The live, the dead, and the very dead: taphonomic calibration of the recent record of paleoecological change in Lake Tanganyika, East Africa

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1 Paleobiology, 30(1), 2004, pp 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 *Present address: School of Oceanography, Box , University of Washington, Seattle, Washington 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 paleoecological 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 2004 The Paleontological Society. All rights reserved /04/ /$1.00

2 THE LIVE, THE DEAD, AND THE VERY DEAD 45 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 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 ( 210 Pb, 14 C), 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-

3 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

4 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 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 ( 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 22- month 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 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 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

5 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 14 C reservoir effect of Lake Tanganyika. By using global atmospheric post-bomb 14 C 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- m, and 63- m sieves, dried at 60 C, and weighed. All ostracode individuals in the sediment fractions of 1 mmand106 m 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- m 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- m 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 assemblages 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

6 THE LIVE, THE DEAD, AND THE VERY DEAD 49 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 distributionsforlive, dead, andfossil data sets, we used paired Kolmogorov- Smirnov 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 subsample 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 for the observed species distribution from the same core interval (following Koch 1987). To estimate the values of Fisher s 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 x, x 2 /2, x 3 /3, x n /n, with being the average species richness for four counted samples, in four observed occurrence categories (Koch 1987; Magurran 1988). Probabilities of occurrence (p n ) 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.

7 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 2 age ranges with 0.1 relative probability. Post-bomb dates were estimated by using atmospheric decay curves for 14 C from Nydal and Lövseth (1983) and Levin and Kromer (1997). Sample number Depth in core (cm) Fraction modern 14 C 14 C age Estimated calendar age range (A.D.) AA AA AA AA AA AA post-bomb post-bomb post-bomb post-bomb 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 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 (N sandy N muddy )N 1 rocky, 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 ( 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 m increasing from % below 9 cm to % 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 m were comparable to those at the top of core MWA-1 at %. 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: g, overall average g), with the average sediment per quadrat being higher at 10 m depth ( g) than at 5 m ( g). Variations in sediment particle size were not correlated with fluctuations in ostracode species richness and abundance. Mean weight percentages of particles 106 m were % and % 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

8 THE LIVE, THE DEAD, AND THE VERY DEAD 51 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 139). Total death assemblage individuals tallied were 18,175 in 37 samples, comprising 87 species (Appendix). Death assemblages contained ,805 individuals per gram dry sediment (median 5915). Fossil assemblages contained 9639 individuals and 79 species in 19 samples (Appendix). Fossil assemblages contained individuals per gram dry sediment (median 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 (H C 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 (Q live dead 9.103, p 0.001; Q live fossil 6.158, p 0.001), with no difference between species richness values of death and fossil assemblage data (Q dead fossil 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 (N time range: , median 1953; N space range: , median 1349). Kruskal-Wallis tests detected no difference among pooled live, dead, and fossil data sets for species richness values (H C 5.804, p 0.10). Interestingly, pooling samples either across space or through time resulted in equivalent numbers of species, al- FIGURE 3. A, Box plots of species richness values for all samples in the live (median 15), dead (median 37), and fossil (median 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 36), life assemblages pooled over time (median 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-

9 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 total number of individuals counted and S 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 speciesin 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

10 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). 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 Romecytheridea ampla, b Mesocyprideis irsacae,c Mesocyprideis pila,d Mecynocypria n.sp. 20, e Tanganyikacypridopsis depressa, f Mecynocypria emaciata, andg Mesocyprideis n.sp. 4 (see text for discussion). 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 (d max(10,79) 3.8, p 0.50), whereas live data were distributed significantly differently from both (live dead: d max(10,64) 13.4, p 0.01; live fossil: d max(10,64) 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 required 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 (r live dead 0.552, r live fossil 0.485, r dead fossil 0.775; p for all). Only the dead fossil abundance correlation was consistently and highly significant across comparisons with various numbers of species included (r , p ) (Fig. 8). Live dead rank-abundance comparisons were not significant with up to 20 species included, but

11 54 SIMONE R. ALIN AND ANDREW S. COHEN with 40 or more species included, live dead rank-order agreement was significant, with r- values of 0.35 to Similarly, live fossil rank-order results were not significant until 60 species were included in the comparison, with r-values of 0.20 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 ( ). 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 5mwasaddedto 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. % 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 5 m 10 m Depths pooled 88% (50/57 spp.) 77% (44/57 spp.) 65% (50/77 spp.) 74% (57/77 spp.) 68% (44/65 spp.) 88% (57/65 spp.) 47% (41/88 spp.) 98% 80% (39/49 spp.) 90% (44/49 spp.) 53% (39/74 spp.) 76% (56/74 spp.) 68% (44/65 spp.) 86% (56/65 spp.) 44% (38/86 spp.) 87% 89% (57/64 spp.) 89% (57/64 spp.) 66% (57/87 spp.) 83% (72/87 spp.) 71% (57/80 spp.) 90% (72/80 spp.) 56% (55/99 spp.) 94%

12 THE LIVE, THE DEAD, AND THE VERY DEAD 55 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 5m: 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 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 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 (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 (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) 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 dominanttaxawashighest 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 5mandonly 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 r 2 -values were 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 ( 88,000), the estimated total species richness for the sample is 85, suggest-

13 56 SIMONE R. ALIN AND ANDREW S. COHEN TABLE 4. Occurrence frequency of species in resampled core interval. TABLE 5. Average species abundance versus probability of detection based on resampling a fossil assemblage. No. of occurrences No. of species Abundance range Median abundance Average abundance No. of species Probability of detection /58 (48%) 12/58 (21%) 7/58 (12%) 11/58 (19%) % % % % 2.00% 0.28% 0.20% 0.05% 0.65% % 0.21% 24/58 (41%) 19/58 (33%) 15/58 (26%) 100% 75% 33% 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 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% abundance had a 50% probability of detection. For estimating numbers of unique species, Fisher s 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 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. p n probability of species in each category occurring in an additional subsample. Observed occurrences No. of species No. of species in (expected) p n new subsample Average total S: 43.3 (100%) Predicted shared S: 31.3 (72%)

14 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 5mand10mindicatedwith enlarged gray square and circle, respectively. Core-top samples from 5mand10mindicatedbylarge 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: 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 higher diversity) and with the abundance of dominant taxa (Mesocyprideis irsacae and Romecytheridea ampla for dead and fossil samples [negative loadings on Axis 1 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 higher abundance]). Abundance of these species varies with substrate, with A. mucronata being the only rockyhabitat species among them. These correlates

15 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 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 ( and , respectively) are both substantially higher than for life assemblage OSI values ( ), 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-