Individual variation in residency and regional movements of reef manta rays Mobula alfredi in a large marine protected area

Mobulid populations are declining on a global scale as a result of both targeted fisheries and indirect anthropogenic threats. In order to implement effective conservation strategies for species of this taxa, it is crucial that movement patterns at a range of spatiotemporal scales are defined. To gain insight into such patterns, we deployed a combination of acoustic (n = 21) and satellite (n = 12) tags on reef manta rays Mobula alfredi in the British Indian Ocean Territory Marine Protected Area (BIOT MPA) annually from 2013 through 2016. An extensive array of acoustic receivers (n = 52) were deployed across the archipelago to record the movements of mantas throughout the MPA. Data revealed large individual variation in horizontal movement patterns, ranging from high local site fidelity (<10 km) for up to 3 yr, to large-scale regional movements (>200 km) around the entire MPA. Depth time-series data recorded vertical movement patterns consistent with other epipelagic elasmobranch species, including oscillatory diving and deep dives to greater than 500 m. Though no individuals were directly recorded departing the MPA throughout the study, the gaps in detections and estimated travel speeds documented here indicate that movement of individuals outside of the BIOT MPA cannot be discounted. Collectively, our data suggests that, with effective enforcement, the current size of the BIOT MPA is providing substantial protection to its reef manta ray population. Characterization of movement patterns across ontogenetic classes, however, is required to fully characterize the spatial ecology of this species and ensure protection across all cohorts of the population.

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Preliminary insights into the spatial ecology and movement patterns of a regionally critically endangered skate (Rostroraja alba) associated with a marine protected area

Marine and Freshwater Behaviour and Physiology ◽

10.1080/10236244.2019.1705805 ◽

2019 ◽

Vol 52 (6) ◽

pp. 283-299

Author(s):

Inês Sousa ◽

Joffrey Baeyaert ◽

Jorge M. S. Gonçalves ◽

Karim Erzini

Keyword(s):

Protected Area ◽

Spatial Ecology ◽

Marine Protected Area ◽

Movement Patterns ◽

Critically Endangered

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The Classic Maya Collapse: Testing Class Conflict Hypotheses

American Antiquity ◽

10.2307/279284 ◽

1980 ◽

Vol 45 (2) ◽

pp. 246-267 ◽

Cited By ~ 25

Author(s):

Robert L. Hamblin ◽

Brian L. Pitcher

Keyword(s):

Large Scale ◽

Time Series Data ◽

Classic Maya ◽

Class Conflict ◽

Series Data ◽

Geographical Patterns ◽

Historical Societies ◽

Testing Class ◽

Lowland Area ◽

Maya Collapse

Several lines of archaeological evidence are presented in this paper to suggest the existence of class warfare among the Classic Maya and of issues that historically have been associated with class conflict. This evidence indicates that class warfare may have halted the rule of the monument-producing, or Classic, elites and precipitated the depopulation of the lowland area. The theory is evaluated quantitatively by testing for time-related mathematical patterns that have been found to characterize large-scale conflicts in historical societies. The information used in the evaluation involves the time series data on the duration of rule by Classic elites as inferred from the production of monuments with Long Count dates at a sample of 82 ceremonial centers. The analyses confirm that the Maya data do exhibit the temporal and geographical patterns predicted from the class conflict explanation of the Classic Maya collapse. Alternative predictions from the other theories are considered but generally not found to be supported by these data.

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Evidence Graphs: Supporting Transparent and FAIR Computation, with Defeasible Reasoning on Data, Methods and Results

10.1101/2021.03.29.437561 ◽

2021 ◽

Author(s):

Sadnan Al Manir ◽

Justin Niestroy ◽

Maxwell Adam Levinson ◽

Timothy Clark

Keyword(s):

Time Series ◽

Large Scale ◽

Time Series Data ◽

Predictive Analytics ◽

Defeasible Reasoning ◽

Series Data ◽

Inference Rules ◽

Deep Networks ◽

Evidence Graph ◽

Over Time

Introduction: Transparency of computation is a requirement for assessing the validity of computed results and research claims based upon them; and it is essential for access to, assessment, and reuse of computational components. These components may be subject to methodological or other challenges over time. While reference to archived software and/or data is increasingly common in publications, a single machine-interpretable, integrative representation of how results were derived, that supports defeasible reasoning, has been absent. Methods: We developed the Evidence Graph Ontology, EVI, in OWL 2, with a set of inference rules, to provide deep representations of supporting and challenging evidence for computations, services, software, data, and results, across arbitrarily deep networks of computations, in connected or fully distinct processes. EVI integrates FAIR practices on data and software, with important concepts from provenance models, and argumentation theory. It extends PROV for additional expressiveness, with support for defeasible reasoning. EVI treats any com- putational result or component of evidence as a defeasible assertion, supported by a DAG of the computations, software, data, and agents that produced it. Results: We have successfully deployed EVI for very-large-scale predictive analytics on clinical time-series data. Every result may reference its own evidence graph as metadata, which can be extended when subsequent computations are executed. Discussion: Evidence graphs support transparency and defeasible reasoning on results. They are first-class computational objects, and reference the datasets and software from which they are derived. They support fully transparent computation, with challenge and support propagation. The EVI approach may be extended to include instruments, animal models, and critical experimental reagents.

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Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

Security and Communication Networks ◽

10.1155/2021/6665984 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jing Zhao ◽

Shubo Liu ◽

Xingxing Xiong ◽

Zhaohui Cai

Keyword(s):

Time Series ◽

Privacy Protection ◽

Large Scale ◽

Differential Privacy ◽

Time Series Data ◽

Sliding Window ◽

Data Publishing ◽

Series Data ◽

Data Publication ◽

Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

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A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽

10.1108/sr-02-2018-0039 ◽

2019 ◽

Vol 39 (2) ◽

pp. 208-217 ◽

Cited By ~ 2

Author(s):

Jinghan Du ◽

Haiyan Chen ◽

Weining Zhang

Keyword(s):

Deep Learning ◽

Sensor Networks ◽

Large Scale ◽

Time Series Data ◽

Temporal Correlation ◽

Data Recovery ◽

Series Data ◽

Learning Method ◽

Content Type ◽

Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

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Movement patterns of a Critically Endangered elasmobranch ( Dipturus intermedius ) in a Marine Protected Area

Aquatic Conservation Marine and Freshwater Ecosystems ◽

10.1002/aqc.3753 ◽

2021 ◽

Author(s):

Edward Lavender ◽

Dmitry Aleynik ◽

Jane Dodd ◽

Janine Illian ◽

Mark James ◽

...

Keyword(s):

Protected Area ◽

Marine Protected Area ◽

Movement Patterns ◽

Critically Endangered

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How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽

10.3390/a13040095 ◽

2020 ◽

Vol 13 (4) ◽

pp. 95 ◽

Cited By ~ 1

Author(s):

Johannes Stübinger ◽

Katharina Adler

Keyword(s):

Time Series ◽

Large Scale ◽

Structural Breaks ◽

Time Series Data ◽

Consumer Price Index ◽

Real Data ◽

Linear Mapping ◽

Series Data ◽

Lag Effects ◽

Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

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