Climate-coastal fisheries relationships and their spatial variation in Queensland, Australia

Fisheries Research 110 (2011) 365–376

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Climate-coastal fisheries relationships and their spatial variation in Queensland, Australia Jan-Olaf Meynecke ∗ , Shing Yip Lee Australian Rivers Institute – Coast and Estuaries and School of Environment, Griffith University, Gold Coast Campus, Queensland 4222, Australia

a r t i c l e

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Article history: Received 29 September 2010 Received in revised form 28 April 2011 Accepted 5 May 2011 Keywords: Coastal fisheries Climate Geographic variability Queensland

a b s t r a c t The notion that climate change may impact coastal fish production suggests a need to understand how climate variables may influence fish catch on a broad scale. The natural variability of freshwater flows, as a result of variable rainfall, has been shown to affect catch, as low levels reduce nutrient input, physical cues for reproduction, and access to nursery habitats. We used fish catch data, coastal sea surface temperature (SST), rainfall and the Southern Oscillation Index (SOI) from 1988 to 2004 for eight distinct climatic regions along the coast of Queensland, Australia, to investigate the relationships between catch and climate parameters and variation between regions. Sea surface temperatures and rainfall were positively correlated with the catch of seven coastal commercial fisheries species but the relationship varied strongly between species and regions, thus indicating possible differences between fisheries stocks in responding to future changes in temperature and rainfall. A forward stepwise regression model that included a measure of rainfall, SST and SOI explained between 30% and 70% of the variance in catch adjusted for effort for the same year for barramundi (Lates calcarifer), mud crabs (Scylla serrata), mullet (e.g. Mugil cephalus), flathead (e.g. Platycephalus fuscus), whiting (Sillago spp.), tiger prawns (Penaeus monodon, P. semisulcatus) and endeavour prawns (Metapenaeus endeavouri, M. ensis). Given that the influence of these climate parameters varies with geographic regions, future catch prediction models should incorporate geographic variation of the relationship between fish catch and climate. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Attempts have been made to link fish catch with oceanographic and climatic variability (Andrade and Garcia, 1999; Zeeberg et al., 2008). Temperature and freshwater flow have been reported to play a major role in the life cycles of many coastal fish species on the east coast of Australia (Blaber and Blaber, 1980; Gillson et al., 2009). In general, warm water temperature can be seen as a proxy for productivity, increasing the primary production (Liston et al., 1992) and metabolic activity in fish by up to 10% for every 1 ◦ C rise in temperature. The increase of metabolic activity is species specific and species that can tolerate or prefer higher temperatures are likely to benefit from warming (Poertner et al., 2001), whereas species that have narrow temperature tolerance limits, e.g. some prawn (Penaeidae) and fish species (Beaugrand et al., 2003; Harley et al., 2006) will be negatively affected. Studies in African estuaries demonstrated temperature effects on temperate fish assemblages (Whitfield, 2005) but these are less distinct in tropical and subtropical regions.

∗ Corresponding author. E-mail address: j.meynecke@griffith.edu.au (J.-O. Meynecke). 0165-7836/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.fishres.2011.05.004

Proposed mechanisms for the connection between estuarineassociated fishery catch, temperature and rainfall include: (1) trophic linkages – changes to primary or secondary production; (2) changes in distribution as a consequence of altered preferred water temperature and salinity and with it, changes of catchability (Loneragan and Bunn, 1999); and (3) changes in population dynamics such as recruitment, growth, survival, abundance, assemblages and migration behaviour as well as cohort or year-class strength ˜ and Montes, 2001). during the first year of life (Quinones Increased freshwater flow enhances connectivity to the estuary and increases the available catch in commercial fisheries. Additionally, freshwater flow contributes to wetland nursery habitat area (Balston, 2009a). Increased freshwater flow can improve fish catch by enhancing survival of early life cycle stages. Freshwater flow also provides nutrient input from the catchments and enhances estuarine productivity, particularly when in combination with warm water temperatures. Therefore, coastal fishery productivity should increase during years and seasons with high rainfall and warm temperatures. In contrast, cold and dry periods should be related to lower catch rates. However, such climatic effects on fishery production and catch are often delayed as a result of the time required for climate-driven recruitment, growth and survival to affect catchable stock. A similar relationship is expected for coastal fish species that rely on certain temperature and salinity gradients.

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Table 1 Selected coastal regions in Queensland and their characteristics for the time period 1988–2004. NP, North Peninsula; SP, South Peninsula; LC, Lower Carpentaria; HNC, Herbert North Coast; ECC, East Central Coast; PSC, Port Curtis South Coast; MSC, Moreton South Coast, BNC, Barron North Coast. Region

Central degrees S

Bioregion

Catch

Total area (km2 )

Total river length (km)

Mean annual SST (◦ C)

Mean max. SST (◦ C)

Mean annual rainfall (mm)

Mean rainfall wet season (mm)

LC NP SP BNC HNC ECC PC MSC

−17.34 −12.50 −14.10 −16.30 −18.20 −19.55 −24.24 −26.32

Gulf Plains Cape York Peninsula Cape York Peninsula Wet Tropics Wet Tropics Central South-East Queensland South-East Queensland

6987 10 426 26 520 9812 6710 10 122 6019 58 102

248 884 85 014 5790 2340 3080 5913 5667 5297

17 729 5235 3919 1585 2313 2975 3905 3449

27.77 28.05 26.95 26.77 26.44 25.29 24.25 24.07

31.41 30.50 29.74 29.74 29.75 28.87 28.02 27.18

700 1423 1062 1503 1475 952 703 925

660 1328 993 1248 1204 757 488 641

Previous studies showed that rainfall and associated freshwater runoff influence the catch of coastal fish species such as mullet (e.g. Mugil cephalus), prawn species (e.g., Penaeus esculentus, P. plejebus) (Meynecke et al., 2006; Vance et al., 1998), barramundi (Lates calcarifer) (Balston, 2009b) and dusky flathead (Platycephalus fuscus) (Gillson et al., 2009). Studies in Australia were concentrated on local regions like the Gulf of Carpentaria (Vance et al., 1998), southeast Queensland (Loneragan and Bunn, 1999), central Queensland (Staunton-Smith et al., 2004) or central New South Wales (Growns and James, 2005). Investigations on a larger geographic scale were only undertaken for single species like barramundi (Balston, 2009a) or for single environmental variables like rainfall (Meynecke et al., 2006). Quantification of the influence of multiple climatic indices on a range of coastal fisheries species in the form of regional comparisons along precipitation and temperature gradients has not been undertaken to date. Beside the large geographic differences in temperature, catch and biology of coastal fisheries species in Queensland it is possible to develop climatic indices capable of improving fisheries management. Given the expected disturbances in rainfall and temperature patterns resulting from climate change, better understanding of climate effects on fish catch is timely. Here we analysed relationships between key climate variables and fisheries catch rates from eight coastal regions in Queensland, eastern Australia. The objective was to determine the influence of climate variables on fisheries landings using effort-adjusted data from individual fisheries. We (1) examined relationships between climate variability measured by rainfall, nearshore sea surface temperature (SST) and Southern Oscillation Index (SOI) values and the Queensland commercial fisheries catch rates from 1988 to 2004; (2) investigated climate variables that were significantly related to fisheries catch rates; and (3) investigated if the relationships vary along temperature and rainfall gradients by cross-regional comparisons.

the Queensland Primary Industries and Fisheries Assessment and Monitoring Unit (Department of Employment, Development & Innovation) (Fig. 1). Effort was calculated for the individual fisheries (line, trawl, net and pot) and the dominant fisheries for each species were used for further analyses to infer an index of abundance. The fish catch data were recorded in 30 nautical mile grids. The catch ranged from 26 to 60 kg/day and between 1 and 260 kg/day per fish catch grid within the eight different regions (Fig. 1). Catch was adjusted for effort using residuals from the regression of logtransformed catch and effort, and the catch adjusted for effort (CAE) was calculated for each year and season to ensure there was no signal from effort in the catch data (Balston, 2009b).

2. Materials and methods 2.1. Study area The Queensland coastline offers a wide range of mean monthly and annual temperatures with annual average water temperatures between 23 and 31 ◦ C for north Queensland and between 19 and 27 ◦ C for southeast Queensland. Between 80% and 90% of annual coastal rainfall occurs in the wet season (October–April) and annual average rainfall ranges from 700 mm in the Gulf Plains Bioregion to 1500 mm in the Wet Tropics Bioregion (Table 1, Figs. 1 and 2). Eight coastal regions were selected to evaluate the influence of climate variables on fisheries catch rates in accordance with the Bureau of Meteorology rainfall districts (BOM, 2004). 2.2. Fisheries data Data on catch and effort (number of fishing days) for coastal species or species groups from 1988 to 2004 were provided by

Fig. 1. Location of study sites showing eight coastal regions along the 7000 km coastline of Queensland and the spatial distribution of catch in kg/day during 1988–2004 for seven coastal fisheries species per region as well as the catch in kg/day recorded in 30 nautical mile grids (spatial resolution of recorded catch data).

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Fig. 2. Time series plots of monthly key environmental data (average rainfall, sea surface temperature and SOI) for eight coastal regions in Queensland, Australia. NP, North Peninsula; SP, South Peninsula; LC, Lower Carpentaria; HNC, Herbert North Coast; ECC, East Central Coast; PSC, Port Curtis South Coast; MSC, Moreton South Coast, BNC, Barron North Coast.

We used coastal catch data from selected fisheries species groups (Table 2) to assess regional differences in the relationship between climate variables and fisheries catch rates. Species were selected based on (1) relatively constant and high market value; (2) estuary association; and (3) common occurrence

throughout Queensland coastal waters (Yearsley et al., 1999). The following species were selected: barramundi (Lates calcarifer), mud crab (Scylla serrata), dusky flathead (Platycephalus fuscus), mullet (Liza vaigiensis, L. subviridis, L. argentea, Valamugil georgii, V. seheli, Mugil cephalus, Trachystoma petardi, Mugilidae), endeavour prawns

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Table 2 Major coastal fish species or species groups selected for analysing the relationship between catch data and climate parameters. Criteria for species selection were that the species should/are (1) have relative constant and high market value; (2) estuaryassociated; and (3) common throughout Queensland (Yearsley et al., 1999). Common name and fish catch class

Dominant taxa

Barramundi Mud Crab Dusky Flathead Mullet

Lates calcarifer Scylla serrata Platycephalus fuscus Liza vaigiensis, L. subviridis, L. argentea, Valamugil georgii, Valamugil seheli, Mugil cephalus, Trachystoma petardi, Mugilidae Metapenaeus endeavouri, M. ensis Penaeus monodon, P. semisulcatus Sillago ciliata, S. analis, S. maculata, S. burrus, S. ingenuua, S. sihama, S. robusta, Sillaginidae spp.

Endeavour Prawns Tiger Prawns Whiting

(Metapenaeus endeavouri, M. ensis), tiger prawns (Penaeus monodon, P. semisulcatus) and whiting (Sillago ciliata, S. analis, S. maculata, S. burrus, S. ingenuua, S. sihama, S. robusta, Sillaginidae spp.). 2.3. Climatic data Monthly mean and maximum sea surface temperature (SST) data points with a 4-km resolution (obtained from http://podaac.jpl.nasa.gov/DATA PRODUCT/SST, NASA JPL Physical Oceanography) for the time period 1988–2004 were selected for a 50-km buffer zone along the coastline of Queensland using ArcGIS 9.3. Monthly patterns are likely to vary significantly between air and water temperatures, with SST better reflecting monthly variations in coastal waters (Eugene et al., 1982) than air temperature. We have analysed coastal weather stations for consistency and air temperatures for the time period 1988–2004 and have found the variation between regions, in particular, between the remote north and urbanised south of Queensland was too high for meaningful analyses. Monthly Southern Oscillation Index (SOI) values and mean and maximum rainfall data for the eight coastal regions were obtained from the Bureau of Meteorology (BOM). Positive val˜ pattern in the ues of the SOI are generally associated with a La Nina central and eastern equatorial Pacific and above average rainfall for northeast Queensland. Negative values of the SOI are associated ˜ conditions and below average rainfall across northeast with El Nino Australia. To align with the climatic patterns of coastal Queensland, seasonal variables were calculated for the wet (October–April) and the dry seasons (May–September). 2.4. Data analyses Exploratory analyses were performed using Pearson correlations for seasonal configuration of the data set for SST and rainfall data only. The correlation analyses were concentrated on the wet season where catch rates (except for mullet) and warm temperatures as well as rainfall are highest. Results were visualised using ArcGIS 9.3 to analyse the spatial distribution of the relationships. Further analyses of fish catch dependence on climatic variables were undertaken with linear regression techniques and corrected with Bonferroni inequality adjustment (Sokal and Rohlf, 1995) in SPSS 17.0, using annual data with lags of up to two years. The general equation used to predict CAE from environmental variables was: Ct = f (xt ) =

n 

ˇi xi,t + et

i=0

where Ct is the CAE at time t, xi,t the covariates representing climate variables (SOI, rainfall, temperature), t the unit of time (year or

season), n the number of covariates, ˇi the coefficient for covariate i and et is the residual term for observation t. We tested a total of 21 relationships for each species and region. Non-metric multidimensional scaling (nMDS) was used to visually represent the similarity of the coastal regions on the basis of temperature, rainfall and catch in kg/day. Data was standardised by sample and resemblance measure was Euclidean distance. We tested annual and seasonal CAE data against seven variables, namely SST max, rainfall max, rainfall, rainfall wet season, SOI, SOI Nov–Apr, SOI May–Oct. The collinearity between variables (e.g. SST and rainfall) and/or autocorrelation within a single variable (e.g. wet season rainfall correlated with dry season rainfall) was tested using a correlation matrix. The correlation between SST values and rainfall were expected to be significant. However, we have not removed the autocorrelation as this can increase the risk of a Type II error, or bias results if the source of autocorrelation is due to covariance (Pyper and Peterman, 1998; Robins et al., 2005). The link between rainfall and SST is also biologically meaningful and should not be removed for the purpose of these analyses. To reduce the risk of spurious relationships that did not have a likely causal link to coastal fish production, comparisons with results from previous studies of the same species were undertaken. One and two-year time lags that were biological meaningful were included in the analyses. 3. Results 3.1. Spatial variation of catch and climate The total catch for all seven fisheries species between 1988 and 2004 was 118 870 t. Highest catches in kg/day were recorded from northern Queensland (South Peninsula, SP, North Peninsula, NP and Barron North Coast, BNC) and southeast Queensland (Moreton South Coast, MSC) whereas the central regions (East Central Coast, ECC Herbert North Coast, HNC) had lower catch rates (Table 1). Mean annual SST during the observation period was highest in NP (28.1 ◦ C) and lowest in MSC (24.1 ◦ C). Mean annual rainfall in the bioregions ranged from 1500 mm in the Wet Tropics (BNC) to about 1000 mm in southeast Queensland (MSC) to 700 mm in the Gulf Plains (LC) (Table 1). 3.2. Relationships of CAE with SST and rainfall An analysis of the relationship between wet season (Nov–Apr) CAE and SST/rainfall indicated a variation of the strength of the relationships but with general trends for the different species within the eight selected coastal regions (Fig. 3). Barramundi CAE correlation with rainfall resulted in significant values for SP (r = 0.63, P < 0.01) and BNC (r = 0.54, P < 0.05) and the Gulf of Carpentaria (r = 0.49, P < 0.05). Positive correlations between mud crab catches during the wet season and rainfall were significant in BNC (r = 0.67, P < 0.01) and with a similar relationship for another five regions but with lower r values. For tiger prawns, CAE relationships were detected for seven regions. Relationships with SST were significant in ECC (r = 0.64, P < 0.01), in PC (r = 0.59, P < 0.01) and BNC (r = 0.43, P < 0.05) and significant with rainfall in MSC (r = 0.48, P < 0.05). However, in this region (MSC), temperature was significant correlated to endeavour prawn CAE (r = 0.51, P > 0.01). Flathead CAE had no significant correlation, whereas Whiting CAE had significant positive relationships with SST in MSC (r = 0.47, P < 0.05) and PC (r = 0.43, P < 0.05). Mullet CAE was also significant positively correlated with SST in MSC (r = 0.43, P < 0.05) (Fig. 3). Significant stepwise multiple regression models for annual fisheries CAE and climate variables with one and two year lags are presented below.

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Fig. 3. Positive Pearson correlation values in eight coastal regions for relationships between CAE (catch adjusted for effort) from seven coastal fisheries species and average wet season rainfall and SST data for the period 1988–2004. Values r > 0.30 are significant at the P < 0.05 level and values r > 0.50 at the P < 0.01 level (n = 17).

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Table 3 Significant stepwise multiple regression models that correspond with the biology of the species for annual (a), one-year lagged (b) and two-year lagged (c) CAE (catch adjusted for effort) and seven climate variables from eight coastal regions. Fisheries parameters and climate variables were from monthly time series. Max indicates the highest SST per year. NP, North Peninsula; SP, South Peninsula; LC, Lower Carpentaria; HNC, Herbert North Coast; ECC, East Central Coast; PSC, Port Curtis South Coast; MSC, Moreton South Coast, BNC, Barron North Coast. a Species

System

Climatic parameter

Adj. r2

Barramundi Barramundi Barramundi Barramundi Barramundi Barramundi Barramundi Mud crab Mud crab Mud crab Flathead Endeavour Prawns Endeavour Prawns Tiger Prawns Tiger Prawns Tiger Prawns Tiger Prawns Whiting

ECC LC PC HNC MSC NP SP PC HNC MSC MSC ECC HNC ECC MSC NP PC PC

SST max + rain wet + SOI Nov–Apr + SST + annual rain Annual rain + SOI years + rain wet SST max + rain wet + SST Rain wet + SOI + SST SST max + SST + SOI + SOI May–Oct SST max + rain wet + SOI + SST + SOI May–Oct Rain wet SOI May–Oct + annual rain SOI May–Oct + annual rain SST + SOI SST + SOI SOI Rain wet + SST + SOI Nov–Apr + annual rain SST max + SST + annual rain Rain wet + SST SST max + SOI Nov–Apr + SOI SST max + SST SST max + SOI + rain wet

0.521* 0.241* 0.291* 0.370* 0.354* 0.432* 0.312** 0.284* 0.647** , † 0.428* 0.241* 0.384** 0.454* 0.503** 0.229* 0.471* 0.430** 0.654** , †

Species

System

Climatic parameter

Adj. r2

Flathead Endeavour Prawns Endeavour Prawns Endeavour Prawns Tiger Prawns Tiger Prawns Tiger Prawns Whiting Whiting

MSC SP BNC NP NP MSC PC MSC HNC

SOI Nov–Apr SOI SOI May–Oct + SOI + annual rain Rain wet + SOI Nov–Apr SOI + rain wet + SST SSTmax + SST + annual rain Annual rain SSTmax + annual rain Rain wet + SOI + SST + SOI Nov–Apr

0.389** 0.369* 0.309* 0.409** 0.732** , † 0.528** 0.415** 0.401** 0.767** , †

Species

System

Climatic parameter

Adj. r2

Barramundi Barramundi Barramundi Barramundi Barramundi Mud crab Mud crab Mud crab Flathead Flathead Mullet Whiting Whiting

NP HNC SP PC ECC HNC PC ECC PC ECC PC HNC MSC

SST max + rain wet + SOI + SST Annual rain + SOI + SST + SOI May–Oct + sum rain SOI May–Oct + SST + annual rain Rain wet + SOI Rain wet + SST + SOI + SOI May–Oct SST Rain wet + SOI Rain wet + SST + SOI + SOI May–Oct SOI May–Oct Rain wet + SOI + SOI May–Oct + annual rain SST Annual rain + SOI Nov–Apr SOI

0.713** , † 0.583* 0.578** 0.421** 0.597** 0.223* 0.443** 0.717** , † 0.295* 0.581** 0.251* 0.570** 0.308*

b

c

* ** †

P < 0.05. P < 0.01. Bonferroni adj. P < 0.002.

3.3. Barramundi

3.4. Flathead

Stepwise multiple regression identified significant models for seven out of eight coastal regions (except BNC), explaining between 24% and 52% of the annual barramundi CAE variation. The models included average wet season rainfall, SOI and SST as predictors. The models were different for each region but all included some measure of rainfall, with average wet season rainfall being the most relevant variable for barramundi CAE variation. Significant models involving a two-year lag of barramundi CAE were recorded for five regions (NP, HNC, SP, PC, ECC), with r2 ranging from 0.42 to 0.71. Wet season rainfall and SOI were the main predictors in all these models (Table 3a–c and Fig. 4).

Stepwise multiple regression identified one significant model based on SST and SOI that could explain 24% of the annual flathead CAE variation in MSC. A one-year lag showed an improved model explaining 39% of the variation based on wet season SOI and a two-year lag provided significant models for PC and ECC that may explain 29% and 58% of the catch variation, respectively (Table 3a–c and Fig. 4). 3.5. Mud crabs Three significant models that could explain from 43% to 68% of the annual mud crab CAE variation were found. The models for PC

J.-O. Meynecke, S.Y. Lee / Fisheries Research 110 (2011) 365–376

and HNC where similar and both included SOI values from May to October and annual rainfall as the best predictors. The primary factors that explained the highest proportion of variability in the annual mud crab CAE in MSC were annual SST and SOI values. A two-year lag of mud crab CAE resulted in significant models for three regions (HNC, PC, ECC), explaining between 22% and 71% of the variation. For HNC, temperature provided the best predictor whereas for PC and ECC wet season rainfall was the most relevant factor explaining lagged mud crab CAE variation (Table 3a–c and Fig. 4).

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3.6. Endeavour and tiger prawns Two significant models could explain, respectively, 38% and 45% of the annual endeavour prawn CAE variation. The model for HNC included both measures of SST and wet season rainfall, SOI values and annual rainfall as possible predictors. The annual endeavour prawn CAE variation in ECC was best explained by annual SOI values. Significant one-year lag models of endeavour prawn CAE were found for four regions (SP, BNC, NP), with r2 values ranging from 0.31 (P < 0.05) to 0.41 (P < 0.01). Endeav-

Fig. 4. Example of linear regression between climate variables and fisheries CAE. The best fit for the same year and for a one or two year lag is shown for each species. Rainfall in mm and sea surface temperature in ◦ C has been log-transformed.

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Fig. 4. (Continued ).

our prawn CAE variations from the northern regions were best explained by SOI values or wet season rainfall while southern endeavour prawn CAE variations were best explained by annual rainfall. Four significant models could explain from 23% to 50% of the variation in the annual tiger prawn CAE for ECC, MSC, PC and NP. For the ECC, PC and MSC regions, SST was the most important predictor. For the NP region, high temperatures and SOI values contributed most to the model. When lagged by one year, significant r2 values were recorded for NP (r2 = 0.73, P < 0.01), MSC (r2 = 0.52, P < 0.01) and PC (r2 = 0.42, P < 0.01) (Table 3a–c and Fig. 4). 3.7. Whiting and mullet A significant model based on high water temperatures, SOI and wet season rainfall for whiting CAE was found for PC. A one and two-year lag of whiting CAE revealed significant models in MSC and HNC based on annual rainfall, high water temperatures and SOI (one-year lag: HNC r2 = 0.77, P < 0.01; MSC r2 = 0.40, P < 0.01; two-year lag: HNC r2 = 0.57, P < 0.01; MSC r2 = 0.31, P < 0.05). A two-year lag for mullet CAE showed SST may explain 25% of the mullet CAE variation in PC (Table 3a–c and Fig. 4).

3.8. Multivariate analyses A nMDS plot based on average annual SST, rainfall and catch in kg/day from eight coastal regions and correlation values from catch, SST and rainfall relationships showed the distribution of annual SST and rainfall correlations with the CAE for seven coastal fisheries species (Fig. 5a and b). SST with catch correlations were in general stronger in the southern regions and strong rainfall/catch correlations were recorded in the northern regions, with exceptions for the Barron and Gulf region where correlations between annual catch and climate variables were non-significant. The nMDS results also suggested three distinct regional groups: (1) southeast Queensland with MSC, PC and ECC; (2) the northern coast with BNC and HMC and (3) the far north Queensland with the LC, SP and NP (Fig. 5a and b). 4. Discussion Commercial fisheries and climate data from eight coastal regions in Queensland with a wide range of temperature and rainfall revealed the possible effects of climate variation on coastal fisheries CAE. Catch rate/climate relationships were often regionand species-specific. Bioregions (Pease, 1999), genetic variation and

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Fig. 5. nMDS plot showing the eight coastal regions falling into three distinct regional groups (southeast Queensland, central and northern Queensland) and the distribution of temperature (a) and rainfall (b) correlations with catch. Size of the circles indicates the strength of the correlation between the climate variables and total annual CAE from seven coastal fisheries species.

physiological adaptation of the species (Colosimo et al., 2003; Salini and Shaklee, 1988) are known to affect fish catch. The distribution of coastal fish species is usually affected by complex interactions between salinity, temperature, turbidity, type of substratum, vegetation and life history (Blaber, 1997). Our results showed that the relationship between climate variables and some coastal fisheries species varies with the species geographical distribution range but the correlation with fish catch remains strong. By examining many regional populations at once, it was possible to detect general patterns (e.g. a positive relationship with temperature evident at the colder limit of a species’ range). Temperature and rainfall are functionally related variables in particular in respect to the life cycle of coastal fish species and should be investigated simultaneously. The collinearity between them therefore has not been removed for this analysis. Separations between rainfall and temperature can, however, be made based on the species’ biology. Trends have been observed for some species, e.g. barramundi (Lates calcarifer) and mud crab (Scylla serrata), where temperature was not as significantly correlated with catch compared with rainfall (Meynecke et al., 2006). Given that correlation does not imply causality, we have considered the species’ biology to provide further support of the results. 4.1. Biological plausibility of results 4.1.1. Influence of rainfall on catch Barramundi, flathead, mud crab, tiger and endeavour prawn catches were in many regions significantly related to rainfall as a

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proxy for freshwater runoff, thus, indicating that freshwater flow supports higher catch rates of these species throughout the eight selected regions in Queensland. In particular wet season rainfall might have a strong effect determining survivorship of juvenile stages and trigger spawning events. This general positive relationship for most species and regions may result from increased food availability (Bennett et al., 1995), improved or altered water quality (Attrill and Power, 2000), possible inland migration of estuarine residents (Dolbeth et al., 2008), stronger environmental cues for spawning (Lytle and Poff, 2004) or by reducing predation pressure from marine species, especially visual predators (Martinho et al., 2007). For example, high rainfall during the wet season two years prior significantly correlated with barramundi CAE (Table 3a–c). These results are congruent with observations (Balston, 2009b) that barramundi fingerlings travel upstream to freshwater for two to four years of growth as males, and then return to marine environments to mature (Davis, 1985; Moore, 1982). Flatheads (Platycephalus spp.) spawn in estuaries and coastal waters during spring and summer (Kailola et al., 1993), with sexual maturity for dusky flathead occurring after approximately two to three years in Queensland (DPI, 2006). Positive relationships between the two-year lagged annual CAE of flathead and rainfall in the wet season in ECC and PC could therefore reflect increased catch due to high flows inducing successful spawning migration two years prior to catch. The positive effect of SOI values in the dry season could be related to higher rainfall and also lower temperatures during spring time when spawning occurs. The significant correlation between wet season rainfall two years prior to mud crab CAE (in the regions HNC, BNC, PC, ECC) could be caused by the reduction in numbers of subadult and adult crabs in the river systems with increased runoff. This may enhance the survival of juveniles because of reduced cannibalism and competition for burrows, thus explaining a two-year lag effect (Loneragan and Bunn, 1999). Increased freshwater flow can also have an immediate effect on catchability. Barramundi catch is likely increased through downstream migration of mature individuals and increased food availability (Balston, 2009a; Griffin, 1993; Robins et al., 2005). The strength of the relationship, however, varies with the geographical region. One contributing factor could be the confirmed differences in the genotype of Australian barramundi populations (Marshall, 2005). Similar to barramundi, the catchability of flathead increases with freshwater runoff (rainfall) stimulating the movement of flathead species (Loneragan and Bunn, 1999). Freshwater flow also stimulates the downstream movement of mud crabs (Hill et al., 1982). Tiger and endeavour prawn catches in northern Queensland (NP, SP) were positively related to rainfall and SOI. This was also true for a one-year lag. Previous studies found significant positive relationships between annual catch and total freshwater flow (or rainfall) in the same or previous year (estimated time for prawns to recruit to the fishery) (Galindo-Bect et al., 2000; Gammelsrod, 1992; Gunter and Hildebrand, 1954; Ruello, 1973; Vance et al., 1985) and a significant negative correlation between wet season temperature and annual prawn catch in the Karumba region (Gulf of Carpentaria), where the water temperature regularly exceeds 30 ◦ C (Vance et al., 1985). This is supported by Staples and Heales (1991), who found the lowest survival rates of juvenile P. merguiensis at warm temperature (>30 ◦ C) and low salinity (<10 ppt). This also seems to be the case for P. semisulcatus (Xu et al., 1995). However, in southern regions low temperatures become a limiting factor, strengthening the correlation with higher temperatures (Myers, 1998). In subtropical Queensland, where the seasonal variation in water temperature is higher than in tropical waters, warm temperatures were associated with higher juvenile catches in a river system in southeast Queensland (Meager, 2003), thus supporting our find-

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ings of temperature as a main correlate for tiger and endeavour prawn catches in this region. The positive relationships between penaeid prawns and warm temperature in the southern regions might also be related to the increase of catchability due to increased activity. Warm temperature during the wet season correlated positively with mud crab (S. serrata) catch in the subtropical south (MSC region), which is likely a result of enhanced crab feeding activity (Loneragan and Bunn, 1999). Williams and Hill (1982) also found that mud crab catches in southeast Queensland were positively correlated with daily water temperature (r = 0.56, n = 44), but not with salinity (r = 0.09, n = 44), when salinity ranged between 24 and 35 ppt. Weaker correlation between wet season rainfall and mud crab catch in NP and SP are likely due to the fact that most areas in Far North Queensland are not accessible during the wet season, in particular during peak rainfall events. However, low catches of mud crabs in the Gulf of Carpentaria have also been attributed by commercial fishers to high migration rates of mud crabs out of fishing areas and recruitment failure occurring as a consequence of extended periods of freshwater runoff (Helmke et al., 1998). Hill (1975) reported that heavy floods reducing salinity to 2 ppt severely reduced the number of mud crabs in two South African estuaries. 4.1.2. Influence of temperature on catch Temperature most likely affects juvenile fish during estuarine residency, explaining variations in assemblage composition, abundance and growth depending on whether they are eurythermic or stenothermic. A significant model for mullet CAE variations in southeast Queensland resulted from a two-year lag of catch with SST as the variable, indicating that salinity (freshwater runoff) is unlikely a main factor determining catchability of mullet in southeast Queensland. Results from observations on sea mullet (Mugil cephalus) in Indo-Pacific estuaries showed that individuals can be concentrated in high salinity sites (Silva and De Silva, 1981) or in oligo- to mesohaline sites (Marais, 1981), but this may be the result of differences in food availability rather than salinity (Trape et al., 2009). However, timing of their reproductive cycle suggests that warmer periods between May and August may stimulate migration out of the estuaries and therefore increases catchability, since most mullet is caught along beaches and not in estuaries (Gillson et al., 2009). The dominant catch season of mullet is during the Australian winter months (April–August), with almost 80% of the Queensland catch harvested during this time, which is mainly due to the breeding migration of this species. However, drier but warmer wet seasons would increase benthic algal productivity (Liston et al., 1992) in the estuaries and therefore strengthen the 0+ year-class with increased catches lagged by 2–3 years, which is the estimated time it takes juveniles of this species group to ‘recruit’ to the fishery. Whiting CAE in MSC and HNC was significantly correlated to high temperatures and wet season rainfall, with temperature during the wet season being the most important factor in southeast Queensland for annual, one and two-year lagged CAE. The spawning of sand whiting in southern Queensland occurs from September to February in the lower reaches of estuaries and nearshore coastal waters (Morton, 1985). Sexual maturity for whiting occurs after approximately two to three years in subtropical waters. Warm waters during the spawning period would increase catchability in estuaries and coastal waters due to higher activity of this species. Similar to analyses on sand whiting in NSW (Gillson et al., 2009), annual rainfall was not correlated to whiting CAE in any of the Queensland regions. Relationships between climate variables and whiting CAE lagged by age at first maturity indicated a likely recruitment effect.

4.2. Confounding factors Relationships between estuarine catch and climate variables are potentially confounded by factors such as fishing effort (Browder, 1985; da Silva, 1985), demand, and distribution of available habitats that are themselves significantly correlated with temperature and rainfall. Analyses can be improved by adjusting catchability through increased fishing power for each species and fishing gear. Vance and Bishop (2003) estimated that fishing power for banana prawns in the Queensland northern prawn fishery increased by 3.5 times from 1979 to 1999. A reason why some regions had no significant relationships with any of the climate variables was likely due to the low catch rates of this species group in those regions. One way to remove the uncertainty of the relationships would be to assess the relationships based on individual estuaries to account for hydrological and biological differences between catchments, which at this stage is not possible for many estuaries due to low catch rates or data gaps between years. The use of catch data has been criticised as an abundance measure as it can be influenced by the economics of the fisheries (Maunder et al., 2006), the dynamics of other species in other fisheries (Dulvy et al., 2003) and changes in management practices (Sampson, 1991; Bene and Tewfik, 2001). Therefore, caution has to be applied when using fish catch data. However, it is in many cases the only available information on many small scale fisheries species. Independent studies, e.g. on mud crab abundance in Queensland have shown similar trends compared to the fisheries catch data (Brown, 2010).

5. Conclusion Coastal fisheries are influenced by variation in temperature and rainfall via impacts on recruitment and catchability affecting catch rates. Temperature may regulate fisheries catch rates by stimulating growth rates, primary productivity and higher activity. Rainfall as an indicator of freshwater runoff stimulates migration and schooling due to salinity fluctuations. Regional differences of climatic effects on fisheries catch were particularly evident between southeast Queensland, the tropical east coast and Gulf of Carpentaria. There is still a lack of understanding about some of the fundamental biological mechanisms behind the relationships detected, e.g. the influence of salinity trigger values on mud crab movement, but these observations could formulate hypotheses for future work. The fact that the relationship between catch and climate variables is not consistent between regions (as previously assumed) is also worth further attention, reiterating the importance of spatiallyexplicit management strategies for these fisheries in the face of climate change. Warming and greater climate extremes predicted for Australia (Hughes, 2003) can alter primary production, regional currents and water quality, which may cause a change in fish migration, abundance, growth and survival. This trend has consequences for the catchability of commercial fish species and the quality or size of the catch. Catch rates may then be reduced in certain areas and fishing pressure increased (e.g. increase of fishing days and net size) as a compensatory response, risking overexploitation and economic loss. Studies about recruitment mechanisms and the effect of climate factors on annual and seasonal catch can provide crucial information for multispecies management and help predict the long-term consequences of climate change (Myers, 1998). There is the need for long-term studies which can provide further insight into the relationships between climate variables and CAE to better explain catch variability. Understanding how interactions between climate variables and coastal fish species can have such a

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marked effect is essential when considering the wider implications of climate change on coastal fisheries.

Acknowledgments We would like to thank Dr. Anthony Richardson (CSIRO) and anonymous reviewers for helpful comments and revision. Many thanks to the Queensland Primary Industries and Fisheries Assessment and Monitoring Unit (Department of Employment, Development & Innovation) for provision of fish catch data and to the Bureau of Meteorology for access to temperature and rainfall data.

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