Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance. A case study on Atlantic salmon populations of Brittany

Estimation of abundance with wide spatio-temporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a Hierarchical Bayesian Modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates and fishing effort. Additional data of total or partial abundance collected in 4 closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied.

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Scottish Inshore Fisheries Integrated Data System (SIFIDS): work package (2) final report WP2A: development and pilot deployment of a prototypic autonomous fisheries data harvesting system, and WP2B: investigation into the availability and adaptability of novel technological approaches to data collection

10.15664/10023.23443 ◽

2019 ◽

Author(s):

R. Ayers ◽

G.P. Course ◽

G.R. Pasco

Keyword(s):

Data Collection ◽

Economic Value ◽

Fishing Effort ◽

Final Report ◽

Executive Summary ◽

Fishing Activity ◽

Business Decisions ◽

Fishing Vessels ◽

Spatio Temporal ◽

Data Deficient

[Extract from Executive Summary] To enhance sustainability and foster resilience within Scotland’s inshore fishing communities an effective system of collecting and sharing relevant data is required. To support business decisions made by vessel owners as well as informing fisheries managers and those involved in marine planning it will be vital to collect a range of information which will provide a robust understanding of fishing activity, the economic value of the sector and its importance within local communities. The SIFIDS Project was conceived to assist in attaining these goals by working alongside fishers to develop and test technology to automatically collect and collate data on board vessels, thereby reducing the reporting burden on fishers. The project built upon previous research funded through the European Fisheries Fund (EFF) and was designed to deliver a step change in the way that inshore fisheries in Scotland could be managed in cooperation with the industry. The project focussed on inshore fishing vessels around Scotland, where spatio-temporal information on the distribution of vessels and associated fishing effort is data deficient. The whole project was broken down into 12 highly integrated work packages. This is the integrated report for work packages 2A and 2B, entitled’ Development and Pilot Deployment of a Prototypic Autonomous Fisheries Data Harvesting System’ (2A) and ‘Investigation into the Availability and Adaptability of Novel Technological Approaches to Data Collection’ (2B).

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Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

Marine Ecology Progress Series ◽

10.3354/meps13211 ◽

2020 ◽

Vol 637 ◽

pp. 117-140 ◽

Cited By ~ 1

Author(s):

DW McGowan ◽

ED Goldstein ◽

ML Arimitsu ◽

AL Deary ◽

O Ormseth ◽

...

Keyword(s):

Temporal Dynamics ◽

Marine Ecosystem ◽

Gulf Of Alaska ◽

Data Series ◽

Limited Information ◽

Important Species ◽

Multiple Data ◽

Spatial And Temporal Dynamics ◽

Spawning Areas ◽

Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

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Integration of statistical and administrative agricultural data from Namibia

Statistical Journal of the IAOS ◽

10.3233/sji-200634 ◽

2021 ◽

pp. 1-22

Author(s):

Emily Berg ◽

Johgho Im ◽

Zhengyuan Zhu ◽

Colin Lewis-Beck ◽

Jie Li

Keyword(s):

Measurement Error ◽

Data Collection ◽

Administrative Data ◽

Statistical Data ◽

Data Sources ◽

Extension Programs ◽

Multiple Data ◽

Administrative Agencies ◽

Crop Area ◽

Using Data

Statistical and administrative agencies often collect information on related parameters. Discrepancies between estimates from distinct data sources can arise due to differences in definitions, reference periods, and data collection protocols. Integrating statistical data with administrative data is appealing for saving data collection costs, reducing respondent burden, and improving the coherence of estimates produced by statistical and administrative agencies. Model based techniques, such as small area estimation and measurement error models, for combining multiple data sources have benefits of transparency, reproducibility, and the ability to provide an estimated uncertainty. Issues associated with integrating statistical data with administrative data are discussed in the context of data from Namibia. The national statistical agency in Namibia produces estimates of crop area using data from probability samples. Simultaneously, the Namibia Ministry of Agriculture, Water, and Forestry obtains crop area estimates through extension programs. We illustrate the use of a structural measurement error model for the purpose of synthesizing the administrative and survey data to form a unified estimate of crop area. Limitations on the available data preclude us from conducting a genuine, thorough application. Nonetheless, our illustration of methodology holds potential use for a general practitioner.

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Preliminary observations on abundance and distribution of fish fauna in a canyon of the Bay of Biscay (ICES Division 8c)

Journal of the Marine Biological Association of the United Kingdom ◽

10.1017/s0025315420001265 ◽

2021 ◽

pp. 1-10

Author(s):

G. Diez ◽

L. Arregi ◽

M. Basterretxea ◽

E. Cuende ◽

I. Oyarzabal

Keyword(s):

Deep Sea ◽

Basque Country ◽

Fish Fauna ◽

Fishing Effort ◽

Fishing Gear ◽

Clear Trend ◽

Depth Ranges ◽

Abundance And Distribution ◽

Sea Fish ◽

Distribution Of Fish

Abstract The changes in abundance and biodiversity of deep-sea fish fauna are described based on an annual deep-water longline survey with data collected during the period 2015–2019 in the Basque Country continental Slope (ICES Division 8c). The sampling scheme included hauls in four 400 m strata, from 650–2250 m deep. The DST sensors installed in the main line have allowed us to set an accurate soak time for each haul, and they were used to calculate fishing effort and CPUE by haul. The catchability of the fishing gear indicated that 15% of the total hooks deployed in the five-year period were able to fish, and that the bottom longline was very effective in fishing a wide number of different species in all depth ranges. The fishing gear caught 14 different species of sharks (13 deepwater and one pelagic), two chimaeras and nine teleosts. The abundance and biomass registered on the hooks attached to the bottom were between three and four times higher than in the floating sections, and the highest CPUE and biomass were recorded between 1051–1450 m, from 2015 to 2017, and in the 1451–1850 m strata, but they do not show any clear trend throughout the five years of the series.

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Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

Renewable Energy ◽

10.1016/j.renene.2021.02.117 ◽

2021 ◽

Author(s):

Carlos A. Severiano ◽

Petrônio de Cândido de Lima e Silva ◽

Miri Weiss Cohen ◽

Frederico Gadelha Guimarães

Keyword(s):

Time Series ◽

Renewable Energy ◽

Energy Systems ◽

Fuzzy Time Series ◽

Renewable Energy Systems ◽

Spatio Temporal

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Proposal for Tower Crane Productivity Indicators Based on Data Analysis in the Era of Construction 4.0

Buildings ◽

10.3390/buildings11010021 ◽

2021 ◽

Vol 11 (1) ◽

pp. 21

Author(s):

Thomas Danel ◽

Zoubeir Lafhaj ◽

Anand Puppala ◽

Sophie Lienard ◽

Philippe Richard

Keyword(s):

Time Series ◽

Data Analysis ◽

Statistical Analysis ◽

Data Collection ◽

Structural Phase ◽

Sampling Technique ◽

Construction Site ◽

Data Logger ◽

Tower Crane ◽

Productivity Indicators

This article proposes a methodology to measure the productivity of a construction site through the analysis of tower crane data. These data were obtained from a data logger that records a time series of spatial and load data from the lifting machine during the structural phase of a construction project. The first step was data collection, followed by preparation, which consisted of formatting and cleaning the dataset. Then, a visualization step identified which data was the most meaningful for the practitioners. From that, the activity of the tower crane was measured by extracting effective lifting operations using the load signal essentially. Having used such a sampling technique allows statistical analysis on the duration, load, and curvilinear distance of every extracted lifting operation. The build statistical distribution and indicators were finally used to compare construction site productivity.

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Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

Earth Planets and Space ◽

10.1186/s40623-020-01293-0 ◽

2020 ◽

Vol 72 (1) ◽

Author(s):

Masayuki Kano ◽

Shin’ichi Miyazaki ◽

Yoichi Ishikawa ◽

Kazuro Hirahara

Keyword(s):

Time Series ◽

Data Assimilation ◽

Evaluation Method ◽

Temporal Evolution ◽

Recovery Process ◽

Postseismic Deformation ◽

Coseismic Slip ◽

Megathrust Earthquakes ◽

Spatio Temporal ◽

Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

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