Large-scale Monitoring Networks Research Articles

Multidimensional patterns of bird diversity and its driving forces in the Yangtze river basin of China

Biodiversity is fundamental to human well-being and economic development. The Yangtze River, the largest river in China, faces biodiversity loss due to habitat degradation, climate change, and other anthropogenic threats. However, the long-term changes in the region's biodiversity remain poorly understood. Here, we constructed an optimized living planet index (LPIO) by combining Partial Least Squares Structural Equation Modeling and Random Forest Modeling. Using data from a monitoring network of 536 sites, we observed an increasing trend in terrestrial bird diversity and functional complexity across the entire watershed from 2011 to 2020. Our findings indicate that a large-scale ecological restoration program has contributed to increases in terrestrial and aquatic bird diversity in the Yangtze River Basin. In contrast, bird diversity in the downstream area has decreased by 2.83 ​%, largely due to a rapid decline in wetland birds. The degradation of wetland habitats and insufficient conservation measures have negatively impacted bird diversity in the downstream region. This suggests that although there have been significant improvements in terrestrial bird diversity, more effective wetland restoration is necessary for biodiversity conservation. We recommend optimizing the national large-scale biodiversity monitoring network and increasing the number of upstream monitoring sites.

Upscaling biodiversity monitoring: Metabarcoding estimates 31,846 insect species from Malaise traps across Germany.

Mitigating ongoing losses of insects and their key functions (e.g. pollination) requires tracking large-scale and long-term community changes. However, doing so has been hindered by the high diversity of insect species that requires prohibitively high investments of time, funding and taxonomic expertise when addressed with conventional tools. Here, we show that these concerns can be addressed through a comprehensive, scalable and cost-efficient DNA metabarcoding workflow. We use 1815 samples from 75 Malaise traps across Germany from 2019 and 2020 to demonstrate how metabarcoding can be incorporated into large-scale insect monitoring networks for less than 50€ per sample, including supplies, labour and maintenance. We validated the detected species using two publicly available databases (GBOL and GBIF) and the judgement of taxonomic experts. With an average of 1.4 M sequence reads per sample we uncovered 10,803 validated insect species, of which 83.9% were represented by a single Operational Taxonomic Unit (OTU). We estimated another 21,043 plausible species, which we argue either lack a reference barcode or are undescribed. The total of 31,846 species is similar to the number of insect species known for Germany (~35,500). Because Malaise traps capture only a subset of insects, our approach identified many species likely unknown from Germany or new to science. Our reproducible workflow (~80% OTU-similarity among years) provides a blueprint for large-scale biodiversity monitoring of insects and other biodiversity components in near real time.

Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions

Soil moisture is an important indicator for monitoring agricultural drought, and is closely related to soil texture. However, the role that soil textures play in agricultural drought evolution in humid climates is not well understood. In the study, we systematically assessed the feasibility of the Soil Water Deficit Index (SWDI) for agricultural drought monitoring in humid regions, examined the impact of soil texture and groundwater depth on agricultural drought evolution, and estimated the field holding capacity for different soil textures in humid areas based on a large-scale regional soil moisture monitoring network. The results show that: (1) The SWDI has reasonably good performance in detecting severe and extreme agricultural drought, but it tends to overestimate the drought intensity during non-severe/extreme drought periods in humid regions, particularly for clay soils. (2) Sand soils are more prone to agricultural drought than clay soils, which are more resistant to severe and extreme agricultural drought with high air temperatures and low precipitation. (3) During drought aggravation, clay soil showed a delay of about 0–18 weeks from the lowest SWDI to the maximum groundwater depth, while the delay was only 0–5 weeks for non-clay soil textures. (4) The 95th percentile is proposed as the best estimator of field holding capacity for clay and loam, while Min(maxgs) is the best estimator for sand, loamy sand, and sandy loam. Overall, these findings provide insights into the role of soil texture and groundwater depth in agricultural drought evolution in humid regions, and offer practical guidance for field holding capacity estimation for different soil textures.

Evaluating PM2.5 element concentration measurements for a nationwide monitoring network

ABSTRACT Particulate matter (PM) concentrations have decreased dramatically over the past 20 years, thus lower method detection limits (MDL) are required for these measurements. Energy-dispersive X-ray fluorescence (XRF) spectroscopy is used to quantify multiple elements simultaneously in the U.S. Environmental Protection Agency (EPA) Chemical Speciation Network (CSN). Inductively-coupled plasma mass spectrometry (ICP-MS) is an alternative analysis with lower MDL for elements. Here, we present a side-by-side comparison of XRF and ICP-MS for elements in PM2.5 samples collected via the EPA’s CSN. For ICP-MS, a simple extraction and ICP-MS analysis technique was applied to a wide variety of samples to minimize effort and cost and serve as a feasibility test for a large monitoring network. Filter samples (N = 549) from various urban locations across the US were analyzed first analyzed via XRF at UC Davis and then ICP-MS at RTI International. Both methods measured 29 of the same elements out of the 33 usually reported to CSN. Of these 29, 14 elements (Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Pb) were found to be frequently detected (i.e. had more than 10% of values above both XRF and ICP-MS MDL). ICP-MS was found to have lower MDL for 26 out of 29 elements, namely Na, Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, As, Se, Rb, Sr, Zr, Ag, Cd, In, Sn, Sb, Cs, Ba, Ce, Pb; conversely, XRF had lower MDL for 3 elements, namely, P, K, Zn. Intra-method quality checks using (1) inter-elemental inspection of scatter plots using a priori knowledge of element sources and (2) scatter plots of routine versus collocated measurements reveal that ICP-MS exhibits better measurement precision. Lower detection limits for element measurements in nationwide PM monitoring networks would benefit human-health and source apportionment research. Implications: We demonstrate that ICP-MS with adilute-acid digestion method would significantly improve the element detection rates and thus be avaluable addition to the current analysis techniques for airborne PM samples in anationwide monitoring network. In this paper, we show that a hybrid method of elemental analysis for airborne particulate matter (PM) would significantly improve the detection rates for elements in PM. This would be a valuable addition to the current analysis techniques for airborne PM samples in nationwide and other large-scale monitoring networks, such as the EPA’s Chemical Speciation Network (CSN). The techniques explored in this study (i.e., X-ray Fluorescence Spectroscopy or XRF and Inductively Coupled Plasma-Mass Spectrometry or ICP-MS) are relevant to the PM monitoring and regulatory community audience of JAWMA, especially agencies and states that are already involved in CSN. In addition, our results outline considerations that give insight on factors to consider for other large-scale and long-term ambient air monitoring efforts.

A Glass-Fiber-Optic Turbidity Sensor for Real-Time In Situ Water Quality Monitoring.

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level of drinking water distribution systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve the manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large-scale distributed fiber-optic turbidity monitoring networks.

Large-scale precipitation monitoring network re-design using ground and satellite datasets: coupled application of geostatistics and meta-heuristic optimization algorithms

Large-scale precipitation monitoring network re-design using ground and satellite datasets: coupled application of geostatistics and meta-heuristic optimization algorithms

A Flow-through Passive Sampler for Microplastics in Air.

Microplastics (MP) in air have attracted increasing attention because of their ubiquitous presence. Accurate atmospheric concentrations of MP are essential for evaluating their capacity for long-range transport and for assessing human inhalation risk. In order to sample airborne MP in locations with limited power supply, we adapted a flow-through passive sampler by placing a glass fiber filter in the inner sampling tube. To test the new sampler's performance under field conditions, two sizes of the flow-through sampler (with diameters of 20 and 10 cm) and a conventionally pumped high-volume air sampler were co-deployed on the Lhasa campus of the Institute of Tibetan Plateau Research. Accurate sampling volumes could be estimated from a relationship between wind speeds recorded outside and inside of the flow-through sampler. Atmospheric concentrations and compositions of MP that compared favorably with those obtained by active sampling indicate that the larger version of the flow-through passive sampler can provide reproducible and quantitative information on atmospheric MP at sites with limited or unreliable power supply. This capability should be useful in large-scale and high-temporal resolution air monitoring networks for MP.

Combining statistical methods for detecting potential outliers in groundwater quality time series

Quality control of large-scale monitoring networks requires the use of automatic procedures to detect potential outliers in an unambiguous and reproducible manner. This paper describes a methodology that combines existing statistical methods to accommodate for the specific characteristics of measurement data obtained from groundwater quality monitoring networks: the measurement series show a large variety of dynamics and often comprise few (< 25) measurements, the measurement data are not normally distributed, measurement series may contain several outliers, there may be trends in the series, and/or some measurements may be below detection limits. Furthermore, the detection limits may vary in time. The methodology for outlier detection described in this paper uses robust regression on order statistics (ROS) to deal with measured values below the detection limit. In addition, a biweight location estimator is applied to filter out any temporal trends from the series. The subsequent outlier detection is done in z-score space. Tuning parameters are used to attune the robustness and accuracy to the given dataset and the user requirements. The method has been applied to data from the Dutch national groundwater quality monitoring network, which consists of approximately 350 monitoring wells. It proved to work well in general, detecting outliers at the top and bottom of the regular measurement range and around the detection limit. Given the diversity exhibited by measurement series, it is to be expected that the method does not give 100% satisfactory results. Measured values identified by the method as potential outliers will therefore always need to be further assessed on the basis of expert knowledge, consistency with other measurement data and/or additional research.

Quantification of major particulate matter species from a single filter type using infrared spectroscopy – application to a large-scale monitoring network

Abstract. To enable chemical speciation, monitoring networks collect particulate matter (PM) on different filter media, each subjected to one or more analytical techniques to quantify PM composition present in the atmosphere. In this work, we propose an alternate approach that uses one filter type (teflon or polytetrafluoroethylene, PTFE, commonly used for aerosol sampling) and one analytical method, Fourier transform infrared (FT-IR) spectroscopy to measure almost all of the major constituents in the aerosol. In the proposed method, measurements using the typical multi-filter, multi-analytical techniques are retained at a limited number of sites and used as calibration standards. At all remaining sites, only sampling on PTFE and analysis by FT-IR is performed. This method takes advantage of the sensitivity of the mid-IR domain to various organic and inorganic functional groups and offers a fast and inexpensive way of exploring sample composition. As a proof of concept, multiple years of samples collected within the Interagency Monitoring of PROtected Visual Environment network (IMPROVE) are explored with the aim of retaining high quality predictions for a broad range of atmospheric compounds including mass, organic (OC), elemental (EC), and total (TC) carbon, sulfate, nitrate, and crustal elements. Findings suggest that models based on only 21 sites, covering spatial and seasonal trends in atmospheric composition, are stable over a 3 year period within the IMPROVE network with acceptable prediction accuracy (R2 &gt; 0.9, median bias less than 3 %) for most constituents. The major limitation is measuring nitrate as it is known to volatilize off of PTFE filters. Incorporating additional sites at low cost, partially replacing existing, more time- and cost-intensive techniques, or using the FT-IR data for quality control or substitute for missing data, are among the potential benefits of the one-filter, one-method approach.

DIGFuPAS: Deceive IDS with GAN and function-preserving on adversarial samples in SDN-enabled networks

Showing a great potential in various domains, machine learning techniques are more and more used in the task of malicious network traffic detection to significantly enhance the ability of intrusion detection system (IDS). When associating with Software-Defined Networks (SDN), the deployment of IDSs can leverage the centralized control plane in SDN to support for large-scale network monitoring. However, machine learning-based IDSs themselves can be attacked and tricked by adversarial examples with additional perturbation from the original ones. It is vital to provide supplementary unknown traffic to evaluate and improve the resilience of IDS against variants of cyberattacks. Thus, this work explores the method of generating adversarial attack samples by Generative Adversarial Model (GAN) to deceive IDS. We propose DIGFuPAS, a framework can create attack samples which can bypass machine learning-based IDSs in SDN with the black-box manner. In this framework, instead of Vanilla GAN, we use Wassertein GAN (WGAN) to improve the ability of GAN convergence training. In addition, the strategy of preserving functional features of attack traffic is applied to maintain the operational aspect of adversarial attacks. Through our implementation and experiments on NSL-KDD and CICIDS2018 dataset, the decreased detection rate of black-box IDSs on adversarial attacks demonstrates that our proposed framework can make IDSs in SDN-enabled networks misclassify on GAN-based synthetic attacks. Also, we utilize DIGFuPAS as a tool for evaluating and improving the robustness of IDS by repetitively retraining classifiers from crafted network traffic flow.

Variable coastal hypoxia exposure and drivers across the southern California Current

Declining oxygen is one of the most drastic changes in the ocean, and this trend is expected to worsen under future climate change scenarios. Spatial variability in dissolved oxygen dynamics and hypoxia exposures can drive differences in vulnerabilities of coastal ecosystems and resources, but documentation of variability at regional scales is rare in open-coast systems. Using a regional collaborative network of dissolved oxygen and temperature sensors maintained by scientists and fishing cooperatives from California, USA, and Baja California, Mexico, we characterize spatial and temporal variability in dissolved oxygen and seawater temperature dynamics in kelp forest ecosystems across 13° of latitude in the productive California Current upwelling system. We find distinct latitudinal patterns of hypoxia exposure and evidence for upwelling and respiration as regional drivers of oxygen dynamics, as well as more localized effects. This regional and small-scale spatial variability in dissolved oxygen dynamics supports the use of adaptive management at local scales, and highlights the value of collaborative, large-scale coastal monitoring networks for informing effective adaptation strategies for coastal communities and fisheries in a changing climate.

Validation of river typologies using molecular information from diatoms

The European Water Framework Directive (2000/60/EC) includes biological assessment of water bodies that has been implemented for many years. Indicator organisms such as diatoms respond to geological and hydrological features of rivers by modifying their structure. Therefore, when implementing the WFD, it was necessary to establish type-specific reference conditions to be able to measure the deviations of sampled communities due to anthropogenic impact.HTS-related eDNA metabarcoding has been developed to complement or even replace traditional approaches for its rapid, low-cost and highly accurate identification of communities for assessment of rivers’ ecological status (e.g. Mortágua et al., 2019; Pérez-Burillo et al. 2020) and proved to provide even more in-depth information about biological elements. The use of this information without assignment to species is being addressed once it eliminates the limiting factor of the reference database incompleteness and may provide new ecological information (e.g. Feio et al., 2020; Rivera et al., 2020). Since WFD requires the establishment of reference conditions for each water body type, for eDNA methods’ implementation it will be essential to review, confirm or reformulate, and perhaps create new typologies. Hereupon, the aim of this study is to analyze diatom communities from different typologies of Portuguese rivers resulting from DNA metabarcoding data and compare it with current typology system. To do so, we will verify the consistency of biological groups included in each type, validate the molecular data, analyze the correspondence of OTU/ISU/ESV to environmental characteristics of rivers. A total of 154 sampling sites were selected from central Portugal and northern Portugal in 2017 and 2019. The biofilm was collected for morphological identification and DNA sequencing of diatoms. Reference sites were selected for 4 river types (mountain, littoral, small and medium-large northern rivers) based on a set of pressure information (water quality, hydromorphology, land use and riparian zones). Diatom inventories were obtained from molecular and morphological analysis. DNA sequences were treated using Mothur software which processed two bioinformatic strategies in order to obtain the final ISU and OTU tables, while ESVs were treated with DADA2 package from R. Identification and counting of diatom valves took place under the light microscope concerning the morphological approach. We expect results to validate the molecular data for each typology either when assigning to species or not, and to understand whether it is necessary to establish new typologies for future use of the molecular approach in ecological assessment of rivers. Directive, W. F. (2000). Water Framework Directive. Journal reference OJL, 327, 1-73. Feio, M. J., Serra, S. R., Mortágua, A., Bouchez, A., Rimet, F., Vasselon, V., &amp;amp; Almeida, S. F. P. (2020). A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms. Science of the Total Environment, 722, 137900. https://doi.org/10.1016/j.scitotenv.2020.137900 Mortágua, A., Vasselon, V., Oliveira, R., Elias, C., Chardon, C., Bouchez, A., ... &amp;amp; Almeida, S. F. P. (2019). Applicability of DNA metabarcoding approach in the bioassessment of Portuguese rivers using diatoms. Ecological indicators, 106, 105470. https://doi.org/10.1016/j.ecolind.2019.105470 Pérez-Burillo, J., Trobajo, R., Vasselon, V., Rimet, F., Bouchez, A., &amp;amp; Mann, D. G. (2020). Evaluation and sensitivity analysis of diatom DNA metabarcoding for WFD bioassessment of Mediterranean rivers. Science of the Total Environment, 727, 138445. https://doi.org/10.1016/j.scitotenv.2020.138445 Rivera, S. F., Vasselon, V., Bouchez, A., &amp;amp; Rimet, F. (2020). Diatom metabarcoding applied to large scale monitoring networks: Optimization of bioinformatics strategies using Mothur software. Ecological indicators, 109, 105775. https://doi.org/10.1016/j.ecolind.2019.105775

Scotch

Sketching algorithms are a powerful tool for single-pass data summarization. Their numerous applications include approximate query processing, machine learning, and large-scale network monitoring. In the presence of high-bandwidth interconnects or in-memory data, the throughput of summary maintenance over input data becomes the bottleneck. While FPGAs have shown admirable throughput and energy-efficiency for data processing tasks, developing FPGA accelerators requires a sophisticated hardware design and expensive manual tuning by an expert. We propose Scotch, a novel system for accelerating sketch maintenance using FPGAs. Scotch provides a domain-specific language for the user-friendly, high-level definition of a broad class of sketching algorithms. A code generator performs the heavy-lifting of hardware description, while an auto-tuning algorithm optimizes the summary size. Our evaluation shows that FPGA accelerators generated by Scotch outperform CPU- and GPU-based sketching by up to two orders of magnitude in terms of throughput and up to a factor of five in terms of energy efficiency.

Local and Landscape-Scale Features Influence Bumble Bee (Hymenoptera: Apidae) Bycatch in Bertha Armyworm Mamestra configurata (Lepidoptera: Noctuidae) Pheromone-Baited Monitoring Traps.

The bertha armyworm (BAW) Mamestra configurata Walker is a significant pest of canola Brassica napus L. (Brassicales: Brassicaceae) in western Canada. Its activity is monitored through a large network of pheromone-baited monitoring traps as a part of the Prairie Pest Monitoring Network across the Canadian Prairies. The unintentional bycatch of bee pollinators in pheromone-baited traps targeting moth pests occurs in many agroecosystems and may have repercussions for biodiversity and pollination services of wild plants and managed crops. We conducted field experiments to determine the abundance and diversity of bees attracted to green-colored BAW pheromone-baited traps across the canola growing regions of Alberta, Canada. A higher species diversity and more bumble bees were captured in BAW pheromone-baited than in unbaited control traps. Bombus rufocinctus Cresson (Hymenoptera: Apidae) was the most commonly captured species. Few other wild bees or honey bees Apis mellifera L. (Hymenoptera: Apidae) were captured during this study. Additionally, we evaluated the influence of local and landscape-level habitat features on bee bycatch. Local flowering plant abundance improved overall model fit but did not directly impact bee bycatch. The proportion of natural and seminatural habitat, and especially forested area, in the area surrounding monitoring traps affected bee bycatch. Both local and landscape-scale factors were important in this study and often have combined effects on bee communities. This study provides recommendations to reduce the bycatch of beneficial bee pollinators in a large-scale pheromone-baited monitoring network.

Field- and model-based calibration of polyurethane foam passive air samplers in different climate regions highlights differences in sampler uptake performance

Abstract Polyurethane foam (PUF) passive air samplers (PAS) are widely used for measurements of persistent organic pollutants (POPs) and other semi-volatile organic compounds (SVOCs) in large-scale monitoring networks as well as in case studies around the globe. Calibration of PUF-PAS is performed by field-based calibration studies or passive sampler uptake models, but both these are typically performed and/or validated in temperate zones, while the sampling rates are more widely applied, including in tropical and polar zones. Here, we present field-based calibrations results for MONET PUF-PAS from a subtropical and tropical site (Nairobi, Kenya and Accra, Ghana) based on side-by-side deployment of PUF-PAS and active air samplers (AAS), as well as model PAS uptake from available passive sampler uptake models. By comparing these results with a similar calibration from a temperate site (Brno, Czech Republic), we show that the higher ambient temperatures result in higher effective sampling rates for intermediate molecular weight SVOCs (logKOA of 7–11) as a result of lower particle-bound fraction, and in lower sample volumes for lighter SVOCs (logKOA

Deep convolutional neural networks to monitor coralligenous reefs: Operationalizing biodiversity and ecological assessment

Monitoring the ecological status of natural habitats is crucial to the conservation process, as it enables the implementation of efficient conservation policies. Nowadays, it is increasingly possible to automate species identification, given the availability of very large image databases and state-of-the-art computational power which makes the training of automated machine learning-based classification models an increasingly viable tool for monitoring marine habitats. Coralligenous reefs are an underwater habitat of particular importance, found in the Mediterranean. This habitat is of a similar biocomplexity to coral reefs. They have been monitored in French waters since 2010 using manually annotated photo quadrats (RECOR monitoring network). Based on the large database of annotations accumulated therein, we have trained convolutional neural networks to automatically recognise coralligenous species using the data gathered from photo quadrats. Previous studies conducted on similar habitats performed well, but were only able to consider a limited number of classes, resulting in a very coarse description of these often-complex habitats. We therefore designed a custom network based on off-the-shelf architectures which is able to discriminate between 61 classes with 72.59% accuracy. Our results showed that confusion errors were for the most part taxonomically coherent, showing accuracy performances of 84.47% when the task was simplified to 15 major categories, thereby outperforming the human accuracy previously recorded in a similar study. In light of this, we built a semi-automated tool to reject unsure results and reduce error risk, for when a higher level of accuracy is required. Finally, we used our model to assess the biodiversity and ecological status of coralligenous reefs with the Coralligenous Assemblage Index (CAI) and the Shannon Index. Our results showed that whilst the prediction of the CAI was only moderately accurate (pearson correlation between observed and predicted CAI = 0.61), the prediction of Shannon Index was more accurate (pearson correlation = 0.74). In conclusion, it will be argued that the approach outlined by this study offers a cost and time-effective tool for the analysis of coralligenous assemblages which is suitable for integration into a large-scale monitoring network of this habitat.

A Lightweight System With Ultralow-Power Consumption for Online Continuous Impact Monitoring of Aerospace Vehicle Structures

Impact monitoring is important to the service safety of aerospace vehicle structures. The implementation of an impact monitoring system is essential because impacts must be monitored online continuously due to their rare, random, and transient nature. Therefore, an impact monitoring system needs to serve as an airborne system of the vehicle so that the system is required to be low in power consumption and lightweight. In this article, a piezoelectric-sensor-based impact monitoring system is proposed and implemented. The system can execute the complete monitoring process by itself, including acquiring sensor data, executing the monitoring algorithm, and storing the results. This monitoring process for each impact can be completed in 3.4 ms by the system. The static power consumption of the system is only 1.44 mW (the static current consumption is 0.38 mA). Furthermore, the system has 32 piezoelectric sensor channels and the weight is only 22 g. Therefore, a large-scale impact monitoring network can be easily constructed by using multiple systems. The system is evaluated on the composite stiffened skin of an unmanned aerial vehicle, and the impact monitoring accuracy rate reaches 96%.

Fewer sites but better data? Optimising the representativeness and statistical power of a national monitoring network

Indicators of large-scale ecological change are typically derived from long-term monitoring networks. As such, it is important to assess how well monitoring networks provide evidence for ecological trends in the regions they are monitoring. In part, this depends on the network’s representativeness of the full range of environmental conditions occurring in the monitored region. In addition, the statistical power to detect trends and ecological changes using the network depends on its structure, size and the intensity and accuracy of monitoring. This paper addresses the optimisation of representativeness and statistical power when re-designing existing large-scale ecological monitoring networks, for example due to financial constraints on monitoring programmes. It uses a real world example of a well-established river monitoring network of 254 sites distributed across Scotland. We first present a novel approach for assessing a monitoring network’s representativeness of national habitat and pressure gradients using the multivariate two-sample Cramér’s T statistic. This compares multivariate gradient distributions among sites inside and outside of the network. Using this test, the existing network was found to over-represent larger and more heavily polluted sites, reflecting earlier research priorities when it was originally designed. Network re-design was addressed through stepwise selection of individual sites to remove from or add to the network to maximise multivariate representativeness. This showed that combinations of selective site retention and addition can be used to modify existing monitoring networks, changing the number of sites and improving representativeness. We then investigated the effect of network re-design on the statistical power to detect long-term trends across the whole network. The power analysis was based on linear mixed effects models for long-term trends in three ecological indicators (ecological quality ratios for diatoms, invertebrates and macrophytes) over a ten-year period. This revealed a clear loss of power in smaller networks with less accurate sampling, but sampling schedule had a smaller effect on power. Interestingly, more representative networks had slightly lower trend detection power than the current unrepresentative network, though they should give a less biased estimate of national trends. Our analyses of representativeness and statistical power provide a general framework for designing and adapting large-scale ecological monitoring networks. Wider use of such methods would improve the quality of indicators derived from them and improve the evidence base for detecting and managing ecological change.

Nitrogen deposition sources and patterns in the Greater Yellowstone Ecosystem determined from ion exchange resin collectors, lichens, and isotopes

Over the past century, atmospheric nitrogen deposition (Ndep) has increased across the western United States due to agricultural and urban development, resulting in degraded ecosystem quality. Regional patterns of Ndep are often estimated by coupling direct measurements from large-scale monitoring networks and atmospheric chemistry models, but such efforts can be problematic in the western US because of complex terrain and sparse sampling. This study aimed not only to understand Ndep patterns in mountainous ecosystems but also to investigate whether isotope values of lichens and throughfall deposition can be used to determine Ndep sources, and serve as an additional tool in ecosystem health assessments. We measured Ndep amounts and δ15N in montane conifer forests of the Greater Yellowstone Ecosystem using canopy throughfall and bulk monitors and lichens. In addition, we examined patterns of C:N ratios in lichens as a possible indicator of lichen physiological condition. The isotopic signature of δ15N of Ndep helps to discern emission sources, because δ15N of NOx from combustion tends to be high (−5 to +25‰) while NHx from agricultural sources tends to be comparatively low (−40 to −10‰). Summertime Ndep increased with elevation and ranged from 0.26 to 1.66 kg ha−1. Ndep was higher than expected in remote areas. The δ15N values of lichens were typically −15.3 to −10‰ suggesting agriculture as a primary emission source of deposition. Lichen %N, δ15N and C:N ratios can provide important information about Ndep sources and patterns over small spatial scales in complex terrain.

An evaluation of the ability of the Ozone Monitoring Instrument (OMI) to observe boundary layer ozone pollution across China: application to 2005–2017 ozone trends

Abstract. Nadir-viewing satellite observations of tropospheric ozone in the UV have been shown to have some sensitivity to boundary layer ozone pollution episodes, but so far they have not yet been compared to surface ozone observations collected by large-scale monitoring networks. Here we use 2013–2017 surface ozone data from China's new Ministry of Ecology and Environment (MEE) network of ∼ 1000 sites, together with vertical profiles from ozonesondes and aircraft, to quantify the ability of tropospheric ozone retrievals from the Ozone Monitoring Instrument (OMI) and to detect boundary layer ozone pollution in China. We focus on summer when ozone pollution in China is most severe and when OMI has the strongest sensitivity. After subtracting the Pacific background, we find that the 2013–2017 mean OMI ozone enhancements over eastern China have strong spatial correlation with the corresponding multiyear means in the surface afternoon observations (R=0.73), and that OMI can estimate these multiyear means in summer afternoon surface ozone with a precision of 8 ppb. The OMI data show significantly higher values on observed surface ozone episode days (&gt;82 ppb) than on non-episode days. Day-to-day correlations with surface ozone are much weaker due to OMI noise and are stronger for sites in southern China (&lt;34∘ N; R=0.3–0.6) than in northern China (R=0.1–0.3) because of weaker retrieval sensitivity and larger upper tropospheric variability in the north. Ozonesonde data show that much of the variability of OMI ozone over southern China in summer is driven by the boundary layer. Comparison of 2005–2009 and 2013–2017 OMI data indicates that mean summer afternoon surface ozone in southern China (including urban and rural regions) has increased by 3.5±3.0 ppb over the 8-year period and that the number of episode days per summer has increased by 2.2±0.4 (as diagnosed by an extreme value model), generally consistent with the few long-term surface records. Ozone increases have been particularly large in the Yangtze River Delta and in the Hubei, Guangxi and Hainan provinces.