Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = -0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the "black-box" nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having "no change" (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

A synergistic future for AI and ecology

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence-they are critical for both persisting and thriving in an uncertain future.

Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

Urban edge trees: Urban form and meteorology drive elemental carbon deposition to canopies and soils

Urban tree canopies are a significant sink for atmospheric elemental carbon (EC)--an air pollutant that is a powerful climate-forcing agent and threat to human health. Understanding what controls EC deposition to urban trees is therefore important for evaluating the potential role of vegetation in air pollution mitigation strategies. We estimated wet, dry, and throughfall EC deposition for oak trees at 53 sites in Denton, TX. Spatial data and airborne discrete-return LiDAR were used to compute predictors of EC deposition, including urban form characteristics, and meteorologic and topographic factors. Dry and throughfall EC deposition varied 14-fold across this urban ecosystem and exhibited significant variability from spring to fall. Generalized additive modeling and multiple linear regression analyses showed that urban form strongly influenced tree-scale variability in dry EC deposition: traffic count as well as road length and building height within 100-150 m of trees were positively related to leaf-scale dry deposition. Rainfall amount and extreme wind-driven rain from the direction of major pollution sources were significant drivers of throughfall EC. Our findings indicate that complex configurations of roads, buildings, and vegetation produce "urban edge trees" that contribute to heterogeneous EC deposition patterns across urban systems, with implications for greenspace planning.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Wet nitrogen (N) deposition to urban Latin America: filling in the gaps with GEOS-Chem

In Latin America, atmospheric deposition is a major vector of nitrogen (N) input to urban systems. Yet, measurements of N deposition are sparse, precluding analysis of spatial patterns, temporal trends, and ecosystem impacts. Chemical transport models can be used to fill these gaps in the absence of dense measurements. Here, we evaluate the performance of a global 3-D chemical transport model in simulating spatial and interannual variation in wet inorganic N (NH₄-N + NO₃-N) deposition across urban areas in Latin America. Monthly wet and dry inorganic N deposition to Latin America were simulated for the period 2006-2010 using the GEOS-Chem Chemical Transport Model. Published estimates of observed wet or bulk inorganic N deposition measured between 2006-2010 were compiled for 16 urban areas and then compared with model output from GEOS-Chem. Observed mean annual inorganic N deposition to the urban study sites ranged from 5.7-14.2 kg ha^-1 yr^-1, with NH₄-N comprising 48-90% of the total. Results show that simulated N deposition was highly correlated with observed N deposition across sites (R² = 0.83, NMB = -50%). However, GEOS-Chem generally underestimated N deposition to urban areas in Latin America compared to observations. Underestimation due to bulk sampler dry deposition artifacts was considered and improved bias without improving correlation. In contrast to spatial variation, the model did not capture year-to-year variation well. Discrepancies between modeled and observed values exist, in part, because of uncertainties in Latin American N emissions inventories. Our findings indicate that even at coarse spatial resolution, GEOS-Chem can be used to simulate N deposition to urban Latin America, improving understanding of regional deposition patterns and potential ecological effects.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales

Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake's watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.

Lakes at Risk of Chloride Contamination

Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but there is little knowledge of the prevalence and spatial distribution of freshwater salinization. Here, we use a quantile regression forest (QRF) to leverage information from 2773 lakes to predict the chloride concentration of all 49 432 lakes greater than 4 ha in a 17-state area. The QRF incorporated 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest road and interstate. Model predictions had an r² of 0.94 for all chloride observations, and an r² of 0.86 for predictions of the median chloride concentration observed at each lake. The four predictors with the largest influence on lake chloride concentrations were low and medium intensity development in the watershed, crop density in the watershed, and distance to the nearest interstate. Almost 2000 lakes are predicted to have chloride concentrations above 50 mg L^-1 and should be monitored. We encourage management and governing agencies to use lake-specific model predictions to assess salt contamination risk as well as to augment their monitoring strategies to more comprehensively protect freshwater ecosystems from salinization.

Integrating fast and slow processes is essential for simulating human-freshwater interactions

Integrated modeling is a critical tool to evaluate the behavior of coupled human-freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human-freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human-freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.

Factors affecting MeHg bioaccumulation in stream biota: the role of dissolved organic carbon and diet

The bioaccumulation of the neurotoxin methylmercury (MeHg) in freshwater ecosystems is thought to be mediated by both water chemistry (e.g., dissolved organic carbon [DOC] and dissolved mercury [Hg]) and diet (e.g., trophic position and diet composition). Hg in small streams is of particular interest given their role as a link between terrestrial and aquatic processes. Terrestrial processes determine the quantity and quality of streamwater DOC, which in turn influence the quantity and bioavailability of dissolved MeHg. To better understand the effects of water chemistry and diet on Hg bioaccumulation in stream biota, we measured DOC and dissolved Hg in stream water and mercury concentration in three benthic invertebrate taxa and three fish species across up to 12 tributary streams in a forested watershed in New Hampshire, USA. As expected, dissolved total mercury (THg) and MeHg concentrations increased linearly with DOC. However, mercury concentrations in fish and invertebrates varied non-linearly, with maximum bioaccumulation at intermediate DOC concentrations, which suggests that MeHg bioavailability may be reduced at high levels of DOC. Further, MeHg and THg concentrations in invertebrates and fish, respectively, increased with δ¹⁵N (suggesting trophic position) but were not associated with δ¹³C. These results show that even though MeHg in water is strongly determined by DOC concentrations, mercury bioaccumulation in stream food webs is the result of both MeHg availability in stream water and trophic position.

Urban Trees Are Sinks for Soot: Elemental Carbon Accumulation by Two Widespread Oak Species

Urban trees could represent important short- and long-term landscape sinks for elemental carbon (EC). Therefore, we quantified foliar EC accumulation by two widespread oak tree species-Quercus stellata (post oak) and Quercus virginiana (live oak)-as well as leaf litterfall EC flux to soil from April 2017 to March 2018 in the City of Denton, Texas, within the Dallas-Fort Worth metropolitan area. Post oak trees accumulated 1.9-fold more EC (299 ± 45 mg EC m^-2 canopy yr^-1) compared to live oak trees (160 ± 31 mg EC m^-2 canopy yr^-1). However, in the fall, these oak species converged in their EC accumulation rates, with ∼70% of annual accumulation occurring during fall and on leaf surfaces. The flux of EC to the ground via leaf litterfall mirrored leaf-fall patterns, with post oaks and live oaks delivering ∼60% of annual leaf litterfall EC in fall and early spring, respectively. We estimate that post oak and live oak trees in this urban ecosystem potentially accumulate 3.5 t EC yr^-1, equivalent to ∼32% of annual vehicular EC emissions from the city. Thus, city trees are significant sinks for EC and represent potential avenues for climate and air quality mitigation in urban areas.

The biology of fog: results from coastal Maine and Namib Desert reveal common drivers of fog microbial composition

Fog supplies water and nutrients to systems ranging from coastal forests to inland deserts. Fog droplets can also contain bacterial and fungal aerosols, but our understanding of fog biology is limited. Using metagenomic tools and culturing, we provide a unique look at fungal and bacterial communities in fog at two fog-dominated sites: coastal Maine (USA) and the Namib Desert (Namibia). Microbial communities in the fog at both sites were diverse, distinct from clear aerosols, and influenced by both soil and marine sources. Fog from both sites contained Actinobacteria and Firmicutes, commonly soil- and air-associated phyla, but also contained bacterial taxa associated with marine environments including Cyanobacteria, Oceanospirillales, Novosphingobium, Pseudoalteromonas, and Bradyrhizobiaceae. Marine influence on fog communities was greatest near the coast, but still evident in Namib fogs 50 km inland. In both systems, differences between pre- and post-fog aerosol communities suggest that fog events can significantly alter microbial aerosol diversity and composition. Fog is likely to enhance viability of transported microbes and facilitate their deposition, making fog biology ecologically important in fog-dominated environments. Fog may introduce novel species to terrestrial ecosystems, including human and plant pathogens, warranting further work on the drivers of this important and underrecognized aerobiological transfer between marine and terrestrial systems.

LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states.

LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600–12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales.

Assessing the effectiveness of Landsat 8 chlorophyll a retrieval algorithms for regional freshwater monitoring

Predicting algal blooms has become a priority for scientists, municipalities, businesses, and citizens. Remote sensing offers solutions to the spatial and temporal challenges facing existing lake research and monitoring programs that rely primarily on high-investment, in situ measurements. Techniques to remotely measure chlorophyll a (chl a) as a proxy for algal biomass have been limited to specific large water bodies in particular seasons and narrow chl a ranges. Thus, a first step toward prediction of algal blooms is generating regionally robust algorithms using in situ and remote sensing data. This study explores the relationship between in-lake measured chl a data from Maine and New Hampshire, USA lakes and remotely sensed chl a retrieval algorithm outputs. Landsat 8 images were obtained and then processed after required atmospheric and radiometric corrections. Six previously developed algorithms were tested on a regional scale on 11 scenes from 2013 to 2015 covering 192 lakes. The best performing algorithm across data from both states had a 0.16 correlation coefficient (R² ) and P ≤ 0.05 when Landsat 8 images within 5 d, and improved to R² of 0.25 when data from Maine only were used. The strength of the correlation varied with the specificity of the time window in relation to the in-situ sampling date, explaining up to 27% of the variation in the data across several scenes. Two previously published algorithms using Landsat 8's Bands 1-4 were best correlated with chl a, and for particular late-summer scenes, they accounted for up to 69% of the variation in in-situ measurements. A sensitivity analysis revealed that a longer time difference between in situ measurements and the satellite image increased uncertainty in the models, and an effect of the time of year on several indices was demonstrated. A regional model based on the best performing remote sensing algorithm was developed and was validated using independent in situ measurements and satellite images. These results suggest that, despite challenges including seasonal effects and low chl a thresholds, remote sensing could be an effective and accessible regional-scale tool for chl a monitoring programs in lakes.

A lake classification concept for a more accurate global estimate of the dissolved inorganic carbon export from terrestrial ecosystems to inland waters

The magnitude of lateral dissolved inorganic carbon (DIC) export from terrestrial ecosystems to inland waters strongly influences the estimate of the global terrestrial carbon dioxide (CO₂) sink. At present, no reliable number of this export is available, and the few studies estimating the lateral DIC export assume that all lakes on Earth function similarly. However, lakes can function along a continuum from passive carbon transporters (passive open channels) to highly active carbon transformers with efficient in-lake CO₂ production and loss. We developed and applied a conceptual model to demonstrate how the assumed function of lakes in carbon cycling can affect calculations of the global lateral DIC export from terrestrial ecosystems to inland waters. Using global data on in-lake CO₂ production by mineralization as well as CO₂ loss by emission, primary production, and carbonate precipitation in lakes, we estimated that the global lateral DIC export can lie within the range of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {0.70}_{-0.31}^{+0.27} $$\end{document}0.70−0.31+0.27 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {1.52}_{-0.90}^{+1.09} $$\end{document}1.52−0.90+1.09 Pg C yr⁻¹ depending on the assumed function of lakes. Thus, the considered lake function has a large effect on the calculated lateral DIC export from terrestrial ecosystems to inland waters. We conclude that more robust estimates of CO₂ sinks and sources will require the classification of lakes into their predominant function. This functional lake classification concept becomes particularly important for the estimation of future CO₂ sinks and sources, since in-lake carbon transformation is predicted to be altered with climate change.

Automatic High Frequency Monitoring for Improved Lake and Reservoir Management

Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.

Dissolved organic carbon modulates mercury concentrations in insect subsidies from streams to terrestrial consumers

Mercury (Hg) concentrations in aquatic environments have increased globally, exposing consumers of aquatic organisms to high Hg levels. For both aquatic and terrestrial consumers, exposure to Hg depends on their food sources as well as environmental factors influencing Hg bioavailability. The majority of the research on the transfer of methylmercury (MeHg), a toxic and bioaccumulating form of Hg, between aquatic and terrestrial food webs has focused on terrestrial piscivores. However, a gap exists in our understanding of the factors regulating MeHg bioaccumulation by non-piscivorous terrestrial predators, specifically consumers of adult aquatic insects. Because dissolved organic carbon (DOC) binds tightly to MeHg, affecting its transport and availability in aquatic food webs, we hypothesized that DOC affects MeHg transfer from stream food webs to terrestrial predators feeding on emerging adult insects. We tested this hypothesis by collecting data over 2 years from 10 low-order streams spanning a broad DOC gradient in the Lake Sunapee watershed in New Hampshire, USA. We found that streamwater MeHg concentration increased linearly with DOC concentration. However, streams with the highest DOC concentrations had emerging stream prey and spiders with lower MeHg concentrations than streams with intermediate DOC concentrations; a pattern that is similar to fish and larval aquatic insects. Furthermore, high MeHg concentrations found in spiders show that MeHg transfer in adult aquatic insects is an overlooked but potentially significant pathway of MeHg bioaccumulation in terrestrial food webs. Our results suggest that although MeHg in water increases with DOC, MeHg concentrations in stream and terrestrial consumers did not consistently increase with increases in streamwater MeHg concentrations. In fact, there was a change from a positive to a negative relationship between aqueous exposure and bioaccumulation at streamwater MeHg concentrations associated with DOC above ~5 mg/L. Thus, our study highlights the importance of stream DOC for MeHg dynamics beyond stream boundaries, and shows that factors modulating MeHg bioavailability in aquatic systems can affect the transfer of MeHg to terrestrial predators via aquatic subsidies.

Out of the fog: Catalyzing integrative capacity in interdisciplinary research

Social studies of interdisciplinary science investigate how scientific collaborations approach complex challenges that require multiple disciplinary perspectives. In order for collaborators to meet these complex challenges, interdisciplinary collaborations must develop and maintain integrative capacity, understood as the ability to anticipate and weigh tradeoffs in the employment of different disciplinary approaches. Here we provide an account of how one group of interdisciplinary fog scientists intentionally catalyzed integrative capacity. Through conversation, collaborators negotiated their commitments regarding the ontology of fog systems and the methodologies appropriate to studying fog systems, thereby enhancing capabilities which we take to constitute integrative capacity. On the ontological front, collaborators negotiated their commitments by setting boundaries to and within the system, layering different subsystems, focusing on key intersections of these subsystems, and agreeing on goals that would direct further investigation. On the methodological front, collaborators sequenced various methods, anchored methods at different scales, validated one method with another, standardized the outputs of related methods, and coordinated methods to fit a common model. By observing the process and form of collaborator conversations, this case study demonstrates that social studies of science can bring into critical focus how interdisciplinary collaborators work toward an integrated conceptualization of study systems.

Managing water services in tropical regions: From land cover proxies to hydrologic fluxes

Watershed investment programs frequently use land cover as a proxy for water-based ecosystem services, an approach based on assumed relationships between land cover and hydrologic outcomes. Water flows are rarely quantified, and unanticipated results are common, suggesting land cover alone is not a reliable proxy for water services. We argue that managing key hydrologic fluxes at the site of intervention is more effective than promoting particular land-cover types. Moving beyond land cover proxies to a focus on hydrologic fluxes requires that programs (1) identify the specific water service of interest and associated hydrologic flux; (2) account for structural and ecological characteristics of the relevant land cover; and, (3) determine key mediators of the target hydrologic flux. Using examples from the tropics, we illustrate how this conceptual framework can clarify interventions with a higher probability of delivering desired water services than with land cover as a proxy.

The importance of lake-specific characteristics for water quality across the continental United States

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

Assessing the influence of topography and canopy structure on Douglas fir throughfall with LiDAR and empirical data in the Santa Cruz mountains, USA

Atmospheric inputs to forest ecosystems vary considerably over small spatial scales due to subtle changes in relief and vegetation structure. Relationships between throughfall fluxes (ions that pass through the canopy in water), topographic and canopy characteristics derived from sub-meter resolution light detection and ranging (LiDAR), and field measurements were compared to test the potential utility of LiDAR in empirical models of atmospheric deposition. From October 2012 to May 2013, we measured bulk (primarily wet) deposition and sulfate-S, chloride (Cl(-)), and nitrate-N fluxes beneath eight clusters of Douglas fir trees differing in size and canopy exposure in the Santa Cruz Mountains, California. For all trees sampled, LiDAR data were used to derive canopy surface height, tree height, slope, and canopy curvature, while tree height, diameter (DBH), and leaf area index were measured in the field. Wet season throughfall fluxes to Douglas fir clusters ranged from 1.4 to 3.8 kg S ha(-1), 17-54 kg Cl(-) ha(-1), and 0.2-4 kg N ha(-1). Throughfall S and Cl(-) fluxes were highest under clusters with large trees at topographically exposed sites; net fluxes were 2-18-fold greater underneath exposed/large clusters than all other clusters. LiDAR indices of canopy curvature and height were positively correlated with net sulfate-S fluxes, indicating that small-scale canopy surface features captured by LiDAR influence fog and dry deposition. Although tree diameter was more strongly correlated with net sulfate-S throughfall flux, our data suggest that LiDAR data can be related to empirical measurements of throughfall fluxes to generate robust high-resolution models of atmospheric deposition.

Effects of acidic deposition and soil acidification on sugar maple trees in the Adirondack Mountains, New York

We documented the effects of acidic atmospheric deposition and soil acidification on the canopy health, basal area increment, and regeneration of sugar maple (SM) trees across the Adirondack region of New York State, in the northeastern United States, where SM are plentiful but not well studied and where widespread depletion of soil calcium (Ca) has been documented. Sugar maple is a dominant canopy species in the Adirondack Mountain ecoregion, and it has a high demand for Ca. Trees in this region growing on soils with poor acid-base chemistry (low exchangeable Ca and % base saturation [BS]) that receive relatively high levels of atmospheric sulfur and nitrogen deposition exhibited a near absence of SM seedling regeneration and lower crown vigor compared with study plots with relatively high exchangeable Ca and BS and lower levels of acidic deposition. Basal area increment averaged over the 20th century was correlated (p < 0.1) with acid-base chemistry of the Oa, A, and upper B soil horizons. A lack of Adirondack SM regeneration, reduced canopy condition, and possibly decreased basal area growth over recent decades are associated with low concentrations of nutrient base cations in this region that has undergone soil Ca depletion from acidic deposition.

A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities

In many older US cities, lead (Pb) contamination of residential soil is widespread; however, contamination is not uniform. Empirically based, spatially explicit models can assist city agencies in addressing this important public health concern by identifying areas predicted to exceed public health targets for soil Pb contamination. Sampling of 61 residential properties in Baltimore City using field portable X-ray fluorescence revealed that 53 % had soil Pb that exceeded the USEPA reportable limit of 400 ppm. These data were used as the input to three different spatially explicit models: a traditional general linear model (GLM), and two machine learning techniques: classification and regression trees (CART) and Random Forests (RF). The GLM revealed that housing age, distance to road, distance to building, and the interactions between variables explained 38 % of the variation in the data. The CART model confirmed the importance of these variables, with housing age, distance to building, and distance to major road networks determining the terminal nodes of the CART model. Using the same three predictor variables, the RF model explained 42 % of the variation in the data. The overall accuracy, which is a measure of agreement between the model and an independent dataset, was 90 % for the GLM, 83 % for the CART model, and 72 % for the RF model. A range of spatially explicit models that can be adapted to changing soil Pb guidelines allows managers to select the most appropriate model based on public health targets.

Ecosystem effects of a tropical cyclone on a network of lakes in northeastern North America

Here we document the regional effects of Tropical Cyclone Irene on thermal structure and ecosystem metabolism in nine lakes and reservoirs in northeastern North America using a network of high-frequency, in situ, automated sensors. Thermal stability declined within hours in all systems following passage of Irene, and the magnitude of change was related to the volume of water falling on the lake and catchment relative to lake volume. Across systems, temperature change predicted the change in primary production, but changes in mixed-layer thickness did not affect metabolism. Instead, respiration became a driver of ecosystem metabolism that was decoupled from in-lake primary production, likely due to addition of terrestrially derived carbon. Regionally, energetic disturbance of thermal structure was shorter-lived than disturbance from inflows of terrestrial materials. Given predicted regional increases in intense rain events with climate change, the magnitude and longevity of ecological impacts of these storms will be greater in systems with large catchments relative to lake volume, particularly when significant material is available for transport from the catchment. This case illustrates the power of automated sensor networks and associated human networks in assessing both system response and the characteristics that mediate physical and ecological responses to extreme events.

Local environmental pollution strongly influences culturable bacterial aerosols at an urban aquatic superfund site

In polluted environments, when microbial aerosols originate locally, species composition of the aerosols should reflect the polluted source. To test the connection between local environmental pollution and microbial aerosols near an urban waterfront, we characterized bacterial aerosols at Newtown Creek (NTC), a public waterway and Superfund site in a densely populated area of New York, NY, USA. Culturable bacterial aerosol fallout rate and surface water bacterial concentrations were at least an order of magnitude greater at NTC than at a neighboring, less polluted waterfront and a nonurban coastal site in Maine. The NTC culturable bacterial aerosol community was significantly different in taxonomic structure from previous urban and coastal aerosol studies, particularly in relative abundances of Actinobacteria and Proteobacteria. Twenty-four percent of the operational taxonomic units in the NTC overall (air + water) bacterial isolate library were most similar to bacterial 16S rRNA gene sequences previously described in terrestrial or aquatic environments contaminated with sewage, hydrocarbons, heavy metals, and other industrial waste. This study is the first to examine the community composition and local deposition of bacterial aerosols from an aquatic Superfund site. The findings have important implications for the use of aeration remediation in polluted aquatic environments and suggest a novel pathway of microbial exposure in densely populated urban communities containing contaminated soil and water.

The effects of the urban built environment on the spatial distribution of lead in residential soils

Lead contamination of urban residential soils is a public health concern. Consequently, there is a need to delineate hotspots in the landscape to identify risk and facilitate remediation. Land use is a good predictor of some environmental pollutants. However, in the case of soil lead, research has shown that land use is not a useful proxy. We hypothesize that soil lead is related to both individual landscape features at the parcel scale and the landscape context in which parcels are embedded. We sampled soil lead on 61 residential parcels in Baltimore, Maryland using field-portable x-ray fluorescence. Thirty percent of parcels had average lead concentrations that exceeded the USEPA limit of 400 ppm and 53% had at least one reading that exceeded 400 ppm. Results indicate that soil lead is strongly associated with housing age, distance to roadways, and on a parcel scale, distance to built structures.

Bromeliad growth and stoichiometry: responses to atmospheric nutrient supply in fog-dependent ecosystems of the hyper-arid Atacama Desert, Chile

Carbon, nitrogen, and phosphorus (C, N, P) stoichiometry influences the growth of plants and nutrient cycling within ecosystems. Indeed, elemental ratios are used as an index for functional differences between plants and their responses to natural or anthropogenic variations in nutrient supply. We investigated the variation in growth and elemental content of the rootless terrestrial bromeliad Tillandsia landbeckii, which obtains its moisture, and likely its nutrients, from coastal fogs in the Atacama Desert. We assessed (1) how fog nutrient supply influences plant growth and stoichiometry and (2) the response of plant growth and stoichiometry to variations in nutrient supply by using reciprocal transplants. We hypothesized that T. landbeckii should exhibit physiological and biochemical plastic responses commensurate with nutrient supply from atmospheric deposition. In the case of the Atacama Desert, nutrient supply from fog is variable over space and time, which suggests a relatively high variation in the growth and elemental content of atmospheric bromeliads. We found that the nutrient content of T. landbeckii showed high spatio-temporal variability, driven partially by fog nutrient deposition but also by plant growth rates. Reciprocal transplant experiments showed that transplanted individuals converged to similar nutrient content, growth rates, and leaf production of resident plants at each site, reflecting local nutrient availability. Although plant nutrient content did not exactly match the relative supply of N and P, our results suggest that atmospheric nutrient supply is a dominant driver of plant growth and stoichiometry. In fact, our results indicate that N uptake by T. landbeckii plants depends more on N supplied by fog, whereas P uptake is mainly regulated by within-plant nutrient demand for growth. Overall, these findings indicate that variation in fog nutrient supply exerts a strong control over growth and nutrient dynamics of atmospheric plants, which are ubiquitous across fog-dominated ecosystems.

Environmental controls on coastal coarse aerosols: implications for microbial content and deposition in the near-shore environment

Coarse aerosols (particle diameter (D(p)) > 2 μm) produced in coastal surf zones carry chemical and microbial content to shore, forming a connection between oceanic, atmospheric, and terrestrial systems that is potentially relevant to coastal ecology and human health. In this context, the effects of tidal height, wind speed, and fog on coastal coarse aerosols and microbial content were quantified on the southern coast of Maine, USA. Aerosols at this site displayed clear marine influence and had high concentrations of ecologically relevant nutrients. Coarse aerosol concentrations significantly increased with tidal height (i.e., decreasing distance from waterline), onshore wind speed, and fog presence. As onshore wind speeds rose above 3 m s(-1), the mean half-deposition distance of coarse aerosols increased to an observed maximum of 47.6 ± 10.9 m from the water's edge at wind speeds from 5.5-8 m s(-1). Tidal height and fog presence did not significantly influence total microbial aerosol concentrations but did have a significant effect on culturable microbial aerosol fallout. At low wind speeds, culturable microbial aerosols falling out near-shore decreased by half at a distance of only 1.7 ± 0.4 m from the water's edge, indicating that these microbes may be associated with large coarse aerosols with rapid settling rates.

Tropical land-cover change alters biogeochemical inputs to ecosystems in a Mexican montane landscape

In tropical regions, the effects of land-cover change on nutrient and pollutant inputs to ecosystems remain poorly documented and may be pronounced, especially in montane areas exposed to elevated atmospheric deposition. We examined atmospheric deposition and canopy interactions of sulfate-sulfur (SO4(2-)-S), chloride (Cl-), and nitrate-nitrogen (NO(3-)-N) in three extensive tropical montane land-cover types: clearings, forest, and coffee agroforest. Bulk and fog deposition to clearings was measured as well as throughfall (water that falls through plant canopies) ion fluxes in seven forest and five coffee sites. Sampling was conducted from 2005 to 2008 across two regions in the Sierra Madre Oriental, Veracruz, Mexico. Annual throughfall fluxes to forest and coffee sites ranged over 6-27 kg SO4(2-)-S/ha, 12-69 kg Cl-/ha, and 2-6 kg NO(3-)-N/ha. Sulfate-S in forest and coffee throughfall was higher or similar to bulk S deposition measured in clearings. Throughfall Cl- inputs, however, were consistently higher than Cl- amounts deposited to cleared areas, with net Cl- fluxes enhanced in evergreen coffee relative to semi-deciduous forest plots. Compared to bulk nitrate-N deposition, forest and coffee canopies retained 1-4 kg NO(3-)-N/ha annually, reducing NO(3-)-N inputs to soils. Overall, throughfall fluxes were similar to values reported for Neotropical sites influenced by anthropogenic emissions, while bulk S and N deposition were nine- and eightfold greater, respectively, than background wet deposition rates for remote tropical areas. Our results demonstrate that land-cover type significantly alters the magnitude and spatial distribution of atmospheric inputs to tropical ecosystems, primarily through canopy-induced changes in fog and dry deposition. However, we found that land cover interacts with topography and climate in significant ways to produce spatially heterogeneous patterns of anion fluxes, and that these factors can converge to create deposition hotspots. For land managers, this finding suggests that there is potential to identify species and ecosystems at risk of excess and increasing deposition in montane watersheds undergoing rapid transformation. Our data further indicate that montane ecosystems are vulnerable to air pollution impacts in this and similar tropical regions downwind of urban, industrial, and agricultural emission sources.

Effects of air pollution on ecosystems and biological diversity in the eastern United States

Conservation organizations have most often focused on land-use change, climate change, and invasive species as prime threats to biodiversity conservation. Although air pollution is an acknowledged widespread problem, it is rarely considered in conservation planning or management. In this synthesis, the state of scientific knowledge on the effects of air pollution on plants and animals in the Northeastern and Mid-Atlantic regions of the United States is summarized. Four air pollutants (sulfur, nitrogen, ozone, and mercury) and eight ecosystem types ranging from estuaries to alpine tundra are considered. Effects of air pollution were identified, with varying levels of certainty, in all the ecosystem types examined. None of these ecosystem types is free of the impacts of air pollution, and most are affected by multiple pollutants. In aquatic ecosystems, effects of acidity, nitrogen, and mercury on organisms and biogeochemical processes are well documented. Air pollution causes or contributes to acidification of lakes, eutrophication of estuaries and coastal waters, and mercury bioaccumulation in aquatic food webs. In terrestrial ecosystems, the effects of air pollution on biogeochemical cycling are also very well documented, but the effects on most organisms and the interaction of air pollution with other stressors are less well understood. Nevertheless, there is strong evidence for effects of nitrogen deposition on plants in grasslands, alpine areas, and bogs, and for nitrogen effects on forest mycorrhizae. Soil acidification is widespread in forest ecosystems across the eastern United States and is likely to affect the composition and function of forests in acid-sensitive areas over the long term. Ozone is known to cause reductions in photosynthesis in many terrestrial plant species. For the most part, the effects of these pollutants are chronic, not acute, at the exposure levels common in the eastern United States. Mortality is often observed only at experimentally elevated exposure levels or in combination with other stresses such as drought, freezing, or pathogens. The notable exceptions are the acid/aluminum effects on aquatic organisms, which can be lethal at levels of acidity observed in many surface waters in the region. Although the effects are often subtle, they are important to biological conservation. Changes in species composition caused by terrestrial or aquatic acidification or eutrophication can propagate throughout the food webs to affect many organisms beyond those that are directly sensitive to the pollution. Likewise, sublethal doses of toxic pollutants may reduce the reproductive success of the affected organisms or make them more susceptible to potentially lethal pathogens. Many serious gaps in knowledge that warrant further research were identified. Among those gaps are the effects of acidification, ozone, and mercury on alpine systems, effects of nitrogen on species composition of forests, effects of mercury in terrestrial food webs, interactive effects of multiple pollutants, and interactions among air pollution and other environmental changes such as climate change and invasive species. These gaps in knowledge, coupled with the strong likelihood of impacts on ecosystems that have not been studied in the region, suggests that current knowledge underestimates the actual impact of air pollutants on biodiversity. Nonetheless, because known or likely impacts of air pollution on the biodiversity and function of natural ecosystems are widespread in the Northeast and Mid-Atlantic regions, the effects of air pollution should be considered in any long-term conservation strategy. It is recommended that ecologically relevant standards, such as "critical loads," be adopted for air pollutants and the importance of long-term monitoring of air pollution and its effects is emphasized.

Effect of climate change between 1984 and 2007 on precipitation chemistry at a site in northeastern U.S.A

Climate change predictions for the northeastern US call for an increase in tropical storms and a decrease in extra tropical cyclones including continental storms. We ran 24-h back trajectories for each precipitation event that occurred at the Cary Institute of Ecosystem Studies in southeastern New York, U.S.A. from 1984 to 2007 and analyzed precipitation chemistry as well as air mass position 24 h prior to the onset of each precipitation event. The results showed an increase in marine precipitation and a slight but statistically insignificant decrease in continental precipitation during the 1984-2007 period. The chemistry of precipitation from the two directions was quite different marine storms were higher in Na4 and Cl- but lower in solutes associated with acid precipitation (H+, SO4(2-), NO3-, and NH4+). Annual mean concentrations of acid precipitation solutes declined for storms from both directions during the period. We used a simple mixing model based on the current rates of increase and decrease of marine and continental precipitation respectively to show that chemical changes in precipitation resulting from the shift in storm tracks are small compared to chemical changes due to emissions reductions.

Long-term sodium chloride retention in a rural watershed: legacy effects of road salt on streamwater concentration

Sodium and chloride concentrations and export increased from 1986 to 2005 in a rural stream in southeastern New York. Concentrations increased 1.5 mg/L per year (chloride) and 0.9 mg/L per year (sodium), and export increased 33,000 kg/year (chloride) and 20,000 kg/year (sodium) during this period. We estimate that salt used for deicing accounted for 91% of the sodium chloride input to the watershed, while sewage and water softeners accounted for less than 10% of the input. Road salt use in the watershed did not increase during the study, but sodium and chloride from sewage and water softeners is likely to have increased slightly due to a small increase in population. Increased input from sewage and water softeners cannot account for the increase in concentration and export from the watershed. Model results suggest that the increase in streamwater concentration and export was likely due to a lag effect of long-term road salt use and subsurface buildup.