Hydrobiologia
DOI 10.1007/s10750-014-1817-5
PRIMARY RESEARCH PAPER
Modern distribution of ostracodes and other limnological
indicators in southern Lake Malawi: implications
for paleocological studies
Margaret Whiting Blome • Andrew S. Cohen
Matthew J. Lopez
•
Received: 19 February 2013 / Revised: 24 January 2014 / Accepted: 27 January 2014
Springer International Publishing Switzerland 2014
Abstract This modern distribution study from the
southwest arm of Lake Malawi quantitatively relates
variables of the lake environment to surficial assemblages of ostracodes and other paleoenvironmental
indicators (molluscs, Botryococcus, fish, and charcoal) from 102 sites, across a gradient of littoral to
shallow profundal conditions. The goal of this
research is to use the resultant relationships to help
quantify paleoecological interpretations of the fossil
record from sediment cores. Site locations varied by
depth (1–60 m) and adjacent shoreline environment.
Thirty-three ostracode species are identified from 54
sites including four new, undescribed species of
Cypridopsinae (2) and Limnocythere (2). Ostracodes
are extremely abundant between 1 and 25 m water
depth, but are rare to absent between 30 and 60 m.
This disappearance is probably taphonomically
controlled, with carbonate dissolution in the death
assemblage since abundant live ostracodes have been
found in the lake at greater than 30 m depth, where
bottom sediments lack calcium carbonate. Constrained correspondence analysis (CCA) of ostracode
species abundance suggests depth and dissolved
oxygen (DO) content to be the primary environmental
variables affecting their distribution. Additional CCA
models using all biological indicators suggest limnologic variables correlated with depth (e.g., bottom
water temperature and DO) and adjacent shoreline
environment were most significant.
Keywords Ostracodes East Africa Lake
Malawi Modern distribution study Paleoecology
Introduction
Handling editor: Jasmine Saros
Electronic supplementary material The online version of
this article (doi:10.1007/s10750-014-1817-5) contains supplementary material, which is available to authorized users.
M. W. Blome (&)
BP America, Houston, TX, USA
e-mail: mwblom@email.arizona.edu
A. S. Cohen
University of Arizona, Tucson, AZ, USA
M. J. Lopez
Tucson High School, Tucson, AZ, USA
Ostracodes are small, bivalved crustaceans with shells
of low-magnesium calcite, typically 0.5–3 mm long.
Although the soft inner tissue is not often preserved in
the sedimentary record, the outer calcite carapace
preserves well in most alkaline environments (pH
[7.5) (Holmes, 1992; Cohen, 2003). The shape or size
of the valve, the presence or absence of nodes, pores
and/or reticulations on the outer surface, and internal
valve characteristics readily distinguish the species
commonly found in East Africa, and in Lake Malawi in
particular (e.g., Martens, 2002, 2003). The
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Hydrobiologia
Fig. 1 Site locations for the
current study within the
southeast arm of the extreme
southern end of Lake
Malawi. The primary depth
transects are labeled A–G, as
is the location of the
SCUBA transect A4/5. Each
cross marks the location of a
sample site. Exact
coordinates for each site can
be found in Table 1. The
inset also shows the site
locations for a previous
study of the ostracodes of
modern Lake Malawi
(Martens, 2002). Satellite
imagery from GeoMapApp
(http://www.geomapapp.
com)
environment in which an ostracode lives is determined
by the ecological interactions it experiences within a
water body and the geology, hydrology, and climate of
the watershed (Delorme, 1969). Ostracode assemblages often reflect lake water chemistry variations
with strong correlations to total conductivity and
alkalinity (Cohen et al., 1983) and solute composition
and concentration (Delorme, 1969; Mezquita et al.,
2005). In addition, lake level histories and water depth
can be inferred based on modern analogs and ecological
tolerances (e.g., Mischke et al., 2002; Horne, 2007).
123
The known relationships between ostracodes and their
environment increase with each new study, however, it
is important to remember that these associations are
strictly demonstrating a correlation and not causation
between the two groups of data (Fritz, 1996).
Lake Malawi, situated between 9 and 14S, is the
southern-most lake in the East African Rift System
(EARS) (Fig. 1—inset map). It is a large
(29,500 km2), deep (706 m), permanently stratified
(meromictic), lake filling the Malawi Rift basin of the
EARS. Within the lake, there is both a thermal and
Hydrobiologia
chemical stratification throughout much of the year
(Patterson & Kachinjika, 1995), however, the lake is
located far enough south to have seasonal variations in
temperature, wind, and precipitation (Eccles, 1974).
These variations are caused by the migration of the
Intertropical Convergence Zone (ITCZ). The ITCZ is
a narrow band of atmospheric convergence and
monsoonal precipitation that migrates across the
tropics following the location of maximum insolation
intensity throughout the year. During the cool, windy
season at Lake Malawi (May–August), the ITCZ is
sufficiently north of the lake to enhance the SE trade
winds at the lake. Sample collection for this study
occurred in mid-March specifically to avoid the windy
season and capture the variation in lake water
chemistry with depth while the lake is stratified. Had
these measurements been taken later in the year when
the surface waters were well mixed, there would have
been no chemical gradient with depth within the
sampling depth interval (e.g., Patterson & Kachinjika,
1995—Figs. 1.18, 1.29).
A modern distribution study quantitatively relates
variables of the lake environment (e.g., water chemistry, physical limnology) to the taxa present at a
variety of sites, over a wide gradient of environmental
conditions (Gasse et al., 1995; Fritz, 1996). This type of
study determines the range of ecological tolerance of
individual taxa based on where they are found at
present, which then allows a paleoecologist to apply
the range determined from modern relationships to
fossil assemblage data to interpret paleo-lake conditions. Paleoecological reconstruction from lacustrine
fossils is not a new technique. It is a well-tested method
that has been used for many years with a variety of
indicators. However, it has not yet been quantitatively
applied to ostracodes from Lake Malawi. Researchers
have long made inferences about lake paleoecology
using an understanding of the relationship between
species assemblages and modern environmental conditions (e.g., Fritz, 1996). In addition, a number of
these relationships have been used successfully to
quantify paleoenvironmental change (e.g., diatoms,
Gasse et al., 1995; pollen, Vincens et al., 2005). The
usefulness of ostracodes as paleoecological indicators
has long been understood (Delorme, 1969). As a result,
ostracodes have been used across the globe in quantitative paleoclimatic reconstructions from China (e.g.,
Mischke et al., 2002) to Europe (e.g., Horne, 2007), and
the United States (e.g., Smith, 1993). Surface shell
collections are time-averaged (in this study each
sample represents *15–20 years of deposition), and
are therefore more appropriate for comparison with
sampled intervals in paleoecological studies than a
snapshot sample of live ostracodes.
In addition to ostracodes, other biota commonly
used as paleoenvironmental indicators include lake fly
larvae (chaoborids), molluscs (bivalves and gastropods), fish, green algae (Botryococcus), and diatoms.
Chaoborids are often found in lakes with an anoxic
bottom layer which they need to escape predation by
fish (Baker et al., 1985; Dawidowicz et al., 1990).
There were no chaoborids found in this study indicating the deepest sampled depths were oxygenated.
Mollusc presence indicates littoral–profundal conditions above a permanent oxicline, similar to ostracodes. Fish fossil abundance in Lake Malawi has been
interpreted as indicative of shallow lake depths, or
widely fluctuating lake levels (Reinthal et al., 2011).
The green algae Botryococcus is commonly preserved
in lake sediments as a result of the high silica content
in its cell walls (Cohen, 2003). Botryococcus has
previously been interpreted as representing a wide
range of environments from arid/semi-arid to fresh
water conditions, and is thought to out-compete other
organisms in rapidly changing environments (GuyOhlson, 1992). Diatoms have been widely used in
paleoenvironmental studies because many species
have very restricted ecological niches and are therefore excellent indicators of past environmental conditions. In this study, a single diatom (Surirella
fuellebornii var. constricta) was frequently observed
and therefore counted purely out of curiosity. It has
previously been documented in two of the in-flowing
rivers (Baka and Songwe) and was assumed to be
present in Lake Malawi itself (Cocquyt & Jahn, 2007).
Finally charred particles provide a sedimentological
indicator of fire frequency in the watershed. Sandsized charred particles (macro-charcoal) cannot be
readily transported long distances before being deposited in a basin and therefore likely represents the local
fire history, whereas micro-charcoal may be windborne, and therefore more indicative of regional fire
history (e.g., Thevenon et al., 2003).
There are a few limitations to be aware of when
conducting a modern distribution study within a single
lake basin, and specifically Lake Malawi. First, there
is very limited water chemistry variance within the
modern lake (Halfman, 1993; Patterson & Kachinjika,
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Hydrobiologia
1995). Second, the range of conditions in the modern
lake at variable depths only bear a partial relationship
to a similar depth range under much lower lake stands
and more saline/alkaline lake waters. Third, the
environmental gradient lengths are short, and fourth,
most of the taxa of modern Lake Malawi are endemics
(Martens, 2002), which limits our ability to draw
comparative conclusions about the lake under past
conditions. A modern distribution study using only the
one lake may not be ideal, however, since a number of
the ostracode species in the paleo-lake were also
endemic (Park & Cohen, 2011; Blome et al., in prep.),
a training set approach using multiple modern lakes
would produce no modern analog for ostracode
species assemblages. The environmental variables
chosen for this study are ones we hope to be able to
reconstruct to interpret past lake conditions.
This study focuses on the modern distribution of
both ostracodes and other commonly preserved biota
within the nearshore (1–60 m) environment of Lake
Malawi, in order to identify the relationship between
ostracode species and these other, frequently cooccurring biotic indicators, and the ecological range of
each of their habitats. The relationships between these
bio-indicators and quantifiable variables not observable in sediment cores (e.g., water chemistry, depth), as
well as to variables of physical limnology, which are
measurable in a sediment core (e.g., grain size, organic
and inorganic carbon content), will inform future
interpretations of the paleoenvironment of Lake
Malawi (e.g., Blome et al., in prep.).
Methods
Water chemistry and ostracode sample collection
Prior to the March 2010 field season, we acquired
detailed bathymetric maps of the Cape MacLear
region at the southern end of Lake Malawi from the
Department of Surveys—Hydrographic Unit in
Malawi (Malawi Government, 2003). Using these
maps and Google Earth images of the shoreline, we
laid out the locations of numerous depth transects
(perpendicular to isobaths). Transects were labeled
A–G with A south of Malembo, and G north of
Chembe Village (Fig. 1). Transect starting locations
were chosen for different types of shoreline environments (Fig. 2): high relief (HR) with coarse sandy
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beach, low relief (LR) near river inflow, and LR near a
marshy environment. Surface sediment samples were
collected every 5 m depth along each transect beginning at the shore (1, 5, 10, 15, …, 60 m) for the
northern-most transects (F, G). Transects A–E were
sampled at 5-m intervals from 1 to 30 m and at 10-m
intervals between 30 and 60 m due to time constraints
in the field. Additional ‘‘in-fill’’ sampling at shallower
depths along isobaths (parallel to shore) most commonly occurred at depths of 5 and 20 m (e.g., Transect
ZA50 located near Transect A along the 50 m
isobath). Samples from Transects A4 and A5 were
collected by hand via SCUBA (table in ES15).
At each site, depth was confirmed using a handheld
depth finder, and surface water temperature was
measured using a Hach pH-meter over the side of the
boat. In addition, at all sites up to 25 m, bottom
temperature and dissolved oxygen (DO) content were
measured using a YSI-83 multi-meter (limited to 25 m
by the length of the probe cord) (tables in ESM 16, 17).
Previous research has shown specific conductivity and
major ion composition in the upper 25 m of the modern
lake to be relatively invariant (Halfman, 1993; Patterson
& Kachinjika, 1995). For this reason, we chose not to
measure major ion concentrations, and only measured
conductivity at a handful of sites (four) to compare to
previously reported measurements (tables in ESM 16,
17). In addition to temperature, DO, and conductivity
measurements, surface sediment samples were collected at each location using a PONAR grab-sampler
(see Strayer, 2010—Fig. 5b for example). Arms
attached to the grab-sampler limited its penetration into
the sediment to approximately 2 cm. There were 20
sites where no sample was collected for a variety of
reasons, most commonly due to repeated improper
closure of the PONAR sampler. These sites are not
included in Table 1. In addition, sites 58–77 were rock
surface scrapings taken by SCUBA sampling which
yielded insufficient sediment for this study, and are
therefore also not included in Table 1. The total number
of sites presented in this study is 102, with a relatively
even spread over the different shoreline environments
and across the depths of interest (Table 1).
Sample preparation: wet-sieving, laser diffraction
granulometry, and loss-on-ignition (LOI)
Approximately 16–20 grams of wet sediment from
each sample was split into two aliquots of 12–16 and
Hydrobiologia
Fig. 2 Categorization of different offshore environment transects. Sites are divided into ‘‘High Relief—Sand,’’ ‘‘Low
Relief—River,’’ and ‘‘Low Relief—Marsh.’’ The first part of the
category describes the topography inland of each transect, and
the second part of the label describes the immediate shoreline
environment: coarse sandy beach, river inflow, and marshland,
respectively. The images to the left typify each subenvironment.
The middle panel is a digital elevation model (DEM) map
(http://www.geomapapp.com, Ryan et al., 2009) of the study
area with the main transects illustrated as solid lines from shore
to their end at 60 m water depth. Extending onto land from each
sampled transect is a dotted line showing the location of the
topographic transects shown in the panel on the right. Vertical
exaggeration (VE) is 15 for transects A–D (‘‘Low Relief’’), and
VE = 3 for transects E, F, and G (‘‘High Relief’’)
4 g. The 12–16 g split was passed through two wiremesh sieves, the first with a mesh size of 2 mm, the
second with a mesh size of 63 lm, yielding sediment
splits of [2 mm (gravel), 2 mm–63 lm (sand), and
\63 lm (silt and clay). The two larger fractions were
dried at low temperatures and weighed to determine
the percent of each grain size within a sample. The
smallest grain size fraction was further prepared by
mixing the sediment with 10 ml sodium hexametaphosphate (25 g/500 ml DI water) to insure complete
disaggregation of particles prior to grain size distribution analysis by laser diffraction. This last analysis
was used to obtain the fraction of silt and clay-sized
particles in each sample.
The 4 gram aliquot was subjected to sequential
heating to estimate the water, organic, and inorganic
carbon content of the sample (Dean, 1974; Heiri
et al., 2001). The samples were first heated for
24–48 hours at 60C, then weighed to get a dry
weight (DW60) and to estimate water content. This
was followed by 4 hour burn at 550C to combust
the organic matter in the sample (DW550), followed
by a short, high-temperature burn at 1,000C for
2 hours of the residual ash to evolve carbon dioxide
from the inorganic carbonate in the sample
(DW1000). The following calculations were used to
determine (1) percent water content to estimate
initial dry weight of the sediment, (2) LOI550, which
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Hydrobiologia
Table 1 Total number of sites sampled by depth (above) and
shoreline environment (below), number of sites with more than
five total ostracodes, and the percentage of total sites with more
than five total ostracodes
Depth
# sites total
# sites [5 cods
1
5
2
5
18
17
10
9
9
% sites [5 cods
40
94.44
100
15
6
5
20
20
17
85
83.33
25
30
4
6
1
0
25
0
35
2
0
0
40
9
0
0
45
2
0
0
50
11
0
0
55
2
0
0
60
8
0
0
Shoreline
environment
# sites
total
# sites
[5 cods
% sites
[5 cods
HR—sand
32
10
31.25
LR—marsh
32
18
56.25
LR—river
38
23
60.53
Olympus SZH binocular microscope. Ostracode identification followed multiple authors’ work including:
Sars (1910, 1924), Klie (1944), Howe (1955), Neale
(1979), Van Harten (1979), Kempf (1980), Martens
(1986, 1988), Rossetti & Martens (1998), and Park &
Cohen (2011), with the bulk of the Lake Malawi
endemic species identified using Martens (2002) and
(2003). In addition, the wet-sieved residues were
counted for absolute abundance of other common
fossil remains in this same size range including:
charred particles, fish (comprising bones, teeth, and
scales), mollusc fragments, and the relative abundance
of algae (Botryococcus) measured as a percentage of
sample material. As each sample contained a different
mass of sediment analyzed for a number of reasons
from sampling to analysis, each sample was weighed
prior to analysis and the ‘‘weight picked’’ was
recorded. In this way, taxa abundance can be compared across samples by their density (number/grams
dry sediment) (e.g., Cohen et al., 2007). Fossil
identifications were confirmed using a Hitachi
3400 N SEM and compared to previously described
modern material.
Rarefaction analysis: cross sample comparison
of species richness
represents the percent weight loss due to combustion
of organic matter, and (3) LOI1000, the percent
weight loss due to combustion of inorganic carbonate (Heiri et al., 2001).
% water ¼
1 ððInitial wet sediment weightÞ=DW60 Þ;
ð1Þ
LOI550 ¼ ððDW60 DW550 Þ=DW60 Þ 100;
ð2Þ
LOI1000 ¼ ððDW550 DW1000 Þ=DW60 Þ 100:
ð3Þ
Wet-sieved sand-sized residue analysis
Wet-sieved residues were counted for total number of
ostracodes per sample, up to *300 valves, although
only five of the 102 samples contained [300 valves.
All other samples found to contain ostracodes were
counted in their entirety. Noted in each sample were
the species and taphonomic condition (% broken,
whole carapace, adult) of each valve using an
123
In order to compare species richness across sites with
differing total abundances of ostracodes, we needed to
account for rarefaction: as abundance increases, so
does the likelihood of finding more species in a given
sample. Rarefaction analysis was conducted using the
computer program ‘‘psimpoll’’ (Bennett, 2007). This
type of analysis estimates the number of taxa (t) one
would expect to find in a random sample of individuals
(n) taken from a larger collection of individuals
(N) containing T taxa (Birks & Line, 1992). N and
T are the actual abundances and number of species
found in a given sample, respectively, and n is the
minimum number of individuals chosen by the
researcher and can be based on a number of factors.
For our analysis, we chose 25 in order to have more
individuals than were found in a single sample and to
maximize the number of samples included in the
analysis. All samples with fewer than 25 individuals
were excluded from this analysis, yielding a total of 35
sites with a combined 33 species.
Hydrobiologia
Constrained correspondence analysis (CCA)
Results
We used CCA to better understand the patterns of
variability in the biological data that are accounted for
by the measured environmental variables. This method
is an extension of correspondence analysis (CA);
whereas CA finds the best theoretical environmental
gradient that maximizes the separation of species with
unimodal distributions (ter Braak and Prentice, 1988),
CCA constrains that axis to be a linear combination of
actual environmental variables (Juggins, 2009; Oksanen, 2004). CCA is considered extremely robust to
noisy data and is not very susceptible to outliers
(Juggins, 2009). Therefore, assuming the biological
assemblages are controlled by the environment, sites
that cluster in ordination space will have similar
species composition and come from a similar environment, and vice versa (Birks, 2003). A ‘‘full’’ CCA
model will analyze the amount of variance constrained
using ALL measured environmental variables, however, for reasons of parsimony and practicality it is
prudent to find a minimal adequate model. This
‘‘reduced’’ CCA model is the one that explains (both
ecologically and statistically) the biological data
(almost) as well as the full model (Oksanen, 2004).
We simplified our models in two steps; first by testing
the significance of each individual environmental
variable in the full model, and then by specifying
those remaining variables as co-variables and asking
whether each remaining variable explains any significant portion of variance once the effect of the other
variables has been taken into account (Juggins, 2009).
Permutation tests on both the full model and the
reduced model were run to test the significance of each.
The following constraints were applied to maintain a
high level of robustness in our analysis: (1) sites
needed to have greater than 5 total ostracodes to be
included and (2) species had to be present in more than
one site to be included. Following these criteria, seven
sites (Nos. 4, 8, 17, 22, 30, 31, and 124) and three
species (Cypridopsine sp.A, Limnocythere s.l. sp.8,
and Limnocythere sp.B n.sp.) were removed from
analysis. Prior to analysis, biological percentage data
were arc-sine transformed, count data were log transformed, and environmental data were standardized to
normalize the distribution of the data. The CCAs and
significance testing were performed using the ‘‘vegan’’
package and the program R, version 2.12.0 (R
Development Core Team, 2010; Oksanen et al., 2011).
Raw limnological results can be found in the Electronic Supplementary Material and their implications
as environmental variables for the distribution of
ostracodes and other biota are discussed further in the
results of the CCA analysis. Photographs of each
species found in this study can be found in figures in
ESM 1–8.
Coarse residue counts: total abundances
of sedimentological and biological indicators
(charred particles, ostracodes, fish, molluscs,
and Botryococcus)
These data are presented in terms of abundance per
gram of dry sediment to provide a density of each of
these taxa per sample and allow their abundances to be
comparable across all samples taken (tables in ESM
16, 17). Sites offshore from the river inlet typically
have orders of magnitude greater charred particle
abundance than the other two sites between 1 and
30 m water depth (figure in ESM 9). In addition, the
highest proportion of sites containing any charred
particles (regardless of abundance) is from the
offshore river group at most depths.
Total abundances of ostracode valves, fish bones,
and molluscs, and relative abundance of Botryococcus
colonies are shown by depth in table (ESM 17) and
Fig. 3. Data are plotted on a logarithmic scale for the
absolute abundances as they differ from site to site by
orders of magnitude. There is a marked difference in
indicator abundance beginning between 20 and 25 m.
Above those depths ostracodes, molluscs and to a
lesser extent, fish remains are all quite abundant, with
ostracodes being the most abundant remains at each
shallow depth. At 25 m, both the frequency of
occurrence and total abundance of ostracodes decrease
precipitously. From 30 to 50 m, ostracodes are found
at only four sites, and never in densities greater than
three valves/gram sediment. Below 30 m, fish bones
become much more abundant. Botryococcus does not
occur above 20 m, and gradually increases in relative
abundance until 50–60 m where its abundance is high,
though with significant variability. Like ostracodes,
molluscs are quite abundant, and present at numerous
sites in water depths less than 25 m. Below that depth
they become less abundant, and occur in fewer
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Hydrobiologia
LOG Total Count/gram sediment
Ostracodes
Molluscs
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
80
Botryococcus
2.5
70
2.0
60
1.5
50
1.0
40
0.5
30
0.0
20
-0.5
10
Percent (%)
LOG Total Count/gram sediment
Fishbone
0
-1.0
0
10
20
30
40
50
60
Depth (m)
0
10
20
30
40
50
60
Depth (m)
Fig. 3 Biological indicator abundance plotted against depth,
with each indicator on its own labeled panel (Ostracodes,
Molluscs, Fishbone, and Botryococcus). Absolute abundances
of ostracodes, molluscs, and fishbones are plotted as the LOG
abundance/gram sediment counted (left-hand axis). The relative
abundance of Botryococcus is given as a percentage of the total
sample (right-hand axis)
samples overall, although they are present at each
sampled depth below 25 m, unlike the ostracodes.
found at depths greater than 30 m have some of the
thickest shells (see figures in ESM 4A–G, 6J–L). Six
species had limited depth ranges including Cypridopsine n.gen. sp.A and Limnocythere s.l. sp. 6 (5 m),
Limnocythere s.l. sp.3 and Cypriodpsine n.gen. sp.X
n.sp. (5–10 m), and Limnocythere s.l. sp.B n.sp. and
Gomphocythere irvinei (20 m). Multiple (13) species
were quite ubiquitous and found at multiple depths
across the entire depth range from 1–5 to 20–25 m and
are marked in Fig. 4 with a star.
In order to compare species abundances across
multiple sites of the same depth and/or offshore
environment, average abundances were calculated for
each species at each depth and within each environment. This involved calculating the average abundance of each species found within each subdivision of
either depth or environment (e.g., (total 9 species at
1 m depth)/(total ostracodes at 1 m depth), then repeat
Coarse residue counts: ostracode species
abundances
A complete list of all species identified in each sample
(including their relative and absolute abundances, and
the number of species per sample) can be found in
table in ESM 18. The depth range of each species
found in this study is illustrated in Fig. 4. For
taxonomic references see Blome et al., in prep. A list
of total number of samples in which each species was
recovered and an index of ESM figure number for each
species can be found in Table 2.
Figure 4 illustrates the precipitous decline in
ostracode abundance below 20 m depth, as was
alluded to in the previous section. The three species
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Hydrobiologia
Fig. 4 Depth range of each
ostracode species found in
this study. Species are sorted
top to bottom first by median
(dark bar across each box
plot), then by average. For
example, the first nine
species all have median of 5
but the first two have the
lowest average and the last
two have the highest. The
width of the box plot is
proportional to sample
size—those species only
found at one or two sites
have the smallest width.
Species labeled with
‘‘asterisk’’ were quite
ubiquitous and found at
multiple depths across the
entire depth range from 1–5
to 20–25 m
Limnocythere sp.8
Cypridopsine sp.A
Limnocythere sp.3
Limnocythere sp.6
Cypridopsine sp.1
Gomphocythere huwi
Limnocythere sp.A
Limnocythere sp.2
*Cyprinotinae sp.2
Cypridopsine sp.N1
*Limnocythere sp.10
*Cypridopsine sp.P
Cypridopsine sp.C
*Cyprinotinae sp.1
*Cyprinotinae sp.3
*Cypridopsine sp.L
*Limnocythere sp.9
Candonopsis sp.
*Cypridopsine sp.K
Cypridopsine sp.G
Cypridopsine sp.D
*Zonocypris costata
*Alicenula serricaudata
*Gomphocythere emyrsi
Cypridopsine sp.O
Cypridopsine sp.B
*Limnocythere sp.1
*Ilyocypris sp.1
Cypridopsine sp.F
Limnocythere sp.4
Cypridopsine sp.N
Gomphocythere irvinei
Limnocythere sp.B
Shallower
O
O
O
O
Intermediate
O
O
Deeper
O
0
at 5 m depth, and so on) (Table 3). One sample
exceeded the rarity requirements at 25 m depth, but
because there were no other samples with which to
average it, this sample is not included in the average
depth dataset (Nsites = 50, Ncods = 6,328). However,
the data from that site are included with the average
abundance by environment dataset (Nsites = 51,
Ncods = 6,361).
Pooling sites of the same depth or environment
allow some trends to be more visible than looking at
each site individually. Those species that comprised
over 5% of the total assemblage of a given depth or
environment are highlighted in Fig. 5. At 1 m nearly
half of the assemblage (Ncods = 27) is unidentifiable
due to valve damage, and although eight total
species are identified, only four comprise [5% of
the total assemblage. This is the only depth at which
G. huwi is found in comparatively large quantities.
At 5 m the greatest total number of ostracodes are
found, nearly 100 times those found at 1 m
(Ncods = 2,716). This depth also has the highest
number of total species identified (28), however,
O
10
O
O
20
O
30
40
50
many of those species were comparatively rare; 22
species had cumulative totals less than 5% of the
total assemblage. The percentage of unidentified
ostracodes decreases to 11% at 5 m and decreases
further to 7% at 10 m (Ncods = 1,706) where valve
damage is minimal. There are 25 total species
identified at this depth with 6 greater than 5%. 15 m
depth had the most even representation of species
with 8 of 21 total species individually comprising
[5% of the assemblage (Ncods = 569). The portion
of unidentifiable valves increases to 16% at this
depth and increases further to 22% of the total
(Ncods = 1,310) at 20 m. There are 26 species found
at this depth with seven [5% of the total. This is the
only depth where Cypridopsine n.gen. sp.N contributes significantly to the total assemblage. Interestingly, a number of species repeatedly account for
[5% of the assemblage, regardless of depth. These
include Cyprinotinae n.gen. sp.2, G. emyrsi, and L.
sp. 9 (the latter with the exception of 1 m depth).
When comparing species composition by adjacent
shoreline environment, HR has the highest percentage
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Hydrobiologia
Table 2 Number of sites in which each species was found
Species
# sites/
species
ESM
figure
Image
Alicenula serricaudata
31
6
A–C
Candonopsis sp.
13
6
D–E
1
1
A–C
Cypridopsine n.gen. sp.A
Cypridopsine n.gen. sp.B
5
1
D–E
Cypridopsine n.gen. sp.C
12
1
G–I
Cypridopsine n.gen. sp.D
3
1
F
Cypridopsine n.gen. sp.F
11
1
J–K
Cypridopsine n.gen. sp.G
3
2
A–B
Cypridopsine n.gen. sp.K
11
2
C–E
Cypridopsine n.gen. sp.L
14
2
F–H
Cypridopsine n.gen. sp.N
14
2
I–L
Cypridopsine n.gen. sp.O
13
3
A–C
Cypridopsine n.gen. sp.P
30
3
D–F
Cypridopsine n.gen. sp.X n.sp.
2
3
G–H
Cypridopsine n.gen. sp.1 n.sp.
Cyprinotinae n.gen. sp.1
5
27
3
4
I–L
A–C
Cyprinotinae n.gen. sp.2
33
4
D–G
Cyprinotinae n.gen. sp.3
36
4
H–J
Gomphocythere emyrsi
37
5
A–D
Gomphocythere irvinei
2
5
E–G
Gomphocythere huwi
7
5
H–J
Ilyocypris sp.1
24
6
F–I
Limnocythere s.l. sp.1
25
7
A–F
Limnocythere s.l. sp.2
10
7
G–H
Limnocythere s.l. sp.3
7
7
I–J
Limnocythere s.l. sp.4
8
7
K–L
Limnocythere s.l. sp.6
5
7
M–N
Limnocythere s.l. sp.8
1
7
O
Limnocythere s.l. sp.9
37
8
A–C
Limnocythere s.l. sp.10
Limnocythere s.l. sp.A n.sp.
20
14
8
8
D–F
G–L
Limnocythere s.l. sp.B n.sp.
1
8
L
20
6
J–L
Zonocypris costata
Also included in this list are the references for the ESM figure
numbers for each species
of unidentifiable valves (24% of Ncods = 508) and also
has the least number of total identified species (26),
though not by a significant amount. This observation is
supported by the rarefaction analysis, which shows that
the range of expected number of taxa is similar for all
environment types regardless of depth, and that the
variance in species richness does not vary significantly
123
with depth between 5 and 20 m (figure in ESM 10). The
offshore marsh sites (Ncods = 2,074) comprise 28 total
species of which six account for greater than 5% of the
total assemblage. The offshore river sites have the
highest total number of ostracodes (Ncods = 3,779) and
the highest number of species identified (29) with
seven accounting individually for [5% of the total.
This is the only environment where Ilyocypris sp.1 is
found in comparatively high abundance, which may
have interesting paleoecological reconstruction ramifications (Blome et al., in prep). A number of species
are found in high abundances in all three environments
including C. n.gen. sp.2&3, G. emyrsi, and L. s.l. sp.9.
This suggests one of two things; either these species
would be poor paleoecological indicators of nearshore
environment as they have a broad range of suitable
ecological niches, or that geographic area is a less
significant factor in determining an ostracode’s ecological niche in Lake Malawi, as was suggested by
Martens (2002).
Constrained correspondence analyses
For all further analyses, sites with five or fewer total
ostracode valves and species found in fewer than two
sites have been removed. Although it is quite informative looking at average abundances of each ostracode
species by geographic location and depth, CCA analyzes the abundance of species data at each site in
multidimensional space and clusters those sites with
similar species assemblages, while using environmental
data to constrain the axes (Figs. 6A, B, C). When
performing a CCA, there must not be gaps in either the
environmental or biological data and the number of
sample sites for each must match. As we were limited by
equipment to measuring DO and bottom water temperature to 25 m and shallower, we ran three separate CCA
models: (1) On ostracode species data alone using all
measured environmental variables (Nsites = 37), (2)
Using all biological data counted and all measured
environmental variables (Nsites = 43—only sites shallower than 30 m could be included), and (3) Using all
biological data from all depths but eliminating DO and
bottom water temperature as environmental variables
(Nsites = 99). We present the results of the three CCA
models below, each of which contributes to our
understanding of the interaction of ostracode species
assemblages with environmental variables and/or other
biological data (graphic abbreviations in Table 4).
1.3
Cypridopsine sp.L
2.9
1.4
Cyprinotinae sp.3
8.5
2.9
1.9
2.8
Limnocythere sp.9
Limnocythere sp.10
Limnocythere sp.A
Zonocypris costata
7.3
9.3
1.9
2.1
11.3
1.6
1.3
15.6
6.3
0.5
0.7
4.6
7.0
1.2
0.9
5.1
22.4
3.6
0.7
0.7
5.3
0.7
0.3
4.4
12.5
23.7
0.4
0.8
1.4
12.7
0.2
1.0
0.8
0.2
2.0
2.7
0.6
11.4
8.2
10.0
3.7
4.7
1.4
0.6
5.3
0.4
0.4
1.6
0.6
1.6
1.0
2.4
HR
Ncods = 508
Nsites = 10
14.5
0.7
3.3
2.4
9.1
0.1
1.0
0.3
2.0
2.3
2.9
0.3
0.4
7.4
9.4
17.6
9.4
0.0
7.5
0.6
0.3
1.7
0.5
0.1
0.3
0.8
1.8
0.3
2.8
LR—marsh
Ncods = 2,074
Nsites = 18
10.8
8.0
0.6
1.9
7.1
1.1
0.1
0.8
0.3
1.5
4.8
0.9
0.0
12.1
7.2
14.7
10.9
0.1
3.8
1.1
3.4
1.3
0.7
0.1
1.0
0.8
0.2
0.2
0.3
4.2
LR—river
Ncods = 3,779
Nsites = 23
Total number of ostracodes and sites included in each pooled calculation is given in the column heading Ncods and Nsites, respectively. Abundance values of 5% or greater are shown in bold and indicate
which species are displayed graphically in Fig. 5
11.1
0.1
Limnocythere sp.6
48.1
0.7
Limnocythere sp.4
Unidentified
0.5
Limnocythere sp.3
0.8
1.8
1.6
3.7
11.1
Limnocythere sp.2
Limnocythere sp.1
Ilyocypris sp.
0.2
9.2
0.7
19.5
2.0
5.4
5.6
1.8
0.7
1.1
17.2
1.6
10.5
6.3
4.6
1.9
8.6
0.3
0.2
0.2
2.2
0.9
0.2
0.2
0.6
8.8
20 m
Ncods = 1,310
Nsites = 17
Gomphocythere huwi
5.3
7.4
17.0
9.8
0.2
4.3
0.4
3.9
4.0
0.7
1.2
1.1
1.4
2.8
15 m
Ncods = 569
Nsites = 5
Gomphocythere irvinei
7.4
7.4
Cyprinotinae sp.2
Gomphocythere emyrsi
20.1
11.1
Cyprinotinae sp.1
12.8
0.0
12.9
Cypridopsine sp.X
7.3
Cypridopsine sp.P
1.2
0.4
Cypridopsine sp.K
0.1
0.1
0.8
0.1
0.0
Cypridopsine sp.G
0.4
0.3
Cypridopsine sp.F
3.7
0.1
Cypridopsine sp.D
0.7
0.2
Cypridopsine sp.O
0.4
Cypridopsine sp.C
0.6
1.0
10 m
Ncods = 1,706
Nsites = 9
Cypridopsine sp.N
0.1
Cypridopsine sp.B
0.1
3.0
1.4
3.7
5m
Ncods = 2,716
Nsites = 17
Cypridopsine 1 n.sp.
3.7
Candonopsis sp.
1m
Ncods = 27
Nsites = 2
Alicenula serricaudata
Species
Table 3 Percent abundance of each species by depth and environment
Hydrobiologia
123
Hydrobiologia
1m Depth
Cyprinotinae 2
11%
n = 27
5m Depth
Unidentified
11%
n = 2716
Cyprinotinae 1
13%
Cyprinotinae 3
8%
Gomphocythere huwi
7%
Unidentified
48%
0 < 5 PCT
22%
Cyprinotinae 2
20%
22 species
Ilyocypris
11%
Gomphocythere
emyrsi
5%
4 species
Limnocythere 9
9%
Cypridopsine P
7%
0 < 5 PCT
15%
Unidentified
7%
Cyprinotinae 1
10%
0 < 5 PCT
21%
10m Depth
n = 1706
15m Depth
n = 569
Unidentified
16%
Cyprinotinae 3
13%
Cyprinotinae 1
6%
Cyprinotinae 2
11%
Cyprinotinae 2
17%
Cypridopsine P
5%
19 species
Zonocypris costata
6%
0 < 5 PCT
20%
Cyprinotinae 3
8%
Gomphocythere
emyrsi
17%
Zonocypris costata
9%
Limnocythere 9
11%
13 species
Limnocythere 6
7%
Limnocythere 9
5%
Ilyocypris
5%
Gomphocythere
emyrsi
19%
Fig. 5 Pie charts showing the most abundant species at each
depth and each shoreline environment. Cutoff for display was
greater than 5% of the total ostracodes in a zone. All other
species are comingled in the wedge labeled ‘‘0 \ 5 PCT.’’ For
individual abundances for all species see Table 3
For the ostracode-only CCA, the full model
included all 11 measured environmental variables
and constrained *40% of the variance with a P value
of 0.01 (Table 5). Whereas the reduced model
explains only 14% of the variance, only two environmental variables are needed to double the F factor and
decrease the P value of the model to 0.005, yielding
the minimal adequate model. Figure 6A shows that
these significant constraining environmental variables
are depth and DO. As would be expected, once fit to
the existing constraining axes, bottom water temperature shows an inverse relationship to depth, and
increases in the same direction as DO (seen in figure a,
b in ESM 11). Many of the ostracodes plotting near the
origin of the CCA plot are those that were starred in
Fig. 4 for being found across multiple sites at a wide
range of depths. Those ‘‘deeper’’ species (Fig. 4) like
Cypridopsine n.gen. sp.N and C. sp. F, Limnocythere
sp. 1 and L. sp. 4, and Ilyocypris sp.1 plot toward
increasing depth, and vice versa for the ‘‘shallower’’
species. Interestingly, although the range of depths
investigated was only 1–25 m, depth was still a key
explanatory variable. Although dropped from the
minimally adequate model, shoreline environment
and the other environmental data added post hoc fit the
constrained model with P values of better than 0.09,
with the exception of surface water temperature
(Table 5).
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Hydrobiologia
Cyprinotinae 1
6%
20m Depth
High Relief
n = 1310
n = 508
Cyprinotinae 2
5%
Unidentified
22%
Cyprinotinae 2
10%
Unidentified
24%
Cyprinotinae 3
8%
Cypridopsine N
9%
Cypridopsine L
5%
Limnocythere 9
5%
Cypridopsine P
5%
Gomphocythere
emyrsi
9%
0 < 5 PCT
22%
Limnocythere 9
13%
0 < 5 PCT
24%
19 species
Ilyocypris
13%
20 species
Alicenula
9%
Unidentified
15%
Cyprinotinae 1
9%
Low Relief - Marsh
Low Relief - River
n = 2074
n = 3779
Unidentified
11%
Cyprinotinae 2
18%
Gomphocythere
emyrsi
11%
Cyprinotinae 1
11%
Cyprinotinae 2
15%
0 < 5 PCT
24%
0 < 5 PCT
25%
22 species
Cyprinotinae 3
7%
Cyprinotinae 3
9%
22 species
Gomphocythere
emyrsi
Cypridopsine P
7%
8%
Limnocythere 9
9%
Ilyocypris
5%
Zonocypris costata
8%
Gomphocythere Limnocythere 9
7%
emyrsi
12%
Fig. 5 continued
For the shallow-only CCA using all biological
indicators, the full model again included all 11 measured
environmental variables and constrained *45% of the
variance with a P value of 0.005 (Table 6). In this case,
the minimal adequate model explains 16% of the
variance with two significant environmental variables
which nearly doubles the F factor of the full model and
keeps the P value at 0.005. Figure 6B shows that these
significant constraining environmental variables are
bottom water temperature and DO. Unlike the ostracode-only analysis, the minimal range of depths covered
here is insufficient to break out depth as a significant
factor, likely because much of the difference in depth
range between ostracodes and the other biological
indicators begins at 25 m (see Fig. 3). However, the two
most significant variables are ones which are known to
inversely co-vary with depth (Fig. 6; figure a, b in ESM
11). Molluscs (M) plot in ordination space near total
ostracode count (Tc) suggesting the environmental
range for both indicators overlap. Conversely, Botryococcus (B) and Surirella (S) plot far away from the
ostracode cluster. Fish and charcoal plot closer to
ostracodes, but may be responding to something other
than bottom temperature and DO (see Fig. 6B).
Although dropped from the minimally adequate model,
the environmental data added post hoc fit the constrained model with P values of better than 0.09
(Table 6).
123
A
B
1.5
Gi
1.5
Hydrobiologia
B
SiltClay
1.0
1.0
CB
CG
HR
A
CF
CN
−0.5
0.5
CCA2
Ge CLRM
L4
L10
L9
CO
CP
Gh
CC Zc CT2
Lx
CT1
CT3
L2
Gi
L6
LRMHR
F
L4
CT3
M
A
CT2 CP
CO
L3
Tc CT1 Lx
C
L9
L10
CC
DO
L1
CL
CB
Cx
−1.0
−1.0
CK
CD
C1
Ge
BTemp
BTemp
Zc
L3
LRR
Gh
STemp
Sand
L2
I
L6
STemp
LRR
Ch
CF
I
0.0
DO
GrSand
CN
−0.5
Depth
0.0
CCA2
0.5
CK Cx
CD
CL
L1
Sand
GrSand
S
CG
C1
−2.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5
CCA1
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5
CCA1
C
1.5
C1
Sand
1.0
C
0.5
Gh
L3
F
Gi
Air
0.0
CB
GrSand CC
HR
L1
LRM CO L2
M
CP
Depth
Lx
CT3
L9L10 Ge
Tc
CT2
I
OrgC
−0.5
CCA2
CL
L4
LRR
−1.0
S
CG
CT1
A
L6
Ch
CK
CF
B
−1.5
CN
Zc
Cx
SiltClay
CD
−2
−1
0
1
CCA1
Fig. 6 Ostracode species are in red, the most significant
environmental variables included in the reduced model are blue
and bold, and those that were initially significant are fitted post
hoc and are black. Where species labels overlapped, a red dot
was placed at the true location, and the labels placed so as to be
legible. For index of abbreviations see Table 4. A Ostracode
species CCA—depths included 1–25 m, Nsites = 37. Depth and
DO were the most significant constraining environmental
variables in the reduced CCA model for explaining the
distribution of the biological data. Model and variable
significance testing results in Table 5. B All biological species
CCA—depths included 1–25 m, Nsites = 43. Non-ostracode
biological indicators are in red and bold to distinguish them.
Bottom water temperature and DO were the most significant
constraining environmental variables in the reduced CCA model
for explaining the distribution of the biological data. Model and
variable significance testing results in Table 6. C All biological
species CCA—depths included 1–60, Nsites = 99. Bottom water
temperature, DO, and inorganic carbon were excluded from the
analysis due to gaps in data. Non-ostracode biological indicators
are in red and bold to distinguish them. Depth and shoreline
environment were the most significant constraining environmental variables in the reduced CCA model for explaining the
distribution of the biological data. Model and variable
significance testing results in Table 7
For the ‘‘all-depths’’ CCA using all biological
indicators, the full model included seven measured
environmental variables and constrained *28% of the
variance with a P value of 0.005 (Table 7). In this case,
the minimal adequate model explains 15% of the
variance with two significant environmental variables
which increases the full model F factor and keeps the
P value at 0.005. Figure 6C shows that these significant
123
Hydrobiologia
Table 4 A listing of all abbreviations used in the CCA plots
Table 4 continued
CCA
Name
CCA
Name
A
Alicenula serricaudata
HR
High relief
C
Candonopsis sp.
C1
CB
Cypridopsine 1 n.sp.
Cypridopsine sp.B
Grouped from top to bottom by ostracode species, other
indicators, and environmental variables
CC
Cypridopsine sp.C
CD
Cypridopsine sp.D
CF
Cypridopsine sp.F
CG
Cypridopsine sp.G
CK
Cypridopsine sp.K
CL
Cypridopsine sp.L
CN
Cypridopsine sp.N
CO
Cypridopsine sp.O
CP
Cypridopsine sp.P
Cx
Cypridopsine sp.X n.sp.
CT1
Cyprinotinae sp.1
CT2
Cyprinotinae sp.2
CT3
Cyprinotinae sp.3
Ge
Gomphocythere emyrsi
Gh
Gi
Gomphocythere huwi
Gomphocythere irvinei
I
Ilyocypris sp.
L1
Limnocythere sp.1
L10
Limnocythere sp.10
L2
Limnocythere sp.2
L3
Limnocythere sp.3
L4
Limnocythere sp.4
L6
Limnocythere sp.6
L9
Limnocythere sp.9
Lx
Limnocythere sp.A
Zc
Zonocypris costata
B
Botryococcus
Ch
Charcoal
F
Fish
M
S
Molluscs
Surirella
Tc
Total ostracodes
Air
Air temperature
Btemp
Bottom temperature
Stemp
Surface temperature
OrgC
Organic carbon content
GrSand
Fraction [ sand
SiltClay
Fraction silt and clay
LRR
Low-relief river
LRM
Low-relief marsh
constraining environmental variables are depth and
shoreline environment. The first CCA axis is likely a
depth gradient; with the exception of molluscs, all other
biological data (in bold) plot in ordination space well
away from ostracodes, with Botryococcus and Surirella
the furthest removed. The second CCA axis may reflect
more of the characteristics of the offshore environment
given the spread along CCA2 of the class variables (HR,
LRM, and LRR) and the trend of the ‘‘fitted’’ data. For
example, the amount of silt and charcoal both increase
significantly in the offshore river environment (figure a
in ESM 12, figure in ESM 13), and both plot toward
negative CCA2 values as does the LRR categorical
variable (Fig. 6B). Although dropped from the minimally adequate model, the environmental data added
post hoc fit the constrained model with P values of better
than 0.09 (Table 7). Although environmental data
measurability forced the running of two separate CCA
models to look at the relationships of all collected
biological data, the results support one another.
Discussion
The interaction of numerous ecological variables
affects both the presence/absence and the relative
species abundances of ostracodes and other biota in
modern Lake Malawi. These factors include DO
content, water and air temperature, substrate grain
size and carbon content (organic and inorganic)
measured in this study, as well as other potential
factors such as conductivity, local pH, and the habitat
preferences of potential predators (e.g., fish—Abdallah, 2003) for which we currently lack data. Figures in
ESM 9, 11, and 14 illustrate how each of these factors
varied with depth and shoreline environment within
the study area. Variables such as bottom water
temperature, DO, inorganic carbon content, and fish
presence (estimated based on abundance of fishbone in
samples—Fig. 3) changed systematically with depth.
Others, including silt/clay content, and charcoal
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Hydrobiologia
Table 5 Ostracode-only CCA (N = 37)
Ostracode CCA
Individual significance
Conditional significance
Reduced model
Variable
P value
Keep/drop
P value
Keep/fit
Fit P value
DO
0.005
Keep
0.025
Keep
**
Depth
0.005
Keep
0.08
Keep
**
Bottom temp
0.005
Keep
0.29
Fit
0.001
Sand
0.015
Keep
0.14
Fit
0.075
Surface temp
0.03
Keep
0.17
Fit
0.101
[Sand
0.035
Keep
0.36
Fit
0.085
Shore env
0.09
Keep
0.17
Fit
0.067
Silt ? clay
0.25
Drop
Organic C
0.25
Drop
Inorganic C
0.28
Drop
Air temp
0.37
Drop
Ostracode CCA
Total inertia
Constrained inertia
% constrained
F factor
P value
Full model
2.4712
0.9997
40.45
1.3588
0.01
Reduced model
2.4712
0.3405
13.78
2.7164
0.005
Data showing the significance testing process for each environmental variable as well as the permutation test statistics for the full and
reduced models. The full model includes all variables listed at left. The two-step significance test follows in the next two columns
with the statistical P value of each variable and the resultant action taken. The variables with ‘‘**’’ in the final column are the only
ones included in the reduced model. The final column evaluates the significance of the fit of each environmental variable to the
constrained axes of the reduced model
abundance, showed greater variation by environment.
Based on the CCA models, three of the four significant
environmental variables affecting the distribution of
biological indicators in Lake Malawi co-vary with
depth, although shoreline environment is also significant when the full biological dataset is used.
Ostracode distribution between 25 and 50 m
There is a drastic decrease in both the number of
ostracode-bearing samples and total ostracode abundance between 25 and 50 m depth. Ostracodes are
completely absent from samples deeper than 50 m. An
ecological threshold relating to lake water conductivity, temperature, or DO content between 20 and 25 m
could cause ostracodes to survive only in the shallowest 25 m of Lake Malawi. The range of conductivities
measured at the four sites in this study (235–247.8 lm/cm,
table in ESM 16) is well within the range of those
previously reported for the upper 100 m of Lake
Malawi (Halfman, 1993; Patterson & Kachinjika,
1995), suggesting there is no conductivity threshold
crossed in the upper 25 m. The rapidly changing
temperature profile in our study area from 29–31 to
123
24–25C over 25 m depth suggests a relatively shallow
thermocline at the time of sampling (mid-March),
which is supported by 2 years of cruise data showing a
similarly shallow thermocline persisting in southern
Lake Malawi from December to April (Patterson &
Kachinjika, 1995). However, water temperatures
below 25C are unlikely an ecological barrier to
ostracodes because other researchers sampling Lake
Malawi during the same month in 1999 (Martens,
2002) found significant numbers of live ostracodes at
depths up to 30 m, where temperatures would presumably have been similar to and likely colder than those
measured at 25 m in our study (temperature was not
explicitly reported). Although measured DO at 25 m is
on average less than that at 20 m, the waters of Lake
Malawi do not become anoxic until 200 m (Patterson
& Kachinjika, 1995), and therefore too little oxygen is
not a likely cause for the decreased abundance of
ostracodes beginning at 25 m. For these reasons, it is
unlikely that one of these ecological variables is
inhibiting the survival of ostracodes at depths below
which they were recovered in our study.
The other variable that decreases systematically
with depth is inorganic carbon in the sediment (figure
Hydrobiologia
Table 6 All-indicators, shallow-depths CCA (N = 43)
Full model: Depth, ShoreEnv, GrSand, Sand, SiltClay, OrgC, InorgC, Air, S.Temp, B.Temp, DO mgL
All Bio CCA N = 43
Individual significance
Conditional significance
Reduced model
Variable
P value
P value
Fit P value
Keep/drop
Keep/fit
DO
0.005
Keep
0.02
Keep
**
Bottom temp
0.005
Keep
0.03
Keep
**
0.001
Sand
0.005
Keep
0.45
Fit
[Sand
0.005
Keep
0.46
Fit
0.001
Silt ? clay
0.005
Keep
0.59
Fit
0.001
Shore Env
0.01
Keep
0.24
Fit
0.003
Surface temp
0.066
Keep
0.17
Fit
0.081
Depth
0.15
Drop
Inorganic C
0.2
Drop
Organic C
0.29
Drop
Air temp
0.46
Drop
All Bio CCA N = 43
Total inertia
Constrained inertia
% constrained
F factor
P value
Full model
1.7398
0.7741
44.49
2.0039
0.005
Reduced model
1.7398
0.2789
16.03
3.8175
0.005
Data showing the significance testing process for each environmental variable as well as the permutation test statistics for the full and
reduced models. The full model includes all variables listed at left. The two-step significance test follows in the next two columns
with the statistical P value of each variable and the resultant action taken. The variables with ‘‘**’’ in the final column are the only
ones included in the reduced model. The final column evaluates the significance of the fit of each environmental variable to the
constrained axes of the reduced model
b in ESM 14). In this study, with the exception of two
sites, samples from depths of 25 m and greater have
less than 2 WT% LOI1000. This corresponds to the
depth at which pH begins to decrease rapidly from
8.65 between 0 and 25 m depth to 8.0 by 75 m
(Patterson & Kachinjika, 1995—Fig 1.26). As carbonate is more soluble in water at lower pH values, it is
likely that the disappearance of ostracodes from our
record results from a taphonomic bias—dissolution
across a lysocline and thus lack of accumulation below
the lake’s current carbonate compensation depth
(CCD). This would explain why live ostracodes have
been recovered from sediment samples taken in 30?
meters of water elsewhere in Lake Malawi (Martens,
2002): ostracodes are able to preferentially take in
carbonate while living, but once dead, the shells
dissolve from the record. This relatively shallow CCD
is in contrast to the supersaturated surface waters of
Lake Tanganyika (pH 8.7–9.2) where thin-shelled
organisms have been recovered from cores taken as
deep as the oxycline (*200–300 m), although a CCD
exists at greater depth (Alin & Cohen, 2004). This
difference in thin-shelled versus thick-shelled organisms may explain why the three ostracode species
found at depths greater than 30 m have some of the
thickest valves (Fig. 4; figures D–H in ESM 4, J–L in
ESM 6), and also why gastropods are still present in
the dataset below 30 m, including a single specimen
found at 60 m. Smaller, more thinly walled shells
(e.g., most ostracode valves) would dissolve faster
than larger, thicker shells as the speed of the reaction
would be the same for both, making it more likely to
find gastropod shells in deeper surface sediments than
ostracodes.
In summary, the data suggest that the sudden
decrease in the number of ostracodes recovered from
sites deeper than 25 m may be the result of postdepositional dissolution caused by carbonate undersaturation and decreased pH at depths greater than
30 m (Patterson & Kachinjika, 1995). Therefore, it is
likely that our data do not accurately reflect the living
assemblage from deeper depths in the modern lake.
This also places a clear limit on our ability to
extrapolate from these results to times in the past
123
Hydrobiologia
Table 7 All-indicators, all-depths CCA (N = 99)
Full model: Depth, ShoreEnv, GrSand, Sand, SiltClay, OrgC, Air
All Bio CCA N = 99
Individual significance
Conditional significance
Reduced model
Variable
P value
Keep/drop
P value
Keep/fit
Fit P value
DO
0.005
Keep
0.02
Keep
**
Depth
0.005
Keep
0.005
Keep
**
Shore env.
0.005
Keep
0.005
Keep
**
Sand
0.005
Keep
0.26
Fit
0.001
Silt ? clay
0.005
Keep
0.29
Fit
0.001
Air temp.
0.01
Keep
0.31
Fit
0.013
[Sand
0.034
Keep
0.58
Fit
0.003
Organic C
0.07
Keep
0.18
Fit
0.082
All Bio CCA N = 99
Total inertia
Constrained inertia
% constrained
F factor
P value
Full Model
1.9218
0.5457
28.31
4.4609
0.005
Reduced Model
1.9218
0.2896
15.07
5.6175
0.005
DO, surface, and bottom water variables not included in analysis. Data showing the significance testing process for each
environmental variable as well as the permutation test statistics for the full and reduced models. The full model includes all variables
listed at left. The two-step significance test follows in the next two columns with the statistical P value of each variable and the
resultant action taken. The variables with ‘‘**’’ in the final column are the only ones included in the reduced model. The final column
evaluates the significance of the fit of each environmental variable to the constrained axes of the reduced model
when the lake was shallower and more alkaline than
present (e.g., 110-95 thousand years ago—Cohen
et al., 2007). It cannot be assumed that ostracodes
would not have lived and been preserved at depths
greater than 30 m during such periods of lowered lake
levels, although a deeper CCD most likely existed
under higher alkalinity conditions than at present.
Ostracode distribution between 1 and 25 m
Post-depositional dissolution may explain the disappearance of ostracodes below 30 m, however, species
assemblage composition changes between 1 and 25 m
as well. Based on the ostracode-only CCA (Fig. 6A),
species distribution is best explained by two ecological factors: water depth and DO content. This finding
supports a similar assertion by Martens (2002), who
found that samples from sites at the same depth in
widely separate geographic locations had a higher
similarity index (more species in common) than
samples from the same location but from different
water depths (8 and 30 m). The influence of depth on
ostracode assemblage is most likely the result of
crossing species-specific tolerance thresholds in DO or
light penetration (not measured in this study) which is
123
typically inversely correlated with turbidity (Halfman,
1996). In contrast, coastline environment (and its
impacts on offshore conditions like substrate grain
size and carbon content) is less significant when
looking only at ostracode species distribution
(Fig. 6A; Table 5). Although standardized species
richness did not vary greatly by either depth or
environment type in this study, it may be interesting to
compare these modern values with those from samples
in the paleo-record to see how ostracode diversity has
changed with time.
Comparison of current study to Martens (2002)
live-assemblage distribution
The largest difference between our results and those of
Martens (2002) is that of the five sites he described,
species richness was between 7 and 14 species/site. As
can be seen in table in ESM 18, the average number of
species per site in our study was 9, and the maximum
was 21. However, this is not an unexpected discrepancy for two reasons. First, Martens (2002) specifically chose to describe those sites with the highest
ostracode diversity. And second, the Martens’ (2002)
report only counted those species that were alive when
Hydrobiologia
collected, and could therefore have soft-parts of the
ostracode preserved for further description. Live
collections typically have fewer total species because
they represent a snapshot in time, whereas surface
shell assemblages have the potential to time average
ostracode species present over the course of one or
more years (Alin & Cohen, 2004). Historically, typical
offshore sediment accumulation rates in Lake Malawi
have been between *1 mm/year (Pilskaln & Johnson,
1991) and *1.3 mm/year (Finney & Johnson, 1991),
however, a recent study suggests near-shore sedimentation rates are increasing due to enhanced regional
soil erosion (Otu et al., 2011). Given the depth of
penetration by the grab-sampler in this study of
*2 cm, it is likely that our samples averaged at most
between 15 and 20 years of deposition.
Since the purpose of choosing the five sites reported
on by Martens’ (2002) was to maximize overall
species diversity (to describe as many new species as
were seen in the entire dataset), and the sites chosen
were at depths of 7, 8, 10, 11 (‘‘shallow’’), and 30
(‘‘deep’’) meters, it is difficult to draw specific
comparisons between the two datasets. However,
some general similarities should be highlighted: In
Martens (2002) atlas, Cyprinotinae sp.2 and G. emyrsi
are found in ‘‘shallow’’ sites at multiple locations,
suggesting that they have a geographically broad
tolerance; these species are also found in abundance in
all three environment categories in our study (Fig. 5;
Table 3). In addition, of the eight species found at the
‘‘deep’’ sites in the live assemblage (Martens, 2002),
four were observed in our study area: Limnocythere
s.l. sp.1, Alicenula serricaudata, Cyprinotinae sp.1,
and Cypridopsine n.gen. sp.N. Each of these species
was present in the deeper samples (20 m) of our study
(Table 3) and three of these four were most abundant
at that depth than any other (Fig. 5). A final point
concerns the extremely similar distributions of Limnocythere s.l. sp.9 and Limnocythere s.l. sp.10 (see
Figs. 6A, B, C). It was suggested by Martens (2002)
that these may, in fact, be males and females of the
same species, and given their similar distribution and
similar valve attributes (figures A–F in ESM 8), our
data does not refute this assertion.
Near-river sediment characteristics
The percentage of silt and abundance of charcoal also
change with depth, however, the variation differs
further by environment. In sites offshore from river
inlets, the values for both variables increase drastically
at *20 m depth and remain high until at least 50 m
(figures in ESM 9, 12, 13). In addition, increased
charcoal and silt/clay plot very close to the low-relief
river region in ordination space (Fig. 6C). This covariation between variables suggests two things: (1)
grain size distribution in near-shore environments of
Lake Malawi is dependent on the subenvironment—
offshore channeling (e.g., figure in ESM 13, Transect
C) from even a small river may redistribute larger
grain sizes (silts) to deeper depths than settling
velocity models would suggest (e.g., Gibbs et al.,
1971); and (2) that macro-charcoal is overwhelmingly
supplied to the lake by rivers. The latter point is of
particular significance for paleoenvironmental interpretation of sediment cores from the lake, since it may
indicate that changes in local, rather than distant
(wind-borne) fire frequency are responsible for the
changes observed in these records (e.g., Whitlock &
Millspaugh, 1996; Thevenon et al., 2003).
Relationship of ostracode abundances to other
paleoecological indicators
Important biological indicators often preserved as
fossils in Lake Malawi paleo-records include ostracodes, fish, and the green alga Botryococcus. For this
reason, understanding the distribution of these indicators in the modern lake may inform interpretations
about the past lake environment (e.g., Blome et al., in
prep.). Since the precipitous decline of ostracodes
below *25 m in this study is likely caused by
taphonomic bias, its potential abundance at deeper
depths in shallower, paleo-lakes cannot be assessed.
However, the coincidence of decreased ostracode
abundance with increased Botryococcus (Figs. 3, 6A,
B) should not necessarily be overlooked as this
dichotomous relationship has also been observed
between the two indicators in the paleo-record of
Lake Malawi (Blome et al., in prep.). There may very
likely be an ecological variable that both taxa are
responding to but its interpretation in this study is
hampered by the taphonomic dissolution of
ostracodes.
Abundant fish remains may relate to geographic
location in addition to depth in this study. In the
CCA models, fish abundance plotted near the highrelief environmental category (Fig. 6C). Since
123
Hydrobiologia
cichlid fish in modern Lake Malawi are primarily
rocky-dwelling littoral communities (Cohen et al.,
2007), increased deposition of fish bones would be
expected in the offshore HR areas as these often had
sandy to rocky shorelines. Fish bones are abundant
across all depths sampled in this study. Although
total abundance increases with depth, the total
variance is similar for all depths (excluding 35, 45,
and 55 m depths due to small sample sizes) (Fig. 3).
Reinthal et al. (2011) suggest that fish are most
abundant at intermediate depths, which may indicate
abundance increases with depth until an ‘‘optimal
depth,’’ where conditions are ideal, before decreasing
in abundance. However, this study did not sample
deep enough to observe this theoretical ideal depth
and subsequent decreased abundance. Often in the
paleo-record discussed in Blome et al., in prep., the
highest abundance of fish fossils occurs at transitions
between ostracode-rich and Botryococcus-rich intervals which are interpreted as relatively shallow and
deep lake environments, respectively. It would be
interesting for a future study to sample deeper depths
in the modern Lake Malawi to find the depth of
highest fish abundance, as this value may be directly
applicable to past studies.
•
•
•
•
Conclusions
•
Modern ecological data from the southwest arm of
Lake Malawi including limnological variables, grain
size, and organic and inorganic carbonate content of
sediments serve as a data set through which to interpret
the paleo-abundance patterns of fish, mollusc, charcoal, Botryococcus, and ostracodes within the study
area. The relationship between modern taxa and
modern lake ecology may be useful for paleoenvironmental reconstruction. Interpretations of the data from
this study allow the following conclusions to be drawn
regarding the distribution and abundance of potentially fossilizeable materials in the littoral zone of
modern Lake Malawi.
•
The general absence of ostracodes below 30 m is
likely the result of a taphonomic bias caused by
shell dissolution in the undersaturated, lower
alkalinity waters below that depth. This is likely
not a direct reflection of the living ostracode
assemblage, although it is probable that ostracodes
123
would be present in lower abundances due to the
increased difficulty in calcifying their valves
below that depth. It is likely that times in the past
when lake level was lower, ostracodes were more
abundant and preserved at deeper depths due to
increased alkalinity and therefore a deeper CCD.
More species per site were found in this study than
Martens (2002) in accordance with predicted time
averaging differences between live and live/dead
assemblages.
Constrained ordination of ostracode abundance data
suggests that species niches are most significantly
affected by depth and DO content rather than
substrate grain size or OC content. Based on these
results, it is possible that a limnological variable not
measured in this study, such as light penetration or
turbidity which also co-vary with depth, may
influence ostracode distribution in Lake Malawi.
As originally suggested by Martens (2002), our
analyses support the hypothesis that Limnocythere
s.l. sp.9 and Limnocythere s.l. sp.10 may be
females and males of the same species,
respectively.
Even a small inflowing river can significantly
affect the particle size distribution of lake sediments, and it can serve as a conduit for macrocharcoal deposition into the basin from the
surrounding watershed.
The distribution by depth of the commonly fossilizable indicators ostracodes (shallow), fishbone
(shallow to intermediate), and Botryococcus
(intermediate) in the modern lake suggests that
changes in their abundances in the past may reflect
relative changes in lake level.
Acknowledgments Funding for the field work for this project
was provided by the U.S. National Science Foundation-Earth
System History Program (EAR-0602350). Funding for sample
preparation and sedimentological analyses was provided to
author Lopez by the SAGUARO (Southern Arizona
Geosciences Union for Academics, Research and Outreach)
program at the University of Arizona. SEM imaging was
performed on a Hitachi S-3400 N housed in the Geosciences
Department at the University of Arizona. Funding for the SEM
facility was through the Arizona LaserChron Center Grant NSF
EAR-0929777. Lead author Blome would like to thank a
number of people for their invaluable assistance during the 2010
field season in Cape MacLear, Malawi including: Linkston
Chataka (captain), Bernard Gulo (diver), Reidwel Nyirenda
(geologist at Malawi Geological Survey Department), and
Jeffery Stone (fellow researcher), as well as Jay Stauffer and
Hydrobiologia
Leonard Kalindekafe for logistical assistance and Howard and
Michelle Massey-Hicks for their hospitality over the course of
the trip.
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