Empirical and other stock assessment approaches FMSP Stock Assessment Tools Training Workshop Bangladesh 19 th - 25 th September 2005

Reference points from minimal population parameters (Beverton & Holt ‘invariants’) FAO Fish. Tech. Paper 487; Section 4.2, Chapter 11 Assume that a species has an average life history pattern, with the following relationships: M / K = 1.5, M t m = 1.65, and L m = 0.66 (where M is natural mortality, K is the growth rate, t m is the age at maturity and L m is the length at maturity as a proportion of the asymptotic length L ∞, see Chapter 11).

Inputs and outputs from Beverton & Holt ‘invariants’ method Notation Constant Recruitment With Stock-Recruit Relationship (SRR) Inputs - Ecological VB Growth rate / curvature parameterKYes Density Dependence in SRR (B&H steepness)hYes Inputs - Management controls L c / L ∞ ratio (allowed range )L c / L ∞ Yes Outputs - Performance indicators Equil. YPR as fraction of Exploitable BPR 0 YPR / BPR 0 Yes Equil. Yield as fraction of Exploitable B 0 Y / B 0 Yes Outputs - Reference points F giving MSYF MSY Yes See FAO Fish. Tech. Paper 487; Section 4.2, Chapter 11

Setting fishing effort in multi-species fisheries Section 4.4, Chapter 12 FMSP Project R5484 derived guidelines for setting F in multi-species, deep reef-slope, hook and line fisheries Management by size limits not practical for hook and line fisheries No detectable evidence of biological interactions (competition, predation, prey release etc) Estimate F MSY as a proportion of M, based on L c50 and L m for each key species (see next slide) Set overall multi-species F as required for most vulnerable species

Setting fishing effort in multi-species fisheries Section 4.4, Chapter 12 L m = 0.5 L ∞ L m = 0.7 L ∞

Empirical approaches Predicting yields from other similar sites: based on resource areas and fishing effort Multivariate modeling of fishery systems GLM approaches Bayesian network approaches See FAO Fish. Tech. Paper 487, Chapter 14 Section 4.7, Chapter 14

Predicting yields from resource areas, by habitat type Asian river fisheriesAfrican lakes loge catch = loge area loge catch = loge area Section 4.7, Chapter 14

Predicting yields from resource areas and fishing effort Section 4.7, Chapter 14 For data sets: FTR for FMSP Project R7834 at Maximum yield (MY) 13.2 t km -2 yr kg ha -1 yr -1 At effort of: 12 fishers km -2

Multivariate modelling of fishery systems Management performance (outcome) variables Production / yield / sustainability / biodiversity Well being of fishers / fishing households etc Institutional performance – equity / compliance with rules etc Explanatory variables Resource / environment Technology – fishing gear / fishing effort / stocking etc Community characteristics Management characteristics – decision making institutions etc Fishing effort is not always the most important factor! Section 4.7, Chapter 14

Multivariate modelling methods General Linear Modeling (GLM) methods for dealing with quantitative management performance indicators (or outcome variables) such as indices of yield or abundance Bayesian network models for qualitative performance indicators such as equity, compliance and empowerment, that must be subjectively measured or scored along with many of the explanatory variables Useful for adaptive management and co-management in inland and coastal fishery systems (divisible into resource/village units) See Final Technical Reports for FMSP Projects R7834 (analysis methods) and R8462 (data collection for co-management) at Section 4.7, Chapter 14

Example of a Bayesian network model Input variables Output variables: Compliance, CPUE change Equity

Example of a Bayesian network model Exploring the effects of government management on outcomes

Example of a Bayesian network model Inputs most likely to achieve favourable states in all three of the main management outcomes simultaneously

Special approaches for inland fisheries Management guidelines for Asian floodplain river fisheries See Hoggarth et al (1999) - FAO Fish. Tech. Pap. 384/1 Stocking models See analysis of eight stocking projects by FMSP Project R6494 (summarised in Hoggarth et al, 1999, Part 2) And forthcoming ParFish-based stocking model… Adaptive management See Garaway and Arthur (2002), and other papers from FMSP projects R7335 and R8292 ( Section 4.8