Elsevier

Fisheries Research

Volume 235, March 2021, 105827
Fisheries Research

Environmental drivers of commercial blue swimmer crab (Portunus armatus) catch rates in Western Australian fisheries

https://doi.org/10.1016/j.fishres.2020.105827Get rights and content

Highlights

  • Blue swimmer crab catch rates were assessed against eight environmental variables.

  • Water temperature was the primary driver of catch rates.

  • Wind speed, depth, lunar illumination and rainfall were also influential.

  • Accounting for environmental effects improved catch rate standardisations.

Abstract

Sustainable fisheries management requires accurate stock assessment and an understanding of how targeted populations vary over space and time and respond to external stressors. Commercial fishery catch rate data (i.e. catch per unit of effort; CPUE) are widely used to assess fishery performance and can provide a measure of stock abundance. However, changes in environmental conditions can influence the two determinants of CPUE, i.e. catchability (q) and abundance (N). We explore the influence of environmental variables (water temperature, wind speed, rainfall, freshwater flow, wave height, lunar illumination, tide height and depth) on commercial blue swimmer crab Portunus armatus CPUE in five Western Australian fisheries. Generalised additive mixed models (GAMMs) were used to identify which variables had the greatest influence on CPUE, and enabled predictions of how these can vary with environmental change. Water temperature had a significant positive effect on CPUE in all fisheries, although some fisheries exhibited a CPUE decline above an optimal temperature range. Wind speed and tide height had significant and generally positive (but lower magnitude) effects in three of the five fisheries investigated, while lunar illumination and freshwater flow caused a decline in CPUE in two or less fisheries. When annual CPUE were standardised for these significant environmental effects, they displayed clear variation from nominal (raw) CPUE. These results highlight the importance of measuring and accounting for environmental effects (e.g. through catch rate standardisation) when using CPUE for fishery assessments.

Introduction

Commercial fishery landings vary over space and time, reflecting both characteristics of the industry (e.g. fisher behaviour, gear efficiency, market demands and regulations) as well as changes in the abundance, distribution and behaviour of target species. Catch variability is particularly evident in invertebrate fisheries, with populations highly susceptible to changes in environmental conditions due to the biological characteristics of many targeted species (e.g. quick growing, short lived and recruiting via broad-scale larval dispersal; Caddy, 1989; Thorpe et al., 2000; Ben-Hasan et al., 2018). For example, annual landings and catch rates of the cuttlefish Sepia officinalis in the Mediterranean Sea fluctuate widely between months and years, reflecting the migratory reproductive behaviour of this species as well as changes in sea surface temperatures and local climatic conditions (Keller et al., 2014). Similarly, recruitment of the western rock lobster Panulirus cygnus in Western Australia is highly correlated with water temperature and the strength of oceanographic currents, which in turn, results in considerable inter-annual catch variability (Caputi et al., 1996, 2001, 2003).

In addition to these broad-scale drivers of population abundance (and therefore the availability of target species), catches are also influenced by catchability, i.e. the proportion of available animals that will be caught by one unit of fishing effort (Arreguín-Sánchez, 1996). The catchability of many invertebrate species has been closely linked to environmental drivers that act over short (and often cyclic) time scales and affect the activity and behaviour of animals. These processes may relate to, or be independent from, effects on abundance (Green et al., 2014). Other authors have determined such factors to include: water temperature (Ziegler et al., 2003; Drinkwater et al., 2006; Spencer et al., 2017); lunar phase (Courtney et al., 1996; Griffiths, 1999); current speed and direction (Zhou and Shirley, 1997; Hill and Wassenberg, 1999; Spencer et al., 2017); wave action (Srisurichan et al., 2005) and water clarity (Addison et al., 2003). Changes in these conditions can influence metabolic rates, sensory ability and mobility, which in turn influence animal distributions and their motivation and capability to feed (Thomas et al., 2000; Stoner, 2004; Green et al., 2014). This is particularly relevant in fisheries using passive and often baited gear (e.g. traps, hook and line and set nets), which rely on animals ‘voluntarily’ entering traps or making contact with hooks and nets (Miller, 1990; Hill and Wassenberg, 1999; Millar and Fryer, 1999). For example, in a commercial trap fishery for spanner crabs Ranina ranina in north-eastern Australia, strengthening bottom current speeds result in increased catches (due to increased feeding activity) until an upper limit of current speed, after which mobility is inhibited and catch rates decline (Spencer et al., 2019a).

Understanding and disentangling these drivers of catch variability is crucial for accurate stock assessment and sustainable fisheries management. Catch per unit of effort (CPUE) is a widely used measure of fisheries performance, and can provide a reliable index of stock abundance when catchability remains constant (Maunder and Punt, 2004). Thus, catch (C) divided by effort (f) is directly proportional to abundance (N): C/f = qN, where q is the catchability coefficient (Ricker, 1975; Maunder and Punt, 2004). However, when catchability changes, CPUE can provide skewed population estimates (Harley et al., 2001; Salthaug and Aanes, 2003; Campbell, 2015). By determining how catchability is influenced by environmental factors and incorporating these variables into catch rate standardisation, it is possible to calculate more reliable measures of abundance (Hinton and Maunder, 2004; Srisurichan et al., 2005; Forrestal et al., 2019).

Blue swimmer crabs (Portunus spp. complex) are widely distributed throughout the Indo-West Pacific region and are of major commercial and recreational fishery importance (Lai et al., 2010). In Western Australia, Portunus armatus fisheries occur along c. 2500 km of coastline, from cool temperate waters of the south coast to warm tropical waters of the north (Johnston et al., 2018). The majority of catch is taken from protected coastal embayments (<20 m depth) and estuaries (Johnston et al., 2018), areas inherently subject to environmental variation. Commercial landings are characterised by highly variable catch rates which fluctuate markedly between months and fishing seasons (Bellchambers et al., 2006; Johnston et al., 2018). Several earlier studies have linked catch rate fluctuations to seasonal environmental patterns (e.g. de Lestang et al., 2003; Bellchambers and de Lestang, 2005) or extreme environmental events (e.g. a severe marine heatwave; Chandrapavan et al., 2019). However, the influence of finer-scale environmental drivers of P. armatus distributions and catchability are not well understood, nor how the magnitude of their influence may vary between habitat types and/or geographic regions.

The objective of this study was to determine how environmental variables affect commercial P. armatus catch rates (i.e. CPUE) in a range of ecosystem types with differing habitats and climatic regimes. In turn, this will allow the anticipation of environmental effects on crab fisheries to inform adaptive management strategies. The specific aims were to: (1) quantify the effects of a suite of environmental variables on commercial P. armatus CPUE in five Western Australian fisheries; (2) relate trends and the magnitude of various environmental influences to the climate, morphology and habitat of the respective commercial fishing areas; and (3) obtain a standardised CPUE index that accounts for environmental variables and better reflects the annual stock abundance of P. armatus in each fishery.

Section snippets

Study areas

Commercial P. armatus catch and effort data and a suite of potential influential environmental variables were collected from five fishery areas in Western Australia (WA); Nickol Bay (NB) on the north-west coast; and Cockburn (CS) and Warnbro (WS) sounds, and the Swan-Canning (SCE) and Peel-Harvey (PHE) estuaries on the south-west coast (Fig. 1). Environmental and commercial fishery characteristics for each area are summarised in Table 1.

Environmental effects of commercial catch rates

Environmental variables significantly influenced commercial P. armatus CPUE in all five fisheries examined. The inclusion of environmental variables improved model fits (model deviation) from null models (year, month and fisher only) by 3 % in NB, 4 % in CS, 8 % in WS and PHE and 33 % in SCE (Table 3). Temperature was selected in the best-fit model for all five fisheries and had a significant and influential effect on CPUE (Table 3; Appendix A). Wind speed and tide height also had a generally

Discussion

Environmental variables significantly influenced commercial P. armatus CPUE in all five fisheries examined in this study and explained 3–26% of overall catch rate variation. This reflects the highly dynamic nature of coastal and estuarine environments which are generally shallow and driven by freshwater inputs (McLusky and Elliott, 2004; Whitfield and Elliott, 2011). Water temperature had a significant and influential effect on CPUE in all fisheries, with catch rates generally increasing in

CRediT authorship contribution statement

Danielle J. Johnston: Conceptualization, Investigation, Writing - original draft, Writing - review & editing, Project administration. Daniel E. Yeoh: Conceptualization, Data curation, Methodology, Formal analysis, Writing - original draft, Writing - review & editing. David C. Harris: Conceptualization, Writing - original draft, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was funded by the Department of Primary Industries and Regional Development (DPIRD), Western Australia. We thank the numerous DPIRD research staff who undertook commercial monitoring surveys and assisted with the deployment and maintenance of water temperature loggers. We also greatly appreciate the cooperation of commercial fishers who voluntarily allowed researchers on their vessels and provided data through monthly returns and daily logbooks. Many thanks to Mark Rossbach for

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