- The Paleontological Society
This paper presents a method for constraining the age of a clade with the ages of the earliest fossil specimens in that clade's outgroups. Given a sufficiently deep, robust, well-resolved, and stratigraphically consistent cladogram, this method can yield useful age constraints even in the absence of specific information about the fossil preservation and recovery rates of individual taxa. The algorithm is applied to simulated data sets to demonstrate that this method can yield robust constraints of clade ages if there are sufficient fossil outgroups available and if there is a finite chance that additional outgroups may be discovered in the future. Finally, the technique is applied to actual fossil data to explore the origin of modern placental mammals. Using data from recently published cladograms, this method indicates that if all Mesozoic eutherians are regarded as outgroups of Placentalia, then the last common ancestor of modern placental mammals and their Cenozoic allies lived between 65 and 88–98 million years ago, depending on the assumed cladogram and the number of outgroups included in the analysis.
The fossil record is an important tool for reconstructing the history of life on Earth, providing critical constraints on when various groups of organisms arose or went extinct. Even analyses of DNA sequence data use fossil evidence to translate measures of genetic similarity into age estimates for cladogenic events. However, fossils represent only a sample of all the organisms that belonged to a given taxon, so it is not always straightforward to translate the ages of recovered fossils into information about the durations of specific clades. In particular, although a clade must be at least as old as its earliest fossil representative, it is not so obvious how to determine the maximum possible age of any given taxon based on fossil data.
Fossil preservation and recovery rates determine how long taxa could have existed prior to their oldest known fossil representatives, so many efforts to constrain clades' true durations first estimate the recovery rates of the relevant taxa in various ways (e.g., Strauss and Sadler 1989; Marshall 1997; Foote et al. 1999). However, when estimating the age of a given clade, one must consider not only that clade's own stratigraphic record but also its phylogenetic relationships with other taxa. For example, a large stratigraphic gap between the earliest known examples of two closely related taxa could imply the existence of an extended “ghost lineage” without fossil representatives (Norell 1993). Although there is still considerable debate on how best to combine stratigraphic and phylogenetic data (especially when there appear to be incongruities between the reconstructed phylogeny and the fossil record; see, e.g., Fisher 2008 and references therein), combinations of fossil age estimates and phylogenetic analyses have already proven useful for estimating rates of character change (Ruta et al. 2006) and placing older limits on clade ages (Marshall 2008). It therefore seems likely that the best constraints on the age of a given taxon will be those that utilize both phylogenetic and stratigraphic information.
This work presents a method for constraining a particular clade's age with the ages of the oldest fossil representatives of that clade's outgroups. Perhaps surprisingly, it turns out that given a sufficiently deep, robust, well-resolved, and stratigraphically consistent cladogram, these fossil dates by themselves can provide interesting constraints on clade ages. The method presented here therefore differs from previously published methods that use estimates of fossil recovery rates derived from the fossil record (Hueslenbeck and Rannala 1997; Wagner 2000) or relative lineage durations derived from molecular data (Marshall 2008) to help constrain the ages of the relevant taxa. This method could therefore provide a useful way to quantitatively evaluate hypotheses regarding the timing of cladogenic events in cases where the fossil recovery rates are difficult to constrain or molecular data are not available. On the other hand, the method presented in this paper relies on a relatively secure phylogeny, so its utility may be limited in situations where the phylogenetic reconstruction has significant uncertainties. Nevertheless, the analyses presented here could still help clarify how much information fossil outgroups can provide on clade ages, and so may be useful in efforts to develop a more generally applicable approach to dating the origins of taxa.
For clarity, the basic algorithm for constraining a clade's age is first illustrated by using the relatively simple case of a well-resolved, stratigraphically consistent cladogram with Hennig-comb topology. This is followed by a briefer discussion of how this method can be extended to handle cladograms with more complex or uncertain topologies, unresolved nodes, stratigraphic gaps, etc. Simulated data sets are used to explore the properties of the constraints derived by using this method and the sensitivity of the results to assumed parameters and sampling rates. These preliminary tests demonstrate that in certain limits this method can indeed produce sensible constraints on clade ages.
In order to illustrate how this technique might be applied to actual fossil data, I investigate the origin of modern placental mammals. The earliest clear fossil examples of Placentalia (the crown group that includes the last common ancestor of modern placental mammals and all of that animal's descendants) date from shortly after the K/T boundary 65 Myr ago (Rose and Archibald 2005). However, recent DNA analyses indicate that the last common ancestor of modern placental mammals lived around 105 Ma (e.g., Springer et al. 2003). This 40-Myr gap between the molecular estimate and the earliest fossil members of Placentalia has been discussed at length in the literature (e.g., Rose and Archibald 2005), but despite some important work by Foote et al. (1999) and Tavare et al. (2002), it has been difficult to quantify this seeming inconsistency between the molecular and fossil evidence. The algorithm developed here provides a new way to evaluate the magnitude and the significance of the difference between the molecular analyses and the fossil data.
The Dating Algorithm
The basic algorithm developed here can best be explained by first considering a simple case involving a cladogram like that shown in Figure 1. (More complex situations will be discussed below.) The nodes on this cladogram are labeled by the numbers j = 1,2,3, …, n and the times for each node are denoted by τ1, τ2, …, τn. Note that the topology of the cladogram requires that τ1 < τ2 < … < τn−1 < τn. The taxa derived from each node are designated by the numbers J = I, II, III, …, N. The earliest fossil representatives of these taxa occur at times tI, tII, tIII, …, tN. For simplicity, assume for now that the fossil record is stratigraphically consistent with this cladogram, so tI < tII < … < tN−1 < tN. For concreteness, the τj and tJ will be described as times throughout this paper, but in practice these parameters could just as well be regarded as depths in a stratigraphic column (Strauss and Sadler 1989) or positions in a sequence of fossiliferous localities (Wagner 1995). In any case, the goal is to obtain constraints on the possible values of τj based on the (known) values of tJ.
This analysis will be performed using a Bayesian statistical framework instead of a classical (frequentist) statistical approach. In classical statistics, unknown parameters like τj have fixed values and observed data are used to obtain estimates and confidence intervals on these parameters. By contrast, a Bayesian analysis yields the probability that the unknown parameters have particular values based on their consistency with the observed data. For example, consider a probabilistic process that yields a set of observable parameters O. The probability of observing particular values for O depends on a set of input parameters X as described by the function P(O|X). The probability that the input parameters have the values X given the observed values of O can then be expressed as follows: where C is a normalization constant and π(X) is the so-called prior probability, a function that specifies the probability that the parameters X have a set of given values in the absence of any information about O. The function p, which specifies the probability of X given the observed values of O, is known as the posterior probability distribution function. Further background on Bayesian methods can be found in statistics textbooks like Lupton (1993), and see Strauss and Sadler (1989) for a demonstration of how this technique can be applied to fossil data.
For this study, the observable parameters are the ages of each taxon's earliest fossil representatives tJ, and the unknown parameters we wish to constrain are the node times τj. However, the probability that the first fossil specimen of a given taxon has a certain age tJ depends not only on the corresponding node time τj, but also on rJ(t), the (possibly time-dependent) fossil recovery rate of taxon J. In general, the probability of obtaining a given value of tJ can be written as follows:
Here 𝛉(x) is the Heaviside step function (𝛉(x) = 1 for x > 0 and 𝛉(x) = 0 for x < 0), which imposes the constraint that tJ > τj. The function f specifies the probability that the first fossil representative of the clade occurs at a given time tJ after τj. If rJ(t) is allowed to vary with time, then f can become almost arbitrarily complex. However, f can always be expressed as the following product: where δt is an infinitely small time step and K = (tJ − τj)/δt. This product is explicitly the probability that a fossil is recovered at time tJ times the probability that no fossil is recovered in any previous time step. If rJ(t) is assumed to be constant in time, then this product reduces to the simple exponential form given in Strauss and Sadler (1989) and other texts.
Given the above form for P(tJ|τj, rJ(t)), equation (1) provides an expression for the posterior probability of τj and rJ(t) given an observed value of tJ: Because the prior probability that τj has a given value is not expected to be correlated with the fossil recovery rate rJ(t), the prior probability can be written as the product of two terms, one that gives the prior probability of τj and another that gives the prior probability of rJ(t): Combining equations (3)–(5) then yields This is the joint posterior probability distribution of τj and rJ(t). To obtain the probability distribution of τj alone, one must integrate the above expression over all possible values of rJ at every time step: Because only the functions π2 and f depend on rJ(t), this expression can be written as follows: where the function F is Further progress requires specifying the priors. In the absence of any other information, it is reasonable to assume that τj is equally likely to occur at any time after the formation of the previous node at τj−1, so one can assume where C′ is a constant.
The optimal choice of π2(rJ(t)) is less obvious, but it turns out to be useful to first rewrite the time-dependent recovery rate as the product of two terms: where RJ is the mean recovery rate and s(t) represents the time variations in rJ(t). The prior on rJ can then be expressed as Together with equations (3) and (9), this yields the following expression for F: Given no other information about the variations in the fossil recovery rate, it is reasonable to assume that at any time s(t) is equally likely to have any value between 0 and some arbitrary maximum value S. In this case, the above expression reduces to where Fo is a constant. This expression can be rewritten as
Now to specify the prior on the mean recovery rate. It might at first seem reasonable to assume that RJ is equally likely to have any value between zero and some maximal value. However, with this prior the function F is sensitive to the arbitrarily chosen maximal value. In order to make the posterior probability less explicitly dependent on the possible range of fossil recovery rates, consider an alternative prior with the following form:
Note that the term in the square brackets prevents this function from becoming infinite as RJ approaches zero. In the limit where the parameter RC approaches zero, the final integral in equation (15) becomes a constant, which removes any explicitly preservation-dependent terms in the expression for p. This choice of priors is therefore equivalent to assuming that tJ − τj is equally likely to have any positive value. In the absence of other information, this is not an unreasonable assumption to make, and furthermore it has practical advantages for this analysis. In particular, equation (8) reduces to a very simple form (note we also make the dependence on the previous node time explicit): where c is just a constant determined by the normalization criterion: which requires that c = 1/(tJ − τj−1), so the posterior probability for τj as a function of tJ and τj−1 can finally be written as
Equation (19) demonstrates that with the above choice of priors, node j is equally likely to occur any time between the age of the earliest fossil representative of a taxon derived from that node and the appearance of the previous node in the cladogram. These are the weakest possible constraints that can be placed on τj, so this choice of priors should produce conservative constraints on the node times. Other choices of the prior π2′ based upon estimates of the fossil recovery rate could potentially place tighter constraints on the clade ages and should be explored in future work. However, as will be shown below, even this very simplistic formulation can provide useful information about the age of certain nodes in the cladogram.
Of course, equation (19) alone does not provide any practical information about the age of node j because it depends explicitly on the age of the earlier node τj−1, which is a priori an unknown parameter just like τj. To obtain useful constraints on node times, it is necessary to extend this analysis to the entire cladogram. The probability that the earliest fossil representatives of the various clades are tI, tII, tIII, … tN, given the node time τ1, τ2, τ3, … τn and the recovery rates rI(t), rII(t), rIII(t), … rN(t) is simply a product of terms: where each term P(tJ | τj, rJ(t)) has the form given by equation (2). The desired probability distribution function for the τj is this expression multiplied by the appropriate priors and integrated over all possible values for the rJ(t). Again, we can choose the priors on the rJ(t) such that the preservation-dependent terms integrate to constants. Finally, the topology of the cladogram suggests that we assume a prior for the τj in which all possible combinations of τj are equally likely subject to the constraint that τ1 < τ2 < τ3 < … < τn−1 < τn. For practical reasons that will become evident later, it is also useful to specify that the first node in the cladogram cannot occur earlier than a time τ0 prior to tI.
With these assumptions, the joint posterior probability of all node times is given by the product where the individual terms p(τj |τj−1, tJ) are given by equation (19). One can obtain an explicit probability distribution function for the age of the most recent node τn from this expression by integrating over all possible values of the earlier node times:
Calculation of the Probability Distribution Functions
The most straightforward procedure for evaluating equation (22) to obtain probability distribution functions for the individual node times is an iterative method illustrated in Figure 2. This approach starts with an explicit probability distribution function for the age of the earliest node in the cladogram τ1, which is equally likely to occur anytime between the known age of the earliest fossil representative of taxon I (tI) and the assumed maximum possible age τ0 < tI. The posterior probability distribution for τ1, given τ0 and tI, is simply:
For a given value of τ1, equation (19) tells us that Integrating the overall possible values of τ1 yields the total probability of τ2 occurring at a given time, given τ0, tI, and tII:
This process can then be repeated for each subsequent node in the cladogram to obtain a probability distribution function for each τn that depends only on the known fossil ages and the arbitrary parameter τ0: where p(τn |τn−1, tN) is given by equation (19) and p(τn−1 |τ0, tI, tII, … tN−1) is the probability distribution function for the prior node's age derived from the previous step in the analysis. In principle, the relevant integrals could be evaluated analytically, but in practice it is easy enough to perform the calculations numerically. A sample computer program that evaluates the probability distribution functions for a given set of tJ and τ0 is provided in the appendix.
Figures 2 and 3 provide examples of the posterior probability distributions derived with this method. The function for any given node j abruptly goes to zero after tJ, the age of the earliest fossil representative of the taxa derived from this node. This edge reflects the hard constraint that the first fossil representative of taxon J must be more recent than node j. The probability of τj is constant between times tJ and tJ−1, because this analysis effectively assumes that any node is equally likely to occur at any time between τj−1 (which must occur prior to tJ−1) and tJ. Prior to tJ−1, the probability of τj declines monotonically toward zero. Heuristically, this is because prior to tJ−1, there is an increasing likelihood that node j − 1 has not yet split, and node j cannot occur until after τj−1. Similarly, prior to tJ−2 there is an increasing chance that neither node j − 2 nor node j − 1 have split, so the probability of τj is even more strongly attenuated. Thus for sufficiently large j, we should expect that the probability τj occurs as early as τ0 will be extremely small and the probability distribution should be very insensitive to this parameter. Quantitative estimates of the influence of τ0 on this analysis are discussed in the next section.
After computing the probability distribution functions, one can compute various parameters to estimate and constrain the age of each node by using whatever criteria are appropriate to the situation. However, because the functions derived with this technique are asymmetric, I must caution that different methods of computing these parameters will yield different results, so the procedures used to obtain these limits should always be made explicit. In this paper I define the age estimate as the point that bisects the probability distribution function into two equal areas, and define the older (younger) age limit as the time where the probability of obtaining an age older (younger) than the given limit is 2.5%, so there is a 95% probability the age of the node is contained between the two limits.
Extensions of the Basic Algorithm
Although the above example considered a simple case of a perfectly resolved Hennig comb cladogram where tI < tII < tIII < … < tN (implying perfect congruence between the cladogram and the stratigraphic record), this method can be extended to provide age estimates for nodes of cladograms with more complex topologies, unresolved nodes, and even incongruities between the cladogram and the stratigraphic sequence.
Applying the above algorithm to cladograms with more complex topologies is relatively straightforward. For any given node in a cladogram with arbitrary (well-resolved) topology, an age estimate can be computed by simply identifying the times tJ with the ages of the earliest members of the various outgroups of the taxon under consideration.
If there are only a few unresolved nodes in the cladogram, these can also be dealt with rather easily. An unresolved node provides insufficient topological information to determine which of several clades is the relevant outgroup for any given taxon, so the most logical way to handle these situations is to treat this part of the cladogram as a single node, with the earliest representative of any of the multiple clades providing the relevant outgroup age.
On the other hand, if many nodes in the tree are unresolved, so that multiple cladograms with very different topologies are viable, then the above approach for constraining node ages will require more substantial modifications. If one can assign relative likelihoods to the different possible cladograms (perhaps using Bayesian methods as discussed in Huelsenbeck et al. 2001), then it should be possible to combine the probability distribution functions computed from the different cladograms into a single age estimate for certain clades. This sort of analysis is beyond the scope of this paper, so at this point I will simply caution that the reliability of the relevant cladogram should be carefully evaluated prior to using the algorithms described here.
Coping with incongruities between the cladogram topology and the stratigraphic record also requires careful consideration. Say … tJ−2 < tJ < tJ−1 < tJ+1 … so the earliest representative of taxon J occurs earlier than the earliest fossil of taxon J − 1. There are two different ways one might handle such a stratigraphic gap, which I will refer to as the “consistent” and “conservative” approaches. With the “consistent approach” we recognize that because the assumed cladogram requires that the node leading to taxon J − 1 must be earlier than that leading to J, some member of J − 1's clade existed as least as early as tJ, and we can extend a “ghost lineage” (Norell 1993) back to this time. Therefore, we replace tJ−1 with tJ and proceed as usual. With the “conservative approach” we simply ignore taxon J − 1 and include only those fossil outgroups that follow the correct stratigraphic sequence. If the cladogram is well resolved and well supported, then the consistent method should obviously be preferred, as it uses all the relevant data. However, if there are uncertainties in the cladogram or many inconsistencies with the stratigraphic record, then it is less clear which approach is most appropriate. If nothing else, the conservative approach provides a way to gauge how strongly the stratigraphically incongruent taxa influence the age estimates. In lieu of a full analysis of the relative merits of these different approaches (which would require dealing with uncertainties in the cladogram's topology), the following section includes a preliminary study of certain aspects of both methods' performance.
Finally, although the basic algorithm presented here does not require any specific information about fossil recovery rates, such information could be incorporated into the age estimations as different priors on the rJ(t) parameters. For example, if there were significant changes in the number of the relevant fossiliferous localities over time, it might be more appropriate to assume priors that result in node j being equally likely to be found in any of the localities dating between tJ and τj−1, rather than at any time between those two events (see, e.g., Wagner 1995). In this case, one could use the same basic algorithms derived above, but interpret the parameters tJ and τj as positions in the sequence of localities (or depths in the stratigraphic column) rather than as absolute times (as mentioned above). Beyond this, there is an extensive literature on estimating fossil recovery rates and how they vary over time and across taxa (e.g., Solow and Smith 1997; Foote 1997, 2001; Wagner 2000). However, incorporating such estimates into this sort of analysis and exploring how variable preservation rates might influence the age constraints is a complex task and therefore must be the subject of a future work.
Tests of the Algorithm and Sensitivity to Assumed Parameters
To explore how the derived constraints on the node ages depend on assumptions (such as the parameter τ0) and the sampling of the fossil record, it is useful to consider some concrete examples using simulated data sets. These preliminary simulations do not test the sensitivity of this method to uncertainties in the clade topology or variations in recovery rates over time and among taxa, so they should not be considered exhaustive tests of the reliability and range of applicability of the above algorithms. Nevertheless, these studies demonstrate the utility of these algorithms and suggest tests that could be done to gauge the results' sensitivity to unknown parameters.
Sensitivity to τ0
The parameter τ0 specifies the assumed maximum possible age of the node closest to the cladogram's root. It initializes the algorithm and enables explicit probability distribution functions to be derived for the ages of all the other nodes in the cladogram. All the age constraints derived with this method therefore depend to some extent on the arbitrarily chosen value of τ0. However, different nodes in the cladogram do not have the same sensitivity to τ0. For example, the solid and dashed lines in Figure 3 show the probability distribution functions derived from a simple cladogram (with equally spaced tJ) assuming two different values of τ0. Clearly the two probability distribution functions of τ1 derived by this method are very different. However, the probability distribution functions for the other nodes are more similar to each other, and for nodes 5 and 6, the two functions are almost indistinguishable. The latter probability distribution functions are already well below their peak values at τ0, so it is relatively unlikely that these nodes will occur as early as τ0. Therefore, it should not be surprising that the exact value of this parameter has limited effects on the shape of these functions.
To better quantify the sensitivity of the probability distribution functions to τ0, I computed the older limit of the confidence interval for each node, assuming two values of τ0 that differ by Δτ0. I then determined the difference in the older limit between these two cases and divided that number by Δτ0 (I do not consider the younger limit here because it is much less sensitive to τ0). Figure 4 shows a plot of the resulting ratios as a function of node number. For the first node this ratio is essentially 1, because the older limit on τ1 is τ0. However, for the other nodes this ratio is less than unity, and indeed the ratio monotonically decreases with increasing n. For n = 6, this ratio is about 0.1, meaning that an uncertainty in τ0 of 100 Myr would correspond to an uncertainty in the older limit of τ6 of only 10 Myr. Note that although the data shown in Figure 4 assume that the ages tJ are regularly spaced in time, trials using tJ with increasingly large or small spacings show the same qualitative trend.
Of course, the degree of uncertainty one is willing to tolerate in τ0 or the chosen node age will depend on the specific situation, but as a rule of thumb it appears that any node with 5 or more outgroups in the cladogram should have an age estimate that is reasonably insensitive to the assumed τ0. Even so, because the calculation of a node age is not computationally intensive, I strongly urge that anyone using this method try several different values of τ0 to determine precisely how sensitive the result is to this parameter and whether the uncertainty in τ0 will be an issue in their particular analysis.
Trends with Sampling Rates
If this method yields reasonable confidence intervals on the clade ages, we should expect that the limits on the age of a given node will converge toward the true age of the node as the fossil sampling and recovery rates improve. To verify that this is indeed the case, the above analysis was run on a collection of simulated cladograms. Each cladogram has the basic topology shown in Figure 1, with a series of nodes that are assumed to be equally spaced in time (with 10 Myr between each node) up to a final node that formed 100 Myr ago. For simplicity, I assume a common sampling rate for all lineages in this cladogram. For each simulation, I assume the taxa derived from the last node persist until the present day (when they are always recovered), assign a randomly determined duration between 0 and 100 Myr to each taxon derived from all earlier nodes, choose a number of fossil specimens for each taxa using the appropriate Poisson distribution for a given sampling rate and distribute the fossils uniformly along the duration of the lineage. Finally, using the techniques described above, I compute the probability distribution function and age limits for the final node in the cladogram. Both conservative and consistent approaches are used.
Figure 5 plots the limits derived from these simulations as a function of the assumed sampling rate. The results of individual runs (40 per sampling rate) are shown as symbols and the lines show the average value of the individual runs as a function of the sampling rate. Note that younger limits from the simulations are almost always more recent that the true age of the node (100 Myr ago), and the older limits from both the conservative and the consistent approaches are almost always earlier than the true age of the node. This suggests that these limits can provide reliable constraints on node ages. We also note that for sampling rates below roughly 0.05/Myr, both the older and younger confidence limits converge toward the true age of the node, as desired.
Because the younger limit on the node's age is set by the age of the oldest fossil representative of the taxa derived from that node, it makes sense that the younger limit on the age steadily moves backward in time as the sampling rate improves. The trends in the older limits, by contrast, result from a more complex interaction between two processes. On the one hand, improving the sampling rate means that the earliest members of a given lineage will typically be found at earlier times, causing the tJ to move back in time and driving the older limit earlier. On the other hand, increasing the sampling rate will also result in the recovery of additional taxa, increasing the number of outgroups within a given time span and thus moving the older limit later in time. In these simulations, the latter effect wins out for sampling rates below about 0.05/Myr, producing the desired trend of increasingly tight limits on the age of the node.
These simulations also indicate that when the sampling rate is above 0.05/Myr, the older limit saturates at a value roughly 20 Myr before the true age of the node, whereas the younger limit continues to move to earlier times. This is partially a limitation of the simulations, which assumed that internal branches in the cladogram are never sampled. Thus, in the limit of perfect sampling, the first fossil representative of the taxa derived from the final node will appear 100 Myr ago and the first fossil representative of its various outgroups will occur at 110 Ma , 120 Ma, and so on. In this limit, the probability distribution function on the node age reaches a limiting form with an older limit around 120 Ma. Although this limiting case is clearly unrealistic, it does demonstrate an important limitation of the method proposed here. These algorithms are most likely to produce confidence intervals that converge toward the true node age if it is probable that additional outgroups will be discovered. By contrast, methods that explicitly include sampling rates (e.g., Strauss and Sadler 1989) are more appropriate if the fossil recovery rate is sufficiently good that it is unlikely that additional outgroup taxa are going to be recovered (or if direct ancestor-descendent relationships between taxa are expected).
Additional simulations (using 1000 replicates for each sampling rate) provide more quantitative data on the validity of the older limits derived by this analysis. Given the criteria used to define this older limit, one expects that these will be more recent than the true node age 2.5% of the time. In reality, for simulations with sampling rates between 0.006/Myr and 0.03/Myr, the older limit is more recent than 100 Ma in roughly 2% of the simulations when the consistent approach is used and only 0.5% of the simulations when the conservative approach is used. The results for the consistent approach are close to expectations, perhaps a little low because of the conservative priors assumed for the fossil recovery rates. The lower value for the conservative approach confirms that this approach gives more conservative constraints on the node age. At sampling rates above 0.03/Myr or below 0.0006/Myr the percentage of simulations with upper limits more recent than 100 Ma decrease to below 0.1%. For high sampling rates, this reflects the limitation of the simulations discussed above. For low sampling rates it becomes unlikely that any fossil is recovered anytime in the interval between the node's true age and the present, so the only evidence for taxa derived from the node of interest comes from the specimens assumed to be recovered at the present. Such a situation violates the assumptions of the above calculations, so deviations from predictions are not unexpected. These models therefore indicate that the consistent approach provides a reasonable older limit on the node time for the range of recovery rates where the assumptions behind this model are justified, and provides conservative constraints on the node age outside of this range.
Although the consistent approach yields limits with the appropriate significance in the above simulations, this result is somewhat dependent on the durations and node spacings of the assumed lineage. If the time interval between adjacent nodes is decreased relative to the lineages' mean duration, then it becomes more probable that the ages of the earliest fossil representatives of the various outgroups will be found out of the expected stratigraphic sequence. This could potentially make the consistent approach more likely to underestimate the older limit on the node age. Simulations where the interval between two nodes in the cladogram were reduced by a factor of two (to 5 Myr) while keeping the lineages durations the same confirm that this in fact occurs, and also show that the conservative approach continues to produce more conservative older limits. These results indicate that the limits derived with this method are most reliable when the fossil record is stratigraphically consistent with the cladogram, and the consistent approach in particular should be used with caution when many taxa are out of the expected stratigraphic sequence.
Application of the Algorithm to the Origin of Placental Mammals
To demonstrate how this method can be applied to actual fossil data, let us consider the origin of placental mammals. The sequence of outgroups used in this analysis derive from two cladograms of stem Mesozoic eutherians from the recent literature, both of which contain at least five stratigraphically consistent outgroups to placentalians (Ji et al. 2002; Wible et al. 2007). The Ji et al. (2002) cladogram only contains a single placentalian (Erinaceus), which has the Cenozoic Protungulatum as a sister group. The Wible et al. (2007) cladogram shows the Cenozoic animals Protungulatum, Purgatorius, and Oxyprimus forming an outgroup to Placentalia, but the authors cannot rule out a cladogram with these animals located inside Placentalia (see supplemental online material of Wible et al. 2007). To keep things simple, we will treat Protungulatum, Purgatorius, and Oxyprimus as “Cenozoic allies” of Placentalia and consider the age of this entire clade, whose earliest members date back to approximately 65 Ma.
Table 1 lists the sequence of outgroups to this placental clade in the two cladograms, with ages obtained from Kielan-Jaworowska et al. (2004). Both cladograms have at least one outgroup that is out of stratigraphic sequence, so we tabulate the dates used in the consistent and conservative approaches separately for clarity (note the Ji et al. 2002 cladogram also contains one trichotomy).
Results and Discussion
Figure 6 shows the probability distribution functions derived from the fossil outgroup data, and the corresponding age estimates and limits are included in Table 1. As expected, the conservative approach yields somewhat looser constraints and higher age estimates than the consistent approach.
Given that the two sequences of outgroups are quite different, it is perhaps surprising that the two cladograms give very similar estimates for the age of modern placentalians and their Cenozoic allies, with central values ranging from 74 to 79 Ma, younger limits at 65–66 Ma, and older limits between 88 and 98 Ma. Upon closer inspection, we can see that in both cladograms numerous outgroups date back to ca. 90 Ma, causing all the probability distribution functions to be strongly attenuated before this time. In fact, the primary difference between the two cladograms for the purposes of this analysis is that the Wible et al. cladogram has the first outgroup (Gypsonictops) dating back to 76 Ma, whereas the Ji et al. data has its first outgroup (Eoungulatum) at 87 Ma. This leads to the narrower probability distribution function for the Wible et al. analysis and the somewhat more recent age estimate.
We may compare these results with the recent molecule-based estimates for the origin of modern placental mammals tabulated in Springer et al. (2003). This source provides multiple age estimates based on different subsets of their molecular data. For the full data set, the estimated age and 95% confidence interval for the base of Placentalia is 107 (98–117) Ma, but some subsets (for 3′ UTRs and mitochondrial RNA) give ages as low as 97 (86–109) Ma. These lower dates from some of the molecular data subsets do overlap with the older limits from this analysis, but even in these cases the hypothesis that the two dates are consistent is disfavored (the probability of no difference being less than 10%). However, this analysis suggests that the magnitude of the difference between the two age estimates may be smaller than previously considered. The fossil-based age limits derived here indicate the present fossil evidence cannot rule out the possibility that placentalians and their Cenozoic allies originated as much as 20 or even 30 Myr before the K/T boundary. If the last common ancestor of placentalians did live this long ago, then the molecular estimates would only be off by 20 Myr (or about 25%), not 40 Myr, as would be required to move the base of Placentalia all the way to the K/T boundary.
Again, I must remind the reader that the age estimates derived here rest on several assumptions, and these must be carefully evaluated. Obviously, this includes the assumed priors on the fossil recovery rates and node ages. As noted above, the priors assumed for this analysis tend to be conservative, so other priors, which might be based on estimates the preservation and fossil recovery rates, will tend to tighten the limits on the age of Placentalia and increase the discrepancy between the fossil and molecular evidence.
A bigger issue is that a given age estimate is based on a particular cladogram, so the estimate is sensitive to the topology of the resulting tree. In this example, two different cladograms gave similar results, which may give us some confidence in the reliability of these calculations. However, the topologies of these two cladograms were similar in that all of the Mesozoic animals formed a highly nested set of outgroups to Placentalia. If some Mesozoic eutherians are not outgroups to Placentalia but instead part of it (Archibald et al. 2001; Archibald 2003), then the age estimate will shift significantly earlier. (Unfortunately, the cladograms showing this pattern contain insufficient stratigraphically consistent fossils below the base of Placentalia to derive a robust date with the present method.) Also, if the eutherian cladogram had fewer discrete outgroups leading to Placentalia, the age estimate would tend to move earlier. Conversely, if more of the late Cretaceous and early Cenozoic eutherians turn out to be closely related outgroups to Placentalia and not part of Placentalia itself, the age estimate will shift later, perhaps more consistent with “explosive” models of placentalian origins (Archibald and Deutschman 2001). An exhaustive analysis of the consistency between the molecular analyses and the fossil analysis would therefore have to account for uncertainties in the topology of the eutherian phylogeny. Even though such an analysis is beyond the scope of this report, the age estimates presented here demonstrate that this technique can be a useful way to investigate the consistency between molecular age estimates and fossil evidence.
The algorithms presented here provide a way to constrain the ages of nodes in a cladogram based on the ages of the earliest fossil specimens of that clade's outgroups. The age estimate for the last common ancestor of modern placentalians and their Cenozoic allies derived by using this method permit us to quantify the significance and magnitude of the difference between molecular-based age estimates and the fossil evidence. Of course, additional work still needs to be done to verify whether the age estimates derived with this method are reliable. In particular, it will be important to evaluate how uncertainties in the cladogram topology and variations in the fossil recovery rate in time and space affect the age estimates derived with this method. Even so, some form of these algorithms could be useful for a wide range of applications, including calibrating DNA-based age estimates and identifying times of rapid cladogenesis. Furthermore, because the calculations presented here are not computationally intensive, anytime researchers create a sufficiently deep, well-resolved, and stratigraphically consistent cladogram of fossil taxa, they could easily also provide age estimates for all of the nodes sufficiently far from the root.
Sample Code for Computing Clade Ages
This is the basic algorithm for calculating the probability distribution function for a given clade, written in the R computing language (thanks to M. Foote for translating the original IDL script into this form).
#function returns p.d.f. for node ages
#t0 is an arbitrary oldest age to consider
#tnodes are the ages of oldest known fossil stemming from each node
#tsteps is a vector of arbitrary time steps on which the p.d.f. is calculated
pnodes<-matrix(0,nn,nt) #initialize array
# first get pdf for node 1, at discrete values
ii<-which(tsteps > t0&tsteps<tnodes)
pnodes[1,ii]<-1.0/length(ii) #assume uniform distribution
for (i in 2:nn) #cycle through remaining nodes
for (j in 1:nt) #cycle through series of time steps
p21<-rep(0,nt) #initialize vector
ii<-which(tsteps > = tsteps[j]&tsteps<tnodes[i])
#cond. probability of this age, given previous node age,
#times prob. of previous node age
#add to cumulative sum
list(pp = as.vector(pnodes[nn,]))
#return the p.d.f. vector for the youngest node
I wish to thank A. McCune, J. D. Archibald, M. Foote, C. R. Marshall, P. Wagner, and several anonymous reviewers for helpful discussions and comments on this manuscript. I also wish to acknowledge the Cornell University astronomy department for providing computer support.
- Accepted 18 July 2009.