Spatial distribution patterns, abundance and ...

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Marine and Freshwater Research http://dx.doi.org/10.1071/MF12263

Spatial distribution patterns, abundance and population structure of deep-sea crab Chaceon macphersoni, based on complementary analyses of trap and trawl data Johan C. Groeneveld A,E, Bernadine I. Everett A, Sean T. Fennessy A, Stephen P. Kirkman B, Jorge Santos C and Wendy D. Robertson A,D A

Oceanographic Research Institute, PO Box 10712, Marine Parade, 4056, Durban, South Africa. Branch Oceans and Coast, Department of Environmental Affairs, Private Bag X2, Rogge Bay 8012, South Africa. C Norwegian College of Fishery Science, University of Tromsø, Breivika, N-9037, Tromsø, Norway. D Deceased 16 May 2003. E Corresponding author. Email: [email protected] B

Abstract. Marine species such as deep-sea geryonid crabs often exhibit high spatio-temporal variability in abundance and size over depth, substratum type and season, therefore data collected from a single gear type may not represent the whole population. Complementary data from trawl (soft substratum) and trap (hard substratum) fisheries were analysed within a general linear modelling (GLM) framework to assess distribution, abundance and population structure of Chaceon macphersoni off eastern South Africa. Catch rates, mean size, maturation size and sex ratio were modelled relative to year, month, depth, latitude and gear effects. Trap and trawl analyses indicated higher abundance as depth increased up to 500 m, and during the austral spring and summer. The mean size of crabs remained constant at all depths sampled, and sex ratios were skewed towards females. Females were smaller than males, and achieved maturity at a smaller size. A standardised index based on trawl data (1988–2010) showed a long-term decline in abundance, with some recovery after 2002, whereas the trap index showed recent local depletions on hard substrata. Using data from two gear types confirmed broad gradients in abundance, but also emphasised subtle trends, such as local depletions on hard substrata, that would not have been apparent from trawl data only. Additional keywords: catch rates, generalised linear modelling, long-term data, population structure, spatial heterogeneity. Received 18 September 2012, accepted 29 November 2012, published online 24 April 2013

Introduction Geryonid deep-sea crabs are widely distributed in world oceans, commonly occurring on continental slopes at depths between 100 m and 1200 m (Hines 1990; Hastie 1995). The genus Chaceon (formerly Geryon) comprises several species that attain a medium to large size with high-quality meat, and supports industrial fisheries in the Atlantic (Le Roux 2001; Wahle et al. 2008; Carvalho et al. 2009; Gutie´rrez et al. 2011), Indian (Groeneveld and Melville-Smith 1995; Melville-Smith et al. 2006) and Pacific Oceans (Guerrero and Arana 2009). Most of these fisheries use baited traps, but in some of them crabs are a by-catch of multispecies trawl fisheries (Fennessy and Groeneveld 1997; Pezzuto et al. 2006). Geryonid crabs inhabit bottom types ranging from sand, mud, flat oozes and gravel to coral mounds and rocky outcrops (Manning and Holthuis 1989; Wenner and Barans 1990; Hastie Journal compilation Ó CSIRO 2013

1995). Species may exhibit habitat partitioning by depth and substratum (Hines 1990) and sexual zonation and seasonal variation with regard to their distribution and catchability (Hastie 1995). An inverse relationship between body size and depth suggests that some species settle at greater depths and subsequently migrate up the slope as they develop (Wigley et al. 1975; Hastie 1995). Older individuals of some species may moult infrequently, with intermoult periods of up to 6–7 years (Lux et al. 1982; Melville-Smith 1989), although MelvilleSmith (1987) suggested that Chaceon maritae females may not moult again after successful copulation. Geryonids are slow-growing and long-lived, and reach sexual maturity at an age of 5–15 years (Hastie 1995; Wahle et al. 2008). Combined with irregular and aperiodic recruitment pulses (Steimle et al. 2001), these features can give rise to considerable variability in stock sizes and a susceptibility to overexploitation. Indeed, www.publish.csiro.au/journals/mfr

Marine and Freshwater Research

ratio, maturity) relative to latitude, depth, season and bottom type, and to construct standardised indices to assess long-term trends in abundance. Material and methods Study area The study area extended along the KwaZulu–Natal (KZN) coast of eastern South Africa, from the Mozambique border at 268540 S to southern KZN at 318420 S (Fig. 1). The continental shelf along this stretch of coastline is narrow with a steep drop-off, except for the central area known as the Natal Bight, where the shelf is up to 50 km wide. The deep-trawling grounds comprise an area of roughly 1750 km2, ranging in depth from 100 to 600 m, with most trawling concentrated in the 300–500-m range, along the continental slope (Fennessy and Groeneveld 1997). The substratum is varied, ranging from mud to hardened accretions of sediment, foraminifera and spicules (Berry 1969). The powerful Agulhas Current flows over the trawl grounds (see Lutjeharms 2006 for a review of the characteristics of this current), and bottom temperature is between 88C and 108C (L. Guastella, c/o University of Cape Town, South Africa; [email protected], unpubl. data). The hard substrata to the north and south of the

AFRICA

28⬚S

St Lucia KWAZULU-NATAL

Richards Bay

Tugela River

00

m

29⬚S

10

several fished stocks have followed a depletion pattern over the past few decades (Melville-Smith 1988a; Armstrong 1990; Hastie 1995; Gutie´rrez et al. 2011), confirming their vulnerability. The geryonid crab Chaceon macphersoni (Manning and Holthuis 1988) occurs at depths of 200–1025 m in the South West Indian Ocean (SWIO) off Mozambique, southern Madagascar and eastern South Africa, and extends westwards up to Cape Columbine along the Atlantic coast of South Africa. Catches of C. macphersoni made off eastern South Africa and Mozambique were attributed to Geryon quinquedens up to 1989 (see Paula e Silva 1985); the family was then revised to include the genus Chaceon, and C. macphersoni was described as a new species (Manning and Holthuis 1988, 1989). C. macphersoni is a large-sized crab, and its distribution overlaps with those of C. maritae and C. chuni off western South Africa, and with that of C. crosnieri off southern Madagascar (Manning and Holthuis 1989). Chaceon macphersoni forms an important retained by-catch in multispecies crustacean trawl fisheries and deep-water trapfisheries for spiny lobsters off southern Mozambique and the KwaZulu–Natal (KZN) coast of eastern South Africa (Paula e Silva 1985; Groeneveld and Melville-Smith 1995; Groeneveld and Cockcroft 1997). The trawl fishery off KZN has been active since the late 1960s, although formal recording of catch and effort statistics did not commence until 1985 (Fennessy and Groeneveld 1997). Historically, C. macphersoni crabs caught by trawlers were discarded, or only larger individuals were retained; however, since the early 1990s, this species has been retained and sold on local markets. Deep-water trapping for spiny lobsters off KZN took place between 1994 and 1997 (Groeneveld and Cockcroft 1997), and between 2004 and 2007 (Groeneveld et al. 2012). Despite its importance to commercial fisheries in the SWIO region, the distribution, abundance and life-history characteristics of C. macphersoni remain virtually unstudied. Whereas the KZN trap fishery operated mainly on hard substrata (gravel and rocks) at depths of 200–500 m, the trawl fishery is restricted to known trawling grounds on soft, muddy and/or sandy substrata extending to .600-m depth. Some overlap occurred along the edges of the trawl grounds, where traps were also set on soft substrata. Nevertheless, the two gear types have very different selectivity properties; trawls catch a proportion of crabs in the tow-path of the net (Melville-Smith 1988b), whereas crabs in the vicinity of a trap may enter it, or not, and may also escape through the trap-opening (Groeneveld et al. 2005). Using data from the two gear types to describe the spatial distribution patterns and population structure of C. macphersoni is therefore preferable to relying on data from a single gear type, which may sample only a part of the population. Given that spatial heterogeneity is a conspicuous feature of the life history of geryonid crabs in general (Hastie 1995; Gutie´rrez et al. 2011), it was hypothesised that gradients in C. macphersoni abundance, size and sex ratios would emerge across depth and area, and that trap and trawl data would show parallel trends, except if bottom type gave rise to habitat partitioning. A generalised linear modelling approach was used to evaluate crab population structure (distribution, size, sex

J. C. Groeneveld et al.

50 200 m 0m

B

Durban 30⬚S

INDIAN OCEAN 31⬚S 0

50

100

Kilometre 30⬚E

31⬚E

32⬚E

Fig. 1. Deep-water trap (2004–2007) and trawl (1988–2010) fishing localities for crustaceans along the coast of KwaZulu–Natal province, South Africa. Symbols indicate hauls by trap (open circles) and trawls (squares).

Geryonid crab fisheries and biology

trawl grounds, which were sampled by traps, consist of gravel and rocky areas (Groeneveld and Cockcroft 1997). Sampling methods All KZN crustacean trawlers are steel vessels, with overall lengths ranging from ,25 to 45 m, and with main engines that generate ,300–600 kW, and are equipped with echo sounders, global positioning systems and track plotters, radar, and VHF/ SSB radios. Since 1985, up to eight permits per year have been available for fishing on the deep KZN trawl grounds (.7 nautical miles from the shore), although only three to five vessels have been active since 2004. In total, 27 individual vessels have participated in the fishery since 1988, several of them for short periods. Trawlers mostly used single otter trawls deployed from the stern, and trawl sizes ranged from 25- to 60-m footrope lengths, with stretched mesh size tapering from 70 mm in the wings to 38 mm in the cod-end, although in 2000, a minimum stretched mesh size of 50 mm was introduced. Trawl speeds were 2–3 kn, and trawling took place on a 24-h basis. The trapping vessel (FV ‘Cape Flower’, 50-m length, 1200 gross register tonnage) operated on an experimental permit between 2004 and 2007, and deployed traps in sets of 70–200 traps along anchored bottom longlines. The majority of traps were barrel-shaped (,0.8 m in length; with a volume of 0.23 m3), made of moulded plastic with a funnelled topentrance, and baited with hake-heads (Merluccius spp.). Metal-framed traps covered with netting (called beehive traps; 0.32 m3) were used to a lesser extent. The median trap soaktime was 48 h, and soak-times .96 h were as a result of strong currents that sometimes submerged marker buoys for several days at a time, delaying the retrieval of traps by the vessel. Trap and trawl catches were sorted by species, size-graded, packed and blast frozen on board. Enquiries of the trawl industry established that there were inconsistencies in the packed category crab sizes over time, so it was decided not to attempt analysis of these size data. Even though crabs were not the target species of the trawl fishery, they have always been kept since the 1990s, because of their relatively high commercial value. Logbooks provided information on the time, duration, depth, position and the estimated catch for each drag, whereas for the trap fishery, an observer accompanied the vessel to record fishing effort (numbers of longlines and traps set, date and time of setting and hauling, latitude and longitude of sets, depth) and details of catches, including the numbers of crabs caught per longline. Biological sampling was conducted by fisheries observers deployed on trawlers (1997 and 2012) and the trapping vessel (2004–2007). The sex of all crabs was determined and carapace width (CW 1 mm) was measured with a Vernier calliper. When catches were too large, however, the first 50 crabs were measured as a subsample. The numbers of egg-bearing females were recorded, and immature and mature female crabs were distinguished on the basis of the shape of the genital opening; a closed slit or partially open slit-shaped opening were considered to indicate immature females, whereas fully opened vulvae blackened at the periphery indicated maturity (Melville-Smith 1987). Damage on the merus of the first pair of walking legs caused by chafing during mating was used as an indicator of


C

maturity of male crabs (Melville-Smith 1987). This technique does not apply to males with a CW .120 mm, because this chafing does not occur in such large specimens, and therefore males .120 mm were assumed to be sexually mature. Samples of a range of crab sizes were taken and individually weighed, and a conversion factor from CW to whole weight (WW 1 g) was determined after fitting a power function by ordinary least-squares. Data analysis Catch rates Nominal catch rates of crabs were determined for trawl data by dividing catch by the number of hours trawled (kg h1 trawled). Catch rates for trap data were defined as the number of crabs caught per trap (numbers trap1). Both datasets comprised a large proportion of zeroes (no crabs caught), and distributions were skewed to the right. The variability in trawl and trap catch rates was explored using generalised linear models (function GLM) in the freely available statistical software package R version 2.14.0 (R Development Core Team 2011). Several approaches are available to model catch-rate series in fisheries (Maunder and Punt 2004). Of these, the delta method, which has been described extensively by Lo et al. (1992) and Maunder and Punt (2004), is one of the most widely applied in fisheries science and was chosen to standardise catch rates for data collected from trap and trawl fisheries in the present study. The method involves fitting two submodels to the data (Lo et al. 1992). In the first submodel, the probability of a non-zero catch is modelled, assuming a binomial error distribution. In the second submodel, only the positive catch is modelled, assuming a log-normal, Poisson, negative-binomial or gamma error distribution. Of these, the gamma model was selected because preliminary tests showed that the relationship between the logarithms of the mean and variance of catch rates (positive values) was close to two (data highly dispersed) (McCullagh and Nelder 1989; Stefańsson 1996). Where trawl catch rate was the response variable (continuous), year, month, depth and vessel were considered as explanatory variables (Table 1). Latitude and longitude were not included in the model because the trawl grounds occupied only a narrow latitudinal range (288S–318S), and because accurate positional reporting commenced only in 2000; before that, vessels simply used a coarse grid-block system. Moreover, records of depth are considered to be more consistent and reliable than those of geographical location, particularly in old records. The explanatory variables year and depth were also used when trap catch rate was the response variable (continuous), although months were combined into seasons because of insufficient data in some months. No traps were set during summer months (December–February) in any year, because the vessel was not available during these months. The latitudes of trap-sampling stations were reliable and were included as a categorical explanatory variable (18 intervals starting from 268300 S and ending at 318290 S). Trap type was considered a blocking variable and correction was also made for trap soaktime (the time that traps were submerged, ranging from 12 to 936 h), which was used as a covariate.

D



Table 1. Candidate factors hypothesised to affect catch rates of crab, Chaceon macphersoni, caught in bottom-trawl and longline trap fisheries off eastern South Africa Variable

Type

Fishery

Description

Year Month Season

Categorical Categorical Categorical Categorical

Trawl Trap Trawl Trap

Depth

Categorical

Trap and trawl

Latitude

Categorical

Trap

Soak time Trap type

Continuous Categorical

Trap Trap

Vessel

Categorical

Trawl

1988–2010 (23 levels) 2004–2007 (4 levels) January to December (12 levels) Autumn ¼ March to May Winter ¼ June to August Spring ¼ September to November Depth stratum in which gear operated 100–199 m 200–299 m 300–399 m 400–499 m $500 m Location of trap sets 26.5–27.498S 27.5–28.498S 28.5–29.498S 29.5–30.498S 30.5–31.498S Time (h) between setting and hauling a longline of traps Moulded plastic traps Metal-framed bee-hive traps covered with mesh 27 individual trawlers that fished between 1988 and 2010

Table 2. Factors retained in the final generalised linear models (GLM) of catch rates, sizes, maturity and sex ratio of crab, Chaceon macphersoni, off eastern South Africa Data models used for catch rates comprised a submodel of proportion of sets with a non-zero crab (binomial) and a submodel of positive sets only (gamma). In the size model, the fishery variable was categorical (trap or trawl) and in the maturity model size was continuous Model

Error

Link

Factors

Trawl catch rate

Binomial Gamma Binomial Gamma Gamma Gamma Binomial Binomial Binomial

Logit Log Logit Log Inverse Inverse Logit Logit Logit

Year þ Month þ depth þ vessel Year þ Month þ depth þ vessel Year þ season þ depth þ latitude þ trap type þ soak-time Year þ season þ depth þ latitude þ trap type þ soak-time Sex þ year þ month þ depth þ fisheryA Sex þ yearB Sex þ size Month þ depth MonthB

Trap catch rate Size Maturity Sex ratio A

Categorical variable with levels for trap and trawl fishery. Final size- and sex-ratio models chosen.

B

For the binomial models, a logit link function was used to relate the response variable to the explanatory variables, and for the gamma models a log link was found to be appropriate. The most parsimonious models (see Table 2) were selected on the basis of Akaike’s information criterion (AIC) and models were validated by visual assessment of residual plots; in the plots, the model residuals were expected to be symmetrical around a zero mean, and to approximate a normal distribution. The standardised catch-rate indices were computed as the product of the binomial and gamma model outputs. For the trawl model, 1988, July, 300-m depth and the most active vessel in the fleet were used as reference points, and for the trap models, the reference points used were 2004, spring, 300-m depth, 298300 S–308290 S,

and plastic traps. These were the most frequently recorded observations of these variables. Size, maturation and sex ratios A GLM framework was further used to explore the response of crab size (CW, continuous, interval-scale) relative to the categorical explanatory variables sex, year, month, depth and gear-effect (Table 2). Size data were highly dispersed, and a gamma error structure and inverse link function were selected as most appropriate after running trials on R software. The AIC and visual assessments of residual plots were used in a hierarchical way to select the most parsimonious model of size.



200

18 000

Catch (tonnes)

160

16 000 14 000

140

12 000

120

10 000

100 8000

80

6000

60 40

4000

20

2000

Effort (hours trawled)

Reported catch Fishing effort

180

0 0 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year Fig. 2. Annual trends in fishing effort (trawl hours) and catch (t) of Chaceon macphersoni reported in logbooks by skippers of the trawl fleet between 1988 and 2010.

E

langoustines (Metanephrops mozambicus), with crabs caught as a by-catch. In total, 1499.4 t of crabs were landed over 23 years, at an average of 65.2 46.1 t year1. The annual crab catches have declined from a high of 180 t in 1992, to an average of ,25 t year1 after 2002 (Fig. 2). In total, 2894 longlines, with a mean of 114.8 9.7 (s.d.) traps line1, were set between May 2004 and November 2007, at an average depth of 319.0 50.4 m. Of these, only 389 longline hauls (16% of the total) captured crabs (between 1 and 1505) as a by-catch of spiny lobsters. More traps were set in 2004 than in the remaining 3 years as a result of vessel availability; however, all 18 latitude categories were sampled in each year. Crab catches over the 4 years amounted to 7.61 t, comprising 5.03 t (66%) in 2004, 1.83 t (24%) in 2007 and ,0.5 t (5%) per year in both 2005 and 2006.

Non-significant variables in the analysis of deviance were omitted in subsequent fits. The coefficients (a and b) of a logistic equation to estimate size at maturity of male and female crabs, respectively, were estimated from a GLM with a binomial error structure and logit link function (Table 2). In the model, sex was a categorical variable, and size was a continuous variable. The proportion of mature crabs at each size was calculated as the inverse logit. The sizes at 25%, 50% and 75% probability of maturation, defining the mean size (L50) and maturation range (L75–L25), were calculated on simulation with the inverse logit and the estimated parameters. Similarly, a binomial model with a logit link function was selected to investigate the effects of month and depth (both categorical) on the sex ratio of crabs caught by traps. The response variable was, thus, the fraction [0, 1] of males in the aggregate of crabs caught in each set.

Trawl catch rate model In the presence/absence (binomial) submodel, the effects of year, month, depth and vessel on trawl catch rates were significant, with the probability of encountering crabs in the catches being highest in November to December, at depths of 400– 499 m, and in the year 1991 (Table 3). In the conditional gamma model for positive occurrences, the effects of year, month, depth and vessel were all significant as well. The final delta model (i.e. product of the probability of capture (binomial submodel) and catch rate of traps with non-zero crab catches (gamma submodel)) resulted in trends very similar to those of the gamma model; standardised catch rates peaked during the early 1990s and gradually declined to a lowest value in 2002 (Fig. 3). In spite of a limited recovery thereafter, the 2010 standardised catch rate (1.73 kg trawl1) amounted to only 17% of the 1992 maximum value of 10.22 kg trawl1. The highest catch rates were recorded from November to January, the lowest from June to July, and catch rates increased from 100- to 499-m depth, and declined thereafter.

Results Fishing effort and catch Whereas most permit-holders collated and provided trawl logbook data (drag and landing sheets) to authorities on a regular basis, others submitted logbooks only irregularly, particularly in the years soon after the logbook system was initiated in 1988. Also, not all caught crabs were packed in the beginning of the time series, and, therefore, the reported catch figures up to the early 1990s underestimate the actual catches (Fig. 2). Nevertheless, a total of 49 736 trawls was recorded over the 23-year period between January 1988 and December 2010. The average duration of each trawl was 4.2 1.1 (s.d.) h. Fishing effort declined over time, from a maximum of 16 230 h year1 trawled in 1989, to 5000 h year1 in 2008 (Fig. 2). The lowest effort (4477 h year1) was recorded in 1994, when a major fishing company did not participate in the fishery because of financial difficulties. Trawl activity increased to .8000 h year1 in 2009 and 2010. Only 57.8% of trawls (28 273 trawls) recorded catches of crabs, quantities ranging between 0.001 and 2.3 t per trawl; the absence of crabs in 42.2% of trawls was because trawls were generally targeted at prawns (Haliporoides triarthrus) and

Trap catch rate model For the final binomial model of trap catch rates, season, depth and latitude were significant as the main effects after correction for soak-time and trap-type (Table 3). The probability of encountering a crab was lowest in winter and highest in spring, and it increased with increasing depth between 200- and 499-m depth. By latitude, the highest probability of encounter fell between 29.58S and 30.498S, and beehive traps captured crabs more often than did plastic traps. Year, depth and latitude were significant explanatory variables in the conditional gamma model for positive occurrences, and as well after correction for soak-time and trap-type (Table 3). The standardised catch rates (numbers trap1) based on the positive data showed an increase with increasing depth and declined continually between 2004 and 2007. Beehive traps had a higher crab catch rate than did plastic traps, and their relative efficiency was e0/e1.492, or ,4.4 times higher for positive catches. Longer soak-times led to a slight increase in crab catch rates of about e0.003 h1, or ,7.2% day1 for positive catches. The final delta model resulted in trends similar to those of the gamma model (Fig. 3), namely a decline in standardised catch rates between 2004 and 2007, higher catch rates with increasing depth and in spring compared

F



Table 3. Coefficients (±s.e.) of the parameters in the different generalised linear models that describe catch rates, size, maturity and sex ratios of Chaceon macphersoni Estimates marked with # were not significantly different (P ¼ 0.05) from the intercept

Error Link n AIC Explained deviance d.f. Chisq (P)

Trawl catch rate

Trawl catch rate

Trap catch rate

Trap catch rate

Size

Maturity

Sex ratio

Binomial Logit

Gamma Log

62 014 7063

189 886 542 299

Binomial Logit 2873 1699.6 2223.2

Gamma Log 384 717.8 1101.9

Gamma Inverse 4803 34 103

Binomial Logit 443 261 359.1

Binomial Logit 4357 4576.7 1483.4

64 0

64 0

15 0

15 ,0.0001

6

3 ,0.0001

9 0

Estimate

s.e.

Estimate

s.e.

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012

0.983 1.270 1.194 0.397 0.562 0.755 0.893 1.052 1.506 0.973 0.737 1.212 1.689 1.732 2.197 1.818 1.943 1.514 1.294 0.917 0.697 1.335 1.110

0.154 0.148 0.150 0.152 0.149 0.147 0.155 0.154 0.149 0.148 0.151 0.149 0.147 0.146 0.147 0.147 0.149 0.151 0.152 0.143 0.149 0.143 0.142

1.583 1.398 1.723 2.505 2.930 2.450 2.884 2.730 2.248 2.546 2.493 2.305 1.941 1.710 1.432 1.448 1.480 1.607 1.737 1.727 1.608 1.586 1.491

0.118 0.116 0.116 0.116 0.116 0.115 0.118 0.118 0.117 0.116 0.116 0.117 0.116 0.116 0.117 0.116 0.118 0.117 0.117 0.113 0.116 0.114 0.112

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0 0.183 0.238 0.383 0.615 0.664 0.652 0.280 0.356 0.424 0.142 0.086#

0.047 0.049 0.050 0.050 0.051 0.050 0.050 0.048 0.047 0.047 0.050

0 0.182 0.297 0.248 0.332 0.367 0.476 0.448 0.416 0.365 0.0499 0.034#

0.024 0.026 0.027 0.029 0.029 0.028 0.027 0.025 0.025 0.025 0.027

0 1.470 1.060 2.369 1.327

0.133 0.139 0.137 0.159

0 0.380 0.639 0.734 0.610

0.109 0.114 0.112 0.125

100–199 m 200–299 m 300–399 m 400–499 m $500 m

Estimate

s.e.

Estimate

s.e.

Estimate

s.e.

1.254# 1.23# 0.948# 0.964#

1.204 1.205 1.208 1.223

5.577 5.952 6.897 7.46

1.738 1.725 1.740 1.751

0.000 0.00018 0.00023 0.00023

0 4.20E–05 3.44E–05 2.78E–05

0.00011

4.05E–05

Autumn 0

Estimate

s.e.

Estimate

s.e.

0

Winter 0.509

0.250

Spring 0.501#

0.320

0.0919# 0.292

0.639#

1.107 1.085 1.102

0 2.444# 3.719 4.795

1.670 1.626 1.641

26.5–27.498S 27.5–28.498S 28.5–29.498S 29.5–30.498S 30.5–31.498S

0 1.644 1.663 1.322 0.518#

0.222 0.378 0.223 0.494

0 0.300# 0.512# 2.714 0.298#

0.327 0.576 0.284 0.728

Soak-time Beehive traps Plastic traps Female Male Size

0.001 0 1.526

0.394

0.003 0 1.492

0.088 0.067 0.073 0.114 0.127

1.691 0.875

0.139 0.266

0.374

0 1.982# 0.804# 2.507

0.001

1.591 1.426 0.892 0.727 0.767

0.001 0.368 0.010 0.008

1.79E–05 2.50E–05

15.194 21.943 0.186

2.036 2.625 0.022

Vessel 1–27: Coefficients ranged from 3.173 0.538 to 1.729 1.059 for the binomial model, and from 1.204 0.118 to 0.529 0.108 for the gamma model of trawl catch rate.



0.6

10

0.5

8

0.4

6

0.3

4

0.2

2

0.1

0

0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

12

2004

2005

0.6

5

0.5

4

0.4

3

0.3

2 1 0 Jan Feb Mar Apr May Jun

2007

Year

6

Jul Aug Sep Oct Nov Dec

Month 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0

CPUE (Numbers/Trap)

CPUE (Kg/Trawl Hour)

Year

2006

G

0.2 0.1 0 Autumn

Winter

Spring

Season 3.0 2.5 2.0 1.5 1.0 0.5 0

100

200

300

400

500

200

100

Depth (m)

300

400

Depth (m) 0.6 0.5 0.4 0.3 0.2 0.1 0 26

27

28

29

30

31

Latitude (Degrees south) Fig. 3. Standardised abundance indices for Chaceon macphersoni on the basis of trawl catch and effort data (kg per trawl hour; first column) and on trapping data (numbers of crabs per trap; second column) for the sampling area along the KwaZulu–Natal coast, on the basis of the final delta model. Depths and latitudes are the lowest points of their respective class intervals, e.g. 100 ¼ 100–199 m, 26.58 ¼ 26.58–27.58.

with winter, and an abundance ‘hotspot’ between 29.58S and 30.498S. Size model Month and depth were not significant in the initial size model, and were therefore removed in a stepwise manner. All crabs

sampled in 2012 were caught with trawls, and all those sampled in 2004–2007 were caught with traps; consequently, there was no overlap in biological sampling by traps and trawls, and gear-effect was therefore also omitted from the model. The final model comprised only sex and year as significant explanatory variables (Table 2). Male crabs were significantly larger than

H


0.35


Trap samples 2004–2007

0.30

Female, n ⫽ 3375 Male, n ⫽ 984

0.25 0.20 0.15 0.10

Frequency

0.05 0 50

60

70

0.35

80

90

100

110

120

130

140

150

Trawl samples 1997 and 2012

0.30

Female 1997, n ⫽ 253 Male 1997, n ⫽ 267 Female 2012, n ⫽ 299 Male 2012, n ⫽ 145

0.25 0.20 0.15 0.10 0.05 0 50

60

70

80

90

100

110

120

130

140

150

CW category (mm) Fig. 4. Size frequency of male and female Chaceon macphersoni sampled from catches made by commercial traps and bottom-trawl fisheries off KwaZulu–Natal.

Proportion mature

1.00

The sex ratio of all crabs sampled was heavily skewed towards females, with a F : M ratio of 1 : 0.29 (n ¼ 4359). Depth was not significant in the initial model of sex ratio and, therefore, it was removed from the final binomial model in which month (7 levels) remained the only explanatory variable (Table 2). The probability of encountering a male crab in the traps as opposed to a female ranged from 0.16 to 0.33 per month. This probability was highest in July to September (0.29–0.33) and lowest in November (0.16). The percentage of egg-bearing females remained below 6.1% of females caught in trawls and 8.1% of those caught in traps, irrespective of CW. It was, therefore, clear that reproductively active females were not fully selected by either fishing gear, and no further analysis of reproductive season was attempted. Conversions of carapace width to whole weight All CW (mm) to WW (g) regressions were significant (P , 0.0001) and the high r2-values indicated that the models fitted the data well. A broad CW range of 78–164 mm was available for male crabs (WW ¼ 0.00008 CW3.2765, n ¼ 261, r2 ¼ 0.91), whereas the female sample comprised crabs of 68–118 mm (WW ¼ 0.0011 CW2.7057, n ¼ 261, r2 ¼ 0.79). A sexes-combined regression is often useful for raising the length composition of unsexed biological samples to reflect that of total catches in biomass calculations; therefore, we provide a combined regression as follows: WW ¼ 0.00009 CW3.2474, n ¼ 522, r2 ¼ 0.92). Discussion

0.75

0.50 Estimate, females Estimate, males Observed, females Observed, males

0.25

0 70

80

90

100

110

120

130

140

150

Carapace width (mm) Fig. 5. Size at maturity ogives for male and female Chaceon macphersoni. Observed values for males were offset from the axes for clarity.

females, being on average 121.3 mm v. 101.0 mm CW for 2007 as a reference year (Table 3, Fig. 4). Crabs caught by traps in 2004 and 2005 were smaller than those caught in 2006 and 2007. Size at maturity and sex ratios Male crabs attained sexual maturity in the standard range of 112–124 mm (L50 ¼ 118 mm), whereas females attained maturity at a much smaller size of 76–88 mm CW (L50 ¼ 82 mm) based on the shape of the genital opening (Table 3, Fig. 5). The estimates of female maturity were substantiated by field observations of egg-bearing female crabs; the smallest eggbearer measured 83 mm, and 4.2% of females with eggs measured ,90 mm CW.

The causes of variation in studies of this kind can be grouped into the following two categories: those caused by fluctuations in population abundance, distribution and population structure (i.e. natural variation) and those introduced by changes in fishing strategy, such as gear-effects, time, location and depth of fishing activities (i.e. man-made variation). These two groups of variation can easily be confounded. Nevertheless, the GLM framework used in the present study explicitly addressed several of the man-made sources of variation, by including factors for vessel effects, trap-type and soak-time. This left gradients over time and space (including depth) to be explained, and partitioning variability into a probability of occurrence (binomial models) and positive values (gamma models) proved to be particularly useful in interpreting model outputs. Although some of the trawl catch and effort data were reportedly not submitted for entry onto the long-term database, particularly during the early part of the time series, the extent of missing data could not be established. Nevertheless, given the volume of data that were available for the trawl fishery, we are of the opinion that the analyses and interpretations presented here adequately and realistically capture the nature of the fishery and C. macphersoni. A key hypothesis tested in the present study supposed that crab catches made by traps on hard substrata and trawls on soft substrata would be able to distinguish population characteristics typical of either habitat. It is important to note, that within this context, both positive and negative results may have biological


meaning; for instance, similar trends in both fisheries over the range of depths fished would confirm that a gradient over depth occurs throughout the population, whereas contrasting trends might indicate that substratum type plays an important role in the spatial organisation of crabs. In most cases, trap and trawl analyses showed similar trends on hard and soft substrata, and, therefore, the role of substratum type appears to be less important than those of depth and season in explaining trends in the crab population. Metal-framed beehive traps caught significantly more crabs per set than did barrel-shaped plastic traps, and presumably crabs found it easier to enter them, or remained in them for longer periods before escaping. Higher catch rates in beehive traps may have been related to their greater volume, weight and stability on the seafloor (see Miller 1990), although this was not tested. The increase in crab catch rates in traps that were left on the seafloor for longer periods suggests that crabs utilise traps as shelter, or find it difficult to escape through the trap-opening after consuming the bait. Both spiny- and slipper lobsters, the target species of the trap fishery under consideration, exhibited constant or lower catches in traps left in the water for .4 days, suggesting that they can exit traps through the trap-opening at will (Groeneveld et al. 2012). The standardised index based on trawl data showed a longterm decline in crab abundance between 1991 and ,2002, whereafter abundance stabilised at a much lower level than during the 1990s (Fig. 3). The index suggested that a limited recovery of the crab population occurred between 2002 and 2008. The yearly index based on trap data showed the reverse, namely, a short-term decline in abundance between 2004 and 2007. The opposing trends may have different explanations, one of them being that intensive trapping between 2004 and 2007 caused local depletions on hard substrata, in spite of an overall limited recovery, as indicated by the trawl index. Whereas recruitment and immigration presumably take place on both substratum types, and removal by fishing definitely does, the balance of these processes may differ; in the present case, we suggest that removals by traps outweighed recruitment and immigration on hard substrata, whereas recruitment and immigration outweighed removals by trawling on soft substrata. Nonetheless, given the length of the time series and the abundance of trawl data, we chose to utilise the standardised trawl index as the main indicator of year trends. Further, the trawl fishery is well established, operational procedures and fishing gear have not changed much over the past two decades (apart from advances in electronic navigation), and the temporal decline observed in the data matches the descriptions given by one of the long-term participants (K. Sorenson, Spray Fishing, pers. comm.). The long-term decline in crab abundance is perhaps not surprising, given typical aspects of geryonid life history such as deep-water distribution at cold temperatures, slow growth and maturation rates and infrequent recruitment. Abundance declined over a long term in spite of relatively small quantities of crabs taken annually by trawlers (mean of 65 t per year) as a retained by-catch of a fishery which targets several other coexisting crustaceans (Groeneveld and Melville-Smith 1995; Fennessy and Groeneveld 1997), suggesting low crab productivity. Similar declines in crab catches have been reported for


I

exploited stocks off Namibia (Melville-Smith 1988a), and the South Atlantic Bight and Gulf of Mexico (Armstrong 1990). Indeed, many fisheries for geryonids are small or only occasional (Hastie 1995; Guerrero and Arana 2009; Gutie´rrez et al. 2011) because of low productivity and high variability in stock sizes. The present information suggests that C. macphersoni fits the general pattern typical of geryonids, and that only limited yields can be expected from the KZN coast. Crab abundance increased with depth between 100 m and 499 m, on the basis of both trap and trawl data (Fig. 3), with low abundance (trawl analysis) or no crabs (trap analysis) occurring at depths of ,200 m. The trawl analysis extended deeper than that for traps, and included a plus-group in which catches made at up to 800-m depth were combined into a single depth category (.500 m), because few trawlers fished that deep. The trap analysis indicated highest abundance in the deepest stratum, and it is therefore likely that if traps were set .500-m depth, they would also have encountered crabs. Although it is clear that a portion of the crab population inhabits depths beyond those that were fished by traps or by trawls on a regular basis, it remains unknown how abundant crabs are at depths below 500 m. Previous studies have suggested that C. macphersoni is abundant at 800-m depth off Mozambique (Paula e Silva 1985), and that some specimens were captured in trawls up to 1025 m deep off Table Mountain, South Africa (Manning and Holthuis 1988). No gradient in the mean size of C. macphersoni with change in depth could be detected, despite the large size range of 68–164 mm in CW covered, and this result corresponds with the findings of Paula e Silva (1984) from southern Mozambique. We could, therefore, provide no evidence to support the hypothesis of Wigley et al. (1975) that geryonid crabs settle at great depths and subsequently migrate up the slope as they develop. Several other authors have shown an inverse relationship between body size and depth of geryonids, including for Chaceon quinquedens (Wigley et al. 1975), C. maritae (Beyers and Wilke 1980), C. notialis (Gutie´rrez et al. 2011) and Geryon trispinosus (Attrill et al. 1990). Conversely, Guerrero and Arana (2009) found that the mean size of C. chilensis at Robinson Crusoe Island increased with depth; therefore, it appears that more than one recruitment strategy may occur within this genus. Sex ratios were significantly skewed towards females over the whole depth range sampled and during all months tested. In Mozambique, female C. macphersoni were up to four times more abundant than males in depths of ,400 m, but equal numbers of males and females occurred at depths of .400 m (Paula e Silva 1985). Sexual zonation has frequently been reported for geryonids (see Hastie 1995 for a summary; Gutie´rrez et al. 2011) and several studies concluded that females are typically more abundant at shallow depths. However, a trap fishery for C. chilensis caught 97.9% males over a 300–1000-m depth range (Guerrero and Arana 2009), and male dominance has been shown for C. quinquedens over the Scotian shelf (McElman and Elner 1982), for C. fenneri off South Carolina and Georgia (Wenner et al. 1987) and for C. affinis off the Azores Islands (Pinho et al. 2001). Hastie and Saunders (1992) suggested that sexual zonation patterns in geryonids may depend on several factors, such as species, water temperature,

J


seasonal effects and spawning migrations. Nevertheless, the dominance of female C. macphersoni over the sampled depth range does suggest that males are elsewhere; the zonation is, however, not driven by bottom type, because females dominated in both trap (hard substratum) and trawl (soft substratum) catches. Therefore depth is a likely factor, males presumably occurring deeper than the sampled area. Crab catch rates were lowest during the meteorological late autumn/winter months, and this was confirmed independently by both trap and trawl analyses. Significant increases in catch rates were apparent during spring (both datasets), and based on trawl data the highest catch rates occurred during midsummer (December and January). However, month was not significant in predicting crab size, and therefore there was no evidence for movement of specific size classes out of the sampled area during winter, when catch rates were lowest. The probability of encountering a male in the depth range sampled was highest during winter months (when catch rates were lowest), which may mean that some females were then absent from the fished area, or were not being selected by fishing gear at that time. Indeed, the paucity of gravid females during all months of the year in the sampled depth range (also shown for C. maritae by Melville-Smith 1987) suggests that during some months, gravid females aggregate outside of the fished area. We suggest that this may be during winter months in C. macphersoni. Although analyses of both the trap and trawl data confirmed a seasonal trend in catch rates, there are no long-term oceanographic data to characterise the seasons experienced in crab habitat off KZN. In situ data have recently been recorded by a thermistor placed at 527-m depth on trawl grounds from April 2009 and August 2010. This showed very little variation in mean monthly temperatures, with both high (9.68C) and low (8.48C) temperatures being recorded in winter (June 2009 and June 2010, respectively), and the lowest temperature (8.38C) in December (summer) 2009 (Lisa Guastella, unpubl. data, c/o Oceanography Department, University of Cape Town, lisagus@ telkomsa.net). It is, therefore, likely that some factor other than temperature underlies the seasonal fluctuations in observed catch rates. Nevertheless, the environmental data now being collected at 527-m depth on the trawl grounds can be used in future studies to clarify relationships between the abundance and environmental conditions, at seasonal and inter-annual levels. To conclude, complementary analyses of crab catches made by traps on hard substrata and trawls on soft substrata showed parallel trends in abundance over depth, season and latitude, and a similar population structure on both substratum types. A standardised index based on trawl data (1988–2010) showed a long-term decline in abundance, with some recovery after 2002; conversely, the trap data suggested local depletions on hard substrata between 2004 and 2007, likely as a result of fishing pressure. C. macphersoni appears to fit the general pattern of low productivity, typical of deep-sea geryonids. Acknowledgements The authors thank Mr Knud Sorenson of Spray Fishing for accommodating fisheries observers on the FV Ocean Spray, and for his insights into the KZN trawl fishery. Likewise, Mr J. G. Fernandes of Lusitania Fishing is thanked for supporting the experimental trap fishery, and for the use of the FV Cape


Flower. CAPFISH observers are thanked for collecting biological samples, and Lisa Guastella and ACEP for providing oceanographic information on the trawling grounds. The project was funded by the South African Association for Marine Biological Research (SAAMBR) and the South West Indian Ocean Fisheries Project (SWIOFP).

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