Croft, 2001 by Felipe Elias

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Diversity and Distributions (2001) 7, 271– 287

B I O D I VE R S I T Y RE S E A RC H

Cenozoic environmental change in South America as indicated by mammalian body size distributions (cenograms) Blackwell Science Ltd

DARIN A. CROFT Department of Organismal Biology and Anatomy, University of Chicago, E. 57th Street. Chicago, IL 60637-1508, U.S.A., E-mail: dacroft@midway.uchicago.edu

Abstract. A cenogram is a rank-ordered body size distribution of non-predatory terrestrial mammal species within a community. Studies of cenograms for modern faunas have shown that certain quantifiable attributes of cenograms are correlated with environmental variables such as rainfall and vegetation structure. Based on these correlations, cenograms of fossil communities have been used to infer palaeoenvironments and palaeoenvironmental variables. The present study uses cenogram statistics to interpret palaeoenvironmental conditions for eight Cenozoic South American mammal faunas, ranging from Eocene to Pleistocene in age. Body sizes for fossil taxa were taken either from the literature or were estimated using regressions of body size on molar length (or femoral bicondylar width) for modern mammals. Cenogram statistics are calculated for the eight fossil faunas and compared to similar statistics calculated for 16 modern South American

mammal faunas, allowing palaeoenvironmental interpretations to be made. The palaeoenvironmental interpretations based on cenogram analyses sometimes support and sometimes contradict interpretations based on herbivore craniodental morphology (e.g. levels of hypsodonty). Simulations of expected errors in body size estimates for fossil taxa suggest that the discrepancies do not result primarily from erroneous body size estimates. It is possible that some of the incongruity in interpretations results from certain non-analogue attributes of South American faunas during much of the Cenozoic (e.g. the relatively depauperate mammalian predator diversity prior to the Great American Biotic Interchange). Key words. Body size distributions, palaeoenvironment, South America, island biogeography, predation.

≤µ

INTRODUCTION The word ‘cenogram’ was originally coined by Valverde (1964) to signify a univariate plot of vertebrate body sizes within a community, arranged in decreasing rank order. Using cenograms with the predator and prey species plotted separately, Valverde (1964, 1967) examined predator– prey body size relationships within modern communities. After examining the structure of these cenograms across a wide variety of habitats, Valverde concluded that each class of terrestrial vertebrates constituted a separate subcommunity that operated nearly independently of the others

and that they could be compared to equivalent subcommunities from other habitats. Legendre (1986) extended the use of mammalian cenograms (excluding bats and predatory mammals) into the fossil record. He demonstrated that cenogram shape tends to vary in a predictable way with certain environmental characteristics in modern communities and suggested that palaeocommunity cenograms should indicate palaeoenvironmental conditions. He illustrated this by studying cenograms for a succession of mammal faunas from the phosphate mines of Quercy, France (Legendre, 1986). Legendre demonstrated that a shift in cenogram

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shape coincided with the major faunal turnover event at the ‘Grande Coupure’, suggesting that the global transition to cooler, drier conditions was evident in the changing body size structure of the European mammalian palaeocommunities. Since Legendre’s first application of cenograms to the fossil record, other studies have used cenograms to interpret palaeohabitats throughout the Cenozoic (Gingerich, 1989; de Bonis et al., 1992; Ducrocq et al., 1994; Gunnell, 1994, 1997; Montuire & Desclaux, 1997; Wilf et al., 1998). Few of these studies have dealt with Southern Hemisphere localities, and none has included palaeocommunities from South America. Recent neontological studies have examined body size frequency distributions for both North and South American mammals (Holling, 1992; Marquet & Cofré, 1999), but these have primarily been concerned with the shape of the distributions themselves rather than how certain attributes of the distributions vary consistently with environmental variables. The present study uses cenograms to interpret palaeoenvironmental conditions for some of the best-known South American fossil mammal assemblages. These interpretations are then compared to more traditional interpretations (based on herbivore craniodental and postcranial adaptations) to judge congruence between the different methods of palaeoenvironmental reconstruction.

MATERIALS AND METHODS Fossil faunal lists Although constructing cenograms for modern communities is relatively straightforward, constructing accurate cenograms for fossil localities is much more difficult. This is especially true in South America, owing to the lack of precise stratigraphic and geographical information for many classic fossil-bearing localities and the often exaggerated diversity of the many fossil groups in need of taxonomic revision. Of the large number of fossil faunas in South America, eight were chosen as most likely to be reliable based on the following characteristics: (a) there should be a restricted time interval represented, (b) they should be collected from a restricted geographical area, (c) there should be a high sampling intensity, and (d) there should be availability of up-to-date

taxonomic information. These represent assemblages from seven fossil localities (one with two faunal zones) ranging from ?middle/late Eocene to late Pleistocene in age (Table 1). Where possible, only specific stratigraphic levels at each locality were used (i.e. rather than all levels within a fossiliferous unit) to minimize problems associated with time-averaging of deposits. Such time-averaging may obscure or alter patterns in cenogram curves. In certain instances, published faunal lists were updated using data from more recent publications or from personal observations. A complete list of the included taxa can be found in Croft (2000) or can be obtained electronically by request from the author.

Body mass data and estimations An entire book (Damuth & MacFadden, 1990) has been written on the challenges associated with estimating body masses from mammalian osteological remains. Most studies using cenograms have regressed body size on molar measurements in extant mammals to estimate body masses for fossil taxa. This method works reasonably well for most animals, especially when the reference data base used to calculate the regressions includes species that are either closely related to or morphologically similar to the extinct animal in question (Legendre, 1986; Janis, 1990). With teeth being the most commonly fossilized and easily identifiable part of a mammal, basing estimates on dental remains has the added benefit of being widely applicable. In South America, however, many fossil mammal taxa are endemic to the continent and have either no living descendants or only morphologically dissimilar ones (e.g. notoungulates, litopterns, ground sloths and glyptodonts; Patterson & Pascual, 1972; Simpson, 1980; Cifelli, 1985b; Flynn & Wyss, 1998). Accordingly, it is more difficult to accurately estimate body masses for these taxa using only dental remains. Therefore, whenever possible (for approximately 40% of included species), mass estimates for specific fossil taxa were taken directly from the literature; it was assumed that these estimates would be more accurate than those calculated by single regressions since most of these articles are authored by specialists on the groups in question and incorporate data from postcranial remains or complete skeletons. For

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Luján Local Fauna, Argentina Not applicable Lujanian 8

Late Pleistocene

Santa Cruz, Argentina Santa Cruz, Argentina La Venta, Colombia Tarija, Bolivia Protypotherium attenuatum Zone Protypotherium australe Zone Monkey Beds Not applicable Santacrucian Santacrucian Laventan Ensenadan 4 5 6 7

Early to middle Miocene Early to middle Miocene Late middle Miocene Middle Pleistocene

Deseadan 3

Late Oligocene

Not available

Salla, Bolivia

Cifelli (1985a) Wyss et al., 1994; Flynn & Wyss (1999); Hitz et al., 2000; J.J. Flynn et al., unpublished MacFadden et al. (1985); Shockey, 1997 Tauber, 1997a Tauber, 1997a Kay & Madden, 1997b Hoffstetter (1963); MacFadden & Shockey (1997) Tonni et al. (1985) Gran Barranca, Argentina Termas del Flaco, Chile Barrancan Not applicable Casamayoran Tinguirirican 1 2

?Late Eocene ?Late Eocene — Early Oligocene

Source Locality Stratigraphic level Age SALMA List

Table 1 Geographic and stratigraphic data for South American fossil mammal localities used in cenogram analyses. SALMA = South American Land Mammal ‘Age’

Environment and mammalian body size distributions

273

most of the remaining taxa (approximately 55% of included species), masses were predicted using regressions of body mass on molar length calculated for recent mammals (see Croft, 2000). Molar length was used instead of area because length values tend to be more highly correlated with body mass in living ungulates (Janis, 1990). Body masses for extant mammals were taken from Olrog & Lucero (1980), Mares et al. (1989), Hayssen (1993), Emmons (1997) and issues of Mammalian Species . For fossil ground sloths, some mass data were taken from the literature, but in most cases masses were estimated using a regression equation of body mass on bicondylar width of the femur. A postcranial measure was used for this group instead of a dental measure because dental regressions are not appropriate for estimating body size in xenarthrans; dental homologies within the group are unclear and many xenarthrans have significantly reduced and /or homodont dentitions (Fariña et al., 1998). Bicondylar width of the femur was chosen because femoral dimensions are usually highly correlated with body mass (Scott, 1990; Fariña et al., 1998) and because, among fossilized postcranial bones, distal femora are common. Additionally, the femoral condyles in ground sloths do not show the extreme enlargement evident in the shaft of the bone, which often leads to overestimates of body mass (Fariña et al., 1998). Because there are no extant ground sloths, the bicondylar regression was calculated using body mass estimates for extinct ground sloths taken from the literature (Fariña et al., 1998; personal communication with R. Fariña and S. Vizcaíno). Although this is mathematically tenuous — using estimated masses to estimate other masses — the resulting ground sloth mass estimates are likely to be much more accurate (see below) than those which would have resulted using only extant mammals, such as tree sloths and /or bears (the extant mammals that are most closely related to, or probably most similar in proportion to, extinct ground sloths, respectively). This is demonstrated in Table 2, which compares ground sloth mass estimates collected from the literature to those calculated using various regressions of body mass on bicondylar width of the femur in extant mammals. Although mass estimates from regressions for some of the large ground sloths are close to those

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Table 2 Comparisons of mass estimates for fossil sloth taxa. See Croft (2000) for regression equations used to calculate body masses from extant taxa Taxon

Mass estimate from literature (source)

Mass estimate based on extant bears, sloths and giant anteater

Mass estimate based on extant bears only

Hapalops longiceps Hapalops longiceps Scelidotherium leptocephalum Glossotherium robustum Lestodon armatus Megatherium americanum

25 kg 40 kg 1119 kg 1713 kg 3397 kg 3950 kg

156 kg 156 kg 2039 kg 1814 kg 3975 kg 11730 kg

164 kg 164 kg 1944 kg 1737 kg 3693 kg 10449 kg

(White, (White, (Fariña (Fariña (Fariña (Fariña

1997)* 1997)* et al., 1998) et al., 1998) et al., 1998) et al., 1998)

* White’s estimate of 25 kg for Hapalops was obtained using the computer program BODYMASS (Gingerich, 1990). Her estimate of 40 kg is based on regressions of body mass on femoral head diameter using extant xenarthrans. The former compares well with an estimate of 25.5 kg calculated using scapular length (Sherman, 1984).

calculated by Fariña et al. (1998), other large ground sloth estimates are two to three times larger. Additionally, the mass estimates from regression for the smaller sloth taxon, Hapalops longiceps, are four to six times larger than estimates collected from the literature. It is quite possible that this tendency to overestimate sloth body masses when using data from extant bears stems from peculiarities of the ground sloth hindlimb and locomotor patterns (Bargo & Vizcaíno, 1997). Masses for cingulates (glyptodonts and armadillos) were either taken from the literature or estimated based on comparisons of carapace size with extant armadillos. Carapace size was used for the estimations rather than some other factor because: (a) cingulates are relatively uniform in their overall morphology (i.e. there are no extreme deviations in the size of the head or the limbs in proportion to the rest of the body, with the exception of the large tail clubs present in some glyptodonts); (b) the carapace represents the external limits of the majority of the body (all except the head and portions of the limbs), and therefore is approximately equal to the volume of the animal (which is proportional to mass); (c) the relative proportion of carapace mass to non-carapace mass should be the same between animals of similar size (this attribute of cingulates makes their mass difficult to estimate when compared to other animals without significant dermal armour); and (d) the carapace (or a portion of the carapace) is the most commonly

recovered element from cingulates. This method also was used by Kay & Madden (1997b) for their body mass estimates of cingulates.

Comparisons with recent faunas As demonstrated by Gingerich (1989), using data from Legendre (1988), certain attributes of cenograms are quantifiable and correlated with habitat, including the slope of the medium-sized mammals (those with mass between 500 g and 250 kg) and the vertical offset at the break between the medium and small mammals (500 g). The number of medium-sized mammals present in a fauna decreases with decreasing annual rainfall, resulting in a greater (steeper) slope in that portion of the cenogram (Fig. 1). The size of the gap (the vertical offset in ln(mass) units) between mammals greater than 500 g and those less than 500 g is large in more open environments (e.g. woodlands and savannas) and is small in closed, forested environments (Fig. 1). Although Gingerich speculated that the relative scarcity of mammals of about 500 g in more open environments is somehow related to the lesser size range, density and diversity of leaves and insects in these habitats as compared with forests, it has yet to be conclusively demonstrated why this gap exists. In order to interpret palaeoenvironmental conditions, data from fossil localities were compared with data from modern South America mammal faunas (Table 3). Faunal lists for these 16 modern

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Fig. 1 Examples of cenogram plots for a moist forest (left) and an open desert (right). Note that high species diversity results in a shallow slope (left) while low species diversity results in a steep slope (right). Also note the lack of an obvious vertical offset at 500 g in the closed environment (left) and the presence of a marked vertical offset at 500 g in the open environment (right). Forest data are from Makokou, Gabon, and desert data are from Monte Desert, Argentina (Legendre, 1988). Table 3 Geographic data for 16 modern South American lowland mammal faunas (modified from Kay & Madden, 1997a) Locality (fauna)

State (province), country

Annual rainfall (mm)

(1) Guatopo

Miranda, Venezuela

1500

(2) Masaguaral

Guarico, Venezuela

1250

(3) Puerto Páez

Apure, Venezuela

1500

(4) Puerto Ayacucho

Amazonas, Venezuela

2250

(5) Esmeralda

Amazonas, Venezuela

2000

(6) Manaus (7) Belém

Amazonas, Brazil Pará, Brazil

2200 2600

(8) Caatingas (9) Federal District

Exu, Pernambuco, Brazil Brasilia, Brazil

< 500 1586

(10) Acurizal

Mato Grosso, Brazil

1120

(11) Chaco

Salta, Argentina

700

(12) Transitional Forest

Salta, Argentina

700–900

(13) Low Montane (14) Cocha Cashu (15) Rio Cenepa

Salta, Argentina Madre de Dios, Peru Amazonas, Peru

(16) Ecuador Tropical

Oriente, Ecuador

800 2000 2880 1795 – 4795

Vegetation

Semideciduous, submontane to montane forest Subtropical vegetation mosaic high savanna Seasonally flooded high grass savanna with scattered patches of low forest and palms Savannas of the Rio Orinoco and evergreen forest/savanna mosaic Nearly continuous evergreen forest in valley up to low dense montane forest Primarily upland terra firma forest Vicinity of Belém, now urban and suburban Semiarid caatinga Seasonal xerophyllous savanna grasslands and gallery forests Pantanal; pastures, secondary forest, cerrado and deciduous forests Subtropical, drought-resistant, thorn forest Transitional deciduous forest with trees 20 – 30 m tall Lower montane moist forest Lowland floodplain rainforest Abandoned fields, secondary regrowth riparian forest, undisturbed humid forest Amazonian lowland evergreen rainforests

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Table 4 Cenogram statistics for modern and fossil South American mammal faunas Fauna

Slope for medium mammals

Size of gap at 500-g

Modern faunas Guatopo Masaguaral Puerto Páez Puerto Ayacucho Esmeralda Manaus Belém Caatingas Federal District Acurizal Chaco Transitional Forest Low Montane Cocha Cashu Rio Cenepa Ecuador Tropical

– 0.305 – 0.324 – 0.415 – 0.247 – 0.165 – 0.199 – 0.185 – 0.325 – 0.245 – 0.241 – 0.301 – 0.286 – 0.632 – 0.169 – 0.191 – 0.156

0.560 1.076 1.299 0.487 0.453 0.160 0.413 0.788 0.487 0.397 0.501 1.277 0.802 0.307 0.374 0.304

Fossil faunas Gran Barranca (Casamayoran SALMA) Tinguiririca (‘Tinguirirican’ SALMA) Salla (Deseadan SALMA) P. attenuatum Zone (Santacrucian SALMA) P. australe Zone (Santacrucian SALMA) Monkey Beds (Laventan SALMA) Tarija (Ensenadan SALMA) Luján (Lujanian SALMA)

– 0.124 – 0.151 – 0.183 – 0.245 – 0.197 – 0.141 – 0.294 – 0.492

0.060 0.970 0.263 0.256 0.256 1.212 0.301 1.030

localities were taken from Kay & Madden (1997a). Body masses for living taxa were taken from the literature or were based on data from mammal specimens in the Division of Mammals, Field Museum, Chicago, U.S.A. For those few taxa for which no mass data were found and which were not present in the mammal collections, masses were estimated based on the average mass of other members of the genus. Complete faunal lists with mass data can be found in Croft (2000).

RESULTS The mass data from the 16 modern South American mammal faunas were log-transformed and ranked in decreasing order. The cenogram statistics discussed above were calculated and are presented in Table 4 along with the same statistics calculated for the fossil localities. Slopes were

calculated using least-squares regression Statview 4.1 for the Macintosh computer.

Modern South American faunas Significance tests support the assertion that predictable relationships exist between cenogram statistics calculated for modern South American faunas and environmental variables. Specifically, a positive relationship exists between the number (slope) of medium-sized mammals and total annual rainfall (P < 0.005, rho corrected for ties = 0.746, Spearman’s rank correlation; see Fig. 2) and a large gap at 500 g tends to characterize open habitats (P < 0.01, Scheffe’s F-test; see Fig. 2). This contrasts with the results of Gingerich for localities world-wide (1989) in which he was able to discriminate combined savanna and woodland faunas from forest faunas but was unable to

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Fig. 2 Bivariate plots for modern South American faunas illustrating the relationship between mean annual rainfall and slope of the medium-sized mammals (left) and between vegetation structure and the gap in the body size distribution at 500 g (right). For specific values for each fauna see Table 4.

discriminate between savanna faunas and woodland faunas. It should be noted in this study, however, that the size of the gaps in two forest localities (localities no. 12 and 13 with gaps of 1.277 ln(g) and 0.802 ln(g), respectively) fall within the range of values exhibited by savanna habitats (0.788–1.299 ln(g) ) and therefore savannas cannot be unequivocally discriminated from forest and woodland faunas based solely on gap size.

Fossil South American faunas In order to examine palaeoenvironmental parameters, the fossil faunas were plotted with the modern faunas, using the slope for the medium mammals (correlated with rainfall) and the gap at 500 g mass (correlated with vegetation structure). These plots are presented in Fig. 3. The oldest of the faunas, the late Eocene Barrancan (a sub-age of the Casamayoran South American Land Mammal ‘Age’ (SALMA) ) from Patagonia, Argentina plots in the lower right-hand corner of Fig. 3. This suggests a closed, moist habitat, and agrees well with traditional interpretations of Eocene environments (Kay et al., 1999). The steep slope of the Tinguiririca Fauna of Chile suggests abundant rainfall, although the large

gap at 500 g is typical of more open habitats. This combination is unique relative to the modern South American faunas under consideration, but is similar to that postulated for the middle Miocene La Venta Fauna (see below). A more thorough study of the palaeoenvironment at Tinguiririca based on several lines of evidence ( J.J. Flynn et al. unpublished) suggests that an open habitat was present, but that rainfall was not nearly as great as that suggested by cenogram analysis. The late Oligocene Deseadan SALMA fauna from Salla, Bolivia, plots in the lower right-hand corner of Fig. 3, near modern closed, moist habitats. The presence at Salla of the earliest South American primate, Branisella boliviana, would seem to support the interpretation of at least a moderately moist environment with some trees. However, the faunal list (Shockey, 1997) used in this study did not distinguish between different stratigraphic levels at Salla, and MacFadden et al. (1985) note that faunal changes are present in the Salla sequence. Therefore, if one were to analyse the lower and upper beds at Salla separately, it is possible that different environmental interpretations would result. However, the lack of published faunal lists for these subdivisions precluded such an analysis as part of this study. The

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1.4

1.2

VEGETATION STRUCTURE 8

1.0

Gap at 500g

0.8

0.6

0.4

Forest or woodland

Savanna

0.2

Unknown (fossil) 1

0 –0.7

–0.6

–0.5

–0.4

–0.3

–0.2

–0.1

1.4

1.2

ANNUAL RAINFALL

1.0

Gap at 500g

6 8 2

0.8

0.6

High (>2500 mm/yr) 0.4

Medium (2000-2499 mm/yr) Low (1000-1999 mm/yr)

0.2

Arid (<1000 mm/yr)

Unknown (Fossil) 0 –0.7

–0.6

–0.5

–0.4

–0.3

–0.2

–0.1

Slope of medium-sized mammals

Fig. 3 Bivariate plots of modern and fossil faunas using slope for the medium-sized mammals and size of gap in body size distribution at 500 g. Faunas are grouped by vegetation structure (above) and mean annual rainfall (below). Numbers refer to fossil faunas: 1 = Gran Barranca (Casamayoran SALMA); 2 = Tinguiririca (‘Tinguirirican’ SALMA); 3 = Salla (Deseadan SALMA); 4 = Protypotherium attenuatum Zone (Santacrucian SALMA); 5 = Protypotherium australe Zone (Santacrucian SALMA); 6 = Monkey Beds (Laventan SALMA); 7 = Tarija (Ensenadan SALMA); 8 = Luján (Lujanian SALMA).

presence of hypsodont and presumably opencountry adapted taxa at Salla implies that at least some open areas were present (Shockey, 1997). Perhaps Salla might better be described as a mixed habitat. The results for the two faunas from the early to middle Miocene Santacrucian SALMA, the Protypotherium attenuatum Zone and the Protypotherium australe Zone, are very similar, suggesting no environmental characteristics distinguish them. Although usually interpreted as a mixed habitat due to the presence of at least one primate

(Homunculus) in addition to many (presumably) savanna-adapted mammals, the cenogram statistics suggest the area was moister and less open than previously thought. The lack of differentiation between the two faunas is notable because it contrasts with Tauber’s (1997b) suggestion (based on various lines of faunal evidence) that the climate deteriorated and became less humid and more open during this interval. If anything, this analysis suggests slightly greater rainfall in the upper (P. australe) zone, as indicated by the slope of the medium-sized mammals. The middle Miocene Monkey Beds fauna from La Venta, Colombia has been the subject of a great deal of systematic and palaeobiological research (see Kay et al., 1997 and references therein) and the results presented here agree with previous palaeoenvironmental interpretations (Kay & Madden, 1997a, 1997b). The position of the Monkey Beds fauna is distant from modern faunas in Fig. 3, suggesting a unique combination of a moist yet partially open environment. It seems best described as a woodland habitat, as suggested by the diversity of primates (five species). Finally, the two Pleistocene localities vary significantly in their positions in Fig. 3. The older Ensenadan SALMA fauna of Tarija, Bolivia, plots in the lower right-hand corner of the graph. The position of Tarija to the left of the other fossil faunas suggests it received lower annual rainfall, but the 500 g gap is relatively small, suggestive of a forest or woodland habitat. Its interpretation as a woodland supports palaeoecological studies of the feeding habits of Tarija’s large mammals (MacFadden & Shockey, 1997), which found a fairly even distribution of herbivores mammals among grazing, browsing and mixed-feeding guilds. In contrast, the cenogram statistics for Luján, Argentina, indicate a dry and open habitat. The presence of a large number of very large animals, many presumably adapted for grazing, supports its interpretation as a mostly open, savannagrassland habitat (Fariña, 1995).

DISCUSSION The results for the South American palaeofaunas vary in their relationship to traditional palaeoenvironmental interpretations. Some results are primarily consistent (e.g. the late Eocene Barrancan

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Environment and mammalian body size distributions level), others are primarily inconsistent (e.g. the middle Miocene Santacrucian faunas), and yet others seem partially consistent with available data (e.g. the late Oligocene Salla fauna). Before exploring potential biological explanations for some of the discordance in these interpretations, certain potential methodological errors should be addressed: specifically, the precision of the faunal associations and the body mass estimates.

Faunal associations As was discussed briefly above, a cenogram analysis ideally includes all mammals known from a single locality, from a restricted interval of time. When dealing with fossil assemblages, it is not possible to know which mammals in the living assemblage have not been preserved. Usually these animals tend to be smaller rather than larger ones, as the bones of smaller mammals are less likely to survive the fossilization process and be recovered. For this reason, screen washing is usually necessary, a process that is not ubiquitous in fossil collecting. While some of the fossil faunas examined above are likely to include most of the mammals that were present at the time of deposition (e.g. Gran Barranca and La Venta) it is quite possible that some small mammals are lacking from some of the others. However, since cenogram statistics primarily rely on the diversity of mammals 500 g or larger, an artificially small sample of the smallest mammals (those less than 500 g) should not have a significant effect on palaeoenvironmental interpretations. Similarly, the period of time represented by fossil-bearing strata is not always known. Detailed biostratigraphy is necessary to determine if changes in faunal composition occur through a section, potentially signalling environmental change. Where such changes do occur, different parts of the section should be analysed separately in an effort to determine the environment at different points in time. If, for example, a forest fauna were gradually replaced by a woodland and then a savanna fauna, all in the same location, lumping these faunas together would result in a single, erroneous, palaeoenvironmental interpretation. Although these sorts of effects cannot be ruled out for the Deseadan Salla Beds and, to some extent, the Barrancan (Casamayoran SALMA) of the Gran Barranca, these effects should be

279

minimal for the other faunas considered. Given the generally short lengths of the Ensenadan and Lujanian SALMAs, time averaging should not be significant at Tarija and Luján, respectively. Both Santacrucian faunal lists come from restricted intervals in a detailed biostratigraphic study of these deposits (Tauber, 1997a) and therefore probably represent the ideal stratigraphic precision for a cenogram study (although the sampling was certainly not as extensive as that at some of the other localities). The same can be said for the La Venta Fauna, which has excellent stratigraphic control and sampling. The Tinguiririca Fauna has been collected from a single fossil-bearing unit ( Wyss et al., 1994, 1996) of unknown temporal length. However, the geographically and stratigraphically restricted nature of the deposit and the restricted taxonomic diversity of the fauna (as compared to the other fossil faunas) suggest that time-averaging was not a significant problem.

Body mass estimates Another potential source of error is body mass estimates for fossil taxa. To investigate how errors in regressions of body mass could affect cenogram statistics (and interpretations), a series of simulations were run using a C-based program for the PC. Two modern faunas were chosen for the simulations, Puerto Páez and Cocha Cashu, which represented a dry savanna and a relatively moist forest, respectively. Since masses for fossil taxa are most commonly estimated using the natural log of body mass (as opposed to body mass itself ) in living mammals, the mass data for the modern faunas were log-transformed for the simulations. Each simulation consisted of 100 replicates. In each replicate, each original mass estimate was recalculated by adding a positive or negative error value, expressed as a percentage of the original mass, taken from a normal distribution with a mean of one and an adjustable standard deviation. The resulting masses were then re-sorted in descending order and cenogram statistics were calculated. For both of the modern faunas, simulations were run using standard deviations ranging from 0.01 to 0.50 (1% to 50% ‘error’ in natural log body mass estimates; see Table 5). Arbitrary values were chosen because a variety of regression equations were used to estimate fossil body

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Table 5 Examples of normally distributed errors (added to the natural log of body mass) converted back into standard mass units. Range represents 95% confidence interval (masses calculated ± 2 standard deviations) Mass

Error

10 g 100 g 1000 g (1 kg) 10 kg 100 kg 1000 kg

10%

25%

50%

9.55 –10.47 g 91.2–110 g 871–1148 g 8.3 –12 kg 79.4 –126 kg 759 –1318 kg

7.94 –12.59 g 63 –158 g 501–1995 g 4 –25.1 kg 31.6 –316 kg 251–3981 kg

6.31–15.85 g 39.8 –251 g 251–3981 g 1.6 – 63 kg 10 –1000 kg 63–15849 kg

3.16 –31.6 g 10 –1000 g 31.6 –31622 g 0.1–1000 kg 0.3 –31622 kg 1–1000000 kg

1–100 g 1–10000 g 1 g –1000 kg 1–100000 kg 1–1 × 107 kg 1–1 × 109 kg

Table 6 Cenogram statistics for error simulations. Data presented include mean ± standard deviation (range). (a) Savanna (Puerto Páez, modern fauna no. 3); (b) Forest (Cocha Cashu, modern fauna no. 14) (a) Savanna Error 0%

(b) Forest Medium slope – 0.415

500-g gap

Error

1.299

Medium slope – 0.169

500-g gap 0.307

– 0.415 ± 0.0082 (– 0.434 – 0.389)

1.298 ± 0.087 (1.073 – 1.511)

–0.165 ± 0.0049 (– 0.173 – 0.154)

0.257 ± 0.066 (0.0451 – 0.409)

– 0.429 ± 0.045 (– 0.542 – 0.300)

1.209 ± 0.421 (0.213 – 2.262)

– 0.162 ± 0.0090 (– 0.180 – 0.142)

0.159 ± 0.099 (0.005 – 0.501)

10%

– 0.462 ± 0.070 (– 0.748 – 0.318)

1.161 ± 0.60 (0.049 – 2.667)

10%

– 0.167 ± 0.018 (– 0.216 – 0.122)

0.185 ± 0.115 (0.013 – 0.435)

25%

– 0.588 ± 0.160 (– 1.120 – 0.291)

1.091 ± 0.653 (0.121 – 2.929)

25%

– 0.190 ± 0.027 (– 0.269 – 0.120)

0.231 ± 0.154 (0.013 – 0.746)

50%

– 0.869 ± 0.36 (– 2.243 – 0.217)*

1.142 ± 0.774 (0.038 – 3.192)

50%

– 0.236 ± 0.045 (– 0.360 – 0.163)

0.308 ± 0.230 (0.020 – 1.120)

* One simulation resulted in only a single medium-sized mammal; slope was undefined.

masses and because the true error variance is unknown. It should be kept in mind that the apparent asymmetry of the estimated masses around the true masses in Table 5 only results from the conversion of natural log values back into standard mass units; this is analogous to the curvature that results when a straight logarithmic plot is plotted in standard mass units. Though the ranges presented in Table 5 may seem counterintuitive, they simulate the ranges of actual errors expected when calculating body masses using log-based regressions. The mean, standard deviation, and range for each cenogram statistic for the combined 100

replicates in each simulation are presented in Table 6. The data in Table 6 demonstrate how much confidence one can have regarding the accuracy of cenogram statistics, given certain assumptions about the accuracy of mass estimates. The range of simulated slopes is large when moderate amounts of error are present in the savanna fauna (10% or greater error), but remains small with greater amounts of error in the forest fauna (up to 50% error). The range in gap size at 500 g follows a trend similar to that for the slopes; the range is large at only 5% error for the savanna fauna and at 25% error in the forest fauna.

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Environment and mammalian body size distributions These results indicate that cenogram accuracy depends considerably on the accuracy of mass estimates. After looking over the examples presented in Table 6, one might assume errors between 5% and 25% to be reasonable. Although this is probably reasonable for medium to large mammals, this may not be so for smaller ones. For example, given the skeleton of a 10-kg dog, one would likely be able to estimate its mass between 4 kg and 25 kg (10% error range in natural log units). However, given the skeleton of a 10-g marsupial, one might have difficulty estimating its mass between 3 g and 30 g (50% error range in natural log units). This is likely due to our own relatively large size and the tendency for measurement errors to be larger when measuring smaller objects. It should be kept in mind, however, that some mass estimate problems are also encountered when constructing cenograms for modern communities; accurate mean mass values for species are difficult to find, and even very accurate mass estimates for species can obscure subspecific or geographical variation in mass that may occur at a locality. Despite these and other problems, statistically significant correlations do exist between environmental attributes and cenogram statistics for modern communities, as demonstrated above. Additionally, cenograms for fossil localities through time do show predicted correlations with environmental change (Legendre, 1986), suggesting that palaeocommunity cenograms also contain useful information and should not be dismissed on the grounds of inaccuracy in body mass estimates alone. A partial explanation may be that the smallest mammals — for which mass is perhaps the most difficult to estimate and which are most prone to be missed in palaeontological sampling — should not have a significant affect on cenogram statistics, which primarily rely on mammals approximately 500 g and larger. The simulations suggest that closed environments with high rainfall (i.e. faunas with small gap values and shallow slopes) are more likely to be captured accurately by cenogram analysis than open environments with little rainfall (i.e. faunas with large gap values and steep slopes). This results from the greater number of data points (mammal species) in wet forest habitats in comparison to dry savanna habitats (and a corresponding lesser importance of any single species

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on slope and gap values). Poor mass estimates have a greater effect on gap values than on slope values, so interpretations about vegetation should be considered more tenuous than estimates of rainfall (which are relatively robust). A paradoxical implication is that because the odds of a fossil savanna fauna being interpreted incorrectly (i.e. as a forest fauna) are greater than the odds of a fossil forest fauna being interpreted incorrectly (i.e. as a savanna fauna), one should have greater confidence in a fossil assemblage interpreted as a savanna than in one interpreted as a forest. However, this may not be entirely true. Since the potential for fossilization tends to be lower in humid forest environments, the faunal lists for these palaeoenvironments may tend be less exhaustive than those from environments with greater potential for fossilization. Intensive sampling of fossiliferous localities should minimize any such problems.

A potential explanation Because it is unlikely that any of the systematic methodological errors discussed above are creating significant errors in cenogram analyses, potential biological factors must be considered. The basic premise of cenogram analysis is that the diversity of medium-sized mammals (as indicated by slope and gap values) depends primarily on rainfall and vegetation structure. If this assumption is violated (i.e. if other factors significantly affect the diversity of medium-sized mammals) then erroneous interpretations could result. I propose that one such confounding factor could be the diversity of mammalian predators. For the 16 modern South America faunas studied here, the diversity of mammalian predators correlates significantly (P = 0.0058, rho corrected for ties = 0.713, Spearman’s rank correlation) with the diversity of medium-sized mammals (500 – 8000 g, as originally defined by S. Legendre). No significant relationship exists between predator diversity and mammals smaller than 500 g nor larger than 8 kg. There also is no significant relationship between predator diversity and total annual rainfall (P = 0.125, Spearman’s rank correlation) nor vegetation (P-values = 0.148, 0.503 and 0.772 (Sheffe’s F-test) for  pairwise comparisons between forests, mixed habitats and savannas), suggesting that the relationship between

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D. A. Croft 18

Predator diversity

predators and prey is not the result of predator diversity and prey diversity being similarly influenced by one of the habitat variables. The causal nature of this apparent relationship between predators and prey is unknown (i.e. whether predator diversity directly depends on the diversity of prey and /or whether prey diversity is partly limited by the diversity of predators). On islands (where predators are often scarce or absent), small taxa tend to increase in size while large taxa tend to undergo dwarfism (Sondaar, 1977; Lomolino, 1985). In both cases, mammals move closer to what might be considered an ‘optimal’ body mass. Based on a mathematical model of fitness, Brown, et al. (1993) suggest this optimum is approximately 100 g. Body mass data from island taxa, however, suggest this optimum is closer to 500 g (Lomolino, 1985). Regardless of the precise optimal body mass, it is unclear what prevents species from approaching this optimum on the mainland (although several hypotheses have been put forth, including resource limitation, interspecific competition and predation (Melton, 1982; Lomolino, 1985; Brown, 1995) ). The fact that small and large mammals tend to converge toward medium size on islands (where predator diversity is often reduced) suggests that predation may be partly responsible for limiting the diversity of medium mammals in their continental habitats. It seems reasonable that an animal would have a more difficult time avoiding predation in open country than in a more forested environment, especially if that animal is of medium size. If so, then the effects of predator diversity in limiting prey diversity might be expected to be more pronounced in open habitat faunas than in a closed ones. In other words, for a given diversity of predators, one would expect a lower diversity of medium-sized mammals in an open (savanna) environment than in a more closed one (woodland or forest). Perhaps such a relationship (i.e. greater predation pressure on medium-sized mammals) might somehow help account for the larger gap at 500 g typical of savanna faunas. Figure 4a presents predator diversity plotted against medium mammal diversity for the 16 modern South American faunas. The figure suggests that for a given number of predators, there generally are fewer medium-sized mammals in open habitats than in the closed ones. However,

Forest or woodland Savanna

4 4

18 16 14 7

Predator diversity

282

12 10 8

8 6 6

Forest or woodland

4 2

Savanna Unknown (fossil)

Medium mammal diversity

Fig. 4 Bivariate plots of predator diversity vs. diversity of medium-sized prey mammals (mass between 500 g and 8000 g). (a) Modern faunas only; (b) Modern plus fossil faunas; solid line represents the least-squares regression line (calculated for the modern faunas only); dashed lines represent 95% confidence intervals for the mean.

since the difference is not statistically significant due to small sample size, the relationship may or may not be real. Still, such a relationship between predators and prey, combined with the unique composition of the mammal fauna present in South America throughout much of the Cenozoic (Patterson & Pascual, 1972; Simpson, 1980; Flynn & Wyss, 1998), may account for some of the discrepancies among palaeoenvironmental interpretations observed in this study. Due to South America’s relative isolation for much of the Cenozoic, many mammal groups present on other continents were absent from South America until the earliest stages of the Great American Biotic Interchange (GABI) of the late Cenozoic (Anaya & MacFadden, 1995). Among others, these groups included the diverse Carnivora (Marshall et al., 1982;

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Environment and mammalian body size distributions

283

Table 7 Predatory (Pred.), medium-sized (Med.), and total non-predatory (Tot.) terrestrial mammal diversity from modern and fossil South American communities, the Great American Biotic Interchanges (GABI) are indicated

Modern faunas Guatopo (1) Masaguaral (2) Puerto Páez (3) Puerto Ayacucho (4) Esmeralda (5) Manaus (6) Belém (7) Caatingas (8) Federal District (9) Acurizal (10) Chaco (11) Transitional Forest (12) Low Montane (13) Cocha Cashu (14) Rio Cenepa (15) Ecuador Tropical (16)

Pred

Med.

Tot.

8 8 5 6 8 7 15 6 9 10 9 14 6 12 14 17

10 8 7 12 19 13 18 8 14 13 9 11 6 19 19 22

32 22 19 39 58 44 47 16 58 32 27 31 20 58 48 64

4 0 5 1 2 5

26 12 16 12 11 21

43 23 35 30 32 49

13 9

8 7

43 42

Pre-GABI fossil faunas Gran Barranca (Casamayoran SALMA) Tinguiririca (‘Tinguirirican’ SALMA) Salla (Deseadan SALMA) P. attenuatum Zone (Santacrucian SALMA) P. australe Zone (Santacrucian SALMA) Monkey Beds (Laventan SALMA) Post-GABI fossil faunas Tarija (Ensenadan SALMA) Luján (Lujanian SALMA)

Webb, 1991). In the absence of the carnivorans, the mammalian carnivore palaeoguild was filled primarily by borhyaenoid marsupials. Additionally, large, terrestrial, phororhacoid birds and terrestrial crocodiles may also have been important large predators (Marshall, 1977). The depauperate nature of the carnivore palaeoguild in South America is evident in Table 7. While the number of mammalian predators in modern South American faunas ranges from five (in Puerto Páez) to 17 (in Ecuador Tropical), the number of predators in pre-interchange faunas (the Monkey Beds and older) ranges from zero (none have yet been found at Tinguiririca) to a maximum of five. The two post-interchange faunas, Tarija and Luján, have predator numbers more typical of modern faunas (13 and 9, respectively). Combining the data from fossil faunas with that

from modern faunas, it is evident that nearly all fossil faunas fall well outside the 95% confidence intervals for the regression line calculated for modern faunas (Fig. 4b). Additionally, the preinterchange and post-interchange faunas show quite different predator–prey distributions; while all pre-interchange faunas plot below the lower 95% confidence interval, the two post-interchange faunas fall above the upper 95% confidence interval. Given the observations regarding predator and prey diversity in modern faunas, it can be hypothesized that pre-interchange faunas could have had greater-than-expected numbers of medium-sized mammals due to the low diversity (and, perhaps, abundance) of mammalian predators. Furthermore, we would expect this effect to be more pronounced in open habitats than in closed ones. Thus, cenogram statistics computed for

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D. A. Croft

a pre-interchange fauna would suggest erroneously high amounts of rainfall (due to the greater than expected diversity of medium-sized mammals) and a possibly erroneous interpretation of a closed habitat (due to the potential for a reduced gap at 500 g). These predictions compare favourably with the results obtained in this study; inconsistencies in palaeoenvironmental interpretations are primarily restricted to pre-interchange faunas, and these tend to err in a direction consistent with greater-than-expected numbers of medium-sized mammals (i.e. smaller-than-expected gaps and shallower-than-expected slopes). The ‘greater-than-expected’ diversity of predators (at least in comparison to modern South American faunas) in the two post-interchange fossil faunas may result from the presence of large, placental carnivores preying on nowextinct large mammals (as has been hypothesized for animals like Smilodon; Akersten, 1985); this would account for the increased predator ‘carrying capacity’ of faunas at that time. Since most large predators and large prey were extirpated in the end-Pleistocene megafaunal extinction, such relationships would not be reflected in analyses of modern South American faunas. Although the South American fossil record suggests that the hypothesized interaction among predator diversity, medium-sized mammal diversity and environment may be real, further study is certainly warranted. In an expanded study of modern habitats, one would expect to find evidence for positive, but different, relationships between predators and medium-sized mammals in open and closed habitats (i.e. fewer mediumsized mammals for a given diversity of predators in open habitats than in closed ones). Preliminary data suggest this may be the case. Total predator and medium-sized mammal diversity were obtained for an additional 28 modern mammal faunas using sources cited in Legendre (1988). The data are presented graphically in Fig. 5. Several trends can be discerned. First, medium-sized mammals are less diverse in mixed habitats than in forests (P-value = 0.024, Sheffe’s F-test) and appear to be less diverse in savannas than in mixed habitats (although this is not statistically significant); nevertheless, average predator diversity remains remarkably constant with respect to vegetation structure (P-values = 0.449, 0.999 and 0.427 (Sheffe’s F-test) for  pairwise

Forest Woodland Savanna

Predator diversity

284

0 0

Medium mammal diversity

Fig. 5 Bivariate plot of predator diversity vs. diversity of medium-sized mammals (mass between 500 g and 8000 g) for 28 modern faunas. Data and sources are from Legendre (1988).

comparisons between forests, mixed habitats and savannas). This observation would tend to support the hypothesized relationship between predator and medium mammal diversity because medium-sized mammals are less diverse in open habitats than in closed ones for a given diversity of predators. However, predator diversity is highly variable and shows no significant correlation with the diversity of medium-sized mammals (P = 0.345, rho corrected for ties = 0.189, Spearman’s rank correlation); this contrasts with the results obtained for South American faunas and would seem to suggest that no special relationship exists. More conclusive results must await further data collection and analysis. Additional research in progress involves examining historical introductions of predators to islands with relatively low diversities of endemic mammalian predators (such as Australia). If the predator hypothesis is correct, these introductions would be expected to have the greatest detrimental effects on medium-sized mammals, especially those living in open habitats. Indeed, among native Australian mammals, scientists have found that extinctions and significant declines are virtually confined to non-flying species weighing between 35 g and 5500 g (the critical weight range or CWR; Burbidge & McKenzie, 1989). Moreover, they have noted that a species’ risk of extinction is positively correlated with arid or semiarid habitat preference, herbivorous or omnivorous diet, and locomotor style that confines the animal

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Environment and mammalian body size distributions to the ground’s surface (Maxwell et al., 1996). All of these attributes are strongly suggestive of the predation hypothesis as a major factor affecting medium mammal diversity. Precisely how such patterns might affect fossil cenogram analyses, however, is not presently known. Continued research in this area is underway. One final aspect of this study warrants comment: that predatory birds and large, terrestrial crocodiles were not included in the predator diversity counts for the fossil faunas, even though they could have played significant roles in predator–prey dynamics. These taxa were not included for several reasons. First, this study was restricted to mammals, and it is difficult to justify adding birds and /or reptiles to the predator side of the equation while not adding them to the prey side of the equation. It is also difficult to justify including predatory birds in the fossil faunas, but not including them in modern faunas. If ‘terror birds’ (phororhacoids) were to be included as predators in the fossil communities, why should not eagles also be included as predators in modern communities? (They certainly prey upon medium-sized mammals.) It may appear to be an arbitrary decision, but at least it is consistent, and actually follows closely with the original interpretations of Valverde (the first to use cenograms): that mammal, bird and reptile communities tend to generate patterns of interaction that are relatively independent of each other. Additionally, birds are much less likely to be preserved in fossil faunas, and including them would require unlikely assumptions about their probability of preservation. Perhaps most importantly, including terror birds and terrestrial crocodiles would not have significantly changed the results. Using the sources of faunal lists cited earlier, adding these predators would add one predator in the Deseadan Salla Beds and one in the Monkey Beds of La Venta. Using an independent data set, this study generally corroborates the findings of Legendre (1986) that cenogram analysis is a useful method for inferring palaeoenvironmental characteristics from fossil mammal assemblages. In general, cenogram rainfall estimates are more likely to be accurate than interpretations about vegetation, but more accurate mass estimates for fossil taxa can improve confidence in both cases. When applying modern methods of ecological investigation

285

to potentially non-analogue fossil communities (i.e. those with ecological associations or conditions not found in present-day communities), careful consideration of the faunas in question is required. In the case of cenogram analysis, incorporating additional information from fossil mammals and taking into account the peculiarities of South American palaeofaunas (i.e. the relatively depauperate predator guild) provide a more accurate (and interesting) picture of fossil mammal communities. It is hoped that this study will increase interest in cenograms and will stimulate new investigations of how they can be applied with precision to fossil assemblages.

ACKNOWLEDGMENTS This research was supported in part by the American Society of Mammalogists, the Hinds Fund, the Palaeobiological Fund, and a NSF Biodiversity Training Grant to The University of Chicago (NSF GRT-9355032). M.S. Bargo, M. Bond, R. Fariña, J. Flynn, J. Hopson, C. Janis, R. Madden, B. Shockey, L. Van Valen, S. Vizcaíno, P. Wagner, and A. Wyss provided helpful input during the development of this project. Additionally, S. Vizcaíno generously permitted the use of unpublished data for some extinct xenarthran body mass estimates. The manuscript was critically reviewed (and improved) by John Flynn and two anonymous reviewers. Guidance from C. Abraczinskas greatly contributed to the quality of the figures. Simulations of body mass errors would not have been possible without the expertise of G. Hunt. The author thanks J. Flynn (Field Museum), R. Pascual (Museo de La Plata), M. Reguero (Museo de La Plata), W. Stanley (Field Museum), and S. D. Webb (Florida State Museum) for access to specimens in their care.

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