Spatial patterns reveal strong abiotic and biotic drivers of
zooplankton community composition in Lake My
REGINA L. G. NOBRE,
CRISTINA M. HERREN,
KYLE C. WEBERT,
´LVEIG R. O
AND ANTHONY R. IVES
University of Wisconsin-Madison, Department of Zoology, Madison, Wisconsin 53706 USA
CREAF, Cerdanyola del Valle`s, Barcelona, Catalonia 08193 Spain
Myvatn Research Station, Iceland, and University of Iceland, Faculty of Life and Environmental Sciences, Reykjavı´k 101 Iceland
Universidade Federal do Rio Grande do Norte, Ecology Department, Natal-RN 59072-970 Brazil
BETA Technological Centre (Tecnio), Aquatic Ecology Group, University of Vic–Central University of Catalonia,
Vic, Catalonia 08500 Spain
Aarhus University, Department of Bioscience, Aarhus 8000 Denmark
Marine Research Institute, Reykjavik 101 Iceland
Citation: Bartrons, M., A
´. Einarsson, R. L. G. Nobre, C. M. Herren, K. C. Webert, S. Brucet, S. R. O
´lafsdo´ttir, and A. R.
Ives. 2015. Spatial patterns reveal strong abiotic and biotic drivers of zooplankton community composition in Lake
´vatn, Iceland. Ecosphere 6(6):105. http://dx.doi.org/10.1890/ES14-00392.1
Abstract. Spatial patterns in the abundance of species are determined by local abiotic and biotic
conditions, and by the movement of individuals among localities. For species distributed among discrete
habitat ‘‘islands’’, such as zooplankton distributed among lakes, local conditions within lakes often
dominate low movement rates among lakes to determine the composition of communities. Here, we ask
whether the same abiotic and biotic environmental conditions can generate spatial patterns in the
distribution of zooplankton within a lake where there are high horizontal movement rates. We conducted
three spatial surveys of zooplankton communities in Lake My
´vatn, Iceland, a moderately sized (37 km
shallow lake with a high outflow rate. The pelagic zooplankton community showed strong spatial
structure (spatial autocorrelation), with species composition varying with spatial variation in chlorophyll-a,
the abundance of Anabaena (cyanobacteria), lake depth, light extinction coefficient, and temperature. These
factors are known from other studies to be strong drivers of among-lake variation in freshwater
zooplankton communities. However, in contrast with among-lake studies, fish (stickleback) abundance
had no measureable effect on the abundance or species composition of the zooplankton community,
although high local stickleback abundance was associated with low zooplankton:phytoplankton biomass
ratios. Finally, a parallel study of the underlying benthic crustacean community showed much finer spatial
variation (spatial autocorrelation to a range 0.6 km vs. 9 km for pelagic zooplankton), suggesting that the
stationary character of the benthos allows finer grained spatial patterns. Given the high flow rate of water
´vatn (.200 m/d), the generation of spatial patterns suggests very strong effects of variation in abiotic
and biotic environmental conditions on the population dynamics of zooplankton in the lake.
Key words: community composition; ecosystem dynamics; Iceland; lake; My
´vatn; spatial patterns; zooplankton.
Received 14 October 2014; revised 8 February 2015; accepted 12 February 2015; final version received 6 March 2015;
published 30 June 2015. Corresponding Editor: D. P. C. Peters.
Copyright: Ó2015 Bartrons et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited. http://creativecommons.org/licenses/by/3.0/
vwww.esajournals.org 1June 2015 vVolume 6(6) vArticle 105
Understanding spatial patterns in the abun-
dance of species and the composition of commu-
nities is a well-established goal in ecology
(Clements 1916, Gleason 1926, Whittaker 1956).
A central issue in understanding spatial patterns
is identifying the local abiotic and biotic envi-
ronmental conditions that drive them. The
environmental conditions that affect freshwater
zooplankton communities are particularly well
studied (Pulliam 1988) and provide striking
examples of the strength of local environmental
drivers of community composition (Carpenter
1988, Pinel-Alloul et al. 1999). These patterns are
especially clear because lakes represent discrete
‘‘islands’’ for zooplankton, and the low dispersal
rates of zooplankton among lakes allow local
environmental conditions to dominate zooplank-
ton abundance and community composition
(Havel and Shurin 2004, Kramer et al. 2008,
Frisch et al. 2012). Here, we ask whether the
abiotic and biotic environmental factors that
drive zooplankton abundance and composition
among lakes could be strong enough to generate
spatial patterns in abundance and composition
within a lake.
Abiotic factors driving variation in freshwater
zooplankton communities among lakes include
water chemistry (e.g., nutrient concentrations,
pH, conductivity, and turbidity; Johannsson et al.
1991, Pourriot et al. 1994), hydrology (e.g., wind
and wind induced currents; Jones et al. 1995,
Lacroix and Leschermoutoue 1995, Rennella and
Quiro´s 2006), temperature (Betsill and Vandena-
vyle 1994, Pinel-Alloul et al. 1999, Winder and
Schindler 2004), and lake morphometry (e.g.,
depth, area or perimeter; Patalas and Salki 1992,
Jeppesen et al. 2001, Amsinck et al. 2006). Biotic
drivers can be both bottom-up factors involving
resources and top-down factors such as preda-
tion (Carpenter et al. 1985, McQueen et al. 1986,
Northcote 1988, Vanni 1988). These abiotic and
biotic drivers are often highly variable among
lakes, making it easy to identify their effects on
zooplankton. In contrast, within a lake many of
these environmental factors are less variable, and
lakes experience horizontal water mixing which
generates high dispersal of zooplankton among
locations (Jeppesen et al. 2003, Havel and Shurin
2004, Kramer et al. 2008, Frisch et al. 2012). Thus,
the main drivers of spatial patterns are generally
thought to consist of factors affecting movement
within lakes, such as wind-induced water cur-
rents and immediate weather conditions (Jones et
al. 1995, Pinel-Alloul et al. 1999, George and
Winfield 2000, Thackeray et al. 2004, Rinke et al.
2009). Nonetheless, it is possible for sufficiently
strong abiotic and/or biotic gradients to generate
spatial patterns in zooplankton composition
within large lakes or among isolated basins
(Stansfield et al. 1997, Pinel-Alloul et al. 1999,
Levesque et al. 2010, Davidson et al. 2011, 2013).
Whether spatial gradients in environmental
factors can drive large differences in abundance
and community composition in smaller well-
mixed lakes, however, has not been shown.
We investigated spatial patterns in the zoo-
plankton community of Lake My
´vatn, Iceland, a
moderately sized (37 km
) lake whose high
throughflow (flushing rate of ;30 d), shallow
depth (maximum 4 m), and windy weather make
it subject to strong horizontal mixing. Despite the
strong mixing, My
´vatn exhibits pronounced
abiotic and biotic environmental gradients (Ei-
narsson et al. 2004). My
´vatn is fed by both hot
and cold springs that establish temperature and
nutrient gradients (Fig. 1). Furthermore, because
it is shallow, most of the primary productivity is
benthic, and variation in depth (and hence light
penetration) leads potentially to variation in
nutrient uptake or release from the benthos.
Also, the phytoplankton community varies spa-
tially in biomass and composition, likely driven
by differential population growth rates of phy-
toplankton species caused by the presence of
particular species and environmental conditions
in different locations. Finally, fish abundances
vary among regions in the lake. Because these
gradients are largely orthogonal (see Results), we
hoped that they would allow us to determine
whether abiotic and biotic drivers are important
in determining zooplankton community struc-
ture and, if so, identify those drivers that have
the largest effects.
Our study consisted of three transect surveys
of 30–31 sites conducted in the summer of 2012,
separated by 23 and 11 days; in the short summer
of Iceland, the surveys spanned from early to
middle-late seasonal succession of zooplankton
´vatn (Adalsteinsson 1979b). In addition to
sampling zooplankton, we also sampled abiotic
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BARTRONS ET AL.
(nutrient concentrations, pH, conductivity, tem-
perature, light extinction coefficient, turbidity,
dissolved oxygen, depth, distance from the
outlet) and biotic (phytoplankton, chlorophyll-
a, phycocyanin) variables, and we used trapping
data from June and August to estimate fish
abundances. To compare pelagic spatial patterns
in zooplankton with the community associated
with a stationary substrate, during the second
transect survey we sampled the benthos at nine
sites for crustaceans, and abiotic and biotic
variables. The timing of the three transect
surveys provided contrasts in the environmental
setting of possible spatial patterns in the lake: the
first survey occurred in early seasonal succession
when phytoplankton and zooplankton abun-
dances were low; the second survey occurred
following a long windy period with concomitant
horizontal mixing; and the third survey occurred
at high growing season following relative calm.
Our overall goal was to use these surveys to ask
whether there are spatial patterns in the distri-
bution of zooplankton within My
´vatn, and if so,
whether they are driven by the same abiotic and
biotic drivers that are known elsewhere to
generate variation in zooplankton community
composition among freshwater lakes.
MATERIAL AND METHODS
´vatn is a shallow eutrophic lake in north-
east Iceland (65840N, 17800W, 278 m a.s.l.) subject
to a oceanic, subarctic climate, and often high
winds (Einarsson 1979). The lake is physically
divided into two main basins, with significant
variation in the chemical composition and
temperature of the artesian springs feeding each
´lafsson 1979, Dickman et al. 1993,
Einarsson et al. 2004). Most of the South Basin
) is 2.5–4 m deep (mean depth of the
sites sampled in the South Basin was 2.3 60.6
m), with benthic substrate that is covered largely
by epiphytic diatoms, free-floating green algae
filaments of Cladophorales, and chironomid
(midge) larval tubes, mainly of the species
Chironomus islandicus (Kieffer) and Tanytarsus
gracilentus (Holmgren). The southeastern part of
the South Basin is influenced by inflowing cold
spring water. The smaller North Basin (8.5 km
is 1–2.5 m deep and is mainly covered by
macrophytes (mean depth of the sites sampled
in the North Basin was 1.4 60.5 m); some areas
of this basin have been dredged for diatomite
mining, which increased depth to a maximum of
Fig. 1. Map of My
´vatn (after Einarsson et al. 2004).
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BARTRONS ET AL.
vegetation. The North Basin is partly fed by
warm springs (up to 308C; O
´lafsson 1979) and
outflows into the South Basin. The discharge of
water is 20.9 m
/s from the South Basin at the
outlet and 7.1 m
/s from the North Basin to the
South Basin (O
The water column is vertically mixed during
the summer (O
´lafsson 1979). External loading of
phosphorus, nitrogen, and silica is estimated to
be 1.5, 1.4 and 340 g m
, respectively, and
nitrogen fixation by cyanobacteria and internal
loading from sediments is important in the total
nutrient budget (O
´lafsson 1979). Resuspension of
sediments occurs frequently in this shallow lake
with no trees on the shoreline, and is estimated to
be three times more common in the North than
the South Basin (Jo´hannesson and Birkisson
Only two crustacean species that are consid-
ered primarily planktonic are regularly found in
´vatn, Daphnia longispina (Mu¨ ll.) and Cyclops
cf. abyssorum Sars, but the tychoplanktonic
Chydorus sphaericus Mu¨ ll. is common in some
years (Adalsteinsson 1979b). Large rotifers dom-
inate the lake in spring, being replaced by small
rotifers in early summer (Adalsteinsson 1979b).
In Transect 1 (2 July), we only counted the large
rotifer Asplanchna Gosse, although we also
counted the small rotifer Keratella Bory de St.
Vincent, in Transects 2 and 3. The benthic
cladoceran community is dominated by Eurycer-
cus lamellatus (Mu¨ ll.), Alona rectangula Sars, Alona
affinis (Leydig), Alona quadrangularis (Mu¨ ll.),
Alonella nana (Baird), Acroperus harpae (Baird),
and Chydorus sphaericus Mu¨ ll.
Diatoms, especially Fragilaria construens
(Ehrnb.) Grun, occur everywhere in the lake. In
2012 the two other common phytoplankton
groups were the cyanobacteria Anabaena flos-
aquae (Lyngb.) Bre´b. and the green alga Oocystis
spp.; these two groups negatively covaried
throughout the lake and therefore were the main
drivers of spatial patterns in the phytoplankton
community. Chrysophyceans, mostly dominated
by colonies of different flagellate cells, occurred
at low abundance early in the season, but
increased as the summer progressed (Fig. 2).
´vatn, three fish species are found; Arctic
char (Salvelinus alpinus (L.)), brown trout (Salmo
trutta L.), and three-spined stickleback (Gaster-
osteus aculeatus L.) (Adalsteinsson 1979a). Stick-
lebacks are the most abundant, show spatial
segregation (Millet et al. 2013), and mostly feed
in the benthos on chironomid larvae, cladocer-
ans, and Cyclops (Adalsteinsson 1979a, Gislason
et al. 1998). Therefore, we focused on sticklebacks
as possible drivers of zooplankton and epi-
Sampling and analyses
Three transects were performed consisting of
30–31 sites located at 500–600 m intervals. The
transects originated in the cold springs in the
southeast of the South Basin, ran to the outlet of
the South Basin (west), and then back through
the outlet of the North Basin towards the warm
springs that feed it (Fig. 2). Transect 1 was
performed on 2 July during the clear phase of the
lake. Transect 2 was performed on 25 July after a
period with rain and very strong winds; during
the 23 days prior to sampling, eight days had
average wind speeds .5 m/s (which is sufficient
to completely mix the water column at most sites;
´lafsson 1979), with gusts exceeding 10 m/s on
11 days. Transect 3 was performed on 5 August
following calm conditions and after pelagic
communities were well developed. Stations were
selected to capture the maximum environmental
variability within the lake, and to sample the
most representative areas of the lake in less than
At each site, measurements of turbidity,
conductivity and phycocyanin (a photosynthetic
pigment found in Cyanobacteria) were made at a
depth of 1 m using a Hydrolab water quality
multiprobe (model DS5X with self-cleaning
turbidity sensor); values were taken every mi-
nute for 10 minutes and averaged. Vertical
profiles of temperature, DO, pH, and light were
made at each station at 0.5-m intervals using a
handheld optical DO meter (model YSI Profes-
sional ODO), a portable pH/Conductivity multi-
parameter meter (Thermo Scientific Orion 4-Star
Plus), and a LI-COR light meter (LI-250A).
Integrated vertical tows of the whole water
column for the analysis of zooplankton and
phytoplankton community composition, and
chlorophyll-a (Chl-a) were made at each station
with a Plexiglas cylinder (length 100 cm, diam-
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BARTRONS ET AL.
eter 3.6 cm; Ramberg 1976) which was used to
sample water at 1-m vertical increments to within
1 m from the benthos.
Zooplankton sampling and processing were
done according to the methods of Edmondson
and Litt (1982). Briefly, zooplankton samples
were integrated over the whole water column,
with two or three vertical water tows made for
each sample depending on depth to equalize the
total volume of water sampled. The pooled
sample of 15 L was filtered through 63-lm mesh
size and counted in entirety under a binocular
microscope. Every other site (i.e., the odd-
numbered stations 1, 3, ...) on the transect was
sampled twice independently using the same
procedure in order to estimate sampling vari-
ability within sites.
Samples for phytoplankton community com-
position, Chl-a, and nutrients were taken using
relative abundance of phytoplankton, identified
Fig. 2. Spatial distributions of zooplankton abundance (number/L) and phytoplankton proportion (%)in
´vatn during the three transects. Distances between sites are about 500 m. The size of the circle denotes the
total abundance of the zooplankton community in relation to the other stations of the transect. The number of the
stations appears in grey.
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BARTRONS ET AL.
to species or genus, was estimated by the
Utermo¨ hl method after fixation with Lugol’s acid
solution (Sournia 1978). At least 400 individuals
were counted from each sample to achieve
counting accuracies of 90%(Lund et al. 1958).
Relative abundances of phytoplankton were
converted to carbon biomass using the conver-
sions of Reynolds (2006).
Water for Chl-a analysis was filtered through a
47-mm Whatman glass fiber filter (GF/F) and
extracted in 100%methanol for 24 h in the dark
at 208C. Following extraction, Chl-a concentra-
tions were measured fluorometrically with acid
correction (0.1 N HCl) by the method of
Welschmeyer (1994) using an AquaFluor Hand-
held Fluorometer (Turner Designs). To determine
zooplankton:phytoplankton biomass ratios, Chl-
a was converted to phytoplankton dry weight
(DW) using a Chl-a:C ratio of 1:30 and a DW:C
ratio of 2.2 (Jeppesen et al. 1994). Zooplankton
biomass was based on Adalsteinsson (1979b)
who estimated species-specific volumes from the
geometric configurations of the organisms.
Total and dissolved nutrients (total phospho-
rus TP, orthophosphate PO
-P, total nitrogen TN,
nitrate þnitrite, ammonium nitrogen NH
and silica SiO
-Si) samples were collected in 250-
mL low-density polyethylene bottles washed
with dilute hydrochloric acid prior to each
sampling. Total nitrogen and phosphorus con-
centrations were obtained after UV-digestion of
the samples. Concentrations of dissolved nutri-
ents were determined using a Seal 3 channel
autoanalyzer, as described in Grasshoff (1970)
but using deionized water for blank and stan-
dards. Except for phosphate, methods were
modified from Murphy and Riley 1962). Silicate
samples were diluted before measurement. The
estimated analytical uncertainties are 60.2 lM
-N and SiO
-Si, 60.03 lM for PO
Sticklebacks were sampled from 11 sites across
the lake during the breeding season (late June)
and again in mid-August, 2012, as part of the
stickleback annual long-term monitoring of the
lake (Gudmundsson 1996, Einarsson et al. 2004).
Five unbaited minnow traps (Dynamic Aqua-
Supply, Surrey, BC, Canada; mesh size 3.2 mm)
per site were set out in two 12-h bouts over a 24-h
period, and the number of captures was counted.
The 11 sites were grouped into four zones based
upon historic sampling patterns that showed
high correlations in catch among traps within the
same zones and an order of magnitude difference
in CPUE (catch per unit effort) between different
zones (Gislason et al. 1998). Genetic studies
showed spatial phenotypic and subtle genetic
differentiation between the four zones as well
(Millet et al. 2013). Stickleback abundances were
assigned to transect sample sites according to
their location among the four zones (see map in
Gislason et al. 1998) and according to each
stickleback sampling season (late June stickle-
back sampling was used for Transect 1 and mid-
August sampling for Transects 2 and 3).
Benthic crustaceans and rotifers were collected
using an activity trap that contained a set of six
jars mounted with inverted funnels. The activity
trap was based on a prototype introduced by
Whiteside and Williams (1975) which was
modified by O
¨rno´ lfsdo´ttir and Einarsson (2004)
for use in My
´vatn. Benthic samples were ob-
tained from nine sites of Transect 2 (25 July)
separated by about 1 km in the South Basin.
Three sampling jars were integrated to produce
three independent samples per site. The two jars
that were integrated were located close to each
other on the sampling rack and 40 cm from the
next nearest set of two jars. The nine benthic sites
corresponded to the pelagic sampling sites 1, 3, 5,
7, 9, 11, 13, 15, 17 from Transect 2. During the
benthic zooplankton sampling, we also obtained
samples for benthic Chl-a, sediment characteris-
tics (rocks, bare sediment, sediment with sparse
chironomid tubes, sediment with .50%chiron-
omid tubes, sediment with Cladophora, sediment
with macrophytes), organic content, and abun-
dance of chironomids. Chironomids were collect-
ed using a Kajak gravity core. Top layers of 10-
cm thickness were sliced from the sediment cores
from which chironomids were counted and
identified to subfamily/tribe.
We used multilevel models, MLMs (Gelman
and Hill 2007), to quantify simultaneously the
composition of zooplankton communities and
the abundance of the constituent species (Jackson
et al. 2012). When applied using predictor
(independent) variables, this approach makes it
possible to assess the effects of abiotic and biotic
drivers on community composition and abun-
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BARTRONS ET AL.
dances, while taking into account spatial corre-
lation in the residuals. When applied without
predictor variables, this approach gives a test for
spatial autocorrelation in community composi-
tion and species abundances.
A formal statement of the model for nspecies
regressed against mpredictor variables distrib-
uted among psites is
This model performs a regression of the log
abundance of nspecies, log Y, on the mpredictor
, simultaneously for all species; thus,
is the log abundance of a given species at a
given site, with qdenoting the species-site
datum. The function spp[q] maps the datum q
onto one of the corresponding nspecies, and the
function site[q] maps the datum qonto one of the
corresponding msites. From this nomenclature, it
follows that the nvalues of the coefficient a
give the mean abundances (intercepts) for each of
the nspecies. The values of a
are treated as a
random variable in the model; they are modeled
with a ‘‘fixed effect’’ agiving the mean among all
species and a ‘‘random effect’’ c
be normally distributed with mean 0 and
. Similarly, the response of
species to predictor variable x
giving the mean
response of all species to predictor variable x
and a random effect e
giving deviation from
the mean response of each of the nspecies which
has variance r
. Finally, the residual varia-
incorporates spatial autocorrelation in the
covariance matrix r
Rthat depends on the
Euclidean distance d[k,l] between sites kand l
(k, l ¼1, ... ,p). Parameter ris the ‘‘ range’’ that
scales the distance between sites at which there is
spatial autocorrelation among residuals, and
parameter gis the ‘‘nugget’’ that scales the non-
spatial variance in the residuals; parameters r
and gare estimated during the fitting process.
The abiotic variables we tested were turbidity,
pH, conductivity, temperature, light extinction
coefficient, DO, lake depth, distance from the
outlet of the South Basin, and latitude and
longitude. The biotic variables were Chl-a and
pelagic phycocyanin concentrations, proportion
of all the phytoplankton that is Oocystis and
proportion that is Anabaena,andstickleback
density. All variables were transformed if needed
to minimize skew. We excluded from the
analyses those variables that were closely corre-
lated to one another, so all abiotic and biotic
variable listed above had pairwise correlations
,0.7 (see Appendix: Table A1 for a complete list
of variables). Because abundances of some
species at some sites were zero, we added 0.5
to all abundances Y
before taking the logarithm.
For analyses of benthic communities, we includ-
ed the same variables as the pelagic communi-
ties, and in addition benthic Chl-a, proportion of
the benthos that is Nostoc, chironomid abun-
dance, and organic content. For the analysis
comparing pelagic and benthic communities, the
response variable log Y
was standardized (mean
0 and variance 1) to facilitate comparison of
regression coefficients. We checked for normality
and homogeneity of residuals by visual inspec-
tion of residuals plotted against fitted values.
This analysis simultaneously provides infor-
mation about abiotic and biotic drivers of the
community composition and the responses of
individual species to these drivers (Jackson et al.
2012). Species-specific differences in responses to
predictor variables are quantified by the coeffi-
. Furthermore, the statistical signif-
icance of the variance terms r
statistical test for whether predictor variable i
plays a role in explaining variation in community
composition; if different species respond differ-
ently to a given predictor variable (r
then the composition of the community must
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BARTRONS ET AL.
vary with this variable. Finally, spatial autocor-
relation is included on top of responses to
predictor variables in the term e
. Thus, the
single analysis reveals species-specific sensitivi-
ties to abiotic and biotic variables, the importance
for these variables for community composition,
and the spatial autocorrelation that is not
explained by the included predictor variables.
We performed these analyses with a linear mixed
model on log-transformed data, rather than
attempt a generalized linear model, because
linear models applied to log-transformed count
data can often give more reliable tests for the
statistical significance of regression coefficients
than generalized linear models (Ives 2015).
In addition to analyzing all species together,
we estimated spatial autocorrelation for each
species separately using both equation 1 and
calculating Moran’s I (Moran 1950). Using Eq. 1,
these analyses were performed excluding predic-
tor variables x
, so the spatial correlation given by
the range rwas determined solely by distances
Data were analyzed using the package lme4
(Bates et al. 2014) in R v. 3.1.0 (R Development
Core Team 2014), and Moran’s I was calculated
using functions in the R package ape (Paradis et
al. 2004). The maps were generated using the R
packages rgdal (projection of latitude/longitude
coordinates; Bivand et al. 2014), maptools (read in
data from a shapefile and plot the map; Bivand
and Lewin-Koh 2014), and mapplots (add the plot
pie charts to the map; Gerritsen 2013). Code is
provided in the Supplement.
Daphnia longispina and Cyclops abyssorum were
the two crustacean species with highest abun-
dance in My
´vatn in summer 2012 (mean abun-
dance for D. longispina, 6.7, 11.6 and 13.6
individuals/L in Transects 1–3, respectively, and
for C. abyssorum, 3.0, 4.7, 3.0 individuals/L; Fig. 2;
Appendix: Table A2). Chydorus sphaericus had
highest abundance in Transect 2 (25 July)
following the prolonged period of strong wind
(mean abundance: 0.3, 12 and 5 individuals/L in
Transects 1–3, respectively). Simultaneously, Cy-
clops nauplii abundance decreased in Transect 2.
The rotifer Keratella, which was only counted in
Transects 2 and 3, was particularly abundant
(mean abundance: 51and 43 individuals/L in
Transect 2 and 3, respectively) and dominated
the zooplankton community in the North Basin.
Almost no zooplankton were found in the
southeastern part of the South Basin where water
temperatures were low due to inflow from cold
springs (ca. 68C).
In Transects 2 and 3, two zooplankton samples
were taken in every other site to assess measure-
ment error. Within-site (among replicate) vari-
ance relative to the total variance was 0.092 and
0.015 for Transect 2 and 3, respectively.
In the benthos sampling during Transect 2,
Cyclops abyssorum had highest activity-abun-
dance (286 CPUE; number individuals/net; Fig.
3, Appendix: Table A3), and it almost completely
dominated the clear, cold southeast part of the
South Basin. Small cladocerans (cladocerans
other than Daphnia longispina) were also abun-
dant, especially Macrothrix hirsuticornis Norman
et Brady and Alona spp. (rectangula,affinis, and
quadrangularis) that had activity-abundances of
229 and 175 CPUE, respectively.
We performed initial analyses to document
possible spatial structure in the pelagic and
epibenthic communities. Spatial structure was
assessed for all species as a group using the
multilevel model (Eq. 1) deployed without
predictor variables. For zooplankton communi-
ties, the estimates of the ranges rwere 6.88, 2.55,
and 5.52 km for Transects 1–3, respectively,
demonstrating statistically significant (all P,
0.001) spatial autocorrelations on the scale of
several kilometers. The nuggets gwere 0.21, 0.33,
and 0.05, demonstrating additional statistically
significant (all P,0.001) variation at a scale
smaller than the minimum distance between sites
(0.5 km). A measure of the unexplained autocor-
related ‘‘regional’’ variance is one minus the
nugget g,1g, multiplied by the residual
(Eq. 1), which was 0.75, 0.61, and 1.74
for Transects 1–3, respectively. Because these
analyses were performed on all zooplankton
species together, they give a synoptic view of
spatial structure for the zooplankton community.
We analyzed spatial autocorrelation for indi-
vidual species using both the multilevel model
without predictor variables and Moran’sI.
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BARTRONS ET AL.
Spatial autocorrelation was found for Asplanchna,
Keratella,Cyclopsabyssorum adults and copepo-
dites, and nauplii, Daphnia longispina, and Chy-
dorus sphaericus, with all of these taxa showing
significant spatial autocorrelation in Transect 3,
and many showing significance in one or both of
the other transects (Appendix: Table A4). Mor-
autocorrelation as statistically significant than
Eq. 1, although this apparent increase in statis-
tical power was slight.
We also performed a direct comparison for
spatial structure between zooplankton and epi-
benthic communities by limiting the number of
pelagic sites to those corresponding to the
benthic sites that were sampled during Transect
2. When pooling samples to give a single value
per benthic site, comparable to the single samples
per pelagic site, spatial correlation in community
composition occurred at a finer spatial scale for
the benthic community, with the range restimat-
ed as 0.61 km versus 9.0 km for the pelagic
samples (Table 1).
Abiotic and biotic variables
We used the multilevel model (Eq. 1) to
investigate abiotic and biotic variables as drivers
of spatial variation in zooplankton community
composition (Table 2). In these analyses, log
abundances of all species were included, and
random effects indicated differences among
species in how they respond to the drivers. The
Fig. 3. Benthic crustacean abundance (mean catch per unit effort given as the number individuals/net, n ¼2) in
stations 1, 3, 5, 7, 9, 11, 13, 15 and 17 of Transect 2 (25 July 2012) in comparison to pelagic crustacean (number/L),
and benthic and pelagic algae composition (%).
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BARTRONS ET AL.
best-fitting models (best AIC) contained only a
small subset of the total predictor variables
(Appendix: Table A1) and included random
effects for Chl-a and proportion Anabaena (Tran-
sect 1); depth, Chl-a and proportion Anabaena
(Transect 2); and Chl-a and proportion Oocystis
(Transect 3). Thus, Chl-a and the makeup of the
phytoplankton community (proportion Anabaena
or Oocystis) were consistent biotic predictors of
differences among zooplankton in their spatial
distributions. In Transects 2 and 3, Anabaena and
Oocystis were the two dominant phytoplankton
throughout much of the lake outside the south-
east corner of the South Basin; thus, the
phytoplankton communities at different sites
could be roughly categorized as Anabaena dom-
inated or Oocystis dominated, with the correla-
tion between logit proportion Anabaena and logit
proportion Oocystis 0.77 and 0.36 in Transects
2 and 3, respectively. Because Transect 1 did not
have sample sites into the North Basin (a storm
blew in), we also performed the same analyses
but removing all samples from the North Basin;
the qualitative results were not changed, sug-
gesting that the results were not sensitive to
differences in sample locations among transects.
In addition to these biotic and abiotic drivers
which had different effects on different zoo-
plankton taxa, there were also negative fixed
effects for temperature and positive fixed effects
for light extinction coefficient which affected all
zooplankton taxa in the same way; higher
abundances of all zooplankton species were
found at lower temperature and lower water
clarity. After accounting for these abiotic and
biotic drivers, there was still residual spatial
variation (range r¼7.7, 1.02, and 3.6 km for
Transects 1–3), thereby implying that the ob-
served spatial variation in zooplankton was not
completely explained by these variables. Further-
more, the autocorrelated component of the
residual variances, given by (1 g)r
were 0.69, 0.13, and 0.71 for Transects 1–3.
Comparing to the autocorrelated components of
the model without environmental variables,
these represent decreases of 8%(0.69 vs. 0.75),
79%(0.13 vs. 0.61), and 59%(0.71 vs. 1.74). These
values imply that in Transects 2 and 3, the
environmental variables we measured explained
more than half of the spatial autocorrelation in
the abundances of zooplankton. Nonetheless, in
all transects there were factors other than the
environmental variables we measured that were
additionally responsible for the spatial autocor-
relation of zooplankton abundances.
These statistical analyses also provide infor-
mation about the responses of individual species
to the abiotic and biotic variables (Table 3). We
Table 1. Environmental determinants of benthic community structure, and comparison of spatial autocorrelation
with the pelagic community during Transect 2 (25 July). Benthic data were analyzed using both 3 samples per
site, and pooling to give one sample per site. The full model (Eq. 1) included abiotic (turbidity, nutrient
concentrations, pH, conductivity, temperature, light extinction coefficient, DO, depth, distance from the outlet,
latitude and longitude) and biotic (phytoplankton and benthic algae community composition, fish densities,
benthic and pelagic Chl-a, pelagic, phycocyanin, benthic chironomid abundance) variables. Variables were
removed to give the model with the lowest AIC. Similar analyses were performed without predictor variables
to estimate spatial autocorrelation in community composition, performing analyses with both 3 samples and
pooled samples per site. For comparison, the model without predictor variables was analyzed for the pelagic
communities at the same sites as the benthic samples. 95%confidence intervals are presented in parentheses for
the range, r, and nugget, g, when they are significant. P,0.1, *P ,0.05, ***P ,0.001.
Full Spatial Spatial
Coefficients Fixed Random Fixed Random Fixed Random
Species 1.46* 1.63 1.44* 1.60 0.07 0.11
Species/Site 1.29 – –
Pelagic Chl-a (log lg/L) 0.49*
Residual variance 0.67 1.53 1.52
Range (km) 0.0036 0.61*** (0.32–1.16) 9.01*** (3.24–25.1)
Nugget 0.07 0.14*** (0.09–9.22) 0.10(0.03–0.28)
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BARTRONS ET AL.
focused on those variables with random effects
included in the model, implying that different
species responded differently to these variables.
The large crustaceans Cyclops abyssorum and
Daphnia longispina responded negatively to the
proportion Anabaena and positively to the pro-
portion of Oocystis, whereas the reverse was
generally true for the small crustaceans. Similar-
ly, Keratella responded negatively to Anabaena
and positively to Oocystis, whereas the reverse
was true for Asplanchna. Thus, high concentra-
tions of ‘‘palatable’’ phytoplankton favor Cyclops
abyssorum,Daphnia longispina, and Asplanchna,
whereas dominance by Anabaena appears to
favor smaller cladocerans and Keratella. Finally,
depth was a driver of differences in zooplankton
community composition in Transect 2, with
greater depth favoring large crustaceans Cyclops
abyssorum and Daphnia longispina, and less depth
generally favoring small crustaceans and rotifers.
We analyzed the benthic samples from Tran-
sect 2 to identify possible abiotic and biotic
drivers of the epibenthic community composi-
tion. The analysis could not identify abiotic or
biotic drivers that were responsible for variation
in community composition among sites; none of
the predictor variables had random effects that
were included in the model. Nonetheless, both
pelagic Chl-a and the proportion of Nostoc in the
benthic samples had negative fixed effects on the
log abundances of epibenthic taxa as a group,
implying that these variables reduced the abun-
dances on average for all taxa in the epibenthic
Bottom-up and top-down control
The results of the multilevel model (Table 2)
suggest that the main determinants of zooplank-
ton community composition are characteristics of
the lower trophic level: Chl-a and the makeup of
the phytoplankton community. Furthermore, in
all three transects there was a statistically
significant decrease in phytoplankton biomass
with increasing zooplankton biomass (r
¼0.45, P,0.001; r
¼0.37, P,0.001, in
Transects 1–3, respectively; Fig. 4), implying high
consumption of phytoplankton by zooplankton.
These results suggest that zooplankton commu-
nity composition is determined primarily by
Sticklebacks varied in abundance within My
vatn, having high densities in the North Basin
and very low densities in the southeast part of
the South Basin. Nonetheless, there was no
statistical evidence that they affected spatial
variation in zooplankton species composition;
sticklebacks were not included in any of the best-
fitting multilevel models (Table 2). Furthermore,
the zooplankton:phytoplankton biomass ratio for
the lake was on average 1, 16, and 11 for
Transects 1–3 (ratio calculated using average
species biomass). These values are characteristic
Table 2. Multilevel model (Eq. 1) for the effects of abiotic and biotic variables on the composition of zooplankton
communities during Transect 1 (2 July), Transect 2 (25 July), and Transect 3 (5 August). Fixed and random
effects are included for predictor variables that were included in the lowest-AIC models, selecting from abiotic
variables (turbidity, nutrients nitrate, phosphate, ammonia, total nitrogen, total phosphorus, silica, N:P, pH,
conductivity, temperature, light extinction coefficient, dissolved oxygen (DO), lake depth, distance from the
outlet, and latitude and longitude) and biotic variables (Chl-a, phycocyanin, proportion of the phytoplankton
that is Oocystis and proportion Anabaena, chironomid abundance, and stickleback densities). 95%confidence
intervals are presented in parentheses for the range, r, and nugget, g, when they are significant.
Transect 1 Transect 2 Transect 3
Fixed Random Fixed Random Fixed Random
Species 0.04 0.03 0.751.32 0.44 1.00
Temperature (exp 8C) ... ... 0.13* ... 0.17* ...
Depth (m) 0.09... 0.13 0.23** ... ...
Light extinction coefficient (k) 0.07* ... 0.09... 0.04 ...
Chl-a (log lg/L) 0.12* 0.07 0.24** 0.22** 0.30*** 0.15
Prop. Oocystis (logit) 0.08* ... ... ... 0.15 0.41***
Prop. Anabaena (logit) 0.11* 0.11* 0.130.16* ... ...
Residual 0.93 0.65 0.90
Range (km) 7.70*** (3.65–16.2) 1.02* (0.21–4.88) 3.60*** (1.16–10.9)
Nugget 0.20*** (0.11–0.33) 0.70 0.12** (0.05–0.27)
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BARTRONS ET AL.
of temperate-zone lakes with little top-down
regulation by fish predators (Jeppesen et al.
Although there is no evidence that sticklebacks
affected the species composition of the zooplank-
ton community, the zooplankton:phytoplankton
biomass ratio was inversely related to stickleback
abundance, except in the cold southeast part of
the South Basin and the connecting area between
the North and South basin of Transect 2 (Fig. 4).
We suspect that the low pelagic biomass of
zooplankton in the southeast part of the South
Basin is primarily due to physical conditions
(temperature ,68C and phytoplankton produc-
tivity ,0.5 lg Chl-a/L) rather than absence of
sticklebacks. Nonetheless, even with this region
excluded, areas with high stickleback abundance
generally had low zooplankton biomass (r
0.05, P¼0. 2; r
¼0.16, P,0.05; r
0.05, in Transects 1–3, respectively; Fig. 4). This
suggests that there is top-down control of
zooplankton biomass, although this does not
affect zooplankton species composition.
The zooplankton community in My
showed strong spatial structure despite the fast
flow rate (.250 m/d) and horizontal mixing due
to high winds. The degree of spatial variation
differed among taxa and transects, but spatial
differences in abundances could reach one and
two orders of magnitude for some crustacean
and rotifer taxa, respectively. Overall, evidence
points to bottom-up forces determining the
composition of the zooplankton community. Sites
with lower phytoplankton biomass had higher
zooplankton biomass, with these sites dominated
by D. longispina and C. abyssorum. These large
crustaceans, along with the rotifer Keratella, were
also associated with either high relative abun-
dance of Oocystis (a palatable green algae) or low
relative abundance of Anabaena (an unpalatable
cyanobacteria) in the phytoplankton community.
These patterns in the zooplankton community
were found in all three transects, but were less
evident after a period of prolonged strong wind
events before Transect 2 when the primarily
epibenthic species C. sphaericus reached high
abundance (Fig. 2). The presence of spatial
patterns in all three transects, even after the
windy period before Transect 2, suggests that the
bottom-up biotic drivers of zooplankton com-
munity composition are sufficiently strong to
overcome the high flow rate and horizontal
mixing within a lake.
The biotic factors driving spatial patterns in
zooplankton community composition in My
are among the most common factors driving
large-scale spatial heterogeneity of freshwater
zooplankton across lakes (e.g., Pinell-Alloul et
al. 1995). Chl-a had positive effects on D.
longispina and C. abyssorum, and was associated
with higher total zooplankton abundance in all
transects. However, it had negative effects on
rotifers and small cladocerans. In among-lake
Table 3. Coefficients from the multilevel model (Eq. 1) giving species-specific responses to Transect 1: Chl-a and
proportion Anabaena; Transect 2: Depth, Chl-a, and proportion Anabaena; and Transect 3: Chl-a and proportion
Oocystis. Values are the species-specific random effect plus the estimate for fixed effects.
Transect 1 Transect 2 Transect 3
Chl-a Anabaena Depth Chl-a Anabaena Chl-a Oocystis
Asplanchna 0.01 0.02 0.13 0.12 0.13 0.05 0.36
Keratella 0.33 0.12 0.07 0.00 0.25
Cyclops abyssorum 0.07 0.14 0.08 0.16 0.23 0.07 0.30
Nauplii 0.06 0.02 0.11 0.16 0.09 0.12 0.20
Daphnia longispina 0.01 0.20 0.36 0.35 0.04 0.17 1.03
Eurycercus lamellatus 0.02 0.07 0.13 0.10 0.06 0.13 0.16
Alona spp. 0.02 0.07 0.06 0.11 0.13 0.05 0.24
Chydorus sphaericus 0.03 0.03 0.24 0.19 0.18 0.12 0.05
Acroperus harpae 0.03 0.06 0.16 0.23 0.07 0.14 0.17
Macrothrix hirsuticornis 0.01 0.05 0.02 0.19 0.05 0.08 0.26
Simocephalus vetulus 0.02 0.06 0.14 0.14
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BARTRONS ET AL.
comparative studies, higher productivity is asso-
ciated with dominance by large-bodied cladocer-
ans, which are superior competitors to smaller-
bodied taxa provided fish predation is low
(Romanovsky and Feniova 1985, Gliwicz 1990).
´vatn, large-bodied crustaceans were asso-
ciated with Oocystis, whereas small-bodied crus-
taceans and rotifers were associated with
Anabaena. Cyanobacteria are often associated
with lower abundance of larger herbivorous
Fig. 4. (A, C, E) Zooplankton and phytoplankton biomass (lg/L) relationships, and (B, D, F)
zooplankton:phytoplankton biomass ratio with stickleback abundance (CPUE; number individuals/net) for
Transects 1–3 (rows). Stations from the south-east corner of the South Basin, where water temperatures are
roughly 68C, are shown in gray, stations from the rest of the South Basin in black triangles; stations in the
transition zone between the South and the North Basin in x; and stations in the North Basin in diamonds.
vwww.esajournals.org 13 June 2015 vVolume 6(6) vArticle 105
BARTRONS ET AL.
zooplankton, because they have low nutritional
value (Muller-Navarra et al. 2000), because they
physically interfere with the zooplankton feeding
apparatus (Webster and Peters 1978, DeMott
1989), or because they produce toxins (Sarnelle
et al. 2010). These effects are less severe for
rotifers and small-bodied cladocerans (e.g., Som-
mer et al. 1986, DeMott 1989), and hence
numerous studies show an association between
cyanobacteria and rotifers or small-bodied cla-
docerans either across lakes or within lakes
through time (Fulton and Paerl 1988, Bednarska
2006). Thus, the main drivers of zooplankton
community composition in My
´vatn, Chl-a and
the relative abundance of unpalatable or palat-
able phytoplankton, have been shown to be
important drivers of variation in zooplankton
community composition among lakes.
Top-down fish predation is also a common
driver of among-lake variation in zooplankton
community composition; generally, lakes with
high fish predation contain small-bodied crusta-
cean species, because large-bodied species are
more vulnerable to predation (Brooks and
Dodson 1965, Jeppesen et al. 2004, Brucet et al.
2010). However, in My
´vatn, we found no
evidence that stickleback density changed zoo-
plankton species composition. This might be due
to high movement rates of sticklebacks within the
lake. Nonetheless, high stickleback density was
associated with a low zooplankton:phytoplank-
ton biomass ratio, suggesting that top-down
effects may partially determine the biomass of
zooplankton (Adalsteinsson 1979a).
Despite the strong bottom-up effects on zoo-
plankton community composition, after factoring
out statistically significance environmental vari-
ables there was still spatial autocorrelation in the
residual variation on the order of 2.5–7 km (Table
2); the inclusion of environment variables re-
duced the autocorrelated component of the
residual variation by 8%,79%,and59%in
Transects 1–3, respectively. This suggests either
that there are important but unmeasured envi-
ronmental variables that are spatially autocorre-
lated, or that water movement has caused mixing
among adjacent areas which are subject to
different abiotic and biotic drivers. Because the
rate of zooplankton population response to
temporal changes in environmental conditions
is limited by their population growth rates, there
will likely be temporal lags in the zooplankton
community response to abiotic and biotic drivers.
In moving water, temporal lags can generate
spatial autocorrelations (lags in space) that are
not immediately explained by the local site
In comparing the spatial autocorrelation struc-
ture of zooplankton and epibenthic communities,
the spatial extent of autocorrelations in the
pelagic zone (9 km) were much greater than in
the benthos (0.6 km) (Table 1). This suggests
that finer-grained spatial patterns can develop in
the stationary substrate of the benthos. Studies
comparing spatial patterns in plankton and
benthos are scarce. A previous study on the
spatial distribution of benthic invertebrates
showed a high degree of similarity in the fauna
occurring at sites 12 km apart which was
attributed to the uniformity of the sediment
(Darlington 1977). A particular problem in
comparing pelagic vs. benthic spatial correlations
in the literature is that the composition of the
fauna in these two zones typically differs
considerably. In the case of My
although differing in relative abundances. The
contrasting extent of spatial structure we found
for pelagic and benthic community composition
implicates the importance of substrate, rather
than taxonomic makeup of the zooplankton
species, in determining spatial patterns.
The strength of the bottom-up forces driving
zooplankton community composition can be
very roughly inferred by estimating the rate of
water flow in My
´vatn and comparing this to the
possible population growth rates of the zoo-
plankton. The discharge rate from the South
Basin is 20.9 m
/s, and its volume is 67.86 10
These give a water turnover rate of 37 d, which in
turn translates roughly into a flow rate of 200 m/
d. Windy periods will likely generate flow rates
of similar or even greater magnitude when
integrated over the water column (Kjaran et al.
2004). Taking the dominant zooplankton, D.
longispina, variation in abundance among sites
can be characterized by the ratio of the density
from the five highest sites to the density from the
5 lowest sites, which are 158, 122, and 1096 for
Transects 1–3, respectively. These differences in
densities occur over a distance of 5 km (Fig. 2).
For these differences to be generated given a
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BARTRONS ET AL.
water movement rate of 200 m/d, the difference
between the intrinsic rates of increase of D.
longispina between low and high density sites
would have to be 0.20, 0.19, and 0.28 for
Transects 1–3, while these values would have to
be 0.41, 0.38, and 0.56 with a flow rate of 400 m/
d. These rough values are mostly within the
range of possible intrinsic rates of increase for D.
longispina, with peak values from the literature
ranging from 0.27 to 0.47 depending on the study
(Lundstedt and Brett 1991, Ojala et al. 1995,
Antunes et al. 2003, 2004). Furthermore, the
intrinsic rate of increase depends strongly on
food type, food abundance, and temperature
(Lundstedt and Brett 1991, Brett 1993, Ojala et al.
1995, Lair and Picard 2000, Antunes et al. 2003,
2004, Gladyshev et al. 2006). We could not find
similar literature on zooplankton less-well stud-
ied than D. longispina, although we suspect
numbers that are similar. Therefore, the spatial
patterns we observed could conceivably be
generated by spatial variation in zooplankton
population growth. Of course, these are very
coarse calculations that do not account for
horizontal mixing, initial colonization of water
entering the lake, potential stickleback predation,
etc. Nonetheless, they suggest large spatial
differences in zooplankton population growth,
and hence strong bottom-up forces, underlying
variation in zooplankton community composi-
Our results show that zooplankton community
composition in My
´vatn has strong spatial pat-
terns that are apparently driven by bottom-up
forces. Productivity (Chl-a) and the composition
of the phytoplankton community, particularly
the abundance of cyanobacteria, are well-known
drivers of among-lake variation in zooplankton
communities, and we have shown they are also
important within My
´vatn. Nonetheless, our
finding that any consistent spatial patterns arise
in the zooplankton community is remarkable
given the very short water turnover times and
the horizontal mixing in this windy environment.
The bottom-up effects must be strong.
We gratefully acknowledge the technical assistance
with the multiprobe of L. Winslow and P. Hanson; the
field assistance of M. Raudenbush, E. de Vries, P. Klas,
S. Jennings, A. Linz and P. Mieritz; and discussions
with all the My
´vatn team: M. J. Vander Zanden, C.
Gratton, P. Townsend, R. Jackson, D. Hoekman and J.
Dreyer, the Ives Lab at UW–Madison, and E. Jeppesen,
J. Blois and B. M. Pracheil. This work was supported
by the National Science Foundation (DEB-0717148 and
LTREB-1052160), a Guyer Postdoctoral Fellowship to
M. Bartrons, the University of Wisconsin-Madison
Graduate School, a postdoctoral grant from the
Spanish Ministry of Education to M. Bartrons, and
the Marie Curie Intra European Fellowship no. 330249
(CLIMBING) to S. Brucet.
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Table A1. Abiotic and biotic variables analyzed.
Transect 1 Transect 2 Transect 3
Mean Range Mean Range Mean Range
Depth (m) 2 1–3 2 1–3 21–3
Temperature (8C) 14.3 8.7–16.0 11.7 6.2–13.3 14.9 7.2–17.4
Light extinction k 0.4 0.1–0.6 0.8 0.2–1.7 0.7 0.2–1.7
Turbidity 8.9 0–4.3 1.8 0– 4.4 2.0 0–24.6
sat (%) 120 88–141 98 79–129 113 79–155
DO (mg/L) 12.3 10.3–15.3 10.69.0–13.7 11.4 9.6–15.0
pH 9.8 9.4–10.1 9.38.3–10.4 9.5 8.6–9.8
Conductivity 188135–288 190133–285 190131–295
Nitrate (lmol/L) 0.3 0.1–1.7 0.2 0–2.9 0.3 0.1–2.8
Phosphate (lmol/L) 0.7 0.2–1.6 1.3 0.2–2.2 1.4 0.2–2.6
TN (lmol/L) 4.3 3.2–5.3 3.3 1.6–4.6 4.6 2.3–6.4
TP (lmol/L) 1.0 0.5–1.8 1.9 0.5–3.0 1.9 0.4 –3.4
NH4 (lmol/L) 1.3 0.5–4.2 0.9 0.3–1.8 0.6 0.3–1.0
Silica (lmol/L) 147 43–360 160 65–402 155 90–318
NP (%) 4.8 2–11 2.6 1–8 3 1–11
Chl-a (lug/L) 0.85 0.08–2.01 5.05 0.10–9.47 2.81 0.07–7.66
Phycocyanine (lg/L) 0.04 0.002–0.16 0.010.004– 0.04 0.010.004 –0.03
Oocystis (%) 22 0–71 430–84 27 0–76
Anabaena (%) 10 0– 63 20 0–98 29 0–89
Nostoc (%)... ... 5 0–16 ... ...
Chironomids (N) ... ... 16 4 –34 ... ...
Organic content (%)... ... 27 16–33 ... ...
Stickleback (number) 80 0–324 112 0–324 102 0–324
Chl-a (lg/L) ... ... 6717 824–13953 ... ...
Nostoc (%)... ... 5 0–16 ... ...
Chironomids (N) ... ... 16 4 –34 ... ...
Organic content (%)... ... 27 16–33 ... ...
Measured variables excluded in the analyses due to pairwise Spearman correlation coefficients with other variables .0.7.
Table A2. Pelagic zooplankton abundance (number per L) in Lake My
´vatn during Transect 1 (2 July), Transect 2
(25 July), and Transect 3 (5 August). Keratella was only measured in Transects 2 and 3.
Transect 1 Transect 2 Transect 3
Mean Range Mean Range Mean Range
Asplanchna 0.8 0–7.7 0.2 0–1 0.9 0–6
Keratella ... ... 50.5 1–99 42.9 0–122
Cyclops abyssorum 3.1 0–14.1 4.7 0–12 3.0 0–11
Nauplii 8.7 0–25.1 3.0 0–8 8.5 0–30
Daphnia longispina 6.7 0.07–34.6 11.6 0–54 13.6 0–72
Eurycercus lamellatus 0.009 0–0.2 0.2 0–1 0.03 0–0.3
Alona spp. 0.008 0– 0.1 0.2 0–1 0.1 0–1
Chydorus sphaericus 0.3 0–1.7 12.1 0–33 5.3 0.1–16
Acroperus harpae 0.02 0–0.3 0.1 0–2 0.01 0–0.2
Macrothrix hirsuticornis 0.1 0–0.7 0.4 0–3 0.1 0–1
Simocephalus vetulus 0.02 0–0.3 ... ... 0.02 0–0.3
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R code and scripts for computing multilevel models with spatial autocorrelation for zooplankton
spatial distribution data (Ecological Archives,http://dx.doi.org/10.1890/ES14-00392.1.sm).
Table A3. Benthic zooplankton abundance (number per jar, with nine replicates pooled per site) in Lake My
during Transect 2 (25 July).
Species Mean Range
Keratella ... ...
Cyclops abyssorum 286 20–1376
Nauplii 126 14–298
Daphnia longispina 2 0.3–7
Eurycercus lamellatus 23 1–114
Alona spp. 175 18–413
Chydorus sphaericus 44 5–139
Acroperus harpae 15 0.3–122
Macrothrix hirsuticornis 229 1–875
Simocephalus vetulus 00–0
Table A4. Spatial correlation structure for pelagic zooplankton species during Transect 1 (2 July), Transect 2 (25
July), and Transect 3 (5 August).
Transect 1 Transect 2 Transect 3
Range Nugget I Range Nugget I Range Nugget I
Asplanchna 724* 0.0002 0.10** 0.003 0.05 0.007 469 0.002 0.06*
Keratella ... ... ... 321 0.67 0.02 3424*** 0.00003 0.26***
Cyclops abyssorum 1205*** 0.0005 0.20*** 1253* 0.25 0.16*** 1669*** 0.00003 0.23***
Nauplii 1778*** 0.001 0.27*** 872* 0.12 0.12** 3721*** 0.09 0.30***
Daphnia longispina 332 0.03 0.07* 678* 0.003 0.10** 1734*** 0.15 0.18***
Eurycercus lamellatus 29 0.12 0.04 381 0.52 0.08* 133 0.11 0.005
Alona spp. 47 0.11 0.06 45 0.12 0.01 3 0.003 0.01
Chydorus sphaericus 598 0.28 0.05* 2884*** 0.19 0.24*** 3729*** 0.01 0.26***
Acroperus harpae 15 0.13 0.05 276 0.007 0.005* 29 0.11 0.05
Macrothrix hirsuticornis 287 0.002 0.01 371 0.01 0.04 287 0.002 0.01
Simocephalus vetulus 35 0.12 0.04 27 0.15 8 0.13 0.04
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