Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

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Modelling Floodplain River Modelling Floodplain River Fisheries – an Fisheries – an Introduction Introduction Training Workshop Training Workshop Materials Materials UK Department for International Development (DFID) UK Department for International Development (DFID) Fisheries Management Science Programme (FMSP) Fisheries Management Science Programme (FMSP) June 2005 June 2005 Ashley S. Halls Ashley S. Halls Aquae Sulis Ltd (ASL) Aquae Sulis Ltd (ASL) www.aquae-sulis-ltd.co.uk www.aquae-sulis-ltd.co.uk

description

Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials. UK Department for International Development (DFID) Fisheries Management Science Programme (FMSP) June 2005 Ashley S. Halls Aquae Sulis Ltd (ASL) www.aquae-sulis-ltd.co.uk. Background. - PowerPoint PPT Presentation

Transcript of Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

Page 1: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

Modelling Floodplain River Modelling Floodplain River Fisheries – an IntroductionFisheries – an Introduction

Training Workshop Training Workshop MaterialsMaterials

UK Department for International Development (DFID)UK Department for International Development (DFID)

Fisheries Management Science Programme (FMSP)Fisheries Management Science Programme (FMSP)

June 2005 June 2005

Ashley S. Halls Ashley S. Halls

Aquae Sulis Ltd (ASL)Aquae Sulis Ltd (ASL)

www.aquae-sulis-ltd.co.ukwww.aquae-sulis-ltd.co.uk

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BackgroundBackgroundThis presentation is one of a series of five presenting key This presentation is one of a series of five presenting key outputs froutputs from FMSP floodplain projects, carried out in the Asian om FMSP floodplain projects, carried out in the Asian region between 1992 and 2005. The five papers focus on:region between 1992 and 2005. The five papers focus on:

– General management guidelines for floodplain river fisheries General management guidelines for floodplain river fisheries (as published in FAO Fisheries Technical Paper 384/1)(as published in FAO Fisheries Technical Paper 384/1)

– Selection and management of harvest reserves (key Selection and management of harvest reserves (key messages)messages)

– Materials for a training course on harvest reservesMaterials for a training course on harvest reserves– Flood Control Impacts on Fisheries: Guidelines for Mitigation Flood Control Impacts on Fisheries: Guidelines for Mitigation – Modelling floodplain river fisheriesModelling floodplain river fisheries

This presentation was prepared by FMSP Project R8486 – This presentation was prepared by FMSP Project R8486 – ‘‘Promotion of FMSP guidelines for floodplain fisheries Promotion of FMSP guidelines for floodplain fisheries management and sluice gate control’management and sluice gate control’

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IntroductionIntroduction

The following materials are provided for adaptation or use in The following materials are provided for adaptation or use in workshops aimed at building awareness of the range of models and workshops aimed at building awareness of the range of models and approaches that can be used to guide the management of floodplain-approaches that can be used to guide the management of floodplain-river fisheries resources. river fisheries resources.

The models and approaches described here were either developed or The models and approaches described here were either developed or applied under research projects funded under the Fisheries applied under research projects funded under the Fisheries Management Science Programme of the UK Government’s Management Science Programme of the UK Government’s Department for International Development (DfID).Department for International Development (DfID).

Further details of the empirical models and methodologies described Further details of the empirical models and methodologies described here can be found in Section 14 of Hoggarth et al (in press) which here can be found in Section 14 of Hoggarth et al (in press) which may be provided as a handout. may be provided as a handout.

Other relevant papers, reports and sources of information are Other relevant papers, reports and sources of information are provided under each section.provided under each section.

Full references and URLs are provided at the end of this Full references and URLs are provided at the end of this presentation.presentation.

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IntroductionIntroduction– What are modelsWhat are models

– Types of modelsTypes of models

– Purpose of modelsPurpose of models

– Further ReadingFurther Reading

1. Empirical models1. Empirical models1.1. Linear models1.1. Linear models

1.1.1. Simple Linear Regression1.1.1. Simple Linear Regression

1.1.2. Multiple Linear Regression (MLR) and General Linear Models 1.1.2. Multiple Linear Regression (MLR) and General Linear Models (GLM)(GLM)

1.2. Non-linear models1.2. Non-linear models

1.2.1 Empirical surplus production models (Non-linear regression)1.2.1 Empirical surplus production models (Non-linear regression)

1.2.2. Bayesian Networks (BNs)1.2.2. Bayesian Networks (BNs)

2. Population Dynamics Models2. Population Dynamics Models

2.1. Age structured Populations Dynamics (ASPD) 2.1. Age structured Populations Dynamics (ASPD)

2.1.1 Dynamic Pool Model for Floodplain Fisheries2.1.1 Dynamic Pool Model for Floodplain Fisheries

2.1.2 BEAM 42.1.2 BEAM 4

ContentContent

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What are models?What are models?

Models are quantitative descriptions of processes or Models are quantitative descriptions of processes or relationships among variables.relationships among variables.

Types of ModelTypes of Model

In the context of fisheries management, models can be In the context of fisheries management, models can be divided into 2 main categories:divided into 2 main categories:

Empirical ModelsEmpirical Models. These are simply statistical . These are simply statistical descriptions of the observed relationships among two or descriptions of the observed relationships among two or more variables of interest.more variables of interest.

Population dynamics modelsPopulation dynamics models. These attempt to explicitly . These attempt to explicitly model the dynamics of fish populations based upon model the dynamics of fish populations based upon established theories of population behaviour, and established theories of population behaviour, and biological and ecological processes.biological and ecological processes.

IntroductionIntroduction

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IntroductionIntroductionPurpose of ModelsPurpose of Models

• Models are used to make predictions about, or improve Models are used to make predictions about, or improve understanding of the response of important understanding of the response of important dependentdependent variables to changes in variables to changes in independentindependent variables. variables.

• DependentDependent variables are often referred to as variables are often referred to as responseresponse, , output output or or performanceperformance variables and typically include catch, variables and typically include catch, indices of abundance, income…etc.indices of abundance, income…etc.

• IndependentIndependent variables are often referred to as variables are often referred to as inputinput or or explanatoryexplanatory variables. Examples include fishing effort, variables. Examples include fishing effort, stocking density, environmental variables (e.g. flood extent)stocking density, environmental variables (e.g. flood extent)…etc.…etc.

Further Reading: Haddon (2001).Further Reading: Haddon (2001).

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Empirical Models: Simple Linear ModelsEmpirical Models: Simple Linear Models

• Simple empirical models are frequently used in floodplain river fisheries Simple empirical models are frequently used in floodplain river fisheries to describe the linear relationships between two variables of interest. to describe the linear relationships between two variables of interest.

• They are typically fitted to estimates of annual catch and a variety of They are typically fitted to estimates of annual catch and a variety of different explanatory variables (e.g. resource area, fishing effort, different explanatory variables (e.g. resource area, fishing effort, hydrological indices…etc) using linear regression methods.hydrological indices…etc) using linear regression methods.

• Variables are often first logVariables are often first logee transformed to ensure that the normality transformed to ensure that the normality assumptions behind the regression method are met.assumptions behind the regression method are met.

• When few or no estimates of annual catch are available for a given When few or no estimates of annual catch are available for a given fishery or management location, some estimate of potential yield may fishery or management location, some estimate of potential yield may be obtained by comparing estimates reported for other river fisheries or be obtained by comparing estimates reported for other river fisheries or management sites in relation to common explanatory variables such as management sites in relation to common explanatory variables such as resource area.resource area.

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Empirical Models: Simple Linear ModelsEmpirical Models: Simple Linear Models

• Models based upon such Models based upon such among fishery comparisonsamong fishery comparisons can provide planners and can provide planners and policy makers with an policy makers with an approximateapproximate indication of the indication of the potential yield from the river potential yield from the river fishery.fishery.

• Figure 1 illustrates the Figure 1 illustrates the relationship between logrelationship between logee catch and floodplain area for catch and floodplain area for Asian river systems reported Asian river systems reported by the DfID-funded Project by the DfID-funded Project R5030 (see MRAG 1993; R5030 (see MRAG 1993; 1994 and Halls 1999). 1994 and Halls 1999).

0 5 10 15

ln floodplain area (km2)

0

5

10

15

ln c

atc

h (

t y-1

)-2 3 8 13

ln lake area (km2)

0

5

10

15

ln c

atc

h (

t y-1

)

Figure 1. Potential yield from Asian floodplain rivers plotted as a function of floodplain area with fitted regression lines on loge transformed scales.

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Empirical Models: Simple Linear Empirical Models: Simple Linear ModelsModels• Details of this and other best fitting models for predicting Details of this and other best fitting models for predicting

annual catches from tropical river fisheries developed under annual catches from tropical river fisheries developed under R5030 are summarised in Table 1 below, together with R5030 are summarised in Table 1 below, together with guidance for estimating 95% confidence intervals around guidance for estimating 95% confidence intervals around the predictions. the predictions.

Table 1 Summary of the best fitting regression models for Table 1 Summary of the best fitting regression models for predicting multispecies potential yield from floodplain-river predicting multispecies potential yield from floodplain-river systems where systems where aa and and bb are the constant and slope parameters of are the constant and slope parameters of the linear regression model: the linear regression model: Y = a + bxY = a + bx, and where , and where n n is the is the number of observations, R is the correlation coefficient, and P is number of observations, R is the correlation coefficient, and P is the probability that the slope parameter, the probability that the slope parameter, bb = 0. = 0. SSbb is the standard is the standard error of the estimate of the slope coefficient, error of the estimate of the slope coefficient, bb, is the , is the residual mean square, and is the mean value of the residual mean square, and is the mean value of the observations of the explanatory variable.observations of the explanatory variable.

Relationship Continent a b Sb n X XYs .2 R P

ln catch vs ln FPA Asia 2.086 0.996 0.083 13 4.31 0.531 0.97 <0.001 ln catch vs ln length Asia -14.88 3.234 0.585 5 8.06 0.680 0.96 0.01 ln catch vs ln DBA S. America -3.60 0.936 0.218 15 12.87 1.457 0.77 0.001

X

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Empirical Models: Simple Linear Empirical Models: Simple Linear ModelsModels

Prediction intervals for yield corresponding to new Prediction intervals for yield corresponding to new observations of X, is given by:observations of X, is given by:

where where is the standard error of the estimate given by: is the standard error of the estimate given by:

where is the residual mean square (the variance of Y where is the residual mean square (the variance of Y after taking into account the dependence of Y on X), and Safter taking into account the dependence of Y on X), and Sbb is the standard error of the estimate of the slope is the standard error of the estimate of the slope coefficient, b (Zar 1984, p272-275).coefficient, b (Zar 1984, p272-275).

}ˆ{)2-;2/-1(ˆnewnew YsntY

}ˆ{ newYs

2

.2

2

.2

)(

)-(11}ˆ{

b

XY

iXYnew

S

s

XX

nsYs

XYs .2

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Empirical Models: Simple Linear Empirical Models: Simple Linear ModelsModels

ApplicationsApplications

• Generally speaking, these types of models provide only very Generally speaking, these types of models provide only very impreciseimprecise predictions because of the significant measurement error predictions because of the significant measurement error associated with the potential yield estimates used to fit the models. associated with the potential yield estimates used to fit the models.

• Potential yields are often estimated using (i) the Generalised Potential yields are often estimated using (i) the Generalised Fishery Development Model (GFDM) approach described by Fishery Development Model (GFDM) approach described by Grainger and Garcia (1996), (ii) as the average annual catch value, Grainger and Garcia (1996), (ii) as the average annual catch value, or worst (iii) from a single observation, all of which are subject to or worst (iii) from a single observation, all of which are subject to potentially significant measurement and estimation error (no potentially significant measurement and estimation error (no account is taken of fishing effort). account is taken of fishing effort).

• The utility of these models is therefore restricted to providing a The utility of these models is therefore restricted to providing a rough indication of the likely potential of the fishery for policy and rough indication of the likely potential of the fishery for policy and development planning purposes.development planning purposes.

• Note that whilst the examples illustrated above are based upon Note that whilst the examples illustrated above are based upon comparisons across wide geographical scales, this modeling comparisons across wide geographical scales, this modeling approach may be equally, if not more, relevant on a more approach may be equally, if not more, relevant on a more local local scalescale, particularly in the context of , particularly in the context of adaptive co-managementadaptive co-management. . (see (see Co-management guidelines presentation)Co-management guidelines presentation)

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Database ResourceDatabase Resource

• Estimates of potential yield for lakes and rivers, and a wide Estimates of potential yield for lakes and rivers, and a wide range of corresponding habitat variables (e.g. resource range of corresponding habitat variables (e.g. resource area, indices of primary productivity and hydrological area, indices of primary productivity and hydrological variables) have been compiled by Project R5030 from the variables) have been compiled by Project R5030 from the literature and entered into a “Lakes and Rivers Database”. literature and entered into a “Lakes and Rivers Database”. This database resource will shortly become available on a This database resource will shortly become available on a CD published by FAO (see Dooley at al. in press).CD published by FAO (see Dooley at al. in press).

Other ModelsOther Models

• Welcomme (1985; 2001) contain other examples of linear Welcomme (1985; 2001) contain other examples of linear empirical models for predicting fish yields and species empirical models for predicting fish yields and species richness in tropical river basins.richness in tropical river basins.

Empirical Models: Simple Linear ModelsEmpirical Models: Simple Linear Models

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• When the response of a variable (e.g. annual catch) to two or more When the response of a variable (e.g. annual catch) to two or more independent variables (covariates) is of interest (e.g. floodplain area independent variables (covariates) is of interest (e.g. floodplain area and annual rainfall), then multiple linear regression (MLR) methods and annual rainfall), then multiple linear regression (MLR) methods would be applicable. would be applicable.

• When using MLR methods it is important to ensure that the When using MLR methods it is important to ensure that the explanatory variables included in the model are indeed independent explanatory variables included in the model are indeed independent to avoid spurious results (e.g. rainfall and flooded area are unlikely to avoid spurious results (e.g. rainfall and flooded area are unlikely to be independent).to be independent).

• It is generally recommend that you should have at least 10 to 20 It is generally recommend that you should have at least 10 to 20 times as many observations (cases, respondents) as you have times as many observations (cases, respondents) as you have variables, otherwise the estimates of the regression line are variables, otherwise the estimates of the regression line are probably very unstable and unlikely to replicate if you were to probably very unstable and unlikely to replicate if you were to repeat the study. repeat the study.

• Beware of automatic (forward and backward) stepwise fitting Beware of automatic (forward and backward) stepwise fitting methods. It is often safer to employ a manual backward stepwise methods. It is often safer to employ a manual backward stepwise fit, starting with all the variables in the model and then dropping the fit, starting with all the variables in the model and then dropping the least significant variables in turn. For unbalanced designs, it is often least significant variables in turn. For unbalanced designs, it is often necessary to return dropped variables to the model to determine the necessary to return dropped variables to the model to determine the effect of different combinations of variables. effect of different combinations of variables.

• Further useful guidance on fitting MLRs can be found at Further useful guidance on fitting MLRs can be found at http://www.statsoft.com/textbook/stmulreg.htmlhttp://www.statsoft.com/textbook/stmulreg.html

Empirical Models: Multiple Linear Regression Empirical Models: Multiple Linear Regression (MLR)(MLR)

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Empirical Models: General Linear Models Empirical Models: General Linear Models (GLM)(GLM)

• Sometimes researchers are interested in understanding the Sometimes researchers are interested in understanding the effects of both effects of both factorsfactors (categorical variables) and (categorical variables) and covariatescovariates (scale variable) on dependent variables such as catch or CPUE.(scale variable) on dependent variables such as catch or CPUE.

• This is often the case in the context of This is often the case in the context of adaptive or co-adaptive or co-managementmanagement when opportunities frequently exist to compare when opportunities frequently exist to compare the outcomes (performance) of local management activities the outcomes (performance) of local management activities among sites. These comparisons can generate among sites. These comparisons can generate lessons of lessons of success and failuresuccess and failure which can be used to adapt management which can be used to adapt management plans accordingly (see Halls et al 2002 and the accompanying plans accordingly (see Halls et al 2002 and the accompanying co-management guidelines presentation). co-management guidelines presentation).

• Examples of important factors of interest might include:Examples of important factors of interest might include:– Community based management: Present (1), absent(0).Community based management: Present (1), absent(0).– Management: Gear bans (1); closed seasons (2); reserves Management: Gear bans (1); closed seasons (2); reserves

(3).(3).– Extent of poaching: low (0); medium (1); high (2).Extent of poaching: low (0); medium (1); high (2).

• Examples of covariates might include:Examples of covariates might include:– Fishing intensityFishing intensity– Ratios describing the morphological characteristics of Ratios describing the morphological characteristics of

waterbodies (eg dry season area: flood season area)waterbodies (eg dry season area: flood season area)– Indices of flooding extent and duration.Indices of flooding extent and duration.

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• In this case, the use of the In this case, the use of the General Linear Models (GLM)General Linear Models (GLM) approach would be applicable.approach would be applicable.

• GLMs are similar to regression models but can deal with GLMs are similar to regression models but can deal with both factors (fixed and random) and covariates. both factors (fixed and random) and covariates. The factor The factor variables effectively divide the population into groups.variables effectively divide the population into groups.

• Detailed guidelines for building interdisciplinary GLMs for Detailed guidelines for building interdisciplinary GLMs for small scale fisheries have been developed by R7834 (see small scale fisheries have been developed by R7834 (see Halls et al 2002 and Hoggarth et al. in press). Halls et al 2002 and Hoggarth et al. in press).

• These include examples of models fitted to data compiled These include examples of models fitted to data compiled from co-management projects worldwide, as well as from co-management projects worldwide, as well as guidance on guidance on identifying sampling units, important variables, identifying sampling units, important variables, data levels and cleaning, exploratory analysis, sample data levels and cleaning, exploratory analysis, sample sizes, sensitivity analysis…etc. sizes, sensitivity analysis…etc.

• More general guidance on GLMs can be found in McCullagh More general guidance on GLMs can be found in McCullagh & Nelder (1989).& Nelder (1989).

Empirical Models: General Linear Models Empirical Models: General Linear Models (GLMs)(GLMs)

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Empirical Models: Empirical Models: Non-linear modelsNon-linear models

• Non-linear models are fitted when Non-linear models are fitted when relationship between two variables is relationship between two variables is not linear, or cannot be ‘linearised’ not linear, or cannot be ‘linearised’ by means of data transformations.by means of data transformations.

• Typically, models are fitted using Typically, models are fitted using non-linear least squares methodsnon-linear least squares methods or or other more sophisticated methods other more sophisticated methods e.g. maximum likelihood methods e.g. maximum likelihood methods and Bayesian estimation.and Bayesian estimation.

• For example, Halls et al (2002) fitted For example, Halls et al (2002) fitted a non-linear modified Fox surplus a non-linear modified Fox surplus production model using non-linear production model using non-linear least squares to estimates of catch least squares to estimates of catch per unit area (CPUA) and fisherman per unit area (CPUA) and fisherman density assembled from a number of density assembled from a number of floodplain rivers to provide some floodplain rivers to provide some estimate of fishing intensity estimate of fishing intensity corresponding to maximum yield. corresponding to maximum yield. (see Figure 2). (see Figure 2).

0 2 4 6 8

Square root number of fishers km -2

-1

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1

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4

Ln C

PUA (t onn

es km

-2 yr

-1)

0 2 4 6 8-1

0

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2

3

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0 2 4 6 8-1

0

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4

0 2 4 6 8-1

0

1

2

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4

Figure 2 CPUA vs. fisher density for floodplain rivers with fitted Fox model. Africa ( • ); Asia ( ▲ ); and South America ( ■ ). R2 = 0.80. Note axis scaling. Source: Halls et al (2002)

The model predicts a maximum The model predicts a maximum yield of 132 kg hayield of 132 kg ha-1-1 yr yr-1-1 at a at a fisher density of about 12 fisher density of about 12 fishers kmfishers km-2-2..

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Empirical Models: Empirical Models: Non-linear modelsNon-linear models

• Further details of this and other related models can be Further details of this and other related models can be found in Halls et al (2002) and Hoggarth et al (in press)found in Halls et al (2002) and Hoggarth et al (in press)..

• Further advice on fitting non-linear models for fisheries Further advice on fitting non-linear models for fisheries applications may be found in Chapter 6 of Hilborn & Walters applications may be found in Chapter 6 of Hilborn & Walters (1992) and Section 3.3 of Haddon (2001).(1992) and Section 3.3 of Haddon (2001).

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Empirical Models: Empirical Models: :: Bayesian networks (BNs)Bayesian networks (BNs)

• Unlike GLMs that deal with quantitative dependent (response) Unlike GLMs that deal with quantitative dependent (response) variables, variables, Bayesian networks (BNs)Bayesian networks (BNs) provide opportunities to model provide opportunities to model more qualitative (categorical) response variables such as equity, more qualitative (categorical) response variables such as equity, compliance, empowerment etc. compliance, empowerment etc.

• BNs comprise nodes (random variables) connected by directed links. BNs comprise nodes (random variables) connected by directed links. Prior probabilities assigned to each link (established via tables of Prior probabilities assigned to each link (established via tables of conditional probabilities) determine the status of each node.conditional probabilities) determine the status of each node.

• Conditional probabilities can be generated from cross-tabulations of Conditional probabilities can be generated from cross-tabulations of the data the data oror by using subjective probabilities encoded from expert by using subjective probabilities encoded from expert opinions. opinions.

• BNs are able to model complex and intermediate pathways of BNs are able to model complex and intermediate pathways of causality in a very visual and interactive manner to improve causality in a very visual and interactive manner to improve understanding of co-management systems and fisher behavior. understanding of co-management systems and fisher behavior.

• BNs can also be used as a management tool or expert system for BNs can also be used as a management tool or expert system for diagnosing strengths and weaknesses among co-management units diagnosing strengths and weaknesses among co-management units and for exploring ‘what if’ scenarios. and for exploring ‘what if’ scenarios.

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Empirical Models: Empirical Models: :: Bayesian networks Bayesian networks (BNs)(BNs)

• Netica software for constructing BNs is user-friendly, Netica software for constructing BNs is user-friendly, inexpensive, and easy to learn. inexpensive, and easy to learn. http://www.norsys.com/http://www.norsys.com/

• Further information about BNs together with detailed Further information about BNs together with detailed guidelines for their construction (with examples) can be guidelines for their construction (with examples) can be found in Halls et al (2002) and Chapter 14 of Hoggarth et al found in Halls et al (2002) and Chapter 14 of Hoggarth et al (in press)(in press)..

Further Reading: Cowell et al (1999).Further Reading: Cowell et al (1999).

Web resources:Web resources:

http://en.wikipedia.org/wiki/Bayes'_theoremhttp://en.wikipedia.org/wiki/Bayes'_theorem

http://en.wikipedia.org/wiki/Bayesian_inferencehttp://en.wikipedia.org/wiki/Bayesian_inference

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• Age-structured population dynamics (ASPD) models apply growth Age-structured population dynamics (ASPD) models apply growth and mortality rates to individual cohorts (age-groups) recruited to and mortality rates to individual cohorts (age-groups) recruited to the fishery in order to determine how the overall population the fishery in order to determine how the overall population number or biomass will respond to age- or size-dependent rates of number or biomass will respond to age- or size-dependent rates of exploitation or management interventions. exploitation or management interventions.

• These types of models are often referred to as These types of models are often referred to as Dynamic Pool Dynamic Pool Models.Models.

• Project R5953 modified a basic ASPD to include density-dependent Project R5953 modified a basic ASPD to include density-dependent growth, mortality and recruitment to explore the effects of growth, mortality and recruitment to explore the effects of hydrological modification (flood control) and management hydrological modification (flood control) and management interventions of floodplain fishery yieldsinterventions of floodplain fishery yields. This . This Dynamic Pool Model Dynamic Pool Model for Floodplain Fisheriesfor Floodplain Fisheries is described in detail by (Halls et al 2001). is described in detail by (Halls et al 2001).

• A combination of hydrological conditions and age-dependent A combination of hydrological conditions and age-dependent fishing mortality rates drives changes to numerical and biomass fishing mortality rates drives changes to numerical and biomass density. These in turn effect rates of recruitment, growth and density. These in turn effect rates of recruitment, growth and natural mortality (Figure 3).natural mortality (Figure 3).

Population Dynamics Models: ASPD Population Dynamics Models: ASPD ModelsModels

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Hydrology

Population Density w

Growth

Natural Mortality

Biomass w

Recruitment

Natural Losses

Biomass w +1

Fishing Mortality Yield

Fishing Effort Catchability

Hydrology

Population Density w

Growth

Natural Mortality

Biomass w

Recruitment

Natural Losses

Biomass w +1

Fishing Mortality Yield

Fishing Effort Catchability

Figure 3 Figure 3 Schematic representation of the population model illustrating the Schematic representation of the population model illustrating the processes by which the biomass in week w becomes the biomass in the following processes by which the biomass in week w becomes the biomass in the following week, w+1. The weekly process is repeated for the 52 weeks of the year, after week, w+1. The weekly process is repeated for the 52 weeks of the year, after which recruitment, determined by the surviving spawning stock biomass, is added which recruitment, determined by the surviving spawning stock biomass, is added at the end of week 52. Solid lines indicate direct influences or operations and at the end of week 52. Solid lines indicate direct influences or operations and broken lines indirect influences or occasional operations. Source: Halls et al (2001)broken lines indirect influences or occasional operations. Source: Halls et al (2001)

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Population Dynamics Models: ASPD Population Dynamics Models: ASPD ModelsModels

• The model has been fitted to landings of a small but The model has been fitted to landings of a small but widely abundant cyprinid, widely abundant cyprinid, Puntius sophorePuntius sophore in Northwest in Northwest Bangladesh (see Halls et al 2001). This species is Bangladesh (see Halls et al 2001). This species is abundant throughout Bangladesh and southern Asia, and abundant throughout Bangladesh and southern Asia, and shares similar life history characteristics with shares similar life history characteristics with HenicorhynchusHenicorhynchus species that dominate catches in the species that dominate catches in the Tonle Sap and Lower Mekong rivers.Tonle Sap and Lower Mekong rivers.

• A simple hydrological model was used to generate A simple hydrological model was used to generate weekly estimates of flooded area and volume required weekly estimates of flooded area and volume required as an input to the model.as an input to the model.

• The model was used to explore the potential effects The model was used to explore the potential effects (benefits) of water level management within a flood (benefits) of water level management within a flood control scheme and introducing closed seasons (to control scheme and introducing closed seasons (to reduce overall effort) on fisheries yield. reduce overall effort) on fisheries yield.

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• The results indicated that The results indicated that beyond a flood water height (~ beyond a flood water height (~ 9m at the study site) fish 9m at the study site) fish production is determined production is determined mostly by dry season water mostly by dry season water levels with production levels with production increasing almost linearly with increasing almost linearly with increasing mean dry season increasing mean dry season water levels (Figure 4).water levels (Figure 4).

• The model predicted that yield The model predicted that yield can be improved by can be improved by retaining retaining moremore water during the water during the dry dry seasonseason. .

• Lost yield arising from the Lost yield arising from the reductions in flood season reductions in flood season water heights caused by flood water heights caused by flood control embankments could be control embankments could be compensated by compensated by increasing increasing the dry season water levelsthe dry season water levels (volumes) on modified (volumes) on modified floodplains. floodplains.

Figure 4. Isopleths of yield kg haFigure 4. Isopleths of yield kg ha--

11yy-1-1 for for P. sophoreP. sophore in response to in response to different combinations of dry and different combinations of dry and flood season water levels. Source: flood season water levels. Source: Halls et al (2001).Halls et al (2001).

Population Dynamics Models: ASPD Population Dynamics Models: ASPD ModelsModels

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Population Dynamics Models: ASPD Population Dynamics Models: ASPD ModelsModels• Closing the fishery during any month of the year was predicted to Closing the fishery during any month of the year was predicted to

increase production by at least 30% (fishery was heavily over-increase production by at least 30% (fishery was heavily over-exploited). exploited).

• Annual production was found to be maximized by removing ~ 85% of Annual production was found to be maximized by removing ~ 85% of the fish biomass during October (and closing the fishery for the the fish biomass during October (and closing the fishery for the remaining 11 months of the year) just prior to the drawdown (ebb remaining 11 months of the year) just prior to the drawdown (ebb flood) when fish have achieved the majority of their year’s growth and flood) when fish have achieved the majority of their year’s growth and before losses due to density-dependent mortality become significant. before losses due to density-dependent mortality become significant. The surviving fraction of the spawning stock maximizes next year’s The surviving fraction of the spawning stock maximizes next year’s density-dependent recruitment. density-dependent recruitment.

• Such a highly seasonal fishery is unlikely to be practicable or Such a highly seasonal fishery is unlikely to be practicable or equitable given the prevailing access rules, particularly in equitable given the prevailing access rules, particularly in Bangladesh. Bangladesh.

• The greatest gains for the smallest initial sacrifices were predicted to The greatest gains for the smallest initial sacrifices were predicted to be achieved by closing the fishery during the dry season (January-be achieved by closing the fishery during the dry season (January-April) when small catches comprise the few remaining spawning April) when small catches comprise the few remaining spawning individuals experiencing low rates of growth and natural mortality. individuals experiencing low rates of growth and natural mortality.

• A closed season toward the end of the dry season could alternatively A closed season toward the end of the dry season could alternatively take the form of dry season reserves (see accompanying take the form of dry season reserves (see accompanying presentations on harvest reserves).presentations on harvest reserves).

• Full details of the model algorithms and results can be found in Halls Full details of the model algorithms and results can be found in Halls et al (2001).et al (2001).

Page 25: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

Other applicationsOther applications

• The model has also been used to explore how water within The model has also been used to explore how water within flood control schemes (compartments) can best be flood control schemes (compartments) can best be managed for the benefit of managed for the benefit of both agriculture and fisheriesboth agriculture and fisheries (see Shanker et al 2004; 2005). The results of these (see Shanker et al 2004; 2005). The results of these investigations have been summarised in the accompanying investigations have been summarised in the accompanying presentation on presentation on sluice gate managementsluice gate management..

• The model has also been used to examine the effects of The model has also been used to examine the effects of dam releasesdam releases of different depth and duration on of different depth and duration on downstream resident fish populations (see Halls & downstream resident fish populations (see Halls & Welcomme 2004). Welcomme 2004).

Page 26: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

BEAM 4 - Bio-Economic Analytical Fisheries BEAM 4 - Bio-Economic Analytical Fisheries ModelModel

• BEAM4 is a multispecies, multigear yield-per-recruit BEAM4 is a multispecies, multigear yield-per-recruit simulation modelsimulation model

• It can be used to assess the potential impacts of different It can be used to assess the potential impacts of different fishery management measures (effort controls, closed fishery management measures (effort controls, closed seasons, minimum size limits etc) on fishery yieldsseasons, minimum size limits etc) on fishery yields

• Software originally published by FAO as 'BEAM4' (Sparre & Software originally published by FAO as 'BEAM4' (Sparre & Willmann, 1991). Now available as the general analytical Willmann, 1991). Now available as the general analytical YPR model in FiSAT software suite (downloadable from FAO YPR model in FiSAT software suite (downloadable from FAO web site)web site)

• Model applied by DFID project R4791 to floodplain river Model applied by DFID project R4791 to floodplain river fishery data from Bangladesh, Indonesia and Thailand (see fishery data from Bangladesh, Indonesia and Thailand (see Hoggarth & Kirkwood, 1996)Hoggarth & Kirkwood, 1996)

Page 27: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

BEAM 4 - Model inputsBEAM 4 - Model inputs

• BEAM 4 has high data requirements, but approximate inputs can be BEAM 4 has high data requirements, but approximate inputs can be estimated from a short time series sample of length frequency data estimated from a short time series sample of length frequency data (or from a sample of aged fish, for species where ageing is possible)(or from a sample of aged fish, for species where ageing is possible)

• Data inputs:Data inputs:

– Biological parameters for each species in the model - growth rates (K, Biological parameters for each species in the model - growth rates (K, LLinfinf, t, t00) and mortality rates (Z and M) - estimated in this analysis from a ) and mortality rates (Z and M) - estimated in this analysis from a 9 month time series of length frequency samples (ELEFAN method)9 month time series of length frequency samples (ELEFAN method)

– Size selectivity of each gear type for each species, determined Size selectivity of each gear type for each species, determined approximately from the length frequency dataapproximately from the length frequency data

– Seasonality of each gear (modelled by entering the actual monthly Seasonality of each gear (modelled by entering the actual monthly fishing efforts of each gear)fishing efforts of each gear)

• For the R4791 analysis, the model was fitted for up to five species For the R4791 analysis, the model was fitted for up to five species guilds in each fishery (each country study site) and up to ten fishing guilds in each fishery (each country study site) and up to ten fishing gear typesgear types

Page 28: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

Study SitesFish Species Guilds

Von Bertalanffy Growth Rates

Mortality Rates(yr-1)

K (yr-1) Linf (cm) Natural, M Fishing, F1

BangladeshLarge CarpsLarge PredatorsMedium-sized BlackfishSmall WhitefishSmall Shrimps

0.30.70.70.7'1.0

75703015'4

0.60.92.43.0'3.0

4.12.12.52.4'1.9

IndonesiaFloodplain PredatorsRiver PredatorsMedium-sized BlackfishMedium/small whitefishLarge Shrimps

0.70.6

0.65'0.7'1.0

655828'20'30

0.90.91.2'1.5'2.0

2.02.02.0'2.0'2.0

ThailandSnakehead PredatorsMedium-sized BlackfishMedium-sized Whitefish

0.50.6'0.6

6530'30

0.91.2'1.2

2.02.0'2.0

Growth and mortality rates used in R4791 BEAM 4 Growth and mortality rates used in R4791 BEAM 4 analysisanalysis

11 Maximum fishing mortality rate, for fish at lengths fully selected by all gear types.Maximum fishing mortality rate, for fish at lengths fully selected by all gear types.

Page 29: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

Example results from R4791 BEAM4 Example results from R4791 BEAM4 AnalysisAnalysis

• Figure shows % Figure shows % change to catch for change to catch for each gear type each gear type (listed on x-axis) for (listed on x-axis) for four alternative four alternative management management measures (shown measures (shown by symbols)by symbols)

• Note variation in Note variation in effects of different effects of different measures on each measures on each gear, but limited gear, but limited overall benefits of overall benefits of any measure, any measure, shown as TOTAL, shown as TOTAL, averaged across all averaged across all gearsgears

• These measures These measures would change the would change the allocation of catch, allocation of catch, but not the totalbut not the total

Page 30: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

ReferencesReferences

Dooley, J., Jenness, J., Aguilar-Manjarrez, J. & Riva, C. (in press.) African Water Dooley, J., Jenness, J., Aguilar-Manjarrez, J. & Riva, C. (in press.) African Water Resource Database (AWRD). GIS based tools for aquatic resource management. Resource Database (AWRD). GIS based tools for aquatic resource management. CIFA Technical PaperCIFA Technical Paper. No. 33. Rome, FAO, 2005. . No. 33. Rome, FAO, 2005. http://www.fao.org/fi/eims_search/publications_form.asp?lang=enhttp://www.fao.org/fi/eims_search/publications_form.asp?lang=en

Grainger, R.J.R. & Garcia, S.M. (1996). Chronicles of marine fishery landings (1950-Grainger, R.J.R. & Garcia, S.M. (1996). Chronicles of marine fishery landings (1950-1994): Trend analysis and fisheries potential. 1994): Trend analysis and fisheries potential. FAO Fisheries Technical PaperFAO Fisheries Technical Paper. . 359359. . Rome, FAO. 51pp. Rome, FAO. 51pp. http://www.fao.org/fi/eims_search/publications_form.asp?lang=enhttp://www.fao.org/fi/eims_search/publications_form.asp?lang=en

Haddon, M. (2001). Modelling and quantitative methods in fisheries, Chapman Haddon, M. (2001). Modelling and quantitative methods in fisheries, Chapman Hall, London, 406pp.Hall, London, 406pp.

Halls, A.S. & Welcomme, R.L. (2004). Dynamics of river fish populations in response to Halls, A.S. & Welcomme, R.L. (2004). Dynamics of river fish populations in response to hydrological conditions: A simulation study. hydrological conditions: A simulation study. River Research and ApplicationsRiver Research and Applications. . 2020: : 985-1000. 985-1000. http://www3.interscience.wiley.com/cgi-bin/jissue/109857602http://www3.interscience.wiley.com/cgi-bin/jissue/109857602

Halls, A.S., Burn, R.W., & Abeyasekera, S. (2002) Interdisciplinary Multivariate Analysis Halls, A.S., Burn, R.W., & Abeyasekera, S. (2002) Interdisciplinary Multivariate Analysis for Adaptive Co-Management. for Adaptive Co-Management. Final Technical Report to the UK Department for Final Technical Report to the UK Department for International Development, MRAG Ltd, London, January 2002, 125pp. International Development, MRAG Ltd, London, January 2002, 125pp. http://p15166578.pureserver.info/fmsp/Home.htmhttp://p15166578.pureserver.info/fmsp/Home.htm

Halls, A.S., Kirkwood, G.P. and Payne, A.I. (2001). A dynamic pool model for floodplain-Halls, A.S., Kirkwood, G.P. and Payne, A.I. (2001). A dynamic pool model for floodplain-river fisheries. river fisheries. Ecohydrology and HydrobiologyEcohydrology and Hydrobiology , , 11 (3): 323-339. (3): 323-339. http://http://www.ecohydro.pl/index.phpwww.ecohydro.pl/index.php

Halls, A.S. 1999. Halls, A.S. 1999. Spatial models for the evaluation and management of Inland FisheriesSpatial models for the evaluation and management of Inland Fisheries . . Final Report prepared for the Food and Agriculture Organisation of the United Final Report prepared for the Food and Agriculture Organisation of the United Nations (FIR 1998 Plansys 232200120).Nations (FIR 1998 Plansys 232200120).

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Hilborn, R. & C.J. Walters (1992). Quantitative Fisheries Stock Assessment. Choice, Hilborn, R. & C.J. Walters (1992). Quantitative Fisheries Stock Assessment. Choice, Dynamics and Uncertainty. London, Chapman Hall.Dynamics and Uncertainty. London, Chapman Hall.

Hoggarth, D.D., Abeyasekera, S., Arthur, R., Beddington, J.R., Burn, R.W., Halls, A.S., Hoggarth, D.D., Abeyasekera, S., Arthur, R., Beddington, J.R., Burn, R.W., Halls, A.S., Kirkwood, G.P., McAllister, M., Medley, P., Mees, C.C., Pilling, G.M., Wakeford, R., Kirkwood, G.P., McAllister, M., Medley, P., Mees, C.C., Pilling, G.M., Wakeford, R., and Welcomme, R.L. (in press). Stock Assessment for Fishery Management – A and Welcomme, R.L. (in press). Stock Assessment for Fishery Management – A Framework Guide to the use of the FMSP Fish Stock Assessment Tools. Framework Guide to the use of the FMSP Fish Stock Assessment Tools. FAO FAO Fisheries Technical PaperFisheries Technical Paper No. XXX. Rome, FAO. 2005. XXX pp. No. XXX. Rome, FAO. 2005. XXX pp. http://www.fao.org/fi/eims_search/publications_form.asp?lang=enhttp://www.fao.org/fi/eims_search/publications_form.asp?lang=en

McCullagh, P. & Nelder, J.A. (1989). Generalized Linear Models. London, Chapman & McCullagh, P. & Nelder, J.A. (1989). Generalized Linear Models. London, Chapman & Hall.Hall.

MRAG (1993) Synthesis of Simple Predictive Models for Tropical River Fisheries. MRAG (1993) Synthesis of Simple Predictive Models for Tropical River Fisheries. Report to the Overseas Development Administration. 85 pp. Report to the Overseas Development Administration. 85 pp. http://p15166578.pureserver.info/fmsp/Home.htmhttp://p15166578.pureserver.info/fmsp/Home.htm

MRAG (1994) Synthesis of Simple Predictive Models for Tropical River Fisheries - MRAG (1994) Synthesis of Simple Predictive Models for Tropical River Fisheries - Supplementary Report. Report to the Overseas Development Administration. 29 pp. Supplementary Report. Report to the Overseas Development Administration. 29 pp. http://p15166578.pureserver.info/fmsp/Home.htmhttp://p15166578.pureserver.info/fmsp/Home.htm

Shankar, B., Halls, A.S., & Barr, J. (2005). The effects of surface water abstraction for Shankar, B., Halls, A.S., & Barr, J. (2005). The effects of surface water abstraction for rice irrigation on floodplain fish production in Bangladesh. Int. J. Water, Vol. 3, No. 1, rice irrigation on floodplain fish production in Bangladesh. Int. J. Water, Vol. 3, No. 1, 2005.2005.

Shankar, B., Halls, A.S., & Barr, J. (2004). Rice versus fish revisited: on the integrated Shankar, B., Halls, A.S., & Barr, J. (2004). Rice versus fish revisited: on the integrated management of floodplain resources in Bangladesh.management of floodplain resources in Bangladesh. Natural Resources ForumNatural Resources Forum, , 2828: : 91-101. 91-101. http://www.blackwell-synergy.com/toc/narf/28/2http://www.blackwell-synergy.com/toc/narf/28/2

ReferencesReferences

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ReferencesReferences

Welcomme, R.L (1985). River Fisheries. FAO Fisheries Technical Paper Welcomme, R.L (1985). River Fisheries. FAO Fisheries Technical Paper 262262, , FAO,Rome. FAO,Rome. http://www.fao.org/fi/eims_search/publications_form.asp?lang=enhttp://www.fao.org/fi/eims_search/publications_form.asp?lang=en

Welcomme, R.L. (2001)Welcomme, R.L. (2001) Inland Fisheries: Ecology and Management Inland Fisheries: Ecology and Management. Fishing News . Fishing News Books, Blackwell Scientific, Oxford, 358pp.Books, Blackwell Scientific, Oxford, 358pp.

Zar, J. H. (1984). Zar, J. H. (1984). Biostatistical AnalysisBiostatistical Analysis. New Jersey, Prentice Hall. 718 pp.. New Jersey, Prentice Hall. 718 pp.

This presentation is an output from a project funded by the UK This presentation is an output from a project funded by the UK Department for International Development (DFID) for the benefit Department for International Development (DFID) for the benefit of developing countries. The views expressed are not necessarily of developing countries. The views expressed are not necessarily

those of the DFID.those of the DFID.

This project was funded through DFID's Fisheries Management This project was funded through DFID's Fisheries Management Science Programme (FMSP). For more information on the FMSP Science Programme (FMSP). For more information on the FMSP and other projects funded through the Programme visit and other projects funded through the Programme visit http://http://

www.fmsp.org.ukwww.fmsp.org.uk

Page 33: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

Project details and Project details and creditscredits

Page 34: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

FMSP Project R5953 – FMSP Project R5953 – Fisheries dynamics of Fisheries dynamics of modified floodplains in southern Asiamodified floodplains in southern Asia

• Start Date: Start Date: 03/1994 03/1994

• End Date: End Date: 03/199703/1997

• Project Collaborators: Project Collaborators: – MRAG (Dan Hoggarth, Ashley Halls); MRAG (Dan Hoggarth, Ashley Halls); – CRIFI, Indonesia (Fuad Cholik, Agus Utomo, Ondara); CRIFI, Indonesia (Fuad Cholik, Agus Utomo, Ondara); – BAU Mymensingh (M.A. Wahab, Kanailal Debnath, BAU Mymensingh (M.A. Wahab, Kanailal Debnath,

Ranjan Kumar Dam)Ranjan Kumar Dam)

• Key References: MRAG (1997); Halls et al (1998); Hoggarth Key References: MRAG (1997); Halls et al (1998); Hoggarth et al (1999); Hoggarth et al (1999b). et al (1999); Hoggarth et al (1999b).

• Project web page: Project web page: http://http://www.fmsp.org.ukwww.fmsp.org.uk/FTRs/r5953//FTRs/r5953/.htm.htm

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FMSP Project R5030 – FMSP Project R5030 – Synthesis of simple predictive Synthesis of simple predictive models for river fish yields in major tropical riversmodels for river fish yields in major tropical rivers

• Start Date: 04/1993Start Date: 04/1993• End Date: 07/1993End Date: 07/1993

• Project Collaborators: Project Collaborators: – MRAG (Ashley Halls)MRAG (Ashley Halls)– FAO (Jim Kapetsky)FAO (Jim Kapetsky)

• Key References: MRAG (1993; 1994); Halls (1999); Key References: MRAG (1993; 1994); Halls (1999); Hoggarth et al (in press); Dooley et al (in press).Hoggarth et al (in press); Dooley et al (in press).

• Project web page: Project web page: http://http://www.fmsp.org.ukwww.fmsp.org.uk/FTRs/r5030//FTRs/r5030/.htm.htm

Page 36: Modelling Floodplain River Fisheries – an Introduction Training Workshop Materials

FMSP Project R7834 – FMSP Project R7834 – Interdisciplinary multivariate Interdisciplinary multivariate analysis for adaptive co-managementanalysis for adaptive co-management

• Start Date: 01/10/2000 Start Date: 01/10/2000

• End Date: 31/01/2002End Date: 31/01/2002

• Collaborators: Collaborators: – MRAG (Ashley Halls) MRAG (Ashley Halls) – Reading University SSC (Bob Burn, Savitri Abeyasekera) Reading University SSC (Bob Burn, Savitri Abeyasekera) – WorldFish Centre (Kuperan Viswanathan) WorldFish Centre (Kuperan Viswanathan) – IFM (Doug Wilson, Jesper Neilsen). IFM (Doug Wilson, Jesper Neilsen).

• Key ReferencesKey References: : Halls et al (2002).Halls et al (2002).