UNIVERSITY OF CINCINNATI
______, 20 _____
I,______, hereby submit this as part of the requirements for the degree of:
______in: ______It is entitled: ______
Approved by: ______
Methodological advances in the use of faunal gradient analysis for regional paleoecological investigations in the type Cincinnatian Series (Upper Ordovician)
A dissertation submitted to the
Division of Research and Advanced Studies of the University of Cincinnati
in partial fulfillment of the requirements for the degree of
DOCTORATE OF PHILOSOPHY (Ph.D.)
in the Department of Geology of the College of Arts and Sciences
2003
by
Andrew J. Webber
B.S., College of William and Mary, 1995 M.S., University of Cincinnati, 1999
Committee Chair: Dr. Arnold I. Miller
Abstract
The purpose of this research is to refine established applications of faunal gradient analysis as a tool of regional high-resolution correlation, using the type Cincinnatian Series as a natural laboratory. Comparisons of stratigraphically arranged, quantified faunal patterns have been employed to establish stratigraphic correlations at regional scales, and have provided a foundation for regional paleoecological studies at high resolution. There are three primary objectives to this research: 1) to assess the meaning of fine-scale stratigraphic change in faunal composition; 2) to extend the lateral breadth of correlations for a more regional coverage; and 3) to assess local spatial variation in the distribution of fossils, and examine the effects this patchiness has on the fine-scale signal of biological variation in the Cincinnatian.
Results of this work show that lithofacies-defined meter-scale cycles in the Cincinnatian do not correspond with water-depth related stratigraphic trends in biotic composition. Therefore, these cycles are likely not caused by small-scale fluctuations in sea-level. Second, previously established correlations, at scales greater than one meter, can be extended farther across the region, and examining the patterns in biotic composition for small stratigraphic intervals rather than individual strata is a promising technique for drawing correlations at scales of less than a meter. Finally, this research has shown that even though local patchiness and differences in the amount of time-averaging with lithology limits the use of faunal gradient analysis when comparing fine-scale (bed-by-bed) trends of biological variation among Cincinnatian localities, patterns at scales greater than a few beds are robust across the region.
Acknowledgements
I would like to acknowledge those who helped me bring this project together. I thank my
dissertation committee, who gave me invaluable suggestions and reviews, and kept me on track.
I owe a very special thanks to my dissertation committee chair, Dr. Arnold Miller, who
generously provided guidance and support, and without whom this project would not have been
possible. I am grateful to the other members of my dissertation committee: Dr. Steven Holland,
for his helpful insight and prompt feedback; Dr. Carlton Brett, for discussions and debate; Dr.
Thomas Algeo, and Dr. David Meyer.
This research was funded by grants from The Paleontological Society, the Geological
Society of America, and the Dry Dredgers. I thank the Geology Department at the University of
Cincinnati for providing a terrific environment in which to learn and to conduct scientific
research. I am indebted to those who served as field assistants: Karen Layou, Heather Ellington,
Brenda Hunda, Bill Garcia, Kim Koverman, and Eric Winhusen. I thank Mark Patzkowsky, three anonymous reviewers, and the editors of PALAIOS for their reviews of the first chapter of this dissertation. Finally, I am grateful for the loving and continual support of my family, especially my wife, LeAnna, and my son, Avery, over the years it took me to work on this project.
1
Table of Contents
Introduction 4 Chapter 1: High-Resolution Faunal Gradient Analysis and an Assessment of the Causes of Meter-Scale Cyclicity in the Type Cincinnatian Series (Upper Ordovician) 8 Abstract 8 Introduction 9 Stratigraphic Framework 13 Development of Analytical Framework 15 Data Collection 15 Quantitative Analysis 17 Comparing Faunal Scores to Meter-Scale Cycles 19 Results and Implications 28 Conclusions 32 Chapter 2: Methodological advances in the use of faunal gradient analysis for regional high- resolution correlation in the type Cincinnatian Series (Upper Ordovician) 34 Abstract 34 Introduction 35 Background 38 The Study Interval 38 Correlation in the Cincinnatian 39 Methods 44 Data collection 44 Data Analysis 45 Results and Discussion 48 Broad-scale correlation 48 Fine-Scale Correlation 52 Conclusions 60 Chapter 3: The Effects of Spatial Patchiness on the Stratigraphic Signal of Biotic Composition in the Type Cincinnatian Series (Upper Ordovician) 62 Abstract 63 Introduction 63 The Study Interval 67 Methods 68 Data Collection 68 Data Analysis 71 Results and Discussion 73 Local Variation 73 Regional Variation 86 Implications 87 Conclusions 93 Final Remarks 94 Bibliography 95 Appendix: Locality Descriptions 108
2
List of Figures
Chapter 1 1.1 Photograph of meter-scale cyclicity in the Kope Formation at the K445 locality 10 1.2 Interpretations of meter-scale cyclicity in the type Cincinnatian Series 11 1.3 Location map 16 1.4 Generalized position of major Cincinnatian taxa along DCA axis 1 21 1.5 Stratigraphic position of meter-scale cycles and DCA axis 1 scores at K445 22 1.6 Binning protocols 26
Chapter 2 2.1 Map of outcrop localities of the Kope and lower Fairview Formations used in faunal gradient analysis 37 2.2 Stratigraphic position of sample ordination scores at K445 41 2.3 A comparison of smoothed ordination curves from K445 and Holst Creek 42 2.4 Large-scale correlation of ordination curves from six Cincinnatian localities 49 2.5 A comparison of the Maysville smoothed ordination curve with the curves from K445 and Holst Creek 51 2.6 A comparison of the raw ordination curve with the smoothed curve and the binned curve from the Maysville locality 55 2.7 Fine-scale correlation of ordination curves for K445, Holst Creek, and Maysville, binned into 0.5 m thick intervals 56 2.8 Fine-scale correlation of ordination curves for K445, Holst Creek, and Maysville, binned according meter-scale cycles 59
Chapter 3 3.1 Graphical depiction of the stratigraphic position of quantified biological Composition for K445 as calculated by gradient analysis 64 3.2 Comparisons of quantified faunal patterns for K445 and Maysville 65 3.3 Map of outcrop localities used in faunal gradient analysis 69 3.4 Composite stratigraphic column of the study interval at the Maysville locality 70 3.5 Graphical depiction of the stratigraphic position of raw DCA Axis 1 ordination scores for the four Maysville sublocalities 74 3.6 Generalized stratigraphic columns of the study interval for each of the four Maysville sublocalities 77 3.7 The stratigraphic position of binned DCA Axis 1 ordination scores for the four Maysville sublocalities 80 3.8 The differences in binned DCA Axis 1 ordination scores between each sublocality and the mean plotted according to stratigraphic position 81 3.9 Raw DCA Axis 1 ordination scores for Maysville and K445 88 3.10 Stratigraphic column of the study interval at K445 89 3.11 DCA Axis 1 ordination scores binned according bedsets 90 3
List of Tables
Chapter 1 1.1 Axis 1 ordination scores of taxa 20 1.2 Faunal patterns within meter-scale cycles for each binning protocol 29
Chapter 2 2.1 Axis 1 ordination scores of taxa 46
Chapter 3 3.1 Axis 1 ordination scores of the 47 taxa included in DCA 72 3.2 List of the primary taxonomic composition of each bin, in order of abundance 82
4
Introduction
The richly fossiliferous rocks of the type Cincinnatian Series have been well studied for over a century. The abundance and diversity of fossil remains, along with numerous outcrops covering a wide geographical area, make Cincinnatian strata an ideal setting for high-resolution paleoecological studies. However, these investigations require an understanding of subtle biotic transitions over broad areas. This has been problematic in the Cincinnatian, where the stratigraphic features necessary for high-resolution correlation are not easily recognizable, and which historically has not been well-correlated at high resolution as a result of difficulties in tracing individual sedimentological horizons among outcrops (Miller et al., 2001; but see Brett and Algeo, 2001a).
In an effort to establish a highly resolved chronostratigraphic framework for the Lower
Cincinnatian (Kope and lower Fairview Formations), Holland et al. (2001) and Miller et al.
(2001) compared high-resolution stratigraphic changes in fossil composition among localities.
To do this, these authors recorded the abundance of taxa from every fossiliferous horizon, and subjected these data to gradient analysis, procedures frequently used by ecologists and paleoecologists to quantify variations in biotic composition. Using gradient analysis, Holland et al. (2001) and Miller et al. (2001) found that the quantified faunal composition of Cincinnatian strata reflects the water depth at which these rocks were deposited, and used this relationship to provide a foundation for regional paleoecological studies at high resolution. Although bed-by- bed gradient analysis in the Cincinnatian has been effective in evaluating trends in biotic composition at stratigraphic scales just greater than a meter, significant amounts of fine-scale fluctuations in quantified biotic composition has hindered paleoecological investigations at finer stratigraphic scales. 5
The purpose of the present study is to assess the meaning of stratigraphic change in
Cincinnatian faunal composition at a finer scale of resolution than has been attempted previously. This has been achieved by refining existing gradient analytical procedures for investigating subtle variations in the fossil record. Here, faunal patterns are characterized for stratigraphic intervals rather than for individual beds. This is accomplished by grouping census samples into stratigraphic bins before conducting gradient analysis. In the three sections of the present study, faunal data have been binned in a variety of stratigraphic combinations, depending on the nature of the investigation at hand. This refinement of existing faunal gradient analyses for high-resolution correlation provides an essential investigative tool in the study of environmental transitions in fossiliferous rocks.
In the first chapter, a refinement of the gradient analytical technique described above is used to assessment of the degree to which small-scale sedimentological patterns were controlled by changes in water depth (Webber, 2002). Many researchers have recognized regular alternations of shale-rich and limestone-rich beds (“meter-scale cycles”) in the Kope Formation, yet the origin of this pattern has long been debated. Most models link meter-scale cyclicity to changes in water depth driven by sea level fluctuations (e.g., Jennette and Pryor, 1993; Brett and
Algeo, 2001b), whereas other models hinted at the possibility that changes in storm intensity and frequency produced this pattern (Holland et al. 1997, 1999, 2001). Here, variations in faunal composition quantified from gradient analysis at a single locality are grouped corresponding to the limestone-rich and mudstone-rich intervals of each meter-scale cycle; consistent differences between scores from the two bins would suggest a water depth control on meter-scale biotic patterns, and, thus, on cyclicity. Results indicate no consistent correspondence of faunal patterns 6 to meter-scale rock patterns, suggesting that water depth does not play a significant role in meter- scale cyclicity.
The second chapter presents two binning protocols designed to improve fine-scale, regional correlation by accounting for variations in the number of samples among localities. One protocol involves binning by 0.5 m intervals, which removes some of the fine-scale variation in faunal composition while retaining enough detail to compare patterns among localities at high resolution. The second protocol again bins samples according to limestone-rich and mudstone- rich intervals of meter-scale cycles, this time at multiple localities, to compare faunal patterns for equivalent stratigraphic intervals. In addition, the lateral breadth of broad correlations established with previous gradient analysis in the region (e.g., Miller et al., 2001) is extended by adding a locality that is beyond the geographic range of previous localities analyzed.
As noted above, high-resolution correlation among localities at finer stratigraphic scales has been problematic owing to significant fine-scale vertical variation in faunal composition.
The source of this variation is not entirely known; however, in Cincinnatian rocks Miller (1997) and Barbour Wood (1999, 2002) recognized that the suite of preserved fauna is not arranged uniformly across bedding surfaces, but into distinct patches dominated by different taxa. That is, there may be considerable spatial variation in faunal composition, even at a single locality.
Therefore, examining just one part of a particular bed might reflect the biotic composition of a single patch and fail to capture the entire suite of fossils from that bed. The final chapter of the present study evaluates local patchiness using gradient analysis to quantify the faunal composition at multiple points along a single locality. Because individual beds are difficult to sample at multiple points along even one outcrop, census samples are binned according to intervals of similar lithology, or bedsets. If Cincinnatian fossil assemblages are patchy in 7 distribution, then stratigraphic patterns of biotic transitions in the same interval may differ from one sampling site to another at a single outcrop. Although local patchiness and differences in the amount of time-averaging with lithology limits the use of faunal gradient analysis for comparing bed-by-bed trends, patterns at scales greater than a few beds are robust across the region. 8
Chapter 1
High-Resolution Faunal Gradient Analysis and an Assessment of the Causes of Meter-Scale
Cyclicity in the Type Cincinnatian Series (Upper Ordovician)
Abstract
The origin of meter-scale cyclicity in the type Cincinnatian Series has long been debated. Some models hypothesize that changes in water depth driven by sea level fluctuations are responsible for producing meter-scale alternations of shale-rich and limestone-rich intervals. Other models link meter-scale cyclicity to changes in storm intensity and frequency, with no change in water depth. Previous interpretations have relied upon lithological variations, which have proven to be ambiguous with respect to meter-scale cyclicity. Here, the role of water depth in producing meter-scale lithologic patterns is assessed using gradient analysis of high-resolution fossil abundance data from the Kope and lower Fairview Formations. Studies have demonstrated that the distribution of biota in this interval is controlled by environmental variables correlated to water depth. Therefore, a direct comparison of stratigraphic variations in faunal composition to meter-scale lithologic alternations is an appropriate test of the influence of water depth on meter- scale cyclicity.
In the present analyses, ordination scores generated from faunal abundance data are grouped into bins that correspond to the upper, proximal and lower, distal parts of each meter- scale cycle, using three different binning protocols. For each cycle, ordination scores from the lower bin are compared to those from the upper bin; consistent differences between the two would suggest a water depth control on meter-scale biotic patterns, and, thus, cyclicity.
However, results indicate no consistent correspondence of faunal patterns to meter-scale lithologic patterns, suggesting that water depth does not play a significant role in the formation 9 of meter-scale cycles. While the different binning protocols did affect analytical outcomes in various ways, the lack of a consistent difference between upper and lower bins within each cycle was robust to all protocols. A model invoking oscillations of storm intensity and frequency appears to provide the most parsimonious explanation for the origin of Cincinnatian meter-scale cyclicity.
Introduction
High-frequency sedimentary cycles are recognized widely in the stratigraphic record and serve as valuable units for high-resolution chronostratigraphic analysis. Meter-scale cycles typically are considered to reflect changes in water depth, particularly shallowing
(parasequences), that stem from high-frequency eustatic sea level oscillation, which often is attributed to Milankovitch-band climate changes (e.g., Bond et al. 1991; House and Gale, 1995).
However, some researchers challenge the recognition of meter-scale cyclicity, suggesting that stratal order more closely resembles randomness (Wilkinson et al., 1996, 1997).
In the Kope Formation of the type Cincinnatian Series (Upper Ordovician), researchers have recognized apparent meter-scale cyclicity characterized by accumulations of alternating carbonate-dominated and siliciclastic-dominated strata (Figs. 1.1, 1.2). General anatomical features of these cycles have been correlated both locally (Tobin, 1982; Holland et al., 2000) and regionally (Jennette and Pryor, 1993; Brett and Algeo, 2001a; Miller et al., 2001), indicating that the recognized pattern of lithological alternation is real, even if not actually cyclical, and was controlled by a region-wide process. That said, the nature of these processes have not been resolved entirely (Holland et al., 1997, 1999, 2001; Brett and Algeo, 2001a). Many workers assign the origin of meter-scale cyclicity to changes in water depth driven by sea level fluctuations (Tobin and Pryor, 1981; Jennette, 1986; Jennette and Pryor, 1993; Holland et al., 10
Figure 1.1 - Photograph of the Kope Formation at the K445 locality, showing the pattern of meter-scale cyclicity. The caps of meter-scale cycles can be seen as ledge-forming limestone beds. 11
Figure 1.2 – Previous interpretations on the structure of meter-scale cycles in the type Cincinnatian Series. Hay (1981; Hay et al., 1981) described regularly occurring, 30-cm thick rippled grainstone beds, spaced, on average, about 1.5 m apart and bounding thicker shale units. Tobin and Pryor (1981) and Tobin (1982) recognized three lithological units within meter-scale cycles: a basal grainstone unit (Unit A), a middle packstone-shale unit (Unit B), and an upper shale and siltstone unit (Unit C). These authors viewed these units as a part of a fining upward and, hence, deepening upward cycle (as indicated by the triangles). Jennette (1986) and Jennette and Pryor (1993) developed an interpretation in which meter-scale cycles represented shallowing upward intervals (parasequences). They applied the storm proximality model of Aigner (1985) to subdivide each cycle into a lower, distal storm facies and an upper, proximal storm facies. In this model, transitions from distal-to-proximal storm facies are viewed as gradual and occurred during continuous shallowing. On the other hand, transitions from proximal-to-distal facies are sharp and indicate an abrupt rise in sea level driven by glacioeustasy. Holland et al. (1997, 1999) and Miller et al. (1997) suggested a greater complexity to Cincinnatian meter-scale cycles, such that many can be viewed as small-scale sequences, rather than only parasequences. These authors observed deepening-upward intervals at the bases of many cycles, rather than simple flooding surfaces, and sharp transitions from distal-to-proximal facies that represent abrupt shallowing. Brett and Algeo (2001b; not illustrated) offered a similar explanation, except that proximal storm facies formed during periods when sediment deposition was dampened by the onset of minor transgressions, allowing the build-up of time-averaged shell beds that frequently were stirred and scoured by storms. Modified from Holland et al. (1997). 12
1997; Brett and Algeo, 2001a). On the other hand, Holland et al. (1997, 1999, 2001) question this view and suggest that alternative mechanisms might better explain meter-scale cycle genesis, including variation in the frequency and intensity of large-scale storms.
In this study, gradient analysis of high-resolution fossil abundance data is used to test the hypothesis that meter-scale cycles in the Kope and lower Fairview Formations of the type
Cincinnatian are governed mainly by changes in water depth. Previous explanations for the origin of meter-scale cyclicity take into account various features of lithology, sedimentary structures, and stratal order, yet none fully address patterns in the biotic composition of these cycles. Fossils can provide information about environmental conditions within depositional sequences, and faunal assemblages should show predictable changes in their composition in relation to sedimentary cycles (Brett, 1998). In fact, few studies have examined fossil variation within the Kope as a whole (but see Anstey and Perry, 1973; Anstey et al., 1987; Anstey and
Rabbio, 1990; Diekmeyer, 1998). However, Holland et al. (2001) and Miller et al. (2001) have analyzed the structure of Kope faunal assemblages at high resolution and found that the distribution of fossils is controlled by environmental factors correlated to water depth, such that vertical trends in faunal abundance data reflect regional water depth history, even where lithofacies change is subtle or nonexistent. Therefore, it is possible to assess the influence of changing water depth on the expression of meter-scale cyclicity by examining the stratigraphic relationship between faunal abundance patterns and the component lithologies within each cycle.
If the delineation of Cincinnatian meter-scale cycles is an accurate reflection of regularly changing water depth, then meter-scale lithologic variations should be matched by concomitant variations in faunal content. Alternatively, if these cycles were controlled by a factor unrelated 13 to changes in water depth, then faunal variations might not exhibit consistent meter-scale lithologic variation.
Stratigraphic Framework
The richly fossiliferous deposits of the study interval, the Kope and lower Fairview
Formations of the type Cincinnatian Series, comprise a nearly 80-m thick mix of siliciclastic and carbonate sediments, consisting of interbedded siliciclastic mudstone, siltstone, calcisiltite, skeletal grainstone, packstone, and minor amounts of lime mudstone and wackestone (Holland,
1993). The Kope Formation is dominated by siliciclastic mudstone (86% shale per 3-ft interval;
Ford, 1967), whereas the Fairview Formation has a higher proportion of limestone (64% shale per 3-ft interval; Ford, 1967). These strata were deposited on a gently north-northwest dipping ramp (Ford, 1967; but see Miller et al., 2001). The region was subject to regular storm activity that controlled deposition in the area by scouring sediments and concentrating shell beds (Hay,
1981; Jennette, 1986; Jennette and Pryor, 1993; Brett and Algeo, 2001b).
Various scales of cyclicity have been identified in the Kope Formation. Patzkowsky and
Holland (1999) recognized six third-order depositional sequences that characterize the entire
Cincinnatian, based on faunal and lithologic criteria (sequences C1 through C6). These sequences have been correlated beyond the Cincinnati Arch into other parts of the North
American continent, namely into the Nashville Dome and Appalachian Basin. Regional correlation led these authors to conclude that most of these sequences were the result of eustatic changes in sea level, represented primarily by thinner, deepening-upward transgressive systems tracts and thicker, shallowing-upward highstand systems tracts. The Kope and lower Fairview
Formations make up the C1 and lower C2 third-order sequences of Holland and Patzkowsky
(1996). The lithologies of both formations exhibit general deepening upward trends overlain by 14 shallowing upward trends, although the Fairview Formation is shallower overall than the Kope
Formation (Holland and Patzkowsky, 1996).
Fourth- and fifth-order cyclicity also has been documented in the Kope Formation.
Fourth-order cycles (“20-m cycles”) have been recognized by upsection transitions from thicker- than-average to thinner-than-average fifth-order cycles (the aforementioned meter-scale cycles), suggesting that 20-m cycles are produced by changes in relative sea level (Jennette and Pryor,
1993; Holland et al., 1997); thicker-than-average meter-scale cycles indicate an increase in accommodation as relative sea level rises, whereas thinner-than-average meter-scale cycles indicate a decrease in accommodation as relative sea level falls (Holland et al., 1997).
Although interpretations for their origin vary, meter-scale cycles in the Kope Formation typically have been recognized by consistent stacking order of lithologies that reflect changes in storm proximality. In the Cincinnatian, as well as similar storm-dominated settings, workers consider vertical trends in proximality to be explained most simply by changes in water depth
(see Aigner, 1985; Brett and Baird, 1986; Jennette, 1986; Jennette and Pryor, 1993; Holland et al., 1997; Brett and Algeo, 2001b; and Fig. 1.2). However, Holland et al. (1997, 1999) did not find consistent stratigraphic patterns in the internal anatomy of meter-scale cycles in relation to larger scale cycles. For example, if the tops of Kope meter-scale cycles reflect shallowing conditions, then cycle-capping limestones, when traced up depositional dip, should grade laterally from offshore and deep subtidal facies into shallower subtidal facies, as is the case with third-order cycles. This transition is not seen; Kope meter-scale cycles are deposited entirely in offshore to deep subtidal environments. Furthermore, Holland et al. (1999) observed that meter- scale cycles are not well expressed in shallow subtidal and peritidal units. These authors pointed out that, if changes in water depth produced the cycles, perhaps they should be best expressed in 15 the shallowest settings (e.g., Markello and Read, 1981; Elrick and Read, 1991). These observations prompted Holland et al. (1999) to suggest that meter-scale cycles did not result from cyclically changing water depth. As an alternative, these authors suggested that meter- scale cycles could have been produced by variations in storm intensity and frequency.
Development of Analytical Framework
The evaluation of stratigraphic faunal abundance patterns and meter-scale cyclicity here relies on data collected from six localities (Fig. 1.3) of the Kope and lower Fairview Formations by Holland et al. (1997, 2001), Miller et al. (1997, 2001), and Webber (unpub. data). In these studies, faunal and lithological data were analyzed for regional correlation, basin floor topographical reconstruction, and biofacies analysis. Techniques of data collection and analysis by these workers, along with modifications made here, are discussed below. These techniques permit a high-resolution comparison of stratigraphic variation in lithology and faunal composition.
Data Collection
The lithology of the section at each locality was logged at 1 cm intervals, and faunal censuses taken at every fossiliferous bed thicker than 0.5 cm, which resulted in a high-resolution database of lithological and biotic information. The following lithological categories were recognized: grainstone, mixed grainstone/packstone, packstone, wackestone, carbonate mudstone, calcisiltite, siltstone, silty shale, and shale. Faunal data were recorded from each of the lithologies listed above, although limestone beds contained the highest abundance of fossils.
Each sample consists of a faunal census conducted on a fossiliferous bed, and was recorded by calculating abundances by eye as rare (1-2 specimens per 1000 cm2), common (3-10 specimens
per 1000 cm2), or abundant (>10 specimens per 1000 cm2). Many faunal elements were 16
Figure 1.3 – Location map. Dots indicate outcrops of the Kope and lower Fairview Formations where data were collected. 17 identified to genus (e.g., brachiopods, crinoids, trilobites, bivalves, cephalopods, and gastropods), but most bryozoans were given the following designations: thin bifoliate (<5 mm), thick bifoliate (>5 mm), thin ramose (<5 mm), thick ramose (>5mm), and encrusting.
Cryptostome and fenestellid bryozoans also were identified, and the distinctive bryozoans --
Escharopora, Aspidopora, Prasopora, Stomatopora, and Parvohallopora -- were identified to the genus level. For analytical purposes, abundance categories later were changed to numeric values of 1, 6, and 12, respectively, which approximate the midpoints of each abundance category. Field collections from all six sections resulted in a relative abundance matrix consisting of 2340 samples and 58 taxa.
Quantitative Analysis
The data matrix was evaluated using Detrended Correspondence Analysis (DCA), a multivariate statistical technique that yields scores for samples and taxa on multiple axes calculated to capture major vectors of variation (Hill and Gauch, 1980). This technique has been used to ordinate taxa along underlying ecological gradients (e.g., Holland et al., 2001; Miller et al., 2001). Unlike other multivariate techniques that distribute the principle gradient underlying the data along axis 1 and 2, such as Correspondence Analysis and Principle Components
Analysis, DCA expresses the primary source for variation along axis 1 (see Hill and Gauch,
1980; Holland et al., 2001, for a detailed description of its application).
Prior to analysis, all samples containing only a single taxon were removed, as were all taxa occurring in only one sample. The culled data matrix from all six sections consisted of 1669 samples and 47 taxa. DCA was run using PC-ORD, Version 4.14 (McCune and Mefford, 1999).
The default program parameters were used: rescaling axes (ON), rescaling threshold (0), and number of segments (26). Rare taxa were downweighted to minimize distortion (PC-ORD 18 defines rare taxa as those with an abundance less than one-fifth of the most abundant taxon).
Varying the number of sections included in analyses resulted only in subtle differences in the arrangement of taxa along axis 1, emphasizing the robustness of structure in the data (Holland et al., 2001). The data from all six localities were included in DCA to maintain consistency with earlier analyses and because this provided the most stable axis 1 ordination.
Using DCA in the Kope and Fairview Formations, Holland et al. (2001) and Miller et al.
(2001) reported that the alignment of taxa along axis 1 strongly suggested correlation with water depth, as in similar ecological gradient analyses (see Cisne and Rabe, 1978; Dattilo, 1996). High axis 1 scores were found to characterize shallow water faunal assemblages, whereas low axis 1 scores characterized deep water assemblages. Holland et al. (2001) cited several lines of evidence for this relationship. High-scoring taxa are most abundant in the Fairview Formation, which consists of an overall shallower lithofacies than the Kope Formation. Low-scoring taxa have been interpreted as deeper water fauna in studies from the Ordovician in the Appalachian
Basin (see Bretsky, 1970; Springer and Bambach, 1985). The overall trend toward higher stratigraphically arranged axis 1 ordination scores in the Kope Formation corresponds with interpretations that this formation shallows upward. Finally, when comparing ordination scores from sections located on different levels of the Cincinnatian ramp, depositionally updip and, therefore, shallower, sections consistently display higher scores than those depositionally downdip.
Although the distribution of taxa along axis 1 likely reflects a gradient related to a combination of depth-related environmental variables rather than water depth itself, low axis 1 ordination scores will be referred to as “deep” and high axis 1 ordination scores as “shallow” for the purposes of discussion throughout the remainder of this paper. High scoring taxa are typical 19 shallow water Cincinnatian forms, such as the robust brachiopods Strophomena, Rafinesquina, and Platystrophia, and thick ramose bryozoans (Table 1; Fig. 1.4). Taxa scoring near the middle of Axis 1 commonly can be found to occur in intermediate water depths; such taxa include
Isotelus, Dalmanella, calymenid trilobites, and Zygospira. Finally, low scoring taxa are deeper water forms such as Cryptolithus, Ectenocrinus, Sowerbyella, and graptolites.
The stratigraphic trend of faunal composition in the Kope and lower Fairview Formations is, therefore, a record of water depth history for a given outcrop locality. To visualize these stratigraphic changes in faunal composition, sample scores at each locality are plotted with respect to their stratigraphic position, providing a curve that displays water-depth related patterns in faunal composition (e.g., Fig. 1.5). Smoothed versions of these curves have been used in a recent correlation study of the Cincinnatian (Miller et al., 2001) by comparing inflection points among several localities.
Comparing Faunal Scores to Meter-Scale Cycles
The quantitative analysis described here is an ideal method for comparing stratigraphic trends in lithology and faunal composition as they correspond to meter-scale cycles. Before such a comparison can be made, however, it is necessary to define a meter-scale cycle and to delineate cycles in the study interval. Although the interpretation of Cincinnatian meter-scale cycles has varied considerably, the recognition of a majority of meter-scale cycle boundaries has been consistent; at an outcrop, most would be readily visible to a casual observer (Holland et al.,
1997; Brett and Algeo, 2001a). In the present analysis, the fifty meter-scale cycle boundaries of
Holland et al. (1997) at an outcrop (K445; Fig. 1.3) of the Kope and lower Fairview are used, because they reflect an extremely high-resolution examination of the study interval, and because 20
Table 1.1 – Axis 1 ordination scores of taxa.
Taxon Score Taxon Score Hebertella 505 Calymenids 147 Strophomena 485 Isotelus 144 Platystrophia 384 Aspidopora 129 Thick ramose bryozoans 366 Proetidella 125 Rafinesquina 358 Ceraurus 122 Thick bifoliate bryozoans 349 Cephalopods 114 Escharapora 331 Prasopora 113 Thin ramose bryozoans 286 Cryptostomes 112 Fenestellids 274 Ostracods 101 Scolecodonts 236 Cyclonema 86 Thin bifoliate bryozoans 236 Cornulites 82 Dalmanella 232 Graptolites 78 Glyptocrinus 230 Iocrinus 74 Hydrozoans 230 Ectenocrinus 71 Plectorthis 218 Acidaspis 61 Ambonychia 205 Craniops 50 Gastropods 200 Sowerbyella 49 Zygospira 200 Stomatopora 36 Encrusting bryozoans 194 Cryptolithus 22 Cyclora 193 Lepidocoleus -2 Pseudolingula 190 Parvohallopora -3 Modiolopsis 166 Schizocrania -5 Nuculoids 163 Merocrinus -8 Cincinnaticrinus -35
21
Strophomena Axis 1 Scores thick ramose bryozoans
Rafinesquina
Platystrophia
Zygospira Isotelus “Shallower” “Deeper” Dalmanella
calymenids
Cryptolithus graptolites
Ectenocrinus Sowerbyella
Figure 1.4 – Generalized diagram showing the position of major Kope and Fairview faunal elements along axis 1 from detrended correspondence analysis. This axis suggests a correlation to water depth-related environmental variables. Fossil photographs from Davis (1992), except Ectenocrinus, and Zygospira, from Feldmann and Hackathorn (1996). 22
Figure 1.5 – Stratigraphic position of sample ordination scores for the Kope and lower Fairview Formations at K445. (A) Generalized stratigraphic column, in which Kope and Fairview lithologies have been grouped into three categories for graphical purposes only. The boundaries of meter-scale and 20 m-scale cycles are indicated at the right of the stratigraphic column. Arrows indicate portions of meter-scale cycles that show features consistent with deepening intervals. Modified from Holland et al. (1997, 1999, 2001). (B) Faunal ordination scores of samples plotted according to stratigraphic position. Higher axis 1 scores reflect shallower water faunal assemblages, whereas lower axis 1 scores reflect deeper faunal assemblages. (C) Binned faunal ordination scores. These bins correspond to shale-rich and limestone-rich hemicycles of each meter-scale cycle. Each bin is designed to characterize the faunal composition of either the lower or the upper portion of each meter-scale cycle. Meter-scale cycles are again shown on the right. 23 these meter-scale cycles have been correlated, at least generally, across much of the regional extent of the study interval (see Brett and Algeo, 2001a; Miller et al., 2001).
Holland et al. (1997) identified meter-scale cycles by successive beds that characterize changes in storm proximality. Ultradistal storm beds are graded or structureless mudstones; distal storm beds are planar-to-rippled siltstones; medial storms are thin packstones with or without a hummocky-to-rippled siltstone cap; and proximal storm beds are characterized by thick, amalgamated grainstones and packstones that frequently contain megaripples and intraclasts (from Holland et al., 1997). Some of the above lithologies can be absent from cycles, particularly those that characterize medial storm beds. Proximality trends in this study were not based on any single storm bed, but among successions of storm beds, because individual storms can vary in intensity. Cycle boundaries were placed at the top of the most proximal succession of beds, typically thick, amalgamated limestone beds. These authors initially assumed that each cycle was a shallowing upward unit bounded by flooding surfaces (parasequence) as per Jennette and Pryor (1993). If this were the case, each cycle would exhibit a smooth upward transition from distal to proximal storm beds. Capping each cycle would be a sharp transition from proximal to distal storm beds representing a flooding surface (recognized by an abrupt increase in the thickness of shale beds). Upon close inspection, however, these authors recognized greater variability in cycle anatomy than anticipated (Fig. 1.2). First, most cycles display an abrupt shift from distal to highly proximal storm beds, rather than a smooth upward succession.
Furthermore, most cycles exhibit an upward decrease in proximality at the base of many cycles
(33 of 50). This interval of decreasing proximality is typically thin (<25% of cycle thickness), with a few exceptions (Fig. 1.5A). 24
With respect to this issue, it is critical to ask whether faunal signatures from more distally occurring storm beds of meter-scale cycles differ consistently from more proximal storm beds.
One way to do this would be to conduct a statistical analysis comparing and contrasting the faunal components of individual proximal storm beds (e.g., thicker limestone) to the faunal components of individual distal or ultradistal storm beds (e.g., shale and siltstone). However, this analysis would prove little. First, larger-scale faunal transitions in the study interval would make comparing beds from different stratigraphic positions difficult. For example, proximal storm beds from the upper Kope would score higher than proximal storm beds from the lower
Kope, because the Kope as a whole shallows upward. Second, because individual storm beds potentially reflect the depth of deposition and the intensity of storms, a proximal storm bed occurring within a succession of more distal beds might be the result of a strong storm in deeper water. The faunal composition of this bed would differ from the composition of a proximal storm bed deposited in shallower water. Because meter-scale cycles are defined by trends in successive storm beds, not individual beds, a better way to assess the influence of water depth on meter-scale cyclicity is to examine the faunal composition of proximality-defined intervals within meter-scale cycles.
With this in mind, the analytical technique described above was modified to provide a characterization of the faunal composition of distal and proximal intervals of meter-scale cycles.
Prior to running DCA, each meter-scale cycle was subdivided into bins according to successive trends in proximality-related lithologies (see below), and then the average abundance value for each taxon within that bin was calculated. The latter step entails summing the abundance of each taxon for all samples within a bin and dividing by the number of samples in that bin. To illustrate, if a cycle bin contains five samples, and Rafinesquina is common in two of the 25 samples, rare in one, and absent from the rest, then the abundance value for Rafinesquina in this bin would be 2.6 (calculated by adding the abundance scores, 6+6+1=13, and dividing by the number of samples, 5). The modified database is subjected to DCA to provide ordination scores for cycle bins, rather than individual samples. The pattern that emerges is broadly faithful to those already generated for localities in previous studies (Fig. 1.5C). Because ordination scores have been interpreted as proxies for water depth, this technique has provided a depth “value” for each proximality-related interval of a meter-scale cycle.
The proximality patterns within meter-scale cycles in the study interval have been delineated using three different protocols. Each protocol bins samples together in an attempt to characterize various interpretations for water-depth control on meter-scale cyclicity. As a first- order subdivision, the samples from each meter-scale cycle can be split into two bins of equal stratigraphic thickness (Fig. 1.6A). This binning protocol would be most effective if meter-scale cycles fit the parasequence model as described by Jennette and Pryor (1993). Because meter- cycles display uniform proximal-to-distal transitions in this model, the upper halves of cycles should contain consistently different faunal properties than the lower halves. However, this binning protocol might fail to account for non-uniform transitions from distal-to-proximal lithofacies. Unequal thickness of the distal, shale-rich and proximal, limestone-rich intervals of cycles and would tend to mask faunal variations associated with this major transition within many cycles. To account for this, the second binning protocol involved dividing each meter- scale cycle into a distal (~70% shale) hemicycle and a proximal (<20% shale) hemicycle, as per
Jennette (1986; Fig. 1.6B). This protocol might provide a more meaningful characterization of the two phases of proximality-related deposition seen within meter-scale cycles, and enhances the possibility of capturing consistent differences in faunal composition associated with the two 26
Figure 1.6 –Binning protocols. (A) Protocol in which each meter-scale cycle is divided into equal bins. The generalized lithology for cycles 31 and 32 is shown on the left. The binned ordination scores are in the middle, and the major faunal elements of each bin, along with their average abundance value for all samples within that bin, are shown on the right. Note that bins with more abundant "shallower" water fauna (e.g., thick ramose bryozoa) score higher than bins with more abundant "deeper" water fauna (e.g., Ectenocrinus). (B) Binning protocol in which each meter-scale cycle is divided into a shale-rich and a limestone-rich hemicycle. Again, generalized lithology for cycles 31 and 32 are shown on the left, the binned ordination scores are in the middle, and the major faunal elements of each bin are shown on the right. 27 phases. Neither of these two binning protocols, however, account for the proximal-to-distal intervals identified at the bases of the majority of meter-scale cycles (33 out of 50) by Holland et al. (1997). If these intervals reflect increasing water depth, then shallower water faunas sampled at the onset of deepening could inflate scores for the lower bin in the cycle. For this reason, a third binning protocol was adopted in which the proximal-to-distal intervals of cycles were separated into a third bin within the relevant cycles. With this protocol, within-cycle comparisons were made between the upper two bins; that is, scores for the limestone-rich bin and the underlying shale-rich bin that is stratigraphically above the proximal-to-distal interval bin were examined. Cycles without this interval are treated in the same manner as the hemicycle binning protocol.
Each type of binning presented above compares the faunal components of a lower, distal interval to an upper, proximal interval for each cycle. If transitions from lower-to-upper bins, that is, distal-to-proximal bins, within meter-scale cycles are the result of changing water depth, then, for a majority of cycles, the upper bin should have a consistently higher or lower depth- related faunal ordination score than the lower bin. If there is no statistical difference between the number of cycles with shallowing upward faunal trends and the number of cycles with deepening upward faunal trends, then there is no consistent relationship of faunal composition to meter- scale cycles. The significance of results from each protocol was evaluated using a test for a binomial proportion that determines whether the number of cycles with a higher-scoring upper bin (“shallowing” upward) relative to the number of cycles with a higher-scoring lower bin
(“deepening” upward) differs from a proportion of 50:50 (Snedecor and Cochran, 1989). At α =
0.05, values derived from this test falling outside a range of ±1.96 indicates a rejection of the
null hypothesis of a 50:50 ratio, and the fauna from distal bins score differently than those from 28 proximal bins for a significant number of cycles. This would show a real pattern in faunal composition with relation to meter-scale cycles, and would suggest a water-depth control.
As an alternative evaluation of each binning protocol, Mann-Whitney U tests were
conducted to determine if the mean of all upper bin ordination scores ( X u ) differed significantly
from the mean for lower bin ordination scores ( X l ). This test is meaningful despite larger-scale
faunal trends within the study interval, because the mean score of all proximal bins taken
together should be different than the mean score for all distal bins if water depth controls meter- scale cyclicity. In both significance tests, cycles in which one of the two bins contained no samples or samples with only a single taxon were excluded.
It is also valuable to look for patterns in the stratigraphic arrangement of the faunal composition trends of meter-scale cycles. For example, perhaps the lower half of fourth-order cycles (20 m cycles), which has been recognized by thicker-than-average meter-scale cycles, contains meter-scale cycles that tend to have faunal patterns that indicate deepening. Runs tests were conducted for each binning protocol to detect the presence of non-random successions of consecutive cycles displaying either shallowing or deepening faunal composition trends (Davis,
1986).
Results and Implications
Results from all three binning protocols reveal that there is no statistical difference in the number of faunally shallowing upward and deepening upward cycles, nor are the means of upper bin faunal ordination scores and lower bin faunal ordination scores significantly different (Table
2). The faunal composition of cycles appears to correspond to lithologic changes only in the twelve cycles from the third binning protocol that do not have basal proximal-to-distal intervals.
In these cycles, the upper bins have higher faunal ordination scores than the lower bins. This 29
Table 1.2 – Faunal patterns within meter-scale cycles for each binning protocol.
Test of Mann-Whitney U tests Proportionality Binning Protocol Z Values P X u X l P
Shallowing Upwards Deepening Upwards No Signal Equal thickness 23 23 4 -0.147 0.8808 202.8 199.2 0.749 Hemicycles 26 18 6 1.05 0.2938 188.5 196.0 0.463 Basal interval 184.9 196.6 0.458 With (33) 14 15 4 -0.371 0.7114 Without (17) 12 3 2 2.07 0.0384*
* Reject H0 at α = 0.05 30 might suggest a shallowing pattern, but it is unclear why stratigraphic patterns in biotic composition for the upper two bins of cycles with deepening intervals would differ from the two bins of cycles without such intervals. Furthermore, the appearance of this pattern in only a handful of cycles still does not display a consistent correlation of faunal trends to meter-scale lithology. Finally, runs tests indicated a random order in the successions of cycles showing either shallowing or deepening faunal trends for all binning protocols.
It is apparent that the stratigraphic distribution of fossil components in lower
Cincinnatian strata does not vary consistently within lithologically defined meter-scale cycles, even with binning protocols designed to enhance the recognition of any such relationship. What does this say about the factors controlling meter-scale cyclicity? In a quantitative analysis on bed thicknesses from the same study interval, Wilkinson et al. (1997) suggest that the style of deposition in the Kope is controlled primarily by random factors. The region-wide correlatability of lithologic packages (see Brett and Algeo, 2001b) and fossil composition (see
Holland et al., 2000; Miller et al., 2001) in these strata, however, show that the control on sedimentation in this interval is widespread and that this meter-scale pattern is real, regardless of whether it was produced by a process that was truly cyclical (Brett and Algeo, 2001b; Holland et al., 1999).
Nonetheless, it is evident that lithological variation in each cycle is produced by variation in the effect of storms on deposition. Shale-rich intervals indicate deposition in settings that receive little or no storm influence, whereas limestone-rich intervals indicate deposition in more storm-influenced settings. Three mechanisms have been invoked to account for the transition from distal into proximal settings: decrease water depth so that the sea floor is above storm wave base more frequently (the parasequence model of Jennette and Pryor, 1993), cut off 31 sedimentation with a minor sea level transgression such that storm-influenced deposits are condensed together (the sediment starvation model of Brett and Algeo, 2001a), or increase the intensity and frequency of storms so that storm wave base is consistently lower (storm model of
Holland et al., 1997, 1999). The first two explanations involve changes in water depth. If the differences between the limestone-rich and shale-rich hemicycles of meter-scale cycles reflect depth-related environmental changes governed by water depth fluctuation, one would expect a consistent difference in the faunal composition between the two hemicycles, given that fauna are sensitive to water depth-related environmental changes. As this is not the case, it cannot be said that water depth is a consistent controlling mechanism of meter-scale cycles in the Cincinnatian.
Therefore, of the existing proposed models, the oscillating storm intensity/frequency model proposed by Holland et al. (1997, 1999) offers the most parsimonious explanation, because it invokes climatic changes that do not infer cyclical changes in water depth as a mechanism for producing meter-scale lithologic change. In this model, the shale-rich portions of cycles represent times of infrequent and relatively weak storms, resulting in the accumulation of significant amounts of mud, with siltstones, calcisiltites, and minor amounts of thin packstones.
Limestone-rich portions of cycles reflect frequent and intense storm intervals, during which sediment bypass and scouring by multiple storms produced condensed, shell-rich bundles of amalgamated limestone, multi-event beds, and little shale. Variability in the sharpness of facies transitions is explained by variability in transitions between periods of strong and weak storms.
This model accounts for many depositional features of Cincinnatian meter-scale cycles that do not correspond to models of fluctuating water depth, as discussed by Holland et al. (1997, 1999).
Climate models suggest that variations in factors such as paleogeography and sea surface temperatures affect the generation and intensity of severe storms on geological time scales 32
(PSUCLIM, 1999a, b). Furthermore, Mason and Jordan (1993) relate variation in storminess with durations of 103 years during the Late Holocene to glacier advance and retreat. This at least
opens up the possibility that climate-related mechanisms affected storm intensity and frequency
variation during the Cincinnatian, recognizing that any such effect would have operated at a
broader temporal scale.
Earlier studies sought unifying explanations for the causes of meter-scale cyclicity,
usually involving links to changes in water depth. However, as Holland et al. (1997) and Miller
et al. (1997) have suggested, there has been a tendency in these studies to underestimate the
extent of variation among cycles within a given study interval. When a series of successive
meter-scale cycles is investigated in detail, fine-scale lithologic structure may reveal important
differences in their depositional characteristics. The present study demonstrates that, in the Kope
and Fairview Formations, when faunal data are added to the mix, these differences are
exemplified further. In this interval, the biota is more diagnostic of depth-related environmental
variation than is the suite of preserved lithologies, because the latter has proven to be ambiguous
with respect to water-depth interpretations (Holland et al., 1999). More broadly, faunal gradient
analysis should be viewed as an essential investigative tool in the study of environmental
transitions within fossil-rich, cyclic intervals.
Conclusions
Previous investigations on meter-scale cyclicity in the Cincinnatian have focused on
stratigraphic patterns in lithology. Quantitative analysis of the stratigraphic distribution of fossil
data in the Kope and lower Fairview Formations of the Cincinnatian has revealed that biotic
transitions generally do not respond consistently to meter-scale lithologic alternations. This 33 would suggest that changes in water depth, which are known to influence the distribution of
Cincinnatian taxa, cannot be invoked readily as causes of meter-scale cyclicity.
It appears that a model linking fluctuations in storm intensity/frequency to meter-scale deposition provides the most parsimonious explanation of meter-scale cycles. This model accounts for: 1) the lack of water depth changes seen in meter-scale cycles of the Kope and lower Fairview Formations, as well as the lack of order in meter-scale cycle anatomy; 2) the failure of meter-scale cycles to mimic the facies transitions of larger-scale, sea level driven cycles; and 3) the expression of meter-scale cycles only in offshore and deep subtidal settings. 34
Chapter 2
Methodological advances in the use of faunal gradient analysis for regional high-resolution
correlation in the type Cincinnatian Series (Upper Ordovician)
Abstract
Historically, strata of the type Cincinnatian Series have not been correlated with precision because of an assumption that individual beds are not regionally persistent. In an effort to establish a regional chronostratigraphic framework for the Cincinnatian, a technique has been developed recently that uses gradient analysis of fossil abundance data to compare vertical trends in biotic composition among five localities that span a distance of 65 km. Using faunal data to establish correlations below the meter-scale has been problematic, because variability in the lateral expression of many beds has made it difficult to sample equivalent fossiliferous strata at all localities.
Here, this correlation technique is modified in an attempt to draw meter-scale correlations of the Kope and lower Fairview Formations. This modification involves grouping fossil census samples into stratigraphic intervals, such that the stratigraphic spacing of samples is identical at all localities. Samples are binned using two protocols: one that groups samples into 0.5 m thick intervals, and another that groups samples by lithologically-delineated meter-scale cycles.
Gradient analysis of binned samples using both protocols produces curves in which patterns in faunal composition below the meter-scale can be correlated with success only for limited intervals at select localities. The inability to consistently correlate at high resolution might indicate that correcting for regional variations in the expression of strata is not enough.
Alternatively, the ability to correlate some intervals below the meter scale suggests that other problems, such as fine-scale lateral variations in biotic composition, might be impeding 35 correlation. In addition, gradient analysis techniques are employed at a new locality to extend the lateral breadth of the existing, broader-scale chronostratigraphic framework by approximately 40 km. Although interpreted to be in an up-ramp direction from previously measured localities, this new locality does not have an overall shallower water faunal assemblage until just below the Kope/Fairview contact.
Introduction
Gaining insight into fine-scale spatio-temporal variations in Late Ordovician regional climatic, oceanographic, and tectonic conditions relies on fine-scale correlation of strata, often across significant lateral distances. Such information has been useful in determining, among other things, how marine organisms respond in the face of environmental change. Regional, high-resolution paleoecological studies have been particularly effective, for example, when attempting to distinguish migrating faunas from those suffering extinction (Patzkowsky and
Holland, 1996), or differentiating between an evolving lineage and ecophenotypes varying across environmental gradients (Dattilo, 1996).
The highly fossiliferous type Cincinnatian Series (Upper Ordovician), with thick, densely-bedded deposits exposed at numerous outcrops over a wide geographic area, is an ideal setting for regional high-resolution geological investigations. However, stratigraphic correlation at these spatial and temporal scales has been lacking in the type Cincinnatian until recently because the features necessary for high-resolution correlation are not readily discernible (see
Background). This is attributable to both lateral variability in the expression of individual beds and the seemingly monotonous nature of stratigraphic variation in major sediment types.
As an alternative to using lithological criteria, Miller et al. (2001) and Holland et al.
(2001) used faunal gradient analysis to establish correlations in the Kope and lower Fairview 36
Formations of the type Cincinnatian Series. Like other faunal gradient analyses (e.g., Cisne and
Rabe, 1978; Anstey et al., 1986; Anstey and Rabbio, 1990; Dattilo, 1996), the technique of
Miller et al. (2001) and Holland et al. (2001) numerically compares stratigraphic changes in the composition of faunal assemblages among localities. Similarities in the vertical pattern of compositional variation at multiple localities provide the basis for establishing correlations. This method is especially promising for developing high-resolution investigations in the Cincinnatian, because it is based on faunal censuses conducted from every fossiliferous horizon within the study interval at several localities arrayed over a lateral distance of approximately 65 km (Fig.
2.1). Although labor intensive, censuses are taxonomically and quantitatively coarse, and, thus, applicable in a variety of regional contexts (Miller et al., 2001). Furthermore, combining the high-resolution faunal data acquired through gradient analysis with other information, such as geochemical and lithological data, provides a powerful tool for a wide variety of geological investigations (e.g., Miller et al., 2001; Holland et al., 2001).
Using these gradient analytical techniques, correlations have been established based on broad trends in Cincinnatian faunal assemblages, but significant amounts of fine-scale variation in quantified faunal composition trends among localities have made regional correlation below the meter scale, as yet, untenable (Miller et al. 2001). Here, the relationship between these fine- scale variations in Cincinnatian faunal composition patterns and variability in the lateral expression of individual strata is addressed directly. In an attempt to compensate for the unequal numbers of strata at each locality, the gradient analysis technique of Holland et al. (2001) and
Miller et al. (2001) is modified to capture the biotic composition of particular stratigraphic intervals rather than individual beds. The purpose of refining the existing gradient analysis techniques is to enable the establishment of fine-scale correlations (meter scale and below) of the 37
Figure 2.1 – Map of outcrop localities of the Kope and lower Fairview Formations used in faunal gradient analysis. Dots indicate localities measured by Holland et al. (2001) and Miller et al. (2001). The star indicates the locality added by the present study. 38
Kope and lower Fairview Formations. Furthermore, the utility of the emerging chronostratigraphic framework of Miller et al. (2001) is evaluated by adding an additional locality about 40 km from those previously analyzed, increasing the lateral breadth of correlations by 60% (Fig. 2.1). Taken together, these aspects of the present study facilitate assessments of the meaning of stratigraphic change in the Cincinnatian, particularly in faunal composition, across a greater distance and at a finer scale of resolution than has previously been achievable.
Background
The Study Interval
The Kope and lower Fairview Formations (the C1 and lower C2 sequences of Holland and Patzkowsky, 1996; Patzkowsky and Holland 1999) consist of a nearly 80-m thick accumulation of interbedded siliciclastic mudstone, siltstone, calcisiltite, and skeletal limestone
(Holland, 1993). Deposition of these strata occurred on a gently north-northwest dipping ramp, as interpreted from the presence of tidal flat facies and the disappearance of deep water facies to the south, increases in the amount and thickness of limestone beds to the south and southeast, and a north to south orientation of gutter casts beds (Ford, 1967; Jennette and Pryor, 1993;
Holland, 1993; Weir et al., 1984; but see Miller et al., 2001). The region was subject to regular storm activity that controlled deposition in the area by scouring sediments and concentrating shells into beds (Hay, 1981; Jennette, 1986; Jennette and Pryor, 1993; Brett and Algeo, 2001a).
Correlation in the Cincinnatian
Early work on the nomenclature of Cincinnatian strata classified units based on generalized lithology and characteristic fauna (e.g., Bucher et al., 1939). Later workers designated units based on lithologic criteria, primarily the ratio of shale to limestone percentages 39
(e.g., Ford, 1967). These lithologic packages historically were not correlated with high precision, that is, at vertical scales of a meter or less, because of an assumption that the sedimentary deposits of the area were not distributed region-wide (e.g., Anstey and Fowler,
1969). The seemingly monotonous nature of stratigraphic variation and the highly variable lateral expression of individual beds in the Cincinnatian led to a perceived lack of lateral persistence of many individual beds from outcrop to outcrop. This impeded tracing stratigraphic packages between outcrops, with few exceptions (Miller et al., 2001). Indeed, many beds pinch out and vary in thickness even along single outcrop faces, especially siltstones and calcisiltites, although many beds that pinch out reappear laterally (Kohrs, 2002; Webber, in prep.).
Several more recent studies endeavored to correlate the entire Cincinnatian stratigraphy in its type area using cyclostratigraphy. On a broad scale, Holland and Patzkowsky (1999) recognized six third-order depositional sequences based on faunal and lithologic criteria characterizing the entire Cincinnatian Series, (sequences C1 through C6). These sequences have been correlated beyond the Cincinnati Arch into other parts of the North American continent, namely into the Nashville Dome and the Appalachian Basin (Holland and Patzkowsky, 1996;
Patzkowsky and Holland, 1996). Likewise, smaller scale cyclicity has been useful in correlating the Cincinnatian. Jennette and Pryor (1993) and Brett and Algeo (2001b) have traced meter- thick sedimentary cycles (meter-scale cycles) over part of the Cincinnatian region. In addition,
Holland et al. (2000) used cross-correlation to correlate meter-scale cycles at two closely-spaced outcrops by comparing successive variation in cycle thicknesses, although they pointed out this method might be problematic over greater distances.
Faunal Gradient Analysis as a Correlation Technique 40
To correlate the Kope and lower Fairview Formations at high-resolution, Holland et al.
(2001) and Miller et al. (2001) established a method comparing vertical trends in fossil composition among localities. They used Detrended Correspondence Analysis (DCA) on rank faunal abundance data to quantify the fossil composition of a given bed, and reported the distribution of fossils in the Cincinnatian was correlated with water depth (see Methods below), as in similar ecological gradient analyses (e.g., Cisne and Rabe, 1978; Dattilo, 1996). The quantified fossil composition of each bed, when arranged graphically in stratigraphic order, provided a visual means of assessing the faunal change at a given locality (Fig. 2.2). In the case of the Cincinnatian, these graphs reflect water depth history for a given locality. By comparing the inflection points on graphs constructed for multiple localities, Miller et al. (2001) were able to recognize similar patterns in the trends of changing fossil composition from each locality, and, thus, were able to correlate these localities and examine the regional water depth history (Fig.
2.3). These authors constructed faunal composition curves for five localities in southeastern
Indiana, southwestern Ohio, and northern Kentucky that covered a lateral distance of 65 km along a northwest to southeast transect (Fig. 2.1).
The analytical technique of Holland et al. (2001) and Miller et al. (2001) has established correlations at a stratigraphic resolution approaching one meter. Fine-scale (less than a meter) patterns have been more difficult to correlate, not only because of difficulties in sampling from laterally variable beds, but also because of significant vertical variation in faunal composition at scales of less than a meter (Fig. 2.2). The source of this finer-scale stratigraphic variation in biotic composition is unknown: it might reflect small-scale environmental differences among localities, or it might indicate random fluctuations in biotic composition (Holland et al., 2001).
Studies that have traced particularly diagnostic beds (Jeanette and Pryor, 1993; Brett and Algeo, 41
Figure 2.2 – The stratigraphic position of sample ordination scores for the Kope and lower Fairview Formations at K445. (A) Generalized stratigraphic column, in which Kope and Fairview lithologies have been grouped into three categories for graphical purposes only. The boundaries of 20 m-scale cycles are indicated at the right of the stratigraphic column. Modified from Holland et al. (1997, 2001). (B) Faunal ordination scores of samples plotted according to stratigraphic position. This curve provides a means of visually assessing stratigraphic trends in faunal composition based on an environmental gradient interpreted here to be water depth. Higher axis 1 scores reflect shallower water faunal assemblages, whereas lower axis 1 scores reflect deeper faunal assemblages. (C) Smoothed ordination curve using an 11-point moving average, designed to permit an easier recognition of broader trends in faunal composition. Modified from Holland et al. (2001) and Miller et al. (2001). 42
Figure 2.3 – A comparison of smoothed ordination curves from K445 and Holst Creek, aligned to the contact between the Kope and Fairview Formations. Stratigraphic faunal trends are similar at both localities despite differences in the absolute value of scores. From Holland et al. (2001) and Miller et al. (2001). 43
2001b; Kohrs, 2002) and unique fossiliferous horizons (Osgood, 1981; Meyer, 1990) across the
Cincinnatian indicate that it is possible to trace at least some individual beds across significant distances. Furthermore, many prominent limestone bundles (10 – 50 cm thick), although highly internally variable, have been traced over much of the region (Barbour Wood, 2001, 2002; Brett and Algeo, 2001b).
To increase the potential of the gradient analysis technique of Holland et al. (2001) and
Miller et al. (2001) in paleoecological and stratigraphic investigations, the chronostratigraphic framework established by these authors must be refined to the highest resolution possible over a greater portion of the Cincinnatian region. It is, therefore, crucial to address the effects lateral variation in the expression and continuity of individual strata has on the use of gradient analysis for correlation. To this end, the ability to correlate the Cincinnatian below the meter scale, despite the lateral discontinuity of many beds, is tested here by presenting a correlation technique that examines the faunal composition of specified stratigraphic intervals rather than individual beds. In the first analysis presented here, patterns of faunal composition are assessed among localities by grouping census samples into intervals that are 0.5 m thick. Interval thicknesses of
0.5 m were selected as a compromise between smaller intervals that contain too few beds to compensate for the significant amounts of fine-scale variations in biotic composition and larger intervals that exceed the desired correlation resolution of less than one meter. Because the delineation of 0.5-m thick intervals is arbitrary with regards to stratigraphy, faunal composition data is also grouped according to meter-scale cycles. Although this method incorporates stratigraphy, the process of recognizing meter-scale cycles is more subjective. In addition, a new locality is included in the present analysis located in Maysville, Kentucky, approximately 80 km 44 to the southeast of Cincinnati, Ohio (Fig. 2.1). This new locality increases the current lateral coverage to approximately 105 km from the northwest to the southeast.
Methods
Data collection
Lithologic and faunal composition data collected in the present study follows the procedures applied by Holland et al. (2001) and Miller et al. (2001). At each locality, lithology is logged at 1 cm intervals, with these lithological categories recognized: shale, fossiliferous shale, silty shale, siltstone, calcisiltite, carbonate mudstone, wackestone, packstone, mixed grainstone/packstone, and grainstone. Faunal censuses are recorded from every fossiliferous horizon throughout the entire section. Because the Kope and Fairview Formations contain a large number of highly fossiliferous strata, these censuses are taxonomically and quantitatively coarse by design to facilitate rapid field assimilation of large stratigraphic intervals (a maximum of 80 m) at high-resolution for multiple localities. Many faunal elements were identified to genus (e.g., brachiopods, crinoids, bivalves, cephalopods, gastropods, and most trilobites), whereas coarser taxonomic designations were given to elements difficult to identify at the genus level (e.g., highly comminuted calymenid trilobites and graptolites). Most bryozoans were given the following designations: thin bifoliate (<5 mm), thick bifoliate (>5 mm), thin ramose (<5 mm), thick ramose (>5mm), and encrusting, although distinctive bryozoans were identified to the genus level (e.g., Escharopora, Aspidopora, and Constellaria). Each faunal census was recorded by visually estimating abundances as rare (1-2 specimens per 1000 cm2), common (3-10
specimens per 1000 cm2), or abundant (>10 specimens per 1000 cm2). For analytical purposes,
these categories were given numeric values of 1, 6, and 12, respectively, which were selected
because they approximate the number of specimens in each abundance category. Field 45 collections from all six sections, including the five used previously by Miller et al. (2001) and the new Maysville locality incorporated here, resulted in an abundance matrix consisting of 2340 samples and 58 taxa.
Data Analysis
To quantify the faunal composition of each fossiliferous bed, the data matrices for the six localities were evaluated using Detrended Correspondence Analysis, a multivariate statistical technique that provides scores for taxa and samples designed to characterize the principal sources of variation in a dataset (Hill and Gauch, 1980). This technique differs from similar approaches by detrending and rescaling scores, such that the primary source of variation in the dataset is reflected along one axis. Taxon ordination along axis 1 is based on the tendencies of fauna to co-occur, and taxa that tend to be found together will be given similar scores (Table 1).
Samples are then scored by calculating the weighted mean average of the scores for all taxa within that sample. The output of DCA is a numerical value for each taxon and for each sedimentary bed at a given locality that reflects the fossil composition of that bed. Following the procedure outlined by Holland et al. (2001) and Miller et al. (2001), all samples containing only a single taxon were removed, as were all taxa occurring in only one sample. The culled data matrix from all six sections consisted of 1669 samples and 47 taxa. DCA was run using the computer program PC-ORD, Version 4.24, which is particularly useful for the manipulation of large datasets (McCune and Mefford, 1999). The default program parameters were used: rescaling axes (ON), rescaling threshold (0), and number of segments (26). Rare taxa were downweighted to minimize distortion (PC-ORD defines rare taxa as those with an abundance less than one-fifth of the most abundant taxon). 46
Table 1 – Axis 1 ordination scores of taxa.
Taxon Score Taxon Score Hebertella 505 Isotelus 144 Strophomena 485 Aspidopora 129 Platystrophia 384 Proetidella 125 Thick ramose bryozoans 366 Ceraurus 122 Rafinesquina 358 Cephalopods 114 Thick bifoliate bryozoans 349 Prasopora 113 Escharapora 331 Cryptostomes 112 Thin ramose bryozoans 286 Ostracods 101 Fenestellids 274 Cyclonema 86 Scolecodonts 236 Cornulites 82 Thin bifoliate bryozoans 236 Graptolites 78 Dalmanella 232 Iocrinus 74 Glyptocrinus 230 Ectenocrinus 71 Hydrozoans 230 Acidaspis 61 Plectorthis 218 Craniops 50 Ambonychia 205 Sowerbyella 49 Gastropods 200 Stomatopora 36 Zygospira 200 Cryptolithus 22 Encrusting bryozoans 194 Lepidocoleus -2 Cyclora 193 Parvohallopora -3 Pseudolingula 190 Schizocrania -5 Modiolopsis 166 Merocrinus -8 Nuculoids 163 Cincinnaticrinus -35 Calymenids 147
47
In ecological studies, DCA has been used to align taxa along underlying environmental gradients. As previously mentioned, Holland et al. (2001) and Miller et al. (2001) interpreted
DCA axis 1 as representing a water depth-related paleoenvironmental gradient, based on the natural histories of Cincinnatian fauna. Common high-scoring taxa are Strophomena,
Rafinesquina, Platystrophia, and thick ramose bryozoans; common middle-scoring taxa are
Isotelus, Dalmanella, calymenid trilobites, and Zygospira; and common low-scoring taxa are
Cryptolithus, Ectenocrinus, Sowerbyella, and graptolites. Higher scoring taxa are more common in shallower water lithofacies, especially in the Fairview Formation, which is interpreted to reflect shallower water depositional conditions than the Kope Formation. Low scoring taxa are more common in deeper water lithofacies, namely in other depositional settings such as the
Appalachian Basin. In addition, the overall shallowing upward trend through the Kope and into the Fairview is reflected in the broad scale DCA scores (see Holland et al., 2001, for a more detailed discussion on axis 1 interpretations).
To display stratigraphic changes in faunal composition, sample scores from each locality were plotted with respect to their stratigraphic position (Fig. 2.2). Each curve reflects stratigraphic changes in water depth based on faunal components, with high scores representing shallower water assemblages, and low scores representing deeper water assemblages. The faunal composition curves produced in this manner have significant amounts of fine-scale variation, potentially obscuring broad-scale trends (Fig. 2.2). To visualize background stratigraphic changes in biotic composition more easily, Holland et al. (2001) and Miller et al. (2001) used 21- point moving averages to smooth raw data curves (Fig. 2.2). The smoothed curves were used in conjunction with prominent lithologic contacts to correlate localities by comparing inflection points (Fig. 2.3). 48
Results and Discussion
Broad-scale correlation
When comparing the smoothed faunal ordination curve for Maysville to the five other localities of Holland et al. (2001) and Miller et al. (2001), it is apparent that the correlation of broad trends in biotic composition can be extended farther across the Cincinnatian region (Fig.
2.4). To corroborate correlations of large-scale patterns, the stratigraphic position of the
Kope/Fairview contact and fourth-order cycles have been mapped onto the curves. The boundaries of fourth-order cycles are based on trends in meter-scale cycle thickness and are independent of the faunal data used in constructing the curves (Holland et al., 1997; Miller et al.,
2001). Rather than using a 21-point moving average to smooth curves, as in Miller et al. (2001), an 11-point moving average is used here because of the greater amount of detail preserved.
Certain smaller-scale inflections, less than 5 m, appear to line up well, notably those above the
60 m mark.
The fact that the inflections on the Maysville curve align with the others upholds the conclusion of Holland et al. (2001) and Miller et al. (2001) that stratigraphically expressed paleoenvironmental changes in the Cincinnatian are basinwide. Despite the fact that each section expresses similar stratigraphic patterns, the absolute values of faunal ordination scores for time- equivalent intervals often differ from section to section (Fig. 2.3). This signifies a difference in the membership of faunal assemblages from time-equivalent intervals among localities (see
Holland et al., 2001). Such differences are not unexpected given that the localities in the analysis lie at different depths along the Cincinnatian ramp, which is generally considered to become shallower to the south and southeast (but see Miller et al., 2001, and below). Even so, 49
Figure 2.4 – Large-scale correlation of faunal ordination curves from the six Cincinnatian localities. The curves are arranged, from left to right, in a generally northwest to southeast transect. The stratigraphic position of 4th order cycle boundaries (solid lines) and the Kope/Fairview contact (dashed line) have been mapped onto the curves to corroborate correlations of large-scale patterns. 50 similar stratigraphic changes in biotic composition are expressed at all localities regardless of the faunal differences attributable to paleoenvironmental differences at each locality.
Given that the control of faunal assemblage distributions in the Cincinnatian is interpreted to be depth related, it might be expected that down-ramp localities would have lower relative ordination scores than up-ramp localities. As noted by Miller et al. (2001), although a comparison of the localities that lie along an expected deep to shallow transect, from Miamitown through K445 to Holst Creek, generally shows this relationship, relative differences in ordination scores among localities are not consistently maintained throughout the study interval, and overlain curves frequently cross (Fig. 2.3). A detailed comparison of Maysville stratigraphic patterns with the other localities further upholds this notion (Fig. 2.5). A visual inspection of
Maysville gives the impression of a greater number of limestone beds than the other sections in this analysis, with the possible exception of Holst Creek, and so it might be expected that
Maysville contains the shallowest faunal assemblages. However, the overall faunal ordination curve for Maysville does not have consistently higher scores until near the Kope/Fairview contact. At this point, the ordination scores for Maysville shift sharply toward highly shallower scores in relation to Miamitown, K445, and Holst Creek (Fig. 2.5). The higher scores at
Maysville for the upper Kope and lower Fairview are reflective of great abundances of the brachiopod Strophomena, which is given a high ordination score by DCA and is not as abundant at the other localities. Furthermore, Hebertella, another high-scoring brachiopod, can be found in the lower Fairview at Maysville, but is absent from equivalent strata at the other localities.
The fact that depth-related DCA curves from different localities cross indicates that the
Cincinnatian ramp did not uniformly dip to the north and northwest, but was uneven with a basin topography that dynamically shifted through time across the region (Miller et al., 2001). This 51
Figure 2.5 – A comparison of the Maysville smoothed ordination curve with the curves from K445 and Holst Creek. Although Maysville has been interpreted to be upramp of K445 and Holst Creek, Maysville ordination scores are not higher until just below the Kope/Fairview contact. 52 was perhaps the result of tectonic activity or differences in sedimentation rate (Miller et al.,
2001). A comparison of the four localities mentioned here implies that during the deposition of the lower and middle Kope Formation, the sea floor at Holst Creek had the highest elevation and shallowest water within the sampled region, not Maysville as initially assumed. It is not until the deposition of the upper Kope and the lower Fairview that the northward-dipping ramp is established. The lack of a non-uniformly dipping ramp throughout much of the study interval is not necessarily surprising, especially since strata in the region were deposited on a sea floor with a limited depth gradient. Depth differences between localities have been estimated by Holland et al. (2001) to be less than 10 m. Even small, local differences in topography would have an affect on faunal assemblage composition and the result of DCA calculations. The conclusions born out of the use of gradient analysis for large scale correlation has revealed that water depth relationships among localities are not as clear-cut as perceived by examining lithology alone, and faunal variation is a more sensitive indicator of water depth than lithology (Miller et al., 2001;
Holland et al., 2001; Webber, 2002).
Fine-Scale Correlation
Correlations of Cincinnatian localities using 11-point moving averaged curves have a resolution of approximately 1 to 5 m. However, smoothed curves lack the detail to examine finer-scale trends in the data. At the same time, the high amount of fine-scale variation of the raw curves also has hampered using ordination curves to correlate below the meter scale.
Because many individual beds in the Cincinnatian potentially display lateral changes in thickness and lithology, even locally, it is difficult to sample the same beds at each locality. Furthermore, census sampling density is not maintained from locality to locality, such that the number of samples per unit stratigraphic interval is different for each locality (Miamitown = 4.8 samples/m, 53
K445 = 7.2, Holst Creek = 5.5, Maysville = 8.0). These differences can hinder fine-scale comparisons among localities for intervals that contain larger and smaller numbers of samples, depending on the locality. It is not known whether differences in sampling density are the result of inaccuracies in the collection of samples or are a reflection of differences in the actual number of fossiliferous horizons at each locality.
As a way to overcome uneven sampling, samples from each locality are grouped into designated stratigraphic interval prior to running DCA. This process (referred to as binning for the remainder of this paper) entails summing the abundance value (1, 6, or 12) of each taxon for all censuses within a given interval and dividing by the number of censuses within that interval.
To illustrate, if an interval (or bin) contains five samples, and Rafinesquina is common in two of the samples, rare in one, and absent from the rest, then the abundance value for Rafinesquina in this bin would be 2.6 (calculated by adding the abundance scores, 6+6+1=13, and dividing by the number of samples, 5). This provides average faunal abundances for all the fossiliferous beds within a particular bin. Because each bin represents a particular designated stratigraphic interval, the same intervals can be delineated at each locality, and, thus, sampling density is standardized.
The new, binned dataset is run through DCA as already described, except the bins are now treated as samples. The output DCA is a depth-related faunal ordination score for each bin.
Binning census data is also useful because samples can be grouped stratigraphically in a variety of ways, depending on the investigation at hand. For example, Webber (2002) grouped samples according to lithologically-defined meter-scale cycles to test whether these cycles coincide with depth-related faunal patterns.
Here, two binning protocols have been designed to facilitate fine-scale correlation by compensating for differences in sampling densities among localities. In the first, samples are 54 arranged into bins representing 0.5 m thick intervals, and one bin boundary was placed at an easily recognizable point at all localities to serve as a datum (here, the Kope/Fairview contact).
As a result, each locality is subdivided into intervals of equal stratigraphic thickness, regardless of the actual number of beds at that locality. In other words, this procedure equalizes the number of sampling units per interval at all localities, with a datum at the Kope/Fairview contact. By doing this, the spacing of DCA axis 1 ordination scores is constant at all localities.
The pattern that emerges from this refinement of the procedure of Holland et al. (2001) and Miller et al. (2001) is faithful to the raw data curves generated for Cincinnatian localities
(Fig. 2.6). However, there is an advantage to this method in that patterns at the meter scale are easier to visualize in the binned curves than in the raw data curves, yet more detail is retained in the binned curves than in the moving average curves, especially where peaks in the raw data have high amplitudes but are short-lived. Subjecting the data to this binning technique teases out finer patterns, some of which can be correlated with success (Fig. 2.7). For example, the pattern of binned ordination scores from 50 to 65 m for Maysville, Holst Creek, and, to a lesser extent,
K445 shows a high degree of similarity, in many cases below the meter scale. Within this interval, two prominent low-scoring bins (Maysville, 56 and 58.5 m; Holst Creek, 56 and 59 m;
K445, 56.5 and 58 m) are not captured in the smoothed curves, yet are detected by binning and appear to be correlated. However, the pattern of binned ordination scores for other portions of
K445 does not match those of Maysville and Holst Creek as well. The inconsistency of recognizable patterns applies to the entire study interval at all six localities; some bin patterns match, whereas others do not.
One potential problem is evident when comparing the stratigraphic position of peaks in the binned curves from Maysville and Holst Creek (Fig. 2.7). Although the patterns are similar, 55
Figure 2.6 – A comparison of the raw ordination curve (left) with the smoothed curve (middle) and the binned curve (right) from the Maysville locality. Fine-scale stratigraphic trends in faunal composition are easier to visualize in the binned curve than in the raw data curve, yet the binned curve preserves more detail than the moving average curves. 56
Figure 2.7 – Fine-scale correlation of ordination curves for K445, Holst Creek, and Maysville, binned into 0.5 m thick intervals. The meter-scale patterns in binned scores for Holst Creek and Maysville are similar in this interval. 57 there is a vertical offset in the position of certain peaks. Such offsets often reflect instances where bin tops do not line up with bed tops, and are a natural manifestation of arbitrarily selecting bin boundaries. Nevertheless, offsets may hinder visual recognition of the patterns. At the same time, thickness differences among localities will also produce offsets in faunal composition patterns. Whereas broad-scale correlations indicate that equivalent stratigraphic intervals have roughly the same thickness at each locality, variations in the number and thickness of individual beds at each locality undoubtedly create fine-scale thickness differences within limited stratigraphic intervals (see Miller et al., 2001). The rate of sediment accumulation is undoubtedly different, both laterally and temporally, across the Cincinnatian region.
Furthermore, because mudstone compacts more than limestone, stratigraphic intervals at limestone-rich localities (e.g., Maysville and Holst Creek) may be thicker relative to the equivalent intervals at mudstone-rich sections (e.g., Miamitown). These differences may be subtle, but enough to obscure the fine-scale pattern even when the binning technique is applied.
Because bins have been arbitrarily defined, it ignores stratigraphy. It is, therefore, susceptible to thickness differences among localities, such that equivalent bins usually do not represent equivalent stratigraphic intervals.
The second protocol bins according to lithologically-defined meter-scale cycles as delineated by Holland et al. (1997) and correlated across much of the Cincinnatian region by
Brett and Algeo (2001b). Meter-scale cycles have been recognized by alternating accumulations of lower, mudstone-rich and upper, limestone-rich strata (see Jennette and Pryor, 1993; Holland et al., 1997; Brett and Algeo, 2001b). Samples are grouped into bins that correspond to the sections displaying these two styles of deposition within each meter-scale cycle. Unlike the previous binning technique, this technique groups samples according to equivalent stratigraphic 58 intervals at each locality. Again, similar fine-scale patterns do appear in some sections at multiple localities (Fig. 2.8), especially in the intervals around the two deeper water assemblage peaks just below the Kope/Fairview contact mentioned previously (the lower, mudstone-rich sections of cycles 39 and 40). Interestingly enough, these peaks, which lined up well in curves from Holst Creek and Maysville but less so in the curve from K445 using the previous binning technique, now line up in all localities. However, other peaks still appear offset among localities
(note the shape of the shallow water assemblage peak leading up to the top of cycle 37), and some fine-scale patterns from equivalent sections at multiple localities do not appear to be traceable. However, the delineation of meter-scale cycles and the intervals of dominating lithology with them is somewhat of a subjective exercise. Incorrect cycle calls may lead to an inability to trace patterns among localities. Despite the problems mentioned earlier, binning by
0.5 m thick intervals has the advantage of removing this kind of subjectivity.
Carrying out the exercise of binning the raw ordination data using these two protocols has revealed that some fine-scale biotic patterns are expressed across at least a portion of the
Cincinnatian region and can be traced, but other patterns are not so clear. Although this might indicate shortcomings of the binning method itself, it is also possible that difficulties in establishing fine-scale correlations consistently throughout the study interval at all localities might indicate a lower limit at which strata in the Cincinnatian can be correlated. Certainly,
Cincinnatian strata can be correlated at the meter scale (Jennette and Pryor, 1993; Holland et al.,
2000; Brett and Algeo, 2001b). Is regional variation in the expression of beds, particularly those that fall between prominent limestone ledges, simply too great to expect correlations finer than one meter across the region? The ability to correlate at least some features below the meter scale in the present study and in others gives reason to believe that this is not true. Why, then, is it 59
Figure 2.8 – Fine-scale correlation of ordination curves for K445, Holst Creek, and Maysville, binned according meter-scale cycles. Note that the y-axis represents meter-scale cycle number rather than stratigraphic position. Each meter-scale cycle has been subdivided into two parts, corresponding to an upper, limestone-rich section and a lower, mudstone-rich section. Cycle calls above the Kope/Fairview contact at Holst Creek and Maysville are difficult to make, so they are not shown. 60 difficult to correlate fine-scale faunal patterns using gradient analysis? One potential problem to the use of gradient analysis for regional fine-scale correlation may be related to the nature of fine-scale faunal distributions. For example, local patchiness in the distribution of fauna may produce significant lateral variation in DCA scores, even at the scale of a single outcrop.
Individual samples taken from each bed at one locality may be from a patch that does not accurately capture the entire faunal composition of that bed. This would likely increase fine- scale stratigraphic variation and make detailed comparisons of DCA curves difficult. For this reason, a study is ongoing to evaluate the relationship between fine-scale variation in DCA scores and patchiness in the lateral distribution of Cincinnatian fauna at the scale of a single outcrop (Webber, in prep.). Results indicate that the calculation of DCA scores is affected by local patchiness, such that the fine scale pattern achieved through gradient analysis are influenced by the composition of the patch sampled.
Ultimately, the combination of the gradient analysis technique employed here with other techniques of high-resolution correlation (tracing lithologic packages of Brett and Algeo, 2001b; cross-correlation of Holland et al., 2000) may provide the key to correlating the Cincinnatian below the meter-scale. However, a complete understanding of the subtle texture of faunal variation in the Cincinnatian first needs to be reached. This will require a more in-depth examination of lateral variation in the character and biotic composition of strata at both local and regional scales.
Conclusions
Adding an additional locality (Maysville) to those already measured reveals that previously established broad-scale correlations using faunal gradient analysis in the Kope and lower Fairview Formations in the type Cincinnatian can be extended farther across the region. 61
Although interpreted to be in an up-ramp direction from Cincinnati, Ohio, the Maysville locality does not have an overall shallower water faunal assemblage until just below the Kope/Fairview contact. This suggests that a uniformly northward-dipping ramp is not established until the deposition of upper Kope and the lower Fairview Formations.
Grouping census samples into small stratigraphic intervals provides easier visualizations of fine-scale patterns in faunal composition and overcomes some of the difficulties in sample beds that are laterally variable. Despite difficulties in sampling equivalent beds among localities across the Cincinnatian region, it is possible to correlate some patterns in faunal composition below the meter-scale using binning procedures. The inability to correlate consistently fine-scale faunal variation throughout the study interval may indicate faunal and depositional patchiness in the Cincinnatian fossil record at the level of individual beds. 62
Chapter 3
The Effects of Spatial Patchiness on the Stratigraphic Signal of Biotic Composition in the Type
Cincinnatian Series (Upper Ordovician)
Abstract
High-resolution faunal gradient analysis in the type Cincinnatian Series has been effective for regional paleoecological studies at vertical scales greater than one meter by numerically comparing stratigraphic trends in fossil composition among localities. Below this scale, significant variation in biotic composition has hampered the ability to compare patterns across the region. Here, spatial patchiness in the distribution of fossils is assessed as a potential cause of fine-scale stratigraphic variation in the biotic signal by examining the amount of lateral variation in biotic composition per level at one locality. Because the lateral expression of individual beds in the Cincinnatian is variable, even locally, patchiness is more effectively evaluated within small stratigraphic intervals of similar lithology, or bedsets, rather than individual beds.
Local spatial patchiness affects the fine-scale stratigraphic pattern of biological variation, such that compositional differences among localities depend on the composition of the patch sampled for gradient analysis. Furthermore, the lateral variability of biotic composition is less for limestone-rich bedsets than for many mudstone-rich bedsets. This supports the idea that limestone beds in the Cincinnatian contain assemblages that are more time-averaged than many, though not all, mudstone beds. Despite the effects of patchiness and time-averaging at scales of one or a few beds, biotic composition patterns acquired through gradient analysis are robust among localities at scales greater than a few beds.
63
Introduction
A fundamental goal of paleoecological studies is to resolve variations in the spatial and temporal distribution of fossilized organisms. In this context, a method of high-resolution faunal gradient analysis, established by Holland et al. (2001) and Miller et al. (2001) and modified by
Webber (2002, in review) for strata in the Kope and lower Fairview Formations of the type
Cincinnatian Series (Upper Ordovician), has provided a valuable means of characterizing the signal of biological variation at a regional scale (Fig. 3.1). Gradient analysis is a multivariate statistical approach that quantitatively compares the composition and abundance of taxa among samples and often is used to ordinate taxa according to underlying ecological gradients (e.g.,
Cisne and Rabe, 1978; Anstey et al., 1987; Anstey and Rabbio, 1990; Patzkowsky, 1995; Dattilo,
1996). As in similar applications of gradient analysis to the fossil record (e.g., Patzkowsky,
1995; Dattilo, 1996), the control of fossil distribution in the Cincinnatian correlates with environmental factors related to water depth (Holland et al., 2001; Miller et al., 2001).
In gradient analyses of the Cincinnatian, patterns of biotic composition have been examined at high levels of stratigraphic resolution throughout a large extent of the Cincinnati region: rank abundance data were collected from every fossiliferous horizon at a series of localities arrayed across a lateral extent of approximately 120 km. Comparisons of quantified faunal patterns were employed to establish correlations among these localities at stratigraphic resolutions approaching the meter scale (Fig. 3.2; Miller et al., 2001; Webber, in review).
Furthermore, gradient analysis in the Cincinnatian has provided a foundation for regional paleoecological studies, such as modeling the ecological distribution of fossilized marine organisms (Holland et al., 2001), biotic response to shifts in basin topography (Miller et al., 64
Figure 3.1 – Graphical depiction of the stratigraphic position of quantified biological composition for a locality (K445) of the Kope and Lower Fairview Formations as calculated by gradient analysis. Plotted to the left is a generalized stratigraphic column for this locality. Abbreviations used in figure: m, mudstone; s, siltstone; cz, calcisiltite; p, packstone; g, grainstone. Modified from Miller et al. (2001). 65
Figure 3.2 – Comparisons of quantified faunal patterns for two localities (K445 and Maysville) aligned to the contact between the Kope and Fairview Formations. In these curves, the raw biological composition scores have been smoothed using an 11-point moving average, designed to permit an easier recognition of broader trends in faunal composition. From Webber (in review). 66
2001), the distribution and ecology of crinoids (Meyer et al., 2002), and the correspondence of faunal assemblages to lithofacies change (Holland et al., 2001; Webber, 2002).
In contrast, stratigraphic patterns in the biotic signal below the meter scale have been more difficult to ascertain, owing to notable fluctuations in local faunal composition at these scales. The source of this variation is unknown: it might reflect small-scale environmental and lithologic differences among localities (see Webber, in review, and Results and Discussion, below), random fluctuations in biotic composition, or both. In a study conducted in the Fairview
Formation of the type Cincinnatian, Miller (1997) recognized significant spatial heterogeneity in the distribution of fossils along bedding planes on lateral scales of tens of meters or less, an observation amplified by Barbour Wood and Miller (in review). In other words, the suite of preserved fauna is not arranged uniformly across bedding surfaces, but into distinct patches that may be dominated by different species. Throughout the geological record, the distribution of fossil assemblages along bedding planes at even confined spatial scales should not be expected to be homogeneous (Miller, 1988; Kidwell and Bosence, 1991; Cobabe and Allmon, 1994;
Bennington and Bambach, 1996). Therefore, sampling a faunal assemblage at a restricted part of a particular bed might reflect the biotic composition of a single patch, not the entire suite of fossils from that bed (Bennington and Rutherford, 1999). Indeed, at each locality, the method of
Holland et al. (2001) and Miller et al. (2001) involved the recording of single censuses from each bed to expedite the large amount of field sampling required to gather data from all fossiliferous horizons of the study interval at multiple localities. It is possible that outcrop-scale spatial variation is appreciable, but is masked by the sampling protocol. At the extreme, successive beds that, overall, have similar fossil composition could appear quite different if each census is from a patch containing a different fauna. 67
The purpose of this study is to assess, at a single outcrop, the potential effects of small- scale spatial heterogeneities in biotic composition on the stratigraphic signal of biotic change.
Here, patchiness is evaluated by using the analytical technique established by Holland et al.
(2001) and Miller et al. (2001), in this case to quantify the faunal composition at multiple closely-spaced locations along individual bedding planes at a single outcrop for a ~7 m stratigraphic interval of the Kope Formation. This provides an assessment of the local variation in faunal composition, in contrast to the regional variation observed when comparing different outcrops across a wide geographic area. If the compositions of fossil assemblages are highly variable within a bed owing to patchiness, then stratigraphic patterns of biotic transitions in the same interval may differ from one sampling site to another at a single outcrop. Given previous studies on patchiness in the fossil record and in Cincinnatian strata in particular, this is to be expected. The present study is unique in that it will evaluate patchiness with respect to broader biotic patterns as established by earlier gradient analyses of this interval.
The Study Interval
The Kope Formation comprises a nearly 60-m thick mix of siliciclastic and carbonate sediments and corresponds with the C1 third-order sequence of Holland and Patzkowsky (1996).
The dominant lithologies of this interval include interbedded siliciclastic mudstone, siltstone, calcisiltite, skeletal grainstone, packstone, and minor amounts of lime mudstone and wackestone
(Holland, 1993). During the Cincinnatian Epoch (443.5 to 451 million years ago; Holland and
Patzkowsky, 1996), much of the North American mid-continent was covered by a shallow, subtropical sea, favoring the development of strong hurricanes that significantly influenced deposition of Cincinnatian strata. Sediments of the Kope Formation are interpreted to have been deposited in an offshore, storm-influenced setting (Hay, 1981; Tobin, 1982; Jennette, 1986; 68
Ettensohn, 1992; Holland, 1993; Jennette and Pryor, 1993; Brett and Algeo, 2001a). Several lines of evidence imply that deposition occurred on a gently north-northwest dipping ramp: the presence of tidal flat facies to the south, the disappearance of deep water facies to the south, increases in the amount and thickness of limestone beds to the south and southeast, and a north to south orientation of gutter casts (Ford, 1967; Jennette and Pryor, 1993; Holland, 1993; Weir et al., 1984; but see Miller et al., 2001).
Methods
Data Collection
Field censuses were recorded from an extensive section of the Kope Formation near
Maysville, Kentucky (Fig. 3.3). This locality is ideal for taking replicate samples of a stratigraphic interval because the outcrop offers broad lateral exposures. Lithologic and faunal abundance data were collected from four vertical transects at the Maysville locality following the procedures applied by Holland et al. (2001), Miller et al. (2001), and Webber (in review) for six localities in the Cincinnatian Kope Formation and the lowermost overlying Fairview Formation
(Fig. 3.3). Faunal data from one transect has been described previously by Webber (in review), to which additional sampling in the present study has added three new transects. Each replicate
(hereafter referred to as Maysville sublocalities and designated MAYS1 through MAYS4) varies in thickness from six to seven meters. The stratigraphic interval covered by the sublocalities corresponds to meter-scale cycles numbered 24 through 30 as delineated by Holland et al. (1997) and correlated to Maysville by Brett and Algeo (2001b). Cycles 25 through 30 correspond to a decameter-scale cycle designated as the Alexandria Submember by Brett and Algeo (2001b).
Deposition in this interval indicates a general shallowing upward trend, as suggested by an upward increase in the frequency and thickness of limestone beds (Fig. 3.4). 69
Figure 3.3 – Map of outcrop localities of the Kope and lower Fairview Formations used in faunal gradient analysis by Holland et al. (2001), Miller et al. (2001), and Webber (in review). The star highlights the Maysville locality, where spatial patchiness is evaluated in the present study. 70
Figure 3.4 – Composite stratigraphic column of the study interval at the Maysville locality. Shown immediately to the right of the column are the meter cycles delineated by Holland et al (1997) at the K445 locality and correlated to Maysville by Brett and Algeo (2001b). The boundaries for the sixteen lithologically-defined bedsets used to evaluate patchiness are shown at the far right. The Ectenocrinus-rich bed shown in bedset 3 is found at all four sublocalities. 71
The lithology of each sublocality was logged at 1 cm intervals, and faunal censuses were taken at every fossiliferous bed thicker than 0.5 cm. The following lithologic categories were recognized: grainstone, mixed grainstone/packstone, packstone, wackestone, carbonate mudstone, calcisiltite, siltstone, silty shale, and shale. Faunal data were recorded from each of the lithologies listed above, although limestone beds contained the highest fossil abundances.
Each census consists of a visual estimate of the relative abundance of each taxon as rare (1-2 specimens per 1000 cm2), common (3-10 specimens per 1000 cm2), or abundant (>10 specimens
per 1000 cm2; e.g., Holland et al., 2001; Miller et al., 2001). Many faunal elements were
identified to genus (e.g., brachiopods, crinoids, bivalves, cephalopods, gastropods, and most
trilobites), whereas coarser taxonomic designations were given to elements difficult to identify at
the genus level (e.g., highly comminuted calymenid trilobites and graptolites). Bryozoans were
classified as thin bifoliate (<5 mm), thick bifoliate (>5 mm), thin ramose (<5 mm), thick ramose
(>5mm), and encrusting. Cryptostome and fenestellid bryozoans were identified as such, and
distinctive bryozoans were identified to the genus level (e.g., Escharopora, Aspidopora, and
Constellaria). For analytical purposes, abundance categories later were changed to numeric
values of 1, 6, and 12, respectively, which approximate the median value of each abundance
class (Holland et al., 2001; Miller et al., 2001).
Data Analysis
To quantify the faunal composition of each fossiliferous bed, the data matrices for the
four sublocalities were evaluated using Detrended Correspondence Analysis (DCA), a
multivariate statistical technique that provides scores for taxa and samples designed to
characterize the principle sources of variation in a dataset along derived axes (Hill and Gauch,
1980). Following the procedure outlined by Holland et al. (2001) and Miller et al. (2001), all 72
Table 3.1. Axis 1 ordination scores of the 47 taxa included in DCA.
Raw Binned Raw Binned Taxon: Taxon: Score Score Score Score Articulate Brachiopods: Trilobites: Dalmanella 207 236 Acidaspis 121 74 Hebertella 432 549 Ceraurus 122 91 Platystrophia 387 373 Cryptolithus 37 11 Plectorthis 277 133 Isotelus 154 149 Rafinesquina 329 366 Proetidella 130 142 Strophomena 475 503 Calymenids 151 169 Sowerbyella 9 67 Bivalves: Zygospira 244 211 Ambonychia 198 205 Inarticulate Brachiopods: Modiolopsis 178 160 Craniops 126 71 Nuculoids 160 123 Pseudolingula 188 134 Other molluscs: Schizocrania 78 -3 Cyclora 196 233 Bryozoans: Cyclonema 117 47 Aspidopora 86 126 Cephalopods 147 149 Indet. Gastropods Escharapora 284 357 202 166 and Parvohallopora -20 -68 monoplacophorans Prasopora 135 120 Crinoids: Stomatopora -56 4 Cincinnaticrinus -33 -26 Cryptostomes 142 69 Ectenocrinus 73 81 Fenestellids 296 334 Glyptocrinus 435 301 Encrusters 217 179 Iocrinus 74 70 Thick bifoliate 468 368 Merocrinus 101 -41 Thick ramose 381 374 Other: Thin bifoliate 263 286 Graptolites 51 5 Thin ramose 283 289 Hydrozoans 309 191 Annelids: Lepidocoleus 27 -20 Cornulites 76 52 Ostracods 118 114 Scolecodonts 255 232 73 samples containing only a single taxon were removed, as were all taxa occurring in only one sample. The final data matrix consisted of 47 taxa (Table 1). DCA was run using the computer program PC-ORD, Version 4.20 (McCune and Mefford, 1999). The default program parameters were used: rescaling axes (ON), rescaling threshold (0), and number of segments (26). Rare taxa were downweighted to minimize distortion (PC-ORD defines rare taxa as those with an abundance less than one-fifth of the most abundant taxon). Censuses from all of the measured localities included in Miller et al. (2001) and Webber (in review) were also used in this analysis to permit direct comparisons with biotic composition at other localities (see below).
The alignment of taxa along DCA Axis 1 implies correlation with a water depth-related paleoenvironmental gradient, as discussed by Holland et al. (2001) and Miller et al. (2001).
High scoring taxa are shallow water Cincinnatian fauna, taxa scoring near the middle of Axis 1 commonly occur in intermediate water depths, and low scoring taxa are deeper water fauna
(Table 3.1). To display stratigraphic changes in faunal composition, sample scores from each sublocality were plotted with respect to their stratigraphic position (Fig. 3.5). Based on earlier gradient analyses it is known that, at scales of more than one meter, each curve reflects stratigraphic changes in water depth based on faunal components, with high scores representing shallower water assemblages, and low scores representing deeper water assemblages. At scales of less than one meter, these patterns are more problematic, as discussed below (see also
Webber, in review).
Results and Discussion
Local Variation
A visual assessment of stratigraphically arranged DCA scores reveals that, although a broad deepening is maintained in the basal two meters of the four Maysville sublocalities, 74
Figure 3.5 – Graphical depiction of the stratigraphic position of raw DCA Axis 1 ordination scores for the four Maysville sublocalities (MAYS1 through MAYS4). 75 followed by shallowing through most of the remaining interval, there is little similarity in finer- scale pattern of biological composition among Maysville sublocalities (Fig. 3.5). Substantial variation obscures direct comparisons of DCA scores at scales less than one meter.
Undoubtedly, a contributing factor is the highly variable expression of individual beds in the
Cincinnatian. Despite the close proximity of these sublocalities to each other, many beds pinch out across the outcrop face. Even though beds that pinch out may reappear laterally, they may not be present, or at least may differ significantly in thickness, at any one of the sublocalities.
The least persistent beds are thinner ones, especially siltstone and calcisiltite, although even thick limestone beds tend to vary in their lateral variation. This variation is a manifestation of the effects of storm-dominated deposition in the Cincinnatian, where scouring and sediment starvation is common and beds may be distinct in one place and amalgamated in another
(Barbour Wood, 1999, 2002).
This architecture presents an obvious problem for the analysis of fine-scale spatial variations in contemporaneous faunal assemblages. First, some beds, particularly thin ones, are difficult to sample at multiple sites separated by distances of even a few meters. Second, storm scouring and reworking could potentially obliterate any original patterns in the distribution of fauna on the Cincinnatian sea floor. With respect to the latter issue, many studies indicate that transport of coarse sediment in storm-influenced, offshore settings is not significant (Kachel and
Smith, 1986; Duke, 1990), and that patterns of biological variation in the fossil record are not likely to be greatly affected by out-of-habitat, post-mortem transport of shelly material (Kreisa,
1981; Westrop, 1986; Miller and Cummins, 1990; Kidwell and Bosence, 1991; Miller et al.,
1992; Kidwell and Flessa, 1996). In the Fairview Formation, which overlies the Kope
Formation, Miller (1997) recognized spatial heterogeneity in the distribution of fossil remains 76 within the amalgamated limestone cap of a meter-scale cycle that cannot be explained by post- mortem transport, and Barbour Wood (2002) found that biotic composition is commonly maintained even through successive microhorizons within this bed. Brett and Algeo (2001c) noted in the Kope Formation that the same mud-adapted faunas tend to occur in both shell-rich limestone beds and the surrounding mud-rich beds, suggesting that fossil assemblages from tempestites were reworked only locally, but not greatly transported during storm events.
Collectively, these studies suggest that the lack of comparable patterns among raw DCA scores from sublocalities is likely not a consequence of out-of-habitat storm transport of fossilized remains that obscures the original biotic signal.
Although not all individual beds are easily found at each of the sublocalities, recognizable packages of beds with similar lithology, less than a meter in thickness, are traceable. For example, limestone ledges and prominent calcisiltite bundles can be followed across the sublocalities. Each bundle of similar beds conforms to Campbell’s (1967) definition of a bedset. Furthermore, distinctive horizons often can be found at multiple locations along a single outcrop; in the study interval, a 2- to 3-cm-thick concentration of partially articulated
Ectenocrinus columnals occurs at each sublocality (Fig. 3.4). Given that bedsets and individual, distinctive beds persist among sublocalities, then comparisons of the faunal compositions of these packages from one sublocality to the next provide a means to overcome the patchy nature of individual beds.
In this manner, the degree of patchiness was evaluated here for bedsets, rather than individual beds. To do so, the same bedsets must be recognized at each sublocality, which was easily accomplished through a visual examination of stratigraphic columns at all sublocalities, coupled with direct observations of the exposures in the field (Fig. 3.6). Boundaries for each 77
Figure 3.6 – Generalized stratigraphic columns of the study interval for each of the four Maysville sublocalities. The sixteen lithologically-defined bedsets at each sublocality are indicated by shading, with dark shading representing limestone-rich bedsets and a calcisiltite-rich bedset, and lack of shading representing mudstone-rich bedsets. Shown on the right are the boundaries for these bedsets, which are depicted according to the composite stratigraphic column for this interval of the Kope Formation. 78 bedset were delineated at the tops of beds marking the transition into a different suite of dominant lithologies. In all sublocalities, the same bedsets could be recognized, and were numbered from stratigraphically oldest to youngest. These bedsets fall into three categories based on dominant lithology: those that are largely shale and siltstone (2, 3, 4, 5, 7, 9, 11, 13, and
15), and those dominated by limestone beds separated by lesser amounts of shale (1, 6, 8, 10, 12,
14, and 16). A distinctive, hummocky-bedded calcisiltite that lies below the Ectenocrinus horizon at all sublocalities serves as the boundary between two consecutive shale bedsets (2 and
3; Figs. 3.4 and 3.6). Bedset thicknesses range from 16 cm to 1.2 m, and the average bedset thickness is 43 cm (Fig. 3.6). It is important to note that sublocality correlation of these bedsets is not in doubt, because they have been traced physically across the Maysville outcrop.
Next, the faunal compositions of these designated bedsets were evaluated at each sublocality (for the rest of this paper, all of the samples within a given bedset at a single sublocality will be referred to as a bin). To do this, the abundance values (1, 6, or 12) for each taxon within a bin were added together, and this sum was divided by the number of samples within that bin. In doing this for all taxa within a bin, a collective bin sample was derived that reflects the average faunal abundances for all taxa and all fossiliferous beds within the bin. As an illustration, if a bin contains five samples, and Rafinesquina is common in two of the samples, rare in one, and absent from the rest, then the abundance value for Rafinesquina in this bin would be 2.6 (calculated by adding the rank abundance scores, 6+6+1=13, and dividing by the number of samples, 5). After binning, DCA was run using the bins as samples. Thus, Axis 1 scores reflect the taxonomic composition of stratigraphic packages with similar lithologic characteristics rather than individual beds. The ordering and score values of taxa along DCA
Axis 1 in this binned approach are broadly faithful to the scores calculated from DCA on raw 79 abundance data (Table 3.1). Again, high scores indicate shallower water taxa and low scores indicate deeper water taxa. Differences in scores produced by the two methods arise because single-taxon samples were not culled from the binned data set as they were from the raw data set
(see Methods).
With this method, the effects of patchiness on the biotic signal can be evaluated by comparing directly the ordination scores for all bins in each designated bedset at the Maysville locality. To facilitate score comparisons among sublocalities, bedset boundaries for each sublocality have been placed according to a composite reference section (Figs. 3.4 and 3.6). For strictly heuristic purposes, this equalizes the stratigraphic thickness of the bins in each bedset, facilitating visual comparisons of graphically arrayed scores (Fig. 3.7). The faunal composition trends in these curves are broadly similar, and imply deepening in the lowest three meters, followed by shallowing throughout the rest of the stratigraphic interval as also indicated in the raw curves (Fig. 3.5). This pattern corroborates the suggestions by Holland et al. (1997, 2001) and Brett and Algeo (2001c) that cycles of this magnitude (10 to 20 m) reflect water depth, although what causes these changes in water depth is still uncertain.
A curve of the mean DCA score from each bedset has been superimposed on the four sublocality curves (Fig. 3.7). This mean curve provides a baseline for assessing variations in biotic content from sublocality to sublocality. Differences in DCA Axis 1 ordination scores between each sublocality and the mean were calculated and plotted according to stratigraphic position (Fig. 3.8). This illustrates significant details about patchiness in this section of the Kope
Formation by providing a means to assess the amount of score variability in any given bin from each of the sublocalities. For example, the bedset 1 score of the MAYS2 sublocality differs from that of the other three sublocalities. Examining the faunal components of this bedset at each 80
Figure 3.7 – The stratigraphic position of binned DCA Axis 1 ordination scores for the four Maysville sublocalities. DCA scores have been binned according to the sixteen lithologically- defined stratigraphic bedsets, whose boundaries are shown the right. The binned scores are plotted in the middle of each bedset. 81
Figure 3.8 – The differences in binned DCA Axis 1 ordination scores between each sublocality and the mean plotted according to stratigraphic position. Examples of significant deviations in score of particular bins relative to others are shown to the right, including the particular sublocality and primary taxa responsible for the deviation. 82
Table 2. List of the primary taxonomic composition of each bin, in order of abundance (left to right).
Bedset Dominant Taxonomic Composition Lithology MAYS1 MAYS2 MAYS3 MAYS4 16 ls Dl, Rf, Dl, nb, Dl, Rf, ca, kr, Ec kr, nr kr, nr 15 ms ca, Dl, Rf, Dl, Rf, Am, Dl, Rf, Zy, kr, nr Zy kr, nr 14 ls Dl, kr, Dl, Rf, nr, Dl, Rf, Dl, Rf, Rf, nr kr, Zy kr kr, nr 13 ms Dl, Rf Dl, Rf
12 ls Rf, Dl, Dl, Rf, Rf, Dl, kr, Rf, Dl, nr, Ec nr, kr nr, Cy kr, nr 11 ms Dl, Cy, Cy, Dl nb, nr 10 ls ca, nr, Rf, Dl, Rf, Ec, nr, Dl, ca, Rf, Dl, Cy, Dl, Ec nr, Cy Rf, Ec, Cy Ec, ca, kr 9 ms Ec, Dl Rf, Ec Ec, gs, Rf, Ca, Pl nb, Zy 8 ls Dl, Rf, Ec, ca, Dl, Ec, Rf, Cy, Ec, ca, Dl, Cy, Dl, Rf, Cy, nr, kr Cy, nr Rf, kr, nr Ec, nr, Pl 7 ms Rf, Dl, nr Dl, Rf gp, Rf gp, Rf, Dl
6 ls Dl, kr, Dl, nr, Dl, Ec, nr, Dl, nr, nb, Rf, Ec Cy, Ec Rf, ca Rf, Cy, Ec 5 ms Ec, ca, Is Ec, ca, gp, gp kr, Is 4 cz Ec, ca, Ec, ca, ca, Ec Ec, ca, Dl, Rf nr gp 3 ms Ec, ca Ec, nr, Ec, ca Ec, Is Dl 2 ms kr, ca Ec, Dl, Dl, ca, Ec, Dl nr Ec 1 ls Dl, nr, ca, Ec, Dl nr, Dl, ca, Dl, nr, Ec, kr, Ec, Rf Ec, nb, Rf ca, nb 83 sublocality (Table 3.2) reveals similar scores for MAYS1, MAYS3, and MAYS4 (244, 229, and
240, respectively) and domination by similar taxa (Ectenocrinus, Dalmanella, Rafinesquina, and thin ramose bryozoans). MAYS2, on the other hand, differs because Rafinesquina and bryozoans do not occur in bedset 1 at this sublocality. The absence of Rafinesquina and bryozoans, both high-scoring taxa, results in a reduction in the Axis 1 score for MAYS2 (135) relative to MAYS1, MAYS3, and MAYS4. This pattern arises throughout the Maysville sublocalities: large deviations from the mean ordination scores result from conspicuously absent, rare, or abundant taxa. A local enrichment or reduction in the abundance of just a few taxa produces these deviations (Fig. 3.8; Table 3.2). As another example, the faunal components of bedset 7 for all bins are similar, but graptolites are much more abundant relative to the other taxa in the MAYS4 bin. The result is a lower score for MAYS4, because graptolites are a low- scoring taxon (Table 3.2).
It is important to determine whether this pattern might be an artifact of unusual occurrences of taxa in bins containing fewer samples. As a hypothetical illustration, suppose one bin has only one sample (i.e., one fossiliferous bed) and Rafinesquina is common in that bed (an abundance value of 6). The same bin at another sublocality has three samples, and Rafinesquina is common in only one sample. The observed abundance of Rafinesquina will be the same for both bins (6), but the abundance values calculated for DCA will be higher in the first bin (6/1 =
6) than for the second bin (6/3 = 2) because the first bin has only one sample. Therefore, the occurrence of Rafinesquina in the first bin will be given greater weight than in the second bin when run through DCA, even though Rafinesquina occurs only once in each bin. However, in an analysis of the number of samples in the six bins with notable deviations (Fig. 3.8), it was found that only two have fewer samples than the rest of the bins in their respective bedsets. 84
In cases where one of the bins in a bedset is an outlier, it does not necessarily follow that all other bins in that bedset are similar in composition to each other. The compositions of bins in some bedsets (e.g., bedset 9) are quite variable, resulting in a wider variation in scores than bedsets containing bins with similar composition (e.g., bedset 13). In general, limestone-rich bedsets display less variation in scores than do mudstone-rich bedsets (the mean standard deviation for limestone bedsets is 18, for mudstone bedsets, 38). The ranges between highest and lowest bin scores for individual limestone-rich bedsets vary from 13 to 38, with the exception of bedset 1, which has a range of 135. Mudstone-rich bedsets are more variable: some have rather high ranges (e.g., 162) whereas others are low (e.g., 9). This indicates that the biotic composition of any given limestone-rich bedset is somewhat similar across the Maysville locality, whereas any given mudstone-rich bedset potentially contains either similar or disparate fauna.
The difference in the amount of variation among bins might be an artifact of binning samples from beds that pinch out along the outcrop. The result is that any given bedset is not guaranteed to have exactly the same component beds sampled at each sublocality. This is problematic for bedsets with many thinner beds, such as siltstones, which more commonly occur in mudstone-dominated bedsets. Therefore, samples within a bedset among sublocalities could be from different beds, and differences in the variability of DCA scores could be reflecting faunal differences among beds rather than within beds. Because beds that tend to pinch out the most also tend to occur in mudstone bedsets, this sampling artifact would result in a higher variation in scores for mudstone bedsets relative to limestone bedsets. If this were the case, it would be expected that the range of DCA scores in bedsets would correlate with differences in the number of samples within bedsets. It is possible that larger differences in the number of 85 samples within a bedset reflect sampling from different beds, and thus would lead to larger DCA score variations. However, the correlation is weak (r = 0.108), suggesting that larger differences in sample size within bedsets does not result in larger DCA score variation.
The more variable, mudstone-rich bedsets may reflect the tendency of fossil assemblages occurring in mudstones to exhibit less time-averaging and within-habitat transport of skeletal remains by storms relative to limestone-rich bedsets. Thick, skeletal limestone beds show evidence of intense reworking and condensation of shelly material, signifying that multiple storm events affect the deposition of even individual limestone beds (Jennette and Pryor, 1993; Brett and Algeo, 2001c). Conversely, mudstone beds tend to display features consistent with episodic sedimentation, and are often graded with silty bases (Jennette and Pryor, 1993; Brett and Algeo,
2001c; Holland et al., 2001). These mudstones have an overall lower density of skeletal remains, and generally contain better preserved specimens, including many smothered faunas (e.g.,
Brandt, 1985; Hughes and Cooper, 1999). Furthermore, limestone-rich bins in the study interval have an overall higher biotic diversity (9.64 taxa per bin) relative to mudstone-rich bins (5.80 taxa per bin). This most likely reflects the greater probability that habitat shifting would add to the diversity of preserved organisms in increasingly time-averaged deposits (Miller and
Cummins, 1990; Kidwell and Flessa, 1996). Although storm reworking and condensation may serve to homogenize lateral faunal differences in limestone beds relative to mudstone beds, as discussed above, this time-averaging is not enough to remove the signal of patchiness entirely
(see Miller and Cummins, 1990; Kidwell and Bosence, 1991; Miller et al., 1992; Kidwell and
Flessa, 1996; Barbour Wood, 2001, 2002).
There also appears to be a pattern in the stratigraphic distribution of score variations.
Variation is generally higher in the lower four to five meters of the study interval than in the 86 upper two meters of the section (Fig. 3.8). This would imply that contemporaneous faunal assemblages become more similar to each other upsection. It might be tempting to ascribe this pattern to some process affecting the style of deposition, such as shallowing. It is plausible that shallowing would result in the increased influence of storms on deposition, which would create more time-averaged and compositionally similar faunal assemblages. However, limestone bedsets have lower variation throughout the entire section, whereas predominantly highly variable mudstone bedsets occur in the lower four-plus meters, and the upper two meters contains three successive mudstone bedsets with low variation in score. Therefore, this pattern is a reflection of changes in the amount of variation in mudstone bedsets. Because the stratigraphic distribution in score variability is dependent on the consecutive occurrence of only three low variation mudstone bedsets, it cannot be said with certainty that this pattern is related to depositional styles without examining more of the Kope section.
Regional Variation
To compare the amount of fine-scale local variation in the study interval with the amount of regional variation, it was necessary to identify the same bedsets at another locality. Fine-scale correlation in the Cincinnatian historically has been problematic (see Holland et al., 1999; Brett and Algeo, 2001c; Miller et al., 2001; and Webber, in review). However, the fifty meter-scale cycles identified by Holland et al. (1997) and correlated across a portion of the Cincinnatian region by Brett and Algeo (2001b) provide the necessary criteria for assessing fine-scale regional variation in biotic composition. The meter-scale cycles (24 through 30 of Holland et al., 1997) that comprise the study interval have been correlated between the Maysville and K445 localities by Brett and Algeo (2001b). A comparison of raw DCA scores from the study interval at these 87 two localities presents little similarity in stratigraphic trends (Fig. 3.9). Besides the nearly two- meter difference in thickness between these localities, fine-scale stratigraphic variation obscures any ability to compare scores among contemporaneous faunas. As with the Maysville sublocalities, binning of samples within the same bedsets is helpful for comparing these patterns.
The same bedsets delineated at Maysville are recognizable at K445 (Fig. 3.10). Binning by these bedsets at K445 and the Maysville sublocalities provides a means to compare local variation
(among Maysville sublocalities) with regional variation (between Maysville and K445) at fine- scales (Fig. 3.11).
The overall deepening and shallowing pattern in faunal composition scores at K445 is similar to Maysville. Even the finer scale faunal trends from bin to bin exhibit some similarities.
Separately comparing trends from each sublocality to K445 again exemplifies the effect of patchiness on the fine-scale signal of biological patterns. Maysville scores tend to be higher overall then K445 scores; however, some bins in some bedsets from Maysville score lower than the equivalent bedsets at K445. Therefore, the compositional relationship between Maysville and K445 depends on the composition of the patch sampled. Of course, an assessment of patchiness at the K445 locality would strengthen the evaluation of local versus regional variation.
Implications
When differences in the lateral expression of beds are taken into account, the fine-scale pattern of biological variation as calculated by gradient analysis is clearly affected by faunal patchiness. Stratigraphic patterns in faunal composition will be subtly different depending on the composition of the patch sampled. As noted by Bennington and Rutherford (1999), patchiness should be taken into account when attempting to draw comparisons of taxonomic composition 88
Figure 3.9 – Raw DCA Axis 1 ordination scores for Maysville (MAYS1) and K445 for the study interval. 89
Figure 3.10 –Stratigraphic column of the study interval at K445, shown alongside the boundaries of the meter cycles of Holland et al (1997) and the sixteen lithologically-defined bedsets used to evaluate patchiness. 90
Figure 3.11 –DCA Axis 1 ordination scores for K445, the four Maysville sublocalities, and the mean Maysville sublocality, binned according to the sixteen lithologically-defined stratigraphic bedsets. The binned scores are plotted in the middle of each bedset. 91 among localities. Even within-outcrop patterns can be significantly different (Miller, 1997;
Barbour Wood, 2002; Barbour Wood and Miller, in review).What does this imply for the use of gradient analysis in the fossil record? The ordering of taxa along Axis 1 and the large-scale patterns in faunal composition do suggest water depth control, such that differences in Axis 1 ordination scores in the Cincinnatian reflect differences in water depth among samples.
Therefore, do fine-scale lateral differences in scores along individual beds indicate differences in water depth among patches? Score differences as calculated by the present study are quite high
(as much as 162). Calculations by Holland et al. (2001) suggest that this corresponds to differences in water depth of 9 to 15 m. This degree of variation is highly unlikely among points on a level-bottom sea floor separated only by a few tens of meters. It is more likely that differences in ordination scores at this small spatial and temporal scale reflect compositional differences not related to depth. This presents a potential problem with the application of faunal gradient analysis to the Cincinnatian: how can longer-term trends in DCA sample scores be useful for recognizing changes in water depth when comparisons of the individual samples used to construct these curves appear to provide little information on relative water depth?
This problem of scale is likely the result of the distribution of organisms on the
Cincinnatian sea floor. As noted by Holland et al. (2001), the occurrence of each taxon along the
Cincinnatian ramp is determined by the water depth tolerance of that taxon. Within its depth tolerance, the abundance of a given taxon is normally distributed and most abundant at its preferred depth (see Holland et al., 2001, for a model of the ecological distributions of Kope fauna using DCA scores). Therefore, the faunal assemblage at any given point on the sea floor is most likely to comprise a higher abundance of those taxa close to their preferred water depth and a lower abundance of those taxa outside their preferred water depth, but within their depth 92 tolerance. At any given depth, the greater influence of the abundant taxa near their preferred depth should dictate the calculation of DCA scores, such that individual DCA sample scores resemble the DCA taxon scores for the most abundant taxa in that sample. Because fauna are spaced patchily on the sea floor, however, there is a chance of a single sample capturing a patch dominated by taxa not at their preferred water depth. In this case, the DCA score for this sample would not resemble DCA scores for the taxa typically abundant at this depth. For example, a sample from a relatively deeper water setting might be from a patch dominated by a typically shallower water taxon, giving that sample an unusually high DCA score. Likewise, a sample from a relatively shallower water setting might contain typically deeper water fauna, resulting in an unusually low score. Patchiness will result in significant fine-scale lateral and stratigraphic variability in scores at any given depth, as exemplified here. However, moving across depth gradients, both up or down the ramp and upsection in shallowing or deepening stratigraphic intervals, it is likely that samples overall will capture patches from progressively shallower or deeper water environments.
Even if patchiness is not taken into account, longer-term trends in DCA scores will reflect progressive water depth-related changes in faunal composition. Moving average curves, like those used by Holland et al. (2001), Miller et al. (2001), and Webber (in review), are a useful way to visualize longer-term trends in DCA scores, and are good proxies for water depth.
The lateral veracity of faunal patterns at scales greater than a few beds indicates the power of the gradient analysis technique employed in this study and others in the Cincinnatian. This is exemplified by the overall fidelity of ordination curves for a stratigraphically limited interval locally and regionally in the present analysis, and by the correlation of larger-scale trends in 93
DCA scores across the region by Miller et al (2001). It is evident that patterns of biological variation at slightly larger scales offset patchiness in the faunal composition of individual beds.
Conclusions
1) Inconsistencies in the lateral expression of beds in the Cincinnatian hinder assessment of faunal patchiness along individual bedding planes, even across limited distances. It is easier to compare faunal composition for bedsets rather than individual beds.
2) Local patchiness affects the fine-scale pattern of biological variation. Ordination scores reflecting compositional differences among samples depend on the composition of the patch sampled.
3) The lower degree of variation in ordination scores from limestone-rich bedsets is likely a result of the higher levels of time-averaging and condensation. These processes tend to homogenize biotic composition along bedding planes, although not enough to remove the signal of patchiness.
4) Faunal patchiness also affects fine-scale comparisons of regional variation.
Patchiness, then, should be taken into account when attempting comparisons at extremely fine stratigraphic scales.
5) At stratigraphic scales greater than the scale of single or few beds, faunal patterns are less affected by patchiness and are robust among localities and sublocalities. 94
Final Remarks
The refinement of existing faunal gradient analyses for high-resolution correlation presented here has provided an essential investigative tool in the study of environmental transitions in fossiliferous rocks. First, faunal rank abundance data were binned according to lithologically-defined meter-scale cycles to determine whether these cycles were deposited under the influence of fluctuating water depth. The lithological patterns that make up these cycles do not correspond to water depth patterns in faunal composition, so it is unlikely that cyclicity at this scale was controlled by fluctuations in water depth. In the second analysis, faunal data were grouped into stratigraphic intervals to facilitate fine-scale correlations by removing high- amplitude variations in DCA scores, and some comparable patterns below the meter-scale were recognized. Finally, the data were binned by bedsets at multiple points along a single outcrop, which allowed an evaluation of the effects of patchiness on the calculation of DCA scores.
These studies have shown that, despite difficulties associated with sampling, it is possible to investigate fine-scale faunal patterns across the regional extent of the type Cincinnatian Series using gradient analysis.
95
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Appendix: Locality Descriptions
K445 Composite: Composite outcrop consists of four sections: K445, CON1, CON2, and
CON3. K445: Roadcut on both sides of Kentucky Route 445, 0.2 km west of intersection with
Kentucky State Route 8, immediately northwest of the I-275 bridge over the Ohio River near Old
Coney Amusement Park. Newport, OH-KY 7 ½´ quadrangle. 39° 03´ 22˝ N, 84° 26´ 10˝ W.
CON1: First roadcut on northwest side of westbound I-275, 0.5 km southwest of intersection of
I-275 and the Kentucky bank of the Ohio River near Old Coney Amusement Park. Newport,
OH-KY 7 ½´ quadrangle. 39° 03´ 15˝ N, 84° 26´ 20˝ W. CON2: Second roadcut on northwest side of westbound I-275, 0.6 km southwest of intersection of I-275 and the Kentucky bank of the
Ohio River near Old Coney Amusement Park. Newport, OH-KY 7 ½´ quadrangle. 39° 03´ 13˝
N, 84° 26´ 24˝ W. CON 3: Third roadcut on northwest side of westbound I-275, 0.8 km southwest of intersection of I-275 and the Kentucky bank of the Ohio River near Old Coney
Amusement Park. Newport, OH-KY 7 ½´ quadrangle. 39° 03´ 10˝ N, 84° 26´ 30˝ W.
Maysville: Roadcut on both sides of US Route 68/US Route 62 (old KY Route 3071), base of section 5.6 km north of KY Route 9 and just before exit for KY Route 8. Maysville
West, OH-KY 7 ½’ quadrangle. 38˚ 40’ 56” N, 83˚ 47’ 29” W.