Historical Sampling Research Articles

The International Pacific Salmon Data Legacy; Historical Sampling of Salmon in the Ocean

The International Pacific Salmon Data Legacy; Historical Sampling of Salmon in the Ocean

Multiple “spaces”: Using wildlife surveillance, climatic variables, and spatial statistics to identify and map a climatic niche for endemic plague in California, U.S.A.

Regional climatic features in endemic areas can help inform surveillance for plague, a bacterial disease typically transmitted by fleas and maintained in mammals. We use 7,954 coyotes (Canis latrans), a sentinel species for plague, screened for plague exposure by the California Department of Public Health - Vector-Borne Disease Section (CDPH-VBDS; 1983-2015) to identify and map plague-suitable local climates within California to empirically inform ongoing sampling and surveillance plans. Using spatial point processes, we compare the distributions of seropositive and seronegative coyotes within the “space” defined by the first two principal components of PRISM Climate Group 30-year average climate variables (primarily temperature and moisture). The approach identifies both regions consistent with CDPH-VBDS mapping of plague-positive rodent and other carnivore samples over the same period and additional plague-suitable areas with climate profiles similar to seropositive samples elsewhere but little or no historical sampling, providing new data-informed insight for prioritizing limited surveillance resources.

Quantification of threats to bats at localized spatial scales for conservation and management.

In a rapidly changing world, where species conservation needs vary by local habitat, concentrated conservation efforts at small spatial scales can be critical. Bats provide an array of value to the ecosystems they inhabit; many bat species are also of conservation concern. San Diego County, California, contains 22 of the 41 bat species that occur in the United States, 16 of which are on conservation watchlists. Thus, management of bat communities in San Diego County is a pressing need. Because bats exploit vast areas of the landscape and historical sampling strategies have shifted over time, a standardized way of prioritizing areas of the landscape for management would provide an integral asset to bat conservation. We leveraged long-term bat community survey data from sampling areas across San Diego County to prioritize areas with the most management need. We calculated two types of scores: species scores and threat scores. Species scores incorporated richness and conservation status, and threat scores included landscape level threats that bats could encounter. We found that urbanization, the presence of artificial lights, and areas sampled on unconserved land were all significantly associated with decreases in species richness. Further, using species and threat scores, each sampling area was placed into one of four conservation categories, in order from greatest to least conservation need, ranging from highest priority (high species score, high threat score) to lowest (low species score, low threat score). Additionally, we focused on sampling areas in which Townsend's big-eared bat (Corynorhinus townsendii) and/or pallid bat (Antrozous pallidus) occurred. These two species are of exceptional conservation concern in San Diego County and across the western United States. We identified urbanization, the presence of artificial lights, and areas sampled on unconserved land as threats that were all significantly associated with the absence of Townsend's big-eared bat, but not pallid bat. The strategy, methodology, and solutions proposed in our study should assist bat conservation and management efforts wherever bats occur, and can be extended to other species that require conservation attention.

A New Look at Diffusion in Vapor Intrusion Assessments; Passive Adsorptive Diffusion Samplers

AbstractVapor intrusion of toxic volatile organic compounds (VOCs) from subsurface soil vapor, through a building slab/floor, and into the indoor air is an important environmental contaminant transport mechanism. It is widely believed that advective flow, driven by the pressure differential between the subslab and indoor air, is the primary mechanism of subslab soil vapor entry into buildings. This paper explores the hypothesis that molecular diffusion through the slab may potentially play a larger role in vapor intrusion than previously believed and may even be the predominant vapor intrusion mechanism when the subslab vapor source strength is sufficiently high or the pressure differential is relatively low. A novel sampling device, referred to as a Passive Adsorptive Diffusion Sampler (PADS), is presented for the purpose of directly measuring the diffusion of VOCs through a building slab. A vacant warehouse was identified as a case study site where historical sampling had determined that vapor intrusion of trichloroethene (TCE) was adversely impacting the indoor air. Calculations using Fick's First Law of Diffusion are presented which demonstrate that diffusion alone can theoretically account for all the TCE observed in the indoor air at this building based on an effective diffusion coefficient for concrete that was calculated from the Johnson and Ettinger Model. Two groups of nine replicate PADS were deployed at two areas on the slab and used to measure the flux and effective diffusion coefficient at each of the 18 total points, which showed an order of magnitude variability within each area and over two orders of magnitude variability overall. These results indicate that diffusion through concrete is inherently variable when measured at a sub‐meter scale. However, when combined over both areas, the overall average approached that calculated from the Johnson and Ettinger Model. An additional 12 PADS were deployed across the building slab (for a total of 30) to quantify the overall building‐wide diffusive flux. This area‐weighted average diffusive flux was consistent with the predicted diffusive flux as calculated from Fick's First Law and the vapor intrusion mass input required to achieve the observed indoor air TCE concentration. The results of this study show that PADS provides a simple way to measure diffusive flux directly without having to drill through the slab. However, significant variability in the measured flux should be expected and will need to be accounted for by the inclusion of a relatively large number of samples including replicates. When using PADs at a new site, the collection of traditional subslab vapors at a select number of locations is recommended for the verification of a building‐specific effective diffusion coefficient, which may not necessarily be the same as for this building.

Using Machine Learning Models for Short-Term Prediction of Dissolved Oxygen in a Microtidal Estuary

This paper presents a comprehensive approach to predicting short-term (for the upcoming 2 weeks) changes in estuarine dissolved oxygen concentrations via machine learning models that integrate historical water sampling, historical and upcoming 2-week meteorological data, and river discharge and discharge metrics. Dissolved oxygen is a critical indicator of ecosystem health, and this approach is implemented for the Neuse River Estuary, North Carolina, U.S.A., which has a long history of hypoxia-related habitat degradation. Through meticulous data preprocessing and feature selection, this research evaluates the predictions of dissolved oxygen concentrations by comparing a recurrent neural network with four other models, including a Multilayer Perceptron, Long Short-Term Memory, Gradient Boosting, and AutoKeras, through sensitivity experiments. The input predictors to our prediction models include water temperature, turbidity, chlorophyll-a, aggregated river discharge, and aggregated wind based on eight directions. By emphasizing the most impactful predictors, we streamlined the model-building processes and built a hindcast system from 2015 to 2019. We found that the recurrent neural network model was most effective in predicting the dissolved oxygen concentrations, with an R2 value of 0.99 at multiple stations. Different from our machine learning hindcast models that used observed upcoming meteorological and discharge data, an actual forecast system would use forecasted meteorological and discharge data. Therefore, an actual operational forecast may have lower accuracy than the hindcast, as determined by the accuracy of the predicted meteorological and discharge data. Nevertheless, our studies enhance our understanding of the factors influencing dissolved oxygen variability and set the basis for the implementation of a predictive tool for environmental monitoring and management. We also emphasized the importance of building station-specific models to improve the prediction results.

Discrete-time graph neural networks for transaction prediction in Web3 social platforms

In Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or posting, to specific relations given by crypto-token transfers facilitated by the blockchain. In this dynamic and intertwined networked context, modeled as a financial network, our main goals are (i) to predict whether a pair of users will be involved in a financial transaction, i.e. the transaction prediction task, even using textual information produced by users, and (ii) to verify whether performances may be enhanced by textual content. To address the above issues, we compared current snapshot-based temporal graph learning methods and developed T3GNN, a solution based on state-of-the-art temporal graph neural networks’ design, which integrates fine-tuned sentence embeddings and a simple yet effective graph-augmentation strategy for representing content, and historical negative sampling. We evaluated models in a Web3 context by leveraging a novel high-resolution temporal dataset, collected from one of the most used Web3 social platforms, which spans more than one year of financial interactions as well as published textual content. The experimental evaluation has shown that T3GNN consistently achieved the best performance over time and for most of the snapshots. Furthermore, through an extensive analysis of the performance of our model, we show that, despite the graph structure being crucial for making predictions, textual content contains useful information for forecasting transactions, highlighting an interplay between users’ interests and economic relationships in Web3 platforms. Finally, the evaluation has also highlighted the importance of adopting sampling methods alternative to random negative sampling when dealing with prediction tasks on temporal networks.

Predicting the future of our oceans-Evaluating genomic forecasting approaches in marine species.

Climate change is restructuring biodiversity on multiple scales and there is a pressing need to understand the downstream ecological and genomic consequences of this change. Recent advancements in the field of eco-evolutionary genomics have sought to include evolutionary processes in forecasting species' responses to climate change (e.g., genomic offset), but to date, much of this work has focused on terrestrial species. Coastal and offshore species, and the fisheries they support, may be even more vulnerable to climate change than their terrestrial counterparts, warranting a critical appraisal of these approaches in marine systems. First, we synthesize knowledge about the genomic basis of adaptation in marine species, and then we discuss the few examples where genomic forecasting has been applied in marine systems. Next, we identify the key challenges in validating genomic offset estimates in marine species, and we advocate for the inclusion of historical sampling data and hindcasting in the validation phase. Lastly, we describe a workflow to guide marine managers in incorporating these predictions into the decision-making process.

Correction to: Historic Sampling of a Vanishing Beast: Population Structure and Diversity in the Black Rhinoceros

Correction to: Historic Sampling of a Vanishing Beast: Population Structure and Diversity in the Black Rhinoceros

Oceanographic dataset collected during the 2021 scientific expedition of the Canadian Coast Guard Ship Amundsen

Abstract. Since 2003, the state-of-the-art Canadian Coast Guard Ship (CCGS) research icebreaker Amundsen has furrowed the Canadian Arctic waters to support novel research endeavors and collect oceanographic data. This paper presents the data acquisition, the processing methods and an overview of the data collected during the 2021 expedition as the ship traveled over 30 000 km during 122 d across the Canadian Arctic Ocean, collecting sea surface, atmospheric and seabed underway measurements. A total of 266 casts of a conductivity, temperature and depth profiler mounted on a Conductivity Temperature Depth rosette (CTD Rosette) were also conducted to monitor the main physical, chemical and biological parameters of the water column. More specifically, the data presented here were collected with the CTD Rosette across historical sampling transects in Davis Strait, the North Water Polynya (NOW) and Cape Bathurst. A 182 km dedicated survey using the Moving Vessel Profiler® (MVP), equipped with CTD, transmissometer, dissolved oxygen, fluorescence and sound velocity sensors, was conducted across Hudson Strait. We also present an overview of the data collected by the underway systems (seabed, thermosalinograph and atmospheric). Such data are essential in understanding the impacts of climate warming on the unique environments of the Canadian Arctic Ocean. Amundsen Science supports and promotes easy access and sharing of such valuable data to the scientific community.

Finite Horizon Memory Control of Networked Systems Using Chain-like Lyapunov Function

Abstract: This paper proposes a novel finite horizon memory control (FHMC) design framework for networked systems by using input delay approach. A chain-like input delay model is established to characterize the networked control system (NCS) under memory control architecture in order to make full use of historic sampled-data. Based on the proposed chain-like delay model, the corresponding chain-like Lyapunov-Krasovskii function, which records the historic sampling information of NCS, is well constructed for facilitating further analysis and synthesis of the proposed FHMC scheme. Both state feedback controllers and static output feedback controllers are derived by solving LMIs (Linear matrix inequalities). The proposed FHMC scheme is skilled in improving control performance of networked systems. Simulations show the effectiveness of the presented FHMC scheme.

Data-Driven Chance-Constrained Schedule Optimization of Cascaded Hydropower and Photovoltaic Complementary Generation Systems for Shaving Peak Loads

The coordinated scheduling of cascade hydropower with photovoltaic (PV) power stations can significantly improve the utilization rate of delivery transmission lines. However, the inherent uncertainty associated with photovoltaic (PV) forecasts challenges the reliable and economic operation of the complementary energy system. Against this background, in this paper, a day-ahead, chance-constrained scheduling for cascaded hydro–photovoltaic complementary generation systems (CHPSs) considering the transmission capacity is proposed. Firstly, the uncertainty of PV forecast errors is simulated by a probability density function fitted using kernel density estimation with historical sampling data. Then, a chance-constrained optimization model considering peak-shaving demands of the receiving-end power grid is developed to determine the day-ahead optimal schedules of CHPSs. Also, complex hydraulic coupling and unit operation constraints of cascade hydropower are considered in the proposed model. To deal with the nonlinear and stochastic constraints, an efficient linearization method is adopted to transform the proposed model into a mixed-integer linear programming (MILP) problem. Finally, the effectiveness and feasibility are verified by case studies. The results show that the day-ahead schedule optimized by the proposed method can fully balance peak-shaving and photovoltaic accommodation while considering photovoltaic output uncertainty.

Southern pine beetle (Coleoptera: Curculionidae) and its associated insect community: similarities and key differences between northeastern and southeastern pine forests.

We examine consequences of climate-induced range expansion on community composition and diversity within trees attacked by the southern pine beetle (Dendroctonus frontalis Zimmermann). At the northernmost limit of the southern pine beetle range where populations have persisted for multiple years (currently Long Island, NY), we collected and reared bark samples and placed emergence traps on southern pine beetle-attacked pitch pine, Pinus rigida Mill. (Pinales: Pinaceae). From these samples, we quantified southern pine beetle gallery length and emergence as well as the diversity and abundance of all associated insects including known and suspected competitors, predators, and parasitoids. We compared our results to that of historic sampling data (1975-1997) in the core of southern pine beetle's range in the southern United States. Key community members were present in both the northern and southern regions; composition and relative abundances differed markedly. A key predator, the clerid beetle Thanasimus dubius (Fabricius) (Coleoptera: Cleridae), was present in similar densities in both regions. Southern pine beetle infested a greater proportion of the length of the tree bole in the North. This increased tree utilization may be a consequence of a lack of resource competition by Ips De Geer (Coleoptera: Curculionidae) engraver beetles, which we found only in very low abundance in the northern sites. We discuss the implications of these results in the context of southern pine beetle range expansion. Continued study of the southern pine beetle community and temporal southern pine beetle dynamics in the North will add to our current knowledge base and aid preservation of rare and ecologically valuable pine barrens of New England.

Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA

Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern. This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions. The statistical and Pearson correlation analysis on historical water samples determines that alkalinity, chloride, hardness, conductivity, and pH are highly correlated, and they decrease with increasing flow rate due to dilution. The flow rate has positive correlations with Escherichia coli, total suspended solids, and turbidity, which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river. The correlation between E. coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E. coli to indicate the bacterial outbreak. A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers, fill missing values, and filter spikes of the sensor measurements. The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover. Therefore, utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality, then in turn to provide early alerts on water resources management decisions.

Historical sampling error: A neglected factor in long-term biodiversity change research

Long-term time series are increasingly used to assess the effects of global change on plant community diversity and to guide management of target plant communities. However, historical biodiversity data may contain neglected sources of error that can have a significant impact on the results and their interpretation. In our study, we focus on historical sampling error, a source of potential bias in long-term biodiversity assessments that has not been systematically addressed. We resampled two historical datasets of a different origin in the floodplain forests of the Czech Republic, with 534 vegetation plots originally sampled in the 1950s and 1960s. We compared temporal trends in alpha diversity and Ellenberg indicator values (EIVs) between the two parallel surveys. To assess compositional differences, we compared temporal changes in species frequencies. Alpha diversity increased by 9.3 % in one resurvey, but decreased by an average of 30.8 % in the second resurvey. The distribution of EIVs for plots also differed, indicating that each resurvey covered a different part of the environmental gradient. We conclude that preferential historical sampling of the vegetation-environment continuum and species omission may have contributed to the differences in biodiversity and environmental change between the datasets. Our study shows that historical sampling error can have a significant impact on assessments of long-term biodiversity trends. We recommend that historical reference datasets should be critically assessed for potential sources of error in assessments of environmental change and management objectives.

Extension of Stochastic Point Location for Multimodal Problems.

Stochastic point location (SPL) involves a learning mechanism (LM) determining an optimal point on the line when the only inputs LM receives are stochastic information about the direction in which LM should move. The complexity of SPL comes from the stochastic responses of the environment, which may lead LM completely astray. SPL is a fundamental problem in optimization and was studied by many researchers during the last two decades, including improvement of its solution and all-pervasive applications. However, all existing SPL studies assume that the whole search space contains only one optimal point. Since a multimodal optimization problem (MMOP) contains multiple optimal solutions, it is significant to develop SPL's multimodal version. This article extends it from a unimodal problem to a multimodal one and proposes a parallel partition search (PPS) solution to address this issue. The heart of the proposed solution involves extracting the feature of the historical sampling information to distinguish the subintervals that contain the optimal points or not. Specifically, it divides the whole search space into multiple subintervals and samples them parallelly, then utilizes the feature of the historical sampling information to adjust the subintervals adaptively and to find the subintervals containing the optimal points. Finally, the optimal points are located within these subintervals according to their respective sampling statistics. The proof of the ε-optimal property for the proposed solution is presented. The numerical testing results demonstrate the power of the scheme.

Historic Sampling of a Vanishing Beast: Population Structure and Diversity in the Black Rhinoceros.

The black rhinoceros (Diceros bicornis L.) is a critically endangered species historically distributed across sub-Saharan Africa. Hunting and habitat disturbance have diminished both its numbers and distribution since the 19th century, but a poaching crisis in the late 20th century drove them to the brink of extinction. Genetic and genomic assessments can greatly increase our knowledge of the species and inform management strategies. However, when a species has been severely reduced, with the extirpation and artificial admixture of several populations, it is extremely challenging to obtain an accurate understanding of historic population structure and evolutionary history from extant samples. Therefore, we generated and analyzed whole genomes from 63 black rhinoceros museum specimens collected between 1775 and 1981. Results showed that the black rhinoceros could be genetically structured into six major historic populations (Central Africa, East Africa, Northwestern Africa, Northeastern Africa, Ruvuma, and Southern Africa) within which were nested four further subpopulations (Maasailand, southwestern, eastern rift, and northern rift), largely mirroring geography, with a punctuated north-south cline. However, we detected varying degrees of admixture among groups and found that several geographical barriers, most prominently the Zambezi River, drove population discontinuities. Genomic diversity was high in the middle of the range and decayed toward the periphery. This comprehensive historic portrait also allowed us to ascertain the ancestry of 20 resequenced genomes from extant populations. Lastly, using insights gained from this unique temporal data set, we suggest management strategies, some of which require urgent implementation, for the conservation of the remaining black rhinoceros diversity.

Long‐Term Standardized Sampling as a Tool for Evaluating Management Actions: BACI Analysis to Reevaluate Predatory Control of Stunted Crappie with Saugeye

Field evaluations of management manipulations are rarely conducted, in part because they are difficult to design and require large sampling effort. However, many commonly used management approaches have <50% success rate, indicating the need for more research. Herein, we provide a case study illustrating how historic standard sampling data from Oklahoma were used to design robust before–after control–impact study designs to evaluate the effectiveness of stocking saugeye Sander vitreus × S. canadensis as a predatory control for stunted White Crappie Pomoxis annularis populations. Comparing six manipulated lakes with six reference lakes (no saugeye stockings), we found that proportional size distribution, proportional size distribution of preferred‐size fish, and mean length at ages 1 and 2 of White Crappie all improved after stockings, but still fell short of statewide median values. Further, similar improvements in these metrics also occurred in reference lakes, suggesting they were likely caused by regional patterns (flood/drought or temperature effects) rather than management intervention. Thus, we conclude saugeye stockings are not likely to routinely improve stunted White Crappie fisheries. We encourage managers who have historic data to use these data to robustly evaluate management interventions (as illustrated in this study), because our field needs additional information regarding when management interventions will be most effective, versus when they may not have the desired outcome. These historic data sets have considerable utility in providing this type of information. Our case study illustrates the importance of replication and reference lakes to avoid erroneous conclusions in such evaluations.

Fast fault diagnosis method of rolling bearings based on compression features in multi-sensor redundant observation environment

High-precision synchronous condition detection and fault diagnosis for bearings are important to reduce the failure rate of rotating machinery products. Therefore, this paper proposes a fast and high-precision diagnosis method based on multi-sensing fusion and compression features. First, the traditional random weighting method is optimized. The fluctuations of each signal are calculated, and used as the basis for balancing the weighting relationship between the current and historical sampling values, in order to achieve high precision fusion of bearings signals. Second, based on the traditional compression sensing method, the reconstruction part that would further increase the diagnosis error and time is omitted. The partial Hadamard matrix is constructed to retain the feature trend in the compressed signal, and the bearings fault diagnosis based on the compressed features is realized. Finally, the combination of these two methods can reduce the number of signal samples during the collection and transmission process, and realize a direct, fast and accurate diagnosis of the bearings state. Simulation and experimental results verify the superiority and effectiveness of the proposed method.

Design and redesign of a bottom trawl survey in Chesapeake Bay, USA

Fisheries-independent surveys that reliably sample a broad size range of exploited and ecologically important species provide valuable data in support of fisheries management and ecosystem science. The operational consistency of surveys over time and space is fundamental to the interpretation of data in the contexts of population dynamics processes, community interactions, policy impacts, and environmental forcing. However, the need to maintain historic sampling protocols over extended time periods limits the utilization of new technologies that could lead to improved data collection. Survey vessel replacements also become inevitable as the maturity of sampling programs becomes multidecadal. This case study describes the motivational origin, initial design, and redesign of a bottom trawl survey operating in Chesapeake Bay, the largest estuary in the United States. Regional aspirations to consider ecosystem principles in fisheries management aided initial development of the survey, and the need to collect specific data types to support that endeavor impacted several early design elements. Following the beginning years of full-scale survey operations, a consistently evolving awareness of potential areas of improvement for the survey grew from formal efforts to engage with scientific and industry partners on trawl gear design, leverage the program for additional survey opportunities, utilize gear testing technology, and analyze extant data. When the delivery of a new, state-of-the-art research vessel forced the transfer of survey operations to a new platform, all potential changes were incorporated simultaneously. A subsequent paired-tow experiment was conducted to build a calibration database that successfully provided estimates of relative selectivity for routinely sampled taxa. This experience yielded several lessons learned that are intended to aid investigators faced with adopting structural changes to fisheries-independent surveys in the future.

Pollen metabarcoding of museum specimens and recently collected bumblebees (Bombus) indicates foraging shifts

Landscape changes, over time, lead to changes of floral resources available to pollinators, which in turn may result in the disappearance of ecologically specialized species. Here, we use pollen metabarcoding to infer historic and recent interactions between plants and bumblebees (Bombus). Bumblebees from Cuxhaven (Germany) were sampled from historical museum collections (1968/69) and in the field (2019). Pollen attached to their bodies was barcoded using multiple plant markers (ITS1, ITS2 and trnL-P6 loop). Our results show shifts in foraging habits between the historic and recent sampling periods, mostly determined by fewer Fabaceae interactions in 2019. The successful implementation of scalable molecular techniques for the analysis of historical pollen samples underscores the value of museum collections as a resource for biodiversity research. This study provides proof of concept of a comparative analysis of recent and historical pollination data. However, to ensure the robustness of our results, it is crucial to consider the broader methodology used. Our study found variation in the efficacy of the three plant barcoding markers. The ITS1 marker exhibited the highest species-level identification success, while the trnL-P6 loop demonstrated utility in amplifying degraded DNA across diverse plant families.