfolios along a spectrum from conventional to nature-based solutions and natural habitats; 3) ecosystem functions reinforce and enhance the performance and lifespan of infrastructure assets; and 4) civil engineering promotes environmental justice by counteracting legacies of social inequity in infrastructure development and nature conservation. This vision calls for a fundamental rethinking of the standards, practices, and mission of infrastructure development agencies and a broadening of scope for conservation science. We critically examine the legal and professional precedents for this paradigm shift, as well as the moral and economic imperatives for manifesting equitable infrastructure planning that mainstreams biodiversity and nature's benefits to people. Finally, we set an applied research agenda for supporting this vision and highlight financial, professional, and policy pathways for achieving it.

Assessing relationships between onsite wastewater treatment system maintenance patterns and system-level variables.

Globally, millions of households rely on onsite wastewater treatment systems (OWTSs), such as septic systems, to safely treat and dispose of wastewater. Conventional subsurface OWTSs are a common and affordable option for many landowners, and effectively remove pathogenic and nutrient pollution from wastewater when properly sited and maintained. However, OWTSs can also be a source of nonpoint pollution in watersheds when they are not functioning properly. To better understand the drivers of OWTS maintenance and failure, we explored relationships between OWTS age, environmental characteristics (edaphic conditions, topographic wetness index, and distance to stream), and repair and pumping records for OWTSs in Athens-Clarke County, Georgia, USA. Repair records indicated that 7.8 % of the 8826 OWTSs in the study were repaired over a 78-year period and that the median age of a repaired OWTSs was 65 years old. Pumping records showed that 12.2 % of the OWTSs were pumped in a 38-month period (an annualized rate of 5.7 %). The suite of widely available environmental variables we used as predictors were likely not granular enough to detect patterns of individual system maintenance at this scale. However, we found that the oldest OWTSs (>50 years) had the highest probabilities of being repaired and exhibiting signs of hydraulic failure. Notably, new OWTSs (2-10 years) were nearly as likely as the oldest systems to exhibit signs of hydraulic failure. These findings suggest that repair and replacement efforts should target older systems that are at or near the end of their serviceable life, and, in addition to continually monitoring older systems, all OWTSs should be inspected one year after installation. By leveraging data that may already exist, practitioners in other localities can use this reproducible approach to estimate the performance of OWTSs. Our data and methods will support efforts to prioritize wastewater infrastructure investments and policies.

Simple statistical models can be sufficient for testing hypotheses with population time-series data.

Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.

A strategic monitoring approach for learning to improve natural infrastructure.

Natural infrastructure (NI) development, including ecosystem restoration, is an increasingly popular approach to leverage ecosystem services for sustainable development, climate resilience, and biodiversity conservation goals. Although implementation and planning for these tools is accelerating, there is a critical need for effective post-implementation monitoring to accumulate performance data and evidence for best practices. The complexity and longer time scales associated with NI, compounded by differing disciplinary definitions and concepts of monitoring necessitate a deliberate and strategic approach to monitoring that encompasses different timeframes and objectives. This paper outlines a typology of monitoring classes differentiated by temporal scale, purpose of data collection, the information benefits of monitoring, and the responsible party. Next, we provide a framework and practical guidelines for designing monitoring plans for NI around learning objectives. In particular, we emphasize conducting research and development monitoring, which provides scientifically rigorous evidence for methodological improvement beyond the project scale. Wherever feasible, and where NI tools are relatively new and untested, such monitoring should avoid wasted effort and ensure progress and refinement of methodology and practice over time. Finally, we propose institutional changes that would promote greater adoption of research and development monitoring to increase the evidence base for NI implementation at larger scales.

Not just trash birds: Quantifying avian diversity at landfills using community science data.

Landfills provide seasonally reliable food resources to many bird species, including those perceived to be pest or invasive species. However, landfills often contain multiple habitat types that could attract diverse species, including those of conservation concern. To date, little is known about the characteristics and composition of bird communities at landfills relative to local and regional pools. Here we used the community science database eBird to extract avian species occurrence data at landfills across the US. We compared species richness and community similarity across space in comparison to similarly-sampled reference sites, and further quantified taxonomic and dietary traits of bird communities at landfills. While landfills harbored marginally lower species richness than reference sites (respective medians of 144 vs 160), landfill community composition, and its turnover across space, were similar to reference sites. Consistent with active waste disposal areas attracting birds, species feeding at higher trophic levels, especially gulls, were more frequently observed at landfills than reference sites. However, habitat specialists including two declining grassland species, Eastern Meadowlark (Sturnella magna) and Savannah Sparrow (Passerculus sandwichensis), as well as migratory waterfowl, were more frequently encountered at landfills than reference sites. Together, these results suggest that landfills harbor comparable avian diversity to neighboring sites, and that habitats contained within landfill sites can support species of conservation concern. As covered landfills are rarely developed or forested, management of wetlands and grasslands at these sites represents an opportunity for conservation.

Phylogenetic conservatism drives nutrient dynamics of coral reef fishes.

The relative importance of evolutionary history and ecology for traits that drive ecosystem processes is poorly understood. Consumers are essential drivers of nutrient cycling on coral reefs, and thus ecosystem productivity. We use nine consumer "chemical traits" associated with nutrient cycling, collected from 1,572 individual coral reef fishes (178 species spanning 41 families) in two biogeographic regions, the Caribbean and Polynesia, to quantify the relative importance of phylogenetic history and ecological context as drivers of chemical trait variation on coral reefs. We find: (1) phylogenetic relatedness is the best predictor of all chemical traits, substantially outweighing the importance of ecological factors thought to be key drivers of these traits, (2) phylogenetic conservatism in chemical traits is greater in the Caribbean than Polynesia, where our data suggests that ecological forces have a greater influence on chemical trait variation, and (3) differences in chemical traits between regions can be explained by differences in nutrient limitation associated with the geologic context of our study locations. Our study provides multiple lines of evidence that phylogeny is a critical determinant of contemporary nutrient dynamics on coral reefs. More broadly our findings highlight the utility of evolutionary history to improve prediction in ecosystem ecology.

Application of multiple-population viability analysis to evaluate species recovery alternatives.

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5-10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning.

Aplicación de un Análisis de Viabilidad Multi-Poblacional para Evaluar Alternativas de Recuperación de Especies Resumen El análisis de viabilidad poblacional (AVP) es una herramienta poderosa de conservación, que desafortunadamente sigue siendo impráctica para muchas especies, en particular para aquellas con poblaciones múltiples distribuidas ampliamente, para las cuales puede ser un reto la recolección de datos apropiados. Sin embargo, un método recientemente desarrollado de análisis de viabilidad multi-poblacional (AVMP) aborda muchas de las limitaciones de los AVP tradicionales. Partimos del desarrollo previo de un AVMP para la trucha degollada lahontana (LCT, en inglés) (Oncorhynchus clarkii henshawi), una especie enlistada bajo el Acta de Especies en Peligro de los Estados Unidos, la cual está distribuida ampliamente a lo largo de los fragmentos de hábitat que se encuentran en la Gran Cuenca (E.U.A.). Simulamos los escenarios potenciales de manejo y evaluamos sus efectos sobre el tamaño de las poblaciones y los riesgos de extinción en 211 arroyos en donde existe la LCT o en donde podría ser reintroducida. Las poblaciones de conservación (aquellas manejadas para su recuperación) tuvieron una tendencia hacia un riesgo de extinción más bajo que las poblaciones sin conservación (media = 19.8% vs. 52.7%), pero no en todos los casos. El manejo activo o la repriorización podrían ser justificadas en algunos casos. La eliminación de las truchas no nativas tuvo un fuerte efecto positivo generalizado sobre las capacidades de carga de las poblaciones de LCT, aunque frecuentemente esto no se transformó en un riesgo de extinción más bajo a menos que las simulaciones también redujeran la estocasticidad asociada (para la media de las poblaciones sin truchas no nativas). Para proporcionar un éxito de reintroducción cercano al máximo, el número mínimo de reintroducción debió ser de 60 peces o una densidad de 5-10 peces/km. Este marco de trabajo para el modelo proporcionó una percepción muy importante y una justificación empírica para la planeación de la conservación y para las acciones de manejo adaptativo para esta especie amenazada. En términos más generales, el AVMP puede aplicarse a una gama amplia de especies que exhiban una rareza geográfica y una disponibilidad limitada de datos de abundancia, además de que expande enormemente el uso potencial de AVP empíricos para la evaluación y planeación de la conservación.

Hierarchical multi-population viability analysis.

Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long-term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi-population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub-models. We present a case study in which we customized this model for historical population monitoring data (1985-2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi-pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi-population viability analysis bridges the gap between site-level field observations and population-level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.

The influence of land cover on the sensitivity of streams to metal pollution.

Identifying freshwater systems that are at risk from anthropogenic stressors is a pressing management problem. In particular, the detection of metal pollution is often constrained by data availability and resources. To address this challenge and develop a tool to identify susceptible systems, we tested whether land cover could be predictive of stream sensitivity to metal pollution, as determined by the biotic ligand model (BLM). We used water chemistry data from the conterminous United States to estimate metal sensitivity in streams using two BLMs (i.e., HydoQual, Bio-Met). Subsequently, we combined the sensitivity estimates with land cover and physiochemical data from the GAGES-II database to build predictive models of sensitivity to metals in streams. When combined, our predictor variables (e.g., land cover, mean annual temperature, mean annual precipitation) generally explained about half of the variation in our dataset. In each model, the percent of wetlands in a watershed was strongly correlated with reduced sensitivity to metals, likely due to increased concentrations of dissolved organic carbon associated with wetlands. To validate the utility of the models, we used them to predict metal sensitivity in sites where metal concentrations had been collected, but where the full suite of BLM parameters were unknown. We were able to classify several hundred sites which are likely at risk to metal pollution. Our work highlights the value in considering metal toxicity at the landscape-scale and describes a new approach to estimate metal sensitivity when site-specific chemical parameters are unknown.

Big biology meets microclimatology: defining thermal niches of ectotherms at landscape scales for conservation planning.

Temperature profoundly affects ecology, a fact ever more evident as the ability to measure thermal environments increases and global changes alter these environments. The spatial structure of thermalscapes is especially relevant to the distribution and abundance of ectothermic organisms, but the ability to describe biothermal relationships at extents and grains relevant to conservation planning has been limited by small or sparse data sets. Here, we combine a large occurrence database of >23 000 aquatic species surveys with stream microclimate scenarios supported by an equally large temperature database for a 149 000-km mountain stream network to describe thermal relationships for 14 fish and amphibian species. Species occurrence probabilities peaked across a wide range of temperatures (7.0-18.8°C) but distinct warm- or cold-edge distribution boundaries were apparent for all species and represented environments where populations may be most sensitive to thermal changes. Warm-edge boundary temperatures for a native species of conservation concern were used with geospatial data sets and a habitat occupancy model to highlight subsets of the network where conservation measures could benefit local populations by maintaining cool temperatures. Linking that strategic approach to local estimates of habitat impairment remains a key challenge but is also an opportunity to build relationships and develop synergies between the research, management, and regulatory communities. As with any data mining or species distribution modeling exercise, care is required in analysis and interpretation of results, but the use of large biological data sets with accurate microclimate scenarios can provide valuable information about the thermal ecology of many ectotherms and a spatially explicit way of guiding conservation investments.

Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity.

The imminent demise of montane species is a recurrent theme in the climate change literature, particularly for aquatic species that are constrained to networks and elevational rather than latitudinal retreat as temperatures increase. Predictions of widespread species losses, however, have yet to be fulfilled despite decades of climate change, suggesting that trends are much weaker than anticipated and may be too subtle for detection given the widespread use of sparse water temperature datasets or imprecise surrogates like elevation and air temperature. Through application of large water-temperature databases evaluated for sensitivity to historical air-temperature variability and computationally interpolated to provide high-resolution thermal habitat information for a 222,000-km network, we estimate a less dire thermal plight for cold-water species within mountains of the northwestern United States. Stream warming rates and climate velocities were both relatively low for 1968-2011 (average warming rate = 0.101 °C/decade; median velocity = 1.07 km/decade) when air temperatures warmed at 0.21 °C/decade. Many cold-water vertebrate species occurred in a subset of the network characterized by low climate velocities, and three native species of conservation concern occurred in extremely cold, slow velocity environments (0.33-0.48 km/decade). Examination of aggressive warming scenarios indicated that although network climate velocities could increase, they remain low in headwaters because of strong local temperature gradients associated with topographic controls. Better information about changing hydrology and disturbance regimes is needed to complement these results, but rather than being climatic cul-de-sacs, many mountain streams appear poised to be redoubts for cold-water biodiversity this century.

Metabolic theory and taxonomic identity predict nutrient recycling in a diverse food web.

Reconciling the degree to which ecological processes are generalizable among taxa and ecosystems, or contingent on the identity of interacting species, remains a critical challenge in ecology. Ecological stoichiometry (EST) and metabolic theory of ecology (MTE) are theoretical approaches used to evaluate how consumers mediate nutrient dynamics and energy flow through ecosystems. Recent theoretical work has explored the utility of these theories, but empirical tests in species-rich ecological communities remain scarce. Here we use an unprecedented dataset collected from fishes and dominant invertebrates (n = 900) in a diverse subtropical coastal marine community (50 families, 72 genera, 102 species; body mass range: 0.04-2,597 g) to test the utility of EST and MTE in predicting excretion rates of nitrogen (E(N)), phosphorus (E(P)), and their ratio (E(NP)). Body mass explained a large amount of the variation in EN and EP but not E(NP). Strong evidence in support of the MTE 3/4 allometric scaling coefficient was found for E(P), and for E(N) only after accounting for variation in excretion rates among taxa. In all cases, including taxonomy in models substantially improved model performance, highlighting the importance of species identity for this ecosystem function. Body nutrient content and trophic position explained little of the variation in E(N), E(P), or E(NP), indicating limited applicability of basic predictors of EST. These results highlight the overriding importance of MTE for predicting nutrient flow through organisms, but emphasize that these relationships still fall short of explaining the unique effects certain species can have on ecological processes.

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change.

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site-specific frequency distributions of occurrence probabilities across a species' range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1-42.5 thousand km; this was predicted to decline to 0.5-7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.

Modelling dendritic ecological networks in space: an integrated network perspective.

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change.

Broad-scale studies of climate change effects on freshwater species have focused mainly on temperature, ignoring critical drivers such as flow regime and biotic interactions. We use downscaled outputs from general circulation models coupled with a hydrologic model to forecast the effects of altered flows and increased temperatures on four interacting species of trout across the interior western United States (1.01 million km(2)), based on empirical statistical models built from fish surveys at 9,890 sites. Projections under the 2080s A1B emissions scenario forecast a mean 47% decline in total suitable habitat for all trout, a group of fishes of major socioeconomic and ecological significance. We project that native cutthroat trout Oncorhynchus clarkii, already excluded from much of its potential range by nonnative species, will lose a further 58% of habitat due to an increase in temperatures beyond the species' physiological optima and continued negative biotic interactions. Habitat for nonnative brook trout Salvelinus fontinalis and brown trout Salmo trutta is predicted to decline by 77% and 48%, respectively, driven by increases in temperature and winter flood frequency caused by warmer, rainier winters. Habitat for rainbow trout, Oncorhynchus mykiss, is projected to decline the least (35%) because negative temperature effects are partly offset by flow regime shifts that benefit the species. These results illustrate how drivers other than temperature influence species response to climate change. Despite some uncertainty, large declines in trout habitat are likely, but our findings point to opportunities for strategic targeting of mitigation efforts to appropriate stressors and locations.

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions.

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.

Use of surrogates to predict the stressor response of imperiled species.

Rare or narrowly distributed species may be threatened by stressors to which they have never been exposed or for which data are very limited. In such cases the species response cannot be predicted on the basis of directly measured data, but may be inferred from the response of one or more appropriate surrogate species. Here, I propose a practical way to use the stressor response of one or more surrogate species to develop a working hypothesis or model of the stressor response of the target species. The process has 4 steps: (1) identify one or more candidate surrogate species, (2) model the relationship between the stressor and the response variable of interest for the surrogate species, (3) adapt the stressor-response relationship from the surrogate species to a model for the target species, possibly using Bayesian methods, and (4) incorporate additional data as they become available and adjust the response model of the target species appropriately. I applied the approach to an endangered fish species, the amber darter (Percina antesella), which is potentially threatened by urbanization. I used a Bayesian approach to combine data from a surrogate species (the bronze darter[Percina palmaris]) with available data for the amber darter to produce a model of expected amber darter response. Although this approach requires difficult decisions on the part of the manager, especially in the selection of surrogate species, its value lies in the fact that all assumptions are clearly stated in the form of hypotheses, which may be scrutinized and tested. It therefore provides a rational basis for instituting management policy even in the face of considerable uncertainty.

Impediments and solutions to sustainable, watershed-scale urban stormwater management: lessons from Australia and the United States.

In urban and suburban areas, stormwater runoff is a primary stressor on surface waters. Conventional urban stormwater drainage systems often route runoff directly to streams and rivers, thus exacerbating pollutant inputs and hydrologic disturbance, and resulting in the degradation of ecosystem structure and function. Decentralized stormwater management tools, such as low impact development (LID) or water sensitive urban design (WSUD), may offer a more sustainable solution to stormwater management if implemented at a watershed scale. These tools are designed to pond, infiltrate, and harvest water at the source, encouraging evaporation, evapotranspiration, groundwater recharge, and re-use of stormwater. While there are numerous demonstrations of WSUD practices, there are few examples of widespread implementation at a watershed scale with the explicit objective of protecting or restoring a receiving stream. This article identifies seven major impediments to sustainable urban stormwater management: (1) uncertainties in performance and cost, (2) insufficient engineering standards and guidelines, (3) fragmented responsibilities, (4) lack of institutional capacity, (5) lack of legislative mandate, (6) lack of funding and effective market incentives, and (7) resistance to change. By comparing experiences from Australia and the United States, two developed countries with existing conventional stormwater infrastructure and escalating stream ecosystem degradation, we highlight challenges facing sustainable urban stormwater management and offer several examples of successful, regional WSUD implementation. We conclude by identifying solutions to each of the seven impediments that, when employed separately or in combination, should encourage widespread implementation of WSUD with watershed-based goals to protect human health and safety, and stream ecosystems.

Importance of riparian forests in urban catchments contingent on sediment and hydrologic regimes.

Forested riparian corridors are thought to minimize impacts of landscape disturbance on stream ecosystems; yet, the effectiveness of streamside forests in mitigating disturbance in urbanizing catchments is unknown. We expected that riparian forests would provide minimal benefits for fish assemblages in streams that are highly impaired by sediment or hydrologic alteration. We tested this hypothesis in 30 small streams along a gradient of urban disturbance (1-65% urban land cover). Species expected to be sensitive to disturbance (i.e., fluvial specialists and "sensitive" species that respond negatively to urbanization) were best predicted by models including percent forest cover in the riparian corridor and a principal components axis describing sediment disturbance. Only sites with coarse bed sediment and low bed mobility (vs. sites with high amounts of fine sediment) had increased richness and abundances of sensitive species with higher percent riparian forests, supporting our hypothesis that response to riparian forests is contingent on the sediment regime. Abundances of Etheostoma scotti, the federally threatened Cherokee darter, were best predicted by models with single variables representing stormflow (r(2) = 0.34) and sediment (r(2) = 0.23) conditions. Lentic-tolerant species richness and abundance responded only to a variable representing prolonged duration of low-flow conditions. For these species, hydrologic alteration overwhelmed any influence of riparian forests on stream biota. These results suggest that, at a minimum, catchment management strategies must simultaneously address hydrologic, sediment, and riparian disturbance in order to protect all aspects of fish assemblage integrity.