The arc trail

This article describes the rationale behind the design and construction of a trail by pupils of Durban Girls' High School. The project was entered for the Natal Schools Symposium on the Conservation of the Environment and Natural Resources, where it came first in the finals

Preliminary model of the impact of pelagic fishing on the South African west coast in the vicinity of seal and penguin colonies

Preliminary work is summarised here concerning the development of a model of the impact of pelagic fishing on the South African west coast in the vicinity of seal and penguin colonies, for report to the CCAMLR Scientific Committee to parallel and inform their similar initiative concerning krill fishing in the vicinity of krill-dependent predator colonies in the Antarctic Peninsula region through which there is a flux of krill. The decision to model the impact of the South African fishery on penguin and fur seal breeding colonies is because it is a topic of local interest and because of the readier availability of data from both predator studies and pelagic fish surveys, which can be used to provide flux estimates. A spatial modelling framework is used to assess what level and localisation of the fishing effort might negatively impact the predators. The models developed build to some extent on an earlier approach (Plagányi et al. 2000) to explore the effect of different geometric distributions and degrees of synchrony in the abundance of anchovy and its zooplankton prey off the South African west coast

Abalone poaching confiscation trends for Zones A-D up until 2007

Poaching confiscation data have been updated using all data currently available up until the end of May 2007. The data have been reworked in terms of a standard Model year y that is taken to run from October of year y-1 to September of year y. This was necessary for reasons of internal consistency in the assessment process which uses a Model year as thus defined

Reference-case 2008 assessment model for abalone in Zones A, B, C and D

A summary is presented of the results obtained from the 2008 Reference-case model and two variants that was fit to Zones A, B, CNP, CP and D in combination (hereafter referred to as the “combined ABCD model”). The full details of the spatial- and age-structured production model (ASPM) are provided in Appendices 1 and 2. The 2008 base-case model uses an updated CPUPE index, and differs from last year’s base-case in estimating one more parameter for Zone A (the historic catch multiplier). The three model versions presented in Table 1 are as follows: Model a) Concern was expressed by the AWG that the CPUPE trend for Zone B declines too steeply in recent years. This may be attributable to an incorrect partitioning of confiscated abalone between Zones A and B. Rather than estimating the amount poached in Zone B in recent years, this model combines the estimates of the amount poached from Zones A and B and then estimates a parameter that describes the proportion of this total that is taken from Zone A versus Zone B from 2000 onwards. The model estimated proportion poached from Zone A is 0.65 [90% Hessian-based confidence interval 0.55 – 0.75. This model version has been refined slightly from a preliminary version presented to the AWG in that parameter estimates for Zone A now result in an improved fit. Model b) The old Reference case model is similar but estimates the Zone B poaching amount (in numbers), that is assume to apply from 2005-2008. Model c) This model version used a single compartment per zone, rather than assuming inshore and offshore model regions. This model had the worst AIC value. The new Reference Case Model estimates a pristine spawning biomass, (in tonnes) with 90% Hessian-based confidence intervals shown in square brackets, of 9760 [6060; 13460], 5840 [5400; 6280], 7290 [7050; 7530] and 9650 [6440; 12850] for Zones A, B, C and D respectively. The current (inshore+offshore) spawning biomasses (and associated 90% confidence intervals) of abalone in Zones A, B, C and D are estimated at ca. 33 % [26%; 39%], 25 % [19%; 30%], 4% [1%; 6%] and 12 % [8%; 16%] respectively of their preexploitation levels. The “nonpoached” CNP and “poached” CP areas of Zone C are estimated at ca. 6 % and 3 % respectively with the inshore region particularly depleted: the model predicts zero remaining abalone in the inshore CNP, CP and Zone D areas. Equivalent estimates for Zones A and B are 21% and 22%. Natural mortality is reasonably estimated (e.g. 0.33 yr-1 for age 0 and 0.14 yr-1 for age 15+) and in Zones C and D, the additional Bsp 0 mortality estimated for 0-yr old abalone (due to the ecosystem-change effect) corresponds to near zero current annual survival rates. Poaching is severely impacting the resource, with Zone A particularly impacted in recent years. The combined Zones A-D model-predicted 2008 poaching estimate is 860 MT and corresponds to the assumption that, on average, 14% of all poached abalone are confiscated

A summary of the assessment and management approach applied to South African abalone (Haliotis midae) in Zones?A-D

The management of abalone stocks worldwide is complicated by factors such as poaching combined with the difficulties of assessing a sedentary (but not immobile) resource that is often patchily distributed. The South African abalone Haliotis midae fishery is faced with an additional problem in the form of a movement of rock lobsters Jasus lalandii into much of the range of the abalone. The lobsters have dramatically reduced sea urchin Parechinus angulosus populations, thereby indirectly negatively impacting juvenile abalone, which rely on the urchins for shelter. The model developed for abalone is an extension of more standard age-structured assessment models because it explicitly takes spatial effects into account, incorporates the ecosystem change effect described above and formally estimates illegal catches using a novel index, the Confiscations Per Unit Policing Effort (CPUPE). The model is simultaneously fitted to CPUE and Fishery-Independent Abalone Survey (FIAS) abundance data as well as several years of catch-at-age (cohort-sliced from catch-at-length) data for the various components of the fishery as well as for different strata. A basic tenet of fisheries modelling is to not go beyond the information content of the data. The model developed involves the efficient use of data to allow a model of greater complexity (as was essential in this instance) than usual. It has provided the basis for management advice over recent years by projecting abundance trends under alternative future catch levels

Schaefer model predictions of abalone dynamics in Zones E and G based on commercial CPUE data from 1980 to 2007

Here we present the revised predictions of abalone dynamics in Zones E and G. As in the previous assessment [1], predictions were based on a discrete Schaefer model [12] of biomass dynamics. In this implementation however, parameter estimates were obtained using Bayesian methods

Update of available data for the African Penguin Spheniscus demersus model to be coupled to the pelagic OMP

This document serves as an updated compilation of all data currently available as inputs to the African penguin spatial model (MCM/2008/SWG-PEL/21b) which is to be coupled to the pelagic OMP. The data are presented here together with some comments as to how they are to be used in the model and notes on their derivation and potential reliability. Note that this is a working group document only and hence should be extended and improved in future, particularly as regards critical evaluation of different data sources

Proposed performance statistics for evaluating the effects of pelagic fishing on African Penguin populations

Given the move towards adopting an ecosystem approach to fisheries in the pelagic sector, the new pelagic OMP needs to be tested in the light of not only the risk parameters as considered previously, along with catch statistics for the anchovy and sardine populations, but also parameters denoting risk to the African penguin population(s) Spheniscus demersus. Penguins have been chosen as a key predator species to consider because of their conservation status, and because of their potential sensitivity to changes in pelagic fish abundance and distribution as a consequence of their land-based breeding sites. A model of penguin dynamics has been developed for use as a penguin Operating Model to be coupled to the pelagic fish OMP. This paper summarises the proposed implementation and suggests performance statistics for use in evaluating the impact on penguins of predicted future pelagic fish trajectories under alternative harvest strategies (OMPs)

Summary of the GLM used to standardize abalone catch-per-unit-effort data for Zones A-D over the period 1980-2006

The General Linear Model (GLM) approach used last year is here updated for use in standardising the commercial abalone CPUE time series (1980 – 2006) of abalone for the influence of other factors on the CPUE apart from resource abundance. The raw data were supplied by A. Mackenzie (MCM). The explanatory variables included in the GLM are as in previous years. The same is true for the interaction terms in the model. The purpose of refitting a GLM to the abalone data is to update the results of Plaganyi (2006) using the data available for the 2006 Model year (October 2005 – September 2006). The method described by Plaganyi (2006) has been replicated as far as possible

A spatial multi-species operating model of the Antarctic Peninsula krill fishery and its impacts on land-breeding predators

The west coast rock lobster assessment of 20061 based on data to 2004 is updated to include data up to 2008. Over the last four years the exploitable biomass trend is upwards for Areas 7 and 8 and the resource as a whole, but downwards for Areas 5+6 and almost level for Areas 1+2 and 3+4. The overall increase since 2006 is significant at the 5% level. While better than median projections at the time the current OMP developed, the increase remains within the 95% probability intervals calculated at the time. An updated version of the Spatial Multi-species Operating Model (SMOM) of krillpredator-fishery dynamics is described. This has been developed in response to requests for scientific advice regarding the subdivision of the precautionary catch limit for krill among 15 small-scale management units (SSMUs) in the Scotia Sea, to reduce the potential impact of fishing on land-based predators. 2. The numerous uncertainties regarding the appropriate choice of parameter values in multi-species models is a major impediment. A pragmatic method proposed involves use of an operating model comprising alternative combinations that essentially try to bound the uncertainty in, for example, the choice of survival rate estimates as well as the functional relationships between predators and prey. 3. The operating model is assumed to simulate the “true” dynamics of the resource and is used to test decision rules for adjusting fishing activities (e.g. catch limits) based on field data forthcoming in the future. 4. An illustrative Management Procedure (MP) that includes a feedback structure is shown to perform better in terms of low risk to predators within each SSMU, than an approach lacking the ability to react and self-correct. 5. This modeling framework provides an example of a method for bounding some of the uncertainty associated with multi-species models used for management. Results are presented as probability envelopes rather than in point estimate form, giving a truer reflection of the uncertainty inherent in outcomes predicted on the basis of multi-species models, as well as highlighting how such probability envelopes could be narrowed given improved data on key parameters such as survival. Results are useful for evaluating the relative merits of different spatial allocations of krill catches. An example is given of 2 how such a framework can be used to develop a management scheme which includes feedback through management control rules