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Plant morphological categories

In order to understand broad phytogeographic patterns, I assigned all Jurassic leaf genera to ten coarser morphological categories (or 'morphocats'). The use of morphocats enables the raw data to be shown, with each floral locality being represented by a pie diagram (see my Late Jurassic example, below). The size of each segment corresponds to the number of genera represented by a particular morphocat, expressed as a percentage of all genera at that locality. The following categories were plotted: sphenophytes, ferns, pteridosperms, microphyllous cycadophytes, unassigned (intermediate or morphologically variable) cycadophytes, macrophyllous cycadophytes, ginkgophytes, microphyllous conifers, unassigned (intermediate or morphologically variable) conifers, and macrophyllous conifers. These groups parallel the major taxonomic subdivisions which in turn reflect the individual physiognomic strategies of their constituent plants. Other paleobotanists might choose to emphasize different morphological categories, but my scheme at least helps to reveal the broad global vegetational patterns. I further believe that these patterns are sufficiently pronounced to remain largely unaltered by fine-scale adjustments.

J3pies.gif
Late Jurassic floral localities, with each leaf genus assigned to a morphological category, represented as a percentage of the total at each locality. For clarity of presentation, the following categories were combined: microphyllous conifers and microphyllous cycadophytes (red), ferns and sphenophytes (green), ginkgophytes and macrophyllous conifers (dark blue). Other morphological categories are: pteridosperms (yellow), macrophyllous cycadophytes (light blue), and unassigned (intermediate or morphologically variable) conifers (brown). Microphyllous (i.e. small-leafed) forms of conifers and cycadophytes tend to occur in lower latitudes, whereas macrophyllous (large-leafed) conifers and ginkgophytes are more common in the higher latitudes. The plants of these two extereme groups rarely co-occur, which makes sense if we consider their leaf morphologies in terms of climate (small-leafed forms of cycadophytes and conifers, often with thick cuticles, adapted to hot dry environments versus large and presumably deciduous leaves of conifers and ginkgophytes adapted to seasonally cool and/or dark conditions).



Correspondence analysis of data

Correspondence analysis is a method commonly used in studies of modern ecology and vegetational succession. With this method, multi-dimensional relationships are reduced to show variance within data sets on a series of two-dimensional axis plots. The advantages of CA are that it provides the same scaling of sample (locality) and character (genus) plots, enabling direct comparison, and can accommodate incomplete data matrices where some information is missing, as normally occurs with the fossil record (e.g. my Early Jurassic example, below). The relative position of each locality is defined by its constituent leaf genera; localities sharing many genera plot closest together, those with little in common plot furthest apart. Likewise, the relative position of each genus is defined by its degree of association with other genera.

CAsimplematrix3.gif

An initial data matrix (Fig. A), comprising a vertical locality axis and a horizontal taxon (e.g. genus, species) axis, may appear to have no structure, but by re-arranging the locality axis (Fig.B) and then the taxon axis (Fig. C), a pattern emerges. The paleontologist may be faced with a data array like Fig. A (or Fig. B if, for example, the paleolatitude is known for each locality) and the computer effectively rearranges the matrix to produce the Fig. C plot. This could be done by hand of course, but with data matrices containing hundreds of columns and rows, this becomes impractical.

Figure C shows a very simple matrix, reflecting perhaps latitude, whereas in reality of course there may be more than one source of variance in the data. CA serves to identify the degree of variance and can ordinate the various influences on the data array but cannot, of course, specify the sources of the variance (examples of which include temperature, precipitation, geography, and ecological succession). This is the job of the ecologist or paleontologist. In my work, I use the physiognomy implicit in the names of individual fossil leaf genera to ultimately enable the determination of global paleoclimates. CA of fossil leaf genera and localities, combined with distributional patterns of climate-sensitive sediments, enable global climate zones (biomes) to be drawn on paleogeographic maps.

J1CAplotgen.gif
Correspondence analysis (CA) axis 1/axis 2 plot for 57 Early Jurassic leaf genera from Northern Hemisphere localities. Genera were assigned to the following broad morphological categories: microphyllous cycadophytes, microphyllous conifers and Pachypteris (red squares); macrophyllous cycadophytes (green vertical crosses); ferns, sphenophytes and lycophytes (green diagonal crosses); 'unassigned' conifers (brown squares); macrophyllous conifers and ginkgophytes (blue squares). Numbers refer to the following leaf genera: 1 Zamites, 2 Otozamites, 3 Brachyphyllum, 4 Pachypteris, 5 Ptilophyllum, 6 Pagiophyllum, 7 Pterophyllum, 8 Taeniopteris, 9 Nilssonia, 10 Elatocladus, 11 Ctenis, 12 Podozamites, 13 Baiera, 14 Ginkgo, 15 Pityophyllum, 16 Sphenobaiera, 17 Czekanowskia, 18 Desmiophyllum.


J1CAplotlocs.gif
CA Axis 1/Axis 2 distribution of Early Jurassic plant localities from the northern hemisphere, colored according to paleolatitude: 0 to 40 degrees N (red circles), 40 to 60 degrees N (green crosses), 60 to 90 degrees N (blue circles). The patterns become even clearer when the CA scores for individual localities are plotted on palaeogeographic maps.


Jurassic floral gradients

My Early Jurassic example shows how Correspondence Analysis can be used to interepret phytogeographic patterns based on the axis 1 scores of individual leaf genera and corresponding plant localities, due to their relative degrees of association. I can then understand these climatically in terms of the basic morphological characteristics of individual leaf genera and the paleogeographic distribution of plant localities (e.g. my Late Jurassic pie diagrams, above). However, this is an improvement on previous work only in that I have adopted a 'whole-flora' approach and applied statistical rules to arrange the data. How can I compare vegetation and climate signals from successive time intervals in a more rigorous and repeatable manner? By repeating the Early Jurassic statistical analysis for Middle and Late Jurassic floras, I arrive at three separate ordinations (for J1, J2 and J3 intervals, comprising 196, 288 and 160 northern hemisphere plant localities respectively). The ordering of leaf genera along axis 1 remains fairly constant in each interval, since overall floristic change was minimal throughout the Jurassic.

By averaging the scaled (0 to 100) axis 1 scores of the 32 genera common to all three intervals, I derive a Jurassic 'floral gradient' (see below). This shows the gradient score for each genus, which is based upon its averaged axis 1 position relative to all other genera throughout the Jurassic. Microphyllous conifers and microphyllous cycadophytes have low scores, whereas macrophyllous conifers and ginkgophytes have high ones. Ferns (e.g. Todites, Cladophlebis, Coniopteris) and macrophyllous cycadophytes occupy the central portion of the gradient, along with sphenophyte genera such as Equisetites (fossil 'horsetails' or 'scouring rushes'). Using the floral gradient, I can assign a value to any Jurassic plant locality (whether J1, J2 or J3) simply by averaging the scores of its constituent leaf genera. Indeed, anyone can place a new locality list on the gradient by averaging the score for each of the 32 genera represented. It should be stressed that the score of each genus on the gradient represents its 'centroid' in the Northern Hemisphere latitudinal spectrum; most genera appear in at least some lists throughout this range. This is due in part to the general uniformity of Mesozoic floras and in part to time-averaging of the taxonomic lists. The gradients are subtle and may only be determined by reference to the entire assemblage. Such a method at least provides the best available proxy for original vegetation and prevailing climate conditions at a given locality.

Jurflorgrad.gi
Jurassic floral gradient, derived from the averaged axis 1 scores of genera common to J1, J2 and J3 floras. Five broad morphological categories ('morphocats') and their constituent genera are highlighted, showing the gradation from microphyllous forms to macrophyllous conifers and ginkgophytes.


Using the floral gradient, I can compare Early, Middle and Late Jurassic plant localities objectively, observe any spatial and temporal changes, and interpret these in terms of floral provinciality, continental motion and global climate change. The following example is of Jurassic floral localities from one basin in Russia and shows how the floral gradient score increases as a function of increasing paleolatitude through the Jurassic, as the colder and more seasonally adapted plants gradually become more common than the smaller-leafed ones more suited to drier low latitude environments.

Jlocgradscore.gif



Summary

The various lines of paleobotanical evidence are combined with information from the distributional patterns of climate-sensitive sediments (e.g. coals and evaporites) to derive interpretations of Jurassic biomes, or climate zones. The summary figure below is an average of my Early, Middle and Late Jurassic floral and lithological data, and shows equator-to-pole variations in the types of climate-sensitive sediment abundances, numbers of plant morphocats, and floral gradient scores. I use this information to interpret biomes through the Jurassic, and to help unravel the multiple effects (or constraints) of floral provinciality and evolution, taxonomic and taphonomic bias, continental motion and true global climate change. By working at the global scale, I provide the matching level of proxy data and interpretations for evaluation of global climate models.

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