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Many researchers have pointed to coastal environments and their use for energy production, aquaculture, and conservation as key to solving profound challenges facing humanity. Navigating this transition in ocean space necessitates tools to quantify impacts on displaced marine users, including recreational fishers. In this study, we present a novel method combining spatial ecological models of recreational catch with fine-scale random utility models of site choice to predict the impacts of site closures on recreational fishing experiences (fisher welfare). We demonstrate our approach by modelling data from over 10 000 fishing trips to fine-scale sites across 1500 km of coastline in the south-west of Australia and simulating the effects of no-take marine reserves recently implemented in the Australian Marine Parks. The results show how the combination of random utility modelling with spatial ecological methods provides a powerful tool for investigating recreational fisheries as joint social-ecological systems and incorporating recreational fishing into marine spatial planning.

Marine recreational fisheries (MRFs) are often highly spatially heterogenous, with effort concentrated into small areas, and fisheries spanning large environmental gradients. However, spatially resolved catch data is rarely collected in MRFs, preventing the study of spatial heterogeneity in catch. This study uses recreational catch reported in 10 × 10 nm blocks across eight degrees of latitude in Western Australia to map spatial predictions of the probability of a recreational catch on an average trip for two key species: West Australian dhufish (Glaucosoma hebraicum) and snapper (Chrysophrys auratus). Two spatial modelling techniques are compared for the analysis, generalized additive mixed models (GAMMs) and boosted regression trees (BRTs). We find that BRTs outperform GAMMs, but performance gains are small. We also find marked spatial variations in recreational catch probabilities: high catches of dhufish are found in the north of the study area, and low catches in the Perth Metropolitan area and in the south; snapper catches are highest in the north and low in the south. These patterns are used to identify important spatial processes in the fishery. The analysis also suggests that modelling approach (GAMMs or BRTs) has only a minor effect on outcomes of spatial catch analysis in MRFs.

Combinatorial auctions allow bidders to bid on individual items or bundles of items. This enables bidders to take advantage of the synergy or complementarity in their valuations of bundles. However, the number of bundles or packages that a bidder could explore may be large making these auctions too complex. Therefore, a combinatorial auction is often run as an iterative auction involving a series of rounds that progressively lead to a final allocation. In each intermediate round, bids are submitted and then provisional winners are determined. A key element in each round is the provision of price feedback to guide bidders in subsequent rounds. This price feedback can focus on packages or on items. Item (also known as linear) pricing feedback schemes have been widely researched for combinatorial auctions on unique items (where packages consist of one or more unique items but not multiple amounts of any item). It has been observed that such item price schemes have performed reasonably well in a wide variety of auction markets. However, they have not yet been thoroughly tested for auctions where the allocation involves multiple units of different items. In this paper, we extend several item pricing designs to the case of multiple unit combinatorial auctions. We identify a set of performance criteria by which an auctioneer could assess the suitability of a design for their particular case. We then numerically compare the algorithms using an agent-based model. Based on the results, we draw some general conclusions about the strengths and limitations of different schemes.