scholarly journals Influence plots and metrics: tools for better understanding fisheries catch-per-unit-effort standardizations

2011 ◽  
Vol 69 (1) ◽  
pp. 84-88 ◽  
Author(s):  
Nokome Bentley ◽  
Terese H. Kendrick ◽  
Paul J. Starr ◽  
Paul A. Breen
Keyword(s):  
New Zealand ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Marine Science ◽  
Catch Per Unit Effort ◽  
Common Procedure ◽  
Explanatory Variables ◽  
Unit Effort

Abstract Bentley, N., Kendrick, T. H., Starr, P. J., and Breen, P. A. 2012. Influence plots and metrics: tools for better understanding fisheries catch-per-unit-effort standardizations. – ICES Journal of Marine Science, 69: 84–88. Standardization of catch per unit effort using generalized linear models (GLMs) is a common procedure that attempts to remove the confounding effects of variables other than abundance. Simple plots and metrics are described to assist understanding the standardization effects of explanatory variables included in GLMs, illustrated with an example based on New Zealand trevally (Caranx lutescens) data.

Eels at the Edge: Science, Status, and Conservation Concerns

2009 ◽  
Keyword(s):  
New Zealand ◽  
Management System ◽  
Stock Assessment ◽  
Catch Per Unit Effort ◽  
Commercial Catch ◽  
The North ◽  
Anguilla Australis ◽  
Unit Effort ◽  
Access Rights ◽  
Quota Management

<em>Abstract.</em>—The New Zealand eel fishery comprises two species, the shortfin eel <em>Anguilla australis </em>and the New Zealand longfin eel <em>A. dieffenbachii</em>. A third species, the speckled longfin eel <em>A. reinhardtii</em>, is present in small numbers in some areas. Major fisheries in New Zealand are managed under the Quota Management System. Individual transferable quotas are set as a proportion of an annual total allowable commercial catch. The Quota Management System was introduced into the South Island eel fishery on 1 October 2000 and the North Island fishery on 1 October 2004. Freshwater eels have particular significance for customary Maori. Management policies allow for customary take and the granting of commercial access rights on introduction into the Quota Management System. Eel catches have remained relatively constant since the early 1970s. The average annual catch from 1989–1990 to 2001–2002 (fishing year) was 1,313 mt. Catch per unit effort remained constant from 1983 to 1989 and reduced from 1990 to 1999. Statistically significant declines in catch per unit effort for New Zealand longfin eel were found in some areas over the latter period. For management, an annual stock-assessment process provides an update on stock status.

More generalized linear modelling.

2021 ◽  
pp. 171-186
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Generalized Linear Models ◽  
Count Data ◽  
Binary Data ◽  
Linear Models ◽  
Explanatory Variable ◽  
Response Variable ◽  
Explanatory Variables ◽  
Continuous Response ◽  
Generalized Linear Modelling ◽  
Linear Modelling

Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.

Climate Influences on Meningitis Incidence in Northwest Nigeria

2014 ◽  
Vol 6 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Auwal F. Abdussalam ◽  
Andrew J. Monaghan ◽  
Vanja M. Dukić ◽  
Mary H. Hayden ◽  
Thomas M. Hopson ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Skill Score ◽  
Additive Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Explanatory Variables ◽  
Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

scholarly journals Generalized Linear Spatial Models to Predict Slate Exploitability

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Angeles Saavedra ◽  
Javier Taboada ◽  
María Araújo ◽  
Eduardo Giráldez
Keyword(s):  
Spatial Distribution ◽  
Generalized Linear Models ◽  
Spatial Dependence ◽  
Spatial Model ◽  
Linear Models ◽  
Spatial Models ◽  
Response Variable ◽  
Explanatory Variables ◽  
Model Residuals

The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality. Modelling the influence of these variables and analysing the spatial distribution of the model residuals yielded a GLSM that allows slate exploitability to be predicted more effectively than when using generalized linear models (GLM), which do not take spatial dependence into account. Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.

Predicting catch per unit effort from a multispecies commercial fishery in Port Phillip Bay, Australia

2020 ◽  
Vol 71 (4) ◽  
pp. 542
Author(s):  
Karina L. Ryan ◽  
Denny Meyer
Keyword(s):  
Time Series ◽  
Environmental Conditions ◽  
Environmental Variability ◽  
Moving Average ◽  
Commercial Fishing ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Explanatory Variables ◽  
Unit Effort ◽  
Stock Abundance

Quantitative models that predict stock abundance can inform stock assessments and adaptive management that allows for less stringent controls when abundance is high and environmental conditions are suitable, or tightening controls when abundance is low and environmental conditions are least suitable. Absolute estimates of stock abundance are difficult and expensive to obtain, but data from routine reporting in commercial fisheries logbooks can provide an indicator of stock status. Autoregressive integrated moving average (ARIMA) models were constructed using catch per unit effort (CPUE) from commercial fishing in Port Phillip Bay from 1978–79 to 2009–10. Univariate and multivariate models were compared for short-lived species (Sepioteuthis australis), and species represented by 1–2 year-classes (Sillaginodes punctatus) and 5–6 year-classes (Chrysophrys auratus). Simple transfer models incorporating environmental variables produced the best predictive models for all species. Multivariate ARIMA models are dependent on the availability of an appropriate time series of explanatory variables. This study demonstrates an application of time series methods to predict monthly CPUE that is relevant to fisheries for species that are short lived or vulnerable to fishing during short phases in their life history or where high intra-annual variation in stock abundance occurs through environmental variability.

scholarly journals Determinants of social startups in Italy

2021 ◽  
pp. 85-90
Author(s):  
Lucio Palazzo ◽  
Pietro Sabatino ◽  
Riccardo Ievoli
Keyword(s):  
Generalized Linear Models ◽  
Social Impact ◽  
Linear Models ◽  
Well Being ◽  
Explanatory Variables ◽  
Technological Value ◽  
Demographic Indicators ◽  
Italian Provinces ◽  
Tax Benefits ◽  
The Empirical Analysis

The so called "Startup Act" (Decree Law 179/2012, converted into Law 221/2012), has introduced in Italy the notion of innovative companies with a high technological value, denoted as the innovative startups. Among them, the Italian government includes the category of SIAVS ("Startup Innovative A Vocazione Sociale"), which represents a relatively new field of interest in both scientific and normative perspective. A social startup must satisfy the same requirement of other innovative startups, usually operating in sectors such as social assistance, education, health, social tourism and culture which can have a direct (social) impact on collective well-being. Furthermore, they must produce specific reporting of the produced social impact, enjoying also some tax benefits. In 2020 more than 200 SIAVS are registered in Italy, more than doubled with respect to 2015. This work is concerned with the empirical analysis of innovative companies focused in funding and implementing solutions to social, cultural, or environmental issues. Specifically, the aim of the paper is to investigate what are the relevant factors for the arise of SIAVS in Italy. The response variable is based on the number of active social startups in Italian provinces while the set of explanatory variables is composed by economic and demographic indicators at the provincial level. Generalized linear models (GLM) for discrete outcomes are applied and compared, even taking into account the zero-inflated issue arising due to the distribution of these particular data.

scholarly journals Generalized Linear Models outperform commonly used canonical analysis in estimating spatial structure of presence/absence data

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9777
Author(s):  
Lélis A. Carlos-Júnior ◽  
Joel C. Creed ◽  
Rob Marrs ◽  
Rob J. Lewis ◽  
Timothy P. Moulton ◽  
...  
Keyword(s):  
Model Selection ◽  
Spatial Structure ◽  
Generalized Linear Models ◽  
Species Interactions ◽  
Linear Models ◽  
Spatial Scales ◽  
Error Rates ◽  
Ecological Communities ◽  
Forward Selection ◽  
Explanatory Variables

Background Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios. Conclusion We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.

scholarly journals FAKTOR-FAKTOR PENTING YANG MEMPENGARUHI CPUE (Catch Per Unit Effort) PERIKANAN HUHATE BERBASIS DI BITUNG

2016 ◽  
Vol 22 (1) ◽  
pp. 43
Author(s):  
Agus Setiyawan ◽  
Lilis Sadiyah ◽  
Syarief Samsuddin
Keyword(s):  
Generalized Linear Model ◽  
Linear Models ◽  
Catch Per Unit Effort ◽  
Katsuwonus Pelamis ◽  
Fishing Vessels ◽  
Unit Effort ◽  
Landing Sites ◽  
Technical Factors ◽  
Live Bait ◽  
Logbook Data

<p>Bitung merupakan salah satu sentra pendaratan untuk perikanan huhate. Perikanan huhate bergantung terhadap ketersediaan umpan ikan hidup dan beberapa faktor teknis. Penelitian ini bertujuan untuk mengkaji faktor yang paling berpengaruh terhadap hasil tangkapan per upaya penangkapan (CPUE) ikan cakalang (<em>Katsuwonus pelamis - </em>SKJ). Pengambilan data primer dilaksanakan di atas kapal huhate dari Januari – Mei 2013 yang berbasis di Pelabuhan Perikanan Bitung – Sulawesi Utara. Data logbook kapal serta data harian kapal diperoleh pada saat melakukan pemancingan. Analisis data dilakukan dengan menggunakan analisis <em>Generalized Linear Models</em> (GLM), uji korelasi dan regresi sederhana. Hasil penelitian ini menunjukkan bahwa terdapat empat faktor signifikan berpengaruh terhadap nilai CPUE cakalang (SKJ). Faktor pertama adalah jenis umpan hidup yang digunakan berpengaruh secara signifikan terhadap CPUE SKJ (P&lt; 0,01). Jenis umpan hidup yang berpengaruh signifikan adalah jenis ikan layang dicampur dengan puri merah. Ketiga faktor lainya yaitu suhu permukaan laut (SPL), jumlah pemancing dan daerah penangkapan mempengaruhi CPUE SKJ dengan nilai P &lt; 0.05.</p><p><strong> </strong><em>Bitung is one of the main landing sites for pole and line fishing vessels. The<strong> </strong>pole and line fisheries depend on the availability of live fish bait and some technical factors. Objective of this study is to assess several factors that may influence catch per unit effort (CPUE) of skipjack (Katsuwonus pelamis – SKJ). Logbook data and record of daily vessel activities during fishing from January – May 2013 were used in the analysis. The data were analyzed using generalized linear model (GLM), correlation and regression. The results showed that type of live bait was significantly affect the SKJ CPUE (P&lt;0.01). Round scad (Decapterus spp) mixed with anchovy (Stelophorus spp) were giving higher SKJ CPUE as live bait. In addition, sea surface temperature, number of fishers, and fishing location also affect the SKJ CPUE with P &lt;0.05.   </em></p>

scholarly journals Algebraic geometry of Poisson regression

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Thomas Kahle ◽  
Kai-Friederike Oelbermann ◽  
Rainer Schwabe
Keyword(s):  
Algebraic Geometry ◽  
Generalized Linear Models ◽  
Poisson Regression ◽  
Linear Models ◽  
Optimal Designs ◽  
Local Optimality ◽  
Unknown Parameters ◽  
Explanatory Variables ◽  
Saturated Design ◽  
Poisson Counts

Designing experiments for generalized linear models is difficultbecause optimal designs depend on unknown parameters.  Here weinvestigate local optimality.  We propose to study for a given designits region of optimality in parameter space.  Often these regions aresemi-algebraic and feature interesting symmetries.  We demonstratethis with the Rasch Poisson counts model.  For any given interactionorder between the explanatory variables we give a characterization ofthe regions of optimality of a special saturated design. This extendsknown results from the case of no interaction.  We also give analgebraic and geometric perspective on optimality of experimentaldesigns for the Rasch Poisson counts model using polyhedral andspectrahedral geometry.

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