6.2 47B Introduction
6.8. e 115B Changing the slope when using the cpue regression method
193
is less inter-simulation variation in the probability of a catch < 2000t for a multiplier of
0.75 but that is offset by a very slightly reduced likelihood of attaining the SMSY by 2015
for both species.
0 5 0 100 150 200 S2010/Smsy , PS C1 C44 C45 0 5 0 100 150 200 S2010/Smsy , PE S2015/Smsy , PS S2015/Smsy , PE p(Cy <2000t) 0. 0 0 .2 0. 4 0 .6 0 .8 1 .0 AAV 0 2 04 06 08 0 1 0 0 CLOW 500 100 0 1 5 0 0 2000 2 500 300 0 DCATCH 1 0000 150 00 20 000 2500 0 3 0 000 3 500 0 p(S2015>Smsy ).PS 0. 0 0 .4 0. 8 p(S2015>Smsy ).PE 0. 0 0 .4 0. 8 p(S2015>Slow).PS 0. 0 0 .4 0 .8 p(S2015>Slow).PE 0. 0 0 .4 0 .8 P(S2015>0.2S0).PS 0. 0 0 .4 0 .8 p(S2015>0.2S0).PE 0. 0 0 .4 0 .8
Figure App.C.11: Management-related performance measures for variants of the cpue regression approach in which the slope of the catch rates are multiplied by 0.5 (C1), 0.25 (C44) and 0.75 (C45). The results in this figure are based on the Base Case operating model.
Table App.C.2: Settings of the scenarios mentioned in Appendix C. Scenario Operating model Management strategy Decision Error Spatially Correlated Recruitment
Recruitment Monte Carlo val- ues Implementa- tion of effort changes (1:3 or 1:1) Decision multi- plier on regression slope Catchability/ Fishing power Catchability/ Fishing power
D1 q/H q/H 0 None Variance-covariance matrix 3 N/A
D32 q/H q/H 0 None Bayesian 3 N/A
B1 q/BCH N/A 0 None Variance-covariance matrix 3 N/A
B13 q/BCH N/A 0.1 None Variance-covariance matrix 3 N/A
C1 q/BCH N/A 0 None Variance-covariance matrix 3 0.5
C13 q/BCH N/A 0.1 None Variance-covariance matrix 3 0.5
D77 q/H q/H 10 None Variance-covariance matrix 3 N/A
B38 q/BCH N/A 0 None Variance-covariance matrix 3 N/A
C40 q/BCH N/A 0 None Variance-covariance matrix 1 0.5
D28 q/H q/H 0 None Variance-covariance matrix 1 N/A
195
CHAPTER 7
6BFUTURE DIRECTIONS
1. No management strategy could be found that left the spawning stock sizes of both
species of tiger prawns above SMSY without reducing catches substantially. Specifi-
cally, most of the Deriso-model based management strategies that were explored
failed to leave the spawning stock size for brown tiger prawns (Penaeus esculentus)
above SSMY. Further exploration of management strategies that incorporate features
that are expected to conserve the P. esculentus resource better may identify one that
achieves the management objectives more successfully. For example, there are indica- tions that the use of a multi-stock assessment in combination with spatially-based effort limits may perform better than any of the management strategies considered to date. Two (of many) ways of implementing the approach are to:
a) assess the resource using an assessment procedure based on the assumption
that there is a single stock of each species, and allocate the total effort level to stock area based on survey resultsF
3
F
; and
b) assess the resource using a four-stock assessment procedure and allocate the
total effort level to stock area based on the effort levels estimated for each stock area. The compliance mechanism for this would have to be investi- gated.
2. There is a lack of knowledge concerning the true underlying stock structure and
movement of prawns among putative stocks. Work on stock structure, mobility, and the reasons for correlations in recruitment among stock areas should be an essential component of working towards a multi-stock NPF assessment.
3. The estimates of the ratio SY/SMSY for the Karumba and Weipa stock area are biased
when the assessment is based on three or four stocks, and this bias increases with time. The reasons for this bias (and its trend over time) need to be explored, understood, and removed before substantial attention is focused on management strategies that rely on multi-stock assessment procedures.
4. This thesis focuses exclusively on two of the target species of the fishery. Given the increasing legislative requirement to consider the broader economic and ecosystem ef- fects of fishing, there is a need to develop performance measures that explicitly consider: a) the ecosystem impacts of fishing (to provide a better way to quantify the broader implications of the different management strategies), and b) the dynamics and behaviour of the fishing fleet. This requires expanding the operating model to include
ecosystem and economic componentsF
4
F.
5. The recruitment surveys in the Gulf of Carpentaria have value beyond their immediate
objectives. Care should be taken that the data have enough information to estimate stock size and catchability if catchability is estimated within the assessment (as is the case for the biomass dynamic model). At present, only logbook data are available for assessment purposes and it seems unlikely that there is enough contrast in stock size and exploitation rate to estimate both stock size and catchability without substantial bias and model instability. The recruitment surveys, started in 2002, have the potential to provide the data required to estimate the values for parameters such as recruitment, and, together with the logbook data, may provide enough information to estimate catchability as well. This is in contrast to the present situation where catchability is either pre-specified (as is the case for the Deriso model) or poorly estimated (as is the case for the biomass dynamic model). Once the time-series of survey data is long enough, say 10 years, the assessment should be updated to include these data and to also estimate catchability.
6. Update the MSE to include economic, effects of trawling and ecosystem components
in both the operating and management procedure. This will move the MSE to investi- gate management strategies for Ecosystem Based Fisheries Management.
197
CHAPTER 8
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