559
Abstract–We analyzed data from Na tional Marine Fisheries Service bottom trawl surveys carried out triennially from 1984 to 1996 in the Gulf of Alaska (GOA). The continental shelf and upper slope (0–500 m) of the GOA support a rich demersal fish fauna dominated by arrowtooth flounder (Atheresthes stomias), walleye pollock (Theragra chalcogramma), Pacific cod (Gadus macrocephalus), Pacific halibut (Hip poglossus stenolepis), and Pacific Ocean perch (Sebastes alutus). Average catch per unit of effort (CPUE) of all ground fish species combined increased with depth and had a significant peak near the shelf break at 150–200 m. Species richness and diversity had significant peaks at 200–300 m. The western GOA was characterized by higher CPUEs and lower species richness and diversity than the eastern GOA. Highest CPUEs were observed in Shelikof Strait, along the shelf break and upper slope south of Kodiak Island, and on the banks and in the gullies northeast of Kodiak Island. Significant differences in total CPUE among surveys suggest a 40% increase in total groundfish biomass between 1984 and 1996. A multivariate analysis of the CPUE of 72 groundfish taxa revealed strong gradients in spe cies composition with depth and from east to west, and a weak but signifi cant trend in species composition over time. The trend over time was associ ated with increases in the frequency of occurrence and CPUE of at least eight taxa, including skates (Rajidae), capelin (Mallotus villosus), three flat fish species, and Pacific Ocean perch, and decreases in frequency of occur rence and CPUE of several sculpin (Myoxocephalus spp.) species. Results are discussed in terms of spatial and temporal patterns in productivity and in the context of their ecological and management implications.
Manuscript accepted 22 March 2002. Fish. Bull. 100:559–581 (2002).
Spatial and temporal patterns in the demersal fish community on the shelf and upper slope regions of the Gulf of Alaska Franz J. Mueter Brenda L. Norcross Institute of Marine Science
University of Alaska Fairbanks
P.O. Box 757220
Fairbanks, Alaska 99775-7220
Present address (for F. J. Mueter): 697 Fordham Drive
Fairbanks, Alaska 99709 E-mail address (for F. J. Mueter):
[email protected]
The continental shelf and upper slope of the Gulf of Alaska (GOA, Fig. 1) support a diverse community of demersal fishes. This community includes many commercial species that support rich bottom trawl and longline fisheries with landings averaging approximately 250,000 metric tons per year over the past two decades (NPFMC1). The commercially most important species during the past decade have been walleye pollock (Theragra chalcogramma), Pacific halibut (Hippoglossus stenolepis), Pacific cod (Gadus macrocephalus), other flatfish species (Pleuronectidae), rockfish (Sebastes spp.), and sablefish (Anoplopoma fimbria). Arrowtooth flounder (Atheresthes stomias), although currently the dominant demersal species by biomass, is a minor component of total landings. Triennial bottom trawl surveys have been carried out since 1984 to assess the abundance and distribution of the main commercial species in the GOA (Martin, 1997). The surveys also provide data on noncommercial fish species and other associated benthic megafauna. At least 140 species of fishes belonging to 33 families were collected during the 1996 GOA bottom trawl survey (Martin, 1997). Rockfish (Scorpaenidae) were the best repre sented family with 25 species, followed by sculpins (Cottidae, 24 species) and flatfishes (Pleuronectidae, 16 species). Data from similar bottom trawl surveys have been used to investigate the ecology and zoogeography of fish assemblages in many parts of the world, including the North Pacific (Gabriel
and Tyler, 1980; Rogers and Pikitch, 1992; Jay, 1996). However, to our knowledge there have been no studies in which demersal fish assemblages from the GOA shelf and slope were examined. To understand, and eventually to predict, the effects of environmental variations on fish stocks, scientists and managers increasingly advocate ecosystem approaches to research and management (Yaffee et al., 1996; Lang ton and Haedrich, 1997). Ecosystem management must be based on mul tispecies relationships and overcome the current focus on individual spe cies (Langton and Haedrich, 1997). An important prerequisite for successful multispecies approaches is an under standing of the species structure of fish communities in space and time. The purpose of this paper is to iden tify spatial and temporal patterns in the composition of the demersal fish community from the GOA. Specific objectives are 1) to identify the main environmental and spatial gradients along which the groundfish community is structured, and 2) to identify long term changes in the composition of the groundfish community.
1
NPFMC (North Pacific Fisheries Management Council). 1997. Stock assess ment and fishery evaluation report for the groundfish resources of the Gulf of Alaska. North Pacific Fisheries Manage ment Council, 605 West 4th Ave., Suite 306, Anchorage, AK 99501.
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Prince William Sound
let
at
Co o
k In
t Yaku
50 0
m
Bering Sea
10
s
m
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0m 20
0 m
ian I Aleut
0
50
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Alaskan Stream
Shum
agin
Chirikof
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Kodiak
east
South 250 km
Figure 1 Bathymetric and geographic features of the Gulf of Alaska between 130°W and 170°W. The black vertical lines separate the five areas used in the survey design and analysis. Thin lines indicate 100 m, 200 m, and 500-m isobaths. The large shaded lines summarize major circulation patterns.
Materials and methods Study area Our study area included the continental shelf and the upper continental slope along the perimeter of the GOA to a depth of 500 m (Fig. 1). The total study area is approxi mately 300,000 km2, extending over a distance of 2600 km from Southeast Alaska to the western Aleutian Islands.
Data sources All fisheries data for this study were obtained from the Alaska Fisheries Science Center (AFSC), National Marine Fisheries Service (NMFS) in Seattle, WA. Personnel from AFSC have executed bottom trawl surveys of groundfish stocks in the GOA on a triennial basis since 1984 (Table 1). All surveys were conducted during the summer with chartered fishing vessels and were based on a stratified random sampling design. The GOA was divided into five areas, three in the western GOA (Shumagin, Chirikof, Kodiak) and two in the eastern GOA (Yakutat, Southeast, see Fig. 1) and four depth strata (0–100 m, 100–200 m, 200–300 m, 300–500 m). Depth strata within each area were further subdivided into 2–5 strata based on the type of geographical area (e.g. banks, gullies, shelf, and slope), resulting in a total of 49 strata. Some modifications were made to the stratification scheme after 1987. Our analysis was based on current stratum boundaries. Details of the
sampling design, data collection, and sample process ing are described in Brown (1986), Martin and Clausen (1995), Stark and Clausen (1995), and Martin (1997). In our analysis we excluded data from the 0–100 m stratum in the southeast area because it is mostly untrawlable and was not sampled during three out of five surveys; thus we used a total of 48 strata in the analysis. We further excluded all hauls that were classified as unsatisfactory in the database. Several types of fishing gear were used in the bottom trawl surveys between 1984 and 1996. A poly-Nor’eastern high opening bottom trawl equipped with rubber bobbin roller gear has become the standard gear for GOA bottom trawl surveys (Munro and Hoff, 1995) and was used for at least part of the survey in all years (Table 1). In addition, an older Nor’eastern trawl (gear type 160) was used during parts of the 1984 and 1990 survey and two different Japa nese trawls (Table 1) were used during the 1984 and 1987 joint Japanese-American surveys. Fishing effort for each gear type was spread out over most of the sampling region within each year and one gear type (gear type 172) was used during all surveys, allowing us to estimate gear differences.
Data analysis Our analysis was based on standardized catch per unit of effort of 72 fish taxa collected in the bottom trawl surveys. Catches were standardized to weight per unit area (kg/ km2), where the area swept by the trawl was estimated
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Table 1 Number of quantitative hauls used in analysis by gear type, start and ending dates of survey, minimum, maximum, and average depth of hauls, and number of haul locations for which bottom temperature was obtained. Note changes in gear from 1984 to 1990, which were associated with a decrease in the average tow duration. Gear types: 160 = Nor’eastern trawl; 172 = poly-Nor’eastern high opening bottom trawl; 710 = Japanese poly trawl; 717 = Japanese poly trawl with roller gear. 1984 Total number of hauls Gear type: no. of hauls
867
1987
1990
758
705
1993 774
1996 807
(172: 380) (717: 378)
(160: 166) (172: 539)
(all: 172)
(all: 172)
Starting date
(160: 430) (172: 133) (710: 304) Jun 3
May 22
Jun 4
Jun 6
May 22
Ending date
Oct 2
Sep 10
Sep 9
Sep 7
Jul 30
Average depth (m)
167
150
167
156
157
Maximum depth (m)
499
499
494
488
479
Minimum depth (m)
16
20
20
22
20
Temp. measurements
563
298
285
726
716
from the measured distance trawled and estimates or measurements of the width of the net opening. Taxa to include in the analysis were selected according to the following criteria: 1) all species consistently identified to species level and occurring at least at 1% of the stations, 2) taxa that were not consistently identified to species level were combined by genus or, if necessary, by family, 3) if, after grouping taxa, a species, genus, or family was pres ent at less than 1% of the stations within each year, it was not included in the analysis. As an initial estimate of abundance trends, we com puted gulf-wide averages of CPUE for each species by year. CPUEs from the Japanese trawls used in 1984 and 1987 were adjusted to the US standard trawl gear by us ing fishing power coefficients provided in Tables 28 and 31 in Munro and Hoff (1995). Following standard NMFS methods (Martin, 1997), we averaged estimated CPUEs by stratum, weighted the averages by stratum area, and combined them to obtain area-wide averages. From a haul-by-taxon matrix of CPUE data, we com puted univariate and multivariate indices to examine spatial and temporal patterns in community structure. For each haul we computed species richness (number of species), species diversity (Shannon-Wiener index), and total CPUE of all groundfish taxa combined as univariate measures of community structure. We considered three in dices of species diversity (Shannon-Wiener, Simpson’s D, Fisher’s α) and chose the Shannon-Wiener index because it showed little dependence on sample size in simulations, was approximately normally distributed (which facilitates statistical comparisons), and is widely used in the ecologi cal literature. In addition, we computed multivariate indi ces of species composition based on Bray-Curtis dissimi larities of root-root transformed CPUEs and nonmetric multidimensional scaling (NMDS) as described in Mueter and Norcross (1999). Indices of species composition were computed separately for each year and were related to
explanatory variables to describe the major gradients in species composition by year. To examine trends in species composition over time, catches were averaged by stratum instead of using individual hauls, reducing the 3911 hauls to 240 within-year strata (5 years × 48 strata). An NMDS ordination of the stratum-by-species matrix was then used to test specifically for significant trends in species compo sition over time. We computed the linear combination of ordination axes that maximized the correlation with year and tested whether the correlation was significantly high er than would be expected by chance by using randomiza tion tests. This linear combination was used as an index representing changes in species composition over time (time index). Species that were most strongly associated with the time index were identified based on scatterplots and Spearman rank correlations between the index and individual species abundances, and changes in these spe cies were examined in more detail. Depth, geographic location, bottom temperature (mea sured by sensors attached to the gear), Julian day, year, gear type, and area swept were included in the analysis as explanatory variables. Geographic location was repre sented in the analysis either as a categorical variable by using the five statistical areas depicted in Figure 1 or by using alongshore distances (AD). Alongshore distance was computed by projecting each station onto a line that ap proximately followed the shelf break and measuring the distance along this line from its origin in the southeast part of the study area (km 0) to the westernmost point (km 2600). Temperature was not measured for all hauls (Table 1); thus all regressions were done both with and without temperature included. Gear type was included in all mod els to account for potential differences among gear types. Area swept was only included for examining trends in spe cies richness and diversity because the number of species per haul was expected to increase with the area sampled. All other indices were based on standardized CPUEs.
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Generalized additive models (GAM, Hastie and Tibshi rani, 1990) and linear models were used to relate univari ate and multivariate indices, individual species CPUEs, and presence-absence data to the independent variables. Additive models were used to allow for nonlinear relation ships between the dependent and independent variables. A GAM is a nonparametric regression that uses smooth functions of the independent variables in place of linear functions and allows different probability distributions for the data. We initially assumed that species richness fol lowed a Poisson distribution (counts), that the ShannonWiener index, log-transformed CPUEs, and all indices of species composition followed a normal distribution, and that presence-absence data followed a binomial distribu tion. The dependent variables were then modeled as the sum of nonparametric functions (smoothing splines) of the hypothesized independent variables. If no evidence of non linearity was found, linear terms were substituted for the smoothing splines. Residuals from each regression model were examined for violations of the underlying assump tions (error distribution and homogeneity of variance) and for outliers. Specifically, we examined residuals graphi cally by means of histograms, quantile-quantile plots, and plots of residuals against time and against other covari ates. We further conducted formal tests of goodness-of-fit of the residuals against the assumed error distributions after each model fit. Model fits were generally deemed ad equate, unless noted otherwise in the results. To identify and evaluate the significance of relationships between species composition and explanatory variables we first chose the most appropriate regression model, then quantified the contribution of each explanatory variable to the model fit. A stepwise procedure based on the Akaike information criterion (Hastie and Tibshirani, 1990) was used to select a subset of significant variables. As a mea sure of model fit we computed a pseudo-coefficient of deter mination (pseudo-r2), or the fraction of the total deviance explained by the model, as a surrogate for the familiar r2 (Swartzman et al., 1992). The importance of individual variables in the model fits was evaluated similarly by us ing a pseudo-coefficient of partial determination based on reduced models that exclude the variable of interest (= 1 – deviance of best model/deviance of reduced model). Trends in the abundance of those species that were strongly associated with the time index were examined in greater detail by haul. For this analysis we treated zero and nonzero catches separately to test for 1) changes in the catch rate of a species over time based on positive catches only (CPUE-where-present) and 2) changes in the frequency of occurrence of a species over time (by estimat ing the probability of a nonzero haul). To test for changes in the CPUE-where-present of these species, we used an analysis of covariance model of the following form: log(CPUE-where-present) = area + depth stratum + gear + (area × depth stratum) + β × year. Errors were assumed to be normally distributed, thus CPUE-where-present was assumed to follow a lognormal
distribution. A separate linear time trend was estimated and evaluated for significance within each depth stratum in each area (β parameters). The interaction term (area × depth stratum) was omitted if it was not significant at the 5% level. Strata within the same statistical area and depth stratum were pooled, unless fitting separate regres sion lines to each of the 48 strata improved the overall fit significantly. If the slope of the regression did not differ among areas or depth strata at the 5% level, data were further pooled across areas or depth strata (or both) to obtain the most parsimonious model. The probability of nonzero hauls was estimated simi larly using a logistic regression model (McCullagh and Nelder, 1989): Pr(CPUE > 0) log = area + depth stratum + 1 − Pr(CPUE > 0) gear + (area × depth stratum) + β × year, where the number of positive catches (CPUE>0) was assumed to follow a binomial distribution. The most par simonious models for each species were used to test the null hypothesis that there was no significant (linear) trend in the catch rate (CPUE-where-present) or frequency of occurrence (Pr{CPUE>0}) over time. The null hypothesis was rejected if the slope β was significantly different from zero at the 5% level.
Results Arrowtooth flounder had the highest average CPUE (kg/km2) and the highest frequency of occurrence during all surveys (Table 2). Walleye pollock was second in most years, followed by Pacific cod and Pacific halibut. However, in 1996, the estimated CPUE of Pacific Ocean perch exceeded that of pollock, cod, and halibut. Sablefish, five other flatfish species, two Sebastes species, and Atka mackerel (Pleurogrammus monopterygius) were other important species by CPUE (> 200 kg/km2, Table 2). The most abundant species, including gadids and most of the flatfishes, were generally also the most widespread spe cies in the survey (>50% frequency of occurrence). In spite of relatively high CPUEs, rockfishes, Atka mackerel, and yellowfin sole (Limanda aspera) had low frequencies of occurrence (4–40%), indicating high local abundances and a more restricted spatial distribution. Independent variables used in the analysis of species richness, diversity, CPUE, and species composition were moderately correlated (Table 3). The largest correlations were between area swept and year (–0.452), due to a reduction in haul duration after 1987, between depth and temperature (–0.407), and between Julian day and alongshore distance (–0.383). Depth and bottom tempera ture on the shelf and slope are invariably confounded and their effects may generally be difficult to separate. The confounding between Julian day and AD was extreme in some years (e.g. r=–0.79 in 1993) because sampling vessels traveled from west to east during most surveys. However
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Table 2 All groundfish taxa included in analysis and their estimated average CPUE (kg/km2) by year and for all years combined. CPUEs were computed for each haul, averaged by stratum, weighted by stratum area, and combined to obtain area-wide averages. FO denotes overall frequency of occurrence for all years combined in percent. Scientific name
Common name
1984
1987
1990
1993
1996
Mean
FO
Atheresthes stomias 1 Theragra chalcogramma 1 Gadus macrocephalus 1 Hippoglossus stenolepis 1 Sebastes alutus 1 Anoplopoma fimbria 1 Hippoglossoides elassodon 1 Lepidopsetta spp. 1 Sebastes polyspinis 1 Pleurogrammus monopterygius Glyptocephalus zachirus 1 Microstomus pacificus 1 Limanda aspera 1 Sebastes ciliatus 1 Sebastes aleutianus 1 Rajidae unident. Clupea pallasi Sebastes zacentrus Albatrossia pectoralis Sebastolobus alascanus Sebastes variegatus Thaleichthys pacificus Isopsetta isolepis 1 Platichthys stellatus 1 Sebastes borealis Sebastes proriger Squalus acanthias Hemilepidotus jordani Sebastes brevispinis Ophiodon elongatus Hemitripterus bolini Lamna ditropis Myoxocephalus spp. Parophrys vetulus 1 Somniosus pacificus Zaprora silenus Pleuronectes quadrituberculatus1 Microgadus proximus Sebastes babcocki Hydrolagus colliei Hexagrammos decagrammus Sebastes reedi Sebastes ruberrimus Sebastes helvomaculatus Sebastes melanops Bathymaster signatus Trichodon trichodon Malacocottus spp. Cryptacanthodes giganteus Lycodes palearis
arrowtooth flounder walleye pollock Pacific cod Pacific halibut Pacific Ocean perch sablefish flathead sole rock sole northern rockfish Atka mackerel rex sole Dover sole yellowfin sole dusky rockfish rougheye rockfish skate (unidentified) Pacific herring sharpchin rockfish giant grenadier shortspine thornyhead harlequin rockfish eulachon butter sole starry flounder shortraker rockfish redstripe rockfish spiny dogfish yellow Irish lord silvergray rockfish lingcod bigmouth sculpin salmon shark sculpin (unidentified) English sole Pacific sleeper shark prowfish Alaska plaice Pacific tomcod redbanded rockfish spotted ratfish kelp greenling yellowmouth rockfish yelloweye rockfish rosethorn rockfish black rockfish searcher Pacific sandfish sculpin (unidentified) giant wrymouth wattled eelpout
3790 2431 1876 1415 753 753 912 515 135 153 187 164 313 87 153 139 186 23 115 126 9 24 80 50 59 18 34 49 16 15 54 27 37 11 1 13 7 5.03 4.88 13.06 3.89 1.69 2.10 1.99 1.14 2.08 7.56 4.33 0.76 0.86
3339 3139 1516 1410 823 1379 783 668 475 98 269 263 192 505 227 129 511 274 96 127 247 56 71 63 137 90 34 46 18 32 35 43 23 28 1 24 16 34.67 6.21 8.16 8.17 0.89 10.17 5.12 3.75 2.05 3.71 2.97 3.26 1.75
6499 2873 1424 1118 535 731 828 531 365 101 335 329 196 91 165 164 60 131 81 68 60 95 59 35 43 92 65 40 48 35 29 42 18 26 6 15 20 6.32 11.20 6.32 13.87 6.39 3.29 2.50 4.65 5.39 2.45 2.40 3.98 3.10
5298 2586 1397 2007 1649 844 643 590 356 73 297 291 277 194 212 205 53 81 155 114 32 119 102 137 69 101 114 40 65 53 19 26 22 28 29 28 9 9.01 12.53 7.08 8.21 12.14 3.88 2.54 0.86 3.04 1.76 3.55 1.17 3.16
5574 2320 1821 1936 2629 492 689 695 337 1179 245 266 162 255 156 274 3 220 175 177 68 110 71 93 69 51 96 61 82 80 14 11 30 15 72 23 17 5.78 15.54 12.65 6.69 3.15 3.70 7.71 7.89 4.38 0.52 1.63 2.77 2.30
4900 2670 1607 1577 1278 840 771 600 334 321 266 263 228 227 183 182 163 146 124 122 83 81 77 76 75 71 69 47 46 43 30 30 26 22 22 21 14 12.16 10.07 9.45 8.16 4.85 4.63 3.97 3.66 3.39 3.20 2.97 2.39 2.23
91 74 73 77 40 52 57 44 20 5 67 55 7 20 34 33 8 10 3 24 11 32 7 5 8 4 16 27 7 10 18 1 9 7 1 11 4 2 11 6 8 1 3 4 1 15 3 14 1 11
continued
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Table 2 (continued) Scientific name
Common name
Eopsetta jordani Mallotus villosus Lycodes brevipes Dasycottus setiger Aptocyclus ventricosus Podothecus acipenserinus Sebastes elongatus Sebastes wilsoni Myctophidae Triglops spp. Stichaeidae Sebastes crameri Hexagrammos stelleri Lyopsetta exilis Cyclopteridae (Liparidinae) Hemilepidotus hemilepidotus Cottidae Gymnocanthus spp. Eumicrotremus orbis Lycodes diapterus Sarritor frenatus Bathyagonus nigripinnis Total
petrale sole capelin shortfin eelpout spinyhead sculpin smooth lumpsucker sturgeon poacher greenstriped rockfish pygmy rockfish lanternfish (unidentified) sculpin (unidentified) prickleback (unidentified) darkblotched rockfish whitespotted greenling slender sole snailfish red Irish lord sculpin (unidentified) sculpin (unidentified) Pacific spiny lumpsucker black eelpout sawback poacher blackfin poacher
1
1984
1987
1990
1993
1996
Mean
FO
0.97 1.47 0.13 1.33 0.74 0.19 0.05 0.00 0.19 0.37 0.58 0.02 0.17 0.10 0.30 0.07 0.25 0.16 0.02 0.08 0.03 0.02 14,786
0.83 0.17 0.84 0.49 1.29 0.42 0.22 1.38 0.19 0.32 0.05 0.13 0.56 0.07 0.23 0.29 0.00 0.03 0.08 0.05 0.03 0.01 17,286
0.61 0.51 0.79 0.89 1.77 1.23 0.59 0.30 0.11 0.68 0.51 0.59 0.64 0.29 0.30 0.05 0.28 0.00 0.03 0.00 0.01 0.01 17,436
2.77 0.42 2.41 1.88 0.37 2.34 0.91 0.01 1.52 0.40 0.70 0.99 0.33 0.77 0.28 0.32 0.08 0.06 0.07 0.01 0.04 0.01 18,400
3.68 4.98 2.17 0.83 0.56 0.33 1.20 0.96 0.53 0.61 0.41 0.41 0.27 0.59 0.04 0.10 0.01 0.06 0.04 0.02 0.04 0.00 20,662
1.77 1.51 1.27 1.08 0.95 0.90 0.59 0.53 0.51 0.48 0.45 0.43 0.39 0.37 0.23 0.16 0.12 0.06 0.05 0.03 0.03 0.01 17,714
2 7 8 11 2 5 1 1 4 6 4 1 1 5 3 1 1 1 2 2 2 1
Catches for 1984 and 1987 were adjusted by using fishing power coefficients in Tables 28 and 31 in Munro and Hoff (1995). Other flatfishes were adjusted by using rock sole coefficients.
Table 3 Pairwise Pearson’s correlation coefficients among independent variables used in the statistical analysis. Depth Alongshore distance Gear temperature Julian day Year Area swept
–0.326* –0.407* 0.129* –0.026 0.012
Alongshore distance
–0.203* –.383* –0.035 0.184*
Gear temperature
Julian day
Year
0.330* –0.266* 0.019
–0.306* 0.145*
–0.452*
* Highly significant (P=0.01)
the direction of sampling was reversed in some years, pro viding some contrast. Species richness, as measured by the number of spe cies per haul, was initially modeled as a Poisson variable (counts). However, an analysis of the distribution of the observed numbers and of residuals from the fitted models suggested that it could more appropriately be modeled as a normally distributed variable (see below). We fitted models assuming either a Poisson or a normal distribu tion and found that the estimated patterns were virtually identical and in both cases the same model was selected as
best model. Only results based on the normal distribution are presented. Species richness was highly variable and the best model explained a small portion of the overall variability (Pseudo-r2=0.25). The average number of spe cies increased with area swept (Fig. 2), which may be ex pected if species area relationships established for terres trial ecosystems also hold in the marine environment. The number of species tended to peak at intermediate depths (200–300 m) and was highest in the eastern GOA (Table 4), decreasing steadily west of Prince William Sound (km 1100, approximately 147°W). Species richness appeared
SE Yak Kod
Ch i
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Shu
2 2 0 0 -2 -2 -4 0
100 200 300 400 500
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1000
0
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Alongshore distance (km)
Depth (m)
2
2
0
0
-2
-2
-4 140
180
220
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2
2
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-2
-4 4
160
Area swept (m2)
172
7
4 8*10 12*10
71
4
71
4*10
0
-4 0
Gear type
Figure 2 Estimated trends in number of species per haul by depth, alongshore distance, Julian day, year, area swept, and gear type. Alongshore dis tance was measured along the 200-m depth contour from east to west. Major geographic regions from east to west are Southeast (SE), Yaku tat (Yak), Kodiak (Kod), Chirikof (Chi), and Shumagin (Shu). Dashed lines indicate approximate 95% confidence limits of the regression lines. Horizontal lines in lower right plots indicate mean response with 95% pointwise confidence intervals. Width of bars is proportional to number of observations. Fitted lines in each panel are adjusted for the effects of all other variables. Standardized effects in each plot (y-axes, no units) are on the same scale for comparison. Effects are standardized because the estimated CPUE at a given value of a vari able is dependent upon the levels of all other variables.
to decrease during the last month of the survey season and showed some variation across years (Fig. 2). There was no consistent trend in species richness with tempera ture (not shown). One of the Japanese trawls (gear type 717, Fig. 2) tended to have a much smaller number of species per haul on average, suggesting a low catchability for at least some species. Residuals from the best model were close to normally distributed (Fig. 3, A and B) and showed no apparent trends with any of the covariates or over time.
Only a very small portion of the variability in species diversity (Shannon-Wiener index) was accounted for by the best regression (Pseudo-r2=0.17). Depth had the stron gest effect on species diversity with highest diversities ob served at intermediate depths (Fig. 4). Diversity generally increased with the number of species, which showed a very similar trend with depth (Fig. 2). Diversity was higher in the eastern Gulf and decreased west of Prince William Sound (Figs. 4 and 5). It showed little variation over time, both within the survey period and across years. However,
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Figure 3 Quantile-quantile plots (left), showing sorted residuals (points) against corresponding quantiles of the standard normal distribution, and plots of residuals against fitted values with smoothing spline (right) for best-fit models of species richness (A, B), species diversity (C, D), and total CPUE (E, F). Points in Q-Q plots fall on the indicated straight line if data are normally distributed.
Table 4 Average number of groundfish species caught per haul by depth stratum and geographic area, not adjusted for effects of other variables. Depth stratum (m) 0–100 100–200 200–300 300–500
No. of species/haul (SE) 9.5 (0.12) 11.5 (0.08) 13.2 (0.13) 10.9 (0.15)
Area
No. of species /haul (SE)
Shumagin Chirikof Kodiak Yakutat Southeast
9.7 (0.11) 10.5 (0.12) 11.6 (0.11) 12.5 (0.14) 12.8 (0.20)
0.4
0.4
567
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0 Effect on species diversity
SE Yak Kod
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Mueter and Norcross: Spatial and temporal patterns in the demersal fish community off Alaska
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0.4
0.4
0
0
-0.4 140
1000
0
Depth (m)
-0.4 180
220
260
'84
Julian day
'87 '90 '93 '96 Year
Figure 4 Trends in species diversity (Shannon-Wiener index) by depth, along shore distance, Julian day, and year (area swept and gear type were not significant). For details see Figure 2.
95% confidence intervals suggest a significant drop in species diversity between 1993 and 1996. Area swept and temperature did not enter the best model, suggesting they had no strong effect on species diversity. Diagnostic plots indicated approximate normality and no apparent trends in the residuals (Fig. 3, C and D). Total biomass (CPUE) showed strong trends with depth and alongshore distance (Fig. 6) but had high variability around these trends (Pseudo-r2=0.14). Average CPUE in creased sharply with depth to about 150 m—a trend that reflected, on average, approximately a doubling of CPUE between the shallowest sampling stations and stations at 150–200 m depth. A similarly strong gradient exists in the alongshore direction between Yakutat (km 700) and the Kodiak Island area (km 1500), where total CPUE has a pronounced maximum. Estimates of the spatial trend suggested that highest CPUEs are found around Kodiak Island, particularly in Shelikof Strait, along the shelf break and upper slope, and on the banks and in the gullies northeast of Kodiak Island (Fig. 5). Total CPUE appeared to decrease significantly after approximately Julian day 240 (Aug. 28); however, Julian day was confounded with alongshore distance (r =–0.38, Table 3) and the effect could in part be due to lower abundance in the eastern GOA, which was often sampled later than other areas. Gear effects were significant and indicated a lower catch rate for one of the Japanese trawls (gear 717, Fig. 6). The esti mated time trend (Fig. 6) indicated a significant increase
in total CPUE between 1984 and 1996 (P0.5) based on linear regres-
SE Yak Kod
Ch i
Mueter and Norcross: Spatial and temporal patterns in the demersal fish community off Alaska
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Figure 6 Trends in total CPUE by depth, alongshore distance, Julian day, year, and gear type. For details see Figure 2
sions). These findings suggest that species composition within the summer remains relatively stable over the 14–16 week survey period. To examine effects of temperature on species composition we used only those hauls from the 1993 and 1996 data for which temperature measurements were available (n=726 and n=716 respectively, Table 1). Results for other years are not reported because of the relatively small number of temperature measurements. The available temperature data for earlier years were often localized in certain strata, whereas large areas had few if any measurements. The lack of contrast did not allow definite conclusions with regards to the temperature effect. For 1993 and 1996, we repeated the GAM analysis for the five axes of species composition with temperature included. Temperature effects were apparently large. However, wide confidence intervals and inconsistent patterns between years suggest that the apparent effects were at least in
Plots of the first two axes from an NMDS (multidimensional scaling) ordination of 48 strata sampled during each of fivfe years. Distances between two points (strata) in the ordination diagram approximately reflect their dissimilarity in terms of species composition. Symbols indicate depth strata (A) and geographic area (B).
part due to confounding of temperature with alongshore distance and depth. Therefore we first adjusted for the effects of depth and alongshore distance using the regression models summarized in Table 6. Temperature explained a small but significant portion of the remaining residual variation in three of the ordination axes in both 1993 and 1996 (Pseudo-r2 values
