Diverse Streams Research Articles

Changes in basal resources mediate the effects of Eucalyptus spp. afforestation on macroinvertebrate functional diversity in subtropical lowland streams

Changes in basal resources mediate the effects of Eucalyptus spp. afforestation on macroinvertebrate functional diversity in subtropical lowland streams

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

AbstractStreambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

Scale effects of riparian forests on fish diversity in streams of the upper Paraná River basin

Scale effects of riparian forests on fish diversity in streams of the upper Paraná River basin

Differential stability of task variable representations in retrosplenial cortex

Cortical neurons store information across different timescales, from seconds to years. Although information stability is variable across regions, it can vary within a region as well. Association areas are known to multiplex behaviorally relevant variables, but the stability of their representations is not well understood. Here, we longitudinally recorded the activity of neuronal populations in the mouse retrosplenial cortex (RSC) during the performance of a context-choice association task. We found that the activity of neurons exhibits different levels of stability across days. Using linear classifiers, we quantified the stability of three task-relevant variables. We find that RSC representations of context and trial outcome display higher stability than motor choice, both at the single cell and population levels. Together, our findings show an important characteristic of association areas, where diverse streams of information are stored with varying levels of stability, which may balance representational reliability and flexibility according to behavioral demands.

The role of the environment and connectivity on multiple facets of local fish diversity in tropical headwater streams

Abstract The identification of multiple facets of biodiversity (e.g., phylogenetic and functional) can help to understand how evolutionary and ecological mechanisms shape the assembly of biological communities. Here, we employed multiple regressions and variance partitions to investigate the effects of environmental conditions, connectivity and space on aspects of the functional and phylogenetic facets of fish fauna in tropical headwater streams of the Xingu River basin, in Eastern Amazonia. We found that the functional and phylogenetic diversities of fish communities are related to local environmental conditions and stream connectivity in the drainage network, with marked differences occurring between the rainy and dry periods. Furthermore, we discovered that functional uniqueness was influenced mainly by environmental factors, whereas phylogenetic uniqueness had a more complex relationship with the influence of local environmental factors and connectivity. Our findings indicate that the fish assemblages of small headwater streams of the Xingu River basin share a close phylogenetic history. This is probably because of the climatic and environmental stability experienced by Amazonian ichthyofauna over evolutionary time. Our findings emphasise the role of niche‐based processes in shaping the unique functional composition of fish assemblages in headwater streams, and highlight the importance of environmental heterogeneity in maintaining the ecological integrity of these freshwater ecosystems.

Fish beta diversity associated with hydrologic and anthropogenic disturbance gradients in contrasting stream flow regimes

Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916–2016) site occurrence records (n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable (n = 77), groundwater (n = 67), groundwater flashy (n = 175), perennial runoff (n = 141), runoff flashy (n = 255), and intermittent (n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.

Assessment of the real‐time pattern recognition capability of machine learning algorithms

AbstractNowadays data streams from different sources, like blockchain‐based and traditional financial transactions, social networks, and interconnected Internet of Things (IoT) devices, are becoming increasingly large in volume and the need to recognize patterns in real time from these streams, while adapting to their velocity and veracity, is emerging. Established machine learning algorithms used for pattern recognition methods have not been designed taking under account the volume, velocity, diversity, and accuracy of the data streams. This research contributes with an approach for assessing the pattern recognition capabilities of established machine learning algorithms when handling volatile data in real time and proposes a system that adapts the algorithms to the requirements of data streams, as well as assesses their pattern recognition capabilities based on established criteria. The system was applied for assessing five machine learning algorithms with input from a data stream from Bluetooth beacons tracking consumers in a retail store. This research can support future data scientists and analysts who need to reveal data patterns in big, volatile data streams in real time in order to support effective decision‐making in the respective application domain. Copyright © 2024 John Wiley & Sons, Ltd.

Mapping the foundations and evolution of career aspiration research: a bibliometric analysis

PurposeThis bibliometric literature review aims to uncover the (1) intellectual foundations and (2) topical evolution in the field of career aspiration research.Design/methodology/approachWe employed the systematic literature review methodology following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. According to the predetermined criteria, 286 articles were included from the Web of Science database. Then, we employed performance analysis, science mapping and natural language processing to address our research objectives.FindingsCo-citation analysis uncovered four foundational themes: (1) gender and leadership aspiration, (2) Social Cognitive Career Theory, (3) process of career development and (4) adolescents’ career aspiration. Moreover, co-word analysis showed that scholarly foci have shifted from adolescents’ career development to diverse streams. This shift was indicated by the exploration of additional research samples, such as university students, and topics related to specific aspirations (e.g. entrepreneurial and managerial aspirations) and gender issues (e.g. women leadership and gender stereotypes).Research limitations/implicationsAlthough this review has limitations related to data selection, it presents implications for practice, theory and future research on career aspirations.Originality/valueThe study illuminated the past and development of a research domain, thus advancing the understanding of career aspirations and inspiring future research.

Hidden results of functional diversity in macroinvertebrates: response of trait groups to flow intermittency in lowland streams

ABSTRACT As flow intermittency increases globally, understanding how macroinvertebrates respond at a functional level in streams becomes crucial for effective water management. This study investigates the functional response of macroinvertebrates to flow intermittency in Hungarian lowland streams over a 10-month period, comparing intermittent and perennial streams across the entire community, trait groups, and trait states. We aimed to demonstrate differences in functional diversity using metrics such as functional richness, evenness, divergence, dispersion, and Rao’s quadratic entropy. For trait groups, we focused on both response and stable traits, anticipating greater differences in response traits. Additionally, we identified which trait states, as indicated by community-weighted means, contribute significantly to variations between streams. Our findings indicate that functional diversity in intermittent streams is generally lower or equivalent to that in perennial streams, suggesting disparities in functional trait composition. Significant differences were observed within trait groups, indicating specific responses of trait groups to intermittency. We identified resilience trait states—including current velocity, reproduction, respiration, female wing length, locomotion and substrate relation, aquatic stages, and resistance forms—that contributed to these differences. The 3-level analysis revealed a hidden relationship: differences at the state level do not consistently manifest at the trait group or whole community levels. Investigating functional changes at the trait group or trait state level can serve as an early indicator of community shifts. Incorporating trait groups as indices into assessment frameworks could facilitate early detection of functional changes during bioassessment. We recommend considering trait groups when evaluating functional responses to flow intermittency in future studies.

Loss of native brown trout diversity in streams of the continental Croatia

Introduction: The genetic diversity of brown trout in the Western Balkans has been disrupted by the introduction of non-native Atlantic phylogenetic lineages and non-native haplotypes of the Danubian phylogenetic lineage. The Western Balkans is characterized by the greatest phenotypic and genotypic diversity of trout populations, and a large part of the internal territory belongs to the Black Sea basin, where the Danubian Da1 haplotype is native. Artificial propagation of non-native lineages in the Western Balkans has a long history, and these populations are often the only available material for stocking rivers attractive for fishing.Material and Methods: Fifteen populations in the Danube basin of the continental Croatia were analysed. The analysis of eight microsatellite loci was performed to determine the structure of brown trout populations, as well as the degree of introgression of non-native genetic material into the native.Results and Disscusion: The results of this study showed significant genetic similarity among brown trout populations, confirming a long history of introduction with non-native genetic material. The main reason was uncontrolled stocking with inadequate material, which is available in fish farms and consists mainly of brown trout of the Atlantic phylogenetic lineage. The results of this study also indicated stocking with brown trout of the non-native haplotypes of the Danubian phylogenetic lineage. The potential breeding origin of brown trout carrying the Danubian Da2 mtDNA haplotype and ways of its introduction into rivers have yet to be investigated. For the survival of the unique gene pool of brown trout in Croatian rivers, it is of fundamental importance to know the structure of wild and farmed populations with the aim of proposing and implementing conservation measures.

Fish diversity of Colombian Andes-Amazon streams at the end of conflict is a reference for conservation before increased land use.

Reference conditions are difficult to find in the Anthropocene but essential for effective biodiversity conservation. Aquatic ecosystems in the Andes-Amazon transition zone of Colombia are now at high risk due to expanded human activities after peace agreements in 2016 ended armed conflict because lands formerly controlled by FARC and other armed groups are now prone to agricultural and urban expansion. Particularly, expanding human land use may reduce fish diversity across the altitudinal gradient, especially in the premontane streams (i.e., <500 m a.s.l.) because lands are more amenable to human use than at greater altitudes. We evaluated fish α-diversity (measured as species richness, total abundance, and effective species number) and β-diversity (spatial and temporal) in 12 sites over 8 years bracketing the end of armed conflict. All α-diversity and β-diversity analyses were evaluated relative to categorical altitude (< or >500 m) and continuous altitude. Strong differences in fish community structure among sites occurred as a function of altitude. Fish communities exhibit altitudinal biodiversity gradients that are consistent in space and time, and that need to be accounted for conservation and management considerations. Our results provide a reference to identify short- and long-term changes due to impending human land use at a critical moment for the conservation of tropical fish diversity. Similar studies in other areas of the upper Amazon Basin are needed to evaluate effects of subsequent human activities on diversity patterns and our study area to compare to reference conditions reported here.

Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

A smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection.

Expanded, compressed, or equal? Interactions between spawning window and stream thermal regime generate three responses in modeled juvenile emergence for Pacific salmon

Climate warming poses challenges to native fish, particularly at high latitudes. We used incubation models to explore how interactions between spawning timing and daily varying water temperature affected emergence timing for five species of Pacific salmon in 33 thermally diverse streams in south-central Alaska. Interactions between spawning timing and stream thermal regime led to three different emergence timing responses: (1) “expanded” by typically 2–3 times the duration of the spawning window for summer spawning salmon at streams with a large annual water temperature range; (2) “equal” in duration to the spawning window, regardless of spawning timing, at streams with upwelling groundwater; and (3) “compressed” for late-spawning salmon where water temperature was cooler at spawning than at emergence. Across all sites, a ±15-day range in spawning timing had influence similar to anomalously warm winters (+2 °C to +3 °C) on the emergence timing window. Differences among species, spawning timing, and thermal regimes suggest that a range of adaptations in spawning behavior will likely enable Pacific salmon populations to accommodate shifting thermal regimes during their early life history.

Local environments, not invasive hybridization, influence cardiac performance of native trout under acute thermal stress.

Climate-induced expansion of invasive hybridization (breeding between invasive and native species) poses a significant threat to the persistence of many native species worldwide. In the northern U.S. Rocky Mountains, hybridization between native cutthroat trout and non-native rainbow trout has increased in recent decades due, in part, to climate-driven increases in water temperature. It has been postulated that invasive hybridization may enhance physiological tolerance to climate-induced thermal stress because laboratory studies indicate that rainbow trout have a higher thermal tolerance than cutthroat trout. Here, we assessed whether invasive hybridization improves cardiac performance response to acute water temperature stress of native wild trout populations. We collected trout from four streams with a wide range of non-native admixture among individuals and with different temperature and streamflow regimes in the upper Flathead River drainage, USA. We measured individual cardiac performance (maximum heart rate, "MaxHR", and temperature at arrhythmia, "ArrTemp") during laboratory trials with increasing water temperatures (10-28°C). Across the study populations, we observed substantial variation in cardiac performance of individual trout when exposed to thermal stress. Notably, we found significant differences in the cardiac response to thermal regimes among native cutthroat trout populations, suggesting the importance of genotype-by-environment interactions in shaping the physiological performance of native cutthroat trout. However, rainbow trout admixture had no significant effect on cardiac performance (MaxHR and ArrTemp) within any of the three populations. Our results indicate that invasive hybridization with a warmer-adapted species does not enhance the cardiac performance of native trout under warming conditions. Maintaining numerous populations across thermally and hydrologically diverse stream environments will be crucial for native trout to adapt and persist in a warming climate.

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

Establishing fluvial silicon regimes and their stability across the Northern Hemisphere

AbstractFluvial silicon (Si) plays a critical role in controlling primary production, water quality, and carbon sequestration through supporting freshwater and marine diatom communities. Geological, biogeochemical, and hydrological processes, as well as climate and land use, dictate the amount of Si exported by streams. Understanding Si regimes—the seasonal patterns of Si concentrations—can help identify processes driving Si export. We analyzed Si concentrations from over 200 stream sites across the Northern Hemisphere to establish distinct Si regimes and evaluated how often sites moved among regimes over their period of record. We observed five distinct regimes across diverse stream sites, with nearly 60% of sites exhibiting multiple regime types over time. Our results indicate greater spatial and interannual variability in Si seasonality than previously recognized and highlight the need to characterize the watershed and climate variables that affect Si cycling across diverse ecosystems.

Influence of land use, occupation, and riparian zones on zooplankton diversity in Cerrado streams

Influence of land use, occupation, and riparian zones on zooplankton diversity in Cerrado streams

A review on diverse streams of interface engineering for organic thin-film transistors

This review article provides a critical overview of the diverse technological streams of interface engineering formed in the field of OTFTs.

Open Design Communities: A bibliometric analysis of community-based management

Online collaboration growing significantly in the development of open-source hardware and software has led to a surge of research interest. However, no comprehensive bibliometric review has investigated the management of digital communities in these ecosystems. In this study, academic contributions to the field of online community management in open-source hardware and software were mapped, highlighting influential research streams and trends. A bibliometric review was conducted based on a keyword search analysis of research databases (IEEExplore, Scopus, ScienceDirect, Web of Science), with a sample comprising an overall 399 papers. The study identifies the most impactful articles in the field, maps the diverse streams of research on online collaboration and community management, visualizes focus areas and trends, and pinpoints areas for further investigation. These findings will support future research within this rapidly evolving domain.

Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia

With the advancement of technologies so does the data collection method which creates a large, rapid, and diverse stream of data. Statistic Indonesia (BPS) has also encouraged to utilize this by starting to collect geospatial information on respondents and public facilities. To keep up with this a change needs to be made in processing methods to accommodate massive, high-dimensional, and multiform data collected in different forms such as machine learning. This progression also opens up a new opportunity for tackling various statistical data problems such as accessibility and location data. Remote sensing is one of the big data sources that undergoes a lot of changes shown in the high spatial and temporal resolution satellite imagery availability, together with the BPS geotagging data shows great promise in classifying land use and geospatial analysis. Even so, there are still some challenges in remote sensing as well as other geospatial data utilization. The goals of this review paper are to study the opportunities and challenges in utilizing remote sensing, geospatial data, and machine learning for accessibility and location information. In this paper, we explore the possibilities and limitations in its implementation into SDGs indicators that involve accessibility and location such as indicators 9.1.1, 11.1.1, 11.2.1, 11.3.1, and 11.7.1 including other variables needed for the calculation like access to public facilities. Moreover, our experiment using geotagging data shows potential in improving proportion estimation when compared to using a simple ratio. Our DEGURBA following the UN definition using machine learning LULC for dasymetric mapping also provides more insight compared to the existing data. We can conclude that there are great opportunities in applying remote sensing and other geospatial data to monitor the accessibility and location to further sustainable development in Indonesia.