Download Empirical and Other Stock Assessment Approaches

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