Influence of local-scale abiotic and biotic factors on stress and nutrition in invasive silver carp

15 Pages·2014·0.26 MB·English

Hydrobiologia(2014)736:1–15
DOI10.1007/s10750-014-1880-y
PRIMARY RESEARCH PAPER
Influence of local-scale abiotic and biotic factors on stress
and nutrition in invasive silver carp
Stephanie A. Liss • Greg G. Sass • Cory D. Suski
Received:26January2014/Revised:30March2014/Accepted:4April2014/Publishedonline:16April2014
(cid:2)SpringerInternationalPublishingSwitzerland2014
Abstract Community structure and dynamics in feeding in warmer months. Patterns in blood-based
aquatic ecosystems are influenced by a variety of stress parameters were most strongly driven by
abiotic and biotic factors including productivity, environmental characteristics, elevating with high
species composition, and temperature. These factors water temperatures and increased turbidity. Nutrient
may also affect local-scale community resilience to levels and community richness parameters did not
nonnativespecies invasions.We used habitat charac- influence the stress or condition of silver carp, likely
teristics,zooplanktonconcentrations,fishabundances, duetotheabsenceoflimitingresourcesorcompetition
and species composition and richness data collected forthisspecies.Together,ourresultsprovideinsights
bytwofishpopulationmonitoringprogramstodefine into the factors that may influence the spread and
relationships that influence stress and nutrition in distributionofsilvercarp,aswellasthecharacteristics
invasive silver carp (Hypophthalmichthys molitrix). of habitats that could be vulnerable to future silver
Wecollectedbloodsamplesandquantifiednutritional carpinvasion.
(alkaline phosphatase, cholesterol, protein, and tri-
glycerides) and stress metrics (cortisol and glucose) Keywords Competition(cid:2)Conservation(cid:2)
from individuals across three distinct time periods. Environmentalcharacteristics(cid:2)Invasion(cid:2)
Nutritional patterns in silver carp were explained by Nonnativespecies
temperature and food resources, indicating elevated
Introduction
Handlingeditor:M.Power
Several theories contribute to our understanding of
S.A.Liss
community structure and dynamics, with numerous
IllinoisNaturalHistorySurvey,1816S.OakStreet,
Champaign,IL61820,USA hypotheses describing the range of complex interac-
tions and interrelationships among species and their
S.A.Liss(&)(cid:2)C.D.Suski
habitats (Tonn, 1990; Tilman, 2004). Community
UniversityofIllinoisatUrbana-Champaign,1102South
interactions may be driven by local-scale factors
GoodwinAvenue,Urbana,IL61801,USA
e-mail:[email protected] including biotic processes (e.g., competition for
limiting resources, species richness/composition)
G.G.Sass
and/or abiotic processes (e.g., habitat complexity)
WisconsinDepartmentofNaturalResources,3110Trout
(Tonn & Magnuson, 1982; Tonn, 1990). Greater
LakeStationDriveBoulderJunction,Madison,WI54512,
USA species richness may improve ecosystem resiliency
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2 Hydrobiologia(2014)736:1–15
(Duffy, 2009), but is inadequate for explaining what Physiology is a discipline that aims to link an
maintains baseline ecosystem processes (Tonn & organism and its population to the environment
Magnuson, 1982). Species composition (e.g., func- (Ricklefs & Wikelski, 2002). Individual nutrition
tional groups, classified by physiological and mor- and stress have previously been shown to vary with
phological differences) is also important because biotic and abiotic conditions ofthe environment, and
compositional differences have the potential to exert can provide insights into population and community
strong influences on ecosystem processes (Tonn & structure and dynamics. Assessing fish health using
Magnuson, 1982; Tilman et al., 1997). The abilityof blood chemistry is valuable, as nutritional condition
assemblagestoresistnonnativeinvasionsmayalsobe represents a relationship between fitness and energy
influenced by species richness and composition, stores (Congleton & Wagner, 2006). Nutritionally
competition,predation,andenvironmentalvariability deficientorganismsmayexperiencereducedforaging
(i.e.,abioticfactors)(Gido&Brown,1999;Ricciardi ability,poorgrowth,reducedswimmingperformance,
&MacIsaac,2011).Alocal-scalestudycanprovidea and/or decreased survival relative to individuals that
snapshot of community and species interactions at a are more satiated (Gingerich et al., 2010; Wagner
specific point in time, but those interactions may be et al., 2010). The metabolism of poikilotherms is
complex and fluid, especially in a large river directlyinfluencedbyenvironmentalfactors(temper-
ecosystem. ature, oxygen, and salinity) (Tonn, 1990; Wootton,
Large river ecosystems are intriguing and under- 1998), which can alter the energy requirements for
studied when addressing questions of community fishes seasonally. During periods of low nutritional
structure (but see Vannote et al., 1980). Large rivers inputs,fishmaynothavesufficientenergytoallocate
experience substantial seasonal and habitat variation toreproduction,potentiallyreducingfitness(Wootton,
that may play a role in influencing community 1998).Reductionsinenergyduetolimitedfoodintake
dynamics at a local scale (Junk et al., 1989). Habitat mayalsoresultintheinductionofastressresponsein
alterations may also occur through processes such as anindividual,makingthequantificationofstressatool
levying or impounding for commercial navigation, thatcanhelpdefinethehealthofanorganism.Stressis
flood control, and/or agriculture purposes, which can also metabolically expensive, can potentially cause a
affect channel morphology, flow regimes, and phys- loss of performance at an organismal level, and can
ical–chemical attributes (Schlosser, 1990). These negativelyimpactfitnessthroughreducingtheenergy
disturbances can lead to large inter-annual variation available for activity and reproduction, which may
in habitat conditions (e.g., more frequent water-level subsequentlylimitpopulationgrowth(Kassahnetal.,
fluctuations, flooding, sedimentation, and nutrient 2009;Schreck,2010).Quantifyingtheinteractionsof
loading) (Koel & Sparks, 2002). Together, these abiotic and biotic factors and their relationship with
alterations may impact local species diversity by organismal stress and nutrition is critical for under-
increasing colonization and establishment potential standing how those various factors are perceivedand
for invasive species (Gido & Brown, 1999). For dealt with by individuals (Wingfield, 2013). Further,
example, water temperature may inhibit or promote such studies can promote a supplementary compre-
theindividualgrowthofaninvaderasitvariesacross hension of what underlies the link between physiol-
timeandspace(Shea&Chesson,2002).Asaresult,it ogy, invasive species, and community structure and
isimportanttoexaminemultipletimepointsannually dynamics.
when studying large rivers due to their unpredictable Silvercarp(Hypophthalmichthysmolitrix)(SVCP)
abiotic characteristics. An organism’s stress and are invasive, planktivorous, filter-feeding fishes that
nutritional condition may also vary seasonally with havesuccessfullyestablishedintheMississippiRiver
changes in habitat conditions, and fish may exhibit Basin (MRB) and have the potential to disperse and
behavioralandphysiologicalresponsestotemperature invade neighboring watersheds depending upon the
(Lowe et al., 2006). Quantifying the nutritional and suitability of habitats (Herborg et al., 2007; Kolar
stress physiology of an invasive species may help et al., 2007). More importantly, SVCP have the
identify abiotic and biotic factors influencing their potential to negatively affect freshwater ecosystems
successandtoallowforpredictionsofsuitablehabitat (Kolaretal.,2007;Sassetal.,2010).Forexample,the
conditionsinotherun-invadedsystems. presence of SVCP has negatively affected the body
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Hydrobiologia(2014)736:1–15 3
conditionofnativegizzardshad(Dorosomacepedia- and a high abundance and biomass of established,
num)(GZSD)andbigmouthbuffalo(Ictiobuscyprin- invasive SVCP (Koel & Sparks, 2002; Sass et al.,
ellus) potentially due to dietary overlap (Irons et al., 2010).Samplingwasdividedintothreetimeperiods:
2007; Sampson et al., 2009). Nevertheless, there is a mid-summer = June 15–July 31, late-summer =
critical need to better define how abiotic and biotic August 1–September 15, and early fall = September
factorsinfluencethenutritionalconditionandstressin 16–October 31. We sampled blood from up to 12
SVCPatalocalscale,asthisinformationcanprovide (range 10–12) SVCP in the LGR during each of the
valuable insights into mechanisms controlling future threetimeperiodsin2011.Althoughweacknowledge
abundance,spread,anddistributionoftheseinvaders. limitations to performing our study at one location
Theobjectiveofourstudywastoquantifytheeffects (i.e.,spatialandtemporalcorrelation,replication),an
of local-scale abiotic, biotic, and community-related ecosystem response is of theoretical and practical
parameters on the stress and nutritional condition of interest because it allows for the inclusion of abiotic
wild-caught SVCP in the La Grange Reach, Illinois and biotic processes that are typically difficult to
River, Illinois, USA (LGR). We hypothesized that incorporate in an artificial system (Carpenter et al.,
SVCPnutritionalconditionwoulddecreaseandstress 1995; Schindler, 1998). The LGR experiences high
levelswouldincreasewithgreatercommunity-related seasonalvariabilityinclimate,fishcommunitystruc-
parametersassociatedwithpotentialcompetition(e.g., ture, and primary production (Koel & Sparks, 2002;
species abundance and richness). We also hypothe- McClellandetal.,2012).Thus,wewereabletosample
sized reciprocal effects on nutrition and stress with fish experiencing a range of abiotic and biotic
higher quality abiotic habitat features (e.g., produc- characteristicsoverthethreedistincttimeperiods.
tive, eutrophic ecosystems based on water quality We collected SVCP primarily using pulsed-direct
characteristics),duetogreaterfoodavailability. current (DC) electrofishing according to methods
outlined in Gutreuter et al. (1995) and Tyszko et al.
(2012).However,abouthalfoftheSVCPanalyzedin
Materialsandmethods thecurrentstudyleaptonboardtheboatpriortobeing
immobilized by electricity. Silver carp exhibit a
Fieldanalysis jumping behavior when startled (Green & Smither-
man,1984),whichcanmaketheircapturebyelectro-
WecollectedSVCPinassociationwithtwolong-term shockingdifficult.Fishthatvoluntarilyleaptonboard
fish population monitoring programs coordinated by weresampledforbloodinamanneridenticaltothose
theIllinoisRiverBiologicalStation(IRBS);theLong- that were stunned by electricity. Immediately after
Term Resource Monitoring Program (LTRMP) and capturing a fish, we collected blood according to
the Long-Term Illinois, Mississippi, Ohio, and methods outlined in Liss et al. (2013). One mL of
Wabash River Fish Population Monitoring Program wholebloodwascollected,placedintoamicrocentri-
(LTEF) (Gutreuter et al., 1995; Tyszko et al., 2012). fuge tube, and spun at 6,600 RPM for 3 min to
The LTRMP and LTEF are designed to sample and separate plasma from red cells. Plasma was removed
enumerate the entire fish community of large rivers andplacedintotwoadditional1.5 mlmicrocentrifuge
usingastandardizedseriesofprotocolsthatdateback tubes.Plasmaandredcellswereflashfrozeninadry
to 1989 and 1957, respectively (Irons et al., 2007; shipper chargedwith liquid nitrogen, andtransported
McClelland et al., 2012). As part of these sampling totheUniversityofIllinoisatUrbana-Champaignand
protocols,wecollectedSVCPfromtheLGR,located storedina\-75(cid:3)Cfreezeruntilprocessing.Wedrew
near Havana, Illinois (LTRMP Field Station 6, blood from all individuals, irrespective of collection
latitude:40(cid:3)180N,longitude:-90(cid:3)30W)duringJune– method,inlessthan3 mintoobtainabaselinevalueof
October, 2011. The LGR is a 129-km stretch of the stress and nutrition that would not be influenced by
Illinois River extending from the La Grange Lock & sampling procedures (Romero & Reed, 2005; Con-
Dam at river km 126 upstream to the Peoria Lock & gleton&Wagner,2006).Wealsorecordedtotallength
Dam at river km 255. The LGR is highly disturbed (mm)andweight(g)ofeachfish.
with extensive anthropogenic modifications (e.g., Water quality parameters within the LGR were
impoundments,agriculture,andalteredfloodregimes) collected from fixed sites every 2 weeks during fish
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4 Hydrobiologia(2014)736:1–15
samplingperiodsandfromasetofrandomlyselected detritivores, omnivores, piscivores, planktivores, and
locations at quarterly intervals according to estab- surface/watercolumninvertivores(Poff&Allan,1995).
lished protocols in 2011 (APHA, 1992; Soballe & Alistofallfishspeciescollectedduringthistimeperiod
Fischer, 2004). Water quality parameters included isavailableathttp://www.umesc.usgs.gov/data_library/
water temperature, Secchi disk transparency, dis- fisheries/fish_page.html.Again,eachpredictorvariable
solved oxygen (DO), pH, turbidity, conductivity, wasaveragedacrossallsamplingepisodeswithinatime
velocity, total phosphorus (TP), total nitrogen (TN), period,resultinginasinglemeanparametervalueper
suspendedsolids,andchlorophylla,whichweusedas timeperiod.
arepresentationofhabitatquality.In2011,zooplank-
toncollectionswereconductedmonthlyduringMay–
Laboratoryanalysis
NovemberintheLGR.Foreachsample,30 lofwater
waspumpedthrougha55 lmfilter,andthisprocedure
We analyzed plasma alkaline phosphatase (ALP)
wasreplicatedthreetimesateachsitepermonth.We
(U l-1), cholesterol (mg dl-1), cortisol (ng ml-1),
examinedtheintegratedzooplanktoncommunityand
glucose(mg dl-1),protein(g dl-1),andtriglycerides
concentration throughout the entire water column by
(mg dl-1) in a manner identical to Liss et al. (2013).
attachingaweighttoahoseandliftingthathosefrom
The cortisol EIA kit used (ADI-900-071, Enzo Life
thebottomtothesurfaceofthewaterwhilepumping.
Sciences, Pennsylvania, USA) has been identified as
Zooplankton samples were preserved in a sugar-
accurateandprecisewhenusedforfishes(Sinketal.,
bufferedformalinsolutionandtransportedtotheIRBS
2008), and has a lower detection limit of
for analysis. We enumerated and identified the mac-
0.0567 ng ml-1. Individual cortisol values that were
rozooplankton samples with a zooplankton counting
below the sensitivity limit of the kit were treated as
wheel. Zooplankters were identified as rotifers to
beingequaltothelowestdetectionlimitvalueforthe
Genus,cladoceranstoGenus,andcopepodstoFamily.
kit (0.0567 ng ml-1) (Haddy & Pankhurst, 1999;
An estimate of the number of zooplankton in each
Ramsay et al., 2006). Triglycerides and ALP have
sample was determined by dividing the sample
been shown to represent a short-term, recent feeding
concentrate volume by the volume of subsamples
nutritional component, while cholesterol has been
requiredtoreach100zooplankton,andmultipliedby
shown to represent a long-term body energy reserve
the number of zooplankton counted in the subsam-
nutritional component. Protein can be representative
ple(s). This number was then divided by the volume
of a short-term feeding or long-term body energy
filteredtogetanestimateofthenumberofzooplank-
reserves(Congleton&Wagner,2006;Guerreiroetal.,
ton in 1 l of river water for each sample. Each
2012). Cortisol and glucose are associated with the
predictor variable was averaged across all sampling
stressresponseofteleostfishes(Barton,2002;Wagner
episodes within a time period, resulting in a single
&Congleton,2004).
meanparametervaluepertimeperiod.
Speciesrichness,communitycompositionrichness,
andcatchperuniteffort(CPUE,numberoffishh-1)for Statisticalanalyses
all fish captured by electrofishing during each time
period were quantified in accordance with LTRMP At theoutset ofanalyses,we developedacorrelation
protocols(http://www.umesc.usgs.gov/ltrmp.html)(Ic- matrix with the different water quality predictor
kes & Burkhardt, 2002). Community composition, variables to identify correlated terms and avoid
quantifiedasthenumberoffunctionalgroupspresent, colinearity within the data. Following colinearity
wasdefinedaccordingtofunctionalgroupsidentifiedin analyses, we identified three abiotic (environmental)
Poff & Allan (1995). To develop these functional variables that were representative of the different
groups,dietinformationwastakenfromBecker(1983), water quality parameters measured: water tempera-
Pflieger (1975), and the Ohio Department of Natural ture, TP, and suspended solids. More specifically,
Resources (http://www.dnr.state.oh.us/Home/species_ water temperature was inversely correlated with
a_to_z/AZFish/tabid/17913/Default.aspx)andusedto conductivity, DO, and Secchi disk transparency
assignfishtothefollowingfunctionalfeedinggroups: (P\0.05) and positively correlated with turbidity,
benthic invertivores, general invertivores, herbivore- chlorophyll a, and suspended solids (P\0.05). TP
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Hydrobiologia(2014)736:1–15 5
Table1 Correlationmatricesshowingrelationshipsbetweenwaterqualitypredictorvariablesusedinregressionanalyses
Waterqualityparameter Negativecorrelation Positivecorrelation Nocorrelation
Temperature Conductivity Turbidity TN
Dissolvedoxygen Chlorophylla Velocity
Secchidisc Suspendedsolids pH
Totalphosphorous
TotalPhosphorous Secchidisc TN Turbidity
Chlorophylla Velocity Temperature
Dissolvedoxygen Suspendedsolids
pH Conductivity
SuspendedSolids Secchidisc Totalphosphorous Velocity
Dissolvedoxygen Chlorophylla Conductivity
pH Turbidity TN
Temperature
Allcorrelationsaresignificantata=0.05
was negatively correlated with Secchi disk transpar- 2011; Symonds & Moussalli, 2011). Based on the
ency, chlorophyll a, DO, and pH (P\0.05) and DAICc, a value 0 B DAICc B 2 shows substantial
positivelycorrelatedwith TN,velocity, conductivity, supportthatamodelisthebestfittothedata,whilean
and suspended solids (P\0.05). Suspended solids DAICc[10showslittlesupport(Symonds&Mouss-
correlated negatively with pH, DO, and Secchi disk alli,2011).Followingbest-fitmodeldeterminationfor
transparency (P\0.05) and correlated positively eachnutritionalandstressparameter(definedhereas
with TP, chlorophyll a, turbidity, and temperature DAICc\4), post hoc analyses were performed to
(P\0.05) (Table 1). Together, water temperature, visualize trends in the data. We performed all statis-
TP, and suspended solids represented the 11 water ticalanalyses usingJMPversion 10.0(SASInstitute,
qualityparametersanalyzed. North Carolina, USA). Rejection of the null hypoth-
After completing colinearity analyses, we used a esis(a)foralltestswas0.05.Allvalueswerereported
two-step statistical approach to define how environ- as mean ± standard error of the mean (SEM) where
mental variables influenced blood-based metrics. appropriate. We used the null hypothesis of no
First, to quantify the variation in environmental variation in stress or nutrition (i.e., physiological
parameters across time periods, we used a one-way parameters) for SVCPacrossabiotic orbiotic habitat
analysis of variance (ANOVA), which allowed us to characteristicsnestedwithintimeperiods.
visualize trends in environmental parameters. Each
predictor variable was treated as a nested parameter
within time period because measurements from all Results
samplingepisodeswereaveragedineachofthethree
time periods. Second, we used simple linear regres- Silver carp total length, weight, and the six measured
sion, followed by Akaike’s Information Criterion physiological parameters were highly variable across
corrected for small sample sizes (AICc), to define thethreetimeperiodsintheLGR(Table 2).Totallength
therelationshipbetweennutritionalandstressparam- and weight were strongly correlated (P\0.0001,
eters (response variables) and total length, water r = 0.94); therefore, only total length was included.
quality variables, zooplankton concentrations, fish Out of 32 cortisol readings for SVCP, nine fish had
relative abundances (CPUE), species richness, and cortisolconcentrationsbelowthedetectionlimitofthe
species composition (predictor variables) over the kit and were treated as being equal to the lowest
threetimeperiodssampled.ComparisonsusingAICc detection limit value (Haddy & Pankhurst, 1999;
allowedustoquantifyandrankthebestapproximating Ramsayetal.,2006).Abioticandbioticenvironmental
modelforeachbloodvariable(Hegyi&Garamszegi, parameters also varied across the three time periods
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Table2 Samplesize,minimum,maximum,mean,median,andstandarderrormeanvaluesfornutritionalandstresscharacteristics
ofsilvercarpH.molitrixsampledintheLaGrangeReach,IllinoisRiverduring2011
Parameter N Min Max Mean Median Standarderrormean
Triglycerides(mgdl-1) 30 14.8 233.4 93.4 86.6 7.9
Cortisol(ngml-1) 32 0.06 102.5 19.3 7.8 5.2
Cholesterol(mgdl-1) 32 159.0 592.0 228.5 213.4 13.2
Glucose(mgdl-1) 32 21.5 63.3 39.6 38.6 1.9
Protein(gdl-1) 32 3.0 4.8 3.8 3.7 0.08
ALP(Ul-1) 32 6.7 71.3 35.0 35.7 3.7
Totalweight(g) 32 800.0 2540.0 1195.6 1130.0 61.0
Totallength(mm) 32 450.0 617.0 493.8 486.5 6.0
Table3 Meanvaluesforwaterqualityparameters,zooplanktonconcentrations,andfishspeciesrichnessandabundancefortheLa
GrangeReach,IllinoisRiverduring2011
Parameter Mid-summer Late-summer Earlyfall
Secchidisktransparency(cm) 20.7±0.6a 20.9±0.7a 21.9±0.6a
Temperature((cid:3)C) 30.9±0.1a 31.6±0.2a 15.1±0.4b
DO(mgl-1) 5.0±0.2c 7.97±0.4b 9.3±0.1a
pH 7.9±0.02c 8.2±0.04a 8.1±0.02b
Turbidity(NTU) 63.7±2.6a 66.4±3.2a 58.5±2.9a
Conductivity(lScm-1) 774.8±7.1a 626.5±8.9b 753.6±12.6a
Velocity(ms-1) 0.2±0.03a 0.04±0.01b 0.12±0.02a
TP(mgl-1) 0.4±0.02a 0.4±0.02a 0.4±0.03a
TN(mgl-1) 2.9±0.1a 1.9±0.08b 2.3±0.1b
Suspendedsolids(mgl-1) 65.0±3.5a 63.8±3.5a 57.2±3.4a
Chlorophylla(lgl-1) 44.3±2.6b 85.9±4.2a 34.6±1.8b
Rotifers(rotifersl-1) 118.5±10.1a 90.8±7.9a 105.7±17.0a
Copepods(copepodsl-1) 3.1±0.3a 0.9±0.1c 1.9±0.3b
Cladocerans(cladoceransl-1) 2.7±0.3a 2.8±0.3a 0.3±0.1b
Totalzooplankton(totalzoop.l-1) 124.4±10.1a 94.5±7.8a 107.9±17.1a
Speciesrichness 38 49 45
Planktivoreandomnivorerichness 13 16 16
Plantivorerichness 3 2 3
CPUE_excludingSVCP(fishh-1) 179.2 319.7 328.5
CPUE_GZSD(fishh-1) 99.2 120.3 34.5
CPUE_SVCP(fishh-1) 8.3 46.8 26.0
CPUEreferstofishcaughtperelectroshockinghour,GZSDreferstogizzardshad(Dorosomacepedianum),SVCPreferstosilver
carp(H.molitrix),andexcludingSVCPreferstotheabundanceofallotherfishescaughtduringsampling.Allvaluesarereportedas
the mean±SEM where appropriate. Results of statistical analyses are reported as superscript letters, with dissimilar letters
indicatingsignificantdifferencesofanindividualparameteracrosstimeperiods(ANOVA,P\0.05)
(ANOVAs, P\0.05; Table 3). Mean water tempera- relativetotheearlyfall(ANOVA,P\0.05;Table 3).
turesweretwiceaswarminthemid-andlate-summer AveragerelativeabundancesofGZSDwerelowestin
relative to the early fall sampling period (ANOVA, theearlyfallcomparedtotheothersamplingperiods.
P\0.05; Table 3). Mean cladoceran concentrations VariabilityinALPactivities(theamountofenzyme
werenearly10-foldgreaterinthemid-andlate-summer causing the hydrolysis of ALP per minute at room
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Hydrobiologia(2014)736:1–15 7
Table4 Model selection results relating predictor variables to variation in ALP activities and plasma glucose concentrations for
wild-caughtsilvercarp(H.molitrix)collectedintheLaGrangeReach,IllinoisRiveracrossthreetimeperiodsin2011
Parameter Model AICc DAICc Modellikelihood AICcweight
ALP CPUE_GZSD(fishh-1) 251.44 0.00 1.00 0.68
Temperature((cid:3)C) 254.06 2.61 0.27 0.18
Cladocerans(cladoceransl-1) 254.73 3.29 0.19 0.13
Suspendedsolids(mgl-1) 260.34 8.89 0.01 0.01
Planktivorerichness 279.92 28.48 0.00 0.00
CPUE_excludingSVCP(fishh-1) 287.91 36.47 0.00 0.00
Planktivoreandomnivorerichness 288.70 37.25 0.00 0.00
CPUE_SVCP(fishh-1) 289.95 38.51 0.00 0.00
Rotifers(rotifersl-1) 289.99 38.54 0.00 0.00
Copepods(copepodsl-1) 290.64 39.19 0.00 0.00
Totalzooplankton(totalzoop.l-1) 290.74 39.29 0.00 0.00
Totallength(mm) 291.01 39.57 0.00 0.00
Speciesrichness 291.05 39.61 0.00 0.00
TP(mgl-1) 291.06 39.62 0.00 0.00
Glucose Suspendedsolids(mgl-1) 233.28 0.00 1.00 0.42
Cladocerans(cladoceransl-1) 234.21 0.92 0.63 0.26
Temperature((cid:3)C) 234.42 1.14 0.57 0.24
CPUE_GZSD(fishh-1) 236.95 3.67 0.16 0.07
CPUE_excludingSVCP(fishh-1) 241.42 8.13 0.02 0.01
Planktivoreandomnivorerichness 242.17 8.89 0.01 0.00
TP(mgl-1) 246.19 12.91 0.00 0.00
Speciesrichness 246.62 13.33 0.00 0.00
Totalzooplankton(totalzoop.l-1) 247.44 14.16 0.00 0.00
Copepods(copepodsl-1) 247.57 14.28 0.00 0.00
Planktivorerichness 247.91 14.63 0.00 0.00
Rotifers(rotifersl-1) 248.09 14.80 0.00 0.00
CPUE_SVCP(fishh-1) 248.10 14.82 0.00 0.00
Totallength(mm) 248.65 15.37 0.00 0.00
ModelsarerankedbydifferencesinAICvalues(DAICc),andthemodelwiththelowestDAICcvalueisthebestfittothedata,with
AICcweightdeterminingthebestapproximatingmodel.CPUEreferstofishcaughtperelectroshockinghour,GZSDreferstogizzard
shad(Dorosomacepedianum),SVCPrefers tosilvercarp,andexcluding SVCPrefers totheabundance ofallotherfishes caught
duringsampling
temperature)intheplasmaofwild-caughtSVCPwas indicated that ALP values were significantly greater
best explained by the CPUE of GZSD; models that inthemid-andlate-summermonthscomparedtothe
included water temperature and cladoceran concen- earlyfall[N = 32,F = 35.57,P\0.0001].
(2,31)
tration also provided substantial support Variability in the plasma glucose concentration of
(DAICc = 2.61 and 3.29, respectively) (Table 4). SVCPwasbestexplainedbysuspendedsolidconcen-
The r2 values between ALP activity (dependent trations, with cladoceran concentration, water tem-
variable) and CPUE of GZSD, water temperature, perature, and the CPUE of GZSD also providing
andcladoceranconcentration(independentvariables) substantial support (DAICc = 0.92, 1.14, and 3.67,
were0.71,0.69,and0.68,respectively,providingthe respectively) (Table 4). Plasma glucose concentra-
variabilityinALPactivityofSVCPattributedtoeach tions were positively correlated with each of the
of these parameters. Analysis of variance results predictor variables from the regression analyses
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Table5 Modelselectionresultsrelatingpredictorvariablestovariationincortisol,cholesterol,triglycerides,andproteinconcen-
trationsforwild-caughtsilvercarp(H.molitrix)collectedintheLaGrangeReach,IllinoisRiveracrossthreetimeperiodsin2011
Parameter Model AICc DAICc Modellikelihood AICcweight
Cortisol Planktivorerichness 309.71 0 1 0.17
CPUE_GZSD(fishh-1) 310.64 0.93 0.63 0.11
CPUE_SVCP(fishh-1) 311.27 1.55 0.46 0.08
Rotifers(rotifersl-1) 311.28 1.56 0.46 0.08
Temperature((cid:3)C) 311.44 1.72 0.42 0.07
Cladocerans(cladoceransl-1) 311.53 1.82 0.4 0.07
Copepods(copepodsl-1) 311.59 1.88 0.39 0.07
Totalzooplankton(totalzoop.l-1) 311.65 1.94 0.38 0.06
Speciesrichness 312 2.29 0.32 0.05
Suspendedsolids(mgl-1) 312.12 2.4 0.3 0.05
TP(mgl-1) 312.15 2.44 0.3 0.05
Planktivoreandomnivorerichness 313.01 3.29 0.19 0.03
CPUE_excludingSVCP(fishh-1) 313.1 3.38 0.18 0.03
Totallength(mm) 313.21 3.5 0.17 0.03
Cholesterol Totallength(mm) 371.91 0 1 0.08
CPUE_SVCP(fishh-1) 372.28 0.38 0.83 0.07
Rotifers(rotifersl-1) 372.28 0.38 0.83 0.07
Copepods(copepodsl-1) 372.29 0.38 0.83 0.07
Totalzooplankton(totalzoop.l-1) 372.29 0.38 0.83 0.07
Speciesrichness 372.3 0.39 0.82 0.07
TP(mgl-1) 372.3 0.39 0.82 0.07
Planktivorerichness 372.31 0.41 0.82 0.07
Planktivoreandomnivorerichness 372.34 0.44 0.8 0.07
CPUE_excludingSVCP(fishh-1) 372.35 0.45 0.8 0.07
CPUE_GZSD(fishh-1) 372.45 0.54 0.76 0.06
Temperature((cid:3)C) 372.46 0.56 0.76 0.06
Suspendedsolids(mgl-1) 372.47 0.56 0.76 0.06
Cladocerans(cladoceransl-1) 372.47 0.56 0.76 0.06
Triglycerides CPUE_GZSD(fishh-1) 314.43 0 1 0.17
Temperature((cid:3)C) 315.86 1.43 0.49 0.08
Cladocerans(cladoceransl-1) 315.88 1.45 0.48 0.08
Suspendedsolids(mgl-1) 316.03 1.59 0.45 0.08
Planktivorerichness 316.18 1.75 0.42 0.07
Totallength(mm) 316.35 1.92 0.38 0.06
CPUE_SVCP(fishh-1) 316.71 2.28 0.32 0.05
Rotifers(rotifersl-1) 316.71 2.28 0.32 0.05
Copepods(copepodsl-1) 316.78 2.35 0.31 0.05
Totalzooplankton(totalzoop.l-1) 316.79 2.36 0.31 0.05
Speciesrichness 316.85 2.42 0.3 0.05
CPUE_excludingSVCP(fishh-1) 316.87 2.44 0.3 0.05
TP(mgl-1) 316.87 2.44 0.3 0.05
Planktivoreandomnivorerichness 316.89 2.46 0.29 0.05
123
Hydrobiologia(2014)736:1–15 9
Table5 continued
Parameter Model AICc DAICc Modellikelihood AICcweight
Protein Totallength(mm) 37.58 0 1 0.11
Totalzooplankton(totalzoop.l-1) 37.78 0.2 0.91 0.1
Copepods(copepodsl-1) 37.78 0.2 0.91 0.1
Speciesrichness 37.81 0.23 0.89 0.1
Rotifers(rotifersl-1) 37.83 0.25 0.88 0.1
CPUE_SVCP(fishh-1) 37.83 0.25 0.88 0.1
TP(mgl-1) 37.86 0.27 0.87 0.09
Planktivoreandomnivorerichness 38.6 1.02 0.6 0.07
CPUE_excludingSVCP(fishh-1) 38.78 1.2 0.55 0.06
Planktivorerichness 39.19 1.61 0.45 0.05
Suspendedsolids(mgl-1) 41.89 4.31 0.12 0.01
CPUE_GZSD(fishh-1) 42.09 4.51 0.1 0.01
Cladocerans(cladoceransl-1) 42.12 4.54 0.1 0.01
Temperature((cid:3)C) 42.14 4.56 0.1 0.01
ModelsarerankedbydifferencesinAICvalues(DAICc),andthemodelwiththelowestDAICcvalueisthebestfittothedata,with
AICcweightdeterminingthebestapproximatingmodel.CPUEreferstofishcaughtperelectroshockinghour,GZSDreferstogizzard
shad(Dorosomacepedianum),SVCPreferstosilvercarp,andexcludingSVCPreferstotheabundanceofallotherfishescaught
(P\0.05), and the r2 value for the relationship andtheCPUEofGZSD,particularlyduringthemid-
betweenplasmaglucoseconcentrationandsuspended andlate-summersamplingperiods.Alkalinephospha-
solids was 0.38, between glucose and cladoceran tase is a nutritional parameter, and previous research
concentration was 0.36, for glucose and water tem- has shown that elevated quantities of ALP can be
peraturewas0.36,andbetweenglucoseandtheCPUE related to the processing of energy substrates by the
of GZSD was 0.31. Results from the ANOVA liver.DecreasedALPactivitieshavebeenobservedin
indicated that plasma glucose concentrations were starving fish (Sandnes et al., 1988; Congleton &
significantly greater in the mid- and late-summer Wagner, 2006). Previous research with Atlantic
periods compared to the early fall [N = 32, F salmon (Salmo salar) also documented a positive
(2,
= 9.06,P\0.001]. correlation between plasma ALP activities and water
31)
All competing AICc models had DAICc\4.0 for temperature(Sandnesetal.,1988).Thereissubstantial
cortisol, cholesterol, and triglycerides (Table 5). The diet overlap between GZSD and SVCP as both are
r2 value for the best-fit model (planktivore richness) filter-feedingfishesthatconsumezooplankton,includ-
and cortisol was 0.11. Cholesterol had an r2 value of ing cladocerans (Shuang-lin & De-shang, 1994;
0.02 with the best-fit model of total length. Triglyc- Sampsonetal.,2009).Wahletal.(2008)documented
eridesandtheCPUEofGZSDhadanr2of0.04.The an increase in cladoceran concentrations in warmer
best-fitmodel(totallength)forproteinhadanr2value water temperatures throughout the summer in the
of 0.13 (Table 5). These models do not appear to Illinois River (i.e., mid- and late-summer), thereby
provide a meaningful interpretation of these physio- elevating potential food resources for filter-feeding
logicalvariables. fishes. This finding is similar to our results, which
showed that cladoceran concentrations were more
than 10-fold greater in the mid- and late-summer
Discussion relativetotheearlyfall.Interestingly,Sampsonetal.
(2009) reported that SVCP found in Illinois and
Forwild-caught,invasiveSVCPcollectedintheLGR, Mississippi river backwaters primarily consumed
ALP activities in plasma were positively correlated rotifers (98%), with cladocerans only comprising
with water temperature, cladoceran concentrations, 0.04% of their diet. Similarly, GZSD consumed
123
10 Hydrobiologia(2014)736:1–15
rotifers as a main prey item (Sampson et al., 2009). increase (McLeese et al., 1994; Pottinger & Carrick,
Themeshsizeusedtocollectzooplanktoninourstudy 2000).Inourstudy,meanwatertemperaturesrecorded
(55 lm)mayhaveunderestimatedtherotifercommu- during the mid- and late-summer sampling periods
nity in the LGR (Chick et al., 2010). As such, wereC30(cid:3)C,atemperatureshowntoinducenegative
cladoceran concentrations may also act as a proxy physiological disturbances in SVCP (Kolar et al.,
for other food quality parameters that were not 2007). Warmer water temperatures can also increase
capturedbyourstudy.Regardless,ALPintheplasma the maintenance energy cost of an individual (von
of SVCP likely correlates positively with GZSD Oertzen, 1985), which can elicit a stress response
CPUE because feeding conditions were optimal for (Akar, 2011). Elevated concentrations of suspended
both species during those time periods. Both species solids in the water may reduce visibility or cause
were likely consuming cladocerans at high rates abrasions,potentiallyresultinginanincreaseinstress
relativetoothertimesoftheyear.TheGZSDrelative for fish (Redding et al., 1987). Silver carp also have
abundances may have increased in collections as a specialized,sponge-likegillrakersthatallowthemto
result of their movement into the area because of the consume very small suspended particles (3–4 lm)
ample food supply. Water temperature also plays a (Omarov, 1970; De-Shang & Shuang-Lin, 1996).
major role in the feeding rates of fishes. Metabolism Elevated concentrations of suspended solids may,
typically increases with warmer water temperatures, therefore, increase the potential for SVCP gill rakers
elevatingtherateatwhichfishesmustacquirefoodto to become clogged, which may trigger a stress
balancemetaboliccostsandoptimizegrowth(Kitchell response. Interestingly, temperature and suspended
etal.,1977;Bureletal.,1996).SpecificallyforSVCP, solids showed negative and positive correlations
feedingisreducedintemperatures\15(cid:3)C(Kolaretal., (respectively) with dissolved oxygen. This could
2007). In our study, the mean temperature was about indicate that dissolved oxygen may have influenced
15(cid:3)Cintheearlyfall,whichmaybewhySVCPALP stress in SVCP. Finally, concentrations of plasma
activities were significantly lower during this time glucose have been shown to decline in fasted fishes
period. Water temperatures were C30(cid:3)C in mid- and (Congleton&Wagner,2006).Ourstudyusedbaseline
late-summer,likelyresultingintheneedforincreased physiologicalmetrics(i.e.,bloodwasdrawninunder
feeding rates. Together, our results demonstrate that 3 min), indicating that it should be possible to use
variation in SVCP feeding rates, quantified by ALP glucoseasareliableindicatorofnutrition(Romero&
activities in plasma, was likely driven by cladoceran Reed, 2005; Gingerich et al., 2010). As such, high
concentrationsandelevatedwatertemperaturesinthe concentrations of plasma glucose may indicate
mid-andlate-summer. increased SVCP feeding (by proxy of increased
PlasmaglucoseforSVCPwaspositivelycorrelated glucose concentrations in our data) in the mid- and
with suspended solids, cladoceran concentrations, late-summercomparedtotheearlyfall.Thisisalsoin
water temperature, and the CPUE of GZSD. Plasma accordance with our results of SVCP ALP values
glucose is a reliable indicator of stress and correlates (similarly acting as a representation for feeding),
withawiderangeofenvironmentalstressors,includ- whichindicatedincreasedfeedinginthemid-andlate-
ing water temperature, suspended solids, and salinity summer relative to the early fall. Irrespective of the
(Menge & Sutherland, 1987; Wells & Pankhurst, mechanism,concentrationsofglucoseintheplasmaof
1999). Our results indicate that, while SVCP feeding SVCP were positively correlated with suspended
was likely not impaired during the mid- or late- solids, water temperature, the CPUE of GZSD, and
summer (i.e., our proxy of SVCP feeding, ALP, did cladoceranconcentrations.
notdecrease withincreasedGZSD CPUE), they may A surprising number of blood-based physiological
still be experiencing external stressors due to the metrics did not have a single, clear best-fit model to
densityofGZSDaspotentialcompetitors,whichmay explain trendsinthedata,indicatingthatvariationin
explain the positive relationship between CPUE of the response variables was not explained by the
GZSDandplasmaglucose.Alternatively,seasonand predictors we quantified. For example, none of the
watertemperaturecanexertastronginfluenceonthe models had DAICc[4 for cortisol, cholesterol, and
hormonal rates of reaction to stress, with greater triglycerides. All protein models had DAICc\5.
temperatures causing biological reaction rates to Thereareanumberofplausibleexplanationsforthese
123