Distribution of Antarctic toothfish Dissostichus mawsoni along East Antarctica: Environmental drivers and management implications

Fisheries Research 219 (2019) 105338

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Distribution of Antarctic toothfish Dissostichus mawsoni along East Antarctica: Environmental drivers and management implications

T

Peter Yatesa, , Philippe Zieglera, Dirk Welsforda, Simon Wotherspoona,b, Paul Burcha,1, Dale Maschettea,2 ⁎

a b

Department of the Environment and Energy, Australian Antarctic Division, 203 Channel Highway, Kingston, Tasmania, Australia Institute for Marine and Antarctic Studies, University of Tasmania, 20 Castray Esplanade, Battery Point, Tasmania, Australia

ARTICLE INFO

ABSTRACT

Handled by George A. Rose

As Antarctic fish species experience increasing anthropogenic pressures there is a growing need to characterise the structure and function of their populations and understand how they may respond to changes in their environment. We integrated fishery-dependent and environmental data from years 2003–2017 to investigate the distribution of Antarctic toothfish Dissostichus mawsoni along a c. 3000 nm expanse of the shelf and slope along East Antarctica (30–150 °E). Spatially-explicit generalised additive mixed models were used to characterise the environmental drivers of relative abundance, mean individual weight, maturity-stage composition and sex ratio. Antarctic toothfish were not randomly distributed across the region, and spatial variations were characterised by complex relationships involving topography, temperature, salinity and sea ice. In particular, catch rates were highest at depths between 1000–1700 m. Mean weight and the proportion of fish that were mature both increased with depth and decreased with bottom temperature. Model predictions were also used to develop hypotheses relating to population function, including the location of nursery and spawning areas. This characterisation of the population can facilitate evaluation of fishing impacts in East Antarctica, and improve our understanding of the role of toothfish in Antarctic and Southern Ocean ecosystems.

Keywords: Fisheries management Habitat use Antarctica Dissostichus mawsoni

1. Introduction Characterising the relationships between species and their environment can improve our understanding of important habitats and population structure. For harvested species it is particularly useful to identify areas that make disproportionally large contributions to population productivity such as spawning or nursery habitats (Beck et al., 2001) and to identify stock structure for management purposes (Hilborn and Walters, 1992; Begg and Waldman, 1999). Understanding the drivers of distribution patterns can also be of broader predictive use for conservation planning, the development of resource management strategies, and evaluating responses to environmental change (Murphy et al., 2007; Goethel et al., 2011; Bestley et al., 2018). Given their role in structuring marine communities, information on the spatial distributions of high trophic-level predators can also facilitate understanding of ecosystem function including the identification of ecologically important regions (Block et al., 2002).

Spatial distributions of marine organisms can be influenced by biological interactions and environmental drivers operating across a range of spatial and temporal scales (Silk et al., 2016). For example, some deep-sea teleosts occupy warmer waters during early life-history stages compared to later stages (Shephard et al., 2007; Dunn et al., 2009; Moteki et al., 2009) which may be related to ontogenetic variation in optimal temperature for growth (Björnsson and Steinarsson, 2002). Individuals may also move into deeper water as they grow (Dunn et al., 2009; Moteki et al., 2009; Péron et al., 2016). These movements are generally attributed to various trade-offs between predation risk and energetic requirements (Björnsson and Steinarsson, 2002; Heithaus, 2007) or size-selective harvesting in shallow waters (Frank et al., 2018). Hence, species-environment relationships are complex and difficult to predict and characterising them is particularly challenging for fish species in the deep sea and in geographically remote or sparsely sampled regions. The remote coastal and pelagic ecosystems along East Antarctica

Corresponding author. E-mail address: [email protected] (P. Yates). 1 Present address: Commonwealth Scientific and Industrial Research Organisation, Castray Esplanade, Battery Point, Tasmania, Australia. 2 Present address: Fisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, 20 Castray Esplanade, Battery Point, Tasmania, Australia. ⁎

https://doi.org/10.1016/j.fishres.2019.105338 Received 18 February 2019; Received in revised form 22 June 2019; Accepted 29 July 2019 Available online 13 August 2019 0165-7836/ Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.

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Fig. 1. Divisions within the area of the Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR) (solid lines). The study region in Divisions 58.4.1, 58.4.2 and 58.4.3b is marked by bold solid lines. Black dashed-line polygons are CCAMLR exploratory fishery research blocks. Grey lines = 2000 m depth contour. Light grey shading = permanent ice shelf. Map projection = Lambert Azimuthal Equal Area. Bathymetry data are polygonised GEBCO Digital Atlas 2003 data courtesy of the Australian Antarctic Data Centre (http://data.aad.gov.au/aadc/). CCAMLR Division and research block data are from the CCAMLR online Geographical Information System (https://www.ccamlr.org/en/data/online-gis).

between 30–150 °E (Fig. 1) have been the subject of increasing research attention since the early 1980s (Nicol and Raymond, 2012). The majority of research has examined the relationships between large-scale physical and chemical environments and biological productivity (reviewed in Nicol, 2006). Circulation patterns and distributions of coastal polynyas, ice shelves and ice bergs have been linked with spatial variation in primary productivity (Arrigo et al., 2015; Ugalde et al., 2016), zooplankton abundance (Nicol et al., 2000; Bestley et al., 2018) and the movements of several air-breathing predators (Raymond et al., 2015). However, the influence of ice and other environmental factors on the distributions of high trophic-level teleosts remain largely unknown. Antarctic toothfish Dissostichus mawsoni are high trophic-level predators and the largest teleost fish in Antarctic waters (Eastman, 1993). They utilise a broad range of habitats throughout their lifespan, from the epipelagic as planktonic larvae to bentho-pelagic slope habitats in excess of 2000 m depth (Hanchet et al., 2010). While this species is usually caught near the seafloor using demersal longlines, adults are

likely to use the entire water column (DeWitt et al., 1990; Stevens et al., 2014). Genetic studies have indicated that discontinuous habitat such as trenches and abyssal plains contribute to the development of genetically distinct populations at the circumpolar scale, although Antarctic toothfish along East Antarctica are regarded as a single stock spread across multiple management divisions (Kuhn and Gaffney, 2008). Over smaller spatial scales, the movements of tagged fish, and spatial variation in length compositions, indicate an ontogenetic habitat shift toward deeper waters as fish grow older (Near et al., 2003; Hanchet et al., 2010; Welsford, 2011). Aside from these broad associations with depth, the distribution and population structure of Antarctic toothfish along East Antarctica remain poorly understood. In particular, spatial variation in demographic factors such as sex ratio and maturity-stage composition remain unknown, and the knowledge of these factors is important for the development of population hypotheses and fisheries stock assessments. The Commission for the Conservation of Antarctic Marine Living 2

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Resources (CCAMLR) is responsible for the management of Antarctic toothfish fisheries within its Convention area. Exploratory longline fisheries targeting Antarctic toothfish have been operating in East Antarctica (Divisions 58.4.1 and 58.4.2, Fig. 1) since 2003. However, integrated stock assessments and catch limits set according to CCAMLR decision rules (Hanchet et al., 2015) have yet to be developed. Recent qualitative assessments indicate that current levels of fishing in the region are ecologically sustainable (Patterson and Mazur, 2018), however Illegal, Unreported and Unregulated fishing activities in this region are likely to have led to localised depletion of Antarctic toothfish, particularly in some areas of the continental slope that are predictably free of sea ice during the Austral summer (Delegation of Australia and CCAMLR Secretariat, 2018). Quantifying stock structure and developing hypotheses relating to the spatial distribution of spawning, settlement and nursery areas are prerequisites for effective fisheries stock assessment and management (Hilborn and Walters, 1992; Begg and Waldman, 1999; Cadrin et al., 2014). Methods to evaluate stock structure and boundaries include the evaluation of life history characteristics (Berger et al., 2012; McBride, 2014), tagging studies (Sibert et al., 2011; Farestveit et al., 2009; Hanchet et al., 2010), morphometric analyses (Tracey et al., 2006; Stransky, 2014), otolith microchemistry (Begg and Brown, 2000; Gillanders, 2001; Fowler et al., 2017) and genetic methods (Antoniou and Magoulas, 2014; Mariani and Bekkevold, 2014; Toomey et al., 2016). The identification of the factors that influence fish distributions are also required in order to evaluate potential responses to environmental conditions and potential changes. Here, we (1) characterise the relationships between environmental factors and Antarctic toothfish catch rates and catch composition in order to evaluate the distribution of the fish population across East Antarctica, (2) generate spatially-explicit predictions of the stock structure, and (3) refine hypotheses regarding potential ecological drivers of stock structure.

data (https://www.ccamlr.org/en/node/74776) and CCAMLR Scheme of International Scientific Observation data (https://www.ccamlr.org/ en/science/ccamlr-scheme-international-scientific-observation-siso). We used data from Divisions 58.4.1, 58.4.2 and 58.4.3b collected during 2003–2017 on-board of 23 authorised commercial fishing vessels from ten CCAMLR Members. Longline gear types included autoline (9.4% of hauls), Spanish longline (59.4%) and trotline (30.6%). Details on gear configuration can be found at the CCAMLR Fishing Gear Library (https://www.ccamlr.org/en/publications/fishing-gear-library). In total, 5153 tonnes of Antarctic toothfish were captured by 4373 longline deployments during 51 fishing voyages. Fishing depths ranged from 550 to 3560 m, with a mean of 1414 m. Sea ice extent generally limits fishing to the period between November and March. All vessels had 100% observer coverage. Biological data, collected from randomly sampled Antarctic toothfish included total length (cm; n = 75,565), sex (n = 75,509) and macroscopic gonad stage (n = 75,694). Sex and macroscopic gonad stage were determined according to the scale in Kock and Kellermann (1991). Fish with macroscopic gonad stage ≥3 were categorised as mature (Yates et al., 2018). Stage 3 females were those with enlarged gonads that contained yellow-orange oocytes of two sizes and were starting to swell the body cavity. Stage 3 males were those with large, white and convoluted gonads with no milt produced when pressed or cut. 2.3. Data analyses Separate analyses were conducted for four dependent variables of interest: 1 Catch rate (kg/1000 hooks). 2 Mean weight (total catch kg/number of fish). 3 Proportion mature (number of fish with maturity stage ≥3/number of staged fish). 4 Proportion female (number of female fish/total number of fish). Males and females were also analysed separately. Individuals < 40 cm length were excluded because of the difficulty in macroscopically sexing juvenile gonad tissues.

2. Materials and methods 2.1. Study area and fisheries

The analyses evaluated a number of potential covariates relating to the fishing operation and environmental factors, including fishing season, fishing vessel, gear type, fishing depth, latitude and longitude, sea ice cover and range, distance to the nearest polynya, sea floor slope, sea floor water temperature, and salinity. Each observation was assigned a depth value from the GEBCO 2014 topographic dataset (http://www.gebco.net/data_and_products/gridded_bathymetry_data/ ), at its initial resolution of 0.0083° × 0.0083°, using the function raster::extract (Hijmans, 2016) in R. Because of the potential significance as a spawning habitat, data from Division 58.4.3b was included in analyses of mean weight, proportion mature and sex ratio. Data from Division 58.4.3b were not included in analyses of catch rate because this alleviated confounding that involved mean ice cover and salinity. Hence mean ice cover and salinity could be included in models of catch rate. The slope of the seafloor was estimated from the GEBCO 2014 topographic dataset using the R function raster::terrain (Hijmans, 2016), and the approach used in Péron et al. (2016). Slope in degrees was calculated based on depths in the eight neighbouring cells, and then converted into a categorical variable in order to facilitate interpretation and reduce the effect of uncertainty in the slope estimations. Six categories were defined ranging from flat areas (0–0.5°) to very steep slopes (> 16°). Sea floor temperature (°C) and sea floor salinity were derived for a 0.1 × 0.1° degree grid from regional ocean modelling system (ROMS) models at a spatial resolution of 5 km (Galton-Fenzi et al., 2012, Online Resource 1). Distance to the nearest polynya area was based on AMSR-E satellite estimates of daily sea ice concentration at 6.25 km resolution.

The study region included CCAMLR Divisions 58.4.1, 58.4.2 and 58.4.3b (BANZARE Bank), which together span approximately 3000 nm along Eastern Antarctica within the Indian sector of the Southern Ocean (56–70 °S, 30–150 °E; Fig. 1). The East Antarctic coastline is relatively linear compared to other stretches of the Antarctic coastline and this region potentially provides contiguous habitat for Antarctic toothfish (Welsford, 2011). Bathymetric features include a number of depressions and canyons on the continental shelf particularly in the region between 30–80 °E (Nicol and Raymond, 2012). Regional oceanic circulation patterns are dominated by the westward-flowing Antarctic Coastal Current close to shore and an eastward-flowing Antarctic Circumpolar Current further offshore (reviewed in Nicol and Raymond, 2012). The highest primary production occurs in conjunction with shallow waters and is linked to the supply of iron from melting ice (Smetacek and Nicol, 2005). In Divisions 58.4.1 and 58.4.2, authorised longline vessels have targeted toothfish since 2003, initially in 10°-longitude-wide smallscale research units (SSRUs), and since 2014 in smaller research blocks (< c. 5°-longitude-wide; SC-CAMLR, 2016a,b; Fig. 1). Demersal fishing was prohibited in waters shallower than 550 m in 2003 (CCAMLR Conservation Measure 41-05 and 41-11). Fishing by authorised longline vessels in Division 58.4.3b occurred between 2004 and 2012. In all three Divisions, illegal, unreported and unregulated (IUU) fishing was prevalent particularly prior to 2014. 2.2. Sampling This study included data from CCAMLR fine-scale catch and effort 3

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Processing steps and metadata for temperature, salinity, and polynya data sources are located at http://data.aad.gov.au/aadc/metadata/ metadata_redirect.cfm?md=AMD/AU/Polar_Environmental_Data. Mean sea ice cover and the range of sea ice cover were estimated in order to investigate ecological relationships that may occur over broad spatio-temporal scales. Calculations used daily percentage ice cover at a grid cell size of 25 × 25 km which were generated from brightness temperature data from Passive Microwave Data (Nimbus-7 Scanning Multichannel Microwave Radiometer [SMMR] and Defence Meteorological Satellite Program Special Sensor Microwave/ImagerSpecial Sensor Microwave Imager Sounder (Cavalieri et al., 1996, updated yearly). Mean sea ice cover was estimated from the average daily percent cover in January–March across years 2003–2017. Range (difference between highest and lowest value) in sea ice cover was estimated from the average magnitude of monthly (January–March) ranges in daily percent cover across years 2003–2017. Hauls were assigned their mean sea ice cover, mean sea ice range, distance to polynya, sea floor temperature, sea floor salinity and slope using the function raster::extract (Hijmans, 2016). Prior to model fitting, data exploration was carried out following Zuur et al. (2009) and Zuur et al. (2010). Cleveland dotplots were used to check for outliers. Conditional boxplots, pairwise scatterplots, Pearson correlation coefficients and variance inflation factors (R package car Fox and Weisberg, 2011) were used to investigate the relationships between variables. Non-linear relationships were visible in scatterplots and boxplots of dependent variables versus covariates, as well as in plots of residuals from saturated generalised linear models for each dependent variable (R package MASS Venables and Ripley, 2002). Data exploration prompted the exclusion of a small number of hauls with a difference of > 500 m between GEBCO and vessel-reported depths (n = 197), depth > 2000 m or salinity > 34.95 (n = 13), unreasonably high hook numbers in relation to line length (n = 5), soak time > 100 h (n = 74), and hauls using the fishing systems other than autoline, Spanish longline or trotline systems (n = 24). Distance to polynya, sea floor slope and sea-ice range were confounded with multiple other covariates and therefore excluded from analyses. With inclusion of data from Division 58.4.3b, correlation was apparent between mean ice cover or salinity and multiple other covariates, and therefore mean ice cover and salinity were excluded from models of mean weight, proportion mature and proportion female. The remaining data had < 2% hauls with zero toothfish catch, zero missing covariate values, variance inflation factors < 3 and no visible relationships between covariates. Spatially-explicit Generalised Additive Mixed Models (GAMM) were fitted using restricted maximum likelihood (REML) and implemented in R 3.4.3 with the function mgcv::gam (Wood, 2006). Catch rate and mean weight data were modelled using a Tweedie error distribution and log link function. Models of proportion mature and proportion female data used binomial error distributions with a logit link function, and observations were weighted according to total number of fish staged and sexed, respectively. Continuous covariates were modelled as splines, and latitude and longitude were combined in a bivariate spline. Based on a priori assumptions of dependence structure in the data for vessel (23 levels) and fishing season (15 levels), these variables were included as independent random effects in all models (Zuur et al., 2013). All combinations of fixed terms were compared, along with the null model, based on the Akaike Information Criterion (AIC). The model with the lowest AIC was considered optimal and models within 2 AIC were considered equivalent (Burnham and Anderson, 2002; Whittingham et al., 2006). The significance of the gear factor was assessed using likelihood ratio tests between models with and without gear (using stats::anova, R Development Core Team, 2016). Deviance residuals of starting models and models with ΔAIC (difference in AIC compared to the model with the lowest AIC) < 2 were plotted as histograms and q–q plots (mgcv::gam.check, Wood, 2006), as well as against observed and fitted values, and included and excluded

covariates to check for homogeneity, independence and model fit. Residuals were then plotted by geographic position according to their sign and magnitude for evaluation of spatial independence. Spatial independence was also assessed using semi-variograms (variogram::gstat, Pebesma, 2004), with horizontal bands of points indicating independence (Zuur et al., 2009; Zuur, 2012). Dependence structure in the data coinciding with the establishment of research blocks in 2014 was tested by comparing the AIC, effect plots, model fits and spatial predictions between lowest-AIC models with and without an additional two-level factor, i.e. with and without research blocks. On the same basis, we also compared (1) alternative bivariate smooth functions for spatial coordinates (spline versus tensor product, Wood, 2006), (2) omission of latitude, (3) omission of latitude and longitude, and (4) alternative dimensionalities of the smoothing matrix for each term (k). We checked the adequacy of k by checking for patterns in model residuals, as well as the basis dimension check (mgcv::gam.check, Wood, 2006) as recommended in Wood (2006). The contributions of each explanatory variable to models (effect plot) were plotted with all other variables held fixed at a representative value (median or most frequent factor level, R package visreg; Breheny and Burchett, 2013a,b). The most parsimonious model with ΔAIC < 2 was used to generate predictions for each dependent variable. Predictions were limited to the observed range of all covariates in the model. This meant that extrapolations were limited to latitude and longitude in two-dimensional space only. Predictions were made with vessel, season and gear fixed at their most common factor level. Spatial cross-validations were performed for the preferred models to evaluate the influence of each spatial cluster on model predictions, and the predictive performance of the models. For each of the four dependent variables, k-means clustering of the observations was performed using the function stats::kmeans in R. This function uses the algorithm of Hartigan and Wong (1979) to partition the spatial data points into 10 groups such that the sum of squares from points to the assigned cluster centres is minimized. Preferred models were then iteratively re-fit, and predicted values generated, with a single cluster omitted from the model. The fitted values from the model containing data from all clusters were compared with the fitted values from models with a single cluster removed. To evaluate the predictive performance of the models, the fitted values from the models with a single cluster removed were plotted against the observed values comprising the omitted cluster. To prevent infinite values of logit transformed proportions, observations of proportion-female and proportion-mature were changed so that maximum values were no higher than 0.99 and minimum values were no lower than 0.01. 3. Results 3.1. Catch rate The preferred model for Antarctic toothfish Dissostichus mawsoni catch rates was given by: Catch rate ∼ β0 + depths + mean ice covers + temperatures + salinitys + longitude, latitudes + vesselre + seasonre where β0 is the intercept and subscripts s and re represent spline smooths and random effects, respectively. Longitude and latitude were included as a bivariate spline smooth. This model was the simpler of two models that were within 2 AIC (Table 1). The addition of gear type resulted in an equivocal decrease in AIC of 1.78. Catch rates increased with increasing depth up to around 1000 m, remained stable between 1000–1700 m and then decreased sharply in depths > 1700 m (Fig. 2). Catch rates were highest for temperatures of −1.2 to −0.8 °C and for mean ice cover between 10–40%. Across the small salinity range, catches were highest at lower salinity levels. Fitted catch rates varied non-linearly with longitude, and generally increased with increasing latitude. 4

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Table 1 Parameters and fit of the final generalised additive mixed model of Antarctic toothfish Dissostichus mawsoni catch rates. All covariates were significant in the model (p < 0.001).

Depth Ice mean Temperature Salinity Longitude, Latitude Vessel Season

Fit

df

k

F

p-value

4.07 6.61 7.01 5.76 21.37 20.41 12.57

9 9 9 9 29 23 15

9.87 6.00 4.42 4.79 8.27 270.19 353.44

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Table 2 Parameters and fit of the final generalised additive mixed model of Antarctic toothfish Dissostichus mawsoni mean weight. All smooth terms were significant in the model (p < 0.001).

Depth Temperature Longitude, Latitude Vessel Season

Fit

Adjusted R2

Deviance explained

n

0.27

31.5%

3092

df

k

F

p-value

3.53 6.77 25.98 20.72 12.87

9 9 29 23 15

99.63 11.87 13.19 303.76 383.04

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Adjusted R2

Deviance explained

n

0.608

61.7%

3843

3.3. Proportion mature

3.2. Mean weight

The preferred model for Antarctic toothfish maturity composition is given by:

The preferred model for Antarctic toothfish mean weight is given by: lati-

Proportion mature ∼ β0 + depths + temperatures + longitude, latitudes + vesselre + seasonre

This model was the simpler of two models that were within 2 AIC (Table 2). The addition of gear type resulted in an equivocal decrease in AIC of 1.93. Mean weight increased with depth and decreased with bottom temperature (Fig. 3). The effect of latitude on mean weight varied across longitude, with a marked latitudinal variation to the east of 80 °E compared to the west (Fig. 3).

This model had the lowest AIC, and was the simpler of two models that were within 2 AIC (Table 3). The addition of gear type resulted in an equivocal increase in AIC of 1.28. The same covariates, plus gear type, also comprised the only model with ΔAIC < 2 for analyses of males and females separately (Online Resources 2–4). Similar to mean weight, the proportion of fish that were mature increased with depth and decreased with bottom temperature (Fig. 4). In general, the

Mean weight ∼ β0 + depths + temperatures + longitude, tudes + vesselre + seasonre

Fig. 2. Effects on Antarctic toothfish Dissostichus mawsoni catch rates from a generalised additive mixed model with vessel and fishing season as random effects. Plots show the partial effects of the respective factor including their 95% confidence intervals (grey shading). Effects were plotted with other variables held at their medians or most common factor level (depth =1410.5 m, temperature = −1.11 °C, salinity = 34.80, mean ice cover = 19.77%, longitude = 99.38°, latitude = −65.07°, vessel = Vessel1, season = 2008). Upper and lower black ticks are for observations with positive and negative residuals, respectively.

5

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Fig. 3. Effects on Antarctic toothfish Dissostichus mawsoni mean weight from a generalised additive mixed model with vessel and fishing season as random effects. Plots show the partial effects of the respective factor including their 95% confidence intervals (grey shading). Effects were plotted with other variables held at their medians or most common factor level (depth = 1445 m, temperature = −1.07 °C, longitude = 97.88°, latitude = −64.74°, vessel = Vessel1, season = 2008). Upper and lower black ticks are for observations with positive and negative residuals, respectively. Table 3 Parameters and fit of the final generalised additive mixed model of proportion of Antarctic toothfish Dissostichus mawsoni that were mature (stage 3+). All smooth terms were significant in the model (p < 0.001).

Depth Temperature Longitude, Latitude Vessel Season

Fit

df

k

χ2

p-value

7.82 8.14 28.45 20.56 13.21

9 9 29 22 15

418.1 133.5 3157.7 23394.5 19926.9

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Adjusted R2

Deviance explained

n

0.673

62.3%

3154

Table 4 Parameters and fit of the final generalised additive mixed model of proportion of Antarctic toothfish Dissostichus mawsoni that were female. Smooth terms for depth, geographic coordinates and season were significant in the model (p < 0.001). Gear (χ22,3397 = 6.76; p = 0.03), vessel and temperature were moderately significant. Note that the parametric terms have a z statistic and the smooth terms have a χ2 statistic estimated. Parametric coefficients

Intercept Gear: Spanish Gear: Trotline

Estimatea

Standard error

z-value

p-value

0.05 −0.01 0.13

0.06 0.07 0.08

0.84 −0.16 1.74

0.40 0.87 0.08

Smooth terms

proportion of mature fish increased with latitude, however these changes were more extreme at longitudes east of 90 °E (Fig. 4).

Depth Temperature Longitude, Latitude Vessel Season

3.4. Proportion female For the proportion of females in the catch, the following model had the lowest AIC and no other models were within 2 AIC (Table 4):

Fit

Proportion female ∼ β0 + depths + temperatures + longitude, latitudes + gear + vesselre + seasonre

a

The proportions of female fish increased with depth from 1000 m

df

k

χ2

p-value

3.67 6.89 24.19 11.06 11.03

9 9 29 22 15

61.15 18.73 231.47 52.90 125.52

< 0.001 0.02 < 0.001 0.02 < 0.001

Adjusted R2

Deviance explained

n

0.183

18.6%

3397

Coefficients are in logit space.

Fig. 4. Effects on proportions of mature Antarctic toothfish Dissostichus mawsoni from a generalised additive mixed model with vessel and fishing season as random effects. Plots show the partial effects of the respective factor including their 95% confidence intervals (grey shading). Effects were plotted with other variables held at their medians or most common factor level (depth = 1442.5 m, temperature = −1.08 °C, longitude = 97.75°, latitude = −64.73°, vessel = Vessel1, season = 2008). Upper and lower black ticks are for observations with positive and negative residuals, respectively.

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Fig. 5. Effects on proportions of female Antarctic toothfish Dissostichus mawsoni from a generalised additive mixed model with vessel and fishing season as random effects. Plots show the partial effects of the respective factor including their 95% confidence intervals (grey shading). Effects were plotted with other variables held at their medians or most common factor level (depth = 1442 m, temperature = −1.07 °C, gear = Spanish, longitude = 97.62°, latitude = −64.70°, vessel = Vessel1, season = 2008). Upper and lower black ticks are for observations with positive and negative residuals, respectively.

(Fig. 5), while temperature and gear were not important drivers of sex ratio in the catch (Table 4, Fig. 5). For comparison, removal of temperature from the preferred model increased the AIC by > 2 and did not substantially influence the model fit, smooths, or spatial predictions across Divisions. Fitted proportions of females were lowest in the far north-west (north of −64 °S and west of 60 °E), however there was a relative lack observations in that region. For proportion mature and mean weight, the covariates explained > 60% of variation in these variables (Table 2, Table 3) that. Catch rates were reasonably well determined (Table 1), whereas the fit to the proportion of female in the catch was relatively poor in comparison (R2 = 0.183, Table 4). For all dependent variables, model residuals were randomly distributed around zero, and there were no relationships between the residuals and fitted values, included variables or excluded variables. Positive and negative residuals of various magnitudes were spread across the geographic area, and there were no trends between semivariance and distance. In some cases, basis dimension (k) checking results were significant (p < 0.001) for mean ice cover, temperature, salinity and latitude/longitude; however there were no visible patterns in the residuals, and p-values remained < 0.05 when k was manually increased two-fold. For each dependent variable, the following modifications resulted in zero or equivocal decrease in AIC: (1) the addition of a two-level factor coinciding with the establishment of research

blocks, (2) changing the bivariate smooth for spatial coordinates from spline to tensor product, (3) removal of spatial coordinates, and (4) two-fold increases in k. In models of mean weight, proportion mature and proportion female, the omission of data from Division 58.4.3b did not substantially influence the model fit, smooths, or spatial predictions across Divisions. For all of the dependent variables, consistency in the slope of fulldata fitted values versus cross-validated fitted values indicated that modelled relationships were preserved when individual spatial clusters of data points were removed (Online Resources 5–8). Hence no single spatial cluster of data drove the modelled relationships. However, some cases of an offset between the predictions with and without a single cluster were most likely due to changes in the bivariate smooth surface in latitude and longitude. This offset was most pronounced for spatial clusters at the edge of the geographic extent in the west (Gunnerus ridge) and north (Division 58.4.3b). Therefore there was some information in the bivariate smooth in latitude and longitude that was not explained by the other covariates. This result provides support for the inclusion of the bivariate smooth in the model, particularly for predictions. There was considerable noise in the observed values, however the observed values in a cluster generally increased with the predictions in the same cluster (Online Resources 9–12). Furthermore, the broad spatial variations in observed catch rate, mean weight, proportion mature and proportion female (Online Resource 1) were also present in 7

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Fig. 6. Prediction maps of Antarctic toothfish Dissostichus mawsoni catch rates (top panel), mean weight (second panel), proportion mature (third panel) and proportion female (bottom panel) from the respective final generalised additive mixed models. Grey lines = Bathymetry contours (−1000, −2000 and −3000; south to north), black lines = small-scale research unit (SSRU) boundaries, green lines = research block boundaries. Predictions were calculated for a 0.25° × 0.125° grid within the observed ranges of all covariates. Black dots indicate grid cells with ≥1 observation. Predictions were made with gear = Spanish, vessel = Vessel1, and season = 2008). Map datum = WGS84 (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

the spatial predictions for these variables (Fig. 6).

across the region (Fig. 6). In some instances, areas of high predicted catch rates were based on extrapolation into un-sampled areas (e.g. SSRUs 58.4.1B and 58.4.1H, Fig. 6), however these did not coincide consistently with either high or low values of any of the covariates included in the model (Online Resource 1).

3.5. Spatial predictions The prediction map indicated spatial heterogeneity in catch rates 8

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Predicted mean weight was relatively high on SSRU 58.4.2A (Gunnerus Ridge) and across 58.4.1E–58.4.1G, and was generally low in shallow waters nearest to the Antarctic continent (Fig. 6). Predicted mean weight was low around Prydz Bay, particularly in shallow water within the bay and to the east of 75 °E. Predictions in areas where there were no observations, such as within Prydz Bay and in SSRU 58.4.1B, will require validation before any meaningful conclusions can be made about mean weight of Antarctic toothfish in those areas. Predicted mean weight was variable in Division 58.4.3b, with relatively low values in the north-west and moderate-to-high values in south-east. Predicted mean weights were aligned with spatial variation in observed length distributions in these SSRUs, which were shifted toward larger sizes in SSRU 58.4.2A, and progressed gradually toward larger sizes moving from west to east starting at SSRU 58.4.2C (Online Resource 13). Predicted proportions of mature fish varied from 1 to 99%, and were spatially heterogeneous across the study region (Fig. 6). The model predicted a number of areas with high proportions of mature fish, particularly on in SSRU 58.4.2A (Gunnarus Ridge), 58.4.1C and 58.4.1D (Bruce Rise Plateau), and the majority of Division 58.4.3b (BANZARE Bank). These locations were within the spatial extend of the data. Mature females were more strongly constrained to these locations compared to mature males which were spread more broadly across the study area (Online Resource 13). Predicted proportions of females for areas within the range of the covariates were spatially heterogeneous albeit generally close to 0.5 (minimum = 0.38, maximum = 0.70; Fig. 6). Again, SSRU 58.4.2A (Gunnerus Ridge) and parts of Division 58.4.3b (BANZARE Bank) differed strongly from other areas, with lowest female proportions predicted here.

(Hanchet et al., 2008), and are common in deep-sea fish including Patagonian toothfish (Péron et al., 2016), orange roughy Hoplostethus atlanticus (Dunn et al., 2009) and roundnose grenadier Coryphaenoides rupestris (Longmore et al., 2011). These patterns may have evolved through multiple ecological processes including competition, morphological and physiological changes, diet shifts, predator avoidance or reproductive activity (reviewed in Péron et al., 2016). Grenadiers (Macrourus spp.) are the most important prey item in the diet of Antarctic toothfish in the Ross Sea (Stevens et al., 2014). High abundances of grenadiers have been observed between depths of 900 and 1900 m (Pinkerton et al., 2013), and toothfish moving into deeper water as they grow may benefit from this abundance of prey. Adult toothfish inhabiting depths greater than c. 1000 m may also benefit from reduced predation pressure given that these depths are beyond the diving limits of killer whales Orcinus orca (Reisinger et al., 2015) and elephant seals Mirounga leonine (Hindell et al., 1991; Kock et al., 2006). Conversely, the utilisation of shallower water by younger cohorts compared to adults may alleviate intra-specific predation pressure exerted by adult toothfish (Stevens et al., 2014; Péron et al., 2016). While ontogenetic movement of older fish into deeper waters has also been attributed to size- and depth-selective harvesting across a population’s range (Frank et al., 2018), active fish movement has been demonstrated in tag-recapture studies of Antarctic toothfish in the Ross Sea (Dunn et al., 2005) and Patagonian toothfish on the Kerguelen Plateau (Welsford et al., 2014). Hence the changes in the East Antarctic toothfish population with depth are likely to be related to nearshore nursery areas and ontogenetic shifts in habitat use by individuals. Depth-related and longitudinal trends in spatial predictions and observed size distributions (Online Resource 13) suggested a progression of fish into deeper water and away from the Prydz Bay area as they grow. Therefore Prydz Bay may serve as a nursery area for Antarctic toothfish (Taki et al., 2011; Welsford, 2011). Beck et al. (2001) argue that the density of young fish should be examined in conjunction with (1) juvenile growth rates, (2) survival and (3) movement to adult habitats to determine whether an area contributes disproportionally to population productivity. Practical application of this framework has proven difficult (e.g. Kraus and Secor, 2005; Heupel et al., 2007) and the required data are not presently available for Antarctic toothfish. Nonetheless, our study provides a useful foundation for future research investigating the nursery value of shallow waters near Prydz Bay and potential links to spawning areas. The majority of fish were in spawning condition in three particular locations on Gunnerus Ridge, BANZARE Bank and Bruce Rise Plateau, indicating that these areas may be important spawning areas. Similarly, northward located bathymetric features such as seamounts are regarded as spawning areas for Antarctic toothfish in the Ross Sea (Hanchet et al., 2008; Welsford et al., 2008). Movements of mature toothfish in spawning condition have previously been used to test hypothesise relating to the locations of spawning areas (Hanchet, 2006; Péron et al., 2016). Limited tag-recapture data are available for Antarctic toothfish in East Antarctica. As of February 2018, 58 tagged Antarctic toothfish had been recaptured and 89% of these were recaptured within 40 km of their tagging location (Delegation of Australia et al., 2018). Across a circumpolar extent, tag-recapture data indicate that while most recaptured Antarctic toothfish remained within c. 200 km of their tagging location, some individuals travel long distances of several thousand kilometres (CCAMLR Secretariat, 2016).Tag-recapture data are especially scarce on BANZARE Bank given that fishing by authorised longline vessels in Division 58.4.3b was limited to years between 2004 and 2012. Nonetheless, the movement of a 121-cm male from the shelf slope around 90°E to BANZARE Bank is congruent with the hypothesised transition from shallow nearshore waters to potential spawning grounds on BANZARE Bank. The transportation of Antarctic toothfish eggs and pelagic larvae, and the influence of active swimming by larvae remain poorly understood. Preliminary simulations of the passive drift of eggs and larvae

4. Discussion This study was the first to integrate environmental, catch, biological and operational data spanning more than a decade to investigate the spatial structure of Antarctic toothfish D. mawsoni along East Antarctica. The results clearly indicated that the fish population is not uniformly distributed. Catch rates, and size and maturity-stage catch composition were strongly linked with bathymetry, temperature and geographic location. These results have important implications for current and future management of Antarctic toothfish and their broader ecosystems. In particular, large-scale prediction maps can inform hypotheses regarding the ecological drivers of habitat use and movement of different life-history stages and sexes. Our characterisation of the relationships between toothfish, temperature, depth and sea ice also provide key information to understand potential responses of Antarctic toothfish to environmental change. Non-uniform distributions of Antarctic toothfish and Patagonian toothfish D. eleginoides have been reported in other locations including the Kerguelen Plateau to the north of the Division 58.4.3 (Welsford et al., 2011; Péron et al., 2016), around South Georgia and on the Falklands shelf (Agnew et al., 1999; Arkhipkin et al., 2003; Arkhipkin and Laptikhovsky, 2010), and in the Ross Sea (Mormede et al., 2013). These studies have provided important information for the development of robust stock assessments for toothfish (e.g. Hanchet et al., 2008). In particular, hypotheses regarding the distribution and connectivity of key life-history stages have been used to inform fisheries management strategies at the appropriate spatio-temporal scales for particular populations (Berger et al., 2012; Péron et al., 2016). Hence the population hypotheses generated in the present study provide crucial information to assess fishing impacts in East Antarctica (Hanchet et al., 2015). Mean weight and proportion of mature fish increased with increasing depth, indicating a gradual migration from shallow to deep waters as fish grow. Similar changes in size and maturity of Antarctic toothfish with increasing depth have been observed in the Ross Sea 9

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around Antarctica suggest that, at a depth of 150 m, eggs and larvae from potential spawning grounds south of ˜60 °S were generally retained near the coast, whereas many of those from BANZARE bank were retained in a local BANZARE gyre or transported to the east by the Antarctic Circumpolar Current (Dunn et al., 2012). Interactions between the Antarctic Circumpolar Current and the westward flowing Antarctic Slope Current, and broad-scale cyclonic recirculation within the Australian–Antarctic Basin (McCartney and Donohue, 2007) may influence the delivery of eggs and larvae to shelf and slope waters along East Antarctica. Further research on the biology of early life-history stages, including vertical distributions and active swimming behaviour, is required to understand the effects of prevailing currents, meandering of currents and high eddy activity (Bestley et al., 2019) on the transport of eggs and larvae from the putative spawning grounds reported here. Furthermore, although our analyses included fish in spawning condition, the confirmation of potential spawning areas and sex-specific differences in their utilisation will require additional sampling of actively spawning fish during the winter spawning season. Sexual segregation is common in many fish species on spawning grounds, with males typically arriving earlier and remaining for longer periods resulting in a higher proportion of males on spawning grounds (reviewed in Péron et al., 2016). In this study, there was overlap between putative spawning grounds and areas with slightly higher proportions of male fish (i.e. Gunnerus ridge and parts of BANZARE Bank). In the Ross Sea, male Antarctic toothfish in spawning condition aggregate earlier than the females, and spawning begins in early July (Stevens et al., 2016). Male-biased sex ratios have been reported on the north-western side of the Kerguelen Plateau, particularly in a known spawning area (Lord et al., 2006; Péron et al., 2016), and our spatial predictions suggest that a similar process may occur along East Antarctica. Species-environment relationships reported here can assist in the evaluation of the responses of toothfish to environmental change. Southern Ocean ecosystems are undergoing increases in ocean temperatures, pole-ward shifts of frontal systems and region-specific changes in sea-ice extent and seasonality (reviewed in Constable et al., 2014). Our regional models provide additional support for previous circumpolar models indicating a link between the global distribution of Antarctic toothfish and temperature and sea-ice extent (Cheung et al., 2008; CCAMLR Secretariat, 2015). Some circumpolar models have predicted a decline in the area of suitable habitat for Antarctic toothfish with increasing temperature (Cheung et al., 2008). Indeed, the adaptation of toothfish to low and stable temperatures might limit their resilience to temperature changes (Hofmann et al., 2000), particularly via the sensitivity of recruitment processes to temperature changes (e.g. Belchier and Collins, 2008). Changes in sea ice dynamics may also influence primary productivity and thus prey availability for polar fishes (Eicken, 1992; Legendre et al., 1992; Fuiman et al., 2002; Constable et al., 2014). Post-settlement Antarctic toothfish are generalist feeders (Fenaughty et al., 2003; Roberts et al., 2011; Park et al., 2015; Kang et al., 2017), can move large distances (CCAMLR Secretariat, 2016) and utilise of a broad depth range (Hanchet et al., 2008). These characteristics may enhance resilience to environmental change via movement to more favourable areas or depths (Constable et al., 2014). Antarctic toothfish exhibit characteristics that may both enhance or inhibit their resilience to change, and this complicates forecasting of population‐level responses to changes in temperature and sea ice dynamics. Investigations of species-environment relationships are influenced by the spatio-temporal scales and variables considered, and the sampling and analysis methods used (Burnham and Anderson, 2002; Whittingham et al., 2006). It was not possible to include all possible drivers of toothfish catch rates or composition in these analyses. For example, a variety of biotic factors can also be important for the habitat use of fish including varied trade-offs between predation risk and energetic requirements (Yates et al., 2015; Péron et al., 2016). Competition for limited resources or interspecific predation may necessitate

inter- or intraspecific partitioning of space and food resources, and these factors may be important for Antarctic toothfish along East Antarctica. In addition, the underlying causative mechanisms cannot be confirmed by correlations. Nonetheless, by identifying relationships between Antarctic toothfish catches and environmental variables, and developing hypotheses regarding the locations of spawning or nursery habitats we provide a useful direction for future data collection. 5. Conclusions This study identified heterogeneity in Antarctic toothfish distributions along East Antarctica, which was characterised by complex relationships with a variety of environmental factors. Broad-scale prediction maps describing population structure allowed us to identify suitable areas for different life-history stages. Antarctic toothfish are likely to play a key role in Antarctic marine ecosystem function, and the spatially-explicit models developed in the present study across a vast expanse of East Antarctica could be used to calibrate ecosystem models (e.g. Murphy et al., 2012). These models can be used to improve understanding of the role of Antarctic toothfish in Antarctic and Southern Ocean ecosystems, and the potential impacts of fisheries on Antarctic toothfish and other dependent or related species. Acknowledgements We gratefully acknowledge the assistance of the crew, skippers and scientific observers aboard vessels of Australian Longline Pty Ltd, Pêche Avenir, Pesquerias Georgia S.L, SUNWOO Corporation, and Taiyo A & F Co. Ltd for collecting data included in this paper. Thanks also to Ben Raymond and Mike Sumner for assistance with sourcing and processing environmental data, the CCAMLR Secretariat for assistance with data requests, and to Tim Lamb and Troy Robertson for crucial contributions to database management. This study was funded through the Australian Antarctic Program, Project 4030; with significant in-kind support from the Australian Antarctic Division, and Australian Longline Pty Ltd. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.fishres.2019.105338. References Agnew, D.J., Heaps, D.L., Jones, C., Watson, A., Berkieta, K., Pearce, J., 1999. Depth distribution and spawning pattern of Dissostichus eleginoides at South Georgia. CCAMLR Sci. 6, 19–36. Antoniou, A., Magoulas, A., 2014. Application of mitochondrial DNA in stock identification. In: Cadrin, S.X., Kerr, L.A., Mariani, S. (Eds.), Stock Identification Methods. Academic Press, San Diego, pp. 257–295. Arkhipkin, A., Brickle, P., Laptikhovsky, V., 2003. Variation in the diet of the Patagonian toothfish with size, depth and season around the Falkland Islands. J. Fish Biol. 63, 428–441. Arkhipkin, A.I., Laptikhovsky, V.V., 2010. Convergence in life-history traits in migratory deep-water squid and fish. ICES J. Mar. Sci. 67, 1444–1451. Arrigo, K.R., van Dijken, G.L., Strong, A.L., 2015. Environmental controls of marine productivity hot spots around Antarctica. J. Geophys. Res. Oceans 120, 5545–5565. https://doi.org/10.1002/2015JC010888. Beck, M.W., Heck, K.L., Able, K.W., Childers, D.L., Eggleston, D.B., Gillanders, B.M., Halpern, B., Hays, C.G., Hoshino, K., Minello, T.J., Orth, R.J., Sheridan, P.F., Weinstein, M.P., 2001. The identification, conservation, and management of estuarine and marine nurseries for fish and invertebrates. Bioscience 51, 633–641. https://doi.org/10.1641/0006-3568(2001)051[0633:TICAMO]2.0.CO;2. Begg, G.A., Brown, R.W., 2000. Stock identification of haddock Melanogrammus aeglefinus on Georges Bank based on otolith shape analysis. Trans. Am. Fish. Soc. 129, 935–945. https://doi.org/10.1577/1548-8659(2000)129<0935:SIOHMA>2.3.CO;2. Begg, G.A., Waldman, J.R., 1999. An holistic approach to fish stock identification. Fish. Res. 43, 35–44. https://doi.org/10.1016/S0165-7836(99)00065-X. Belchier, M., Collins, M.A., 2008. Recruitment and body size in relation to temperature in juvenile Patagonian toothfish (Dissostichus eleginoides) at South Georgia. Mar. Biol. 155, 493. https://doi.org/10.1007/s00227-008-1047-3. Berger, A.M., Jones, M.L., Zhao, Y., Bence, J.R., 2012. Accounting for spatial population structure at scales relevant to life history improves stock assessment: the case for Lake

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