Palaeontologia ElectronicaPalaeontologia Electronica palaeo-electronica.org Daley, Gwen M. and Bush, Andrew M. 2020. The effects of lithification on fossil assemblage biodiversity

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Daley, Gwen M. and Bush, Andrew M. 2020. The effects of lithification on fossil assemblage biodiversity and composition: An experimental test. Palaeontologia Electronica, 23(3):a53. https://doi.org/10.26879/1119palaeo-electronica.org/content/2020/3204-lithification-and-biodiversity

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The effects of lithification on fossil assemblage biodiversity and composition:

An experimental test

Gwen M. Daley and Andrew M. Bush

ABSTRACT

Lithification of unconsolidated sediment into solid rock can bias the tabulation oftaxonomic diversity from fossil assemblages in several ways. Methodological biasesresult from the relative difficulty of extracting fossils from lithified sediments, whereasdiagenetic biases result from poor preservation or destruction of fossils by dissolutionor other processes. Here, we use an experimental approach to isolate the effects ofmethodological biases. Replicate samples of Pleistocene mollusks were collected fromthe same series of horizons at the same outcrop. One set of replicates was sieved andthe other was cemented into artificial rocks before identifying and counting species.These procedures probably represent a best-case scenario for recovering biologicalsignals: the artificial rocks were only poorly lithified, the sampling process was nothighly destructive, and the smallest mollusks (< 4 mm) were excluded. However, minorbiases were still introduced by the non-random nature of locating and identifying spe-cies in the lithified samples, despite efforts at random sampling. After standardizingsampling effort, the cemented samples actually appeared slightly more diverse thanthe uncemented replicates due to the oversampling of rare species. Assemblage com-position was affected slightly by lithification, notably in the undersampling of oysters,whose irregular shells were probably more difficult to identify in matrix. The method-ological effects of lithification doubtlessly vary – sample processing will be moredestructive for some rocks and fossils – but methodological biases can be quite mild insome cases.

Gwen M. Daley. Department of Chemistry, Physics and Geology, Winthrop University, Rock Hill, South Carolina 29733, USA. [email protected] M. Bush. Department of Geosciences and Department of Ecology and Evolutionary Biology, University of Connecticut, 354 Mansfield Rd, Storrs, Connecticut 06269-1045, USA. [email protected]

Keywords: taphonomic bias; species richness; aragonite dissolution; Mollusca; paleoecology; Pleistocene

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Submission: 17 August 2020. Acceptance: 3 November 2020.

INTRODUCTION

Understanding the processes that generateand eliminate biodiversity is a central focus in bothbiology and paleobiology. However, paleobiologistsface unique challenges in accurately estimatingbiodiversity because the formation and sampling ofthe fossil record can introduce biases not encoun-tered in studies of the living biota. Here, we exam-ine some of the potential biasing effects oflithification, the transformation of unconsolidatedsediment into solid rock. Some effects of lithifica-tion (methodological biases) occur because fossilsare prepared or extracted from lithified sedimentsusing different techniques than those applied tounlithified sediments (Kowalewski et al., 2006;Nawrot, 2012). Many lithified fossil assemblagesrequire time-consuming preparation by mechanicalprocesses like hammering and scraping, leading tosmaller sample sizes and fewer observed species.In contrast, unlithified sediments can frequently bewet-washed through sieves with no chemical treat-ment, and highly fossiliferous formations can yieldhundreds to thousands of fossil specimens perkilogram of sediment with relatively little effort.

Variation in sample size can be counteractedby analytical standardization (e.g., Sanders, 1968;Hurlbert, 1971; Powell and Kowalewski, 2002;Kowalewski et al., 2006; Bush and Bambach,2004; Chao and Jost, 2012), but sampling-stan-dardization will not fix other methodological biasesintroduced by lithification (e.g., Hendy, 2009, 2010;Sessa et al., 2009). For example, sieved fossilscan be manipulated and examined from all angles,making identification easier, whereas many fossilsfrom lithified sediments cannot be removed fromthe rock matrix. It may be more difficult to locatesmall specimens in lithified samples, which couldreduce their contribution to observed diversity,although small size classes are sometimes deliber-ately excluded from sieved unconsolidated mate-rial as well. In addition, the more intensivepreparation methods applied to solid rocks coulddamage fossils (Kowalewski et al., 2006; Hendy,2009; Nawrot, 2012). Silicified assemblages dis-solved out of limestone can be more comparable tounlithified assemblages, although the silicificationprocess itself can introduce other biases (e.g.,Clapham, 2015; Pruss et al., 2015).

Lithification can also occur in conjunction withdiagenetic biases introduced by poor preservation

of some or all fossils due to compaction, dissolu-tion, or other processes. In particular, aragoniticshells and skeletons dissolve more readily thanthose built of calcite or other minerals, which canlead to the underrepresentation or loss of commontaxa like scleractinian corals and many mollusks(e.g., Koch and Sohl, 1983; Cherns and Wright,2000, 2009; Cherns et al., 2008; Foote et al, 2015;Sanders et al., 2015). Fossil dissolution need notbe directly linked with lithification (e.g., Nawrot,2012; Sanders et al., 2015), although dissolutionsometimes provides a source of cement (Cherns etal., 2008), in which case the two processes areconnected. Just as small fossils may be harder tosample in lithified sediments, they may be particu-larly vulnerable to diagenetic loss (Cooper et al.,2006; Sessa et al., 2009; Sanders et al. 2015).Aragonite dissolution can impair the measurementof sample-level diversity (Koch and Sohl, 1983),but regional and global analyses of biodiversityappear to be more robust (Kidwell, 2005), probablydue to patchy preservation of aragonitic taxa, oftenas molds (Bush and Bambach, 2004; Cherns et al.,2008; Dean et al., 2019).

It is critical to understand the strength of bothmethodological biases and diagenetic biases, aswell as how each varies among geological con-texts. For example, if methodological biases arestrong and diagenetic biases are weak then addi-tional sample preparation and/or modified prepara-tion techniques might improve diversity estimatesby revealing additional species. However, if diage-netic biases are strong and some fossil taxa aretruly lost from the assemblage, then additionalpreparation of existing samples might not be fruit-ful. Instead, one’s time might be better spentsearching for new samples on which diagenesishad less impact. As another example, consider acomparison of the rarefied species richness ofbivalves from unlithified Cenozoic sediments tothat of brachiopods from lithified Paleozoic rocks. Ifmethodological biases are strong, then the com-parison will be compromised by undercounting ofdiversity in the Paleozoic samples. However, ifmethodological biases are weak, then the compari-son could be legitimate, because the preferentialdissolution of aragonite is not an issue when allfossils are calcitic or phosphatic.

Most previous tests of the effects of lithifica-tion on fossil content were based on comparisons

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of unlithified and lithified samples from the samegeological context that contained generally similarfauna. In some cases, the samples came from anarrow stratigraphic interval at a single outcropwhere cementation was patchy (Nawrot, 2012;Sanders et al., 2015), whereas others included abroader range of samples (Hendy, 2009, 2010;Sessa et al., 2009). Several general patterns areevident from these studies, which all focused onCretaceous and Cenozoic assemblages. First, lithi-fication accompanied by aragonite dissolution sig-nificantly reduces apparent biodiversity, particularlyin small size classes (< 5 mm or so). Some authorsreported that biases were more severe for fully lith-ified sediments than for “poorly lithified” sediments,for which some disaggregation is possible witheffort (Hendy, 2009). However, when lithificationwas not accompanied by aragonite dissolution, theeffects on sample-level diversity were relativelymild, although, again, fossils less than 5 mm is sizewere under-represented (Nawrot, 2012; Sanders etal., 2015). For example, in a study of a patchily lith-ified Eocene shell bed, Sanders et al. (2015, table1) found that rarefied species richness of lithifiedsamples was 15 to 18, versus 17 to 19 for unlith-ified samples.

These studies suggest that methodologicalbiases, in the absence of diagenetic biases, maynot have a large effect on biodiversity tabulation.Taking another approach to the same problem,Hawkins et al. (2018) isolated the effects of meth-odological biases through computer simulation.After randomly placing virtual fossils within a pre-scribed space (the virtual rock), they passedplanes through the space (i.e., sliced the rock) atdifferent orientations and counted any fossil inter-sected by the plane. Rarefied species richnesswas 5-23% lower in the sliced, “lithified” samplesrelative to random draws of individuals from theoriginal population, equivalent to a sieved sample.The “lithified” samples had lower evenness andhigher mean size.

Here, we further test the effects of method-ological biases associated with lithification using anexperimental approach. We took replicate bulksamples of unconsolidated sediment from twohighly fossiliferous Pleistocene formations fromFlorida (the Fort Thompson and Bermont), sievingone set of samples and counting all molluscan taxafollowing typical procedures for unlithified bulksamples (Daley et al., 2007). We then artificiallycemented a subset of the replicate samples usinghydraulic cement and tabulated diversity fromthese artificial rocks. Our treatments did not chemi-

cally or physically alter the sediment or fossils, andwe therefore isolated the methodological effects oflithification from diagenetic effects like the dissolu-tion of aragonitic shells (cf. Nawrot, 2012; Sanderset al., 2015; Hawkins et al., 2018). We examine theeffects of lithification on both species richness andspecies composition.

MATERIALS AND METHODS

Samples were collected from the Bermont andFort Thompson formations at Caloosa ShellQuarry, Ruskin, Florida (Portell et al., 1995; Daley,2001; Bush et al. 2002; Daley et al., 2007) (Figure1A, B). These formations consisted of coarseshelly material with very fine quartz sand and asmall amount of mud (Figure 1C). There was nosignificant natural cementation or evidence of dia-genetic alteration such as recrystallization of ara-gonite.

The quarry wall was sampled at 30-cm incre-ments using a shovel and pick. At each verticalposition, replicate samples were taken directlyadjacent to each other (Figure 1B). Each sample isreferenced by a formation abbreviation (Ber or Ftt),a number that indicates vertical position, and a let-ter that distinguishes replicates (A or B). As part ofprevious paleoecological studies, one replicatefrom each set was sieved, and all mollusksretained on a 4-mm sieve were examined andidentified (Daley, 2001; Daley et al. 2007). Tapho-nomic research indicates that death assemblagesof these larger shells generally reflect the long-term composition of the adult living assemblage,whereas smaller shells are more affected by pre-burial processes like current sorting and by fluctua-tions in juvenile settlement (e.g., Cummins et al.,1986; Kidwell, 2001, 2002; Nawrot, 2012). To avoiddouble-counting, shells were counted following theprocedures outlined by Daley (2001). For example,bivalve specimens were only counted if theyincluded enough of the hinge to allow species-levelidentification, and the number of bivalve specimensfor each species was divided by two to account foreach bivalve shell having two valves (e.g., Bam-bach and Kowalewski, 2000; Kowalewski and Hoff-meister, 2003).

Artificial Cementation

Three replicate samples from each formationwere cemented using SakreteTM bolt and railcement anchor. This product was designed toanchor bolts in concrete slabs, and thus was engi-neered to flow into tight spaces, like those betweenshells. The fine-grained cementing mixture filled in


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most of the visible void spaces within the body ofthe unconsolidated sediment, creating a fair simu-lacrum of a real rock. One kilogram of dry uncon-solidated sediment sample was gently mixed byhand with 250 grams of dry cementing agent,ensuring that the dry cement was evenly distrib-uted throughout the sediment. Five hundred mL ofwater was then poured into the sample and mixedby hand. The wet, pliable mixture was transferredto a 36×8 cm mold, where it was allowed to setundisturbed for an hour, at which time it was suffi-ciently rigid to transfer from the mold. To allowample time for the cementation reactions to run tocompletion, the newly formed rock tiles wereallowed to sit for at least five days out of direct sun-light.

The cement adhered both to the quartz sandand the shelly material, resulting in samples of arti-ficially cemented, highly fossiliferous, argillaceoussandstone (Figure 1D). The degree of cementationwas sufficient that fossils could be removed usingtools with some effort. Similar to Nawrot’s (2012)assemblages, these samples could be describedas “poorly lithified.”

Natural rocks vary in degree of cementation,and methods for extracting and counting fossilsfrom lithified rocks vary as well. Thus, no singleexperimental protocol can perfectly replicate all sit-uations. Our approach represents one reasonablescenario and replicates the contrast between siev-ing an assemblage on one hand and finding andpreparing fossils from a cemented matrix on theother. However, as we address in the Discussion,

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FIGURE 1. Photographs of the outcrop and fossil samples. A. Outcrop at Caloosa Shell Quarry, Hillsborough County,Florida, USA. B. Close-up of outcrop showing sampling scheme. Pairs of replicate samples were taken immediatelyadjacent to each other at 30 cm vertical increments. C. Fossil sample prior to either sieving or cementation. D. Artifi-cially cemented sample.


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the exact effects of lithification certainly depend tosome extent on rock type, fossil type, and prepara-tion methods.

Data Collection

The rock matrix was slowly removed to revealthe fossils using dental picks and soft nylonbrushes. Larger hand tools, wire brushes, chisels,and mechanical devices like air abrasive units anddrills were not used. The samples were not washedduring processing nor was sediment removed fromthe rock sieved for small fossils.

To keep the experimental and control datacomparable, we used the same rules for recogniz-ing a countable bivalve or gastropod specimen thatwere used by Daley (2001) and Daley et al. (2007)for the unlithified replicates. For bivalves, the pres-ence of the hinge structure had to be confirmed byexcavating enough of the matrix and/or overlyingshell material to see that the structure was present.Similar excavations were necessary for gastro-pods. It was not necessary to remove the speci-men from the rock tile matrix to count it as aspecimen. Given that the unconsolidated sampleswere processed on a 4 mm sieve, only specimensthat were at least 4 mm in at least one dimensionwere counted from the experimental samples. Anyspecimen that was less than 1 mm long in anydimension was not counted. The exclusion of smallspecimens was required for consistency with thepreviously collected data. In addition, however, allprevious studies agree that fossils smaller thanseveral millimeters are under-represented in lith-ified assemblages (e.g., Nawrot, 2012; Sanders etal., 2015), so excluding them allows us to concen-trate on the fate of the larger size classes.

Each rock slab was examined using a 4xmagnifying lamp, starting with the upper left-handcorner. Any shells protruding from the body of thetile were further examined and excavated if therewas a chance the shell was a countable fragmentas defined above. A combination of picking with thedental picks and vigorous brushing with a nylonbrush was a slow but effective method of excava-tion. Specimens that came loose from the rock tilewere given an identification number, identified tothe species level, and placed in a bag. If the speci-men could be identified without removing it fromthe rock tile, it was given an identification numberand left in place. If it later became necessary toremove the specimen, it would be transferred tothe same bag as the other members of its species.

As specimens became harder to find, the topsand sides of the rock were brushed and scraped,

exposing more shells. Collection continued until100 specimens had been identified or a maximumof 10 hours of processing had been completed.The number of bivalve hinge fragments counted foreach species was divided by two and the fractionsrounded up to account for each bivalve having twovalves to its shell (Bambach and Kowalewski,2000; Kowalewski and Hoffmeister, 2003). The rawdata set is provided in the Appendix.

Data Analysis

Sampling intensity was standardized usingclassical rarefaction (Sanders, 1968; Hurlbert,1971) and coverage-based rarefaction (Chao andJost, 2012), which is similar to Alroy’s (2010 a,b)shareholder quorum subsampling method. The rar-efaction analyses were conducted using the iNEXTfunction in the iNEXT package (Hsieh et al., 2016)in the R programming environment (R Core Team,2017). This function both conducts rarefaction(reducing sampling intensity below the observedvalue) and extrapolation (predicting diversity athigher sampling intensity than observed). We focuson the rarefaction results, but include the extrapo-lation results in Figure 2 and Figure 3 (dashedlines). Shannon-Wiener diversity (H) and evenness(exp[H]/S) were determined using PAST softwarepackage (Hammer et al., 2001). To examine differ-ences in the abundance structure of the lithifiedand unlithified samples, rank abundance plotswere created using the rank abundance function inthe BiodiversityR package in R (Kindt and Coe,2005).

Given that lithification is generally presumedto suppress the abundance and diversity of small-bodied species, we also examined the effects oflithification on body-size distribution of sampledspecimens (bearing in mind that specimens < 4mm, which are most vulnerable to this bias, werenot included in the study). We categorized all spe-cies into three size categories based on maximumdimension following Daley (2017): small (4-15mm), medium (15-50 mm), and large (> 50 mm).These assignments were based on measurementsof a subset of specimens. Although this analysiswill not capture subtle changes in the body-sizedistribution, it should detect large, obvious effects.Confidence intervals on proportions were calcu-lated using Wilson’s method (Brown et al., 2001).

Variation in species composition was visual-ized using principal coordinates analysis based onBray-Curtis dissimilarity (e.g., Tyler and Kow-alewski, 2014). Species abundances were stan-dardized to proportions due to the great differences


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in sample size between the lithified and unlithifiedsamples. Methods like detrended correspondenceanalysis and non-metric multidimensional scalingare often preferred because they can counteractthe “arch” or “horseshoe” effect (e.g., Holland et al.,2001; Bush and Brame, 2010). However, this effectis observed when samples vary considerably inspecies composition, and the Fort Thompson andBermont samples contain the same set of species.For example, the only species that did not occur inboth formations were ones whose average sample-level abundance was less than 0.2%.

RESULTS

Diversity

Given the slower pace of data collection, thesample sizes of the experimentally cemented sam-ples were much smaller than those of the uncon-solidated control samples (Table 1, Table 2). It tookapproximately 10 hours to find 40-50 countableindividuals from the cemented samples after thebivalve material had been halved (4-5 individuals/hour). In contrast, the unconsolidated samples ofthe same material required approximately threehours to yield 400-650 countable individuals (133-217 individuals/hour). As a result, the raw number

FIGURE 2. Classical (A) and coverage-based (B) rarefaction of the Bermont Formation samples. In each panel, thered curve marks the lithified sample, and the blue curve marks the corresponding unlithified replicate sample. The graycurves mark other unlithified samples taken from different horizons of the same formation at the same outcrop. Thered and blue bands mark 95% confidence intervals.


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of species recovered was lower for the lithifiedsamples.

However, the cemented samples had slightlyhigher species richness than their unconsolidatedreplicates when the latter were rarefied to matchthe sampling intensity of the former, although thedifference was not always significant (Figure 2,Figure 3). With one exception (Ber6), thecemented samples also had higher species rich-ness than all of the rarefied unconsolidated sam-ples from the same formation. Results were similarfor both rarefaction methods.

For both formations, the rank abundancecurves for the lithified samples generally over-

lapped those of the unlithified samples for the 10most common species (i.e., rank ≤ 10; Figure 4). Athigher ranks, the curves for the lithified samplesflatten out, reflecting the presence of numerousspecies represented by a single specimen (the lith-ified samples contained between 43 and 62 speci-mens, so calculated proportional abundances forsingleton species range from 2.3% to 1.6%). Incontrast, the curves for the unlithified samples con-tinued to decline. A randomization test suggeststhat the number of singleton species in the lithifiedreplicates is in fact unexpectedly high. We subsa-mpled each unlithified replicate down to the samplesize of the corresponding lithified sample 10,000

FIGURE 3. Classical (A) and coverage-based (B) rarefaction of the Fort Thompson Formation samples. In eachpanel, the red curve marks the lithified sample, and the blue curve marks the corresponding unlithified replicate sam-ple. The gray curves mark other unlithified samples taken from different horizons of the same formation at the sameoutcrop. The red and blue bands mark 95% confidence intervals.


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times, tabulating the number of singletons for eachiteration. The number of singletons in the lithifiedsamples always fell in the upper ends of these dis-tributions, with percentiles of 99.6, 96.8, 97.4, 99.8,71.5, and 85.0 for Ftt7, Ftt11, Ftt12, Ber5, Ber6,and Ber9, respectively. The number of singletonsin the lithified samples is statistically significantlyhigher than expected for some samples (percentile> 95.0 for individual single-sided tests with α =

0.05, or > 99.2 with a Bonferroni correction), andthe fact that all percentiles are high indicates anoverall tendency towards increased singletons.

The cemented samples also had significantlyhigher evenness than the unconsolidated samplesfrom the same formation (Table 1, Table 2), and theproportion of specimens belonging to small-bodiedspecies (4-15 mm) was slightly higher in each

TABLE 1. Diversity indices for samples from the Bermont Formation. Artificially lithified samples are marked with aster-isks (*). All other samples were composed of unconsolidated sediment.

TABLE 2. Diversity indices for samples from the Fort Thompson Formation. Artificially lithified samples are marked withasterisks (*). All other samples were composed of unconsolidated sediment.

Species Richness Specimens Shannon-Weiner H

Evenness exp(H)/S

Ber1B 31 562 2.282 0.3160

Ber2B 36 667 2.444 0.3198

Ber3B 41 636 2.285 0.2397

Ber4B 38 778 2.510 0.3239

Ber5B 38 620 2.355 0.2772

Ber6B 37 643 2.326 0.2766

Ber7B 40 419 2.505 0.3061

Ber8B 44 447 2.461 0.2662

Ber9B 45 405 2.703 0.3315

Ber6A* 15 43 2.152 0.5733

Ber9A* 23 58 2.669 0.6259

Ber5A* 21 59 2.501 0.7606

SampleSpecies

Richness Specimens Shannon-Weiner HEvenness exp(H)/S

Ftt1B 41 621 2.063 0.1919

Ftt2B 44 515 2.403 0.2513

Ftt3B 46 703 2.210 0.1983

Ftt4B 39 494 1.923 0.1754

Ftt5B 47 631 2.066 0.1679

Ftt6B 45 573 2.042 0.1713

Ftt7B 41 650 1.856 0.1560

Ftt8B 47 921 1.714 0.1181

Ftt9B 51 768 2.349 0.2054

Ftt10B 52 575 2.381 0.2080

Ftt11B 47 592 1.711 0.1178

Ftt12B 44 708 1.786 0.1356

Ftt7A* 22 62 2.445 0.5290

Ftt11A* 17 57 1.978 0.4251

Ftt12A* 23 65 2.12 0.4388


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cemented sample than in the correspondingunconsolidated replicate (Figure 5).

Species Composition

The first principal coordinate axis separatedthe samples from the Fort Thompson Formationfrom those from the Bermont (Figure 6), reflectingthe different composition of the two faunas, whichcan be seen more directly in the relative abun-dance data for the 10 most abundant species (Fig-ure 7). For each formation, the second principalcoordinate axis separated the unlithified samplesfrom the lithified ones, suggesting a consistent

change in composition caused by lithification. Forthe Fort Thompson samples, the lithified andunlithified samples are separated by a clear gap(Figure 6). One of the lithified Bermont samplesplots closer to the cluster of unlithified samples(lowermost red circle, representing sample Ber9A).However, the unlithified replicate of this sample,Ber9B, is represented by the most negative bluecircle in the cluster, so Ber9A is in fact shifted in apositive direction from its direct replicate, consis-tent with the direction of displacement for the otherlithified samples.

FIGURE 4. Rank abundance plots for the Bermont (A) and Fort Thompson (B) samples.

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FIGURE 5. Percentage of individuals in each sample belonging to small-shelled species, defined as less than 15 mm.A. Bermont Formation. B. Fort Thompson Formation.


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DISCUSSION

In some cases, mechanical preparation of fos-sils from lithified sediments may destroy fossils,particularly delicate ones, and preferential destruc-tion of certain species could affect studies of diver-sity and assemblage composition. However, fossildestruction was not an important bias in our poorlylithified samples. Instead, minor biases likelystemmed from changes in the probability that aparticular species was noticed, identified, andcounted during data collection.

With sieved samples, one can systematicallyexamine all specimens, viewing each in threedimensions for identifying characteristics. Wholefossils can be separated from unidentifiable frag-ments, and each species can be placed in a sepa-rate tray or container. In the case of our sievedsamples, we counted all bivalve and gastropodspecimens that met certain criteria of size andcompleteness. However, in a lithified sample, it ismore challenging to count every specimen due tothe increased difficulty of fossil extraction. Thepaleontologist’s ability to notice and identify a spec-imen imposes another filter: certain types of speci-mens are more likely to be noticed and/or morelikely to be identifiable when entombed in matrix.Both species richness and species compositionwere affected by this perceptual filter, albeit in dif-ferent ways.

Species Richness and Rarity

Surprisingly, the experimentally cementedsamples tended to have slightly higher speciesrichness after sampling standardization than didtheir unconsolidated replicates (Figure 2, Figure 3).

Given that pairs of replicates were drawn from thesame sampling distribution, the differences indiversity must reflect differences in how fossilswere identified and counted.

The rank abundance plots show that rare spe-cies were oversampled in the lithified samples rela-tive to the unlithified ones – had sampling beenrandom in the lithified samples, then their rankabundance curves would overlap the others (Fig-ure 4). Instead, the lithified samples generallyinclude an unusual number of single-individualspecies. Oversampling of rare species explains theelevated standardized diversity (Figure 2, Figure 3)and evenness (Table 1, Table 2). We attribute this

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FIGURE 6. Principal coordinates analysis. The eigen-values for the first two axes accounted for 72% of thevariation in the data.

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FIGURE 7. Relative abundances of the ten most abun-dant species in the overall dataset. Relative abundanceis plotted on a square-root scale so that low values canbe more easily distinguished. A. Bermont Formation. B.Fort Thompson Formation.


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oversampling to the natural human tendency tonotice the unusual and overlook the mundane. Forsieved bulk samples, it is much easier to overridethis instinct.

Previous studies indicated that lithificationcombined with aragonite dissolution notably sup-pressed the apparent diversity of molluscan fossilassemblages (e.g., Sessa et al., 2009; Hendy,2009). However, other studies have indicated thatthe methodological effects of lithification on diver-sity tabulation (the effects related to sample prepa-ration and not diagenesis) were fairly mild formacrofossils greater than a few millimeters in size,once sample sizes were standardized by rarefac-tion (e.g., Nawrot, 2012; Sanders et al., 2015). Ourresults indicate that, somewhat surprisingly, lithifi-cation can even be associated with slightly higherobserved diversity.

Species Composition

Species richness is highly sensitive to thepresence of rare species, but the results of theprincipal coordinates analyses largely reflect varia-tion in the relative abundances of common spe-cies, particularly considering that we did not log-transform species abundances. Despite contain-ing the same set of species, the faunas of the twoformations are easily distinguishable based on rel-ative abundance, and lithification did not obscurethese basic patterns (Figure 6, Figure 7).

The lithified samples are all displaced in apositive direction on principal component axis two(Figure 6), suggesting that lithification affected fau-nal composition in similar ways in both sets of sam-ples. However, lithification did not clearly alter theapparent relative abundance of many species, andothers were affected differently in the two faunas(Figure 7). For example, Chione elevata has lowerrelative abundance in the lithified samples (red)than in the unlithified replicates (blue) in the FortThompson, but not in the Bermont.

There are, however, a few consistent differ-ences in species’ relative abundances between thelithified and unlithified samples. Most notably, Ost-rea equestris, the fifth most abundant species inthe dataset, is under-represented in the lithifiedsamples relative to the unlithified replicates in bothdata sets (Figure 7). Ostrea shells are irregular andmay have been harder to distinguish from shellfragments when embedded in matrix, which couldlead to undercounting – a reverse of the “Chlamyseffect,” which describes the over-counting of easilyidentified species (Kowalewski et al., 2003). Parvi-lucina multilineata, the tenth most abundant spe-

cies overall, is over-represented in the lithifiedsamples, although it is unclear to us why this mightbe, other than, perhaps, random chance. Crepidulaaculeata, the ninth most abundant species, isunder-represented in the lithified samples, but thecongeneric Crepidula convexa is not under-repre-sented as consistently, so the pattern for this mor-photype is not entirely clear.

Given the common assumption that lithifica-tion preferentially obscures small fossils, it issomewhat surprising that individuals belonging tosmall-bodied species were slightly over-repre-sented in the cemented samples (Figure 5). How-ever, specimens less than 4 mm in size werecompletely excluded from the analysis, and previ-ous literature suggests that the size-bias would pri-marily affect this size class (e.g., Cooper et al.,2006; Sessa et al., 2009). In other words, onceshells smaller than a few millimeters wereexcluded, the size-bias was not particularly import-ant (also see, for example, Nawrot, 2012).

In sum, lithification had relatively mild effectson the apparent species composition of these sam-ples. The faunas of the two formations, which con-tained heavily overlapping sets of species, couldstill be distinguished. Lithification might havedepressed the apparent relative abundance of theoyster Ostrea equestris, possibly because it isirregular in morphology and less easy to distin-guish as a countable specimen when ensconced inmatrix. Further studies would be needed to confirmthis effect.

Effects of Lithification and Diagenesis on Diversity: General Models

In Figure 8, we illustrate some of the waysthat lithification and diagenesis could alter theapparent diversity of a sample of shelly fossilsusing classical rarefaction curves. The blue rar-efaction curves represent bulk samples from pris-tine, unaltered assemblages, from whichspecimens were tabulated comprehensively (everyspecimen tabulated) or at random. The red rarefac-tion curves represent assemblages affected by lith-ification or some other diagenetic process. Thetotal species richness of a sample is representedby the rarefaction curve’s asymptote, and even-ness is represented by the initial slope (e.g., Olsze-wski, 2004). In general, fewer individuals will becounted from lithified samples due to the increaseddifficulty of data collection, but we show the samemaximum samples size for all rarefaction curvesfor the sake of comparison.


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In Figure 8A-C, no species were lost due todiagenesis or sample processing, such that theblue and red rarefaction curves asymptote at thesame value given high enough sampling intensity.In Figure 8B, lithification did not alter the relativeabundance distribution of the assemblage, suchthat the rarefaction curves coincide. However, inFigure 8A and 8C, the apparent relative abun-dances of species in the lithified sample werealtered by either diagenetic or methodologicaleffects. In Figure 8C, rare species were preferen-tially sampled from the altered sample, increasingthe apparent species richness at low sampling lev-els. This preferential sampling could occur due tocollector bias, as in the present study, or if commonspecies were poorly preserved or difficult to recog-nize due to small size, partial dissolution, irregularmorphology, etc. In Figure 8A, common specieswere oversampled in the diagenetically alteredsample, lowering its apparent species richness atlow sampling levels. In cases like these (Figure 8A,C), additional collecting should improve the diver-sity estimate (i.e., rarefaction curves should beginto converge) because no species were entirelylost.

In Figure 8D-F, some species were lostentirely due to aragonite dissolution, preferentialdestruction of fragile species during processing, orsome other process. No amount of extra samplingwould recover these species, which is indicatedgraphically by the red and blue rarefaction curvesrising to different asymptotes. If rare species are

preferentially lost, the affected sample will be lesseven and have fewer species than the pristinesample at all sampling intensities (Figure 8D).However, if common species are preferentially lost,the remaining rare species will be sampled morequickly, and species richness can appear higher atlow sampling intensity before asymptoting at alower value (Figure 8F).

Other Rock Types

When lithification is accompanied by arago-nite dissolution (Sessa et al., 2009; Hendy, 2009),the effects on diversity can be large, correspondingto Figure 8D-F (i.e., decrease in observable spe-cies richness). In the absence of preferentialdestruction of certain fossil species by diagenesisor preparation, the effects of lithification are rela-tively limited for larger fossils (Nawrot, 2012; Sand-ers et al., 2015), reflecting changes in the observedrelative abundance distribution rather than loss ofspecies (Figure 8A-C). However, it is worth empha-sizing that our cemented samples were equivalentto “poorly lithified” sediments, and the methodolog-ical effects of lithification may differ for more com-pletely cemented rocks prepared with othermethods. It is also worth emphasizing that studiesof the effects of lithification on biodiversity havefocused on late Mesozoic to Cenozoic molluscanshell beds (e.g., Sessa et al., 2009; Hendy, 2009;Nawrot, 2012; Sanders et al., 2015), although ara-gonite dissolution has been discussed more

Lithified (altered) sampleUnlithified (pristine) sample

Decreased Evenness Increased EvennessUnchanged Evenness

Num

ber o

f spe

cies

Number of specimens

Num

ber o

f spe

cies

Number of specimens

Dec

reas

ed

Ric

hnes

sU

ncha

nged

R

ichn

ess

Number of specimens

D

CBA

E F

FIGURE 8. Hypothetical rarefaction curves illustrating the potential effects of lithification or other diagenetic processeson the observed biodiversity of a fossil assemblage. The rarefaction curves are shown in blue for unlithified, unalteredassemblages and in red for the lithified, altered assemblages. A-C, Total species richness is the same in the pristineand altered samples. D-F, Total species richness is lower in the altered samples. A,D, Evenness is lower in the alteredsamples. B,E Evenness is the same. C,F, Evenness is higher in the altered samples.


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broadly (e.g., Cherns and Wright, 2000, 2009;Cherns et al., 2008; Hsieh et al., 2019).

What about Paleozoic and older Mesozoicrocks, which are often more completely cemented?The exact effects of lithification probably dependon how the fossils are distributed through the rock,the physical properties of the rock, the mode of fos-sil preservation, and the preparation and samplingmethods. For example, many Paleozoic fossilassemblages are preserved in thin shell beds or onbedding planes (Kidwell and Brenchley, 1994), andthese assemblages can be exquisitely exposed iflithified limestone or sandstone beds are separatedby easily eroded mud. This style of preservation iscommon, for example, in the Ordovician of the Cin-cinnati, Ohio, region, which has served as thebasis for many paleobiological studies (e.g., Hol-land et al., 2001). The methodological effects oflithification are probably minimized in these assem-blages; fossils that are more than a couple millime-ters in size can be readily observed, and acomprehensive census of a bedding plane canreduce human-induced non-randomness. Also,destructive preparation in not needed. A studyrestricted to brachiopods might suffer little bias,corresponding perhaps with Figure 8B. However, awhole-fauna analysis might correspond insteadwith Figure 8E due to diagenetic loss of mollusks.

The methodological biases of lithification maybe most severe for carbonate fossils ensconcedwithin well-cemented limestones, in which caseextraction can be quite difficult. Fossils in well-cemented siliciclastic rocks can be easier to finddue to the contrast in composition between rockand fossil – when prepared using hammers, chis-els, and rock splitters, cemented sandstones willtend to fracture along planes of weakness, whichare often created by fossil shells or their molds. Iffossils are concentrated along a particular horizon,that horizon will be exposed, providing an assem-blage somewhat like a naturally exposed bedding-plane assemblage.

If fossils are distributed more randomly in awell-cemented rock, or in a thicker shell bed, theycan be found by breaking a sample into smallerand smaller pieces. It is possible that small fossilsmay be somewhat under-sampled during this typeof processing because large fossils create largerareas of weakness within a rock. However, under-sampling of small fossils will be reduced by morecomplete processing of a sample (i.e., breaking itinto more and smaller pieces). However, this pro-cessing could reduce large fossils to unidentifiableor uncountable fragments, even as it exposes com-

plete small fossils on fracture faces. Thus, theextent of bias depends on the choices made duringsample preparation. Also, size-related bias will onlyoccur if large and small fossils co-occur in thesame sample; if all fossils are similar in size, thensize will not affect the probability of collection. Inour experience with Paleozoic fossils (e.g., Bush etal. 2015), fossils that are 4-5 mm or larger are rela-tively easy to find, although fine morphologicaldetails may not be preserved in molds in coarsersediments. Again, if diagenetic processes or sam-ple preparation have eliminated some taxa, thenthe complete diversity of a sample cannot berecovered (Figure 8D-F). In other cases, however,methodological biases could have a range ofeffects (Figure 8A-C), depending on how theyaffect the relative abundance distribution.

In our poorly lithified samples, we had somecapacity to remove sediment from shell surfaces,which may have mitigated perceptual biases tosome extent by improving our ability to identify allspecies. The under-counting of certain taxa mightbe more severe in well-cemented samples wheresediment cannot be removed easily from fossils.Moldic preservation may alleviate this problem,however, if the impressions of shell surfaces arewell preserved.

In sum, the effects of lithification on biologicalpatterns are probably highly variable, and for somestudies, methodological biases are probably minor.Even when lithification imparts more seriousbiases, these biases may not be an impediment ifall samples in a study are biased similarly. Giventhat lithification and fossil dissolution can be linked,lithification may provide a useful warning that dia-genetic biases should be carefully evaluated, evenif the methodological effects of lithification are notthe primary source of bias.

CONCLUSIONS

Lithification can affect the measurement ofspecies richness both through methodologicalbiases related to the sampling of solid rock andthrough diagenetic biases related to the alterationand/or destruction of fossils by dissolution andother processes. To isolate the effects of method-ological biases, we cemented samples of uncon-solidated, fossiliferous Pleistocene sediment intoartificial rocks. We compared the observed biodi-versity of these samples to that measured fromreplicate control samples that were not cementedand that were sieved. The comparison focusesspecifically on the biases introduced by changing


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the physical nature of the matrix surrounding thefossils without altering or destroying any fossils.

Our sampling of the poorly lithified artificialrocks was not destructive to the fossils, and weexcluded fossils smaller than 4 mm, so this testprobably represents a best-case scenario forrecovering biological patterns. Even so, mildbiases were introduced by non-randomness inlocating and identifying specimens in the lithifiedsamples. The lithified samples had slightly highersampling-standardized species richness than theunlithified control samples due to over-representa-tion of rare species, which we ascribe to the humantendency to notice new and rare items and over-look common items.

The effects of lithification on assemblagecomposition were similarly mild. Lithification did notobscure the differences in species relative abun-dance between samples from two different forma-tions, such that these two faunas were still

distinguishable. However, the oyster Ostrea eques-tris was under-represented in all lithified samples,perhaps because its irregular morphology was diffi-cult to identify when obscured by matrix.

Several studies have now shown that lithifica-tion has only mild effects on the observed diversityand composition of fossil assemblages, at leastwhen sampling intensity is standardized, very smallfossils are excluded, fossils can be exposed forexamination without the preferential destruction ofsome species, and diagenetic destruction is not anissue. In real rocks, the effects of lithification onbiological patterns will vary considerably andshould be evaluated on a case-by-case basis.

ACKNOWLEDGMENTS

Our thanks to two anonymous reviewers forhelpful comments on the manuscript.

REFERENCES

Alroy, J. 2010a. Fair sampling of taxonomic richness and unbiased estimation of origination and extinction rates, p. 55-80. In Alroy, J. and Hunt, G. (eds.), Quantitative Methods in Paleobiology. Paleontological Society Papers, 16. https://doi.org/10.1017/s1089332600001819

Alroy, J. 2010b. Geographical, environmental and intrinsic biotic controls on Phanerozoic marine diversification. Palaeontology, 53:1211-1235. https://doi.org/10.1111/j.1475-4983.2010.01011.x

Bambach, R.K. and Kowalewski, M. 2000. How to count fossils. Geological Society of America Abstracts with Programs, 32(7):332.

Brown, L.D., Cai, T.T., and DasGupta, A. 2001. Interval estimation for a binomial proportion. Statistical Science, 16:101-133. https://doi.org/10.1214/ss/1009213286

Bush, A.M. and Bambach, R.K. 2004. Did alpha diversity increase through the Phanerozoic? Lifting the veils of taphonomic, latitudinal, and environmental biases. Journal of Geology, 112:625-642. https://doi.org/10.1086/424576

Bush, A. M. and Brame, R.I. 2010. Multiple paleoecological controls on the composition of marine fossil assemblages from the Frasnian (Late Devonian) of Virginia, with a comparison of ordination methods. Paleobiology, 36:573-591. https://doi.org/10.1666/07022.1

Bush, A.M., Csonka, J.D., DiRenzo, G.V., Over, D.J., and Beard, J.A. 2015. Revised correlation of the Frasnian-Famennian boundary and Kellwasser events (Upper Devonian) in shallow marine paleoenvironments of New York State. Palaeogeography, Palaeoclimatology, Palaeoecology, 433:233-246. https://doi.org/10.1016/j.palaeo.2015.05.009

Bush, A.M., Powell, M.G., Arnold, W.S., Bert, T.M., and Daley, G.M. 2002. Time-averaging, evolution, and morphologic variation. Paleobiology, 28:9-25. https://doi.org/10.1666/0094-8373(2002)028<0009:TAEAMV>2.0.CO;2

Chao, A. and Jost, L. 2012. Coverage-based rarefaction and extrapolation: Standardizing samples by completeness rather than size. Ecology, 93:2533-2547. https://doi.org/10.1890/11-1952.1

Cherns, L. and Wright, V.P. 2000. Missing molluscs as evidence of large-scale, early skeletal aragonite dissolution in a Silurian sea. Geology, 28:791-794. https://doi.org/10.1130/0091-7613(2000)28<791:MMAEOL>2.0.CO;2


15

Cherns, L. and Wright, V.P. 2009. Quantifying the impacts of early diagenetic aragonite dissolution on the fossil record. Palaios, 24:756-771. https://doi.org/10.2110/palo.2008.p08-134r

Cherns, L., Wheeley, J.R., and Wright, V.P. 2008. Taphonomic windows and molluscan preservation. Palaeogeography, Palaeoclimatology, Palaeoecology, 270:220-229. https://doi.org/10.1016/j.palaeo.2008.07.012

Clapham, M.E. 2015. Ecological consequences of the Guadalupian extinction and its role in the brachiopod-mollusk transition. Paleobiology, 41:266-279. https://doi.org/10.1017/pab.2014.15

Cooper, R.A., Maxwell, P.A., Crampton, J.S., Beu, A.G., Jones, C.M., and Marshall, B.A. 2006. Completeness of the fossil record: Estimating losses due to small body size. Geology, 34:241-244. https://doi.org/10.1130/G22206.1

Cummins, H., Powell, E.N., Stanton, R.J., and Staff, G. 1986. The size-frequency distribution in palaeoecology: Effects of taphonomic processes during formation of molluscan death assemblages in Texas bays. Palaeontology, 29: 495-518.

Daley, G.M. 2001. Creating a paleoecological framework for evolutionary and paleoecological studies: An example from the Fort Thompson Formation (Pleistocene) of Florida. Palaios, 17:419-434. https://doi.org/10.1669/0883-1351(2002)017<0419:capffe>2.0.co;2

Daley, G.M., Ostrowski, S., and Geary, D.H. 2007. Paleoenvironmentally correlated differences in a classic predator-prey system: the bivalve Chione elevata and its gastropod predators. Palaios, 22:166-173. https://doi.org/10.2110/palo.2005.p05-057r

Daley, G.M. 2017. Diversity and faunal composition in fragments. Palaios, 32:639-646. https://doi.org/10.2110/palo.2016.101

Dean, C.D., Allison, P.A., Hampson, G.J., and Hill, J. 2019. Aragonite bias exhibits systematic spatial variation in the late Cretaceous Western Interior Seaway, North America. Paleobiology, 45:571-597. https://doi.org/10.1017/pab.2019.33

Foote, M., Crampton, J.S., Beu, A.G., and Nelson, C.S. 2015. Aragonite bias and lack of bias, in the fossil record: lithological, environmental, and ecological controls. Paleobiology, 41:245-265. https://doi.org/10.1017/pab.2014.16

Hammer, Ø., Harper, D.A.T., and Ryan, P.D. 2001. PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica, 4.1.1:1-9. http://palaeo-electronica.org/2001_1/past/issue1_01.htm

Hawkins, A.D., Kowalewski, M., and Xiao, S. 2018. Breaking down the lithification bias: the effect of preferential sampling of larger specimens on the estimate of species richness, evenness, and average specimen size. Paleobiology, 44:326-345. https://doi.org/10.1017/pab.2017.39

Hendy, A.J.W. 2009. The influence on lithification on Cenozoic marine biodiversity trends. Paleobiology, 35:51-62. https://doi.org/10.1666/07047.1

Hendy, A.J.W. 2010. Taphonomic overprints on Phanerozoic trends in biodiversity: Lithification and other secular megabiases, p. 19-77. In Allison, P.A. and Bottjer, D.J. (eds.), Taphonomy. Topics in Geobiology, 32. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8643-3_2

Holland, S.M., Miller, A.I., Meyer, D.L., and Dattilo, B.F. 2001. The detection and importance of subtle biofacies within a single lithofacies: the Upper Ordovician Kope Formation of the Cincinnati, Ohio region. Palaios, 16:205-217. https://doi.org/10.2307/3515600

Hsieh, S., Bush, A.M., and Bennington, J.B. 2019. Were bivalves ecologically dominant over brachiopods in the late Paleozoic? A test using exceptionally preserved fossil assemblages. Paleobiology, 45:265-279. https://doi.org/10.1017/pab.2019.3

Hsieh, T.C., Ma, K.H., and Chao, A. 2016. iNEXT: An R package for interpolation and extrapolation of species diversity (Hill numbers). Methods in Ecology and Evolution, 7:1451-1456. https://doi.org/10.1111/2041-210x.12613

Hurlbert, S.H. 1971. The nonconcept of species diversity: a critique and alternative parameters. Ecology, 52:577-586. https://doi.org/10.2307/1934145

Kidwell, S.M. 2001. Preservation of species abundance in marine death assemblages. Science, 294:1091-1094. https://doi.org/10.1126/science.1064539

Kidwell, S.M. 2002. Mesh-size effects on the ecological fidelity of death assemblages: A meta-analysis of molluscan live-dead studies. Geobios, 35:107-119. https://doi.org/10.1016/s0016-6995(02)00052-9

Kidwell, S.M. 2005. Shell composition has no net impact on large-scale evolutionary patterns in mollusks. Science, 307:914-917. https://doi.org/10.1126/science.1106654


16

Kidwell, S.M. and Brenchley, P.J. 1994. Patterns in bioclastic accumulation through the Phanerozoic: changes in input or in destruction? Geology, 22:1139-1143. https://doi.org/10.1130/0091-7613(1994)022<1139:pibatt>2.3.co;2

Kindt, R. and Coe, R. 2005. Tree diversity analysis: a manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre, Nairobi, Kenya. http://www.worldagroforestry.org/output/tree-diversity-analysis

Koch, C.F. and Sohl, N.F. 1983. Preservational effects in paleoecological studies: Cretaceous mollusc examples. Paleobiology, 9:26-34. https://doi.org/10.1017/s0094837300007351

Kowalewski, M. and Hoffmeister, A.P. 2003. Sieves and fossils: Effects of mesh size on paleontological patterns. Palaios, 18:460-469. https://doi.org/10.1669/0883-1351(2003)018<0460:SAFEOM>2.0.CO;2

Kowalewski, M., Carroll, M., Casazza, L., Gupta, N.S., Hannisdal, B., Hendy, A., Richard. A., Jr, LaBarbera, M., Lazo, D.G., and Messina, C. 2003. Quantitative fidelity of brachiopod-mollusk assemblages from modern subtidal environments of San Juan Islands, USA. Journal of Taphonomy, 1:43-65.

Kowalewski, M., Kiessling, W., Aberhan, M., Fürsich, F.T., Scarponi, D., Barbour Wood, S.L., and Hoffmeister, A.P. 2006. Ecological, taxonomic, and taphonomic components of the post-Paleozoic increase in sample-level species diversity of marine benthos. Paleobiology, 32:533-561. https://doi.org/10.1666/05074.1

Nawrot, R. 2012. Decomposing lithification bias: Preservation of local diversity structure in recently cemented storm-beach carbonate sands, San Salvador, Bahamas. Palaios, 27:190-205. https://doi.org/10.2110/palo.2011.p11-028r

Olszewski, T.D. 2004. A unified mathematical framework for the measurement of richness and evenness within and among multiple communities. Oikos, 104:377-387. https://doi.org/10.1111/j.0030-1299.2004.12519.x

Portell, R.W., Schindler, K.S., and Nicol, D. 1995. Biostratigraphy and paleoecology of the Pleistocene invertebrates from the Leisey Shell Pits, Hillsborough County, Florida. Bulletin of the Florida Museum of Natural History, 37:127-164.

Powell, M.G. and Kowalewski, M. 2002. Increase in evenness and sampled alpha diversity through the Phanerozoic: Comparison of early Paleozoic and Cenozoic marine fossil assemblages. Geology, 30:331-334. https://doi.org/10.1130/0091-7613(2002)030<0331:iieasa>2.0.co;2

Pruss, S.B., Payne, J.L., and Westacott, S. 2015. Taphonomic bias of selective silicification revealed by paired petrographic and insoluble residue analysis. Palaios, 30:620-626. https://doi.org/10.2110/palo.2014.105

R Core Team. 2017. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org

Sanders, H.L. 1968. Marine benthic diversity: a comparative study. American Naturalist, 102:243-282. https://doi.org/10.1086/282541

Sanders, M.T., Merle, D., and Villier, L. 2015. The molluscs of the “Falunière” of Grignon (Middle Lutetian, Yvelines, France): Quantification of lithification bias and its impact on the biodiversity assessment of the Middle Eocene of Western Europe. Geodiversitas, 37:345-365. https://doi.org/10.5252/g2015n3a4

Sessa, J.A., Patzkowsky, M.E., and Bralower, T.J. 2009. The impact of lithification on the diversity, size distribution, and recovery dynamics of marine invertebrate assemblages. Geology, 37:115-118. https://doi.org/10.1130/g25286a.1

Tyler, C.L. and Kowalewski, M. 2014. The utility of marine benthic associations as a multivariate proxy for paleobathymetry. PLoS One, 9:e95711. https://doi.org/10.1371/journal.pone.0095711


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APPENDIX

Raw data set in a .csv file. Artificially lithified samples are marked with asterisks (*). All othersamples were composed of unconsolidated sediment. This file is supplied in a zipped file athttps://palaeo-electronica.org/content/2020/3204-lithification-and-biodiversity.

Documents

Palaeontologia ElectronicaPalaeontologia Electronica palaeo-electronica.org Daley, Gwen M. and Bush, Andrew M. 2020. The effects of lithification on fossil assemblage biodiversity