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Information on shore‐based recreational fishing is essential for the sustainable management of nearshore fish stocks. However, obtaining estimates of catch and effort from such fishing activity can be complex and expensive due to the large spatial scales over which surveys are typically conducted and the fine‐scale temporal resolution that is desired. Complementary surveys are one option for improving the accuracy of estimates. A pilot study was conducted in Perth, Western Australia, from April to June 2010 to test an expanded aerial–roving survey design that incorporated remotely operated cameras. Cameras recorded the distribution of shore‐based fishing activity across a 24‐h day, highlighting an afternoon peak as well as some nighttime activity, which is rarely captured in existing survey designs. This information was combined with instantaneous counts of shore fishers from aerial surveys and trip length data that were obtained from 1,194 incomplete trip interviews conducted during roving creel surveys; the resulting estimate of fishing effort was 213,460 angler‐hours (SE = 18,141; relative SE [RSE] = 8%). Catch rates, which were calculated from roving creel survey data on the numbers of retained fish, were combined with fishing effort to estimate a total retained catch of 355,801 fish (SE = 41,446; RSE = 12%). The Australian herring Arripis georgianus was the dominant species, with a retained catch of 229,779 fish (SE = 39,007; RSE = 17%). In comparison with other on‐site techniques, the incorporation of cameras into an aerial–roving survey design provides a generic, cost‐effective method for measuring the distribution of shore‐based fishing activity across a 24‐h day. Our findings improve the understanding of the exploitation of nearshore fish stocks along the Perth coast. Our method has broad application to many other recreational fisheries around the world, especially where nighttime fishing is popular. Received June 28, 2012; accepted August 24, 2012

Implementing ecosystem‐based fisheries management requires indicators and models that address the impacts of fishing across entire ecological communities. However, the complexity of many ecosystems presents a challenge to analysis, especially if reliant on quantification because of the onerous task of precisely measuring or estimating numerous parameters. We present qualitative modelling as a complementary approach to quantitative methods. Qualitative modelling clarifies how community structure alone affects dynamics, here of exploited populations. We build an array of models that describe different ecosystems with different harvesting practices, and analyse them to predict responses to various perturbations. This approach demonstrates the utility of qualitative modelling as a means to identify and interpret community‐level indicators for systems that are at or near equilibrium, and for those that are frequently perturbed away from equilibrium. Examining the interaction of ecological and socio‐economic variables associated with commercial fisheries provides an understanding of the main feedbacks that drive and regulate exploited ecosystems. The method is particularly useful for systems where the basic relationships between variables are understood but where precise or detailed data are lacking.

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During progression of a mass mortality of Australian pilchards in late 1998 and early 1999, quantities of dead pilchards on the sea-surface, sea-floor and along beaches were estimated in three regions along southern Western Australia (WA) by use of transects. Total mortality was estimated at 17 590, 11193 and 144.4 t for Esperance, Bremer Bay and Albany respectively. Mortality rates at Esperance and Bremer Bay were similar at 74.5% and 64.7% respectively, with a mean of 69.6%. In contrast, estimated mortality at Albany was only 2.4%. Although the difference in total mortality between regions is probably related to differences in stock size, as determined by simulation models, the much lower estimate for Albany is probably an artefact of an over-estimated pilchard biomass and not due to large differences in actual mortality rates. Variability in estimates of both pilchard biomass and quantities killed resulted in a wide range of estimated mortality rates, with lower estimates for Esperance and Bremer Bay of 28.0% and 22.9% respectively. This represents a significant decline in the breeding stock of WA pilchards. If the impact was closer to the mean (69.6%), then pilchard stocks in WA are severely depressed.