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Empirical and other stock assessment approaches FMSP Stock Assessment Tools Training Workshop Bangladesh 19th - 25th September 2005 Reference points from minimal population parameters (Beverton & Holt ‘invariants’) Assume that a species has an average life history pattern, with the following relationships: M / K = 1.5, M tm = 1.65, and Lm = 0.66 (where M is natural mortality, K is the growth rate, tm is the age at maturity and Lm is the length at maturity as a proportion of the asymptotic length L∞, see Chapter 11). FAO Fish. Tech. Paper 487; Section 4.2, Chapter 11 Inputs and outputs from Beverton & Holt ‘invariants’ method Notation Constant Recruitment With Stock-Recruit Relationship (SRR) VB Growth rate / curvature parameter K Yes Yes Density Dependence in SRR (B&H steepness) h Inputs - Ecological Yes Inputs - Management controls Lc / L∞ ratio (allowed range 0.05-0.95) Lc / L∞ Yes Equil. YPR as fraction of Exploitable BPR0 YPR / BPR0 Yes Equil. Yield as fraction of Exploitable B0 Y / B0 Yes Outputs - Performance indicators Yes Outputs - Reference points F giving MSY FMSY Yes Yes See FAO Fish. Tech. Paper 487; Section 4.2, Chapter 11 Setting fishing effort in multi-species fisheries 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 FMSY as a proportion of M, based on Lc50 and Lm for each key species (see next slide) Set overall multi-species F as required for most vulnerable species Section 4.4, Chapter 12 Setting fishing effort in multi-species fisheries 4 Lm = 0.5 L∞ 3 2 Lm = 0.7 L∞ 1 0 0.2 0.3 0.4 0.5 0.6 0.7 Lc Section 4.4, Chapter 12 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 African lakes loge catch = 2.668 + 0.818 loge area 15 15 10 10 -1 ln catch (t y ) -1 ln catch (t y ) Asian river fisheries loge catch =0.9 + 0.096 loge area 5 0 0 5 10 ln floodplain area (km 2) 15 5 0 -2 3 8 ln lake area (km 2) 13 Section 4.7, Chapter 14 Maximum yield (MY) 13.2 t km-2 yr-1 132 kg ha-1 yr-1 At effort of: 12 fishers km-2 -2 -1 Ln CPUA (tonnes km yr ) Predicting yields from resource areas and fishing effort 4 3 2 1 0 -1 0 For data sets: FTR for FMSP Project R7834 at http://www.fmsp.org.uk/FTRs.htm 2 4 6 Square root number of fishers km 8 -2 Section 4.7, Chapter 14 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 http://www.fmsp.org.uk/ 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 • http://www.fao.org/DOCREP/006/X1357E/X1357E00.HTM • http://p15166578.pureserver.info/fmsp/r8486.htm 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 (http://www.adaptivelearning.info/) Section 4.8