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Timing and Extent of Drift of Shortnose SturgeonLarvae in the Saint John River, New Brunswick, CanadaSima Usvyatsov a , Jeffrey Picka b , Andrew Taylor c , James Watmough b & Matthew KennethLitvak ca Golder Associates Limited , 201 Columbia Avenue, Castlegar , British Columbia , V1N 1A8 ,Canadab Department of Mathematics and Statistics , University of New Brunswick , Post Office Box4400, Fredericton , New Brunswick , E3B 5A3 , Canadac Department of Biology , Mount Allison University , 63B York Street, Sackville , NewBrunswick , E4L 1G7 , CanadaPublished online: 09 Apr 2013.

To cite this article: Sima Usvyatsov , Jeffrey Picka , Andrew Taylor , James Watmough & Matthew Kenneth Litvak (2013)Timing and Extent of Drift of Shortnose Sturgeon Larvae in the Saint John River, New Brunswick, Canada, Transactions of theAmerican Fisheries Society, 142:3, 717-730, DOI: 10.1080/00028487.2012.760484

To link to this article: http://dx.doi.org/10.1080/00028487.2012.760484

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Transactions of the American Fisheries Society 142:717–730, 2013C© American Fisheries Society 2013ISSN: 0002-8487 print / 1548-8659 onlineDOI: 10.1080/00028487.2012.760484

ARTICLE

Timing and Extent of Drift of Shortnose Sturgeon Larvaein the Saint John River, New Brunswick, Canada

Sima Usvyatsov*Golder Associates Limited, 201 Columbia Avenue, Castlegar, British Columbia V1N 1A8, Canada

Jeffrey PickaDepartment of Mathematics and Statistics, University of New Brunswick, Post Office Box 4400,Fredericton, New Brunswick E3B 5A3, Canada

Andrew TaylorDepartment of Biology, Mount Allison University, 63B York Street, Sackville,New Brunswick E4L 1G7, Canada

James WatmoughDepartment of Mathematics and Statistics, University of New Brunswick, Post Office Box 4400,Fredericton, New Brunswick E3B 5A3, Canada

Matthew Kenneth LitvakDepartment of Biology, Mount Allison University, 63B York Street, Sackville,New Brunswick E4L 1G7, Canada

AbstractLittle is known about the dispersal of Shortnose Sturgeon Acipenser brevirostrum larvae in the wild. In the Saint

John River, New Brunswick, we captured a total of 2,251, 460, 2,100, and 2,083 larvae in 2008–2011, respectively;abundance estimates ranged between 21,000 (2009) and 244,687 larvae (2008). A substantial reduction in larvalnumbers (49–76%) was recorded over the 4.5-km distance between the two sampling transects deployed in 2008–2010. We found no consistent pattern of larval distribution across the channel, but we recorded a consistent, significantpreference for nighttime (dusk to dawn) over daytime dispersal. Generalized linear models were used to examine thetiming and extent of larval migration in the Saint John River during the study period. Logistic models incorporatingwater temperature and Mactaquac Dam discharge provided good predictions of the timing of larval migration. Theprobability of larval presence was highest when water temperature reached 15◦C. At this temperature, larvae werepredicted to disperse when nighttime total dam discharge was 20 × 106 to 30 × 106 m3. The extent of larval migrationwas described using negative binomial models, which indicated that dam discharge and transect location significantlyinfluenced the number of drifting larvae. However, data variability was high, reducing predictive capability. Ourfindings include the first report of Shortnose Sturgeon larval abundances in the Saint John River. The predictions oftiming and extent of drift provide information for future sampling and conservation efforts during this vulnerableperiod as well as insight into the relationships between environmental variables and larval drift in this protectedspecies.

*Corresponding author: sima.usvy@gmail.comReceived April 16, 2012; accepted December 10, 2012Published online April 9, 2013

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The Shortnose Sturgeon Acipenser brevirostrum is foundfrom Florida, United States, to New Brunswick, Canada, andis protected throughout its range (COSEWIC 2005). The SaintJohn River in New Brunswick represents the northernmostoccurrence of Shortnose Sturgeon and is the only known habitatfor this species in Canada (Dadswell et al. 1984; Kynard 1997).The Saint John River population of Shortnose Sturgeon is oneof the largest throughout the species’ range, second only to theHudson River population (Kynard 1997). Due to the reducedsizes of many populations, larval research in the wild is verydifficult, as often only few larvae can be captured (e.g., Taubert1980; Duncan et al. 2004). Since high numbers of wild Short-nose Sturgeon larvae can be obtained from the Saint John River(Usvyatsov et al. 2012), the Canadian population provides anextremely useful model for studying larval dispersal.

Reproductive activity and early life history strategies are sim-ilar in many sturgeon species, such as the Shortnose Sturgeon(Kynard 1997; Kynard and Horgan 2002), Lake Sturgeon A. ful-vescens (Auer and Baker 2002), and Chinese Sturgeon A. sinen-sis (Wei et al. 2009). Ripe adult sturgeon move as far as 100–200 km upriver to spawn (Kynard 1997; Bruch and Binkowski2002). After hatching, the larvae migrate downstream to thenursery grounds, sometimes for tens of kilometers (e.g., Auerand Baker 2002). Larvae are rendered extremely vulnerable dur-ing their migration to the nursery grounds.

Sturgeon larvae suffer from high mortality, and 90.5–98.3%of the larvae do not survive to age 0 (Caroffino et al. 2010),thus making the early life stages a likely bottleneck to recruit-ment (Gross et al. 2002). Since the survival of early life stagesis crucial for population persistence, larval dispersal has beenstudied in several sturgeon species, including Lake Sturgeon(Auer and Baker 2002; Smith and King 2005), Pallid SturgeonScaphirhynchus albus (Kynard et al. 2002; Braaten et al. 2010),and Shortnose Sturgeon (Richmond and Kynard 1995; Kynardand Horgan 2002).

A thorough understanding of the spatial and temporal extentof larval drift and the relationship between larval drift and envi-ronmental variables is needed for adequate sturgeon protectionand management efforts. The development, dispersal, and set-tling of sturgeon larvae depend on a variety of factors, such astemperature (Kynard and Horgan 2002; Hardy and Litvak 2004),light levels (Kynard and Horgan 2002), flow velocity (Brannonet al. 1985), and predation. The speed and distance of larvaldispersal depend on the current velocity, since larval swimmingspeeds are negligible in comparison with water velocity on thespawning grounds (Kynard 1997; Deslauriers 2011). This de-pendence is further complicated by the fact that sturgeon spawn-ing often occurs in the vicinity of hydroelectric dams (Kynard1997; Bruch and Binkowski 2002; Wei et al. 2009).

We used a combined approach that incorporated fieldworkand modeling to examine the timing and extent of ShortnoseSturgeon larval dispersal. Our main objectives were to (1) es-timate the abundance of migrating Shortnose Sturgeon larvaein the Saint John River during 2008–2011 and (2) model the

dependence of larval drift on environmental variables by usinglogistic models (based on larval presence–absence) and nega-tive binomial models (based on larval counts). In addition, wedescribe the dispersal behavior of Shortnose Sturgeon larvaein relation to spatial distribution and preference for nighttimeor daytime migration. This work provides the first estimatesof Shortnose Sturgeon larval abundance, predicts the timing oflarval migration, and assesses the influence of environmentalvariables on larval dispersal.

METHODSStudy site.—The Saint John River is New Brunswick’s largest

river, extending 673 km through Maine, Quebec, and NewBrunswick (Benke and Cushing 2005). The lower Saint JohnRiver is an estuary; its main stem spans approximately 150 kmbetween Mactaquac Dam and the river mouth at Reversing Falls,where it flows into the Bay of Fundy (Figure 1). The onlyknown spawning site of the Shortnose Sturgeon population inthe Saint John River is immediately downstream of MactaquacDam (COSEWIC 2005). Therefore, we focused our efforts oncollecting larvae in a 17-km stretch of river downstream ofthe dam (Figure 1c). Apart from spring freshet and rainfallfloods, the dam operates in peak-load mode. The discharge lev-els change according to electricity demand (Jessop and Harvie2003), thereby subjecting the larvae to altered and fluctuatingflow regimes.

Field collections.—Ten to fifteen anchored drift (D-frame)nets were used to collect larvae. Each net consisted of a 3.5-m-long mesh cone made of 1.6-mm knotless delta mesh. Thecone’s mouth (0.85 m in diameter) was held open by a D-shaped,stainless-steel hoop (0.9 m high, 1 m wide). The net’s end (0.3 min diameter) opened to a cod end that collected the sample. Eachnet was held in place by a Danforth anchor, which was attachedto the net via a short bridle connected to a 15-m-long rope. Thenets were positioned in two transects (5 stations/transect) acrossthe river channel at 12.5 and 17 km below Mactaquac Dam(Figure 1c). The number of stations per transect was chosen torepresent shore, channel, and midpoint habitats. In 2008, thetwo transects consisted of five nets each, with the nets evenlyspaced across the channel. In 2009 and 2010, the downstreamtransect had five nets, while the upstream transect had 10 netsthat were positioned at five stations evenly spaced across thechannel; each station along the upstream transect consisted oftwo nets set 3–5 m apart. In 2011, only the upstream transectwas deployed, with five nets evenly spaced across the channel.

The mean channel width was 538 m (SD = 12.7) at theupstream transect and 661 m (SD = 2.3) at the downstreamtransect, as measured after the spring floodwater receded. In theupstream transect, the deepest station was close to the southernshore of the river, and the depth gradually decreased toward thenorthern shore. The depths (mean ± SD) of the five stations(beginning with the station closest to the southern shore) were2.98 ± 0.56, 2.27 ± 0.53, 2.91 ± 0.43, 2.49 ± 0.38, and

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FIGURE 1. (a) Map of the Atlantic coast, showing the Maritime Provinces of Canada as well as Quebec and Maine; (b) the lower Saint John River, extendingfrom Mactaquac Dam (upstream of Fredericton) to the Saint John Harbour (Fredericton and Saint John Harbour are marked with stars; rkm = river kilometer);and (c) study site, indicating the locations of transects used to collect Shortnose Sturgeon larvae (Fredericton is downstream of both transects and is not shown inthe panel).

1.68 ± 0.37 m (values averaged across 2008–2011). In thedownstream transect, the deepest point was in the middle ofthe channel; station depths (mean ± SD) were 3.54 ± 0.43,3.57 ± 0.65, 4.35 ± 0.38, 3.71 ± 0.35, and 2.39 ± 0.61 m.In May 2010, we mapped the bathymetry of the study reachby using a Lowrance LCX-MT15 sonar. The data were thenimported into ArcMap GIS software (version 3.9; ESRI) withthe CrossView plug-in; cross-sectional area was estimated to be1,412 m2 at the upstream transect and 2,105 m2 at the down-stream transect.

Nets were usually deployed for approximately 12 h to ac-count for migration during nighttime (from 2000–2100 hoursto 0800–0900 hours) and daytime (from 0800–0900 hours to2000–2100 hours). However, throughout the 4 years of sam-pling, setting times ranged from 4 to 26 h; deployment lengthsvaried due to weather conditions and the extent of debris load.We collected larvae daily during several periods in May andJune 2008–2011. In 2008, sampling was performed between 14and 22 May; between 10 and 15 June; on 19–20 June; and be-tween 23 and 26 June. In 2009, we sampled continuously from2 May to 23 June, except during 30 May–1 June. In 2010, wesampled continuously from 3 May to 15 June. In 2011, samplingwas performed between 5 and 19 June.

In 2008, the contents of each net were examined for thepresence of Shortnose Sturgeon larvae immediately after liftingthe net and before lifting the next net. In 2009 and 2010, thecod ends of the nets were rinsed into individual 2-L pails andwere examined on the shore after all nets were checked. Sincethis method increased the mortality rates of captured larvae(S. Usvyatsov, unpublished observations), in 2011 the nets werechecked as soon as they were lifted, similar to the procedure usedin 2008. In all 4 years of sampling, the mesh in the first 70 cm ofthe net adjacent to the cod end was examined for the presence

of larvae. If larvae were found, they were transferred into thepails. The contents of the pails were then gently transferredinto shallow trays, and the captured items were examined. Alllarvae were separated into “live” and “dead” groups, counted,and photographed by using a Pentax Optio W60 10-megapixeldigital camera. Due to time constraints, no images of larvaewere taken in 2011.

Images were analyzed using ImageJ software (Abramoffet al. 2004) to determine SL and the presence of a yolk sac.Larval condition was recorded as live or dead; if dead, the esti-mated larval condition was scored as 1 (pristine), 2 (altered), or 3(decomposed) based on the images. A previous study on taphon-omy (i.e., the decay of tissue over time) indicated that the fastest-degrading body characteristics were barbels, fin structures, andcoloration (S. Usvyatsov, unpublished data). In pristine larvae,the images showed no degradation of these or any other charac-teristics. In decomposed larvae, the images indicated a loss ofbody firmness, a complete loss of coloration, and degradationof eyes, head, and fin structures. Altered larvae displayed decaylevels that varied between pristine and decomposed. Experimen-tal taphonomy indicated that the majority of decomposed larvae(score = 3) were probably larvae that drifted into the nets afterthey had already died, while pristine larvae (score = 1) prob-ably drifted into the nets while alive but then died during thesampling period (S. Usvyatsov, unpublished data). The alteredlarvae (score = 2) were hard to assign to either group, as theintermediate decomposition level may have been a result of ei-ther mortality type. Therefore, only live larvae and pristine deadlarvae were used for subsequent analyses, as these two groupsrepresented the population of live, migrating larvae.

Daily averages of water temperature were collected by us-ing a temperature logger that was deployed 1 km downstreamof Mactaquac Dam throughout 2008–2011 by Fisheries and

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Oceans Canada. Dam discharge levels (hourly averages; m3/s)were provided by New Brunswick Power, which recorded theoverall discharge of the dam (i.e., combined generation and spillflows).

Catch per unit effort.—Water velocity is a function of thecross-sectional area of the channel and the volume of water:

V = Q/A, (1)

where V is velocity (m/s), Q is dam discharge (m3/s), and A iscross-sectional area (m2). The difference in cross-sectional areabetween the two transects resulted in differences in the velocityof water sampled by the nets and therefore differences in thenumbers of captured larvae per sampling period. To addressthis, the numbers of larvae captured in the downstream transectwere scaled by the ratio of cross-sectional areas (2,105 : 1,412).This scaling was used for CPUE analysis only.

Catch per unit effort was calculated as follows:

CPUE = N

t × A, (2)

where N is the number of larvae found in the net, t is deploy-ment time (h), and A is the area of the net’s opening (m2).Paired randomizations of CPUE values were used to determine(1) whether time of sampling (nighttime or daytime) signifi-cantly affected CPUE at the two transects and (2) whether tran-sect location (upstream or downstream) significantly affectednighttime or daytime CPUE. All tests were performed with theexactRankTests package (Hothorn and Hornik 2010) in the sta-tistical environment R (R Development Core Team 2010) byusing a Bonferroni-corrected type I error rate of 0.025 to ac-count for testing the same data twice.

To distinguish between nighttime and daytime larval collec-tions, we only retained samples from nets that were deployed forless than 16 h. In addition, to reduce noise in the data, we only in-cluded samples collected on days that contributed at least 1% ofthe year’s larval abundance. This approach can be viewed as one-sided trimming of data—that is, the removal of very small valuesthat might influence the robustness of the model (Ott and Long-necker 2010). The 1% cut-off value was chosen by preliminaryinspection of the data set, as it successfully reduced noise in thedata set while maintaining an adequate case-per-covariate ratio.

Larval abundance estimates.—Larval abundance was esti-mated at the two transects separately in order to account for set-tlement and mortality between transects. We did not have to scalethe numbers of larvae captured at the downstream transect whencalculating abundances, since the nets were deployed as soon asthey were checked for larvae from the previous deployment.

Two types of estimates were calculated. One was based on auniform distribution of larvae across the channel,

Ntot = Ac

An× N , (3)

where Ntot is the estimate of the total larval population, N isthe number of larvae obtained in the entire transect, An is thetotal area (m2) of the net openings along the transect, and Ac

is the channel area (m2) from the river bottom to 0.85 m (i.e.,the height of the net opening). The second estimate of Ntot wasobtained by using the specific distances between the nets andthe numbers of larvae collected at each net,

Ntot =S∑

i=1

K∑j=1

(ni, j−1 + ni, j

2 × An× A j

), (4)

where S is the number of sampling times; K is the number ofstations (0 designates the south shore; station K is the stationclosest to the north shore); ni, j is the number of larvae collectedat sampling i at station j; An is the area (m2) of each net opening;and Aj is the channel area (m2), measured in width betweenstation j and station j − 1 and in height from the river bottom to0.85 m (the diameter of the net opening).

Transect-level analysis: larval counts (negative binomialmodel).—Count data, which are used to explain the number ofoccurrences, are inherently heteroscedastic and right-skewed.Such data are traditionally described using Poisson models(Hilbe 2011); however, the application of Poisson models isrestricted to data sets in which the variance equals the mean.If the data exhibit higher variance (i.e., the variance is largerthan the mean), they are considered overdispersed and are mostoften described by using negative binomial models (Zuur et al.2009). In this study, negative binomial regression models wereconstructed using larval collection data, where the independentvariables were averaged across all nets in a transect at each sam-pling and the response variable was the total number of larvaecaptured at each transect at every sampling. Examination of rawlarval catches on an individual-net level was highly variable dueto differences in catch among nets, whereas the transect-levelapproach had lower variability in larval counts since it preservedonly the differences between the two transects and the temporaldifferences between daytime and nighttime sampling.

The database of larval collections was separated according tolarval size (<15 mm and ≥15 mm SL), since larval behavior wasreported to change from “swim-up and drift” to active migrationat 15 mm (Kynard and Horgan 2002; Usvyatsov et al. 2012). Allthree data sets (larvae < 15 mm, larvae ≥ 15 mm, and all larvaecombined) were used to construct dispersal models. To reducenoise in the data, we restricted our data sets to sampling days thatcontributed at least 1% to the total number of larvae collectedduring that year. In addition, the 2009 data were removed fromour data sets since larval counts in 2009 were approximately fivetimes lower than those in other years, and preliminary modelingconsistently predicted higher larval counts than were observedin 2009.

Pearson’s product-moment correlation coefficients (r) amongtransect-level environmental variables were calculated to es-timate collinearity among predictors. For the data set thatincluded only larvae ≥ 15 mm, nighttime deployment and

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nighttime discharge had a Pearson’s r of 0.76 (P < 0.001),which was marginally below the 0.80 cut-off value recom-mended for variable retention (Menard 1997). Variance inflationfactors (VIFs) were calculated using the package “car” (Fox andWeisberg 2010). High VIF values are indicators of collinearity;although there is no standard VIF cut-off point that signifies highcollinearity, values of 5 or 10 are the most commonly appliedas cut-off points (Zuur et al. 2009).

The following generalized linear model was used to describethe relationships between the counts of larvae and environmentalvariables for each data set:

g(µ) = β0 + β1(night.discharge) + β2(day.discharge)

+ β3(night.deployment) + β4(day.deployment)

+ β5(N .nets) + β6(transect)

+ β7(night.discharge × night.deployment)

+ β8(night.discharge × transect), (5)

where µ = E(Y); Y is the larval count; night.discharge orday.discharge is the cumulative volume of water (m3) dischargedduring dark or light hours of net deployment; night.deploymentor day.deployment is the number of hours for which the netswere deployed during darkness or daylight; N.nets is the numberof nets deployed in the transect; and transect is a dichotomousvariable, where upstream transect = 0 and downstream tran-sect = 1. Due to the small size of the data sets, only two-way,biologically reasonable interactions were fitted. Larval countsfrom the downstream transect were not scaled by using the cross-sectional areas since the transect variable was used in the modelsto account for any differences between the two transects (e.g.,location, cross section, flow, etc.). Nighttime lengths of deploy-ment were calculated from dusk (∼2100 hours in early May and2200 hours throughout June) until dawn (∼0600 hours in earlyMay and 0500 hours throughout June) and daytime deploymentwas calculated from dawn to dusk based on civil twilight charts(NRCC 2011). The day.discharge and night.discharge values(m3) were then calculated by summing the Mactaquac Damhourly discharge over the recorded daytime or nighttime lengthof deployment. The variable Y has a negative binomial distribu-tion, g is a logarithmic link function, and log[E(Y)] is assumedto be linearly dependent upon predictors (Zuur et al. 2009). Allmodeling was performed using the libraries MASS (Venablesand Ripley 2002) and Design (Harrell 2008) in R (R Devel-opment Core Team 2010). The least significant variables in theconstructed model (starting from interactions) were dropped oneat a time, and the resulting reduced model was tested againstthe full model using the χ2 test of deviance (α = 0.05). Thefinal model was examined for residual patterns and influentialcases. The model’s goodness of fit was estimated using the χ2

test of deviance. Following Zuur et al. (2009), the percentage ofexplained deviance was used as an expression of the amount of

captured variation:

100×[(null deviance−residual deviance)/null deviance]. (6)

Since two of the data sets (larvae < 15 mm and larvae≥ 15 mm) only included 2 years of data after the removal ofthe 2009 data set, they could not be tested by use of the leave-1-year-out approach. Therefore, only the “all-larvae” data setwas cross validated. The parameter used to evaluate the models’predictive ability was mean absolute error (MAE), calculatedas the mean of absolute differences between the predicted andobserved values. The MAE was calculated both for the origi-nal data used to construct the model and for cross validation ofthe all-larvae data set. The interpretation of the MAE values isstraightforward, as it represents the average number of larvaethat are over- or underestimated by the models’ predictions. Theperformance of cross validation was evaluated by comparing theMAE values for cross validation with the MAE values forthe original data set; the latter MAE values are expected tobe the lowest, as models usually fit the training data set best.

Day-level analysis: larval presence–absence (logisticmodel).—We constructed logistic models using data aggregatedto day level, where independent variables were averaged acrossall transects over the entire day and the response variable wasthe daily presence–absence of larvae. The day-level models onlyretained the major temporal pattern associated with larval drift,yielding simpler and more-robust models. Hence, aggregatingthe data to day level allowed us to examine the overall timingof larval drift.

Pearson’s r-values among day-level environmental variableswere calculated to estimate collinearity among predictors. Cor-relation between temperature and nighttime discharge was lowand nonsignificant (Pearson’s r = −0.08, P = 0.431); however,nighttime discharge and daytime discharge were strongly corre-lated (Pearson’s r = 0.86, P < 0.001). Preliminary data analysisindicated that nighttime discharge was more significant thandaytime discharge; therefore, only nighttime discharge valueswere used for modeling. Two generalized linear models wereconstructed to describe the presence–absence of larvae:

g(µ) = β0 + β1(temp) + β2(temp2) (7)

and

g(µ) = β0 + β1(temp) + β2(temp2)

+ β3(night.discharge) + β4(night.discharge2)

+ β5(night.discharge × temp), (8)

where µ = E(Y), Y is larval presence–absence, temp iswater temperature in ◦C (temp2 is its quadratic term), andnight.discharge is the cumulative volume of water (× 106 m3)discharged during the dark hours of deployment (night.discharge2 is its quadratic term). The variable Y has a binomial

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distribution, g is a logit link function, and g[E(Y)] is assumed tobe linearly dependent upon predictors (Hosmer and Lemeshow2000).

In all 4 years of the study, sampling was discontinued oncelarval numbers decreased below 10 larvae/d (mid- to late Junein 2008–2010; in 2011, sampling had to be terminated slightlyearlier). To provide “absence” cases after the larval drift period,we designated 28–30 June as “no-larvae” days for 2008–2011.In addition, we may have missed the beginning of larval mi-gration in 2008 and 2011, since we captured larvae as soon aswe started sampling. That said, the number of larvae capturedin 2008 and 2011 was similar to the number captured in 2010,when sampling was continuous for several weeks prior to larvaldispersal; this indicates that even if the exact onset of disper-sal was not recorded, only few larvae migrated before sam-pling began. The timing of peak larval dispersal was similar in2008 and 2011, and in both years dispersal occurred later thanin 2009 and 2010. We designated 1–3 June as “absence” daysin 2008 and 2011. Since only three larvae were captured on 5June and on 7–8 June 2011, we felt confident in this decision.

The logistic models were constructed in R. A multivariatemodel was built, containing all main effects and interactions.The least significant variables in the constructed model (startingfrom interactions) were dropped one at a time, and the resultingreduced model was tested against the full model by using the χ2

test of deviance (α = 0.05; Hosmer and Lemeshow 2000). Thefinal model was examined for residual patterns and influentialcases. The percentage of explained deviance and the goodness-of-fit statistics were calculated as described for the negativebinomial models.

After model construction, we evaluated the sensitivity, speci-ficity, accuracy, and predictive ability of the models. Sensitivityis the proportion of true positives that are correctly identified bythe model, specificity is the proportion of true negatives that arecorrectly identified by the model, and accuracy is the percent-age of the sample that is correctly classified as either positiveor negative. Since the output of the model is a probability rang-ing between 0 and 1, the model’s performance depends on thechoice of a decision threshold such that probabilities above thethreshold are considered “presence” and those below the thresh-old are considered “absence.” The intuitive cut-off value is 0.5.However, approaches such as receiver operating characteris-tic (ROC) curves allow for plotting the sensitivity and speci-ficity levels of a model at different cut-off thresholds (Hosmerand Lemeshow 2000). The choice of an operational thresholdcan then be made, attempting to make a better trade-off betweenthe two parameters. We chose a cut-off value that maximizedthe difference between sensitivity and (1 − specificity), thusmaximizing sensitivity and minimizing the false positive rate;we aimed for sensitivity greater than 80% and a false positiverate less than 20%.

The area under the curve (AUC) for the ROC curve wasused to characterize the discriminating ability of the model;an AUC value of 0.5 is equivalent to random classification of

cases, values of 0.7–0.8 are considered acceptable, values of0.8–0.9 are excellent, and values of 0.9–1.0 are outstanding(Hosmer and Lemeshow 2000). The models’ predictive abilitywas examined by using a leave-1-year-out approach. Cross-validation sensitivity, specificity, and accuracy were estimatedby using the original model’s threshold value.

Autocorrelation.—The nature of ecological sampling oftencreates pseudoreplication in data sets. In this study, spatial au-tocorrelation may have existed between nets within the sametransect as well as between the two transects. In addition, sam-ples that are collected at short intervals are expected to be moresimilar in contents than samples collected at longer intervals,due to temporal autocorrelation. Incorporation of these spatialand temporal patterns into the predictive model increases ourability to describe the system (i.e., to predict the values of thedependent variable). In this study, we used several approachesto account for these processes:

1. Explicit incorporation of temporal variability. We used tem-perature as an indicator of time, since water temperature inthe Saint John River increased from 5◦C in early May to 15–20◦C by the end of June. The quadratic term of temperatureaccounted for the unimodal distribution of larval presence.

2. Examination of net-level catches. This allowed us to visuallyidentify spatial patterns.

3. Aggregation of data to the transect or day level. This stepreduced the spatial autocorrelation between the samples andthe noise associated with individual samples.

4. Explicit incorporation of spatial structure. The transect vari-able in the transect-level data analysis accounted for differ-ences between the two transects. If data from more transectswere available, this dichotomous parameter could be trans-formed into a continuous gradient describing distance fromthe spawning site or distance from the dam.

RESULTS

Field CollectionsRiver flow and temperature regimes differed between years.

The daily averages of discharge from Mactaquac Dam decreasedto post-spring-flood levels during early June in 2008 and 2011and during late May in 2009 and 2010. Once the freshet ended,the dam discharge became noticeably pulsed, with high dis-charge during daytime (1200–1800 hours) and low flow at night(minimum at 0200–0700 hours). Water temperatures differedbetween years as well, reflecting the differences in dischargerates; in 2008 and 2011, when the freshet ended later in the sea-son, the temperature increase was slower and began later thanthat in 2009 and 2010 (Figure 2).

The total number of Shortnose Sturgeon larvae captured was2,251 in 2008, 460 in 2009, 2,100 in 2010, and 2,083 in 2011.The number of larvae and the exact timing of larval drift changedbetween years (Figure 2). However, in all years, larval countsfollowed an approximately unimodal distribution, with the main

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FIGURE 2. Daily averages of Mactaquac Dam discharge (solid line) and watertemperature (dashed line) and daily counts of live Shortnose Sturgeon larvaeand pristine-condition (dead) larvae in the Saint John River, 2008–2011. Larvaecollected in 2008–2010 are separated according to size-classes (<15 mm and≥15 mm SL) to account for behavioral changes. Note that the scale of theprimary y-axis differs among the panels.

wave of larval drift occurring in the first 2.5 weeks of June. Ofthe total number of larvae captured, the percentage of fish thatwere at least somewhat decomposed (condition score = 2 or 3)was 16% in 2008, 44% in 2009, 33% in 2010, and 40% in 2011;these larvae were not used for subsequent analyses.

The SL of live larvae and pristine-condition larvae capturedin 2008–2010 ranged between 9.5 and 23.4 mm (mean ± SD =16.4 ± 1.6 mm). No consistent spatial pattern of larval countswas seen (Figure 3). In 2009–2011, most of the upstream-transect larvae were captured in the shallow area close to thenorthern shore (60–90% channel width), whereas in 2008 thelarval catches at the upstream transect were high across the entire

FIGURE 3. Number (open circles and gray-shaded area) and standard length(solid black circles; + SD) of Shortnose Sturgeon larvae captured at each stationin the upstream transect (left panels) and downstream transect (right panels) onthe Saint John River during 2008–2011. Open circles indicate the position ofeach station across the channel (5 stations/transect; 0% of channel width = thesouthern shore; 100% of channel width = the northern shore). In 2009 and2010, each station along the upstream transect consisted of two nets; data arepresented for each net within a station. The y-axis scale is consistent within thesame year but differs among years.

channel (Figure 3). Larval catches at the downstream transectalso indicated no spatial preference: in 2008, most of the lar-vae were captured closer to the southern shore (∼40% channelwidth); in 2009, they were captured closer to the northern shore(60–80% channel width); and in 2010, they were captured inthe middle of the channel (40–60% channel width). Like thelarval counts, larval sizes displayed no consistent patterns be-tween the two transects or within the same transect among years(Figure 3).

Catch per Unit EffortThe CPUE differed between transects and between times

of day. The upstream transect had higher CPUE values thanthe downstream transect, and nighttime samples had higherCPUE values than daytime samples, indicating a preferencefor dispersal during the night (Figure 4). Significant differences(Bonferroni-corrected α = 0.025) between transects were foundfor nighttime samples in 2009 and 2010 (P = 0.002 and P =0.008, respectively); marginally significant differences betweentransects were found for daytime samples in 2009 and 2010(P = 0.031 for both). However, since the tests were restricted to

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FIGURE 4. Average (+SE) Shortnose Sturgeon larval CPUE during spring2008–2011 for each transect (upstream or downstream) and sampling time(white bars = daytime; gray bars = nighttime). Note that the y-axis scale differsbetween the panels. Letters designate significant differences between transectsor between daytime and nighttime samples.

TABLE 1. Shortnose Sturgeon larval abundance estimates based on collec-tions obtained in the Saint John River, New Brunswick, during 2008–2011.Larval abundances were estimated by using a uniform distribution and a dis-tribution that incorporated the distances between nets. Downstream transectabundances expressed as a percentage of upstream abundances are shown inparentheses.

Year DistributionN larvaeupstream

N larvaedownstream

(% ofupstream)

2008 Uniform 244,687 73,159 (30)Distance based 204,082 57,874 (29)

2009 Uniform 24,386 5,734 (25)Distance based 20,995 5,098 (24)

2010 Uniform 139,068 71,577 (51)Distance based 118,570 55,134 (47)

2011 Uniform 206,130Distance based 197,957

daily transect averages of nets deployed for less than 16 h, thenumber of cases in 2008 and 2011 was exceedingly low, withonly three cases for each year. Significant differences betweennighttime and daytime samples were found for 2009 (upstreamtransect: P = 0.016); marginal significance was identified forthe upstream transect in 2010 (P = 0.031).

Larval Abundance EstimatesThe estimates of larval abundance differed greatly among the

4 years (Table 1). However, in all 4 years the uniform abundanceswere higher than estimates based on weighted net distances.Abundances for both 2008 and 2011 underestimated the totalnumber of larvae, as sampling was initiated after the beginningof larval migration in both years. In addition, in 2011, most ofthe nets were deployed during the nighttime, therefore missingdaytime migrants. All estimates reflected the substantial drop inlarval abundance between the two transects; downstream abun-dance estimates ranged between 24% and 51% of the upstreamabundances.

Transect-Level Analysis: Larval Counts (NegativeBinomial Model)

The three models that described transect-level dispersal oflarvae < 15 mm, larvae ≥ 15 mm, and all larvae combined weresimilar. All models included significant effects of nighttimedischarge, transect, and number of nets, as well as either daytimedischarge or length of nighttime deployment, and explainedabout 60% of the overall variation (Table 2). No lack of fit wasindicated by the goodness-of-fit statistics; therefore, we couldnot reject the null hypothesis that the models fit the data (modelfor larvae < 15 mm: χ2 = 66.08, df = 54, P = 0.125; model forlarvae ≥ 15 mm: χ2 = 62.01, df = 51, P = 0.138; model for alllarvae: χ2 = 77.48, df = 63, P = 0.103). The analysis of VIFs

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TABLE 2. Negative binomial models of transect-level Shortnose Sturgeon larval count data, detailing models’ mean absolute error (MAE; ± SD) for the originaldata set and the leave-1-year-out cross validation (CV) data set. See Methods for definitions of the variables. All models were highly significant (P < 0.001).

Variable Slope SE PMAE, original

data set MAE, CV data set % deviance

Model 1: all larvaeIntercept 1.094 0.395 0.006 27.6 ± 40.3 23.6 ± 40.8 (2008) 62Night.discharge 0.033 0.012 0.009 39.0 ± 35.4 (2010)Night.deployment 0.276 0.040 <0.001 125.5 ± 165.3 (2011)Transect −0.771 0.221 <0.001N.nets 0.187 0.055 0.001

Model 2: larvae ≥ 15 mmIntercept 1.414 0.472 0.003 16.7 ± 22.5 64Night.discharge 0.124 0.034 <0.001Night.deployment 0.145 0.058 0.013Day.discharge −0.027 0.010 0.009Transect −0.747 0.224 0.001N.nets 0.191 0.053 <0.001

Model 3: larvae < 15 mmIntercept 0.916 0.439 0.037 3.18 ± 4.02 57Night.discharge 0.167 0.021 <0.001Day.discharge −0.044 0.010 <0.001Transect −0.857 0.251 0.001N.nets 0.112 0.056 0.046

indicated little collinearity between the predictors in all threemodels, as all VIF values were below 2.

In each of the three models, the coefficient for the transectvariable was negative because of the decline in larval num-bers between the upstream and downstream transects. All threemodels provided a good fit to the data (Figure 5a–c), with fittedvalues closely following the observed values. However, testingof the all-larvae model by using the leave-1-year-out approachdecreased the model’s fit (Figure 5d–f), including a nonlinearpattern with the 2010 data set (Figure 5e; Table 2). The pat-tern is a result of higher larval counts during daytime in 2010than in 2008 and 2011, which caused the predicted larval countsfor daytime samples in 2010 to be lower than the observedcounts. Cross validation with the 2008 data set generated thelowest MAE value, while cross validation with the 2011 dataset yielded the highest MAE value (Table 2).

The model describing larvae smaller than 15 mm had anextremely low MAE value due to the low counts of young larvae,as most of the migrating larvae were larger than 15 mm. Inaddition, the lower MAE values for the data sets including larvae< 15 mm and larvae ≥ 15 mm in comparison with the MAE ofthe all-larvae data set are probably due to the smaller size of thedata sets, as neither contained the 2011 larvae.

Day-Level Analysis: Larval Presence–Absence(Logistic Model)

In both logistic models, temperature and its quadratic termwere highly significant, providing the temporal distribution of

larval presence–absence (Table 3). Both models had high P-values associated with the goodness-of-fit statistics, indicatingno lack of fit (temperature-only model: χ2 = 40.749, df = 93,P = 0.874; temperature–discharge model: χ2 = 54.981, df =91, P = 0.998). The AUC values for the ROC curve were veryhigh for both models, indicating excellent classification ability;both the AUC values and the percentage of explained variationsuggested that the temperature–discharge model was superiorto the temperature-only model (Table 3). The 2011 data sethad low specificity and accuracy values in model training andtesting, and the 2009 temperature-only and 2010 temperature–discharge data sets had low sensitivity and accuracy when usedfor testing (Table 4).

Estimates of specificity and sensitivity were generated usinga decision threshold value of 0.7 for the temperature-only modeland 0.5 for the temperature–discharge model. However, whenwe considered the entire timeline of the predicted probabili-ties of larval presence instead of classifying them as presence–absence, both of the models predicted elevated probabilities oflarval presence during the appropriate time period in all 4 years(Figure 6). Overall, the curves generated by the two models weresimilar. Both models predicted larval presence approximately5 d before sampling resumed in 2008, successfully predicted alow probability of larval presence at the end of June 2009, andestimated a high probability of larval presence in late May 2010,when larvae indeed were present but constituted less than 1%of the yearly catch (Figure 2). It is especially interesting thatthe models developed by using the 2008–2010 data generated

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FIGURE 5. Negative binomial models based on observed, fitted, and cross-validated Shortnose Sturgeon larval counts: (a) the all-larvae data set for 2008(open circles), 2010 (+ symbols), and 2011 (solid black circles); (b) 2008 and2010 data including only 15-mm SL and larger larvae; (c) 2008 and 2010 dataincluding only larvae that were smaller than 15 mm SL; (d) cross validation(CV; using the leave-1-year-out analysis) of the all-larvae model for 2008 data;(e) CV of the all-larvae model for 2010 data; and (f) CV of the all-larvae modelfor 2011 data.

reasonably good predictions of the timing of larval drift in June2011, when three large flood events caused dam discharge toincrease up to 2,000 m3/s—a level that was two to three timeshigher than discharge levels observed in 2008–2010 (Figure 2).The high discharge in 2011 caused false predictions of larval

presence in mid-May 2011 and resulted in constant high prob-abilities of larval presence for June 2011.

The main differences between the two models were seenin 2010 and 2011. Whereas the temperature-only model pre-dicted a high probability of larval presence starting in late May2010, the temperature–discharge model had a much narrowermain peak, with several smaller peaks preceding it. For 2011,the temperature-only model indicated a gradually increasingprobability of larval presence starting in early June, followedby a low probability during a period when larvae were ac-tually present. On the other hand, the temperature–dischargemodel predicted a high probability of larval presence from thebeginning of June until late June, including the entire period ofknown larval presence. The sudden drop-off in probability on18 June 2011 was probably due to the high-flow event recordedat that time (Figure 2).

The presence of quadratic terms in the two models allowedfor a unimodal relationship between the environmental vari-ables (i.e., temperature and discharge) and the probability oflarval presence. Since larval migration occurs when water tem-perature has not reached its highest yearly level and when riverdischarge has not reached its lowest yearly value, unimodal rela-tionships between these variables and larval presence–absenceare very appropriate. Overlaying the frequency distributionsof temperature and discharge values observed during samplingwith curves of predicted larval presence identified the preferredperiod of migration (Figure 7). In the all-larvae data set, larvaewere predicted to be present throughout the range of environ-mental conditions, although the highest probability of presencewas at intermediate temperatures (13–16◦C) and at low to in-termediate nighttime discharge. When the data sets were re-stricted to days that contributed at least 1% of the yearly larvalcatches, the probability curve became considerably steeper. Inthe temperature-only model, the probability of larval presenceincreased between 12◦C and 17◦C and peaked at 14–15◦C (Fig-ure 7). Restriction of the temperature–discharge model to daysthat contributed over 1% indicated that at 15◦C, the probability

TABLE 3. Logistic models of Shortnose Sturgeon larval presence–absence in 2008–2011. See Methods for definitions of the variables. All regressions werehighly significant (P < 0.001); area under the curve (AUC) for the ROC curve is a measure of predictive ability.

Variable Coefficient SE P % deviance AUC

Temperature onlyIntercept −180.638 40.056 <0.001 41 0.894Temp 24.869 5.485 <0.001Temp2 −0.849 0.187 <0.001

Temperature and dischargeIntercept −292.027 73.630 <0.001 55 0.949Temp 39.718 9.970 <0.001Temp2 −1.359 0.339 <0.001Night.discharge 0.537 0.154 <0.001Night.discharge2 −9.801 × 10−3 3.00 × 10−4 0.001

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TABLE 4. Classification ability of logistic models of Shortnose Sturgeon larval presence–absence in 2008–2011, as assessed using sensitivity, specificity, andaccuracy (all in percentages) for the original data set and the leave-1-year-out cross validation (CV) data set.

Original data set CV data set

Year Sensitivity Specificity Accuracy Sensitivity Specificity Accuracy

Temperature-only model, classification threshold = 0.72008 100 92 94 100 92 942009 73 92 81 53 83 672010 92 83 86 100 83 892011 33 75 53 22 88 53

Temperature and discharge model, classification threshold = 0.52008 100 100 100 100 93 882009 100 83 93 80 75 782010 83 96 91 58 96 832011 78 75 76 89 38 65

of larval presence was maximized at a nighttime discharge of10 × 106 to 40 × 106 m3 (Figure 7b). When the data set wasfurther restricted to days that contributed at least 10% of yearlycollections, the probability of larval dispersal peaked at night-time discharge values of 20 × 106 to 30 × 106 m3. Most of thesampling days had nighttime discharge levels of 5 × 106 to 15 ×106 m3 (Figure 7b). These low levels of nighttime discharge,observed already in early June, corresponded to an average flowvelocity of 14–42 cm/s at the upstream transect and 9–28 cm/sat the downstream transect. However, larvae were predictedto disperse at higher discharge values corresponding to flow

FIGURE 6. Probabilities of Shortnose Sturgeon larval presence, predictedusing polynomial temperature terms (left panels) and polynomial temperatureand discharge (right panels). The models were constructed using the leave-1-year-out approach and tested on the respective year’s data. Light-gray areasrepresent larval absence, and dark-gray areas represent larval presence; whiteareas represent periods of no sampling. Threshold values of 0.7 (left panels)and 0.5 (right panels) are shown as dotted lines, above which the probability isclassified as larval presence.

velocities of 56–84 cm/s at the upstream transect and 38–57 cm/sat the downstream transect.

DISCUSSION

Larval Abundance EstimatesThe collection of 2,251, 460, 2,100, and 2,083 Shortnose

Sturgeon larvae in 2008, 2009, 2010, and 2011, respectively,provided an opportunity to calculate the first estimates of thisspecies’ larval abundance during dispersal and to estimate theyearly variability in numbers of drifting larvae. The high num-bers of larvae collected for this study are in stark contrast tofield-based work on Shortnose Sturgeon in other systems, whereonly few larvae have been captured due to the small populationsizes (e.g., Taubert 1980; Duncan et al. 2004). The numbers ofcollected larvae may be used in the future to back-calculate theabundance of spawning adults or to provide a 4-year baselinefor comparison with future collections.

In 2008–2011, the abundance estimates of dispersing Short-nose Sturgeon larvae ranged between 20,955 and 244,687 lar-vae, depending on the year and estimate assumptions. Theseestimates are probably very conservative due to (1) the useof only live larvae and pristine-condition dead larvae, (2) thepotential escapement of larvae from the D-frame nets duringdeployment, and (3) the sampling of only the bottom 0.85 mof the river channel. Although the highly decayed larvae wereprobably environment-related mortalities, some of the partlydecayed specimens were probably alive when entering the nets(S. Usvyatsov, unpublished observations). However, since it isnot currently possible to determine which of the partly decayedlarvae died before entering the nets and which died afterward, wepreferred to underestimate larval abundance rather than overesti-mate it. In addition, experiments with the D-frame nets indicatedthat the nets were only retaining 40–60% of the larvae that ini-tially entered them (S. Usvyatsov, unpublished data). Moreover,our abundance estimates may also be inaccurate if the larvae

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FIGURE 7. Fitted logit curves of the probability of Shortnose Sturgeon larval presence, presented in relation to frequency histograms of environmental variables:(a) temperature in the temperature-only model and (b) nighttime river discharge (with temperature held constant at 15◦C) in the temperature and discharge model.Lines represent data from all days (solid line), only days that contributed at least 1% to yearly larval catches (dashed line), and only days that contributed at least10% to yearly larval catches (dotted line).

dispersed above the height of the net’s opening. However, whilelaboratory-based studies have shown that Shortnose Sturgeonlarvae may disperse as high as 1.2 m off the bottom, the averagedrifting height was 0.3 m (Kynard and Horgan 2002), which iswell within the range of our study nets’ openings. In the SaintJohn River, preliminary successful collections at the ShortnoseSturgeon spawning area in spring 2002 retrieved larvae fromthe bottom of the river but not from the surface (M. K. Litvak,unpublished data).

Larval abundances were 49–76% lower at the downstreamtransect than at the upstream transect. Similar abundance de-creases were reported for Lake Sturgeon (Auer and Baker 2002;Smith and King 2005), although Auer and Baker (2002) alsoreported variable results, including some downstream increasesin CPUE. In a previous study, we found no difference in theages of larvae captured at the two transects (Usvyatsov et al.2012), which indicates either that (1) mortality and settlementrates are even across all ages or (2) younger larvae have greatermortality, while older larvae exhibit increased settlement.

Generalized Linear ModelsIdentifying the variables that influence the timing of larval

presence can provide guidelines for lower-impact dam opera-tion during the period of larval migration. Water temperaturewas clearly a driving factor in determining the timing of larvaldispersal, as temperature alone was able to predict the timingof larval migration in each of the tested years. A similar re-sult was reported for Lake Sturgeon, with larval drift beginningonce the water temperature exceeded 16◦C (Smith and King2005). However, in our study, nighttime discharge increased themodel’s predictive ability, indicating that both temperature andnighttime discharge may be important for Shortnose Sturgeonlarval migration.

Count models of larvae < 15 mm and larvae ≥ 15 mm werelargely similar. We expected to see differences in the directionor magnitude of coefficients that would reflect the two types ofmigration: passive swim-up and drift by the younger, smallerlarvae and active migration by the older, larger larvae. The simi-larity in model structure between the two data sets suggests thatthe influence of temperature and nighttime discharge is similarfor the two larval size ranges.

Our larval collections are larger than any previous collec-tions of Shortnose Sturgeon larvae, allowing us to model thetiming of larval dispersal. However, the cross validation of thecount models indicated potential pitfalls. For example, crossvalidation of the all-larvae model using the 2008 data showedmedium to good predictive ability. On the other hand, the non-linear pattern and the high MAE values found for the 2010 and2011 data sets, respectively, indicated that the model did notexplain a large portion of the variability associated with the sys-tem. One reason may be that our data set only contained 4 yearsof data, and several more years of monitoring may be neededto construct a robust predictive model for larval counts. Alter-natively, additional variables may be required to make reliablepredictions of larval counts.

Dispersal BehaviorWe found no consistent pattern of larval distribution across

the channel. This differs from Pallid Sturgeon larvae, whichpreferred the deeper, faster part of the channel (Braaten et al.2010), and Lake Sturgeon larvae, which preferred gradual slopesoutside of the main channel (Auer and Baker 2002). That said,throughout 2008–2011 we found that one to three nets usu-ally displayed higher CPUE values than the rest of the netsin a given transect, indicating a clumped distribution; in otherwords, although there may not be preference for lateral position,the larvae appeared to travel together. The yearly variability in

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spatial distribution of drifting larvae may be due to drift patternsbeing altered by (1) changes in the exact location of spawningsites between years, (2) differences in flow rates, or (3) both.

Our data indicated that Shortnose Sturgeon larvae preferredto disperse during the nighttime. Some laboratory studies onShortnose Sturgeon larvae have reported a similar preferencefor nighttime dispersal (Richmond and Kynard 1995; Parker2007), whereas Kynard and Horgan (2002) reported higher lar-val activity during the daytime. A preference for nighttime dis-persal would allow larvae to evade detection by visual predatorsand would likely lead to increased larval survival. However, inrivers where flows are regulated, nighttime discharge is oftenlowered due to reduced electricity demand, thereby leading tolower flow rates and causing the larvae to drift at lower veloci-ties. This may result in three outcomes: (1) larvae may remainhidden in the substrate for a longer period of time, waiting forflood events that will increase nighttime discharge; (2) larvaemay spend a longer time drifting in order to reach their nurserygrounds (constant drifting distance); or (3) larvae may settlecloser than they would have under natural conditions (constantdrifting time). The three outcomes may be deleterious due tostarvation, increased predation risk, and settling in a subopti-mal habitat, respectively. Therefore, an understanding of therelationships between larval migration, flow, and light levels isnecessary to develop discharge guidelines that are optimal forlarval dispersal.

Management Implications and Concluding RemarksSpawning grounds’ vicinity to hydroelectric dams may pose

risks to larvae due to dam operations, which are associated withfluctuating flow and water levels that may lead to stranding(Weyers et al. 2003; Caroffino et al. 2010). A variety of damoperation guidelines has been introduced to protect downstreamfish. Such guidelines include the use of minimum flows (e.g.,Travnichek et al. 1995), a reduction in ramping rates (e.g.,Bradford et al. 1995), and the combined implementation offlow limits, ramping rates, and timing of flow changes (Connorand Pflug 2004).

To develop the best dam operation strategy for protectingShortnose Sturgeon larvae, managers must be informed of thetiming, duration, and level of required flows. The timing andduration of larval presence in the Saint John River have beenpreviously identified (Usvyatsov et al. 2012). The current studyrefines these parameters and addresses the flow levels that areoptimal for larval migration. In addition, the estimated larvalabundances can be used to calculate correlations with juvenileyear-class strength and rates of survival to the juvenile stage.Moreover, by using two estimates of larval abundance, we il-lustrate the sensitivity of abundance estimates to assumptions.Based on our findings, we suggest that in similar studies, morethan one abundance estimate should be calculated to ascertainthe robustness of the estimates.

The availability of Shortnose Sturgeon larvae from the SaintJohn River population is a source of invaluable data on thespecies’ sensitive early life stages. The described relationships

between larval dispersal and important environmental variablescan also be used for other sturgeon species due to the similaritiesin spawning sites and behavior of hatched larvae. All sturgeonspawn in freshwater rivers (Bemis and Kynard 1997), usuallyjust downstream of dams as observed for Lake Sturgeon (Bruchand Binkowski 2002), Chinese Sturgeon (Wei et al. 2009), andWhite Sturgeon A. transmontanus (Parsley et al. 1993). Theproduced larvae must migrate downstream to reach the nurserygrounds. Therefore, it is highly likely that the dependence onwater temperature and discharge exists across sturgeon species,although species-specific changes in coefficients or preferencesfor nighttime versus daytime migration are to be expected. Fur-ther refinement of our understanding of the influence of watertemperature, light levels, and flow rates on the behavior of stur-geon larvae will advance our knowledge of the early life historyof Shortnose Sturgeon in the wild and will provide tools for fu-ture management and conservation efforts focused on this andother sturgeon species.

ACKNOWLEDGMENTSWe are grateful to Brent M. Wilson, Laura Qi, Faith M.

Penny, Andrew Hazen Brown, Jennifer R. Adams, ChristineAdams, and Joel R. Chase for their help with field collections oflarvae and image analysis in the laboratory. We also thank theHartt Island RV Resort for their support during the 2008–2011sampling seasons. This study was supported by Mathematics ofInformation Technology and Complex Systems research grantsto J.W. and M.K.L., the New Brunswick Wildlife Trust Fund,and Natural Sciences and Engineering Research Council Dis-covery and Strategic grants to M.K.L. The comments and sug-gestions provided by the anonymous reviewers greatly improvedthe manuscript.

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