Paleobiology, 30(1), 2004, pp. 44–81
The live, the dead, and the very dead: taphonomic calibration of
the recent record of paleoecological change in Lake Tanganyika,
East Africa
Simone R. Alin and Andrew S. Cohen
Abstract.—High-resolution (annual to decadal) paleoecological records of community composition
can contribute a long-term perspective to conservation biology on baseline ecological variability
and the response of communities to environmental change. We present here a detailed comparison
of species assemblage characteristics (species richness, abundance, composition, and occurrence
frequency) in live, dead, and recent fossil ostracode samples from Lake Tanganyika, East Africa.
This study calibrates the fidelity of paleoecological samples (i.e., both death and fossil assemblages)
to live diversity patterns for the purpose of reconstructing community dynamics through time.
Both life and death assemblages were collected from rocky sites in a mixed substrate habitat
(total of ten sampling visits over 22-month period) over spatial scales of less than a meter to about
3–12 meters. Fossil assemblages were derived from sediment cores collected in sandy substrates
adjacent to the rocky sites. Species richness in paleoecological assemblages is comparable to that
in a year’s accumulation of life assemblages sampled approximately monthly. The temporal resolution of the fossil samples in Lake Tanganyika could thus be as short as one year. Species abundance distributions were statistically indistinguishable among data sets. Rank abundance tests
demonstrated that death and fossil assemblages were quite similar, although life assemblages differed substantially in the composition of their dominant species. Species composition differences
between life and paleoecological assemblages appear to reflect the area of spatial integration represented by an assemblage—i.e., death and fossil assemblages are integrated over multiple habitat
types, whereas life assemblages dominantly represent the rocky habitats where they were collected.
Species occurrence frequencies in paleoecological data identified ecologically persistent species
and may be useful for delimiting local species pools. Analysis of sampling efficiency indicates that
approximately 28% of species in each paleoecological assemblage are ‘‘unique’’; i.e., they are not
likely to be present in an additional subsample from the same sample. Ordination reveals that life
assemblages of ostracodes are characterized by high spatiotemporal heterogeneity. Variability in
species composition was lower in paleoecological assemblages, presumably as a result of spatial
and temporal averaging.
Death and fossil assemblages of Lake Tanganyika appear to preserve many characteristics of living benthic ostracode assemblages with high fidelity. Spatiotemporal averaging allows paleoecological assemblages to render information about the average composition of ostracode communities
over short timescales, at spatial scales of several meters, and across habitat types. Sampling shell
assemblages in surficial sediments thus represents a more efficient way of assessing the average
ecological conditions at a locality than repeated live sampling. Furthermore, paleoecological analyses can generate novel insights into long-term community variability and membership with direct
relevance to conservation.
Simone R. Alin* and Andrew S. Cohen. Department of Geosciences, University of Arizona, Tucson, Arizona 85721
*Present address: School of Oceanography, Box 355351, University of Washington, Seattle, Washington
98195. E-mail: simone.alin@stanfordalumni.org
Accepted:
23 July 2003
Introduction
Paleoecological reconstruction is playing an
increasingly important role in addressing
conservation biological problems (e.g., Binford et al. 1987; Steadman 1995; Pandolfi 1996;
Brenner et al. 1999; MacPhee 1999; Miller et al.
1999; Davis et al. 2000; Finney et al. 2000; Jackson et al. 2001; Rodriguez et al. 2001). One of
the perennial problems encountered in paleoq 2004 The Paleontological Society. All rights reserved.
ecological reconstruction and interpretation is
assessing whether the sedimentary archive of
ecological and environmental change faithfully records the conditions that existed at the
time of deposition. Two transitions are inherent to the formation of the fossil record: transition from life to death assemblage and from
death to fossil assemblage. Copious studies on
the fidelity of death assemblages to living
communities have been performed (references
0094-8373/04/3001-0000/$1.00
THE LIVE, THE DEAD, AND THE VERY DEAD
in Kidwell and Bosence 1991; Kidwell and
Flessa 1995; Kidwell 2001a,b). Other permutations on live–dead–fossil comparisons include Valentine’s (1989) classic study on live
and fossil molluscan faunas in the California
Province, comparisons of death assemblages
with recent fossil assemblages formed in the
same environment (e.g., Russell 1991, also in
the California Province), and the occasional
comparison among life, death, and fossil assemblages (e.g., Fürsich and Flessa 1987;
Wolfe 1996).
Paleoecological baseline studies are increasingly being used for conservation purposes to
reconstruct environmental conditions before
human intervention (Brenner et al. 1993; Kowalewski et al. 2000; Rodriguez et al. 2001).
Microfossils like ostracodes allow investigators to collect statistically robust sample sizes
for analysis at low cost. However, the taphonomy of lacustrine microfossils has received
less attention than marine taphonomy (cf.
Wolfe 1996). Because of their small size and
susceptibility to transport, microfossil distributions within a locality may not map their
life habitat with the same fidelity seen in marine molluscs, which is quite high (Kidwell
and Bosence 1991; Kidwell 2001a,b). Although
high habitat fidelity has been observed in marine foraminiferal assemblages (Martin and
Liddell 1988), Wolfe (1996) observed low spatial fidelity in lacustrine diatom assemblages.
Kidwell (2001b) also reported poorer preservation of species rank order in marine molluscan death assemblages including small specimens (#1 mm) than among those with exclusively large-bodied specimens (.1 mm). Such
studies raise the question of how representative microfossil assemblages are of the onceliving communities that contributed to them
in terms of species richness, abundance, composition, and occurrence frequency. Also, differences in the spatial and temporal scales of
analyses in ecology and paleoecology may affect the conclusions that can be drawn from
life versus death or fossil assemblages with respect to conservation (cf. Levin 1992; Anderson 1993; Pandolfi 1996; Cohen 2000).
Lake basins can provide extensive and highly resolved paleorecords for reconstructing
past environmental and ecological changes in
45
terrestrial milieus. Lakes are also excellent settings for studying the responses of ecosystems to natural and anthropogenic environmental change on human timescales. High lacustrine sedimentation rates often result in
sedimentary records of annual to decadal resolution, allowing high-resolution reconstruction of environmental and ecological change.
Lakewide average sedimentation rates in large
lakes are typically on the order of 1.0 mm/yr
when measured over tens to thousands of
years (Johnson 1984; Cohen 2000). Furthermore, many lakes contain annually laminated
sediments, reflecting seasonal cycles of productivity and/or stratification. Using highresolution dating techniques (210Pb, 14C), it is
possible to estimate the temporal resolution of
the sedimentary record on the basis of sediment accumulation rates, sampling resolution,
presence or absence of laminae, and the depth
of the taphonomically active zone (TAZ: the
post-burial zone through which biotic and
physicochemical processes continue to alter
death assemblages prior to their ascension to
the fossil record [Davies et al. 1989]). Maximum depths of bioturbation and the TAZ tend
to be shallower in lakes (2–5 cm in Lake Tanganyika [Cohen 2000]) than in nearshore marine settings (up to about 1.4 m in rare cases
[Kidwell and Bosence 1991]), as a result of the
lower densities and burrowing depths of lacustrine bioturbators. These factors combine
to make lacustrine sediments amenable to
very high-resolution paleoenvironmental and
paleoecological reconstruction.
In this study, we compared assemblage
structure and composition of ostracode life,
death, and fossil assemblages from Lake Tanganyika in order to calibrate the sedimentary
record of community composition with respect to living communities. Lake Tanganyika
is a tropical rift lake housing extensive radiations of fish and invertebrate species. Because
of the evolutionary and economic significance
of its fauna, much attention has been focused
on the conservation of the Tanganyikan ecosystem (e.g., Cohen et al. 1993; Alin et al.
1999). Using paleoecological reconstruction,
Wells et al. (1999) showed that areas of the lake
that had experienced intensive watershed deforestation were also characterized by sub-
46
SIMONE R. ALIN AND ANDREW S. COHEN
stantial declines in the diversity of their ostracode faunas in recent decades or centuries. To
place these observations in a long-term, natural context, it is necessary to calibrate the fidelity of the fossil record to living communities and to estimate the resolution of lake sediment record.
Working in an extant ecosystem with a continuously accumulating sedimentary record
allows us to simultaneously examine the biases in the transition from life to death assemblage and those intrinsic to the translation
from death assemblages into the fossil record.
The quality of preservation of species richness
and abundance patterns determines the extent
to which paleoecological insights can be applied to problems in conservation biology.
Here we examine changes in variability associated with sampling at different temporal
and spatial scales. We further show how some
paleoecological data may be better suited for
some conservation applications than data
based on live collections alone.
Methods
Sampling. Ostracode life and death assemblages were derived from surface sediment
samples collected from rock surfaces offshore
from Mwamgongo, Tanzania, in Lake Tanganyika (Fig. 1). Fossil ostracode assemblages
came from short sediment cores collected in
the silty sand adjacent to the rocky habitat
sampling sites for life and death assemblages.
The shallow benthic habitat at this locality is
dominantly composed of silty sands with
patches of rocky habitat, a common habitat
type in the nearshore zone of Lake Tanganyika. Rocky habitats are particularly noted for
their high biodiversity; hence it is desirable to
understand the reliability of the soft-substrate
paleoecological record in representing community composition patterns across a mosaic
of habitat types. This locality was chosen as
part of a disturbance comparison between this
small, deforested watershed and the adjacent,
protected watersheds in Gombe Stream National Park (Alin et al. 2002). Although the
shallow-water ostracode fauna at this locality
has undergone changes in the composition of
dominant species over the last few hundred
years, there is no evidence that the watershed
FIGURE 1. Location of study area (Mwamgongo, Tanzania), with inset map of Africa showing Lake Tanganyika.
disturbance near the study sites has affected
species richness, abundance, or composition
patterns substantially over the time period
represented by this study (a roughly 30-yr
sediment record). Other regions of the lake basin have experienced much more extensive
sedimentation impacts related to deforestation. Thus, the results presented here are relevant to the reliability of the sediment record
under moderate-to-low disturbance conditions and may hold for higher-disturbance situations as well.
Sampling locations were chosen at two
depths (5 m, 10 m) because the rocks sampled
had different aspects (horizontal surfaces
dominated at 10 m, with steeper-sided, more
exposed rocks at 5 m) and were thus likely to
retain different amounts of sediment on their
surfaces (Fig. 2A). Also, ostracode diversity
varies with depth in Lake Tanganyika (Alin et
al. 1999). Sites at both depths were sufficiently
shallow to be affected by wave activity. Colored bolts were affixed with underwater epoxy to four rocks, two each at 5 m and 10 m
water depth, to identify sampling locations. A
THE LIVE, THE DEAD, AND THE VERY DEAD
47
FIGURE 2. A, Schematic diagram of spatial sampling layout. Rocks sampled are depicted by black shapes, with
regular quadrat sampling locations indicated by white squares. White asterisks denote the approximate location of
permanent, colored bolts that marked each sampling site. Quadrat numbers in squares show the sampling pattern.
Rocks at 10 m depth are flat and crop out from the surrounding sand horizontally. Rocks at 5 m depth stand relatively high above the sand and are steeper sided. Core collection locations (in silty-sand substrate, indicated by
background pattern) are shown with solid circles. Note: figure not drawn to scale. B, Water-depth curve for quadrat
series 1A–2A throughout the sampling period. Sampling dates are indicated by asterisks under the water-depth
curve. Water depths for other quadrat series are 0.6 m deeper for series 3A–4A and 5.3 m shallower for series 5A–
8A. Periods of high rainfall, lake-level rise, and sample-site deepening are shaded in light gray.
quadrat (25 3 25 cm) was used to collect surface sediments from two patches adjacent to
the marker bolts on each rock. Each month,
eight surface sediment samples were collected
(i.e., two sites at each of two depths, and two
samples per site per visit) using a diver-operated suction sampler modified from Gulliksen and Derås (1975). The suction sampler allowed for semiquantitative collection of all
surface sediment within the quadrat on the
rock surface. Collected sediments thus represent a constant area of substrate surface, but
not a constant sediment volume. Both life and
death assemblages originated in the surface
sediment collected from these rocky habitat
sites. Quadrat series were numbered 1A–4A
(10 m) and 5A–8A (5 m). Rocks sampled at
each depth were located a few to several (ø 3–
12) meters apart.
Sediment samples were collected monthly
during the October–December 1997 and February–July 1998 intervals and once in July
1999 (i.e., ten visits total per site during a 22month study period; eight samples per visit
for a total of 80 total samples of live and dead
fauna) (Fig. 2B). Sediments were transferred
immediately to 95% ethanol for storage and
soft-part preservation. This period spanned
the transition from a very dry year (1997) to a
very wet, El Niño year (1998). During the sampling interval, lake level rose rapidly by nearly
2.5 m and then declined again by 1.0–1.5 m
(Birkett et al. 1999). Water depth data were recorded on a dive computer and were calibrated by using NASA satellite altimetry data (C.
Birkett personal communication 2000).
Ostracode fossil assemblages were obtained
from two short sediment cores collected with
a hand-coring device in July 1999. Cores were
sectioned into 1-cm intervals in the field. Core
MWA-1 (16 cm total length, of which top 8 cm
discussed here) was collected at 10 m between
the two sampling sites, and core MWA-2 (11
cm) was taken adjacent to one of the 5-mdepth sampling sites (Fig. 2A). Dead and fossil faunas were collected from different habitat
types for logistical reasons—i.e., it was too
difficult to permanently mark and reliably return to soft-sediment sites, whereas cores
were necessarily collected from soft substrates. However, we note that comparing
death and fossil assemblages from different
substrate types has allowed us to assess the
spatial averaging of lacustrine microfossils
across habitat types.
Six radiocarbon dates were obtained for
core MWA-1 through the National Science
Foundation Accelerator Mass Spectrometer
48
SIMONE R. ALIN AND ANDREW S. COHEN
Facility at the University of Arizona. All dates
were derived from single terrestrial leaf fragments to avoid the dual problems of mixing
carbon sources of varying ages and the 14C
reservoir effect of Lake Tanganyika. By using
global atmospheric post-bomb 14C decay
curves, single-leaf fragments allow the assignment of calendar ages to within a few years of
leaf production (based on data in Nydal and
Lövseth 1983; Levin and Kromer 1997). Prebomb radiocarbon dates were assigned using
CALIB 4.3 (Stuiver et al. 1998a,b). No Southern
Hemisphere correction was applied because
the study location is equatorial.
All surface sediments and core intervals
were sieved with 1-mm, 106-mm, and 63-mm
sieves, dried at 608C, and weighed. All ostracode individuals in the sediment fractions of
.1 mm and 106 mm–1 mm that remained articulated and retained soft parts and coloration typical of live specimens were included in
life assemblages and were identified to the
species level. The 106-mm sieve retains both
adults and advanced instars of Tanganyikan
ostracodes (podocopid ostracodes have nine
instars: eight juvenile, one adult). All 80 surface sediment samples were tallied for live diversity.
For death and fossil assemblages, we added
ostracodes from the .1 mm size fraction to
the 106-mm–1-mm size fraction before removing subsamples for counting and identification of individuals. Standard sample sizes of
500 were used for death and fossil assemblages. Both adults and advanced instars of ostracode species were tallied, as it is difficult to
differentiate advanced juveniles (many of
which have heavily calcified valves) from
adults in the Tanganyikan fauna, introducing
the possibility that multiple valves from the
same individual have been included in the
sampling. However, given the immense number of ostracode valves available in surface
and buried sediments (hundreds to thousands
per gram dry sediment) and the apparent efficiency of wave-mixing and mobilization of
surface sediments, it is unlikely that resampling of individuals occurs on a frequent
enough basis to introduce significant bias to
the results.
Thirty-seven of the 80 possible death assem-
blages were counted. Samples from November
1997, July 1998, and July 1999 were counted
for all quadrat series (1A–8A) in order to sample changes in death assemblages at the beginning, middle, and end of the collection period. In addition, all remaining monthly samples were counted for two (3A, 7A) of the eight
samples taken to examine higher-frequency
changes in death assemblages at both depths.
For cores, all 1-cm intervals were counted. In
addition, we made multiple ostracode counts
for one core interval (0–1 cm of core MWA-1)
in order to assess the reliability of a given
sample in representing the full fossil ostracode assemblage present in that interval.
To identify ostracodes, we followed Rome
(1962), Martens (1985), Wouters (1988), Wouters and Martens (1992, 1994, 1999, 2001),
DuCasse and Carbonel (1994), and Park and
Martens (2001). For the many Tanganyikan ostracode species not yet described, we used extensive reference collections at the University
of Arizona to identify individuals to the level
of genus, using a numbered species designation.
Data Analysis. Ranges of species richness
values in live, dead, and fossil samples were
compared in box plots. Individual samples of
the life assemblage were successively pooled
both across space (all quadrats in a given sampling visit) and through time (each quadrat
through 22-month sampling period) to estimate the minimum amount of spatial and
temporal averaging represented by death and
fossil assemblage data. We used Kruskal-Wallis nonparametric analysis of variance to test
for significant differences in species richness
among data sets, because the Shapiro-Wilk
test of normality rejected the hypothesis that
the live data were normally distributed (Sall
and Lehman 1996). To localize the difference
among samples, we used Dunn’s test for multiple comparisons with samples of different
sizes (Zar 1984).
Average abundance values were calculated
for each species across all samples in each data
set. We compared species abundance distributions among data sets by using paired Kolmogorov-Smirnov tests for goodness-of-fit
(Zar 1984).
Occurrence frequencies were computed for
THE LIVE, THE DEAD, AND THE VERY DEAD
all species in each data set. Live, dead, and fossil data sets contained different numbers of
samples (80, 37, and 19, respectively). Occurrence frequency bins varied in size such that
each bin represented 10% of samples in a data
set (resulting in bin sizes of eight, four, and
two samples for live, dead, and fossil data, respectively). To compare species occurrencefrequency distributions for live, dead, and fossil data sets, we used paired KolmogorovSmirnov tests, using one data set for expected
values, the other for observed values.
For rank abundance tests, we ordered species in all data sets on the basis of their abundance in the total live data set, followed by
dead-only and then fossil-only species, in
rank-order. Rank abundance data were compared by using Spearman’s coefficient of rank
correlation (Sall and Lehman 1996). We used
the Bonferroni correction to avoid obtaining
spuriously significant results. Spearman’s
rank-abundance test is influenced by the number of species and individuals in the comparison. To test the effects of the number of species included and of truncating rare live species from the list, we calculated p-values and
r-values for Spearman’s coefficients for various subsets of the ranked species-abundance
data. For example, in the ten-species comparison, only the first ten species in order of live
rank were retained; in the 20-species comparison, the first 20 live species were retained;
and so on.
We used a variety of methods to compare
fidelity of species composition among live,
dead, and fossil data sets. Percentages of live
species found dead and vice versa were used
as fidelity metrics (following Kidwell and Bosence 1991; Kidwell 2001a). Comparisons of
species composition were also extended to assess the fidelity of the fossil data to both life
and death assemblages. We also determined
percentages of dead individuals from species
found alive as a means of gauging spatial fidelity (Kidwell and Bosence 1991).
Additional sediment subsamples tallied for
ostracodes from the 0–1-cm interval of core
MWA-1 were used to generate a species sampling curve for the uppermost core interval.
Six subsamples of 100 individuals each, three
subsamples of 500, and an additional subsam-
49
ple of 610 were counted. For one of the subsamples of 100, a running tally was kept of
each new species occurrence. A logarithmic
curve was fitted to the sampling curve in order to determine whether our standard sample size of 500 was sufficient to pass the inflection point of the diversity curve. In addition, we tallied numbers of occurrences for all
species in four subsamples of 500 (five of six
subsamples of 100 were pooled for this comparison) and calculated detection probabilities for species in different average abundance
classes.
Another means of judging the adequacy of
sample sizes is to calculate the predicted percentage of unique species in each sample,
where ‘‘unique’’ species are those unlikely to
be resampled in an additional subsample,
based on the value of Fisher’s a for the observed species distribution from the same core
interval (following Koch 1987). To estimate the
values of Fisher’s a and x needed to generate
the expected number of species in each occurrence category, code from Rosenzweig (1995:
p. 194, modified by M. Rosenzweig) was used.
The expected number of species in an additional sample of 500 was calculated as a x, a
x2/2, a x3/3, 43.3-Saxn/n, with 43.3 6 1.5 being the average species richness for four
counted samples, in four observed occurrence
categories (Koch 1987; Magurran 1988). Probabilities of occurrence (pn) for four samples of
500 were then used to calculate the predicted
similarity in species composition for one additional sample of 500 (Koch 1987: Table 3, Appendix).
To explore the combined live, dead, and fossil database for differences in community
structure among sample types, detrended correspondence analysis (DCA) was performed
with CANOCO 4 software (ter Braak and Smilauer 1998). In order to avoid some of the gradient distortions reported for DCA (Pielou
1984; Minchin 1987), detrending was executed
by using polynomials rather than segments
(ter Braak and Prentice 1988). Species relativeabundance data failed the Shapiro-Wilk test of
normality; hence all data were log-transformed. In addition, the CANOCO option to
downweight rare species was used in the DCA
analysis.
50
SIMONE R. ALIN AND ANDREW S. COHEN
TABLE 1. Radiocarbon dates for core MWA-1 from 10 m water depth at Mwamgongo, Tanzania. Calendar ages
reported for pre-bomb dates include all 2s age ranges with .0.1 relative probability. Post-bomb dates were estimated by using atmospheric decay curves for 14C from Nydal and Lövseth (1983) and Levin and Kromer (1997).
Sample
number
Depth in
core (cm)
Fraction
modern 14C
AA-38063
AA-38064
AA-41870
AA-41871
AA-38065
2–3
4–5
7–8
8–9
10–11
1.0934
1.1254
1.1729
1.5133
0.9689
post-bomb
post-bomb
post-bomb
post-bomb
254 6 39
AA-38066
12–13
0.9590
336 6 41
To examine the effects of spatial averaging
across substrate types, we defined an ostracode substrate index (OSI) for ostracode species assemblages based on the output of a canonical correspondence analysis (CCA) of a
database of live ostracode species abundance
data from various locations, substrate types,
and water depths around Lake Tanganyika
(Cohen unpublished data). CCA Axis 1 was
significantly and strongly correlated (r 5 0.67)
with substrate type (rocks, sand, mud). OSI
was defined by using Axis 1 species scores to
assign species to rocky, sandy, or muddy categories. Separation of samples along Axis 1
with respect to substrate type was good although not complete, as many species are
commonly collected alive in more than one
habitat type. OSI is defined as (Nsandy 1
Nmuddy) N21rocky, where N is the number of individuals in each category, such that smaller
values correspond to a greater proportion of
individuals from rocky species, and larger
values to more individuals belonging to sandy
or muddy species.
Results
Sedimentological and Radiocarbon Data for
Cores. Visual inspection revealed three zones
differing in organic content and particle size
in core MWA-1. A transition occurred between 3.5 cm and 7.5 cm from reddish brown
silty sand at the core top (0–3.5 cm) to darkerbrown silty sand with organic fragments and
some pebbles in the lower core (ø 8–16 cm).
Grain-size data showed a slight fining of particles upwards of 9 cm in the core, with average weight percentages of particles #106 mm
14
C age
Estimated
calendar age
range (A.D.)
1997
1992
1987
1971–1972
1632–1670
1527–1553
1780–1797
1466–1644
increasing from 19.9 6 4.0% below 9 cm to
31.4 6 5.3% above 9 cm.
Visual inspection of core MWA-2 suggested
a possibility of finer sediments above 5 cm
and higher organic content below, with reddish brown sand throughout, although granulometry of the core revealed no overall trend
in grain size. Mean weight percents of particles #106 mm were comparable to those at the
top of core MWA-1 at 31.3 6 4.9%.
Granulometric data for surface sediments
showed dramatic month-to-month fluctuations in quantity and particle-size distribution. The amount of sediment in each quadrat
varied substantially from month to month
(range: 0.30–41.71 g, overall average 5 11.15
6 10.01 g), with the average sediment per
quadrat being higher at 10 m depth (16.98 6
8.73 g) than at 5 m (5.33 6 7.56 g). Variations
in sediment particle size were not correlated
with fluctuations in ostracode species richness
and abundance. Mean weight percentages of
particles #106 mm were 38.4 6 14.3% and 65.0
6 9.3% at 5 m and 10 m, respectively.
Radiocarbon dates obtained from singleleaf fragments in core MWA-1 are shown in
Table 1 and suggest a midcore depositional hiatus of about 300 years. Judging from the
jump in radiocarbon ages, the hiatus probably
lies between 9 cm and 10 cm. In this paper, we
present ostracode data only from the upper 9
cm of core MWA-1, because our aim was to
calibrate the currently accumulating paleoecological record with the extant living and
death assemblages. Post-bomb radiocarbon
dates indicate that the upper 9 cm of core
MWA-1 represents approximately the last
THE LIVE, THE DEAD, AND THE VERY DEAD
three decades of deposition (Table 1). Material
from core MWA-2 suitable for radiocarbon
dating was not available. For the purpose of
this paper, we assume that sediment accumulation rates, and thus sample resolution,
were comparable for both cores. Estimated
ages in upper MWA-1 suggest recent sediment accumulation rates between 0.6 mm/yr
and .4 mm/yr in the nearshore zone.
Characteristics of Ostracode Assemblages.
The live data set, composed of 80 samples,
consisted of 15,765 individuals and 64 species
(Appendix). Life assemblages contained from
26 to 1229 individuals (median 5 139). Total
death assemblage individuals tallied were
18,175 in 37 samples, comprising 87 species
(Appendix). Death assemblages contained
1290–11,805 individuals per gram dry sediment (median 5 5915). Fossil assemblages
contained 9639 individuals and 79 species in
19 samples (Appendix). Fossil assemblages
contained 816–6974 individuals per gram dry
sediment (median 5 1641).
Ranges of values in species richness data are
shown for ostracode life, death, and fossil assemblages in Figure 3A. Kruskal-Wallis tests
for analysis of variance soundly rejected the
hypothesis that the live, dead, and fossil data
sets shared a common range of species richness values (HC 5 99.2, p K 0.001). Dunn’s
multiple comparison test showed significant
differences between live species richness data
and both death and fossil assemblage data
(Qlive–dead 5 9.103, p , 0.001; Qlive–fossil 5 6.158,
p , 0.001), with no difference between species
richness values of death and fossil assemblage
data (Qdead–fossil 5 0.793, p . 0.20). However, after species richness data were pooled across
all samples either through time or across
space (Fig. 3B), species richness values of live
data were comparable to those of death and
fossil assemblage data (Fig. 3C), although the
numbers of individuals per pooled sample
were consistently higher (Ntime range: 1101–
3210, median 5 1953; Nspace range: 783–3611,
median 5 1349). Kruskal-Wallis tests detected
no difference among pooled live, dead, and
fossil data sets for species richness values (HC
5 5.804, p . 0.10). Interestingly, pooling samples either across space or through time resulted in equivalent numbers of species, al-
51
FIGURE 3. A, Box plots of species richness values for all
samples in the live (median 5 15), dead (median 5 37),
and fossil (median 5 36) data sets. B, Species accumulation curves resulting from pooling sequential life assemblage samples within each quadrat location through
the duration of the sampling period (ten monthly samples over 22-month study; squares) and from pooling sequential quadrats within each month up to the total
number (eight) of quadrats collected each month (circles, offset from squares on x-axis for clarity). C, Box
plots of species richness for life assemblages pooled
across space (median 5 36), life assemblages pooled
over time (median 5 34.5), and death and fossil assemblages from 3A.
though the spatial accumulation curve (circles
in Fig. 3B) ascended more steeply initially, indicating greater spatial than temporal heterogeneity in ostracode life assemblages.
Histograms of average species abundance
(%) per sample are shown in Figure 4. All
plots show a predominance of species represented by fewer than 1% of individuals (average) in a sample, with nearly half of the spe-
52
SIMONE R. ALIN AND ANDREW S. COHEN
FIGURE 4. Histograms of species abundance per sample
for life (A), death (B), and fossil (C) assemblage data. For
all data sets, N 5 total number of individuals counted
and S 5 total number of species encountered. Insets for
each panel show the abundance distribution for species
in the ,1% bin across finer bin-size intervals.
cies in each data set represented by ,0.1% of
individuals (Fig. 4 insets). Even below 0.1%
average abundance (in 0.01% intervals), the
species distributions were quite similar to
each other and skewed toward the lowest average abundance bin (not shown). Paired Kolmogorov-Smirnov tests for goodness-of-fit indicated no difference in the species abundance
structure of the live, dead, and fossil data sets
with any of these bin sizes (p $ 0.20 in all comparisons).
Occurrence frequencies tallied for species in
all data sets are shown in Figure 5. Live species occurrence frequencies show a unimodal
FIGURE 5. Species occurrence frequency histograms for
life (A), death (B), and fossil (C) assemblage data. All
inset plots have the same occurrence frequency bins as
the large histograms, with different y-axis values. Life
assemblage inset (A): Histograms show distribution of
live species in the #10% bin across the dead (diagonal
stripes) and fossil (white) data sets. Death (B) and fossil
(C) assemblage insets: Histogram shows the distribution of dead and fossil species in the .90% bin across
the live data set.
distribution, with a prominent peak representing species that occurred in #10% of samples. In contrast, death and fossil assemblage
occurrence frequencies are bimodally distributed, with large peaks at both ends of the distribution representing species present in
#10% and .90% of samples. The distribution
of species in the lowest live occurrence category across death and fossil assemblages (Fig.
5A, inset) shows that most species remain in
the lowest occurrence categories, but several
appear in the highest occurrence category,
representing species that are rare in terms of
abundance but persistent. In contrast, species
constituting the .90% bins for both death and
fossil assemblages are somewhat more evenly
distributed across the live data set (insets in
Fig. 5B,C), indicating that persistent species
THE LIVE, THE DEAD, AND THE VERY DEAD
53
FIGURE 6. Species occurrence frequency histograms for
life assemblage data pooled through time (A) and across
space (B).
occur in all live occurrence classes. Interestingly, when live data were pooled either
through time or across space, a bimodal distribution similar to those for the death and
fossil assemblages resulted (Fig. 6). Thus,
pooled live data again display patterns comparable to death and fossil assemblages.
Kolmogorov-Smirnov tests confirmed that
the shapes of the dead and fossil occurrence
frequency distributions were indistinguishable (dmax(10,79) 5 3.8, p . 0.50), whereas live
data were distributed significantly differently
from both (live–dead: dmax(10,64) 5 13.4, p ,
0.01; live–fossil: dmax(10,64) 5 15.0, p , 0.005).
One possible caveat for interpreting the shape
of occurrence frequency histograms is that
when values in the expected data set differ
markedly from equality (as ours did), the robustness of this test may become unreliable
(Pettitt and Stephens 1977). However, with regard to inequality of expected values and the
strength of our results, the variability in test
p-values reported by Pettitt and Stephens
(1977) indicates that it is highly unlikely that
this test has misidentified the direction of
these relationships. In other words, the variability of p-values is smaller than the offset re-
FIGURE 7. Rank-abundance histograms based on live
species ranked abundance for life (A), death (B), and
fossil (C) assemblage data. Labeled species in 7B and 7C
correspond to: a 5 Romecytheridea ampla, b 5 Mesocyprideis irsacae, c 5 Mesocyprideis pila, d 5 Mecynocypria n.sp.
20, e 5 Tanganyikacypridopsis depressa, f 5 Mecynocypria
emaciata, and g 5 Mesocyprideis n.sp. 4 (see text for discussion).
quired to change the significance of our results.
Ranked species-abundance data differ substantially between life assemblages and both
death and fossil assemblages (Fig. 7). Spearman’s test of rank correlation (comparing full
data sets) reveals significant correlation for all
three comparisons, although only the correlation between death and fossil rank-orders is
strong (rlive–dead 5 0.552, rlive–fossil 5 0.485,
rdead–fossil 5 0.775; p , 0.0001 for all). Only the
dead–fossil abundance correlation was consistently and highly significant across comparisons with various numbers of species included
(r 5 0.7745–0.9515, p , 0.0001) (Fig. 8). Live–
dead rank-abundance comparisons were not
significant with up to 20 species included, but
54
SIMONE R. ALIN AND ANDREW S. COHEN
with 40 or more species included, live–dead
rank-order agreement was significant, with rvalues of 20.35 to 0.56. Similarly, live–fossil
rank-order results were not significant until
60 species were included in the comparison,
with r-values of 20.20 to 0.21 for 40 or fewer
species and r-values of 0.44 to 0.48 for 60 or
more species. The fact that live–dead and live–
fossil rank-order agreement improves with
more species included suggests that the rank
order of the dominant live species is more
mismatched with respect to paleoecological
assemblages than the rank order of rarer species.
Live–Dead–Fossil Agreement in Species Composition. Fidelity measures for species composition among data sets were generally high
(Table 2; range: 53–90%, median: 78.5%).
Agreement was closest in percentages of live
species also found dead, live species also
found as fossils, and fossil species also found
dead (range: 77–90%, median: 88%). Appearance of much weaker compositional similarity
in the percentages of dead species found also
alive, dead species also found as fossils, and
fossil species also found alive is largely an artifact of differences in species richness between samples being compared—i.e., when
the more species-rich fauna is in the denominator, agreement will necessarily be lower. Interestingly, agreement among species lists decreased when the lists were truncated to include only the more abundant species, again
indicating that some rare species belong to the
persistent species pool at this location.
Percentages of dead individuals that are
from species also found alive at the same
depth were 98% at 5 m and 87% at 10 m. When
FIGURE 8. A, Distribution of p-values for Spearman’s
rank-order correlations coefficients for comparisons including different subsets of ranked species (i.e., first ten
live ranked species, first 20, etc., up to all 99 taxa).
Dashed line across graph represents Bonferroni-corrected significance criterion (a 5 0.0167). B, Spearman’s
correlation coefficient (r) values for comparisons of all
subsets of data. Symbols: live vs. dead (squares, solid
line), live vs. fossil (diamonds, coarse dashed line), dead
vs. fossil (circles, fine dashed line).
the percentage of dead individuals at 10 m
that are only found alive at 5 m was added to
the number of dead individuals found alive at
10 m, the agreement increased from 87% to
TABLE 2. Fidelity of ostracode death and fossil assemblages to life assemblages, based on pooled samples. For life
and death assemblages, all quadrats were pooled across the entire sampling interval (22 months) at each depth
separately and with combined depths. For fossil assemblages, all core samples were pooled at each depth and across
depths.
5m
%
%
%
%
%
%
%
%
Live species also found dead
Live species also found as fossils
Dead species also found live
Dead species also found as fossils
Fossil species also found live
Fossil species also found dead
Species found in all assemblages
Dead individuals also found live
88%
77%
65%
74%
68%
88%
47%
(50/57
(44/57
(50/77
(57/77
(44/65
(57/65
(41/88
98%
10 m
spp.)
spp.)
spp.)
spp.)
spp.)
spp.)
spp.)
80%
90%
53%
76%
68%
86%
44%
(39/49
(44/49
(39/74
(56/74
(44/65
(56/65
(38/86
87%
Depths pooled
spp.)
spp.)
spp.)
spp.)
spp.)
spp.)
spp.)
89%
89%
66%
83%
71%
90%
56%
(57/64
(57/64
(57/87
(72/87
(57/80
(72/80
(55/99
94%
spp.)
spp.)
spp.)
spp.)
spp.)
spp.)
spp.)
55
THE LIVE, THE DEAD, AND THE VERY DEAD
TABLE 3. Ten most abundant species in life, death, and fossil assemblages at both depths in order of abundance.
Numbers in parentheses after species names in death and fossil assemblages represent species rank in life assemblages at the same depth. Asterisks indicate species that are absent alive.
Live
Dead
Fossil
5 m:
Allocypria mucronata
Cypridopsis n.sp. 6C
Cypridopsis n.sp. 18
Allocypria inclinata
Cypridopsis n.sp. 6A
Romecytheridea tenuisculpta
Romecytheridea ampla
Allocypria n.sp. 11
Cypridopsis colorata
Cypridopsis n.sp. 25
Romecytheridea ampla (7)
Mesocyprideis irsacae (15)
Mecynocypria emaciata (46)
Cypridopsis n.sp. 18 (3)
Cypridopsis n.sp. 6A (5)
Romecytheridea tenuisculpta (6)
Mesocyprideis n.sp. 4 (*)
Mesocyprideis pila (28)
Tanganyikacypridopsis depressa (42)
Cypridopsis n.sp. 23 (16)
Mesocyprideis irsacae (15)
Romecytheridea ampla (7)
Cypridopsis n.sp. 6A (5)
Tanganyikacypridopsis depressa (42)
Mesocyprideis n.sp. 4 (*)
Mesocyprideis pila (28)
Gomphocythere curta (13)
Cypridopsis n.sp. 18 (3)
Mecynocypria emaciata (46)
Gomphocythere alata (14)
10 m:
Romecytheridea tenuisculpta
Romecytheridea longior
Allocypria n.sp. 11
Romecytheridea ampla
Allocypria inclinata
Allocypria mucronata
Cypridopsis n.sp. 6C
Tanganyikacythere burtonensis
Tanganyikacypridopsis n.sp. 8
Cypridopsis n.sp. 25
Mesocyprideis irsacae (16)
Romecytheridea ampla (4)
Cypridopsis n.sp. 6A (12)
Romecytheridea tenuisculpta (1)
Cypridopsis n.sp. 18 (38)
Mesocyprideis n.sp. 4 (*)
Tanganyikacypridopsis depressa (*)
Mecynocypria emaciata (*)
Mesocyprideis pila (24)
Mecynocypria n.sp. 20 (27)
Mesocyprideis irsacae (16)
Romecytheridea ampla (4)
Mesocyprideis n.sp. 4 (*)
Tanganyikacypridopsis depressa (*)
Cypridopsis n.sp. 6A (12)
Gomphocythere curta (30)
Mecynocypria emaciata (*)
Cyprideis spatula (10)
Cypridopsis n.sp. 23 (11)
Mesocyprideis pila (24)
99%. This implies a role for down-slope transport in determining the species composition
of death and fossil assemblages, at least in
shallow water.
Fidelity can also be examined by comparing
numbers of dominant taxa shared among data
sets (Table 3) (Kidwell and Bosence 1991).
Agreement among dominant taxa was highest
between the dead and fossil data sets, with
eight of ten dominant species in common at 5
m and seven of ten shared at 10 m. Live–dead
FIGURE 9. Species accumulation curve for core MWA-1.
Open squares represent species tallied in subsamples
from core interval 0–1 cm. Solid squares indicate cumulative diversity from core interval 7–8 cm through interval 0–1 cm. The logarithmic curve is fitted only to
data from the resampled core interval 0–1 cm.
agreement was substantially lower, with only
four of ten dominants shared at 5 m and only
two of ten at 10 m. Finally, the fewest matches
occurred between live and fossil species lists,
with three of ten matching at 5 m and only one
at 10 m. However, agreement between 5 m and
10 m within each data set was quite good. In
the live data set, seven of ten dominant taxa
were shared. Dead and fossil data sets had
nine and eight species, respectively, of ten
dominants in common between depths.
Analysis of Core Interval Resampling. The
species accumulation curve resulting from resampling a single core interval is shown in
Figure 9. A total of 2710 individuals were
counted, yielding 61 species. The order in
which samples were added to the accumulation curve affected the regression equation
minimally, and all r2-values were .0.95. Extrapolation of the logarithmic curve to 10,000
individuals yielded an estimate of approximately 66 species, suggesting that .90% of all
species in this core interval had been sampled.
However, if the curve is extrapolated to the total number of individuals contained in this
core interval (ø88,000), the estimated total
species richness for the sample is 85, suggest-
56
SIMONE R. ALIN AND ANDREW S. COHEN
TABLE 4. Occurrence frequency of species in resampled
core interval.
No. of
occurrences
4
3
2
1
No. of
species
28/58
12/58
7/58
11/58
(48%)
(21%)
(12%)
(19%)
Abundance
range
Median
abundance
0.30–21.0%
0.25–0.60%
0.10–0.25%
0.05–0.25%
2.00%
0.28%
0.20%
0.05%
ing that, at our standard sample size of 500,
our sampling could be as poor as ca. 50%.
Eighty-five species is not an unreasonable
number for this location, as 99 species were
tallied in the live, dead, and fossil data sets together, although it is unclear that extrapolation to such sample sizes would be robust. In
any case, our sample size of 500 was sufficient
to have crossed the inflection point on the
sampling curve. Total species richness of the
resampled core interval is approximately 1.5
times as high as that observed in a similar
analysis on another core from Lake Tanganyika (Wells et al. 1999) and can probably be
explained by diversity differences between
water depths of the cores (40 m in Wells et al.
1999 vs. 10 m here).
Species composition comparisons for four
samples containing 500 individuals revealed
that almost half the species occurred in all
four samples (Table 4). Another third of species appeared in two to three samples. Table 5
shows the observed probabilities of detection
for species based on their average percent
abundance in samples of 500. Except for the
$0.65% category, groups contained species
with varying numbers of occurrences, as they
were grouped by abundance rather than occurrences. All species present in $0.65%
abundance fell into the 100% detection probability category, and only species with ,0.21%
TABLE 5. Average species abundance versus probability of detection based on resampling a fossil assemblage.
Average
abundance
$0.65%
0.21–0.64%
#0.21%
No. of
species
Probability
of detection
24/58 (41%)
19/58 (33%)
15/58 (26%)
100%
75%
33%
abundance had a ,50% probability of detection.
For estimating numbers of unique species,
Fisher’s a and x were determined to be 11.2
and 0.994, respectively, for a total species richness of 58 in 2000 individuals (four subsamples of 500). The log-series distribution apparently fits our data well, as the values of a and
x changed minimally when calculated with
various subsets of the data. Table 6 shows the
values for expected number of species in each
category, probability of species in each category being present in one additional sample,
and predicted number of species from that
category to appear in the additional sample.
Our results indicate that 28% of the species in
each of our samples of 500 may be unique.
This is essentially equivalent to the 26% of
species present in #0.20% average abundance
in Table 5. Comparison of these two results
suggests that, on average, only those species
represented by a single individual in a subsample of 500 are unlikely to be resampled in
an additional tally from the same sample.
Ordination. Ordination plots based on
DCA of the live–dead–fossil database reveal
fairly good separation of live species assemblages at 5 m and 10 m, although some overlap
is apparent (Fig. 10A). In contrast, close association of death and fossil assemblages from
both depths is evident (Fig. 10A,B), with dead
and fossil samples offset from live samples.
TABLE 6. Calculation of predicted overlap in species composition among subsamples from the interval 0–1 cm in
core MWA-1. pn 5 probability of species in each category occurring in an additional subsample.
Observed
occurrences
No. of species
(expected)
1
2
3
4
11.1
5.4
3.5
23.2
Average total S: 43.3 (100%)
pn
0.25
0.50
0.75
.0.99
No. of species in
new subsample
2.8
2.7
2.6
23.2
Predicted shared S: 31.3 (72%)
THE LIVE, THE DEAD, AND THE VERY DEAD
57
FIGURE 10. A, Ordination plot of life (solid circles, 10 m; solid squares, 5 m), death (open circles, 10 m; open
squares, 5 m), and fossil (open diamonds, 10 m; crosses, 5 m) ostracode assemblages. Mean values for life assemblages at 5 m and 10 m indicated with enlarged gray square and circle, respectively. B, Enlarged view of death and
fossil assemblage distribution in 10A. Mean death assemblage values for 5 m and 10 m indicated with enlarged
gray square and circle, respectively. Core-top samples from 5 m and 10 m indicated by large gray triangle and
diamond, respectively. Other symbols as in 10A. C, Temporal trajectory of monthly quadrat life assemblages at 10
m in ordination space (1A: 3 symbols, dashed lines; 3A: solid inverted triangles, solid lines). For 10C and 10D, the
first sample in each quadrat series (i.e., Oct. 1997) is circled, with subsequent sampling months connected in order.
Note that scale is enlarged with respect to 10A for clarity. D, Temporal trajectory of monthly quadrat life assemblages at 5 m in ordination space (5A: solid diamonds, solid line; 8A: crosses, dashed line).
Indirect gradient analysis indicated that DCA
Axis 1 was strongly correlated with the species richness of samples (more negative Axis 1
loadings 5 higher diversity) and with the
abundance of dominant taxa (Mesocyprideis irsacae and Romecytheridea ampla for dead and
fossil samples [negative loadings on Axis 1 5
higher abundance]; Allocypria mucronata for
live samples at 5 m, and Romecytheridea tenuisculpta for 10 m live samples [positive loadings
for both on Axis 1 5 higher abundance]).
Abundance of these species varies with substrate, with A. mucronata being the only rockyhabitat species among them. These correlates
58
SIMONE R. ALIN AND ANDREW S. COHEN
of Axis 1 indicate that death and fossil assemblages are offset from life assemblages by virtue of being richer in species and having species compositions reflecting a degree of spatial
averaging across habitat type. We interpret
Axis 1 to represent dominantly substrate texture/grain size (positive Axis 1 loadings corresponding to coarse-grained [rocky] habitats,
negative loadings to fine-grained [sandy,
muddy] substrate). DCA Axis 2 was correlated
with both depth (higher Axis 2 loadings 5
greater depth) and abundance of dominant
taxa.
Ostracode substrate index (OSI) values support the interpretation of Axis 1 as related to
substrate grain. Average OSI values for death
and fossil assemblages (3.1 6 0.9 and 4.2 6
1.0, respectively) are both substantially higher
than for life assemblage OSI values (1.7 6 1.6),
confirming that fine-grained substrate species
comprise a greater percentage of individuals
in death and fossil assemblages.
The pattern within the live ostracode data
set was not simple to interpret. What overlap
did occur may be somewhat attributable to
lake-level fluctuations and depth preferences
of the samples’ constituent ostracode species.
Samples from 5 m and 10 m in closest proximity were those from the 5-m locations during high-water months (March–May 1998, Fig.
2B) and from 10-m quadrats in relatively lowwater months (October 1997, July 1999), although this pattern was not consistent
throughout the remaining samples. Samples
collected from adjacent quadrats (i.e., on the
same rock) tended to plot closer together in
ordination space, but samples collected from
different rocks in the same month were sometimes more similar. Finally, sample series from
single quadrat locations tended to follow complex trajectories that frequently ended at a
point in ordination space closer to the origination point than many of the intervening
samples (Fig. 10C,D). These patterns simply
confirm the high degree of spatiotemporal
heterogeneity in living ostracode assemblages
reported by Cohen (1995, 2000). This heterogeneity is probably caused by numerous factors such as changes in lake level, species population levels in preceding months, and seasonal to inter-annual climate cycles.
Dispersion in ordination space of death and
fossil assemblages was substantially lower
than among live samples. Samples were distributed along both axes, indicating the importance of at least two environmental gradients in determining their species composition
(Fig. 10B). Many of the death assemblage samples are so similar to assemblages from core
MWA-1 that they are superimposed in ordination space. In general, dead samples had
slightly higher loadings on Axis 2 than fossil
samples. In both data sets, samples from 10 m
have higher loadings on Axis 2 than samples
from 5 m, a pattern that matched the results
for live samples. Thus, death and fossil assemblages retained relationships observed among
the living assemblages with respect to water
depth to some extent, although overall sample
variability was muted in death and fossil assemblages relative to live samples.
Discussion
Preservation of Life Assemblage Attributes in
Death and Fossil Assemblages. Numerous lines
of evidence suggest that both death and fossil
assemblages accurately preserve the community structure and composition attributes of
the living ostracode fauna at high resolution,
despite the fact that spatial and temporal averaging reduce the between-site variability
that is characteristic of life assemblages. Species richness of death and fossil assemblages
exceeds that of unpooled live richness in
quadrats by two- to threefold, although
comparable and statistically indistinguishable
numbers of species were present alive when
temporal replicates of live samples were
pooled. According to the pooled live data,
minimum estimates for time-averaging of
death and fossil samples were on the order of
one year. Alternatively, minimum spatial integration seen in death and fossil assemblages
was roughly equivalent to several square meters of habitat area. Thus, it appears that death
and fossil assemblages have potential to accurately represent the diversity of living ostracode assemblages at annual resolution and
a spatial scale of several meters.
Species abundance distributions are not significantly different among live, dead, and fossil data sets (Fig. 4). Thus, translation of os-
THE LIVE, THE DEAD, AND THE VERY DEAD
tracode life assemblages into death and fossil
assemblages does not appear to introduce significant bias to species-abundance distributions.
Ostracode rank-abundance histograms indicate substantial compositional differences
between life assemblages and paleoecological
assemblages (Fig. 7). Obvious outliers in death
and fossil assemblages relative to life assemblages can be accounted for by considering
habitat preference, and to a lesser extent, variation in preservation potential of species related to shell thickness. Results of the live ostracode CCA indicated that many of the outlying species in Figures 7B and 7C—Romecytheridea ampla (a), Mesocyprideis irsacae (b),
Mesocyprideis pila (c), and Tanganyikacypridopsis depressa (e)—reach their highest abundance
in sandy sediment. Mecynocypria emaciata (f) is
most abundant in rocky habitats, although it
is commonly collected in sandy habitats as
well. Mecynocypria n.sp. 20 (d) is a rocky-habitat species but is never very abundant alive
(0.064% average abundance in this study,
0.089% average abundance in a larger live database). All five taxa, with the possible exception of M. emaciata, have well-calcified valves
relative to many of the cypridoidean ostracode species in Lake Tanganyika. Of the six
most abundant live species (with depths
pooled, in order [dead and fossil ranks in
brackets]: Romecytheridea tenuisculpta [6, 13],
Allocypria mucronata [18, 32], Allocypria inclinata [23, 46], Cypridopsis n.sp. 6C [17, 16], Romecytheridea ampla [2, 2], and Romecytheridea
longior [16, 19]), the species R. tenuisculpta, A.
mucronata, A. inclinata, and C. n.sp. 6C are
known primarily from rocky habitats. R. longior is a shallow-water, sandy species, frequently observed as poorly calcified juvenile
instars in sediment cores. R. ampla is a shallow-water species common in both rocky and
sandy habitats. Of the six most abundant live
species, only A. mucronata is relatively poorly
calcified.
It remains possible that there is some taxonomic bias against the most poorly calcified
cypridoidean species. However, the surface
waters of Lake Tanganyika are supersaturated
with respect to carbonate and are alkaline (pH
8.7–9.2), so carbonate encrustation is a more
59
common phenomenon than dissolution in the
well-oxygenated surface waters of the lake.
Even thin-shelled ostracode valves are typically found in abundance in sediment cores,
although carbonate dissolution is sometimes
observed in cores with organic-rich sediments
or those collected below the oxycline in Lake
Tanganyika.
Ordination analyses implicate spatial mixing across habitat types as a likely cause of the
disagreement in rank order between life and
paleoecological assemblages. Wave action at
the depths of the sampling sites is sufficient to
transport surface sediments, and thereby the
ostracode valves contained in them, over centimeter-to-meter-scale distances (personal observation). The dominance of the rocky–sandy
habitat species M. irsacae and R. ampla in both
death and fossil assemblages suggests that
these species are the dominant species across
all habitat types at this locality. The dominant
live species (R. tenuisculpta) registers as sixth
most abundant dead species and 13th most
abundant as a fossil, showing that rocky-habitat species are also well-represented in paleoecological samples from this locale. Although
life and death assemblages for this study were
collected only from rocky habitat patches at
our locality, death assemblages clearly comprised individuals from both the rocky and
sandy habitats, the latter of which are more areally extensive at this location. Cores were collected in the silty-sand facies, and ordination
indicated that they contained assemblages
very similar to the death assemblages. Thus,
ostracode death assemblages appear to be
spatially integrated to an extent that renders
them more representative of the fauna at an
entire locality than are individual live samples collected at discrete points in the locality.
The similarity between death and recent fossil
assemblages suggests that this fidelity is carried through to the paleoecological record as
well.
The fidelity of microinvertebrate death and
fossil assemblages to life assemblages reported here differs in several respects from results
of comparable studies on marine macroinvertebrate death assemblages. Kidwell and Flessa
(1995) concluded, on the basis of a review of
published studies, that in level-bottom set-
60
SIMONE R. ALIN AND ANDREW S. COHEN
tings, transport out of the immediate life habitat is rare. However, Kidwell (2001b) concludes that molluscs ,1 mm are more prone
to postmortem transport out of the life habitat
based on a meta-analysis of live–dead studies.
We argue here that death and fossil ostracode
assemblages are spatially integrated within
localities across substrate types. For the purposes of paleoecological reconstruction, we
deem this a positive outcome of taphonomic
processes, in that it renders the averaged samples more representative of a larger habitat
area. Kidwell and Flessa (1995) make a similar
argument for the virtues of time-averaging.
In marine molluscan assemblages, ‘‘most
species with preservable hardparts are . . .
represented in the local death assemblage,
commonly in the correct rank order abundance’’ (Kidwell and Flessa 1995). Marine
mollusc assemblages collected with coarsemesh sieves have reported r-values of 0.54 6
0.05, whereas those collected with fine-mesh
sieves tend to show lower rank-order agreement, with r-values of 0.38 6 0.06 (Kidwell
2001b). Rank-order agreement of Tanganyikan ostracode assemblages compares quite favorably to these estimates (rlive–dead 5 0.552,
rlive–fossil 5 0.485, rdead–fossil 5 0.775), despite the
small size of ostracodes and the large disagreement in ranks of the dominant ostracode
species between life and paleoecological assemblages. However, we note that the amount
of variability accounted for by the correlation
is quite low for live–dead and live–fossil comparisons (r2-values of 0.30 and 0.24, respectively) and is high only in the case of the
dead–fossil comparison (r2 5 0.60). Rank-order differences among ostracode life, death,
and fossil assemblages probably stem from
transport within the locality and homogenization of spatially heterogeneous ostracode
populations.
Fidelity Metrics. Values obtained for the fidelity metrics of Kidwell and Bosence (1991:
Table 1) lend further support to the interpretation that fidelity of ostracode death assemblages to contemporaneously collected life assemblages is quite high. Agreement ranged
between 77% and 90% for all comparisons of
percentage of live found dead, percentage of
live found as fossils, and percentage of fossil
species found dead. Agreement was weaker
for the complementary comparisons (percentage of dead found alive, percentage of fossil
species found alive, percentage of dead found
as fossils), ranging between 53% and 83%, because of differences in the total species richness among the data sets. Applying the ‘‘maximum possible agreement’’ correction suggested by Kidwell (2001a) to account for this
problem restores the agreement values to
those obtained in the first set of comparisons
(i.e., ranging between 77% and 90%). This is
because the correction to ‘‘maximum possible
agreement’’ values involves a conversion from
values of ‘‘percentage dead found live’’ back
to ‘‘percentage live found dead.’’ Thus, any
comparison between data sets using the richness of the smaller fauna in the denominator
will inherently be the metric of maximum possible agreement, which is also equivalent to
Simpson’s index of similarity (number of species shared divided by the number of species
in the smaller fauna [Simpson 1960]). In this
case, Simpson’s index equals our values for
percentage live found dead, percentage live
found as fossils, and percentage fossil found
dead. Any estimate of similarity will be influenced by the total species richness of one sample or the other. Using the larger fauna in the
denominator only serves as an indirect indicator of the discrepancy of species richness between the two samples. Thus, it seems reasonable to report only the Simpson-equivalent
(i.e., maximum) fidelity value in live–dead–
fossil comparisons, as the disparity in species
richness can be assessed more efficiently by
simply comparing numbers of species between samples.
Sampling Efficiency. Counting multiple samples from the same core interval allows assessment of the adequacy of sampling for our death
and fossil assemblages. Analysis of the data
from the resampled core interval indicated that
approximately 28% of species in a sample of
500 can be expected to be unique. This corresponds closely with the percentage of species
represented by fewer than 0.20% of individuals
on average. Therefore, downweighting rare
species in ordination analyses is appropriate
on the basis of their inconsistent detection in
death or fossil assemblages. However, predom-
THE LIVE, THE DEAD, AND THE VERY DEAD
inance of rare species is an important characteristic of Tanganyikan ostracode assemblages
(Fig. 4). We would not want to exclude rare species entirely from paleoecological analyses on
the basis of incomplete sampling. Indeed, previous studies have suggested that disappearance of rare species from ostracode assemblages can be an important indicator of severe anthropogenic disturbance in adjacent watersheds (Wells et al. 1999). Furthermore,
ecological studies have demonstrated the importance of rare species in assessments of ecological integrity at localities (e.g., Cao et al.
1998), and it makes sense to extend this perspective to the paleoecological record wherever possible. Although inclusion of rare species is desirable, it would not serve to place
much importance on the particular identities of
the rarest species, as their exact composition is
likely to change with additional sampling from
the same core interval or surface sediment
sample. Maximum similarity metrics such as
Simpson’s should not surpass 72% on average
if none of the predicted unique rare species
were shared among samples from the same interval. Despite caveats about the unreliability
of detecting rare taxa, observed agreement in
pairwise comparisons of samples from interval
0–1 exceeds this value (79% on average), demonstrating greater actual species similarity
than predicted.
When cumulative species richness from
core MWA-1 is superimposed on data from
the uppermost core interval (0–1 cm), the
trends are indistinguishable (Fig. 9). Rare species appeared at the same rate throughout the
core as in the resampled interval, reflecting
sampling intensity and the abundance distribution of rare species. This suggests continuity in numbers and abundance of rare taxa
through the core.
Scaling Issues and Comparability of Fish and
Ostracode Community Dynamics. Levin (1992)
discussed the importance of crossing scales in
ecology to facilitate the prediction of the effects of global environmental change on communities and ecosystems. In this context, the
two aspects of scale most frequently addressed are spatial and temporal scales of
sampling and analysis. Levin (1992) emphasized that the scales inherent to the observer
61
of the environment are also important, with
the observer here being an individual of any
species in its environment. Thus, in addition
to consideration of objective scales of time and
space, investigators should also consider the
scale perspective of the organisms under
study, as each species’ response to environmental change will be influenced by its life
history characteristics, resource requirements,
and disturbance responses (Levin 1992).
Ideally, ostracodes could be used as paleoecological indicators of the entire benthic
community. However, body size differences
between ostracodes, larger invertebrates, and
fish suggest that organismal scaling issues
must be considered. Cohen (2000) discussed
the implications of the disparity between fish
and ostracode community dynamics in Lake
Tanganyika for the application of paleoecological insights to conservation. Nakai et al.
(1994) demonstrated the stability of fish assemblages at several locations in Lake Tanganyika for over a decade and attributed this stability to deterministic and highly coevolved
species interactions. In contrast, Cohen (2000)
described ostracode community dynamics as
highly patchy in space and time, at the scale of
hundreds of meters, and invoked metapopulation dynamics as the mechanism for the
maintenance of ostracode diversity. In our
study, species richness rose more steeply
when live diversity data were pooled across
space than when pooled through time (Fig.
3B). This confirms that spatially heterogeneous patterns in ostracode life assemblages
are also borne out at the scale of less than a
meter to tens of meters. The shallower rise in
the temporally pooled curve implies some degree of stability in the species pool, despite the
patchiness of ostracode distributions, and
may represent a seasonal succession of species.
Cohen (2000) concluded that ‘‘the contrast
between cichlid and ostracode diversity structure in space and time suggests that no one
taxonomic group is likely to serve as a robust
model for how diversity is maintained.’’ Thus,
in order to use ostracode paleoecology as a
general indicator of benthic community integrity, it is necessary to understand the relationship between fish and ostracode diversity dy-
62
SIMONE R. ALIN AND ANDREW S. COHEN
namics. Apparent differences in stability between fish and ostracode communities were
equalized to some extent when ostracode
death and fossil assemblages were studied instead of life assemblages. Our ordination results indicated that, although overall variability among live samples was quite high, death
and fossil assemblage variability was far lower. Death assemblages, which we have shown
to preserve spatiotemporally averaged characteristics of live communities with good fidelity, were thus better indicators of the summation of ecological conditions at our locality
than single live-collected samples were. In addition, samples in sediment cores were compositionally very similar to death assemblages, suggesting minimal change in the ostracode species pool at this locality during the
past few decades.
Inter-sample relationships in ordination
space were largely determined by the abundance of common taxa. Identity of rare species
was less consistent among samples in all data
sets, but rare taxa were downweighted and
played relatively minor roles in the outcome of
the ordination. The reduced variability seen in
death and fossil assemblages is thus largely
attributable to the influence of common taxa.
Lower variation in death and fossil samples
supports the notion of stability in the dominant component of the local species pool, despite high spatiotemporal heterogeneity of
live populations and the instability of the
large proportion of rare taxa in ostracode assemblages. When viewed through death and
fossil assemblages, with their inherent spatiotemporal averaging, ostracode community
dynamics no longer appear so disparate from
those of fish.
After thorough consideration of spatiotemporal scaling issues, the question again arises:
can ostracodes safely be used as paleoecological indicators of the integrity and function of
benthic communities as a whole? Dominant
taxa in ostracode assemblages show similar
stability to fish assemblages when viewed in
time-averaged death or fossil assemblages.
One caveat to the utility of ostracodes as indicator taxa for the entire benthos is that ostracodes may have a different response
threshold than either fish or molluscs to sed-
iment inundation, which is the dominant anthropogenic threat to Tanganyikan habitats
and species (Alin et al. 1999). This makes
sense, as small-scale influx of sediment may
constitute a disruption of habitat or food quality for fish but may simply represent additional food supply to ostracodes. Larger-scale sedimentation changes may render habitat unsuitable for both taxa. Therefore, ostracodes
effectively provide a conservative estimator of
paleoecological change in the benthic community. For the purposes of ecological monitoring, an investigator would use taxa more
susceptible to the environmental impact of interest (e.g., increased sedimentation), in order
to detect species responses in a timely fashion.
However, for the purposes of reconstructing
biodiversity dynamics by using paleoecological assemblages, a conservative indicator decreases the likelihood of falsely interpreting
anthropogenic impacts.
Novel Contributions of Paleoecological Observations to Conservation Biology. Two observations suggest that sampling death and fossil
assemblages provides a more efficient means
of gauging the response of ecological communities to natural or anthropogenic environmental change than live sampling alone. First,
ordination of live, dead, and fossil data sets
showed lower variability among dead and fossil samples than among live samples. Postmortem mixing of ostracode assemblages
leaves its signature on the species composition
and richness of death and fossil assemblages
by integrating assemblage membership across
habitat types and through time. Spatiotemporal averaging allows death and fossil assemblages to retain high-resolution information about the ostracode life assemblages that
contributed to them, while simultaneously
rendering information about the average composition of these communities averaged over
short timescales.
Second, occurrence frequencies of species
indicate which species are ecologically persistent. Some persistent species are rare and
would not be identified as persistent on the
basis of live sampling. Species occurrence frequencies give us a means of identifying species, using paleoecological data, that may be
more likely to recolonize a habitat after a dis-
THE LIVE, THE DEAD, AND THE VERY DEAD
turbance, based on their tendency to recur
through time. Wells et al. (1999) observed the
disappearance of rare species at a highly disturbed study area following watershed deforestation. Examining the occurrence frequency
of species in paleoecological assemblages may
provide a predictive tool for identifying components of an invertebrate community that are
relatively extinction-prone versus those that
are extinction-resistant.
Conclusions
Death and fossil assemblages of ostracodes
in Lake Tanganyika accurately record attributes of living ostracode communities, such as
species richness, abundance, and composition.
Spatial and temporal averaging of death and
fossil assemblages, on scales of less than one
to tens of meters and months to years, reduce
between-sample variability in species diversity and composition compared with life
assemblages. Because death and fossil assemblages represent communities integrated
across habitat type and through the vicissitudes of fine-scale ostracode population dynamics, these assemblages are thus more representative of community dynamics at the
whole habitat scale than individual live-collected samples can be. Paleoecological sampling of ostracode assemblages therefore
should provide a high-resolution tool for
analyzing benthic community dynamics
through time.
Furthermore, paleoecology can generate insights useful for conservation that are not
amenable to analysis based on live sampling
alone without costly, labor-intensive, and
time-consuming effort. Paleoecological analyses can also generate insights into the
ecological persistence of individual species
whose abundance and stability may not be
correlated. Ordination of ostracode fossil assemblage data shows promise as a reliable, informative means of assessing trends in the
richness and composition of the benthic community through time. Overall, the sedimentary record of ostracode community dynamics
reliably represents the live population at a locality through time.
63
Acknowledgments
We gratefully acknowledge assistance with
field collections by C. M. O’Reilly, K. Fadhili,
J. Houser, R. Shapola, and A.B. Thompson. We
thank the Tanzanian Commission for Science
and Technology for research permits; the Tanzanian Fisheries Research Institute and the
United Nations Development Program/Global Environmental Facility’s Lake Tanganyika
Biodiversity Project for logistical assistance;
the National Science Foundation (NSF)/University of Arizona AMS Facility for radiocarbon dates; D. L. Dettman and O. K. Davis for
assistance in preparing radiocarbon samples;
and C. Birkett at the National Aeronautics and
Space Administration for satellite altimetry
lake-level data used to calibrate sampling location water depths. We are particularly grateful to S. M. Kidwell, J. B. Bennington, K. W.
Flessa, R. Robichaux, and P. N. Reinthal for detailed, constructive comments on various versions of the manuscript; and K. W. Flessa, S.
M. Kidwell, J. Pandolfi, P. N. Reinthal, D. L.
Dettman, L. Kaufman, J. T. Overpeck, R. Robichaux, and D. Goodwin for helpful conversations and correspondence. S.R.A. appreciates financial support for this project from: a
University of Arizona Graduate College
Dean’s Fellowship, National Science Foundation Graduate Research Fellowship, National
Security Education Program Graduate International Fellowship, Geological Society of
America Graduate Student Research Grant,
Wilson R. Thompson Scholarship (Department of Geosciences, University of Arizona),
and University of Arizona Graduate Student
Final Project Fund. This study was also supported by NSF grants EAR-9627766 and ATM9619458 (The Nyanza Project). This is contribution no. 148 of IDEAL (International Decade
of East African Lakes).
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Appendix
Species Abundance Data for Life, Death, and Fossil
Assemblages
Samples identifications are abbreviated as follows: for life and
death assemblages (e.g., 1LNo, 1DNo), quadrat number, L for
life and D for death assemblage, and a two-letter code for collection month (except J8, July 1998; J9, July 1999); for sediment
cores (e.g., MWA-1:0), core number:upper sample interval depth
(0, 0–1 cm), lowercase letters denote multiple samples from the
same core interval. Species, in columns, are indicated by numbers as follows: 1, Allocypria aberrans; 2, A. cf. aberrans; 3, A. claviformis; 4, A. humilis; 5, A. inclinata; 6, A. cf. inclinata; 7, A. mucronata; 8, A. n.sp. 5; 9, A. n.sp. 10; 10, A. n.sp. cf. 10; 11, A. n.sp.
11; 12, A. n.sp. 17; 13, A. n.sp. 18; 14, A. n.sp. 20; 15, Archaeocyprideis tuberculata; 16, A. n.sp. 2; 17, Cyprideis profunda; 18, C.
spatula; 19, C. n.sp. 24; 20, Candonopsis depressa; 21, C. cf. depressa;
22, C. n.sp. 2; 23, C. n.sp. 7; 24, C. n.sp. 8; 25, C. n.sp. 9; 26, C.
n.sp. 12; 27, C. n.sp. 15; 28, Cypridopsis bidentata; 29, C. colorata;
30, C. cf. lacustris; 31, C. obtusa; 32, C. serrata; 33, C. n.sp. 5; 34,
C. n.sp. 6A; 35, C. n.sp. 6B; 36, C. n.sp. 6C; 37, C. n.sp. 15; 38, C.
n.sp. 17; 39, C. n.sp. 18; 40, C. n.sp. 23; 41, C. n.sp. 25; 42, C. n.sp.
; 43, Darwinula stevensoni; 44, cf. Elpidium; 45, Gomphocythere alata; 46, G. coheni; 47, G. cristata; 48, G. curta; 49, G. wilsoni; 50, G.
woutersi; 51, G. n.sp. 11; 52, Kavalacythereis braconensis; 53, Mecynocypria complanata; 54, M. cf. complanata; 55, M. connoidea; 56,
M. declivis; 57, M. cf. declivis; 58, M. deflexa; 59, M. emaciata; 60,
M. opaca; 61, M. parvula; 62, M. subangulata; 63, M. n.sp. 9; 64,
M. n.sp. 17; 65, M. n.sp. 19; 66, M. n.sp. 20; 67, M. n.sp. cf 20; 68,
M. n.sp. 21; 69, M. n.sp. 22; 70, M. n.sp. 29; 71, M. n.sp. 30; 72,
M. n.sp. 31; 73, M. n.sp. cf 32; 74, M. n.sp. 33; 75, M. n.sp. cf 34;
76, M. n.sp. 36; 77, M. n.sp. 37; 78, M. n.sp. 39; 79, M. n.sp. 40;
80, Mesocyprideis irsacae; 81, M. nitida; 82, M. pila; 83, M. n.sp. 2B;
84, M. n.sp. 4; 85, Romecytheridea ampla; 86, R. longior; 87, R. tenuisculpta; 88, Tanganyikacypris matthesi; 89, T. n.sp. 1; 90, Tanganyikacypridopsis acanthodes; 91, T. calcarata; 92, T. depressa; 93, T.
n.sp. 3; 94, T. n.sp. 4; 95, T. n.sp. 5; 96, T. n.sp. 8; 97, Tanganyikacythere burtonensis; 98, T. caljoni; 99, T. n.sp. 1.
66
APPENDIX FIGURE 1.
SIMONE R. ALIN AND ANDREW S. COHEN
THE LIVE, THE DEAD, AND THE VERY DEAD
Appendix.
Extended.
67
68
Appendix.
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Extended.
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Extended.
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Extended.
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Extended.
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