METHODS

Management scenarios
Modeling approaches

Management scenarios

Single species and ecosystem models were used to evaluate the expected impacts of adopting different exploitation rates for sardine on the fisheries yield, the stock biomass and on the biomass of other ecosystem components.

To examine the short term effects of fishing strategies, dynamic simulations were run for 5 years according to fishing scenarios that halve, completely stop, and double the fishing rates of the four major fleets in the region, i.e., Purse seiners, Bottom trawlers, Shrimp trawlers, and Pole-and-line vessels (Table 1). Scenarios 1, 2 and 3 represent fishing strategies solely directed to the Purse seine fleet, resulting in a 50% decrease, 100% decrease, and a 100% increase in F for sardine in 5 years, respectively. Strategies were also tested where all fleets (Purse Seiners, Bottom trawlers, Shrimp trawlers, and Pole-and-line vessels) are decreased by 50% (scenario 4); decreased by 100%, i.e., all fisheries closed (scenario 5); and increased by 100% (scenario 6) during a 5 years period. No by-catch is included in the model, as the objective of the simulations was to investigate the likely effects of changes in fishing mortality rates for the target species. Catch composition of each fishing fleet is shown in Appendix I, Table I.2.

Table 1. Simulation scenarios used to evaluate the effects of fishing policies applied to the four main fleets in the Southeastern Brazilian Bight. Policies are defined by changes in fishing rates of each fleet. Simulations were ran for 5 years.

Simulation	Purse seiners	Bottom trawlers	Shrimp trawlers	Pole-and-line
Scenario 1	50% decrease	no change	no change	no change
Scenario 2	close fishery	no change	no change	no change
Scenario 3	100% increase	no change	no change	no change
Scenario 4	50% decrease	50% decrease	50% decrease	50% decrease
Scenario 5	close fishery	close fishery	close fishery	close fishery
Scenario 6	100% increase	100% increase	100% increase	100% increase

In the multi-species model, equilibrium simulations were run to evaluate the potential yield for sardine over a range of exploitation rates and to evaluate the long term effect of fishing sardine on the abundance of other ecosystem components. In the single-species model, the expected yield and the probability of stock collapse over a range of exploitation rates were evaluated by simulating the population model for 10 years in a Monte Carlo simulation procedure. The probability of stock collapse was calculated as the frequency of cases (simulations) in which the spawning stock was driven below the historical lowest size, that is approximately 50,000 tons.

Modeling approaches

A complete description of modeling approaches used in this analysis is presented in Appendix I. The single species approach combines functions representing growth, natural and fishing mortality, and recruitment in a Monte Carlo simulation model. Simulations are based on three hypothesis proposed to describe sardine spawner-recruit population dynamics:

i) Recruitment decreased in response to a gradual decline of the spawning stock biomass due to overfishing. This hypothesis is consistent with the high fishing mortality rates, the decrease in recruitment and spawning biomass estimated between 1977 and 1990 (Cergole, 1995; Vasconcellos, 2000).

ii) Recruitment declined in response to overfishing and is forced to stay at low levels due to depensatory mechanisms. According to this scenario, recruitment is a function of stock size but recruitment is depressed by depensation at low stock sizes. This hypothesis is consistent with the slow recovery of the stock after the collapse. Several processes can contribute to depensation in stock production including competitive exclusion, increased predation mortality at low stock size, reduction of intraspecific diversity, or even behavioural processes such as the “school trap” phenomenon (Cury et al., 2000)

iii) Recruitment declined as a result of overfishing and recruitment failures caused by long-term, low-frequency environmental effects, according to Bakun’s “dome-shaped” regime hypothesis (Figure 3). According to this scenario, recruitment changes in response to a sinusoidal trend in marine carrying capacity with a period of 10 years between “good” and “bad” conditions (Appendix I; Figure I.2).

First, the assumption is made that the Southeastern Brazilian Bight (SBB) encompass an area of distinct environmental characteristics and types of activities which defines an appropriate unit for ecosystem management purposes. An Ecopath trophic mass-balance model of the SBB is constructed and used to quantify biomasses and flows among important exploited functional groups in the system.

A dynamic trophic model, Ecosim, structured from the mass-balance assessments in (i) is parameterized and used to provide dynamic predictions of changes in sardine biomass and recruitment as affected directly by fishing and predation, changes in food availability, and indirectly by fishing and predation on other groups with which sardine interacts.

Ecosim is used to evaluate hypotheses of changes in sardine population and ecosystem and to evaluate the outcomes of exploitation rates for sardine and other exploited groups in the system.

A sensitivity analysis was carried for the type of control of trophic relationships in the ecosystem, which has been shown to influence the magnitude and direction of changes in the ecosystem when subjected to fisheries (Appendix I; Walters et al., 1997; Mackinson et al., 1997). Two types of trophic controls were tested: a bottom-up control, where the amount of prey available to predators is limited so that the mortality rate of a species in the ecosystem is largely independent of the abundance of predators; and a “wasp-waist” control (Bakun, 1996; Cury et al., 2000), where the abundance of small pelagic fish control both their predators and prey. Following Cury et al. (2000), the interaction between small pelagic fish (sardine, anchovy, and other forage fish) and their prey (phyto and zooplankton) is assumed to be top-down controlled, while the interaction between small pelagics and their predators is bottom-up controlled.