Post on 02-Feb-2023
Mind the Data Gap: Identifying and Assessing Driversof Changing Eutrophication Condition
Benjamin Fertig & Michael J. Kennish &
Gregg P. Sakowicz & Laura K. Reynolds
Received: 12 February 2013 /Revised: 8 October 2013 /Accepted: 19 November 2013 /Published online: 18 December 2013# Coastal and Estuarine Research Federation 2013
Abstract This study identified drivers of change in BarnegatBay–Little Egg Harbor Estuary, NJ, USA over multiplelong-term time periods by developing an assessmenttool (an “Eutrophication Index”) capable of handlingdata gaps and identifying the condition of and relationshipsbetween ecosystem pressures, ecosystem state, and bioticresponses. The Eutrophication Index integrates 15 indicatorsin 3 components: (1) water quality, (2) light availability, and(3) seagrass response. Annual quantitative assessments ofcondition and its consistency for three geographic segmentsrange from 0 (highly degraded) to 100 (excellent condition).Eutrophication Index values significantly declined (p <0.05)by 34 and 36 % in central and south segments from 73 and 71in the early 1990s to 48 and 45 in 2010, respectively. Ongoingdeclines despite periods of improvement (e.g., 1989–1992,
1996–2002, and 2006–2008) suggest these estuarine seg-ments are currently undergoing eutrophication. The northsegment had highest nutrient loading and lowestEutrophication Index values (2010 Eutrophication Index val-ue=37) but increased over time (from 14 in 1991 to 50 in2009) in contrast to trends in central and south segments.Rapid initial declines of Eutrophication Index values withincreasing loading highlight that the estuary is sensitive toloading. Ecosystem response to total nutrient loading, asdescribed by the Index of Eutrophication, exhibited nonline-arity at loading rates of >1,200 and <5,000 kg TN km−2 year−1
and >100 and <250 kg TP km−2 year−1, values similar toresponses of seagrass to nutrient loading in many ecosystems.While nutrient loading is initially a critical driver of ecosystemchange, other factors, e.g., light availability and drive ecosys-tem condition, yield nonlinearity. Empirical evidence forswitches in the driving factors of ecosystem stress adds com-plexity to the conceptualization of ecosystem resiliency due tofeedback from multiple dynamic, nonlinear stressors.
Keywords Eutrophication . Biotic indices . Long-term data .
Data gaps . Ecological status .Multivariate analysis
Introduction
Many coastal estuarine ecosystems undergo long-term non-linear ecosystem-scale changes and have been classified ashighly eutrophic (Bricker et al. 2007; Kennish et al. 2007;Duarte 2009; Fertig et al. 2013b). Barnegat Bay–Little EggHarbor (BB–LEH), a Mid-Atlantic shallow coastal lagoon inNew Jersey, USA, exemplifies systems exhibiting multiplebiotic responses to a myriad of stressors (Kennish et al.2011; Fertig et al. 2013a) to the extent that BB–LEH has beenused as a eutrophic end-member ecosystem to test metrics ofincipient eutrophication (Kennish and Fertig 2012). Stressors
Communicated by Mark J. Brush
Electronic supplementary material The online version of this article(doi:10.1007/s12237-013-9746-5) contains supplementary material,which is available to authorized users.
B. Fertig (*) :M. J. Kennish :G. P. Sakowicz : L. K. ReynoldsInstitute of Marine and Coastal Sciences, Rutgers University,New Brunswick, NJ 08901, USAe-mail: bfertig@ccbcmd.edu
B. Fertige-mail: ben.fertig@gmail.com
L. K. Reynoldse-mail: lkreynolds@ucdavis.edu
Present Address:B. FertigSchool of Math and Science, Community College of BaltimoreCounty, Essex, MD 21237, USA
Present Address:L. K. ReynoldsDepartment of Evolution and Ecology, University of CaliforniaDavis, Davis, CA 95616, USA
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221DOI 10.1007/s12237-013-9746-5
to BB–LEH include increases in concentrations and loads ofnutrients (Hunchak-Kariouk and Nicholson 2001; Baker et al.2013). Human population in the catchment (>575,000year-round and >1,200,000 in summertime) has rapidlyexpanded (US Census 2010), altering the catchment’sland use–land cover to >34 % developed including>10 % impervious surface (Lathrop and Conway2001). Multiple symptoms of eutrophication have beenobserved (Kennish 2001a; Kennish et al. 2007, 2012),including low dissolved oxygen concentrations, harmfuland benthic algal blooms (Anderson et al. 2002;Gastrich et al. 2004; Kennish et al. 2011), heavy epi-phytic loading, and declines in seagrass biomass(Kennish et al. 2010; Fertig et al. 2013a). A holistictool for assessing condition and identifying the driversof these changes is needed for BB–LEH.
Identifying drivers of long-term changes can be ac-complished by assessing a suite of bioindicators. Manytechniques and tools are available (Bricker et al. 2007;Williams et al. 2009; Ferreira et al. 2011) includingstatistical methods for establishing reference nutrientconditions (Dodds and Oakes 2004), comparing indica-tors to threshold values (Wazniak et al. 2007), andsampling benthic community composition with respectto the degree of sensitivity/tolerance to a stressor toindicate local condition across ecosystems (Borja et al.2000, 2008).
Synthesizing existing datasets to evaluate the ecolog-ical condition of shallow estuarine systems can provideextensive information globally (Srebotnjak et al. 2012),but they are often time consuming, labor-intensive, cost-ly, and often target individual stressors. To avoid thesedeficiencies, there has been an effort to develop analyt-ical techniques and environmental indicators that spanthe multiple levels of ecosystem components (Carvalhoet al. 2011), biological organization, and are broadlyapplicable (Niemi and McDonald 2004). Relatively fewecosystems are extensively studied globally, and datagaps (both paucity and spatiotemporal inconsistencies)pose a major challenge for extensive assessments formany ecosystems including shallow coastal lagoonssuch as BB–LEH. Long-term ecosystem-wide datasetsfor BB–LEH exist but have not previously been synthe-sized to assess drivers of change in ecosystem condi-tion, and, as with many systems, available data containsgaps and spatiotemporal inconsistencies.
The goal of this study was to identify drivers ofchange in BB–LEH over multiple long-term time pe-riods by developing an assessment tool (an “Index ofEutrophication”) capable of handling data gaps andidentifying the condition of and relationships betweenecosystem pressures, ecosystem state, and bioticresponses.
Methods
Study Location
The BB–LEH catchment covers an area of 1,730 km2 (catch-ment, estuary areal ratio is 6.5:1) approximately overlappingOcean County, NJ, USA. Population density and developmentdecrease along a north–south gradient (Conway and Lathrop2005), as does nutrient loading to the estuary (Hunchak-Kariouk and Nicholson 2001; Seitzinger et al. 2001; Wiebenand Baker 2009; Baker et al. 2013). Nitrogen loadingfrom the BB–LEH catchment is positively correlatedwith total nitrogen concentrations in the estuary (Kennishand Fertig 2012).
BB–LEH estuary (Fig. 1) is a back-bay basin complexforming an irregular contiguous lagoonal system with a sur-face area of 280 km2 and a volume of 3.54×108 m3 (Kennish2001a, b). Water residence time is 74 days in summer (Guoet al. 2004), semidiurnal tides range from <0.5 to 1.5 m, andthe estuary is well-mixed by wind and currents (<0.5 to1.5 m s−1). Water temperatures range from −1.5 to 30 °C,and salinity ranges from 10 to 32.
Significant differences in long-term catchment, estuarinewater quality, and sediment indicators (Psuty 2004; Psuty andSilveira 2009) indicated the necessity of partitioning the estuaryinto three spatial segments (north, central, and south; Fig. 1),differing from previous assessment (Kennish et al. 2007).
Index Calculation
Fifteen biotic and abiotic indicators were selected at the outsetbased on potential response to total nitrogen and total phos-phorus loadings and organized into three components: (1)water quality, (2) light availability, and (3) seagrass(Table 1). Annual mean (or median) data (1989–2010) foreach indicator in each of the three spatial segments (north,central, and south; Fig. 1) were input to calculate “raw scores”,“weighted scores” , component indices , and the“Eutrophication Index”.
First, each variable was rescaled by comparing the mea-sured value to “threshold” values (Tables 2 and 3). Thesethresholds were defined to distinguish optimal conditionsfrom degraded conditions for each variable based upon (a)the literature, (b) data analysis, (c) best professional judgment,and (d) some combination of a–c (Table 2). Best professionaljudgment was reserved only for indicators where previousthresholds are not established in the literature and data analy-sis yielded limited insight. Thresholds were set at values ofindicators that indicated a change in response values—such aschanges in the slope or abrupt breaks in response indicators(Table 2). As a result, this rescaling transforms each variableinto common spatial and temporal units and intervals. Data for2011 were calculated independently for validation.
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S199
Component scores are comprised of “raw” and “weighted”Scores, each contributing 50 % to the component’s index.Raw scores for each indicator are calculated by rescalingannual means (Table 4) according to an equation describedby a set of threshold values (Tables 2 and 3; see below). Rawscores range from 0 (degraded) to 50 (excellent) for eachvariable. Component raw scores were determined by comput-ing the mean of these raw scores.
Weighted scores are calculated independently from variablesmeasured in each year and each component (e.g., water quality,light availability, and seagrass; Table 1). For each component,covariance matrices are computed from raw scores for all mea-surements in a given decade. Principal component analysis
(PCA) is then applied to these covariance matrices to find theeigenvector (first principle component) that gives the linearcombination of model variables that explain most of the vari-ance in the given system in a given decade (∼50–75 % of thevariability in our data). The larger the magnitude in an elementof the eigenvector, the more the corresponding variable explainsthe variability in the system. Therefore, weightings are deter-mined for each variable by squaring the elements of the eigen-vector (Table 5). Weighted scores were then calculated for eachyear by multiplying the raw scores in a given year by theseweightings. The resulting scores also range from 0 (degraded) to50 (excellent) for each variable and are summed to obtain aweighted component score.
Fig. 1 Map of Barnegat Bay–Little Egg Harbor Estuary and itslocation within New Jersey, USA.The boundaries for the north,central, and south segments areindicated, as well as the arealextent of seagrass beds (green)and locations of transects used forseagrass sampling (black dots)
S200 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Table1
Availableindicatordataforpotentialinclusion
intheEutrophicationIndexforBarnegatB
ay–L
ittleEgg
HarborEstuary
summarized
byyear
(1989–2010)andestuarinesegm
ent
Com
ponent
Variable
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Water
quality
Temperature
aa
ac
aa
aa
aa
aa
aa
aa
aa
aa
aa
a
Dissolved
oxygen
aa
ac
aa
aa
aa
aa
aa
aa
aa
aa
aa
a
Totaln
itrogen
concentratio
n
aa
ac
aa
aa
aa
aa
aa
aa
aa
aa
aa
a
Totalp
hosphorus
concentratio
n
cc
cc
cc
cc
cc
aa
aa
aa
aa
aa
aa
a
Light
availability
Chlorophylla
cc
cc
cc
cc
aa
aa
aa
aa
aa
aa
aa
a
Totalsuspended
solid
sa
aa
ca
cc
ca
aa
aa
aa
aa
aa
aa
aa
Secchi
depth
aa
ac
aa
aa
aa
aa
aa
aa
aa
aa
aa
a
Macroalgaepercent
cover
ca
aa
aa
aa
aa
aa
aa
ab
aa
cb
bb
a
Percentsurface
light
cc
cc
cc
cc
cc
cc
cc
cb
bb
cb
bb
a
Epiphytebiom
ass
cc
cc
cc
cc
cc
cc
cc
cb
bb
cb
bb
a
Seagrass
response
Zosteraaboveground
biom
ass
cc
cc
cc
cc
cc
cc
cc
cb
bb
cb
bb
b
Zosterabelowground
biom
ass
cc
cc
cc
cc
cc
cc
cc
cb
bb
cb
bb
b
Zosteradensity
cc
cc
cc
cc
cc
cc
cc
cc
cc
cb
bb
b
Zosterapercentcover
cc
cc
cc
cc
cc
cc
cc
cc
cc
cb
bb
b
Zosteralength
cc
cc
cc
cc
cc
cc
cc
cb
cc
cc
cc
c
aDataavailablein
north
,central,andsouth
bDataavailablein
centraland
south
cDatanotavailable
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S201
Table2
Determinationof
thresholds
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
Ecosystem
Pressures
TotalN
itrogen
(kgTN
yr-1
estuarine
km-2)
50kg
TNestuary
km-2yr
-1=100
250kg
TNestuary
km-2yr
-1=75
1000
kgTNestuary
km-2yr
-1=50
3000
kgTNestuary
km-2yr
-1=25
Thresholds
fortotalnitrogenandphosphorus
loadingweredeterm
ined
byexam
iningbiotic
responsestonutrientloading
reportedinthe
literature,andby
dataanalysisof
thenutrient
loadingmodelingoutputfro
mPL
OADandits
relationshiptoecosystemstateandbiotic
response.Inexam
iningandcompiling
inform
ationfro
mtheliterature,loadingratesfor
totalnitrogen
andtotalphosphoruswere
convertedtokg
Nyear-1
forcom
parison
with
common
units
tomodeled
loadsfro
mBB-
LEH.Inlookingforpotentialthresholdsamong
theserelationships,w
esoughtvaluesofnutrient
loadings
thatmarkachange
inrateofdeclineof
seagrassresponses.How
ever,w
ealso
looked
forv
aluesthatmarkthestartofd
eclines
(regardlessof
rate),andvalues
aboveor
below
which
itappearsthatnitrogenloadingisno
longeradominantfactorinthechange
ofthe
bioticresponse.First,weexam
ined
relationships
betweennutrientloading
and
estuarineresponsesintheliterature(see
for
exam
ple,Wazniak
etal.,2007;B
rickeretal.,
1999;B
rickeretal.,2007;T
omasko
etal.,
1996;ShortandBurdick,1996;Deegan,2002;
Valielaetal.,2000;B
urkholderetal.2007;
Boynton
etal.,1996;K
ennish
andFertig,2012;
Stevensonetal.,1993;D
uarte
1995;and
Kiddonetal.2003).Pertinentfigures
fromthe
literaturewereincluded
inKennish
etal.,2012.
Tomasko
etal.(1996)and
Burkholderetal.
(2007)
reportthatas
nutrientloading
increases,
seagrassbiom
assandproductivity
decline
exponentially
with
very
sharpdeclines
starting
at~50kg
Nday-1,an
inflectionpointinthe
curveat~100
kgNday-1andaslow
errateof
declineabove~225
kgNday-1(Figure3-10).
Asimilartypeof
response
isseen
forseagrass
arealcoverageinthattheinflectionpointofthe
curvewasbelow1,000kg
Nkm
-2year-1anda
slow
errateof
declinewas
observed
above
5,000kg
Nkm
-2year-1
(Figure3-11in
Kennish
etal.2012,Shortand
Burdick,1996,
Valielaetal.,2000,B
urkholderetal.2007)
Also,seagrassarealcoveragedeclined
most
dram
aticallyatincipientlevelsof
eutrophication,early
oninthelong-term
analysis(Valielaetal.,2000).Seagrassshoot
density
ishighlyvariableanddeclines
rapidly
with
nitrogenloadingaslowas50
kgNyear-1,
which
slow
swith
greaterthan1,000kg
Nyear-
1though
atthishigherloadingratethedensity
approaches
(butdoes
notreach)0
shootsm-2
(Deeganetal.2002,Burkholderetal.2007,
Figure3-1
2inKennish
etal.2012).Seagrass
declines
aremediatedby
linearincreases
in
Thresholds
fortotalnitrogenand
phosphorus
loadingwere
determ
ined
byexam
iningbiotic
responsestonutrientloading
reportedintheliterature,andby
dataanalysisof
thenutrient
loadingmodelingoutputfro
mPL
OADandits
relationshipto
ecosystem
stateandbiotic
response.First,weexam
ined
relationships
betweennutrient
loadingandestuarineresponsesin
theliterature(see
forexample,
Wazniak
etal.,2007;B
rickeret
al.,1999;B
rickeretal.,2007;
Tomasko
etal.,1996;S
hortand
Burdick,1996;Deegan,2002;
Valielaetal.,2000;B
urkholderet
al.2007;Boynton
etal.1996;
Kennish
andFertig,2012;
Stevensonetal.,1993;D
uarte
1995;and
Kiddonetal.2003).A
snutrientloading
increases,
seagrassbiom
assandproductivity
declineexponentially
(Tom
asko
etal.,1996,alsoFigure3-9
inKennish
etal.2012),asdoesareal
coverage
(ShortandBurdick,
1996,alsoFigure3-1
0in
Kennish
etal.2012andValielaet
al.,2000,alsoFigure3-11in
Kennish
etal.2012).Seagrass
shootdensitysimilarly
declines
(Deeganetal.2002,also
Figure3
-11inKennish
etal.2012).
Seagrassdeclinesaremediatedby
linearincreases
inestuarinetotal
nitrogenconcentrations,ashas
been
foundinMaryland’scoastal
bays
(Boynton
etal.,1996,also
Figure3-1
2inKennish
etal.
2012)and
inBB-LEH
(Kennish
andFertig,2012,alsoFigure3-
13inKennish
etal.2012).In
lookingforthresholdsam
ong
theserelationships,w
ehave
looked
forv
aluesof
nutrient
loadings
thatmarkachange
inrate
ofdeclineof
seagrassresponses.
How
ever,w
ehave
also
looked
for
values
thatmarkthestartof
declines
(regardlessof
rate),and
valuesabovewhich
itappearsthat
nitrogenloadingisno
longera
dominantfactorinthechange
ofthebioticresponse.Similarly,w
e
Thresholdsandrescalingequations
have
been
calibratedforBB-
LEHas
acoastallagoon.
How
ever,w
hiletheremay
beapplicability
ofthesethresholds
toothersimilarcoastallagoons
inNew
Jersey
orelsewhere
(suchas
GreatSo
uthBay,N
Y,
ChincoteagueBay,M
D/VA,
Hog
Island
Bay,V
A,etc.),
the
thresholds
establishedmay
beof
limitedutilityforotherNew
Jersey
waters(e.g.R
aritanBay,
NY/NJHarbor,andDelaw
are
Bay)thatdonotshareim
portant
characteristics.BB-LEHisin
partextremelysusceptibleto
even
smallamountsof
nutrient
loadingdueto
itsenclosed
geom
orphologyandslow
water
circulationandflu
shingtim
e.In
contrast,coastalwatersalong
the
AtlanticCoast,R
aritanBay,and
NY/NJHarbor,andDelaw
are
Bay
have
muchquickerand
strongercirculationpatternsand
thereforerespondto
nutrient
enrichmentatd
ifferenttim
escales.A
dditionally,w
hile
heavymetals,inorganic,and
organictoxicantsmay
beim
portant
considerations
for
ecologicalhealth
insomeNew
Jersey
waters,they
may
beof
lowerpriorityforBB-LEH.
Toxicologicalanalysisof
sedimentsandthewatercolumn
arebeyond
thescopeof
this
projectand
have
notb
een
included
intheIndexof
Eutrophicationor
itscomponent
indices.
Weexam
ined
totaln
itrogen
loading
impactson
waterquality
indicatorsincluding
temperature,dissolved
oxygen,
andestuarytotaln
itrogen
and
totalp
hosphorusconcentrations
(Figure3-14
inKennish
etal.
2012),light
indicatorsincluding
totalsuspended
solids,
chlorophylla,S
ecchid
epth,the
ratio
ofepiphytebiom
assto
seagrass
biom
ass,macroalgae
percentcover,and
thepercentof
light
reaching
seagrass
leaves
(Figure3-15
inKennish
etal.
2012),andseagrass
indicators
includingabovegroundand
belowground
biom
ass,shoot
density,percentcover,andblade
length(Figure3-1
6inKennish
etal.2012).A
dditionalpotential
thresholds
fortotaln
itrogen
loadingwereidentifiedfrom
changesin
response
indicators
with
changesin
loading.
Wazniak
etal.2007,Brickeretal.
1999,2007To
masko
etal1996,
Shortand
Burdick
1996,D
eegan
2002,V
alielaetal2000,
Burkholderetal2007,B
oynton
etal1996,K
ennish
andFertig
2012,S
tevenson
etal1993,
Duarte
1995,K
iddonetal.2003,
dataanalysisby
thisstudy
TotalPhosphorus
(kgTPyr
-1
estuarine
km-2)
25kg
TPestuarykm
-2yr-1
=100;50
kgTPestuary
km-2
yr-1
=75;100
kgTPestuarykm
-2yr-1
=50;250
kgTPestuary
km-2
yr-1
=25
Weexam
ined
totaln
itrogen
loading
impactson
waterquality
indicatorsincluding
temperature,dissolved
oxygen,
andestuarytotaln
itrogen
and
totalp
hosphorusconcentrations
(Figure3-14
inKennish
etal.
2012),light
indicatorsincluding
totalsuspended
solids,
chlorophylla,S
ecchid
epth,the
ratio
ofepiphytebiom
assto
seagrass
biom
ass,macroalgae
percentcover,and
thepercentof
light
reaching
seagrass
leaves
(Figure3-15
inKennish
etal.
2012),andseagrass
indicators
includingabovegroundand
belowground
biom
ass,shoot
density,percentcover,andblade
length(Figure3-1
6inKennish
etal.2012).A
dditionalpotential
thresholds
fortotaln
itrogen
loadingwereidentifiedfrom
changesin
response
indicators
with
changesin
loading.
Brickeretal.1999,2007
Tomasko
etal1996,S
hortandBurdick
1996,D
eegan2002,V
alielaetal
2000,B
urkholderetal2007,
Boynton
etal1996,K
ennish
and
Fertig2012,S
tevenson
etal.
1993,K
iddonetal.2003,
Wazniak
etal.2007,Duarte
1995,dataanalysisby
thisstudy
S202 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
estuarinetotalnitrogen
concentrations,w
ithtotalnitrogen
concentrationinμM
=39.4+
0.53
*theannualtotalnitrogen
load
ingNm-2
year-1,ash
asbeen
foundinMaryland’scoastal
bays
(Boynton
etal.,1996,B
urkholderetal.,
2007,Figure3-13inKennish
etal.2012)and
inBB-LEH
,with
totalnitrogen
concentrations
inμg
NL-1=52.42+1.76
*theareal
norm
alized
subw
atershed
totalnitrogen
loading
inkg
TNkm
-2year-1(Figure3-14inKennish
etal.2012).Inan
analysisof
62estuarine
embaym
ents,L
atimerandRego(2010)
found
that≤50
kgTN
loadingha-1
year-1,seagrass
extentisvariableandislikelycontrolledby
otherecosystem
factorsunrelatedtonutrient
loading,butabove
thatrateeelgrasscoverage
declined
markedlyandwasessentially
absentat
loadinglevels≥100kg
TNloadingha-1year-1
(Figure3-15inKennish
etal.2012).Thereare
fewerestuarinestudiesthatexam
inethe
relationshipbetweentotalphosphorusloading
andbioticresponsesthan
forthe
relationship
betweentotalnitrogen
loadingsinceingeneral,
nitrogen–notphosphorus–isthelim
iting
nutrientfor
estuarinesystem
s.Nevertheless,
bothphosphorus
andnitrogenareimportantto
controlfor
estuarinewatersheds,particularly
thosewith
high
levelsofnutrientloading,asthe
receivingestuariescanbe
phosphorus
limited,
nitrogenlim
ited,or
co-limited,andthenutrient
thatismostlimiting
canchange
bothseasonally
andspatially
(Conleyetal.2009,Conley,1999,
Maloneetal.1996).T
heanalyses
ondata
assembled
forthisp
rojectdescribed
abovewere
also
perfo
rmed
fortotalphosphorus.Similarly,
weexam
ined
therelationships
between
seagrassresponsesandnutrientloadings
observed
inBB-LEH
compiledforthisproject.
Additionalpotentialthresholdsfortotal
nitrogenloadingwereidentifiedfro
mchanges
inresponse
indicatorswith
changesinloading.
Thisisparticularly
importanttocalibratethe
thresholds
tobe
relevantforB
B-LEH
.We
exam
ined
totalnitrogen
loadingimpactson
waterquality
indicators:tem
perature,dissolved
oxygen,and
estuarinetotalnitrogen
andtotal
phosphorus
concentrations
(Figure3-1
6in
Kennish
etal.2012),lightindicatorsas
wellas
from
totalphosphorusloading.Similarly,w
eexam
ined
theimpacton
lightindicators:
chlorophylla,totalsuspendedsolids,Secchi
depth,macroalgaepercentcover,and
theratio
ofepiphytetoseagrassbiom
assfro
mtotal
nitrogenloading(Figure3-19inKennish
etal.
2012)and
totalphosphorusloading(Figure3-
20inKennish
etal.2012).A
lso,weexam
nined
exam
ined
therelationships
betweenseagrassresponsesand
nutrientloadingsobserved
inBB-
LEHcompiledforthisproject.
Thisisparticularly
importantto
calibratethethresholds
tobe
relevantforB
B-LEH
.We
exam
ined
totalnitrogen
loading
impactson
waterquality
indicatorsincludingtemperature,
dissolvedoxygen,and
estuary
totalnitrogen
andtotalphosphorus
concentrations
(Figure3-1
4in
Kennish
etal.2012),light
indicatorsincludingtotal
suspendedsolids,chlorophylla,
Secchidepth,theratio
ofepiphyte
biom
asstoseagrassbiom
ass,
macroalgaepercentcover,and
the
percentoflightreaching
seagrass
leaves(Figure3-15inKennish
etal.2012),and
seagrassindicators
includingabovegroundand
belowground
biom
ass,shoot
density,percentcover,andblade
length(Figure3-16inKennish
etal.2012).A
dditionalpotential
thresholds
fortotalnitrogen
loadingwereidentifiedfro
mchangesinresponse
indicators
with
changesinloading.
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S203
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
theimpacton
seagrassindicators:aboveground
andbelowground
biom
ass,shootdensity,
percentcover,and
bladelengthfro
mtotal
nitrogenloading(Figure3-21inKennish
etal.
2012)and
fromtotalphosphorusloading
(Figure3-2
2inKennish
etal.2012).A
gain,
herewelooked
forv
aluesof
nutrientloadings
thatmarkedachange
intherateof
declineof
response
indicatorsandforv
aluesof
nutrient
loadings
thatmarkedthestartofd
eclines
(regardlessofrate),andforvaluesabovewhich
itappeared
thatnutrientloading
wasno
longera
dominantfactorinthechange
ofthebiotic
response.Totalnitrogenconcentrations
increasedwith
totalnitrogen
loadingandwith
totalphosphorusloadingintheNorthsegm
ent
(Figure3-1
8inKennish
etal.2012).
Chlorophyllaconcentrations
didnotappearto
vary
below2,000kg
totalnitrogen
km-2
yr-1,
butincreased
linearly
above~5,000
kgtotal
nitrogenkm
-2yr-1
andabove~250
kgtotal
phosphorus
km-2
yr-1
(Figure3-1
9in
Kennish
etal.2012).A
llseagrassindicators
declined
substantially
with
increasedtotal
nitrogenloadingandtotalphosphorusloading
(Figure3-2
1,Figure3-2
2inKennish
etal.
2012).Th
esedeclines
wereexponential
decreasesforb
iomass(bothabovegroundand
belowground
(Fertig
etal.2013a)aswellas
bladelengthandshootdensity(Figure3-21in
Kennish
etal.2012,Figure3-22inKennish
etal.2012).B
ased
ontheaboveobservations
and
analyses,thresholdsfortotalnitrogenloading
andtotalphosphorusloadingweredefined.
Defined
thresholds
forE
cosystem
Pressuresare
listedinTable3-11inKennish
etal.2012.Th
erescalingequations
thataregeneratedfro
mthesethresholds
arelistedinTable3-4
inKennish
etal.2012.Notethatsincethe
EcosystemPressuresonlyreceiveRaw
Scores,
thescores
forthese
indicatorsrangefro
m0to
100.Th
isisbecausethereareonlytwo
indicatorsandthus
PCAisnotm
eaningfuland
weightings
arethus
notcalculated.Raw
Scores
forthese
indicatorsareaveraged
togetherto
createthePressureIndex.Maximum
and
minimum
nutrientloading
values
forrescaling
arelistedinTable3-2inKennish
etal.2012.As
described
inmoredetailbelow,E
cosystem
Pressurescoresarekeptseparatefro
mtheother
indicatorsused
intheIndexof
Eutrophication
toavoidconfoundingassessmentofcausal
indicatorsfro
mresponse
indicators.
Water Quality
Temperature(°C)
18°C
=50;22°C=38;26°C
=25;3
0°C=13
Welooked
foro
ptim
altemperaturesforseagrass
grow
thandphotosynthesis,m
inim
umoxygen
concentrations
required
Waterquality
thresholds
werealso
defin
edby
exam
iningthe
literatureandthroughanalysisof
Temperaturedataareonly
available
from
quarterly
insitu
observations
form
anyyears.
effectsof
increasedtemperatureon
seagrass
condition
(including
density,percentcover,biom
ass)
Borjaetal.2004,Lee
etal.2007,
Wazniak
etal.2007,Williamset
S204 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
physiologically
fora
varietyoffish,shellfish,
andinvertebratespecies,andnutrient
concentrations
thatspur
phytoplanktonand
macroalgalg
rowth
(Table3-3in
Kennish
etal.2012).K
empetal.(2004)liststatistically
deriv
edconcentrations
ofdissolved
inorganicnitro
gen(D
IN)anddissolved
inorganicphosphorus
(DIP)beyond
which
subm
ergedaquatic
vegetationisnotp
resent
atavariety
ofsalinity
regimes
(Table3-4in
Kennish
etal.2012).A
roughguidelinehas
been
oneforChincoteagueBay,w
hich
isa
shallow,w
ell-m
ixed
coastallagoon
ecosystem,sim
ilarto
BB-LEH.W
azniak
etal.(2007)summarized
pertinentthresholds
regardingdissolvedoxygen,and
fortotal
nitro
gen,totalphosphorus,andchlorophylla
(Table3-9in
Kennish
etal.2012)
for
Maryland’scoastalb
ays.Optim
altemperaturesforg
rowth
andphotosynthesis
ofseagrass(Lee
etal.2007)
guided
determ
inationof
temperaturethresholds
(Table3-10).Fo
rBB-LEH,dissolved
oxygen
thresholds
weredefin
edrelativeto
theNew
Jersey
standard
ofim
pairm
ent,
which
isestablishedat4mgL-1.D
eviations
from
optim
altemperatureswereconsidered
forthresholdvalues.Tem
peraturefrom
April
toOctober(in
clusive)was
considered
with
respecttothesevalues
fordeterm
ining
thresholds.Ingeneral,seagrass
haspeak
abovegroundbiom
assdurin
gsummer
monthsandminim
alabovegroundbiom
ass
durin
gwintermonths(see
Com
ponent
2in
Kennish
etal.2012).L
eeetal.(2007)report
theoptim
altemperatureforeelgrass
grow
this15.3±1.6°C
andtheoptim
altemperature
foreelgrassphotosynthesisis23.3±1.8°C
(Table3-10
inKennish
etal.2012).
Temperaturesabove30
°Cstress
eelgrass
though
even
prolongedexposureto
26°C
canalso
induce
physiologicalstress
(Burkholderetal.2007,Lee
etal.2007).In
additionto
physiologicalstressof
seagrass
reportedintheliterature,analysisof
theBB-
LEHdatabase
revealed
severalrelationships
with
temperature.T
herewas
greater
variabilityof
chlorophylla
concentrations
above15
°C(Figure3-23
inKennish
etal.
2012).To
talsuspended
solidsandSecchi
depthwereinverselyrelatedto
temperature
(Figure3-2
3in
Kennish
etal.2012).T
here
was
anapparent
inflectionpointo
fmacroalgaepercentcoverat~12°C
(Figure3
-23in
Kennish
etal.2012).S
eagrassshoot
dataassembled
inthisproject.
Specifically,w
elooked
for
optim
altemperaturesfor
seagrassgrow
thand
photosynthesis,m
inim
umoxygen
concentrations
required
physiologically
fora
variety
offish,shellfish,and
invertebrate
species,andnutrient
concentrations
thatspur
phytoplanktonandmacroalgal
grow
th(Table3-3in
Kennish
etal.2012).K
empetal.(2004)list
statistically
deriv
edconcentrations
ofdissolved
inorganicnitro
gen(D
IN)a
nddissolvedinorganicphosphorus
(DIP)beyond
which
subm
erged
aquatic
vegetationisnotp
resent
atavariety
ofsalinity
regimes
(Table3-4in
Kennish
etal.
2012).Aroughguidelinehas
been
oneforChincoteagueBay,
which
isashallow,w
ell-m
ixed
coastallagoonecosystem,
similartoBB-LEH.W
azniak
etal.(2007)sum
marized
pertinent
thresholds
regardingdissolved
oxygen
(Table3-5in
Kennish
etal.2012),and
fortotal
nitro
gen,totalp
hosphorus,and
chlorophylla
(Table3-6in
Kennish
etal.2012)
for
Maryland’scoastalb
ays.
Optim
altemperaturesforgrow
thandphotosynthesisof
seagrass
(Lee
etal.2007)
guided
determ
inationof
temperature
thresholds
(Table3-7in
Kennish
etal.2012).F
orBB-
LEH,dissolved
oxygen
thresholds
weredefin
edrelative
totheNew
Jersey
standard
ofim
pairment,which
isestablished
at4mgL-1.
Light
availabilityiscriticalto
maintainathigh
levelsfor
shallowcoastallagoon
ecosystemsin
orderto
maintain
healthydominance
ofbenthic
communities
(Figure3-18
inKennish
etal.2012).Indeed,
Burkholder(2001)
foundthat
light
reductionhadagreater
negativeeffecton
seagrassshoot
Thisfrequency
ofdatacollection
isnotsufficienttocapture
naturald
aily
fluctuations.
Furth
er,thisdatacollection
frequencyintro
ducesbias
with
theconfoundingwith
sunlight
irradiance.Continuous
monitorin
g(observations
recorded
at15
minuteintervals)
would
bettercharacterize
temperature;h
owever,such
measurementsareoftenonly
abletobe
madeinshallowwater
alongshorelines
dueto
capacity
forsondedeployments,and
sosuch
observations
wouldneed
tobe
reconciledwith
observations
atdepthorinopen
waterareaso
ftheestuary.
al.2009,,dataanalysisby
this
study
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S205
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
density
hadan
apparentinflectionpointat
~12°C
(Figure3-24inKennish
etal.2012).
productionthan
didincreased
nitro
genavailability(Figure3-
19inKennish
etal.2012).L
ight
availabilitythresholds
are
determ
ined
from
theliterature
associated
with
physiological
requirementsof
seagrass
(Dennisonetal.1993,Figure3-
20in
Kennish
etal.2012)
and
associated
light
attenuationby
vario
usfactorssuch
asplankton
(chlorophylla),totalsuspended
solids,macroalgae(K
ennish
etal.2011,Table3-9in
Kennish
etal.2012),and
epiphytic
cover
(Brush
andNixon,2002;
Figure
3-21
inKennish
etal.2012,
Figure3-22
inKennish
etal.
2012),as
wellasmeasuresof
waterclarity
such
asSecchi
depthandthepercento
fsurface
irradianceavailableto
seagrass
leaves.L
ight
availability(%
oflight
availableto
seagrass
leaves,’PL
L’)isim
portantanda
potentially
bettermeasurement
than
Secchi
depthbecauselight
oftenpenetratestothebottomof
BB-LEHsuch
thatSecchi
disks
canbe
seen
atthebottom,
renderingSecchi
depthreadings
inaccuratewhilealso
not
providingagood
measurement
ofhowmuchlight
isactually
available.PL
Liscalculated
accordingto
equations
deriv
edfrom
empiricalobservations
describ
edby
Kem
petal.2004
show
nin
Appendix3-1in
Kennish
etal.2012.Additional
analysison
availabledata
indicatesthatseagrassindicators
respondednegativelyto
increasesin
chlorophylla
(Figure3-23
inKennish
etal.
2012)and
totalsuspended
solids
(Figure3-24
inKennish
etal.
2012).
Dissolved
Oxygen
(mgL-1)
10mgL-1
=50;9
mgL-1
=38;7
.5mgL-1
=25;
4mgL-1
=13
Dissolved
oxygen
isaphysiologicalrequirement
forfish,shellfish,and
otherinvertebrates.A
sdissolvedoxygen
concentrations
reach
hypoxicandanoxicconditions,lethality
increases(Figure3-25
inKennish
etal.
2012)andbenthiccommunities
become
stressed,decreasingbiom
assanddiversity
(Figure3-2
6inKennish
etal.2012,Table3
-7in
Kennish
etal.2012,Ritterand
Montagna1999)Weexam
ined
theliterature
andtheBB-LEHdatabase
forphysiological
stress
andlethalminim
umoxygen
concentrations
(Breitburg2002,D
iazand
Solow1999).Wazniak
etal.(2007)report
cutoffvalues
ford
issolved
oxygen
(Table3-
8inKennish
etal.2012)as<3mgL-1‘D
oes
notm
eeto
bjectives’,3-5mgL-1
‘Com
munity
threatened’,5-6mgL-1
‘Borderline’,>6mgL-1
‘Meetsobjectives’,
and>7mgL-1
‘Betterthan
objectives’.
Breitburg(2002)
reports
seasonalpatternsof
dissolvedoxygen
inthebottom
layero
fa
seasonally
stratifiedtemperateestuarythat
hasundergonesubstantiald
egradationand
experiences
seasonalhypoxia(Figure3-27
inKennish
etal.2012).W
hennotseasonally
stressed
(i.e.in
winterm
onths),dissolved
oxygen
concentrations
canreach~10to
14mgL-1
inthebottom
layer.Due
toits
shallowdepthandthorough
mixing,BB-
LEHdoes
notstratifyseasonallyandismore
similarto
thesurfacelayerof
stratified
estuariesandso
dissolvedconcentrations
inBB-LEHshould
exceed
thoseof
bottom
layersof
stratifiedestuaries.Thresholdsfor
dissolvedoxygen
inBB-LEHconsidered
the
aboveliteratureinform
ation,theNew
Jersey
standard
ofim
pairm
entthatiscurrently
establishedat4mgL-1,and
analysisof
the
assembled
database.Y
etlim
itations
ofthe
dissolvedoxygen
monitorin
gprogram
noted
abovein
previous
sections
createa
system
aticbias
thattendsto
misslow
nighttimeconcentrations.T
hese
differences,
inconjunctionwith
acomparison
ofthe
prim
aryproductionin
BB-LEHto
thatof
similarcoastallagoons
(Fertig
etal.2009,
2013a,Kennish
andFertig2012)
necessitatedadjustingthedissolvedoxygen
thresholds
upwards
from
theliteraturevalues
inaccordance
with
values
ofdissolved
oxygen
observed
inBB-LEH.C
hlorophylla
concentrations
wereinverselyrelatedto
dissolvedoxygen
concentrations,but
total
Dissolved
oxygen
dataareonly
availablefrom
quarterly
insitu
observations
formanyyears.
Thisfrequency
ofdatacollection
isnotsufficient
tocapture
naturald
aily
fluctuatio
nsdueto
processessuch
asphotosynthesisandrespiration.
Further,thisdatacollection
frequencyintro
ducesbias
with
theconfoundingof
temperature
andsunlight
irradiance.
Continuous
monito
ring
(observations
recorded
at15
minuteintervals)would
better
characterizedissolvedoxygen;
however,suchmeasurements
areoftenonlyabletobe
madein
shallowwateralongshorelines
dueto
capacity
forsonde
deployments,and
sosuch
observations
would
need
tobe
reconciledwith
observations
atdepthor
inopen
waterareasof
theestuary.
effectof
dissolvedoxygen
concentrationon
stress
and
survivalof
aquatic
fauna
includingfish,benthic
invertebrates
(including
shellfish)
Brickeretal.1999,2007Wazniak
etal.2007,Williamsetal.2009,
How
elland
Simpson
1994,
Boynton
etal.1996,Diazand
Solow1999,B
reitburg2002,
Breitburgetal.2001,Breitburg
2002,K
iddonetal.2003,Borja
etal.2004,dataanalysisby
this
study
S206 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
suspendedsolids,Secchi
depth,macroalgae
percentcover,and
epiphyteto
seagrass
biom
assratio
wereallcorrelatedpositively
with
dissolvedoxygen
(Figure3-2
9in
Kennish
etal.2012,Figure3-3
1inKennish
etal.2012).
TotalN
itrogen
Concentra-
tion(ugL-1)
135TNug
L-1
=50;1
75TNug
L-1
=38;2
50TNug
L-1
=25;4
00TNug
L-1
=13
Elevatednutrientconcentrations
spur
phytoplanktonandmacroalgalgrowth
and
degradeseagrass
(Burkholderetal.2007).
Kem
petal.(2004)documentstatistically
deriv
edconcentrations
ofdissolved
inorganicnitro
gen(D
IN)anddissolved
inorganicphosphorus
(DIP)beyond
which
subm
ergedaquatic
vegetationisnotp
resent
(<0.15
mgL-1
and<0.01
mgL-1,
respectively,which
equatesto
<150μgL-1
DIN
and<10
μgL-1
DIP)in
mesohaline
regions(Table3-6in
Kennish
etal.2012).
Kem
petal.(2004)n
otethatthesethresholds
aretobe
appliedtomedianvaluesofrawdata
collected
durin
gthegrow
ingseason
(April-
October,inclusive).Fu
rther,K
empetal.
show
thelogarithm
icrelationshipbetween
increasing
TotalD
INconcentrationand
increasing
epiphytebiom
assunderavariety
ofdimensionless
opticaldepthregimes,
whereopticaldepth=Kd*Z=the
attenuationcoefficient*depth(Figure3-4
4in
Kennish
etal.2012).Inflectionpointsfor
theserelatio
nships
rangefrom
10μM
Total
DIN
(equivalentto140μgL-1
totalD
IN)
whereopticaldepthisgreatest(i.e.clearer
water)to
30μM
TotalD
IN(equivalentto
420μgL-1
totalD
IN)inmoreopaque
water
(Figure3-44
inKennish
etal.2012).
Dissolved
inorganicnitrogen,how
ever,only
comprises
asm
allfractionof
thetotal
nitro
genin
thewatercolumnthatcanbe
bioavailable,undergouptake
andrecycling
viathemicrobialloop
andfood
webs,and
thus
thresholds
fortotaln
itrogen
concentrationin
BB-LEHmustaccount
for
this.W
azniak
etal.(2007)reportcutoff
values
fortotalnitro
genandtotalphosphorus
concentrations
used
forMaryland’sCoastal
Bays(Table3-9in
Kennish
etal.2012)
asfollows(inmgL-1):To
talN
itrogen
<0.55
mgL-1,<
0.64
mgL-1,0.65–1.0mgL-1,
1.0–2.0mgL-1,>
2.0mgL-1
(thisis
equivalent
to<550μgL-1,<
640μgL-1,
650–1000
μgL-1,1000-2
000μgL-1,and
>2000
μgL-1)andTo
talP
hosphorus<
0.025mgL-1,<
0.037mgL-1,0.038
–0.043
mgL-1,0.044
–0.100mgL-1,and
>0.100
mgL-1
(thisisequivalent
to<25
μgL-1,<
37μgL-1,38-4
3μgL-1,44-1
00μgL-1,
Nutrient
concentrations
weremade
quarterlyin
theearlieryearsof
theprojectstudy
perio
d(1989-
2010)and
thus
confidence
ofthe
assessmentislowerdurin
gthese
earlieryearswith
morelim
ited
data.C
ollectionfrequency
increasedovertim
eandthus
confidence
increasesas
wellfor
lateryearsof
thestudyperio
d.Thresholdsandrescaling
equations
have
been
calibrated
forB
B-LEHas
acoastallagoon.
How
ever,w
hiletheremay
beapplicability
ofthesethresholds
toothersimilarcoastallagoons
inNew
Jersey
orelsewhere
(suchas
GreatSo
uthBay,N
Y,
ChincoteagueBay,M
D/VA,
Hog
Island
Bay,V
A,etc.),
the
thresholds
establishedmay
beof
limitedutility
forotherNew
Jersey
waters(e.g.R
aritanBay,
NY/NJHarbor,andDelaw
are
Bay)thatdonotshareim
portant
characteristics.BB-LEHisin
partextremelysusceptibleto
even
smallamountsof
nutrient
loadingdueto
itsenclosed
geom
orphologyandslow
water
circulationandflushing
time.In
contrast,coastalwatersalong
the
AtlanticCoast,R
aritanBay,and
NY/NJHarbor,andDelaw
are
Bay
have
muchquickerand
strongercirculationpatternsand
thereforerespondto
nutrient
enrichm
entatd
ifferenttim
escales.A
dditionally,w
hile
heavymetals,inorganic,and
organictoxicantsmay
beim
portant
considerations
for
ecologicalhealth
insomeNew
Jersey
waters,they
may
beof
lowerpriorityforBB-LEH.
Toxicologicalanalysisof
sedimentsandthewatercolumn
arebeyond
thescopeof
this
projectand
have
notb
een
included
intheIndexof
bioavailabilityandnutrient
recyclingresulting
inincreases
infrequencyandintensity
ofmacroalgalbloom
swhich
isan
indicatoro
feutro
phication,as
wellasotherprim
aryproducers
inclduingmicroalgae(as
indicatedby
chlorophylla
concentration)
Brickeretal.1999,2007,
Burkholderetal.2007
Kiddonet
al.2003,Kem
petal.2004,
Wazniak
etal.2007,Boynton
etal.1996,Duarte
1995,
Stevensonetal1993,K
ennish
andFertig2012,K
iddonetal.
2003,B
orjaetal.2004,
Williamsetal.2009,data
analysisby
thisstudy
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S207
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
and>100μgL-1).Analysisof
the
assembled
database
revealed
thatin
BB-
LEH,seagrassbiom
ass(bothaboveground
andbelowground)decreasedmarkedlyat
totalnitrogen
concentrations
greaterthan400
μgL-1
(Figure2-1
4inKennish
etal.2012,
Fertigetal.2013a).How
ever,sum
mertim
echlorophylla
inMaryland’sC
oastalBaysh
ashistoricallyandrecently
been
measuredat>
40μgL-1
(Boynton
etal.1996,Fertigetal.
2013a),w
hich
is~5tim
eshigher
concentrationthan
the<8μgL-1
observed
inBB-LEHsince2004
(Fertig
etal.2013a)
andarealcoverageof
seagrass
isroughly
twiceas
largein
ChincoteagueBay
(Orth
etal.2006)
than
itisinBB-LEH(Lathrop
etal.
2001).
Eutrophicationor
itscomponent
indices.
Total
Phosphorus
Concentration
(ugL-1 )
10TPu
gL-1=50;13TPug
L-1
=38;2
2TPu
gL-1
=25;40TPug
L-1
=13
Elevatednutrientconcentrations
spur
phytoplanktonandmacroalgalg
rowth
and
degradeseagrass
(Burkholderetal.2007).
Kem
petal.(2004)documentstatistically
deriv
edconcentratio
nsof
dissolved
inorganicnitro
gen(D
IN)anddissolved
inorganicphosphorus
(DIP)beyond
which
subm
ergedaquatic
vegetationisnotp
resent
(<0.15
mgL-1
and<0.01
mgL-1,
respectively,which
equatesto
<150μgL-1
DIN
and<10
μgL-1
DIP)in
mesohaline
regions(Table3-6in
Kennish
etal.2012).
Kem
petal.(2004)n
otethatthesethresholds
aretobe
appliedtomedianvaluesofrawdata
collected
durin
gthegrow
ingseason
(April-
October,inclusive).Fu
rther,K
empetal.
show
thelogarithm
icrelationshipbetween
increasing
TotalD
INconcentrationand
increasing
epiphytebiom
assundera
variety
ofdimensionless
opticaldepthregimes,
whereopticaldepth=Kd*Z=the
attenuationcoefficient*depth(Figure3-4
4in
Kennish
etal.2012).Inflectionpointsfor
theserelationships
rangefrom
10μM
Total
DIN
(equivalentto140μgL-1
totalD
IN)
whereopticaldepthisgreatest(i.e.clearer
water)to
30μM
TotalD
IN(equivalentto
420μgL-1
totalD
IN)inmoreopaque
water
(Figure3-44
inKennish
etal.2012).
Dissolved
inorganicnitro
gen,however,only
comprises
asm
allfractionof
thetotal
nitro
genin
thewatercolumnthatcanbe
bioavailable,undergouptake
andrecycling
viathemicrobialloop
andfood
webs,and
thus
thresholds
fortotalnitrogen
concentrationin
BB-LEHmustaccount
for
this.W
azniak
etal.(2007)reportcutoff
valuesfortotalnitro
genandtotalphosphorus
concentrations
used
forMaryland’sCoastal
bioavailabilityandnutrient
recyclingresulting
inincreases
infrequencyandintensity
ofmacroalgalb
loom
swhich
isan
indicatorof
eutro
phication,as
wellasotherprim
aryproducers
inclduingmicroalgae(as
indicatedby
chlorophylla
concentration)
Burkholderetal.2007,B
rickeretal.
1999,K
iddonetal.2003,Kem
petal.2004,Wazniak
etal.2007,
Boynton
etal.1996,Stevenson
etal.1993,Kiddonetal.2003,
Borjaetal.2004,Duarte
1995,
Kennish
andFertig2012,
Williamsetal.2009,data
analysisby
thisstudy
S208 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
Bays(Table3-9in
Kennish
etal.2012)
asfollows(in
mgL-1):To
talN
itrogen
<0.55
mgL-1,<
0.64
mgL-1,0.65–1.0mgL-1,
1.0–2.0mgL-1,>
2.0mgL-1
(thisis
equivalent
to<550μgL-1,<
640μgL-1,
650–1000
μgL-1,1000-2
000μgL-1,and
>2000
μgL-1)andTo
talP
hosphorus<
0.025mgL-1,<
0.037mgL-1,0.038
–0.043
mgL-1,0.044
–0.100mgL-1,and
>0.100
mgL-1
(thisisequivalentto
<25
μgL-1,<
37μgL-1,38-4
3μgL-1,44-1
00μgL-1,
and>100μgL-1).Analysisof
the
assembled
database
revealed
thatin
BB-
LEH,seagrassbiom
ass(bothaboveground
andbelowground)decreasedmarkedlyat
totalnitrogen
concentrations
greaterthan400
μgL-1
(Figure2-1
4inKennish
etal.2012,
Fertigetal.2013a).How
ever,sum
mertim
echlorophylla
inMaryland’sC
oastalBaysh
ashistoricallyandrecently
been
measuredat>
40μgL-1
(Boynton
etal.1996,Fertigetal.
2013a),w
hich
is~5tim
eshigher
concentrationthan
the<8μgL-1
observed
inBB-LEHsince2004
(Fertig
etal.2013a)
andarealcoverageof
seagrass
isroughly
twiceas
largein
ChincoteagueBay
(Orth
etal.2006)
than
itisinBB-LEH(Lathrop
etal.
2001).
Light
Availability
TotalS
uspended
Solids
(mgL-1)
10mgL-1=50;12.5mgL-
1=38;1
5mgL-1
=25;
17.5mgL-1
=13
Kem
petal.2004,Brickeretal.1999,2007
Stevensonetal.1993,Lee
etal.2007,
Wazniak
etal.2007,Ralph
etal.2007,
Williamsetal.2009,Brush
andNixon
2002,
dataanalysisby
thisstudy
samplingfrequencyandlocationis
limited
degraded
conditionsof
seagrassand
otherb
enthicprim
aryproducers,
smotherin
gof
benthicfauna
(especially
sessile
species
includingfilterfeeders)
Kem
petal.2004,Brickeretal.
1999,2007Stevensonetal.
1993,L
eeetal.2007,Wazniak
etal.2007,Ralph
etal.2007,
Williamsetal.2009,Brush
and
Nixon
2002,dataanalysisby
thisstudy
Chlorophylla
(ugL-1)
2.5ugL-1
=50;3
ugL-1
=38;4
ugL-1
=25;6
ugL-1=1
3
Kem
petal.2004,Burkholderetal.2007,
Wazniak
etal.2007,Boynton
etal.1996,
Brickeretal.1999,2007,K
iddonetal.2003,
Stevensonetal.1993,Borjaetal.2004,Lee
etal.2007,Ralph
etal.2007,Duarte
1995,
Williamsetal.2009,Brush
andNixon
2002,
dataanalysisby
thisstudy
samplingfrequencyislim
ited
reducedlight
conditionsforbenthic
prim
aryproducersincluding
seagrass
andbenthicmicroalgae
insediem
nts.Discolorationof
water.D
ecreased
dissolved
oxygen
follo
wing
decompositionof
microalgal
detrituswhich
followscrashof
algalbloom
s
Kem
petal.2004,Burkholderetal.
2007,W
azniak
etal.2007,
Boynton
etal.1996,Brickeret
al.1999,2007,K
iddonetal.
2003,S
tevenson
etal.1993,
Borjaetal.2004,Lee
etal.2007,
Ralph
etal.2007,Duarte
1995,
Williamsetal.2009,Brush
and
Nixon
2002,dataanalysisby
thisstudy
Macroalgaeareal
cover
(%cover)
3%=50;5
%=38;8
%=
25;1
4%=13
Kennish
etal.2011,Lee
etal.2007,Williamset
al.2009,Brush
andNixon
2002,data
analysisby
thisstudy
Macroalgaeandseagrass
dataare
notavailablepriorto
2004,
creatingsomeuncertainty
regarding‘reference’or
‘pristine’conditionsof
seagrass
inBB-LEH,thoughthesecanbe
estim
ated
basedon
empirical
relationships
describ
edin
the
literatureforothersimilartypes
ofcoastallagoonestuaries.
shadingandlight
reductionfor
benthicprim
aryproducers
inclusingseagrass
andbenthci
microalgae.Sm
otherin
gof
benthicinvertebrates.N
oxious
odorsupon
decomposition
resulting
from
macroalgal
bloom
populationcrash
Kennish
etal.2011,Lee
etal.2007
Williamsetal.2009,Brush
and
Nixon
2002,dataanalysisby
thisstudy
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S209
Table2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
Epiphyteto
seagrass
ratio
(gdrywt
epiphytesper
gdrywt
seagrass)
0.25gdrywtepiphytes
per
gdrywtseagrass=50;
0.50
gdrywtepiphytes
perg
drywtseagrass=
38;1
.0gdrywt
epiphytesperg
drywt
seagrass
=25;1
.5gdry
wtepiphytes
perg
dry
wtseagrass=13
Brush
andNixon
2002,K
empetal2004,L
eeet
al.2007,dataanalysisby
thisstudy
Epiphyticdatahave
been
calculated
basedon
empiricalobservations
andstatisticalrelationships
with
otheravailableobservations,and
though
thereisvery
good
agreem
entb
etweenvalidation
datasetsandthecalculations,
additionaly
earsof
measurementswould
strengthen
theconfidence
inthese
estim
ates.
shadinig
andlight
reductionto
seagrass.N
oxious
odorsupon
decomposition
Brush
andNixon
2002,K
empetal
2004,L
eeetal.2007,data
analysisby
thisstudy
Secchi
depth(m
)0.5m
=50;0.4m=38;0.3m
=25;0
.2m
=13
Dennisonetal.1993,Kem
petal.2004,
Burkholderetal.2007,Brickeretal.
1999,2007,Kiddonetal.2003,Stevensonet
al.1993,Boynton
etal.1996,Borjaetal.
2004,L
eeetal.2007,Ralph
etal.2007,
Wazniak
etal.2007,Williamsetal.2009,
Brush
andNixon
2002,dataanalysisby
this
study
Secchidepthmustbeconsidered
atype
of‘censoreddata’–
atechnicalstatisticalterm
defined
asdatathathave
cutoffpointsdueto
someexternalfactor
resulting
ina
discreteendpointon
oneendof
thedatadistribution.In
thiscase,
data‘censorship’
isduetothe
Secchidisk
hitting
thebottom,
which
thus
placesan
externallim
it(i.e.,w
aterdepth)totheupperend
oftheobservations
ofSecchi
depth.Given
thesameconditions
indeeperwater,the
recordings
(and
theirm
eans)forSecchidepth
may
have
been
ofgreater
magnitude.
decreasedlightavailabilty
tobenthic
popiulations
Dennisonetal.1993,Kem
petal.
2004,B
urkholder2
001,Bricker
etal.1999,2007,K
iddonetal.
2003,S
tevenson
etal.1993,
Boynton
etal.1996,Borjaetal.
2004,L
eeetal.2007,Ralph
etal.
2007,W
azniak
etal.2007,
Williamsetal.2009,Brush
and
Nixon
2002,dataanalysisby
this
study
PercentL
ight
Reaching
Seagrass
Leaves(%
)
32%=5
0;23%=3
8;19%=2
5;15%=1
3Kem
petal.2004,Burkholderetal.2007,
Dennisonetal.1993,Stevensonetal.1993,
Boynton
etal.1996,Lee
etal.2007,Ralph
etal.2007,Wazniak
etal.2007,Williamsetal.
2009,B
rush
andNixon
2002,dataanalysis
bythisstudy
calculated
estim
ates
basedon
availabledata
physiologicallight
requirem
ents
Kem
petal.2004,Burkholderetal.
2007,D
ennisonetal.1993,
Stevensonetal.1993,Boynton
etal.1996,Lee
etal.2007,
Ralph
etal2007,W
azniak
etal.
2007,W
illiamsetal.2009,
Brush
andNixon
2002,data
analysisby
thisstudy
Seagrass
Aboveground
Biomass(g
m-2)
400g
m-2=5
0;300g
m-
2=38;2
00gm-2=2
5;100g
m-2=1
3
Burkholderetal.2007,Kem
petal.2004,
Stevensonetal.1993,Kennish
etal.2011,
Kennish
andFertig2012,L
eeetal.2007,
Wazniak
etal.2007,Duarte
1995,W
illiams
etal.2009,Dennisonetal.1993,Lea
etal.
2003,dataanalysisby
thisstudy
Thresholds
forseagrassresponsewere
defined
throughdataanalysiswith
thisproject.Because
few
extensivedataexistonseagrassin
BB-LEH
priorto2004,itis
difficulttoestablishstable
referenceconditionsforthis
estuary.Asdiscussedin
Com
ponent2inKennish
etal.
2012,eelgrassbiom
asshasbeen
ingeneraldeclinesince
monitoringcommencedin2004.
Datawereanalyzed
toidentifyif
changesinratesof
declinewere
evidentw
ithrespecttototal
nitrogenloading(Figure13
-16in
Kennish
etal.2012),to
chlorophylla
(Figure3-2
3in
Kennish
etal.2012),and
total
suspendedsolids(Figure3-24in
Eelgrassdatado
notstartuntil
2004
anddo
notextendfarback
enough
tocaptureinform
ation
durin
gsteady
state,andthus
referenceconditionsfor
BarnegatB
ay-LittleEgg
Harbor
aredifficulttoestim
ate.
Com
parisons
tohistorical
coverage
have
been
madefor
datacollected
durin
gthestudy
perio
d(1989-2010).Sp
atially,
eelgrass
arenotd
ominantinthe
north
segm
ento
fBB-LEHdue
tophysiologicalsalinity
requirements,although
they
have
been
observed
indiscrete
patchy
areasthatarenot
necessarily
locatedalong
establishedtransects.T
hecomparison
towidgeongrass
populationdemographics,biom
ass
declines
Burkholderetal.2007,K
empetal.
2004,Stevenson
etal.1993,
Kennish
etal.2011,Kennish
and
Fertig2012,L
eeetal.2007,
Wazniak
etal.2007,Duarte
1995,
Williamsetal.2009,Dennisonet
al.1993,Leaetal. 2003,data
analysisby
thisstudy
Below
ground
Biomass
(gm
-2)
800g
m-2=5
0;600g
m-
2=38;4
00gm-2=2
5;200g
m-2=1
3
Burkholderetal.2007,Kem
petal.2004,
Stevensonetal.1993,Duarte
1995,K
ennish
andFertig2012,L
eeetal.2007,Wazniak
etal.2007,Dennisonetal.1993,Lea
etal.
2003,dataanalysisby
thisstudy
populationdemographics,biom
ass
declines
Burkholderetal.2007,K
empetal.
2004,S
tevenson
etal.1993,
Duarte
1995,K
ennish
andFertig
2012,L
eeetal.2007,Wazniak
etal.2007,Dennison1993,L
eaetal.2003,dataanalysisby
this
study
AreaCover(%
)50%=5
0;25%=3
8;10%=2
5;5%
=13
Lee
etal.2007,Burkholderetal.2007,K
empet
al.2004,Valielaetal2000,D
uarte
1995,
Deegan2002,Stevenson
etal1993,K
ennish
andFertig2012,W
azniak
etal.2007,
populationdemographics,coverage
ofseagrass
Lee
etal.2007,Burkholderetal.
2007,K
empetal.2004,Valiela
etal2000,D
uarte
1995,D
eegan
2002,S
tevenson
etal1993,
S210 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
The final index was computed annually by summing the rawand weighted component scores to account for both the condition(mean) and consistency (variability). Thus, the final index rangesfrom 0 (degraded) to 100 (excellent) for each component. Indicesfor the components were then averaged to calculate theEutrophication Index, which thus also ranges from 0 (degraded)to 100 (excellent). All analyses were performed using SAS.
Assembling the Database
Existing and generated datasets were assembled together intoSAS libraries (SAS Inc., Table 1; Electronic SupplementaryMaterial (ESM)). Variables were selected for analysis of po-tential ecosystem impacts of nutrient loading. Nutrient loadingwas modeled by the U.S. Geological Survey generating out-puts for 1989–2011 in the three segments of the catchment, asa collaborating project (Baker et al. 2013). Water quality,chlorophyll a , total suspended solids, and Secchi depth datawere obtained from the New Jersey Department ofEnvironmental Protection (http://www.nj.gov/dep/bmw). Theratio of epiphyte to seagrass biomass is available asmeasurements throughout central and south BB–LEH from2009 to 2011 and is estimated backwards to 1997 based onempirical relationships (Kemp et al. 2004). Percent lightavailable to seagrass leaves are estimated according to empir-ical relationships (Kemp et al. 2004) from 1997 to 2011 data.Macroalgae and seagrass data were assembled from previous-ly collected datasets (Fertig et al. 2013a, b).
Analyzing Sensitivity of the Index of Eutrophication
Sensitivity to (a) the length of time over which variability ismeasured and (b) availability of indicators for a given segmentin a given year was assessed. Note that PCA—used forcalculating weightings—cannot handle missing data. Also,the power of the index increases with the size of the referencedataset. Two scenarios were considered: (1) annual weightingand annual assessment and (2) multiyear weighting and annu-al assessment. The two scenarios test sensitivity for waterquality because most data were available for that component(except 1992 for temperature, dissolved oxygen, and totalnitrogen and except for 1999–2010 for total phosphorus).Raw, weighted, and index scores for each scenario are plottedagainst each other and the slope was tested for difference from1.0. This enables examining a component’s response to theinclusion/omission of an indicator. Eigenvectors give higherweighting for higher variability. Variability of unavailable datais null and thus the weighting is defined as 0 %.
To assess sensitivity under scenario 1, eigenvectors andweightings are calculated for each metric for each year. Forscenario 2, eigenvectors and weightings are calculated in twosets: 1989–1998 (except 1992) and 1999–2010. These sets ofyears were determined by availability of total phosphorusTa
ble2
(contin
ued)
Water
component
Threshold
indicator
Threshold
values
Threshold
valuedeterm
ined
by:
Summaryof
methods
used
todeterm
inethreshold
Threshold
limitations
Biotic
responsesshow
ninBarnegat
Bay
todeterm
inethresholdvalues
References
Kennish
etal.2012).H
owever,
declines
hadbegunpriorto
monitoringandso
assessments
wereadjusted
giventhe
uncertaintyassociated
with
identifying
‘reference’conditions
ofseagrassinBB-LEH
.
(Ruppiamaritima)ultim
ately
needsmoretim
eforthis
secondaryspecieswith
lower
salinity
requirementsto
beutilizedas
acomparable
biologicalindicatordueto
physiologicald
ifferences
betweenthesetwospecies.
Williamsetal.2009,Dennisonetal.1993,
Lea
etal.2003,dataanalysisby
thisstudy
Kennish
andFertig2012,
Wazniak
etal.2007,Williamset
al.2009,Dennisonetal.1993,
Lea
etal.2003,dataanalysisby
thisstudy
ShootD
ensity
(shootsm-2)
1910
shootsm-2=5
0;1146shootsm-2=3
8;764shootsm-2=2
5;382shootsm-2=1
3
Burkholderetal.2007,L
eeetal.2007Kem
pet
al.2004,Kennish
andFertig2012,W
azniak
etal.2007,Duarte
1995,L
eaetal.2003,
Williamsetal.2009,Dennisonetal.1993,
dataanalysisby
thisstudy
populationdemographics,shoot
density
ofseagrrass
Burkholderetal.2007,Lee
etal.
2007
Kem
petal.2004,Kennish
andFertig2012,W
azniak
etal.
2007,D
uarte
1995,L
eaetal.
2003,W
illiamsetal.2009,
Dennisonetal.1993,data
analysisby
thisstudy
Blade
Length
(cm)
80cm
=50;6
0cm=3
8;40cm
=25;
20cm
=13
Burkholderetal.2007,K
ennish
andFertig2012,
Lee
etal.2007,Wazniak
etal.2007,Duarte
1995,L
eaetal.2003,Dennisonetal.1993,
Williamsetal.2009,,dataanalysisby
this
study
populationdemographics,length
ofseagrass
Burkholderetal.2007,K
ennish
and
Fertig2012,L
eeetal.2007,
Wazniak
etal.2007,Duarte
1995,L
eaetal.2003,Dennison
etal.1993,Williamsetal.2009,,
dataanalysisby
thisstudy
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S211
data. Effectively, the weighting for all metrics of water qualityin 1992 is 0. Note that in both scenarios, indicators receivemultiple weightings over the course of the entire study period(1989–2010) due to data availability.
Results
Summary Data Available as ESM
Annual means (or medians), 1989–2011, for each indicator ineach of the three spatial segments used for calculating index
values are available online as ESM. Outputs of nutrient load-ing models are available in a U.S. Geological Survey Report(Baker et al. 2013).
Sensitivity Analysis
Although weighted scores for each water quality indicator (tem-perature, dissolved oxygen, total nitrogen, and total phosphorus)differed between the annual weighting vs. multiyear weighting(slope≠1.00, p<0.05; Fig. 2a–d), the weighted scores for theWater Quality Index did not differ (slope=0.89, p=0.4; Fig. 2e)between these two scenarios. The range of the weighted scores
Table 3 Equations used to rescale mean annual indicator data into unitless unweighted scores for each component of the Eutrophication Index
Component Metric Units Rescalingequation
Maximumscore
Minimumscore
Reference
Pressures Total nitrogenloading
kg TN estuarykm−2 year−1
y=−19*ln(x)+177.52 x ≤50 x ≥10,000 Boynton et al. (1996),Short and Burdick (1996),Tomasko et al. (1996),Valiela et al. (2000),Deegan et al. (2002),Burkholder et al. (2007),Kennish and Fertig (2012),this study
Pressures Total phosphorusloading
kg TP estuarykm−2 year−1
y=−32.81*ln(x)+204.01 x ≤25 x ≥500
Water quality Temperature °C y=−3.125*x+106.25 x ≤18 x ≥34 Stevenson et al. (1993),Howell and Simpson (1994),Boynton et al. (1996),Bricker et al. (1999),Diaz and Solow (1999),Breitburg et al. (2001, 2002),Kiddon et al. (2003),Borja et al. (2004),Kemp et al. (2004),Burkholder et al. (2007),Kennish and Fertig (2012),Lee et al. (2007),Wazniak et al. (2007),Williams et al. (2009),this study
Water quality Dissolved oxygen mg L−1 y=4.8641*e0.228*x x ≥10 x ≤4Water quality Total nitrogen μg L−1 y=26,721*x−1.274 x ≤135 x ≥750Water quality Total phosphorus μg L−1 y=475.95*x−0.977 x ≤10 x ≥45
Light availability % surface irradianceavailable
% y=50.084*ln(x)−122.18 x ≥32 x ≤7.818 Dennison et al. (1993),Burkholder (2001),Brush and Nixon (2002),Kemp et al. (2004),Burkholder et al. (2007),Lee et al. (2007),Ralph et al. (2007),Kennish et al. (2011),this study
Light availability Chlorophyll a μg L−1 y=−41.67*ln(x)+85.351 x ≤2.5 x ≤100Light availability Total suspended solids mg L−1 y=−5*x +100 x ≤10 x ≥20Light availability Secchi depth cm y=0.125*x −12.5 x ≥500 x ≤100Light availability Macroalgae
percent cover% y=−24.52*ln(x)+76.782 x ≤3 x ≥20
Light availability Epiphyte/seagrassbiomass
g epiphyte/gseagrass
y=−20.32*ln(x)+22.744 x ≤2.5 x ≥2.0
Seagrass response Abovegroundbiomass
g m−2 y=0.125*x x ≥400 x ≤0 Dennison et al. (1993),Duarte (1995),Valiela et al. (2000),Deegan et al. (2002),Lea et al. (2003),Kemp et al. (2004),Burkholder et al. (2007),Lee et al. (2007),Ralph et al. (2007),this study
Seagrass response Belowgroundbiomass
g m−2 y=0.0625*x x ≥800 x ≤0
Seagrass response Percent cover % x=15.925*ln(x)−12.713 x ≥50 x ≤0Seagrass response Shoot density shoots m−2 y=0.0243*x +5.7143 x ≥1910 x ≤0Seagrass response Blade length cm y=0.625*x x ≥80 x ≤0
S212 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Table 4 Mean ± standard deviation of raw scores and weighted scores for each indicator in each of the three segments of Barnegat Bay–Little Egg Harbor. Seagrass and macroalgae percent cover data wereunavailable in the North segment
Measurements Raw scores Weighted scores
Units North Central South North Central South North Central South
Water quality 1989–2010 Temperature °C 14.3±3.8 14.5±2.9 14.2±3.5 41±5 39±4 41±7 15±10 15±10 15±10
Dissolved oxygen mg L−1 7.9±1.0 7.9±1.3 7.7±1.5 26±4 25±5 24±8 5±3 5±3 4±3
Total nitrogen μg L−1 598±141 408±93 433±141 10±5 17±6 15±5 1±1 1±1 1±1
Total phosphorus μg L−1 27±6 32±14 44±15 18±5 18±8 15±7 12±3 12±5 10±5
Light availability 1998–2010 Chlorophyll a μg L−1 4.8±1.9 4.0±1.7 4.9±3.9 36±8 37±12 41±7 1±0 1±0 1±0
TSS mg L−1 12.1±3.7 19.5±18.0 19.4±5.1 43±5 33±15 37±10 14±2 10±5 12±3
Secchi depth 3.6±1.2 4.0±1.5 4.1±1.0 25±11 23±12 26±13 1±0 1±0 1±1
Macroalgae cover % No data 5.9±4.8 10.4±4.0 25±23 13±17 0±0 0±0
Epiphyte/seagrass g epiphyte g−1
seagrass0.9±0.2 0.7±0.4 1.0±0.6 32±9 26±15 31±13 9±3 8±4 9±4
% Light at seagrass % 12.6±4.1 19.5±4.6 15.7±11.5 16±13 18±14 23±16 5±4 6±4 7±5
Seagrass 2004–2010 Aboveground biomass g m−2 No data 18.9±10.4 21.7±23.2 2±1 2±3 0±0 0±0
Belowground biomass g m−2 No data 51.6±23.7 48.4±21.9 3±1 3±1 0±0 0±0
Shoot density shoots m−2 No data 318±121 276±161 8±2 6±2 0±0 0±0
Percent cover % No data 21.8±2.9 25.1±7.1 19±3 24±6 10±2 13±3
Blade length cm No data 25.2±7.5 21.7±10.0 9±3 9±6 3±1 3±2
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was greater for each indicator under scenario 1 (100 for totalnitrogen, 93 for dissolved oxygen, 87 for total phosphorus, and40 for temperature) than scenario 2 (92 for dissolved oxygen, 56for total phosphorus, 11 for temperature, 4 for total nitrogen).Weighted scores for total nitrogen and temperaturewere very lowunder scenario 2 (0–4 and 2–12, respectively; Fig. 2a–e).
Including or omitting an indicator (total phosphorus)changed the weighting for the other three water quality indi-cators during 1999–2010. When total phosphorus was includ-ed, temperature, dissolved oxygen, and total nitrogen wereweighted 0.15, 0.08, and 0.13, with the remaining 0.65 ofweighting for total phosphorus. When total phosphorus wasexcluded from the 1999 to 2010 data, temperature, dissolvedoxygen, and total nitrogen were weighted 0.34, 0.21, and0.45, respectively. If total phosphorus was excluded entirelyfrom the study period (1989–2010), the weightings were 0.61temperature, 0.29 dissolved oxygen, and 0.10 total nitrogen.
Indicator Scores and Weightings
Of the water quality indicators, total nitrogen had the lowestmean raw score and weighted score in all estuarine segments
(Table 4). Of the light availability indicators, the lowestmean rawscore was the percent of light reaching seagrass in the north andcentral segments and was the percent of macroalgae cover in thesouth segment (Table 4). The lowest mean weighted scores weremacroalgae percent cover in the central and south segments (0±0), chlorophyll a (1±0 in all segments), and Secchi depth (1±0in the north and central segments, and 1±1 in the south segment;Table 4). Aboveground biomass (central, 2±1; south, 2±3) andbelowground biomass (3±1 in central and south segments) hadthe lowest seagrass indicator raw scores while these and shootdensity has the lowest weighted scores (0±0 for each indicator inboth the central and south segments; Table 4).
Eigenvectors of water quality (Fig. 3a, b), light availability(Fig. 3c), and seagrass (Fig. 3d) indicators were plotted byeigenvectors calculated through PCA to determine the dominantcontributors to each component. During 1989–1999, the firstprincipal component correlated positively with temperature (r=0.42, p<0.05) and total nitrogen (r=0.54, p<0.01); the secondprincipal component correlated positively with dissolved oxygen(r=0.42, p<0.05) and total nitrogen (r=0.62, p<0.01; Fig. 3a).During 2000–2010, the first principal component for water qual-ity indicators correlated positively with temperature (r=0.83, p<0.01) and total phosphorus (r=0.57, p<0.01) but negativelywith dissolved oxygen (r=−0.70, p<0.01), while the secondprincipal component correlated positively with dissolved oxygen(r=0.38, p<0.05) and total nitrogen (r=0.47, p<0.01; Fig. 3b).The first principal component for the light availability indicators(1998–2010) correlated positively with the ratio of epiphyte toseagrass biomass (r=0.80, p<0.01) while the second principalcomponent positively correlated with total suspended solids(r =0.54, p <0.01) and chlorophyll a (r =0.62, p <0.01;Fig. 3c). The first principal component for seagrass indicatorspositively correlated with eelgrass aboveground and below-ground biomass (r=0.91, p<0.01 and r=0.93, p<0.01, respec-tively), blade length (r=0.73, p<0.05), and percent cover (r=0.81, p<0.01). The second principal component for seagrassindicators positively correlated with eelgrass shoot density (r=0.86, p<0.01; Fig. 3d).
During 1989–1999, the highest weighting of the waterquality indicators was temperature (0.66), but this changedto total phosphorus (0.64) during 2000–2010 (Table 5). Lightavailability indicators were nearly evenly weighted amongtotal suspended solids (0.32), percent light reaching seagrass(0.31), and the ratio of epiphytes to seagrass biomass (0.30)during 1998–2010 (Table 5). Eelgrass percent cover wasweighted 0.53, the highest of the seagrass indicators during2004–2010 (Table 5).
Barnegat Bay–Little Egg Harbor Estuary Is Degraded
Though raw scores of the Water Quality Index did not changeover time, weighted scores in all segments decreased over time,leading to significant declines over time in the final Water
Table 5 Weighting for each indicator in each component and for eachcomponent within the Eutrophication Index
Component Years Variable Weighting
Water quality 1989–1999 Temperature 0.66
Water quality 1989–1999 Dissolved oxygen 0.33
Water quality 1989–1999 Total nitrogen 0.02
Water quality 1989–1999 Total phosphorus 0.00
Water quality 2000–2010 Temperature 0.15
Water quality 2000–2010 Dissolved oxygen 0.08
Water quality 2000–2010 Total nitrogen 0.13
Water quality 2000–2010 Total phosphorus 0.64
Light availability 1998–2010 Chlorophyll a 0.02
Light availability 1998–2010 TSS 0.32
Light availability 1998–2010 Secchi depth 0.04
Light availability 1998–2010 Epiphyte/seagrass 0.30
Light availability 1998–2010 Macroalgae % cover 0.00
Light availability 1998–2010 % Light reaching seagrass 0.31
Seagrass 2004–2010 Aboveground biomass 0.08
Seagrass 2004–2010 Belowground biomass 0.02
Seagrass 2004–2010 Shoot density 0.01
Seagrass 2004–2010 Percent cover 0.53
Seagrass 2004–2010 Blade length 0.35
Eutrophication 1989–1997 Water quality 1.00
Eutrophication 1998–2003 Water quality 0.50
Eutrophication 1998–2003 Light availability 0.50
Eutrophication 2004–2010 Water quality 0.33
Eutrophication 2004–2010 Light availability 0.33
Eutrophication 2004–2010 Seagrass 0.33
S214 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Quality Index values for the north (p <0.01) and central seg-ments (p <0.05), but not in the south segment (Fig. 4a–c). Rawscores, weighted scores, and final Light Availability Indexvalues increased significantly (p <0.01) in the north segmentbut not the other two segments (Fig. 4d–f). Raw scores, weight-ed scores, and final Seagrass Index values did not change overtime in either central or south segments (Fig. 4g–i). None ofthese index values differed between segments.
Values of the Index of Eutrophication in the central andsouth segments significantly declined (p <0.01 and p <0.05,respectively) 34 and 36 %, respectively, over time, yet valuesincreased (p <0.01) over time in the north segment (Fig. 5a).The rates of change in Eutrophication Index values over timedid not differ between the central and south segments.
Nutrient loading to the catchment from the north segmentwas significantly higher than loading rates from the central orsouth segments of the catchment. Eutrophication Index valuesexhibited a significant logarithmic decline in values as nutrientloading from the catchment increased (Fig. 5b).
Discussion
Advantages, Limitations, and Comparisons of the Tool
The “Index of Eutrophication” described here (Tables 2 and 3;Figs. 3 and 4) builds upon the myriad of existing eutrophica-tion assessment tools and indices (Borja et al. 2004; Ferreira
Central
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Fig. 2 Sensitivity analysiscomparing annual weighting(scenario 1) on x-axis tomultiyearweighting on y-axis (scenario 2).The symbols represent weightedscores for a temperature, bdissolved oxygen, c totalnitrogen, d total phosphorus, ande the Water Quality Index in thenorth (gray square), central(white circle), and south (blacktriangle) segments of BarnegatBay–Little Egg Harbor. Statisticsrefer to the slope (m), F statistic(F), and p value (p) forcomparison of regression slope tothe 1:1 line (shown)
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S215
et al. 2011) by integrating multiple data types (Tables 1 and 2)to assess condition of water quality, light availability, andseagrass (Fig. 4) while addressing data gaps (Table 1) andidentifying drivers of ecosystem-level change (Table 5 andFig. 5). This index reduces the number of dimensions at theecosystem level similar to other uses of PCA to integratemultimetric analyses of indicators (Oliva et al. 2012).
An indicator’s “weighting” (Table 5) and “weighted score”(Fig. 4b, e, and h; Table 4) can be used to identify drivers ofchange in eutrophication condition. Combining a direct com-parison of indicators to thresholds along with the variabilityidentifies estuarine condition and its consistency. Utilizationof PCA to generate a weighting maintains the flexibility ofadding additional components or indicators, providedrescaling equations could be established based on thresholds.Weighted scores, particularly for total nutrient concentrations(Table 4), drove declines in Water Quality Index values overtime, since raw scores did not change in any segment (Fig. 4),consistent with the general conceptual model of eutrophica-tion (Cloern 2001). Identification of dominant drivers of con-dition in a particular system (e.g., temperature during 1989–1999 and total phosphorus during 2000–2010; Table 5) mayhelp rank stressors in order of importance and thus increaseefficiency of management efforts (Duarte 2009; Fertig et al.2013b). Future statistical studies should consider the relativeimpact of raw scores and weighted scores, and ascertain theweighting between the two that optimizes the accuracy ofthese rankings in controlled experimental systems with knownstressors.
Integrating multiple indicators representing components ofthe ecosystem, e.g., water quality, light availability, and seagrass(Borja et al. 2004) into multiple indices enables handling of datagaps through weighting each indicator (Table 5) yet limits thesensitivity of each index to variability in theweighting (Fig. 2) byusing the eigenvectors calculated from the multiyear scenario.Potentially, data availability could limit the practicality ofconducting assessments of Barnegat Bay–Little Egg Harborand many other data-poor estuaries, even though data quantitygenerally increases over time (Table 1) yet the quality of analysisis ultimately constrained by data quality (Kimmel et al. 2010).
Analyzing data with shared spatiotemporal resolutionsamong datasets required aggregation to the three segments(Fig. 1) at annual intervals (Table 3) to minimize data gaps(see ESM). Yet sampling intervals widely varied with respect toecological variability (e.g., quarterly in situ dissolved oxygenmeasurements, frequent depth limitation for Secchi disk read-ings, etc., http://www.nj.gov/dep/bmw/; (Fertig et al. 2013a)).Epiphytic data (see ESM) were estimated from empirical rela-tionships (Kemp et al. 2004) while historical macroalgae(Kennish et al. 2011) and seagrass (Fertig et al. 2013a) coverand biomass have undergone rate changes but not years ofstable “reference conditions”, and species-specific salinity tol-erance yields varying distributions of primary producers.
Consistency is important to include in an Index ofEutrophication because it highlights times and places whenand where conditions of each indicator are changing (eitherpositively or negatively) so that these indicators can be targetedfor attention (e.g., for monitoring, management, or research).
Water Quality 1999-2010
PC 1-1.0 -0.5 0.0 0.5 1.0
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LengthLength
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Below SecchiSecchi
BeBe
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TSSTSS
Ke
BdeBde
Ke
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Secchi
North SouthCentral
a b
c d
Fig. 3 Principle component (PC)analysis of indicators for eachcomponent for each multiyeardataset used: a water quality(1989–1998), b water quality(1999–2010), c light availabilityexcept macroalgae percent cover(1998–2010), d seagrass (2004–2006 and 2008–2010). Ttemperature, DO dissolvedoxygen, TN total nitrogen, TPtotal phosphorus, Chl chlorophylla , TSS total suspended solids, Keattenuation due to epiphytes, Beepiphyte biomass, Bde ratio ofepiphyte to seagrass biomass
S216 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
Calculating unique weightings for each segment would bestatistically inappropriate and would invalidate comparisonsacross segments (Sokal and Rohlf 1981; Quinn and Keough2002; Underwood 1997). Therefore, a single weighting foreach indicator is applied to data from each segment. We notethat imbalances in sampling in given segments may result inweighting variables that explain the most variance in data-richregions.Moreover, estimates of the covariancematrices input tothe PCA may have greater error when all segments are data-poor, which occur frequently in under observed systems.Therefore, future studies should determine the minimum num-ber of observations for and impact of imbalanced data avail-ability on the resulting Water Quality Index.
The substantial data gaps required that we compute thePCA based weighting in the index independently for each
decade, instead of all years of the study. Comparisons acrossyears to assess spatiotemporal variability would ideally com-pute PCA across complete datasets for all years. Nonetheless,our analyses comparing the Water Quality Index resultingfrom annual and decadal weightings for the 1999–2010 periodsuggested the robustness of that index. Changes in annualversus multiyear weightings could either implicate temporalvariability in ecosystem stressors or measurement errors inthat variable for a given year. Similarly, missing variableschange the relative weighting of other data points, and there-fore potentially impact the overall Water Quality Index albeitless than it would in a univariate index. Therefore, futurestatistical studies should formally assess the sensitivity ofmissing or biased data from a given year or variable on theWater Quality Index.
ssargaeSytilibaliavA thgiLytilauQ retaW
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1990 2000 2010 1990 2000 2010 1990 2000 2010
N: R2 = 0.033 NS C: R2 = 0.001 NSS: R2 = 0.86 NS
N: R2 = 0.52 ** C: R2 = 0.40 **S: R2 = 0.25 *
N: R2 = 0.37 ** C: R2 = 0.20 *S: R2 = 0.05 NS
N: R2 = 0.51 ** C: R2 = 0.037 NSS: R2 = 0.095 NS
N: R2 = 0.63 ** C: R2 = 0.001 NSS: R2 = 0.25 NS
N: R2 = 0.60 ** C: R2 = 0.007 NSS: R2 = 0.20 NS
C: R2 = 0.27 NSS: R2 = 0.35 NS
C: R2 = 0.28 NSS: R2 = 0.39 NS
C: R2 = 0.28 NSS: R2 = 0.38 NS
CentralNorth South
1990 2000 2010 1990 2000 2010 1990 2000 2010
1990 2000 2010 1990 2000 2010 1990 2000 2010
b
a
c
e
c
f
h
g
i
Fig. 4 Raw Values (top), weighted values (middle), and final values (bottom) of Water Quality Index (left), Light Availability Index (middle), andSeagrass Index (right). N north segment (gray squares), C central segment (white circles), S south segment (black triangles). *p <0.05; **p <0.01. NSdenotes nonsignificant relationship
Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221 S217
As with other indices of ecosystem health (Williams et al.2009), the Index of Eutrophication (Fig. 5) provides a flexibleframework and integrates observations by converting all indica-tors to a common unit and scale based on comparison of theobservations to defined thresholds for each indicator (raw scores;Tables 2 and 3, references therein). This approach is somewhatdifferent from identifying direct/indirect effects on symptoms ofeutrophication (Bricker et al. 2003; OSPAR 2008; HELCOM2010). Additionally, there a limited possibility when creatingmultimetric indices that there is an element of double countingand circularity in the arguments—e.g., a change in water qualitywill influence light availability and seagrass response. Hence,
quantifying eutrophication and ecosystem response may bereflected by several elements and over-emphasize eutrophicationand continues to need to be addressed by future studies.
Threshold definition (Tables 2 and 3), unlike multiple linearregression analysis (Dodds and Oakes 2004), requires a prioriknowledge of the ecosystem to be assessed, and ecosystemsmay vary in their susceptibility or resilience due to characteris-tics such as residence time (Dettmann 2001; Daskin et al. 2008;Fertig et al. 2013a). As such, locality matters (Fig. 5) andcalibration of the defined thresholds is required for applicationin other systems, unlike studies conducted at much larger scales(Bricker et al. 2003, 2007). Determining and quantifying how
a
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South y= –0.51x + 1100 R2=0.21 p=0.03
y=31.7 + (23700x-1) R2=0.63 p<0.0001
Fig. 5 Eutrophication Index forBarnegat Bay–Little Egg HarborEstuary a vs. time (1989–2010),b vs. total nitrogen loading (kgTN km−2 year−1), and vs. totalphosphorus loading (kg TPkm−2 year−1). Total nutrientloading data from (Baker et al.2013)
S218 Estuaries and Coasts (2014) 37 (Suppl 1):S198–S221
co-factors of locality interact with nutrient loading may yieldcapacity for predictions of ecosystem state and/or trajectory.Currently, the existence and quantification of ecosystem stateand nonlinearity of ecosystem change remains an active area ofresearch (Steckbauer et al. 2011).
Eutrophication Index Values Reveal Degradation of BarnegatBay–Little Egg Harbor
Decline in values of the Index of Eutrophication in the Centraland South segments since the 1990s (Fig. 5a) indicated continu-ing eutrophication in those segments, beyond the extent ofprevious assessments that characterized the estuary as “moder-ately eutrophic” (Bricker et al. 2007; Kennish et al. 2012). Smallincreases of total nutrient loading were associated with increasesin estuarine total nitrogen concentration (Kennish and Fertig2012) and sharp decreases in values of the Index ofEutrophication (Fig. 5b), indicating these areas are highly sensi-tive and have been undergoing eutrophication since the early1990s, as development (Fig. 1) has increased (Lathrop andConway 2001). Land use–land cover in the BB–LEH catchmenthas changed rapidly over the past three decades, and is nowmorethan 30 % urban with more than 10 % impervious cover. Suchchanges in land use and land cover alter hydrologic dynamics byincreasing the percentage of impervious surface resulting indecreases in recharge, increases in runoff, and more extremehydrologic peaks and low-flow events in streams and rivers.Nutrient loading from the catchment is an important driver ofbiotic change in the estuary. Nutrient enrichment and resultingeutrophic effects are leading to long-term, ecosystem-wide de-cline, impacting biotic resources, essential habitats (e.g., seagrassand shellfish beds), ecosystem services, and human uses(Kennish and de Jonge 2011).
The north segment of BB–LEH has historically been themost degraded segment (Fig. 5) but values of the Index ofEutrophication increased significantly (p <0.05) over time,generally with increasing light availability scores. There, wa-ter quality conditions deteriorated (Fig. 4a–c) as nutrient load-ing increased (Baker et al. 2013). The highest nutrient loadingin the BB–LEH catchment occurs via the Toms River andMetedeconk River, located in the north segment, containingthe peak human population and delivering >60 % of totalnitrogen load to the estuary (Wieben and Baker 2009). Yetthis area was not subject to the severity of macroalgae bloomsfound elsewhere in the estuary (Kennish et al. 2011). LightAvailability generally increased over time (Fig. 4d–f), andZostera marina is largely absent due to oligohalinity, all ofwhich contribute to the modest increases in Index ofEutrophication scores in the northern segment. Dramatic yettemporary declines of light availability during 2004–2007were observable in the decline of the Eutrophication Indexscores in the central segment during that time period.Concurrently, as the influential light availability indicators
were improving in the north, Eutrophication Index scores inthe north improved.
Eutrophication Driven by Nutrient Loading and LightAvailability
The different response of the Index of Eutrophication in thenorth as compared to the central and south segments (Fig. 5) isalso reflected in a nonlinearity of ecosystem response tonutrient loading (>1,200 and <5,000 kg TN km−2 year−1 and>100 and <250 kg TP km−2 year−1; Baker et al. 2013) similarto nonlinearities observed for seagrass condition (Short andBurdick 1996; Tomasko et al. 1996; Deegan 2002; Valiela andCole 2002; Hauxwell et al. 2003; Hauxwell and Valiela 2004;Burkholder et al. 2007; Latimer and Rego 2010). Thus, whilenutrient loading was initially a critical driver of ecosystemchange in BB–LEH, another set of factors, such as lightavailability indicators (Fig. 4, Table 4), drive ecosystem con-dition once this ecosystem threshold has been crossed. Moregenerally, empirical evidence for switches in the driving fac-tors of ecosystem stress (Fig. 5) adds complexity to the con-ceptualization of ecosystem resiliency (Elliott et al. 2007;Elliott and Quintino 2007) as ecosystem response may bedynamically and nonlinearly related to multiple stressors.
Acknowledgments We acknowledge technical support and construc-tive comments by R. Baker, R. Nicholson, and C. Wieben (U.S. Geolog-ical Survey New Jersey Water Resources Center), R. Lathrop (Center forRemote Sensing and Spatial Analysis, Rutgers University), and D. Ad-ams, T. Belton, R. Connell, M. Ferco, L.S. Hales, D. Hammond, S. King,D. Ringel, R. Schuster, B. Spinweber, and J. Vasslides (Technical Advi-sory Committee of the New England Interstate Water Pollution ControlCommission, NEIWPCC). We thank E. Fertig for reviewing statisticalsections of this contribution. This work was conducted with researchawards from the Estuarine Reserves Division, Office of Ocean andCoastal Resource Management, National Ocean Service, National Oce-anic and Atmospheric Administration, the New Jersey Department ofEnvironmental Protection, and NEIWPCC. This is Contribution Number13-257 of the Institute of Marine and Coastal Sciences, Rutgers Univer-sity, New Brunswick, New Jersey.
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