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Keywords:

  • bivalves;
  • latitudinal gradient;
  • morphological disparity;
  • taxonomic diversity

Abstract

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Geographical Variation of Shell Shape
  5. Latitudinal Gradient
  6. Disparity and Diversity Over Space and Time
  7. Summary and Conclusions
  8. References

Abstract:  We studied morphological variation of the bivalve Buchia over its geographical and temporal range. Buchia was widely distributed during the Late Jurassic and Early Cretaceous, and, while previous quantitative studies have shown that species are characterized by large amounts of variation, there have been no prior attempts to measure how morphology varies geographically. We employed traditional morphometric techniques using nine linear/angular measurements on 1855 buchiid shells from eight localities taken from widely separated, but mostly coeval, sections across its range. Principal component and canonical variate analyses indicate that geographical morphospace of buchiids varied significantly, but we did not find evidence of a latitudinal gradient in shell shape. The amount of variation between localities was similar to the amount of variation between species, indicating the importance of geographical effects on morphology. Disparity (morphological diversity within a taxon, calculated by the sum of variances) and diversity (number of species) were calculated for each location and time period (age). Disparity and diversity reached ultimate lows just before the genus’ extinction in the Hauterivian, and is suggestive that extinction was morphologically selective. We did not find significant trends for either metric, but there were discordances throughout its temporal range. Latitudinal trends of disparity and diversity within Buchia are not apparent. This research adds to the growing body of work on geographical variation and is a preliminary step to understanding the nature and variation of buchiid species and of biodiversity in general.

G eographical variation can result from adaptation to local environments and/or genetic drift and is an important factor when considering the nature of species and how they evolve. Every population of a species differs in some way from all others and the degree to which they differ varies; species compared between populations can range from being nearly identical (intraspecific variability is minimal) to having a distinctness almost at the species level (intraspecific variability is large) (Mayr 1963).

The study of geographical variation has been the topic of many palaeontological and biological investigations, with research involving interspecific vs environmental variation (e.g. Crampton 1996; Stempien et al. 2002; Stempien and Kowalewski 2004); disparity (morphological diversity) estimates (e.g. Foote 1993; Neige et al. 1997, 2001; Villier and Korn 2004); comparisons of taxonomic diversity and disparity (e.g. Moyne and Neige 2007); and palaeoenvironmental reconstruction potential (e.g. Aguirre et al. 2006). Few studies have looked at the variation of a genus over its range (but see Courville and Cronier 2005 and Crampton and Gale 2005), however, and this avenue of research can provide evidence of how morphological patterns vary both spatially and temporally. The study of geographical variation is critical as there is a growing body of evidence showing that it is a primary determinant of biodiversity gradients (Allen and Gillooly 2006) – a timely topic as many scientists believe that the world has entered into a modern biodiversity crisis (Pimm et al. 1995).

Buchiid bivalves, and in particular the genus Buchia Rouillier, 1845 (Late Oxfordian – Hauterivian), are well known for their biostratigraphic utility (e.g. Jeletzky 1965; Kauffman 1973); they are also useful for the study of evolutionary patterns (e.g. Grey et al. 2008) and geographic variation. Buchiids are abundant and often well-preserved in a variety of facies, widely distributed geographically, and readily determined taxonomically through the use of morphometric analyses, despite a large amount of intraspecific variability (Grey et al. 2008). Traditional morphometric techniques have shown that while the most important discriminatory variables for differentiating species of Buchia are the angles of the crest-line and inflation (see Text-fig. 1), variables can and do change depending on the species that are being compared; it is therefore important to retain and utilize all previously defined variables for analyses (Grey et al. 2008).

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Figure TEXT-FIG. 1..  Features used to characterize shell morphology in buchiid bivalves: 1, dorsalmost point (umbo) on hinge margin; 2, posteriormost point; 3, ventralmost point; 4, anteriormost point; 5, intersection of lines measuring maximum valve length and valve width; 6, point directly beneath highest point on left valve; 7, highest point on left valve. Morphological variables measured: ventral angle of crest-line* (I); dorsal angle of crest-line (J); dorsal length (Ld; 1–5); ventral length (Lv; 5–3); anterior width (Wa; 4–5); posterior width (Wp; 5–2); dorsal distance to highest point on valve (Dd; 1–6); ventral distance to highest point on valve (Dv; 6–3); and inflation (In; 6–7). *The ‘crest-line’ is defined as the imaginary line joining the highest points of the left valve of Buchia (Pavlow 1907) and has been recognized as a taxonomically valuable feature (Jeletzky 1966).

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In this study, we examine morphological variability of buchiids over their geographical and temporal range by:

  • 1
     Quantifying geographical variation within the genus and identifying its role on morphology;
  • 2
     Determining potential latitudinal gradients; and
  • 3
     Comparing disparity and diversity over time and space.

Material and Methods

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Geographical Variation of Shell Shape
  5. Latitudinal Gradient
  6. Disparity and Diversity Over Space and Time
  7. Summary and Conclusions
  8. References

Material

We undertook statistical comparisons of eight geographically widely separated and roughly co-eval collections of buchiids from the Oxfordian (Late Jurassic) to Hauterivian (Early Cretaceous). The examined material is from: Antarctica (Alexander Island); Arctic Canada (Ellesmere Island); China (Eastern Heilongjiang); East Greenland (Jameson Land and Wollaston Forland); Far East Russia (Southern Primorye and Far North); Grassy Island and Taseko Lakes, British Columbia, Canada (these are located on the Wrangellia and Cadwallader terranes, respectively); and New Zealand (North Island, Murihiku Terrane) (Table 1 and Text-fig. 2). The bulk of this material was obtained from institutional collections and represents stratigraphically constrained series. The number of specimens ranged from 28 to 765 for each collection, with a total of 1855 specimens measured (Table 1). Of the approximately 40 species within the genus Buchia (and the related Southern Hemisphere genus Australobuchia Zakharov, 1981), we have 30 represented in our study. Thus, we have a nearly complete view of taxonomic diversity because most other species not represented here are endemic to areas that we did not include in this study. We have also included specimens of Praebuchia Zakharov, 1981 (of Oxfordian age, Late Jurassic) as this genus is considered to be the direct ancestor of Buchia (Zakharov 1987).

Table 1.   Summary of collections used in analyses.
LocalityPalaeo-latitude (°)Age rangeCollection locationIndividuals studied/locationNumber of speciesPrimary reference
  1. AU, Auckland University; BAS, British Antarctic Survey; UC, University of Copenhagen (Geological Museum of Denmark and Geological Institute); GNS, Institute of Geological and Nuclear Sciences (NZ); GSC, Geological Survey of Canada; NIGP, Nanjing Institute of Geology and Palaeontology; VSEGEI, All Russian Geological Research Institute.

  2. aNote that the palaeolatitude for China, Grassy Island and Taseko Lakes are approximated as there is no current consensus on the placement of these areas in the Late Jurassic – Early Cretaceous.

Antarctica (Alexander Island)75°STithonianBAS283Butterworth et al. 1988; Crame and Howlett 1988
Canadian Arctic (Ellesmere Island)81°NMiddle Tithonian – BerriasianGSC395Jeletzky 1966
China (Eastern Heilongjiang)a46–48°NMiddle Tithonian – mid-ValanginianNIGP596Sha et al. 2003; Sha and Fursich 1994
East Greenland (Jameson Land and Wollaston Forland)49.5°NLate Oxfordian – Late ValanginianUC20614Surlyk and Zakharov 1982; Alsen 2006
Far East Russia71°NKimmeridgian – mid-ValanginianVSEGEI30119Sey and Kalacheva 1993, 1999
Grassy Island, B.C. (Wrangellia Terrane)a49°NLate Tithonian – ValanginianGSC7655Grey et al. 2007
New Zealand (Murihiku Terrane)65°SLate Oxfordian – Middle TithonianAU and GNS1934Hikuroa and Grant-Mackie 2008
Taseko Lakes, B.C. (Cadwallader Terrane)a51°NLate Tithonian – HauterivianGSC24611Jeletzky and Tipper 1968
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Figure TEXT-FIG. 2..  Approximate buchiid palaeogeographical localities utilized in this study (modified after Crame 2002). A = Antarctica; B = Canadian Arctic; C = NE China; D = East Greenland; E = Far East Russia; F = Grassy Island (B.C.); G = New Zealand; H = Taseko Lakes (B.C.).

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Palaeolatitudes for each study location were estimated and were, for the most part, taken from Crame (2002), whereas others (i.e. Ellesmere Island and terranes in British Columbia) were estimated based on current research (Torsvik et al. 2001 and Stamatakos et al. 2001; Carter and Haggart 2006; Schröder-Adams and Haggart 2006; Smith 2006, respectively).

Taxonomic validity of Buchia species using multivariate morphometrics has been undertaken for the section at Grassy Island, British Columbia (Grey et al. 2008), and preliminary work has also been carried out for the other localities used for this study. Results for all the localities are similar to those from Grassy Island: while there is considerable intraspecific variability within previously recognized species, all are deemed statistically valid based on the morphological characters we measured. We therefore accept all the previously designated species as such for the purposes of this research.

General methods

The exterior and side views for all unbroken left valves from each geographical area were photographed using a Nikon D70 digital camera. We used the left valve for consistency and also because it exhibits greater morphological variance. Images measured with a custom-designed morphometrics program created in MatLab (MorphLab 1.0; available from the authors).

Nine morphometric variables, log-transformed to minimize the effect of size (Kowalewski et al. 1997), were used to describe shell shape; these include angular and linear measurements that have been used previously (Grey et al. 2008) to describe morphology between species of Buchia (Text-fig. 1). We applied multivariate morphometrics using phenetic discrimination of linear and angular measurements to define the buchiid morphospace. Phenetic discrimination included a combination of principal component analyses (PCA) and step-wise canonical variate analyses (CVA). PCAs, based on the correlation matrix and executed using the program PAST (version 1.38, Hammer et al. 2001), were used to explore the morphospace of buchiids. CVAs, executed using SPSS (version 11.0), were used for predictive classification (high percentages indicate that specimens were correctly classified to their a priori groups based on morphological characteristics). A priori groupings for CVAs were based either on geographical location or taxonomic group (=species). We utilized a step-wise method for the CVA because it selects variables that contribute the most discriminatory power to the model (refer to Cheetham et al. 2006), and this method is advantageous when there are a number of characters measured. We also employed a jackknifed approach in SPSS (version 11.0) by selecting the ‘leave-one-out classification’ option.

Geographical Variation of Shell Shape

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Geographical Variation of Shell Shape
  5. Latitudinal Gradient
  6. Disparity and Diversity Over Space and Time
  7. Summary and Conclusions
  8. References

Morphometric analyses were performed in a variety of ways in order to determine the effect of geography on buchiid morphology. We applied the above methods to investigate geographical variability within a single species that was found in at least three locations, using location as the a priori grouping variable. Separate PCA and CVA analyses were therefore performed for each of Buchia volgensis, Buchia unschensis, and Buchia okensis. We also grouped data from the three species and performed another analysis using species as the a priori grouping variable (for CVAs); this was to determine if PCAs and CVAs discriminate between species (i.e. species used as the grouping variable) better than, or as well as, geography (i.e. location used as the grouping variable). Geography is an important factor influencing morphology if the data cluster by location and if the percentages of correctly classified specimens are high (or as high as when species is the grouping variable).

Results indicate that geographic variation plays an important role in buchiid morphology (Text-figs 3–7). There is variable separation between locations in the morphospace (Text-figs 3–5, see Table 2 for principal component loadings for all tests) and the percentage of correct classification according to location ranges from 53 per cent (B. unschensis) to 94 per cent (B. okensis) (Table 3). Higher classification rates indicate large intraspecific variability across the geographic range of that species; therefore, B. okensis is more variable than B. unschensis across its geographical range.

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Figure TEXT-FIG. 3..  A, scatterplot from the principal component analysis (PCA) showing overlap in the morphospace for Buchia okensis using location as the grouping variable. B, scatterplot from the canonical variate analysis (CVA) for Buchia okensis where location is the a priori grouping variable. Ninety-four per cent of specimens were correctly classified according to their geographical location; this indicates that most specimens differ enough in morphology between locations that they can be categorized to their geographical origin. CV1 was a function of primarily posterior width (Wp, Text-fig. 1), and CV2 was a function of primarily anterior width (Wa, Text-fig. 1).

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Figure TEXT-FIG. 4..  A, scatterplot from the PCA for Buchia volgensis, using location as the grouping variable, showing overlap in the morphospace. B, scatterplot from the CVA for Buchia volgensis. Seventy-five per cent of specimens were correctly classified according to their geographical location. CV1 was a function of primarily inflation (In, Text-fig. 1), and CV2 was a function of primarily ventral angle of crest-line (angle I, Text-fig. 1).

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Figure TEXT-FIG. 5..  A, scatterplot from the PCA for Buchia unschensis, using location as the grouping variable, showing considerable overlap in the morphospace. B, scatterplot from the CVA for Buchia unschensis. Fifty-three per cent of specimens were correctly classified according to their geographical location. CV1 was a function of primarily inflation (In, Text-fig. 1) and CV2 was a function of primarily dorsal angle of crest-line (angle I, Text-fig. 1).

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Figure TEXT-FIG. 6..  A, scatterplot from the PCA of data from three species (Buchia volgensis, Buchia unschensis and Buchia okensis) that are found in some or all of the following six locations: Arctic, China, Grassy Island, Greenland, Russia and Taseko Lakes. Species was used as the grouping variable. B, scatterplot from the CVA of data from three species (B. volgensis, B. unschensis and B. okensis) that are found in some or all of the six locations, using species as the a priori grouping variable. Seventy-five per cent of specimens were correctly classified according to their species designation. CV1 was primarily a function of ventral length (Lv, Text-fig. 1) and CV2 was primarily a function of inflation (In, Text-fig. 1).

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Figure TEXT-FIG. 7..  Scatterplot from the CVA of data from three species (Buchia volgensis, Buchia unschensis and Buchia okensis) that are found in some or all of the six locations, using location as the a priori grouping variable. Seventy-one per cent of specimens were correctly classified according to their geographic location. CV1 was primarily a function of posterior width (Wp, Text-fig. 1), and CV2 was primarily a function of anterior width (Wa, Text-fig. 1).

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Table 2.   Correlation of all morphological variables on the first two principal components for all five principal component analyses performed in this study.
VariablePC1PC2
  1. 1–3 = Buchia okensis, Buchia volgensis and Buchia unschensis, respectively, across their geographical ranges; 4 = all three species using location as the grouping variable; and 5 = all data from all locations (used for latitudinal gradient analysis). Variables are defined in Text-figure 1.

Dorsal angle of crest-line (J)(1) 0.15 (2) 0.00 (3) −0.70 (4) 0.14 (5) −0.01(1) 0.70 (2) −0.70 (3) −0.68 (4) 0.68 (5) 0.70
Ventral angle of crest-line (I)(1) −0.18 (2) 0.01 (3) 0.70 (4) −0.10 (5) 0.02(1) −0.67 (2) 0.70 (3) 0.71 (4) −0.71 (5) −0.69
Dorsal length (Ld; 1–5)(1) 0.37 (2) 0.37 (3) 0.04 (4) 0.39 (5) 0.39(1) −0.11 (2) 0.04 (3) 0.08 (4) −0.09 (5) −0.01
Ventral length (Lv; 5–3)(1) 0.37 (2) 0.38 (3) −0.03 (4) 0.39 (5) 0.37(1) −0.08 (2) −0.03 (3) 0.01 (4) −0.04 (5) 0.07
Anterior width (Wa; 4–5)(1) 0.37 (2) 0.38 (3) 0.00 (4) 0.38 (5) 0.38(1) −0.05 (2) 0.00 (3) 0.00 (4) −0.02 (5) 0.09
Posterior width (Wp; 5–2)(1) 0.37 (2) 0.37 (3) 0.04 (4) 0.32 (5) 0.37(1) −0.15 (2) 0.03 (3) −0.00 (4) −0.08 (5) −0.05
Dorsal distance (Dd; 1–6)(1) 0.37 (2) 0.39 (3) 0.02 (4) 0.38 (5) 0.38(1) −0.05 (2) 0.02 (3) −0.02 (4) −0.03 (5) −0.05
Ventral distance (Dv; 6–3)(1) 0.37 (2) 0.39 3) −0.04 (4) 0.37 (5) 0.38(1) −0.12 (2) −0.04 (3) 0.09 (4) −0.07 (5) 0.08
Inflation (In; 6–7)(1) 0.36 (2) 0.37 (3) −0.04 (4) 0.37 (5) 0.37(1) −0.05 (2) −0.04 (3) 0.14 (4) −0.11 (5) −0.07
Table 3.   Percentage of variation attributed to each axis in the principal component (PC) and canonical variate (CV) analyses, and classification rates for analyses performed for each species.
SpeciesLocations (grouping variable)PC1 (%)PC2 (%)CV1 (%)CV2 (%)CV1CV2Classification rate (%)
Buchia okensisGrassy Island, Greenland, Russia, Taseko Lakes641785.613.5Posterior width (Wp)Anterior width (Wa)94
Buchia volgensisChina, Greenland, Russia74.318937Inflation (In)Ventral angle of the crest-line (J)75
Buchia unschensisArctic, China, Greenland, Russia732170.216.1Inflation (In)Dorsal angle of the crest-line (I)53

The fairly large number of correctly classified specimens according to location for B. volgensis and B. okensis means that there are morphological differences between populations of the same species that can be attributed to geography. The morphological characters that best distinguish between locations (i.e. CV1 and CV2) vary according to the species analysed (Table 3). For instance, B. okensis is primarily distinguished by width across its geographical range, while B. volgensis and B. unschensis are distinguished by angles of the crest-line and inflation (Table 3).

A PCA using morphological data for all three species (species used as the posteriori grouping variable) shows considerable overlap in the morphospace (Text-fig. 6A), but a moderately high classification rate of 75 per cent was obtained from the CVA (Text-fig. 6B). Comparatively, a CVA of data from the three species above that are found in some or all of the six locations, using location as the a priori grouping variable, gives a classification rate of 71 per cent (Text-fig. 7). These similar rates of classification indicate that there is nearly as much variation between geographical locations as there is between species.

Overall, results indicate that geographical variation is an important factor influencing morphology of buchiids. These results are similar to those of Stempien and Kowalewski (2004), who found the geographical morphospace of the bivalve Mulinia varies significantly and that the amount of variation between geographical regions is comparable to the amount of interspecific variation between two species. Crampton (1996), on the other hand, found geographical variation is negligible compared to interspecific variation for the bivalve Actinoceramus– species differences outweigh those between geographical locations, suggesting that genetics are more important for morphological variation than are environmental differences. Our results suggest that geographical differences can have a similar role on morphology as genetics, and also raise important issues of taxonomy: how morphologically different do two populations from different locations need to be before they are considered separate species? For the case of B. okensis in particular, it may be prudent to assign species variant names.

Latitudinal Gradient

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Geographical Variation of Shell Shape
  5. Latitudinal Gradient
  6. Disparity and Diversity Over Space and Time
  7. Summary and Conclusions
  8. References

Latitudinal gradients with respect to morphology and speciation are well documented in the fossil record (see Crame 2002 and Aguirre et al. 2006 for examples). An example of a gradient commonly found between populations, both fossil and Recent, is termed Bergmann’s size rule, whereby there is a positive relationship between latitude and size of organisms of the same species. This has been found in organisms from fossil ostracodes (Hunt and Roy 2006) to modern birds (Ashton 2002). Latitudinal gradients have also been observed in Devonian and Carboniferous brachiopod shell ornamentation that may have resulted from differential predation (Dietl and Kelley 2001).

We explored whether gradients existed for buchiids over the range of palaeolatitudes sampled in this study (from 75°S to 66°N) by performing a PCA using all data for all locations studied. We plotted the primary principal component (PC1; see Table 2 for loadings), which accounted for 67 per cent of the variation, against palaeolatitude (Text-fig. 8A). A Spearman correlation indicates that there is not a significant relationship between latitude and morphology (Spearman’s rho = −0.29, p = 0.5). This was also the case for PC2 vs palaeolatitude (not figured; Spearman’s rho = −0.19, p = 0.65). Next, we plotted the primary canonical variate (CV1), representing the combination of variables (mainly a function of ventral length) that best separates locations and accounts for the most variation (53 per cent), against palaeolatitude (Text-fig. 8B). We found that the resulting Spearman correlation is not significant (Spearman’s rho = −0.38; p = 0.35).

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Figure TEXT-FIG. 8..  A, the first principal component (using location as the grouping variable) vs palaeolatitude. B, the primary canonical variate (using location as the grouping variable) vs palaeolatitude. CV1 was primarily a function of ventral length (Lv, Text-fig. 1), and CV2 was primarily a function of posterior width (Wp, Text-fig. 1).

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Our results suggest that a gradient may exist for buchiids along CV1 and it is possible that with more data points from more regions (especially those from lower latitudes, i.e. Tibet and Indonesia) one may be found. It is also important to note that palaeolatitudes are approximated and, in some cases, may not be entirely accurate. Those from East Greenland, Far East Russia, New Zealand, and Antarctica are fairly well supported in the literature (see Crame 2002), and evidence suggests that Ellesmere Island (Canadian Arctic) may have been in its current latitude in the Jurassic (Torsvik et al. 2001). The latitude of Eastern Heilongjiang in China is not known, and we have instead used its current latitude for approximation (the palaeolatitude may consequently be off by 10–20 degrees). Lastly, the location of the two terranes (Wrangellia and Cadwallader) in British Columbia, Canada are also debatable, but there is growing support in the literature that these two terranes may have been close to their current latitude by the Middle–Late Jurassic (Stamatakos et al. 2001; Carter and Haggart 2006; Schröder-Adams and Haggart 2006; Smith 2006).

The previous analyses included geographic-temporal data (i.e. time is not held constant), and we therefore explored the effect of time on latitudinal gradients. PC1 and CV1 were plotted against palaeolatitude (not shown) for each time period, or age, in the buchiid lineage: Oxfordian, Kimmeridgian, Tithonian, Berriasian, Valanginian, and Hauterivian. We did not find evidence of a gradient in any case. Overall, however, and with the approximations of palaeolatitudes we have used, our results thus indicate that, while morphology differs according to geographic region, there is no evidence for a trend in those differences along a latitudinal gradient.

In our study of gradients, we included all data from all locations, giving a general view of buchiid morphology by latitude (and this type of analysis is commonly carried out for other taxonomic ranks, such as genera; e.g. Stempien 2002 and Aguirre et al. 2006), but future studies should focus on individual species of Buchia and/or Australobuchia over their range to add to our understanding of gradients within the two genera.

Disparity and Diversity Over Space and Time

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Geographical Variation of Shell Shape
  5. Latitudinal Gradient
  6. Disparity and Diversity Over Space and Time
  7. Summary and Conclusions
  8. References

Calculations of taxonomic diversity are often supplemented with those of disparity (morphological diversity), leading to an improved understanding of the processes of diversification and extinction. The relationship between the two metrics also can yield useful information about evolutionary mechanisms. Disparity and diversity have been studied over geographical ranges (Moyne and Neige 2007), during periods of known extinctions (e.g. Villier and Korn 2004), during periods of evolutionary expansions (e.g. Ediacara: Shen et al. 2008), during faunal invasions (Navarro et al. 2005), and over the range of a taxon (e.g. angiosperms: Lupia 1999; cuttlefish: Neige 2003; echinoids: Villier and Eble 2004).

Diversity measures were obtained by species counts, and the corresponding error bars were calculated as √D, where D is the number of species (Moyne and Neige 2007). We calculated disparity using the sum of variances of scores on the first three principal components from the PCA in the above section. The sum of variances method is particularly useful for this study because it is relatively insensitive to sample size (Moyne and Neige 2007). The first three axes account for more than 88 per cent of the variation and, as outlined in Villier and Eble (2004), adding more axes would not affect patterns because the axes are less influential on disparity and would add approximately the same amount of disparity to any group. Following the methods of Villier and Eble (2004) to reduce the artificial weighting of variables and emphasize the main sources of variance, we scaled principle component scores by multiplying them by the square root of the eigenvalue. Note that, as in Neige et al. (1997), we used many specimens within each species to calculate disparity. All disparity estimates were bootstrapped (1000 replicates), where the mean value of the bootstrap distribution was used as the disparity estimate for the sample and standard deviations from that were also calculated (Villier and Eble 2004). The bootstrap was performed using MatLab and followed procedures of Foote (1992, 1999).

Our sample sizes for the locations used in this study varied between 28 and 765; in order to ensure that this did not affect our measures of diversity and disparity, we determined that sample number was not correlated to either metric using a Spearman correlation (disparity: Spearman’s rho = −0.5, p = 0.21; diversity: Spearman’s rho = 0.48, p = 0.23). Hence, our results appear not to be influenced by sample size.

Moyne and Neige (2007) studied diversity and disparity signals over the geographical range of Middle Jurassic ammonites and found that three biodiversity crises during this time had left different palaeogeographical signatures. We therefore calculated disparity and diversity values for each geographical location and time period. Latitudinal and temporal trends were considered by employing Spearman’s correlation analyses (Villier and Eble 2004).

Disparity over the eight geographical locations ranged from 19 to 60, and diversity ranged from 3 to 19 (Table 4). Higher values of disparity indicate greater morphological variability across specimens; therefore, buchiids from Antarctica and China had the most wide-ranging morphological variability, while the localities from two different terranes in British Columbia (Grassy Island and Taseko Lakes) had more constrained morphologies (Table 4). Although the method we used to calculate disparity is relatively insensitive to sample size, results from China and the Arctic should still be viewed with caution because they represent the locations with some of the smallest sample sizes (future work will need to include more specimens to confirm this result). In addition, we performed identical analyses for disparity within a single species, B. okensis (this was the species found in the most locations: Grassy Island and Taseko Lakes, B.C., Russia and Greenland), over its entire geographical range and found similar results in that Grassy Island and Taseko Lakes have the lowest disparity values (23 and 24, respectively); Russia and Greenland have much larger disparities (40 and 60, respectively). We did not find a clear relationship between diversity and disparity (Text-figs 9, 10), and this finding supports others’ work that taxonomic diversity is a poor predictor of morphological diversity (disparity) (e.g. Foote 1993; Neige 2003).

Table 4.   Disparity (with standard deviation (SD)) and diversity estimates for each location studied.
LocationDisparity (SD)Diversity
Antarctica60 (12)3
Arctic34 (10)5
China57 (8)6
Grassy Island (B.C.)19 (1)5
Greenland54 (5)14
New Zealand40 (4)4
Russia46 (4)19
Taseko Lakes (B.C.)23 (2)11
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Figure TEXT-FIG. 9..  The relationship between disparity and diversity over the geographical range of buchiids used in this study. There is not a significant correlation between the two metrics.

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Figure TEXT-FIG. 10..  Discordance and concordance between diversity and disparity through the temporal range of buchiids.

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Latitudinal trends are not apparent in either disparity (Spearman’s rho = −0.60; p = 0.21) or diversity (Spearman’s rho = −0.26; p = 0.62) (Text-fig. 11) for our spatio-temporal data. As in the above section, we explored the effect of time by performing the same analysis, but only for the Tithonian (this is the only time in which all localities are represented). The results are similar: there is no latitudinal trend for either disparity (Spearman’s rho =  −0.48; p = 0.91) or diversity (Spearman’s rho = 0.49; p = 0.91) and there is no relationship between diversity and disparity (not figured).

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Figure TEXT-FIG. 11..  Disparity and diversity vs palaeolatitude.

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There is discordance between disparity and diversity through much of the genus’ time range (Text-fig. 10). Patterns of discordance have been found in many previous studies (e.g. Shen et al. 2008; Moyne and Neige 2007) and are perhaps suggestive of a decoupling of taxonomic and morphological evolution (Shen et al. 2008). However, as Buchia heads towards extinction in the Hauterivian, both disparity and diversity decline, and disparity reaches an ultimate low (Text-fig. 10), perhaps suggesting in this case that the extinction was selective of morphology. This pattern has also been documented by other groups, such as the Cambrian blastoids (Foote 1992) and some trilobite groups (Foote 1993). Villier and Korn (2004) also found that, over 30 million years, disparity for Palaeozoic ammonoids declined and reached a minimum just before the Permian mass extinction.

Discordances have often been attributed to biases such as taxonomic, temporal, or the choice of morphological characters measured, but there is increasing evidence that discordances are real events that are caused by phenomena such as biodiversity crises (e.g. Moyne and Neige 2007) or clade evolution (Foote 1991, 1996). Our results are similar to those found for Old World cuttlefishes (Neige 2003), where there were no latitudinal gradients found and no linear relationship between diversity and disparity (i.e. disparity is not predicted by the number of species).

Disparity and diversity are often positively correlated during the beginning of a radiation (Foote 1993), but our results do not show this trend: we found that disparity was high and diversity low at the initial diversification of buchiids in the Late Oxfordian (Text-fig. 10). Our results may be confounded here because the only data we have for this time period includes ten specimens of Praebuchia (proposed ancestors to the genus Buchia) from two localities (seven are from Indonesia and are in Auckland University’s collection, New Zealand; three others are from East Greenland). Further study will require additional data from early species of Buchia (such as stratigraphically lower occurrences of B. concentrica in the Boreal Realm) during this time period to test if our results for the Oxfordian are accurate.

Summary and Conclusions

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Geographical Variation of Shell Shape
  5. Latitudinal Gradient
  6. Disparity and Diversity Over Space and Time
  7. Summary and Conclusions
  8. References

This research adds to a growing body of work on patterns of geographical variation and is the first to do so for the genus Buchia. Our research highlights three key points:

  • 1
     Geography is a significant variable affecting morphology, perhaps nearly as important as the morphological effects of speciation;
  • 2
     While geography can have an important influence on morphology, we found no evidence to support a correlation between geography (palaeolatitude) and morphology (shell shape), disparity (morphological diversity), or diversity (number of species); and
  • 3
     Disparity and diversity are not always correlated; however, both metrics decreased as the genus became closer to extinction, a pattern that has been documented for a variety of other taxa.

Acknowledgements.  This study is part of the doctoral research of MG, supported by a PGS-D NSERC scholarship and NSERC grant 588493 to PLS. We are extremely grateful to all the people who made this research possible by allowing us to work with their and/or their institutions’ collections: T. Bogdanova, E. Kalacheva, and I. Sey (All-Russian Geological Institute (VSEGEI)); J.A. Crame (British Antarctic Survey); J.S. Crampton (Geological and Nuclear Sciences of New Zealand); J. Dougherty (Geological Survey of Canada); J. Grant-Mackie, D. Hikuroa and N. Hudson (Auckland University); D. A. T. Harper and J. Rasmussen (Geological Museum, Copenhagen); P. Alsen and F. Surlyk (Geological Institute, Copenhagen); and J. Sha (Nanjing Institute of Geology and Palaeontology). We also thank P.G. Lelièvre for creating the MatLab morphometrics program (MorphLab 1.0). Two reviewers, M. Kowalewski and P. Neige, significantly improved the quality of this paper, and we thank them for their thoughtful comments.

Editor. Svend Stouge

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  7. Summary and Conclusions
  8. References
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