scholarly journals Finding the signal in the noise: objective data-selection criteria improve the assessment of western Baltic spring-spawning herring

2009 ◽  
Vol 66 (8) ◽  
pp. 1673-1680 ◽  
Author(s):  
Mark R. Payne ◽  
Lotte Worsøe Clausen ◽  
Henrik Mosegaard
Keyword(s):  
Selection Criteria ◽  
Time Series Data ◽  
Stock Assessment ◽  
Clupea Harengus ◽  
Data Selection ◽  
Series Data ◽  
Distribution Area ◽  
Fish Stock ◽  
Objective Data ◽  
Spatial Coverage

Abstract Payne, M. R., Clausen, L. W., and Mosegaard, H. 2009. Finding the signal in the noise: objective data-selection criteria improve the assessment of western Baltic spring-spawning herring. – ICES Journal of Marine Science, 66: 1673–1680. In the art of fish-stock assessment, it is common practice to include all available data without properly testing their validity in terms of their signal-to-noise ratio. The western Baltic spring-spawning herring (Clupea harengus) stock has been historically difficult to assess in a reliable manner. The population is spread between the Skagerrak, Kattegat, the Danish islands, and the western Baltic, but the distribution depends on age and season. Although the distribution area is covered by five separate surveys, none covers the entire stock. Using all time-series data may cause high noise levels and could lead to a poor-quality assessment. We examine the temporal and spatial coverage of each survey in terms of current biological understanding of stock distribution and, employing the observed internal consistency between age classes within cohorts as additional criteria, select the most appropriate data subsets. Assessments based on the revised dataset show greatly improved quality in terms of both accuracy and precision. The results highlight the often-ignored principle that a judicious choice of input data, based on rational and justifiable selection criteria, can enhance the ultimate quality of a stock assessment.

scholarly journals Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Dimitrios M. Vlachogiannis ◽  
Yanyan Xu ◽  
Ling Jin ◽  
Marta C. González
Keyword(s):  
Air Quality ◽  
Time Series Data ◽  
Series Data ◽  
Pollution Transport ◽  
Concentration Data ◽  
Significant Drop ◽  
Pm 2.5 ◽  
Correlation Networks ◽  
Spatial Coverage ◽  
Haze Pollution

AbstractOver the last decades, severe haze pollution constitutes a major source of far-reaching environmental and human health problems. The formation, accumulation and diffusion of pollution particles occurs under complex temporal scales and expands throughout a wide spatial coverage. Seeking to understand the transport patterns of haze pollutants in China, we review a proposed framework of time-evolving directed and weighted air quality correlation networks. In this work, we evaluate monitoring stations’ time-series data from China and California, to test the sensitivity of the framework to region size, climate and pollution magnitude across multiple years (2014–2020). We learn that the use of hourly $$\hbox {PM}_{2.5}$$ PM 2.5 concentration data is needed to detect periodicities in the positive and negative correlations of the concentrations. In addition, we show that the standardization of the correlation function method is required to obtain networks with more meaningful links when evaluating the dispersion of a severe haze event at the North China Plain or a wildfire event in California during December 2017. Post COVID-19 outbreak in China, we observe a significant drop in the magnitude of the assigned weights, indicating the improved air quality and the slowed transport of $$\hbox {PM}_{2.5}$$ PM 2.5 due to the lockdown. To identify regions where pollution transport is persistent, we extend the framework, partitioning the dynamic networks and reducing the networks’ complexity through node subsampling. The end result separates the temporal series of $$\hbox {PM}_{2.5}$$ PM 2.5 in set of regions that are similarly affected through the year.

scholarly journals Growth Model and Forecasting Prices of Some Agricultural Products in Bangladesh

2021 ◽  
Vol 26 (2) ◽  
pp. 64-71
Author(s):  
Md Hossain
Keyword(s):  
Time Series ◽  
Growth Model ◽  
Selection Criteria ◽  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Trend Model ◽  
Exponential Method

The aim of this paper was to explore the appropriate deterministic time series model using the latest selection criteria considering the price pattern of onion, garlic and potato products in Bangladesh (January 2000 to December 2016). It appeared from our analysis that the time series data for the prices of potato was first order homogenous stationary but onion and garlic were found to be the second order stationary. Four different forecasting models namely, linear trend model, quadratic trend model, exponential growth model, and S-curve trend model were used to find the best fitted model for the prices of above mentioned products in the Bangladesh. Three accuracy measures such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used for the selection of the best fitted model based on lowest value of forecasting error. Lowest values of these errors indicated a best fitted model. After choosing the best growth model by the latest model selection criteria, prices of selected agricultural commodities were forecasted using the following time-series analysis methods: Simple Exponential Method, Double Exponential Method using the time period from January 2017 to December 2021. The findings of this study would be useful for policy makers, researchers, businessmen as well as producers in order to forecast future prices of these commodities.

Integrating data from multiple surveys and accounting for spatio-temporal correlation to index the abundance of juvenile Pacific halibut in Alaska

2017 ◽  
Vol 75 (2) ◽  
pp. 572-584 ◽  
Author(s):  
Kotaro Ono ◽  
James N Ianelli ◽  
Carey R McGilliard ◽  
André E Punt
Keyword(s):  
Survey Data ◽  
Stock Assessment ◽  
Temporal Correlation ◽  
The United States ◽  
Fish Stock ◽  
Sources Of Information ◽  
Considerable Uncertainty ◽  
Pacific Halibut ◽  
Spatial Coverage ◽  
Spatio Temporal

Abstract Survey indices of abundance are one of the main sources of information used in fish stock assessment. Many regions around the world, including the United States and Europe, develop survey protocols to aid in creating these indices. As ecosystems function as a continuum across borders, there is a need to develop a standardized framework for combining information across surveys. Such methods could help resolve differences in timing, spatial coverage, catchability, and selectivity among surveys. We present a method that uses survey data by length class. These data come from several regional surveys each with differing sampling designs, spatial and temporal coverage, and sampling gear. The method accounts for the spatio-temporal correlation structure in length-specific catch rates and occurrence, and allows for differences in catchability and selectivity among regions. The approach was applied to Pacific halibut (Hippoglossus stenolepis) in Alaska, a broadly distributed species for which there is considerable uncertainty in recent recruitment estimates. Results confirm the major recruitment event of 1987 recorded in the most recent stock assessment and also detected recruitment peaks in more recent years (1998 and 2004–2005). The signal seems to be mostly related to the Eastern Bering Sea shelf survey data. The approach introduced in this study is general and could be expanded to other regions and species where standardized survey data are collected that include extensive length and/or age measurements.

scholarly journals Changes in Nutrient Concentrations in Shenzhen Bay Detected Using Landsat Imagery between 1988 and 2020

2021 ◽  
Vol 13 (17) ◽  
pp. 3469
Author(s):  
Jingjing Huang ◽  
Difeng Wang ◽  
Fang Gong ◽  
Yan Bai ◽  
Xianqiang He
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Hong Kong ◽  
Time Series Data ◽  
Inorganic Nitrogen ◽  
Landsat Imagery ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Nutrient Concentrations ◽  
Spatial Coverage

Shenzhen Bay (SZB), situated between Shenzhen and Hong Kong, is a typical bay system. The water quality of the bay is notably affected by domestic and industrial discharge. Rivers and various types of drainage outlets carry terrestrial pollutants into SZB, resulting in elevated concentrations of nitrogen and phosphorous as well as relatively poor water quality. For over 200 years, Hong Kong has practiced oyster farming within brackish estuarine waters. Oyster farming is a type of mariculture which includes oyster breeding in oyster rafts. Remote sensing is a monitoring technique characterized by large spatial coverage, high traceability, and low cost, making it advantageous over conventional point-based and ship-borne monitoring methods. In this study, remote-sensing models were established using machine-learning algorithms to retrieve key water-quality factors (dissolved inorganic nitrogen (DIN) and orthophosphate-phosphorous (PO4_P) concentrations, CDIN and CPO4_P, respectively) from long-term time-series data acquired by the Landsat satellites. (1) Spatially, the water quality in Inner SZB was worse than that in Outer SZB. (2) The water quality temporarily deteriorated between the end of the 20th century and the beginning of the 21st century; then it gradually improved in the late 2000s. (3) Monitoring the water quality in an oyster-farming area revealed that oyster farming did not adversely affect the water quality. (4) The result of monitoring the water quality in river estuaries in SZB shows that water quality was mainly affected by river input.

scholarly journals Inferring Ecosystem Networks as Information Flows

2021 ◽  
Author(s):  
Jie Li ◽  
Matteo Convertino
Keyword(s):  
Species Interactions ◽  
Network Inference ◽  
Time Series Data ◽  
Ecosystem Model ◽  
Series Data ◽  
Fish Stock ◽  
Community Patterns ◽  
Predator Prey Model ◽  
Biodiversity Changes ◽  
Structural Networks

AbstractThe detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed Optimal Information Flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.

Semidiscrete biomass dynamic modeling: an improved approach for assessing fish stock responses to pulsed harvest events

2012 ◽  
Vol 69 (10) ◽  
pp. 1710-1721 ◽  
Author(s):  
Michael E. Colvin ◽  
Clay L. Pierce ◽  
Timothy W. Stewart
Keyword(s):  
Time Series Data ◽  
Reference Points ◽  
Series Data ◽  
Fish Stock ◽  
Parameter Estimates ◽  
Maximum Sustained Yield ◽  
Biomass Dynamic ◽  
Biomass Dynamics ◽  
Discrete Manner ◽  
Dynamics Models

Continuous harvest over an annual period is a common assumption of continuous biomass dynamics models (CBDMs); however, fish are frequently harvested in a discrete manner. We developed semidiscrete biomass dynamics models (SDBDMs) that allow discrete harvest events and evaluated differences between CBDMs and SDBDMs using an equilibrium yield analysis with varying levels of fishing mortality (F). Equilibrium fishery yields for CBDMs and SDBDMS were similar at low fishing mortalities and diverged as F approached and exceeded maximum sustained yield (FMSY). Discrete harvest resulted in lower equilibrium yields at high levels of F relative to continuous harvest. The effect of applying harvest continuously when it was in fact discrete was evaluated by fitting CBDMs and SDBDMs to time series data generated from a hypothetical fish stock undergoing discrete harvest and evaluating parameter estimates bias. Violating the assumption of continuous harvest resulted in biased parameter estimates for CBDM while SDBDM parameter estimates were unbiased. Biased parameter estimates resulted in biased biological reference points derived from CBDMs. Semidiscrete BDMs outperformed continuous BDMs and should be used when harvest is discrete, when the time and magnitude of harvest are known, and when F is greater than FMSY.

scholarly journals Distribution of Chlorophyll in Coastal Borneo Island Using Modis Terra Satellite Data

2021 ◽  
Vol 934 (1) ◽  
pp. 012011
Author(s):  
N F Yunita ◽  
M Usman ◽  
D Merdekawati
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Chlorophyll Concentration ◽  
Fishing Ground ◽  
Series Data ◽  
Distribution Area ◽  
Data Series ◽  
Aquatic Area ◽  
Marine Aquaculture ◽  
Chlorophyll Distribution

Abstract Clorophyll is the colour pigment most common found in phytoplankton. Its concentration is one of the indicator of the high of productivity of aquatic area, especially in coastal area. Information of chlorophyll concentration and distribution is very important to determine the suitable location of marine aquaculture and prediction of fishing ground. The aims of this research were to: 1) find out and analyze the concentration of chlorophyll and its distribution in Borneo Island Indonesia and 2) the pattern of chlorophyll distribution for each provinces using modis terra data for five years (from January 2016 to December 2020) in monthly and annually data series. In addition, it used Seadas 7.5.3 for data visualization. The result of this research showed that the chlorophyll concentration ranged 0,045 – 20 mg/m3 and clorophyll distribution affected by the location that seen in all variation data series. In annually time series data, the highest value of concentration shown by west borneo province and central borneo province with the distribution area were larger as well. The distribution of chlorophyll in monthly data showed almost same with annually data time series. The difference was just in large area distribution. The pattern of chlorophyll distribution also showed that in the west Kalimantan and central Kalimantan area had the highest values.

Density-Dependent Mortality and the Relationship between Size Composition and Fishing Effort in Lobster Populations

1986 ◽  
Vol 43 (11) ◽  
pp. 2360-2367 ◽  
Author(s):  
J. T. Addison
Keyword(s):  
Time Series Data ◽  
Stock Assessment ◽  
Size Composition ◽  
Fishing Effort ◽  
Series Data ◽  
Density Dependent ◽  
Density Dependent Mortality ◽  
Yield Per Recruit ◽  
The Relationship ◽  
Dependent Mortality

Examination of time series data from lobster fisheries shows that although in some cases fishing mortality (estimated from size composition data) is directly related to fishing effort, that of many stocks remains relatively unchanged despite changes in the pattern of exploitation. This suggests that conventional stock assessment models used for finfish populations may not be adequate for lobster populations. A model is described which incorporates into a standard yield per recruit model density-dependent mortality in large lobsters due to limited availability of suitable holes in the substrate. The model predicts that size composition may change little with changes in fishing effort, and it is shown that current stock assessment methods may fail to detect those changes. The underlying assumptions of current methods are undermined further by the possible delay of moulting of lobsters that cannot find shelters.

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