Spatial distribution of ocean habitat of yearling Chinook(Oncorhynchus tshawytscha) and coho (Oncorhynchuskisutch) salmon off Washington and Oregon, USA

HONGSHENG BI,1,* RACHEL E. RUPPEL,1

WILLIAM T. PETERSON2 AND EDMUNDOCASILLAS3

1Cooperative Institute for Marine Resources Studies, OregonState University, Hatfield Marine Science Center, Newport, OR97365, USA2NOAA Fisheries, Northwest Fisheries Science Center, HatfieldMarine Science Center, Newport, OR 97365, USA3NOAA Fisheries, Northwest Fisheries Science Center,

Montlake Blvd. East, Seattle, WA 98112-2097, USA

ABSTRACT

We determined the habitat usage and habitat con-nectivity of juvenile Chinook (Oncorhynchustshawytscha) and coho (Oncorhynchus kisutch) salmonin continental shelf waters off Washington and Ore-gon, based on samples collected every June for 9 yr(1998–2006). Habitat usage and connectivity wereevaluated using SeaWiFS satellite-derived chlorophylla data and water depth. Logistic regression modelswere developed for both species, and habitats were firstclassified using a threshold value estimated from areceiver operating characteristic curve. A Bernoullirandom process using catch probabilities fromobserved data, i.e. the frequency of occurrence of a fishdivided by the number of times a station was surveyed,was applied to reclassify stations. Zero-catch proba-bilities of yearling Chinook and yearling coho salmondecreased with increases in chlorophyll a concentra-tion, and with decreases in water depth. From 1998 to2006, � 47% of stations surveyed were classified asunfavorable habitat for yearling Chinook salmon and� 53% for yearling coho salmon. Potentially favorablehabitat varied among years and ranged from 9 856to 15 120 km2 (Chinook) and from 14 800 to16 736 km2 (coho). For both species, the smallesthabitat area occurred in 1998, an El Nino year.Favorable habitats for yearling Chinook salmon were

more isolated in 1998 and 2005 than in other years.Both species had larger and more continuous favorablehabitat areas along the Washington coast than alongthe Oregon coast. The favorable habitats were alsolarger and more continuous nearshore than offshorefor both species. Further investigations on large-scaletransport, mesoscale physical features, and prey andpredator availability in the study area are necessary toexplain the spatial arrangement of juvenile salmonhabitats in continental shelf waters.

Key words: Bernoulli random process, Chinook,chlorophyll a, coho, favorable habitat, habitatclassification, juvenile salmon, logistic regression,receiver operating characteristic

INTRODUCTION

Variation in salmon growth, production and survivalin the ocean has been linked to conditions in theocean environment. For example, in 2000–2001,juvenile salmon in the Strait of Georgia were larger insize and had greater volumes of prey in their stomachs,associated with a shift in climate conditions in thesubarctic Pacific (Beamish et al., 2004). Salmon oceansurvival was also higher under favorable ocean con-ditions such as strong upwelling (Pearcy, 1992;McFarlane et al., 2000; Logerwell et al., 2003). Oceanconditions along the Washington and Oregon coastsare often described by the Pacific Decadal Oscillation(PDO) index, with positive values indicating warmerocean temperatures, weaker upwelling-favorablewinds, and relatively poor ocean conditions. Con-versely, negative PDO values indicate cooler temper-atures, stronger winds that favor upwelling and goodocean conditions (Mantua et al., 1997). An inverserelationship between salmon production in the Cali-fornia Current and the PDO index has been found topersist since the mid-1900s (Mantua et al., 1997;Logerwell et al., 2003; Peterson and Schwing, 2003).

However, little is known about juvenile salmonocean distribution at a regional scale, particularlywhen they first enter the sea, a critical time period

*Correspondence. e-mail: hongsheng.bi@oregonstate.edu

Received 28 April 2008

Revised version accepted 23 September 2008

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when mortality is high (Pearcy, 1992). The spatialdistribution of juvenile salmon is likely the result ofnumerous factors in the local ocean environment(Beamish et al., 2005; Bi et al., 2007). Juvenile Chi-nook and coho salmon primarily reside in coastalwaters along the Washington and Oregon coasts(Beamish et al., 2005); hence, water depth is oneimportant factor defining their distribution in theocean (Brodeur et al., 2004; Bi et al., 2007). Chloro-phyll a concentration, a proxy for primary production,has been correlated with fish production (Ware andThomson, 2005) and with juvenile Chinook and cohosalmon distributions along the West Coast of NorthAmerica (Brodeur et al., 2004; Bi et al., 2007). Thepotential linkage between juvenile salmon and chlo-rophyll a concentration might be explained by indirecttrophic interactions. The major prey items for bothyearling Chinook and yearling coho salmon werehyperiid amphipods, larval and juvenile fish, variouscrab megalopae, and euphausiids (Peterson et al., 1982;Emmett et al., 1986; Brodeur, 1991; Schabetsbergeret al., 2003). High chlorophyll a concentrations likelylead to high prey abundance, which in turn shouldbenefit juvenile salmon.

Detailed analysis of the environmental factors thatcontrol the spatial distribution of salmonids and theirprey can be difficult due to the spatial scales of phy-sical processes and associated temporal changes in theocean environment. Fortunately, data from opera-tional satellite systems can provide information onsome environmental variables at a high enough reso-lution for analysis. In the past two decades, use of thisinformation has been expanding in fisheries oceano-graphy from fish distribution (Akesson, 2002; Palacioset al., 2006) to ocean ecosystem modeling (Polovinaand Howell, 2005). When combined with geographicinformation system techniques, satellite data canprovide useful information on designation of essentialfish habitat (Valavanis et al., 2004a), and identifica-tion of biological hotspots (Valavanis et al., 2004b).

Defining juvenile salmon ocean habitat remains ahuge challenge because juvenile salmon are relativelyrare in the ocean. Very often, no fish are caught at agiven station, i.e. excessive zeros in the data. For suchorganisms, there could be a vast region that appears tocontain a favorable set of habitat parameters and yetcontains no fish because there are too few fish in theocean to occupy all the available habitat. The preva-lence of zeros results in lack of normality and homo-geneous errors. Logistic regression is a widely used toolin wildlife habitat modeling, which models the pres-ence or absence of a species at a set of survey sites inrelation to environmental variables and predicts

occurrence of the species at unsurveyed sites (Pearceand Ferrier, 2000; Zarnetske et al., 2007). Combinedwith geographic information techniques and remotesensing data, logistic regression can provide usefulinformation on the spatial distribution of importantspecies (Austin et al., 1996; Bian and West, 1997;Luoto et al., 2002; Zimmermann et al., 2007), and theresponse of species to environmental perturbation(Reckhow et al., 1987).

In a previous study, we successfully modeled thezero-catch probability of juvenile salmon, and zero-catch probability was used to qualitatively describejuvenile salmon ocean habitat (Bi et al., 2007). Thatstudy used environmental variables collected at eachsampling station during regular fish surveys, includingtemperature, salinity, chlorophyll a concentration, andwater depth. However, in situ field measurements forthese environmental variables are costly and at lowspatial resolution on many occasions, and operationalsatellite measurements provide useful alternatives withgood spatial and temporal resolution. The objectives ofthe present study are to: (1) investigate the potential ofusing chlorophyll a concentrations measured from theSea-viewing Wide Field of View Sensor (SeaWiFS)satellite and bathymetry data derived from a globalmodel (ETOPO2, 0.03�) to model the presence ofyearling coho (Oncorhynchus kisutch) and yearlingChinook (Oncorhynchus tshawytscha) salmon; and (2)quantify the area of juvenile Chinook and coho salmonocean habitat and the potential impact of habitat areaon ocean survival of salmon.

METHODS

Study area and sampling

Yearling Chinook and yearling coho salmon popula-tions were sampled in coastal waters from northernWashington to central Oregon during June researchcruises from 1998 through 2006. A fixed grid of sta-tions along eight transects was sampled (Fig. 1).Juvenile salmon were assigned to length-based ageclasses modified from those of Pearcy and Fisher(1990) with yearling Chinook salmon ranging from141 to 280 mm and yearling coho salmon <330 mmfork length (FL). For details on fish sampling meth-odology, see Bi et al. (2007).

Geographic data

Satellite images from the SeaWiFS onboard the Orb-View-2 satellite were processed using a standardchl_oc4 version 4 algorithm with standard atmo-spheric correction (ftp://samoa.gsfc.nasa.gov/pub/franz/

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Figure 1. Chlorophyll a concentration (mg m)3). SeaWiFS satellite imagery, 4-km resolution, corresponds to the cruise dates inJune 1998–2006. Black triangles indicate sampling stations.

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code/src/l2gen/get_chl.c). Because of cloud cover inthe study area, daily data were compiled into 8-daycomposites of ocean chlorophyll a concentration at0.04� (� 4 km) spatial resolution. Data wereprocessed in the West Coast Regional Node of theNational Oceanic and Atmospheric AdministrationCoastWatch program (http://coastwatch.pfeg.noaa.gov/coastwatch/CWBrowser.jsp). Bathymetry datawere derived from a global model (ETOPO2, 0.03�),and were available on the same website. For eachcruise, two composite SeaWiFS images were selectedto coincide with the sampling period (Table 1).Gridded data for chlorophyll a concentration andbathymetry were downloaded for the study area as*.mat files into MATLAB 7.2. Due to cloud cover, eachSeaWiFS image had missing data for some grids. Topartially remedy this, we selected the image thatcoincided with the most days of the 10-day cruises andfilled in data gaps with pixels from the second image.Despite cloud cover, we were able to extract chloro-phyll a concentrations for 399 of 405 sampling stationsin June 1998–2006. The station-based chlorophyll aconcentration and bathymetry data were also used tomodel the presence of juvenile salmon. Full griddeddata were used to map the spatial distribution ofjuvenile salmon habitat. The presence of juvenilesalmon for unsurveyed portions of the grid was pre-dicted using models developed from the station-baseddataset. Note the difference between SeaWiFS imag-eries (Fig. 1) in the present study and the previouswork (fig. 11 in Bi et al., 2007). In the previous work,we only used the imageries that coincided with themajority of the cruise and did not fill data gaps withother imageries, whereas in the present study, datagaps were filled using information from the secondimage.

Data analysis

A logistic regression model was used to predict thezero-catch probabilities with 95% confidence intervalsfor yearling Chinook or yearling coho, with a highzero-catch probability indicating unfavorable habitatand a low zero-catch probability indicating favorablehabitat. Juvenile salmon abundance data were con-verted to binary data (presence⁄absence) as theresponse variable and chlorophyll a and water depthwere used as predictor variables (Equation 1)

Pðy ¼ 0jxÞ ¼ expðb0XÞ1þ expðb0XÞ ð1Þ

where X = (1, x1, x2)¢ is a vector holding chlorophyll aand water depth, b¢ = (1, b1, b2)¢ is a vector of coef-ficients for the intercept, chlorophyll a and waterdepth, and P is zero-catch probability. Logisticregression was only applied to the full dataset (1998–2006) because we did not have enough samples percruise to run the model for each individual year.

To determine the discrimination threshold proba-bility (Pt) between favorable and unfavorable habitat,a receiver operating characteristic (ROC) curve wasconstructed (Pearce and Ferrier, 2000), where a zero-catch probability higher than Pt would indicate theabsence of fish and a zero-catch probability lower thanPt would indicate the potential presence of fish. In thepresent study, habitat was classified as: (1) locationswith zero-catch probabilities higher than Pt indicatingunfavorable habitat; (2) locations with zero-catchprobabilities lower than 1–Pt indicating favorablehabitat; and (3) locations with zero-catch probabili-ties in [1–Pt, Pt] indicating potentially favorablehabitat.

Table 1. Temporal duration of fieldsampling cruises in June 1998–2006 andcorresponding 8-day composite SeaWiFSchlorophyll imagery. Chlorophyll aconcentration was estimated for eachstation (Fig. 1) from the correspondingSeaWiFS imagery.

Year Field survey Satellite

Proportion ofpixels fromsecond imagery

1998 June 17–25 June 14–21, June 22–29 0.521999 June 16–24 June 14–21, June 22–29 0.342000 June 17–25 June 13–20, June 21–28 0.442001 June 24–July 1 June 22–29, June 30–July 6 0.282002 June 21–28 June 14–21, June 22–29 0.282003 June 26–July 3 June 22–29, June 30–July 6 0.252004 June 22–29 June 21–28, June 29–July 5 0.252005 June 12–22 June 6–13, June 14–June 22 0.342006 June 19–28 June 14–21, June 22–29 0.25

Dates shown in bold indicate that SeaWiFS imagery from that 8-day period wasthe primary image used to map the spatial pattern of salmon habitat. Data fromthe secondary image (not bold) were used to fill in gaps in the primary images.

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To construct the ROC curve, two indices, ‘sensi-tivity’ and ‘specificity’, were calculated. First, athreshold value (an arbitrary value ranging from 0 to1) was chosen to classify the predicted zero-catchprobabilities into presence and absence, so that loca-tions could be divided into two categories: zero-catchprobability higher than the threshold, indicating fishabsence, and probability lower than the threshold,indicating the potential presence of fish. Secondly, wecompared the predicted values (presence⁄absence)with the observed values. Stations were divided intofour groups: stations where we predicted the presenceof fish and observed them, stations where we predictedfish absence and observed no fish, stations where wepredicted fish absence but observed fish, and stationswhere we predicted fish presence but observed no fish.Sensitivity was calculated as number of stations withobserved and predicted fish absence divided by numberof stations with observed fish absence, and specificitywas calculated as number of stations with observed andpredicted presence divided by number of stations withobserved fish presence. By increasing the thresholdvalue gradually from 0 to 1, sensitivity and specificitycould be calculated for each increment, and a plot forspecificity versus sensitivity could be constructed. Thearea under the curve has been statistically shown to bethe probability cutoff for fish absence (Metz, 1978;Pearce and Ferrier, 2000). In the present study, logisticregression models and ROC curves were constructedby using the PROC LOGISTIC procedure in SAS 9.1for yearling Chinook and yearling coho salmon, andchi-square tests were used to test whether coefficientsfor chlorophyll and depth were equal to zero at the95% significance level.

Sampled stations were first classified into threegroups – favorable, unfavorable, and potentiallyfavorable – based on results from logistic regressionmodels. The logistic regression model only includedfixed effects and error terms, whereas fish distributionmay involve random processes. Meanwhile thethreshold value determined from the ROC curveindicated where zero catches were most likely tooccur. Preliminary results indicated that only a fewsampling stations were classified as unfavorable habi-tat (<5% of sampled stations for yearling Chinook),whereas many stations with fish absence were classi-fied as favorable habitat. Thus a second step was ta-ken to reclassify these stations. For each station, acatch probability (pi) was calculated as the number ofsamples with at least one fish divided by the totalnumber of samples collected at that station. Then, arandom number was generated from a Bernoulli dis-tribution with probability pi. For each station with

zero-catch in the favorable or potentially favorablegroups, if the random number was 0, the station wasreclassified as unfavorable habitat, whereas if therandom number was 1, the station remained in thesame group. After reclassification, juvenile salmonabundances were compared among the three groups.The null hypothesis that the three groups had thesame abundance was tested by variance analysis at the95% significance level (PROC GLM, SAS 9.1; SASInstitute, 2005).

To map the spatial distribution pattern of juvenilesalmon, gridded 8-day composite chlorophyll a con-centrations from SeaWiFS and bathymetry data fromETOPO2 were used to predict fish absence for eachgrid cell (� 16 km2) using the models describedabove. Each grid cell was first classified based on pre-dicted zero-catch probability and then reclassifiedusing catch probability and a Bernoulli random pro-cess. To estimate the catch probability for each gridcell, a non-parametric procedure in ARCGIS 9.1 wasapplied to interpolate the station-based catch proba-bilities (thin plate spline interpolation). Thus, theextent and distribution of habitat for yearling Chinookand coho salmon were estimated for each cruise. Totalarea of potential habitat for each cruise and eachspecies was estimated on a pixel-by-pixel basis. Per-centages of area for the three types of habitat werethen calculated. The grid cells of (1) favorable habitat,(2) potentially favorable habitat, (3) unfavorablehabitat, and (4) missing data in each block were spa-tially queried in ARCGIS 9.1.

RESULTS

Descriptive statistics of salmon abundance, chlorophyll aconcentration and bathymetry

Total abundance of yearling Chinook salmon was9 240 (N km)2) at 405 stations surveyed from 1998 to2006 with a mean of 23 ± 57 fish km)2 (mean ± 1SD) and a median of 0 fish km)2. The highest meanabundance occurred in 1999 (51 ± 109 fish km)2),and the lowest occurred in 2005 (5 ± 11 fish km)2).Mean abundances were also relatively low in 1998(9 ± 19 fish km)2), 2001(14 ± 34 fish km)2), and2004 (14 ± 21 fish km)2). Mean abundances in 2000,2002, 2003 and 2006 ranged from 21 to 33 fish km)2

(2000: 33 ± 53; 2002: 26 ± 47; 2003: 21 ± 43; 2006:22 ± 65).

Yearling coho salmon had higher abundances thanyearling Chinook salmon in the study area during allJune cruises. Total abundance of yearling coho salmonwas 31 539 at 405 stations surveyed from 1998 to 2006

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with a mean of 765 ± 154 fish km)2 (mean ± 1 SD)and a median of 11 fish km)2. The highest meanabundance occurred in 2003 (120 ± 240 fish km)2),and the lowest in 2005 (9 ± 18 fish km)2). Meanabundances were relatively low in 1998 (10 ± 27 fishkm)2), 2004 (38 ± 96 fish km)2) and 2006 (38 ± 75fish km)2). Mean abundances from 1999 to 2002ranged from 53 to 94 fish km)2 (1999: 73 ± 215; 2000:53 ± 95; 2001: 94 ± 151; 2002: 84 ± 153).

SeaWiFS-derived chlorophyll a concentrationsshowed large interannual variation (Fig. 1). Chloro-phyll a concentrations were generally higher along theWashington coast and around the Columbia Rivermouth than along the central Oregon coast. Meanchlorophyll a concentrations ranged from 0.2 to23.7 mg m)3 over all 9 yr with a mean of 3.2 ±2.8 mg m)3 at 399 stations. The highest mean chlo-rophyll a concentration occurred in 2003 (5.3 ±4.7 mg m)3). Mean chlorophyll a concentrations in1999, 2000, 2001 and 2004 were similar, ranging from3.1 to 3.5 mg m)3. Mean chlorophyll a concentrationsranged from 2.2 to 2.6 mg m)3 in 1998, 2002, 2005,and 2006 with the lowest in 1998 (2.2 ± 1.5 mg m)3).The mean depth of stations surveyed was135 ± 175 m, with more than half of the stations lo-cated in waters shallower than 100 m. Chlorophyll aconcentration was positively correlated with bothyearling Chinook salmon abundance (Spearmancoefficient = 0.40, P < 0.01) and yearling coho sal-mon abundance (Spearman coefficient = 0.23,P < 0.01). Depth derived from the bathymetry modelwas negatively correlated with both yearling Chinooksalmon abundance (Spearman coefficient = )0.48,P < 0.01) and yearling coho salmon abundance(Spearman coefficient = )0.12, P = 0.02).

SeaWiFS-derived chlorophyll a concentration wassignificantly correlated with field measurements butwith large variation (R2 = 0.20, P < 0.001). Log-transformation improved the correlation considerably(R2 = 0.46, P < 0.001). Model-derived bathymetrydata were consistent with shipboard measurements(R2 = 0.86, P < 0.01).

Modeling zero-catch probability and classification of habitat

SeaWiFS-derived chlorophyll a concentrationsshowed an inverse relationship with the zero-catchprobability for yearling Chinook salmon, but did notshow a significant correlation with the zero-catchprobability for yearling coho salmon (Table 2). Thatis, there is a greater chance of catching a yearlingChinook salmon at locations with high chlorophyll aconcentrations. To capture the interannual variation,chlorophyll a concentration was included in themodel. The zero-catch probability for both yearlingChinook and yearling coho salmon also increased aswater depth increased (Table 2). The predictivemodel for yearling Chinook salmon was

Pchinook ¼ expð�0:25�0:20�chlþ0:99�10�2�depthÞ1þexpð�0:25�0:20�chlþ0:99�10�2�depthÞ and the

predictive model for yearling coho salmon was

Pcoho ¼ expð�0:44�0:07�chlþ0:30�10�2�depthÞ1þexpð�0:44�0:07�chlþ0:30�10�2�depthÞ :

The proportion of stations with yearling Chinooksalmon present showed an inverse relationship withthe predicted zero-catch probability, and the propor-tion of surveyed stations with no yearling Chinooksalmon increased as the predicted zero-catchprobability increased (Fig. 2a). When the predictedzero-catch probability was high, fish were absent atmost stations (zero-catch was 67–93% when predictedzero-catch probability was higher than 0.60). Whenthe predicted zero-catch probability was lower than0.4, at least one yearling Chinook salmon was caughtat 67–80% of the stations surveyed. The area coveredby the ROC curve was 0.77 (Fig. 2b). That is, if astation had predicted zero-catch probability higherthan 0.77, it generally should be classified as ‘fish ab-sent’, which was consistent with Fig. 2a.

The proportion of present⁄absent versus predictedzero-catch probability for yearling coho salmon wassimilar to that for yearling Chinook salmon (Fig. 3a).When predicted zero-catch probability was higherthan 0.60, no fish were caught at 89–100% of thestations sampled, but when it was lower than 0.4, atleast one yearling coho salmon was caught at 47–100%

Table 2. Oncorhynchus tshawytscha andOncorhynchus kisutch. Results fromlogistic regression models. Total df = 405.A P-value < 0.05 indicates significanteffects.

Species Statistics InterceptChl a(mg m)3) Depth (m)

Likelihoodratio

YearlingChinook

Coefficient )0.25 )0.20 0.99 · 10)2

Chi-square value 0.68 13.54 22.61 83.28P 0.41 <0.01 <0.01 <0.01

Yearlingcoho

Coefficient )0.44 )0.07 0.30 · 10)2

Chi-square value 3.84 2.80 10.02 25.18P 0.05 0.09 <0.01 <0.01

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of surveyed stations. The area covered by the ROCcurve was 0.61 (Fig. 3b), i.e. if a station had a pre-dicted zero-catch probability higher than 0.61, thestation was classified as ‘fish absent’.

Based on the threshold value estimated by the ROCcurve, yearling Chinook salmon were more likely to bepresent at 7% of the stations (28 stations) with a meanabundance of 40 ± 65 fish km)2, potentially present at78% of the stations (312 stations) with a meanabundance of 24 ± 60 fish km)2, and more likely to beabsent at 15% of the surveyed stations (59 stations)with a mean abundance of 2 ± 5 fish km)2 from 1998to 2006. Yearling coho salmon were more likely to bepresent at 37% of the stations (147 stations) with amean abundance of 75 ± 173 fish km)2, potentiallypresent at 58% (233 stations) with a mean abundanceof 64 ± 147 fish km)2, and more likely to be absent at5% of the surveyed stations (19 stations) with a meanabundance of 4 ± 17 fish km)2.

Catch probabilities for yearling Chinook salmonbased on observed samples were generally higher atstations along the northern Washington coast and at afew nearshore stations along the Oregon coast (Fig. 4).Catch probabilities for yearling coho salmon showed

the same pattern (Fig. 4). When the surveyed stationswere reclassified for yearling Chinook salmon based ona Bernoulli random process, and using the catchprobabilities estimated from observed data, 5% of sta-tions (20 stations) were favorable habitat, 40% (160stations) were potentially favorable habitat, and 55%(219 stations) were unfavorable habitat. The meanyearling Chinook salmon abundances in the favorable(50 ± 74 fish km)2) and in the potentially favorable(35 ± 74 fish km)2) were significantly higher than inthe unfavorable group (8 ± 30, P < 0.01, Table 3).When the surveyed stations were reclassified for year-ling coho salmon, 24% of stations (97 stations) werefavorable habitat, 23% (90 stations) were potentiallyfavorable habitat, and 53% (212 stations) were unfa-vorable habitat. The mean abundances in the favorable(94 ± 205 fish km)2) and potentially favorable groups(81 ± 141 fish km)2) were significantly higher than theunfavorable group (45 ± 125, P = 0.02, Table 3).

Spatial distribution of habitat

Yearling Chinook salmon potential habitatThe spatial extent of favorable habitat for yearlingChinook salmon from 1998 to 2006 ranged widely,

Figure 3. Yearling coho salmon (Oncorhynchus kisutch).Discrimination capacity of the logistic regression model.(a) Distribution of predicted zero-catch probability valuesassociated with observed presence or absence of a fish. (b)The receiver operating characteristic curve (ROC), in whichthe area under the curve indicates the threshold value forabsence.

Figure 2. Yearling Chinook salmon (Oncorhynchus

tshawytscha). Discrimination capacity of the logistic regres-sion model. (a) Distribution of predicted zero-catch proba-bility values associated with observed presence or absence ofa fish. (b) The receiver operating characteristic curve(ROC), in which the area under the curve indicates thethreshold value for absence.

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from 15 120 km2 in 2003 to 9856 km2 in 1998 (Fig. 5).Every year, favorable habitat existed from 46 to 48�N,and potentially favorable habitat mostly occurred alongthe northern Washington coast. In 1998 and 2005,relatively larger nearshore areas of the northernWashington coast were classified as potentially favor-able habitat, which contrasts with 1999, 2000, 2003,2004 and 2006, when most areas were classified asfavorable habitat. In 1998 and 2005, most favorablehabitat areas were less connected than in other years.

The extent of potential habitat was significantlydifferent between the Oregon and Washington coasts(Fig. 5). The amount of favorable and potentiallyfavorable habitat north of 46�N was � 10 times largerthan south of 46�N, and this pattern was consistentamong years. In most years, favorable habitat areasalong the Oregon coast were mostly isolated from eachother, whereas favorable habitat areas along the

Washington coast were often connected. The extentof potential habitat for yearling Chinook salmon alsodisplayed a clear cross-shelf pattern. Fewer favorablehabitat areas occurred offshore than nearshore, andthe offshore favorable habitat areas were more isolatedthan the nearshore areas.

Yearling coho salmon potential habitatFor yearling coho salmon, the extent of favorablehabitat was much larger than for yearling Chinooksalmon but similar among years: the year 2000 had thelargest habitat area, 16 736 km2, and 1998 the small-est, 14 800 km2 (Fig. 6). Distributions of favorablehabitat tended to be more continuous and moreexpansive north of 46�N. The size of favorable habitatshowed relatively small interannual variation.

Favorable habitat extent for yearling coho salmonwas significantly different between coastal waters offOregon and Washington. The amount of favorableand potentially favorable habitat to the north of 46�Nwas � 6 times larger than to the south of 46�N, andthis pattern was consistent among years. In most years,favorable habitat areas along the Washington coastwere mostly continuous and more expansive thanthose along the Oregon coast. The extent of favorablehabitat for yearling coho salmon displayed a similarcross-shelf pattern to that of yearling Chinook salmon,with fewer favorable habitat areas offshore thannearshore and more continuous favorable habitatnearshore than offshore.

DISCUSSION

Habitat classification

Understanding the spatial distribution of juvenilesalmon in the ocean is important for understandinghow ocean and climate variability may influence

Figure 4. Oncorhynchus tshawytscha andOncorhynchus kisutch. Catch probabilities(calculated as the frequency of occur-rence of fish divided by the total numberof times a station was surveyed) in 1998–2006. Station-based data were inter-polated using the Thin Plate procedurein ARCGIS to map the spatial pattern.

Table 3. Yearling Chinook salmon (Oncorhynchustshawytscha) and yearling coho salmon (Oncorhynchuskisutch). Mean abundance comparison among three differentgroups: favorable, potentially favorable, and unfavorable.Total df = 399. A P-value < 0.05 indicates significant dif-ference.

YearlingChinook salmon

Yearlingcoho salmon

F P F P

Overall difference 16.67 <0.01 4.19 0.02Favorable versusPotential

0.57 0.44 0.38 0.54

Favorable versusUnfavorable

10.32 <0.01 7.12 <0.01

Potential versusUnfavorable

28.50 <0.01 3.56 0.05

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Figure 5. Yearling Chinook salmon (Oncorhynchus tshawytscha). Modeled habitat in June 1998–2006. Favorable, potentiallyfavorable, and unfavorable habitats were recorded by the number of pixels. Each pixel is � 16 km2.

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Figure 6. Yearling coho salmon (Oncorhynchus kisutch). Modeled habitat in June 1998–2006. Favorable, potentially favorable,and unfavorable habitats were recorded by the number of pixels. Each pixel is � 16 km2.

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salmon survival. That is, we need to understand salmonhabitat preference before we can begin to exploremechanisms that might govern their growth and sur-vival. In the present study, we characterized juvenileChinook and coho salmon ocean habitat based onsatellite-derived chlorophyll a concentrations andmodel derived water depth using logistic regression.However, in the ocean environment, many factorscould influence juvenile salmon distribution, includinglarge-scale transport and the distribution and avail-ability of prey and predators. Moreover, salmon are rarein the ocean. Simple habitat variables like chlorophylla concentration and water depth represent goodstarting points for habitat studies because data arereadily available, e.g. satellite-derived chlorophyll aconcentration has good spatial and temporal coverage.

Spatial autocorrelation is a top concern for fishdistribution in the ocean. In the present study, we useda Bernoulli random process based on catch probabili-ties estimated from 10 yr of observation to capturespatial features that might not have been explained bychlorophyll a concentration and water depth. Theinclusion of a Bernoulli random process based onobserved catch probabilities had two advantages in thepresent study. First, the classification procedure basedon threshold values estimated from ROC curves isconservative and tends to overestimate the area offavorable habitat. Only a small fraction of the sur-veyed stations were classified as unfavorable habitat,i.e. for yearling coho the percentage of stations atwhich zero-catch was most likely to occur was � 5%,whereas many zero-catch stations were classified asfavorable or potentially favorable habitat. When pre-dicted zero-catch probabilities ranged from 0.4 to 0.6for yearling Chinook salmon and from 0.2 to 0.5 foryearling coho salmon, the logistic regression modelshad difficulty classifying habitat because the observedpresence and absence among these stations was nearlyequal (Figs 2 and 3). Introducing the Bernoullirandom process helped to reclassify some zero-catchstations from favorable or potentially favorable groupsback into unfavorable habitats. As a result, � 47% ofsurveyed stations for yearling coho salmon and � 50%of surveyed stations for yearling Chinook salmon werereclassified as unfavorable habitat. The secondadvantage is that introducing catch probabilities helpsto capture spatial patterns. It is well established thatjuvenile salmon are more abundant along the Wash-ington coast than the Oregon coast (Pearcy andFisher, 1990; Bi et al., 2007). Such spatial patternsmight be related to several factors including varyingnumbers of fish entering the ocean from coastal rivers,large-scale transport, migration routes, and mesoscale

ocean features, all of which are unlikely to beexplained by simple habitat variables like chlorophylla concentration and depth. Inclusion of the Bernoullirandom process combined with observed catch prob-abilities helped to address concerns for spatial auto-correlation.

The potential of chlorophyll a concentration andwater depth in defining juvenile salmon ocean habitathas been documented by several studies. Pearcy andFisher (1990) sampled juvenile salmonids off Wash-ington and Oregon in summer 1980–85, and theyreported that catch rates of coho were highest in yearswith highest chlorophyll a concentrations, suggestingthe possibility that chlorophyll a concentration definesareas of potential habitat better than do temperatureor salinity. Bi et al. (2007) explicitly showed that thepresence of yearling Chinook and yearling cohosalmon were both correlated with chlorophyll aconcentration based on station data. The present studyextends the result to satellite-derived chlorophyll aconcentration, particularly for yearling Chinook sal-mon, and raises the potential of using satellite data tocharacterize the area of potential habitat for juvenilesalmon in the coastal ocean.

The correlation between chlorophyll a concentra-tion and juvenile salmon may reflect trophic inter-actions. Chlorophyll a concentration may provide anindex of high biomass associated with preferred foodresources of salmon, such as amphipods, juvenile fish,euphausiids, and copepods. Some of the prey itemsmay not directly feed on phytoplankton, but may relyon copepods, which in turn depend on phytoplankton.Further information on prey distribution is necessaryto explain the trophic interactions. Additionally,regions of high chlorophyll a potentially have higherturbidity, which may reduce the risk of being eaten byvisual predators. An alternative explanation for therelationship between chlorophyll a concentration andsalmon could be that juvenile salmon mostly reside inshallow nearshore waters where chlorophyll a con-centrations are high. However, in this study chloro-phyll a concentration was not highly correlated withwater depth (R2 = 0.08), suggesting that althoughsalmon are only found in shallow shelf waters, they aremore likely to be found where chlorophyll a concen-trations are high. Given that the study area wasinfluenced by local upwelling, the Columbia Riverplume, and the Juan de Fuca eddies, SeaWiFS-derivedchlorophyll a concentration is likely to reflect bothlocal biological and physical features.

The obvious advantages of using satellite data arethe high spatial and temporal resolution. Tradition-ally, mapping a spatial pattern from station-based

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survey data is done by kriging, or non-parametricinterpolation techniques. However, such techniquesmay not be appropriate when potential spatial patternsexist at a finer scale than the sampling grid. Mean-while, SeaWiFS satellite data are almost continuous,and continuity is important in addressing issues likeinterannual variations in salmon ocean survival.Chinook salmon generally spend at least 2.5 yr in theocean environment, and coho salmon spend 1.5 yr.Consequently, changes in habitat availabilitythroughout the entire ocean period may contribute tointerannual variation in subsequent survival. Thus astudy of week-by-week or seasonal changes in habitatarea might be instructive.

Habitat utilization

Higher abundance of juvenile salmon in the northernpart of the study area could be related both to thesource of the fish (since most fish exit from theColumbia River, which forms the border betweenWashington and Oregon), and to the tendency forjuvenile salmon to swim north once they enter theocean. Given that there are far fewer salmon enteringthe ocean from Oregon’s coastal streams, the proba-bility of catching these juvenile salmon is far lower offthe Oregon coast than off the Washington coast.Regardless, based on chlorophyll a concentration theWashington coast had a larger area of favorablehabitat than the Oregon coast. Higher abundance inthe coastal ocean off Washington could also beaffected by physical characteristics of the continentalshelf, which may promote better habitat for the sal-mon. On the shelf off Washington, upwelling isweaker, the shelf is broader, and circulation is moresluggish (favoring retention) compared to the conti-nental shelf off Oregon. Each of these factors leads tohigher chlorophyll a concentrations off Washington(Hickey and Banas, 2003; Ware and Thomson, 2005).Based on our analyses, these characteristics seem togenerate good salmon habitat consistently from year toyear on the Washington shelf.

Both salmon species primarily resided in nearshorewaters. Brodeur et al. (2004) considered the timing ofocean entrance as a factor, i.e. many of the coho andchinook salmon juveniles collected may have recentlyentered the ocean with little time to disperse offshore.However, data on daily hatchery releases suggestedthat most of the fish were released in April and a smallnumber of fish were released after mid-May (http://www.fpc.org). That is, most fish caught in June hadbeen out in the ocean for more than a month, whichwas long enough to disperse offshore. As a result,nearshore distribution indicates a preference for shal-

low coastal waters as favorable habitat for both species,although yearling coho salmon extended their favor-able habitats further offshore than did yearling Chi-nook salmon.

Interannual variation

Interannual variations in habitat were pronounced,and were reflected in habitat area and habitat conti-nuity. Larger interannual variation in the size offavorable habitat was found for yearling Chinooksalmon than for yearling coho salmon. For yearlingChinook salmon, the years with the smallest amountof favorable habitat were 1998, an El Nino year, and2005, a warm El Nino-like year (Hickey et al., 2006;Kosro et al., 2006). For yearling coho salmon, the sizeof favorable habitat showed less interannual variationthan for yearling Chinook salmon, with relativelysmall favorable habitat areas in 1998, 1999 and 2005.The lowest total habitat area (favorable and poten-tially favorable) in 1998 and 2005 coincided withlowest ocean survival in the study period: 1.3% in1998 and 1.8% in 2005 contrasting with 4.6% in 2000and 4.0% in 2002 (PMFC, 2007). The relationshipbetween habitat size and survival was not clear inother years. This probably suggests that the size offavorable habitat may be only one of several factorsinfluencing salmon ocean survival. Perhaps whenhabitat area is not a limiting factor, the relationshipbetween survival and habitat size may not be clear.Further investigations of habitat quality and otherpotential factors are necessary to understand salmonocean survival.

Connectivity among habitats

The importance of the spatial arrangement of habitatshas emerged as an important factor in terrestrialecology in recent years (Mansergh and Scotts, 1989;Hanski, 1999; Wulf, 2004). In the ocean environment,it is difficult to evaluate habitat connectivity (i.e. theextent of spatial continuity of favorable habitat)because the ability to sample over broad regions at finescales is limited, as is our knowledge of the role ofdifferent ocean processes in setting habitat parameters.In the present study, habitat connectivity and conti-nuity may have implications for the success of juvenilesalmon populations. For example, the favorablehabitats for both yearling Chinook and yearling cohosalmon were more connected nearshore than offshoreand more connected along the Washington coast thanthe Oregon coast. Habitat continuity and connectivityalso showed large interannual variation. For example,the favorable habitat areas for yearling Chinook sal-mon were more isolated in 1998 and 2005 than in

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other years. Such spatial and temporal variation inhabitat connectivity and continuity might be relatedto large-scale ocean conditions and mesoscale ocean-ographic features. Further analyses are necessary toaddress these issues.

SUMMARY

This is the first study to explicitly use satellite-deriveddata to model juvenile salmon ocean habitat. Weclassified habitat using logistic regression and a Ber-noulli random process based on catch probabilityestimated from observed data. Zero-catch probabilityshowed an inverse relationship with chlorophyll aconcentration and a positive relationship with depthfor both yearling Chinook and yearling coho salmon.Both species had significantly higher abundances infavorable and potentially favorable habitat than inunfavorable habitat. The spatial pattern of favorablehabitat showed a clear cross-shelf pattern with largerarea and more continuous habitat nearshore thanoffshore. Favorable and continuous habitat occurredmore often along the Washington coast than along theOregon coast. The smallest amount and most isolatedpatches of favorable habitat for yearling Chinooksalmon were found in 1998 and 2005. In the highlydynamic ocean environment, the spatial and temporalarrangement of habitats through the ocean period of asalmon’s life history may be an important factor for thesuccess of yearling Chinook and yearling coho salmon.To investigate the effects of habitat dynamics onjuvenile salmon populations, information on meso-scale physical features and prey and predator fields arenecessary.

ACKNOWLEDGEMENTS

This synthesis research was supported by U.S. GLO-BEC ⁄ National Oceanic and Atmospheric Adminis-tration and data collection was funded by theBonneville Power Administration. We thank the manypeople who contributed greatly to the collection andprocessing of data: Brian Beckman, Paul Bentley,Richard Brodeur, Cindy Bucher, Robert Emmett, Jo-seph Fisher, Troy Guy, Susan Hinton, Cheryl Morgan,Robert Schabetsberger, Laurie Weitkamp and Jen Za-mon. We are especially indebted to our database team:Cheryl Morgan, Cindy Bucher and Susan Hinton.

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