Home > Data Exploration, Key Concepts, Relevant Research > On continua and categories in paleoecology. . .or, an example application of ODP data

On continua and categories in paleoecology. . .or, an example application of ODP data

Today we’re delighted to have a guest post from Dr. Chris Noto, a new assistant professor at University of Wisconsin-Parkside, and an old friend of mine from our graduate school days together at Stony Brook University. Chris has had a long-running interest in dinosaur paleoecology, and thus it only seemed natural for him to apply these interests to the ODP data. Enjoy!

Chris Noto

Chris Noto, at home in the field

In describing the ecology of an organism our first inclination may be to simply go observe it in its day-to-day existence. Therefore, at its core, ecology is primarily a science of the living and this is reflected in the methods and theories one finds in the literature. Over the past couple decades there has been a growing interest in the relationship between organismal morphology and ecology, which is now often referred to as “ecomorphology”(Losos 1990; Ben-Moshe et al. 2001; Aguirre et al. 2002; Zeffer et al. 2003; Sacco and Van Valkenburgh 2004). This has opened entire new areas of research into the covariation between organisms and their environment, which is also the basic foundation for understanding evolutionary change over longer spans of time.

Some paleontologists have applied ecomorphological principles to reconstructing the paleoecology of certain extinct groups, including carnivorans (Palmqvist et al. 1999), birds (Hertel 1995), and especially ungulates (Solounias and Semprebon 2002; Meehan and Martin 2003; DeGusta and Vrba 2005; Klein et al. 2010). Changes in the types and/or proportions of ecomorphs in a fossil community have also been used as evidence of environmental and evolutionary responses to climate change(Van Valkenburgh 1995; Meehan and Martin 2003; Badgley et al. 2008; Noto and Grossman 2010). You will note though that a lot of this research relies on comparison to living analogs related to the fossil groups in question. How do we explore the paleoecology of groups that completely lack extant analogs?

If there’s one thing we’ve learned through all this research, it’s the fact that:

  1. Certain morphological adaptations occur regardless of species (convergence) because of specific habitat constraints, and
  2. Morphological differences between species will occur due to diverging ecologies, even if we don’t know exactly what ecological functions those morphological differences actually represent.

But, we won’t know those differences exist until we look for them. Paleoecology is first and foremost comparative: we take our fossils and compare them to other related taxa and living forms to better understand their place in the original community. Often we assign categories to taxa, such as “carnivore”, “biped”, etc.; however, differences between species are often better described by a continuum than a set of categories(Carrano 1999). The morphology of an organism reflects the amount of time it spends doing certain activities or performing certain functions. For example, a sloth can swim on occasion even if it is not particularly well adapted for it. Dinosaur paleoecology is finally moving in the direction of our mammalian colleagues by using quantitative measures of morphology (which allow for continua) instead of assigning discreet categories.

The ODP is one of the first large-scale projects to bring together the kind of dataset necessary to study dinosaur ecomorphology. In a recent paper I published looking at differences between dinosaur fossil communities (Noto and Grossman 2010), I was forced to use categories in assigning ecomorphs, which artificially restricted the analysis by forcing me to choose a category when uncertainty existed. In this case it was whether certain non-hadrosaur ornithopods were bipedal or quadrupedal. With ODP data, it is now possible to take a more quantitative (and nuanced) approach to this question.

To explore possible trends in ornithischians, I used humerus length and mediolateral width measurements to calculate Mike Taylor’s Gracility Index (GI; Taylor 2009) using only the largest individual from each species. These data were log transformed and plotted against the log of humerus length (to help minimize the effects of size and codependency). The resulting plot clearly separates the taxa, with more bipedal taxa having relatively gracile humeri and quadrupedal groups have more robust humeri. There are two ways to use this graph. First, we can look at the distribution of taxa from each group and see whether they fit more towards a bipedal or quadrupedal type; those intermediate to the extremes are referred to as facultative. These are my own divisions based on where I see breaks in the data. Basal ceratopsians, for example, occur mainly towards the bipedal or facultative ends of the spectrum, while derived Neoceratopsians are firmly on the quadrupedal end of things. Another way to look at the plot is how robust we may expect the humerus to be for a taxon of a given size. The dashed line is drawn across the middle of the plot. For a given humerus length we can compare GI between taxa. For example, Iguanodon has a more robust humerus (lower GI) than most ornithopods in the dataset. Furthermore, we can spot outliers, which may point to either extreme specialization or faulty data. The theropod Mononykus has an extremely robust humerus, approaching the level of Triceratops, which is related to its specialized digging forelimb. On the other hand, Cerasinops appears to have the most robust humerus of all, however as Mike pointed out to me, the original paper describes the humerus as extremely gracile and gives no width measurement. So where did the width measurement come from? This particular data point is worth another look.

Ecomorphology Plot

Humeral robustness as a function of humeral length. Select taxa labeled in gray. Cera.=Cerasinops, Gypo.=”Gyposaurus”, Herr.=Herrerasaurus, Igua.=Iguanodon, Mono.=Mononykus, Post.=Postosuchus, Psit.=Psittacosaurus, Scut.=Scutellosaurus, Stego.=Stegosaurus, Thec.=Thecodontosaurus, Tric.=Triceratops.

As you can see, the distribution of humeral morphologies indicates a gradual continuum of locomotor strategies from fully bipedal to full quadrupedal. Quantitative data such as this could then be fed into a paleoecological analysis instead of categories, allowing for more refined analysis of ecological differences and similarities among paleocommunities over space and time. While evolutionary trends are certainly important, we must not forget the ecological context of the morphological patterns we are studying.

References

Aguirre LF, Herrel A, van Damme R et al. (2002) Ecomorphological analysis of trophic niche partitioning in a tropical savannah bat community. Proceedings of the Royal Society of London Series B-Biological Sciences 269:1271-1278

Badgley C, Barry JC, Morgan ME et al. (2008) Ecological changes in Miocene mammalian record show impact of prolonged climatic forcing. Proc Natl Acad Sci U S A 105:12145-12149

Ben-Moshe A, Dayan T, Simberloff D (2001) Convergence in morphological patterns and community organization between Old and New World rodent guilds. Am Nat 158:484-495

Carrano MT (1999) What, if anything, is a cursor? Categories versus continua for determining locomotor habit in mammals and dinosaurs. J Zool 247:29-42

DeGusta D, Vrba E (2005) Methods for inferring paleohabitats from discrete traits of the bovid postcranial skeleton. J Archaeol Sci 32:1115-1123

Hertel F (1995) Ecomorphological indicators of feeding behavior in recent and fossil raptors. Auk 112:890-903

Klein RG, Franciscus RG, Steele TE (2010) Morphometric identification of bovid metapodials to genus and implications for taxon-free habitat reconstruction. J Archaeol Sci 37:389-401

Losos JB (1990) Ecomorphology, performance capability, and scaling of West Indian Anolis lizards: an evolutionary analysis. Ecol Monogr 60:369-388

Meehan TJ, Martin LD (2003) Extinction and re-evolution of similar adaptive types (ecomorphs) in Cenozoic North American ungulates and carnivores reflect van der Hammen’s cycles. Naturwissenschaften 90:131-135

Noto CR, Grossman A (2010) Broad-scale patterns of Late Jurassic dinosaur paleoecology. PLoS ONE 5:e12553

Palmqvist P, Arribas A, Martinez-Navarro B (1999) Ecomorphological study of large canids from the lower Pleistocene of southeastern Spain. Lethaia 32:75-88

Sacco T, Van Valkenburgh B (2004) Ecomorphological indicators of feeding behaviour in the bears (Carnivora : Ursidae). J Zool 263:41-54

Solounias N, Semprebon G (2002) Advances in the reconstruction of ungulate ecomorphology with application to early fossil equids. Am Mus Novit:1-49

Taylor M (2009) A re-evaluation of Brachiosaurus altithorax Riggs 1903 (Dinosauria, Sauropoda) and its generic separation from Giraffatitan brancai (Janensch 1914). J Vert Paleontol 29:787-806

Van Valkenburgh B (1995) Tracking ecology over geological time – evolution within guilds of vertebrates. Trends Ecol Evol 10:71-76

Zeffer A, Johansson LC, Marmebro A (2003) Functional correlation between habitat use and leg morphology in birds (Aves). Biol J Linn Soc 79:461-484

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  1. Jordan
    July 14, 2011 at 11:50 pm | #1

    This approach reminds me a bit of trying to separate mammalian browsers, grazers, and mixed feeders based on dental microwear features. I think the next step might be to try and see how well separated the clusters are using a discriminant analysis.

    Great work, Chris! Just out of curiosity, is there a particular reason why you’ve drawn the dividing lines at an angle like that?

  2. Chris Noto
    July 15, 2011 at 1:36 am | #2

    Jordan, I don’t have a particular reason for those lines. They appear to be breaks in the data, and fitting the lines to taxa for which we could be pretty sure fall towards one end of the spectrum or the other. However, with a larger dataset those gaps could disappear, so to a certain extent they’re arbitrary. I would eventually like to use an ordination method (or discriminant analysis) to more quantitatively separate the groups. What I’m really interested in is the horizontal line and using biomechanical proncipals along with scaling to assess the gracility of these elements.

  3. Jordan
    July 15, 2011 at 2:41 am | #3

    In retrospect, I suppose discriminant methods might not be the way to go because the groups were not established independently of the data. Maybe cluster analysis might be the way to go?

    Cool stuff. Interesting to see Thecodontosaurus come out so firmly quadrupedal.

  4. 220mya
    July 15, 2011 at 7:04 am | #4

    Jordan, we can use independent contrasts or a similar method to phylogenetically “correct” our data before throwing them into these sourts of analyses – if thats what you’re thinking of?

  5. Jordan
    July 15, 2011 at 5:56 pm | #5

    Yes, I suppose PICs would be necessary, too. I’m just wondering whether there might be a more objective means of clustering the data points into discrete groups (bipedal/facultative/quadrupedal) rather than simply eyeballing it. Cluster analysis might be the way to go. Or even serial k-means cluster analyses if we expect to see the aforementioned three groups in the dataset.

  6. Chris Noto
    July 15, 2011 at 7:53 pm | #6

    Thanks for the suggestions. Luckily I’m reasonably familiar with different clustering methods. Interesting to see where this will all go.

  7. July 18, 2011 at 10:57 am | #7

    Jordan said: “Interesting to see Thecodontosaurus come out so firmly quadrupedal.”

    But that can’t be right. According to the data, Thecodontosaurus has a longer humerus than any ornithischian; but in fact a whole Thecodontosaurus was shorter than a Triceratops humerus. So this is an example of the other value of the plot that Chris mentioned — it highlights incorrect data points. Could it be that we have a measurement in mm but plotted it it as cm?

  8. July 18, 2011 at 2:26 pm | #8

    @Chris

    “I would eventually like to use an ordination method (or discriminant analysis) to more quantitatively separate the groups.”

    Hi Chris,

    You might be familiar with these methods, but if you could separate your data into two groups of definitely bipedal and definitely quadrupedal you could do a Generalized Linear Discriminant Analysis to find an optimal linear transformation to separate the groups. In this case a 1-D direction. Alternately, you could do a Non-negative Matrix Factorization (mathematically related to k-means) to find ‘outliers’ of your data so the rest are convex combinations of these templates.

    Would finding intermediaries between bipedal and quadrupedal species interesting; maybe showing a lineage evolving through time from bipedal to quadrupedal?

  9. Leo W Sham
    August 10, 2011 at 12:46 pm | #9

    Simple estimation for the Thecodontosauru data point: if humerus length in X mm is entered as X cm and LOGging the wrong datum results in circa 3.15, then the corrected LOGged datum would be 3.15 – 1 = 2.15, making Thecodontosaurus bipedal or marginally facultative bipedal.

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