Summary of activities -- Workshop:

"Habitat and Climate Inference from the Structure of Mammal Communities"

 August 21-27, 1998

John Damuth


In Attendance: 

    Peter Andrews  
    Catherine Badgley  
    John Damuth (Convenor)  
    Mikael Fortelius  
    Elizabeth Hadly  
    Sylvia Hixson  
    Christine Janis  
    Richard Madden  
    Kaye Reed  
    Jessica Theodor  
    Jan van Dam  
    Blaire Van Valkenburgh  
    Lars Werdelin  

    Paleomammalogists have explored in recent years a variety of techniques for inferring past climate and vegetation from structural characteristics of fossil mammal communities and faunas. These techniques hold enormous promise for expanding temporal and geographic ranges over which we can make accurate, objective reconstructions of terrestrial habitat characteristics (chiefly, vegetation) and local or regional climate. However, we badly need a comparison, evaluation, and synthesis of the various methods. This working group developed a global dataset of mammal faunas, by pooling and standardizing the data participants contributed, as well as by compiling additional information at the workshop. This dataset will allow us to evaluate different variables and methods by checking them against the same global dataset, in order to generate the most effective means of using mammalian community data to infer climate and habitat. In future work we will apply the techniques to Quaternary and Tertiary data, both to test out the techniques in practice and to shed light on selected paleoecological questions.

Initial Presentations and Definition of the Problems

     The group spent the first day and a half presenting brief descriptions of previous and ongoing research, describing their datasets, and discussing general topics and approaches. The wide range of subjects considered defies easy summary, but several themes and issues emerged that were discussed at greater length.

The Nature and Adequacy of the Variables ---

    Our basic aim is to be able to use metrics that are likely to be available from fossil mammal local faunas or individual localities to reconstruct past vegetation and/or climate. Traditionally, this has been approached by assigning fossil mammals to a number of functional categories, such as diet classes, locomotor classes, and so forth, on the basis of morphometric measurements (ecomorphology). Likewise, vegetation is usually expressed in terms of some kind of classification scheme. Clearly, any statistical results will be affected by the particular classifications used. An appealing alternative is to use directly morphometric measures that are considered to represent important ecological attributes such as diet or locomotion, and to use combinations of continuous climatic variables rather than classes. Both class-variables and continuous variables have theoretical strengths and weaknesses. Because the group contains a number of researchers at the forefront of ecomorphological research with fossil mammals, we are able to compare an extensive array of such continuous morphometric and categorical data in our combined dataset.

The Adequacy of Coverage of Extant Ecosystems --

    We need to establish that we have covered as large a range of extant climatic conditions as possible, with data of similar quality, if we want our results to apply as broadly as possible. Also, we need to know that our vegetational classifications are related coherently to climatic variables if we want to use similar methods to predict each.

What we know about the Relationship between Climate and Mammals --

    Andrews and Badgley both presented their unpublished research in progress on the relationship between mammal species diversity and climate. Working independently, they have converged on very similar methods for treating southern Africa and North America, respectively, and find significant relationships between mammal diversity and temperature and moisture variables. Madden also finds similar relationships between species richness and rainfall in South America.

Task Groups

The participants then broke down into three task groups for most of the remaining time.

Workshop Results

Global Dataset -- The compilation and integration of the global dataset turned out to be the most time-consuming task, as the participants had brought a very large number of data. Using Badgley's global dataset as the basis, we ended up with a dataset with 141 variables: Locality geographic, vegetational and climatic variables (Badgley, the working group, and Madden [not yet fully integrated]); Species diet and behavioral variables (Andrews, Badgley, Janis); Postcranial morphometric variables (Hixson); "Mesowear" dental variables (Fortelius & N. Solunias); Craniodental morphometric variables (Reed, Janis).

    There are a total of 265 localities worldwide. Madden's data including 80 localities and 556 species are not yet fully integrated into the global dataset. Not counting Madden's data, there are 185 localities and 2016 species, with 8954 distinct species occurrences. This means that the flat-file representation of these data is an Excel spreadsheet with 1.2 million cells! This spreadsheet had repeatedly to be transformed from one file format to another, modified, and proofread. The dataset still needs additional entries, particularly an expansion of the morphometric data, with, ideally, additional information for small mammals.

Figure 1. The 256 localities, by vegetation type (variable VEGNO).

African data integration -- This group's work was also integrated into the global dataset. In preliminary multivariate analyses it was discovered that continuous locomotor and dental characters separate species extremely well according to their habitat preferences -- and represent open-closed and wet-dry dichotomies on separate axes. This suggests that such variables will have considerable predictive power in the global dataset once the data become available for a wider range of species.

Locality data visualization -- Since this activity was dependent upon the global dataset, time constraints were severe. With Frank Davis' help, the group was able to plot our localities on a global map of vegetation zones -- a good check on both our coverage and vegetation assignments. Figure 2 shows mean annual rainfall plotted against mean annual temperature for dataset localities identified to vegetation type. It can be seen that for the majority of our vegetation types there is a relatively restricted range of climatic variables within which they occur. For grasslands and deserts, there is an upper limit of moisture (green and red lines), and bushlands and woodlands overlap considerably (polygons). It is evident that our major vegetation categories are in fact representing climatic signals as well.

Figure 2. Climatic distribution of locality types.

Multivariate Analysis of the Global Dataset -- Example: Forests vs non-forests worldwide, predicted from faunal structure

    As an example of the results anticipated from the global dataset once it is complete and in a form that allows more effective data manipulation, I present a logistic regression* analysis, predicting vegetation type from six faunal variables (such as percent hypsodont species). These six variables jointly properly classify 73% of the localities overall (Table 1). We expect ultimately to do better than this, but the details here are instructive. In 90% or more of the cases we can distinguish forests from all other vegetation types, and temperate forests are never confused with tropical forests. The next step, then, is to discover -- among the large number of variables yetr to be evaluated -- ways to distinguish among grassland, bushland and woodlant vegetation types. This result is consistent with what previous results from Africa suggested, but this is the first time it has been statistically shown in a global context. Also, note that this is consistent with an interpretation of Figure 2, where bushlands and woodlands overlap considerably on major climatic variables and clearly will be the most difficult to tell apart.
Table 1. Logistic regression analysis. 97 Localities (119 Desert, 5 Grassland, 12 Bushland, 17 Woodland, 14 Temperate Forest, 30 Tropical Forest)
Observed: Predicted Bushland Predicted
Predicted Grassland Predicted Temperate Forest Predicted Tropical Forest Predicted Woodland Pct. Correct
Bushland 7 3 0 0 0 2 58.33 %
Desert 1 14 1 0 1 2 73.68 %
Grassland 1 1 2 0 0 1 40.00 %
Temperate Forest 0 0 0 13 0 1 92.86 %
Tropical Forest 1 1 0 0 27 1 90.00 %
Woodland 1 4 0 1 3 8 47.06 %
Overall 73.20 %

Anticipated Product

It is anticipated that we will produce a large joint paper, thoroughly exploring and documenting the potential for reconstructing past climates and habitats using fossil mammal faunas. This may require a monographic treatment, or may be published as a series of papers in a special issue of a journal. Plans were also made for production of a "News & Views" type of piece for Nature or Science.

Future work

The group needs to continue to fill in the holes in the dataset, and to refine the ecomorphic and vegetation categories. In addition, the group recognized the difficulty of working with the dataset in flat-file format. Instead, we propose to store the data in a proper relational database, from which it will much easier to generate subset data files for analysis. An outline of the schema for such a database is depicted in Figure 3.