Abstract
We introduce a widely applicable species delimitation method based on the multispecies coalescent model that is more efficient and more biologically realistic than existing methods. We extend a thresholdbased method to allow the ancestral speciation rate to vary through time as a smooth piecewise function. Furthermore, we introduce the cuttingedge proposal kernels of StarBeast3 to this model, thus enabling rapid species delimitation on large molecular datasets and allowing the use of relaxed molecular clock models. We validate these methods with genomic sequence data and SNP data, and show they are more efficient than existing methods at achieving parameter convergence during Bayesian MCMC. Lastly, we apply these methods to two datasets (Hemidactylus and Galagidae) and find inconsistencies with the published literature. Our methods are powerful for rapid quantitative testing of species boundaries in large multilocus datasets and are implemented as an open source BEAST 2 package called SPEEDEMON.
Similar content being viewed by others
Introduction
There are many concepts of what defines a species^{1}, making species delimitation a field of study that is fraught with pitfalls^{2}. Of all the species concepts, the coalescentbased species concept is one of the few that allows quantitative testing of different hypotheses^{3,4,5}. These methods rely on the multispecies coalescent model, where one or more gene trees are constrained within a single species tree^{6,7}. The data used in a multispecies coalescent analysis can consist of multilocus biological sequence alignments, and explicit representations of the gene trees are used in the inference of the species tree, as in the *BEAST^{8,9} model. Alternatively, the data can consist of independently evolving single nucleotide polymorphic (SNP) sites, in which case the gene trees are integrated out^{10}. Multispecies coalescent methods can overcome numerous statistical pitfalls underlying traditional phylogenetic analyses which infer species phylogenies from concatenated genomic data^{6,8,9,11,12,13}.
In multispecies coalescent models, the different ways that samples are assigned to species allow us to perform species delimitation in a variety of ways. With Bayes factor delimitation^{3,4} (BFD for gene alignments, BFD* for SNP alignments), hypotheses consist of explicitly stated species assignments. By estimating the marginal likelihood of each of the assignments, the Bayes factor can be estimated in order to compare competing hypotheses in a pairwise fashion. The species tree does not need to be known beforehand, and can be estimated from the data. The methods are implemented in BEAST 2^{14,15}, which means they can be applied with a wide choice of site models, clock models, and tree prior distributions, and combined with a variety of other data, such as morphological features or geographical locations.
An alternative approach is to use reversible jump^{16}, which allows switching between models during the execution of the Markov chain Monte Carlo (MCMC) algorithm where a species is assigned a set of sequences to one where the sequences are split over multiple species, as implemented in BPP^{5}. The elegance of this approach is that no explicit sequence assignments to species are required, since these can be either guided through a predefined species tree, or jointly inferred with the species tree. The posterior samples produced by the MCMC algorithm contain a distribution of species assignments from which the various hypotheses under consideration can be tested. Unfortunately, BPP does not support as wide a set of models as BEAST and reversible jump moves are nontrivial to extend for general application to a wide range of models such as optimised relaxed clocks^{17}.
There also exist numerous rapid likelihoodbased approaches to species delimitation, such as GMYC^{18}, mPTP^{19}, and SpedeSTEM^{20}. These approaches are likely to outperform any Bayesian implementation in their computational runtime. However, they are often restricted to singlelocus data and are limited in their abilities to report statistical uncertainty. Moreover, as is the case with BPP, they are not readily incorporated with other data types (such as morphological, geographical, or linguistic data) or models. For the remainder of this article, we consider species delimitation under a modular Bayesian framework.
The birth–death collapse model (implemented in DISSECT^{21}, and STACEY^{22}) is a simple but flexible method that does not rely on reversible jump, while still allowing joint inference of sequence assignments to individuals, the phylogeny, and other parameters. First, samples are either given an a priori species assignment, or each individual is assigned to its own species. Then, samples whose divergence time falls below some userdefined threshold ϵ are considered to be part of the same species, or cluster. This forms the basis of a prior distribution behind the species tree (Fig. 1). This spikeandslab prior is a mixture of a birth–death tree prior^{23} and a collapse model. For nodes above the threshold, only the standard tree prior has an impact (the “slab”), but below the threshold the tree prior is dominated by the “spike”, thus encouraging nodes to remain below the threshold when the userdefined weight of the spike ω is large. To this day, the approach is widely applied to species delimitation, and has found its use across a range of taxonomies including amphipods^{24}, fungi^{25}, and clingfishes^{26}.
Recently, advances have been made in efficient inference under multispecies coalescent models for both gene tree based models (StarBeast3^{27}), and SNP based models (SNAPPER^{28}). Namely, StarBeast3 benefits from parallelised gene tree inference and highly efficient relaxed clock proposals, while SNAPPER benefits from its fastbutaccurate likelihood approximation. The threshold approach to species delimitation is readily incorporated into both of these packages as a tree prior distribution.
In this article, we extend the collapse model to allow the speciation rate to vary through time and we demonstrate that this method is a valid approach to performing species delimitation using SNPs with SNAPPER and using gene sequences with StarBeast3. This opens up the way to perform species delimitation in a Bayesian framework using larger datasets and more biologically realistic models compared with previous approaches. We apply these methods to two biological datasets (geckos and primates consisting of lorises and bush babies). Our methods are implemented as the opensource SPEciEs DEliMitatiON (SPEEDEMON) package for BEAST 2^{14,15}.
Results
Validating the Yuleskyline collapse model
We combined the collapse model^{21} with the Yuleskyline model^{29} to allow the speciation rate to vary through time as a smooth piecewise function. In this model, the birth rates are analytically integrated and therefore these parameters do not need to be estimated^{29}. We call this new tree prior distribution the Yuleskyline collapse (YSC) model.
We validated the YSC model for both SNAPPER (with SNPs) and StarBeast3 (with genes) using a wellcalibrated simulation study. In either case, 100 species trees (and their associated gene trees/parameters) were sampled from the prior distribution, and the parameters were recovered using Bayesian MCMC on datasets simulated under the trees. The “true” value of each parameter was compared with the 95% highest density posterior (HPD) interval in order to calculate the coverage. A coverage close to 95% (i.e., from 90 to 99 based on a binomial with p = 0.95 and 100 trials) indicates that the model is valid. These experiments suggested that our implementation of the YSC model is valid for the multispecies coalescent. The two wellcalibrated simulation studies are presented in Fig. S1, S2.
We also validated these methods for their abilities to identify species assignments, using the same simulated datasets. To do this, we discretised cluster posterior supports into 20 evenlyspaced bins, and for each bin we counted the number of times each of its clusters existed in the tree from which the data was simulated under. If, for example, a cluster has 52% posterior support, then this hypothesis should be true 50–55% of the time. This experiment confirmed that SNAPPER and StarBeast3 were both able to accurately estimate cluster support probabilities (top panel of Fig. 2). Lastly, we performed this same experiment with varying thresholds ϵ used during inference on datasets simulated under a known threshold. This sensitivity analysis suggested a moderate degree of robustness to ϵ and is presented in Fig. S3.
Benchmarking performance in a Bayesian multilocus framework
We benchmarked the performance of STACEY and StarBeast3 for their abilities to achieve convergence of phylogenetic parameters under the birthcollapse model. Although it is a nontrivial problem to determine if an MCMC chain has converged, the effective sample size (ESS) can serve as a useful metric. Thus, we computed the number of effective samples generated per hour of runtime (ESS/hr) across multiple replicates of MCMC, across an array of parameters. Both software packages were benchmarked under the same phylogenetic model, however, with effective population sizes analytically integrated by STACEY and estimated by StarBeast3. We considered a lizard dataset with 89 samples across 107 loci^{30}, and a simulated dataset with 48 samples across 100 loci^{27}. Each MCMC replicate was run until the effective sample size of the posterior density p(θ∣D) (after a 50% burnin) exceeded 200.
StarBeast3 gains efficiency over similar software packages through two primary means. First, inference under the multispecies relaxed clock model^{9} is highly efficient under StarBeast3 because of its constantdistance relaxed clock operators^{17,31}. Here, however, we employ a strict clock, as the former is not implemented in STACEY. Second, StarBeast3 can operate on gene trees (and their substitution models) in parallel, while the species tree and other parameters are proposed only in the main thread. Here, we parallelised StarBeast3 with both 1 thread and with 4 threads while STACEY was run with just 1 thread (as it does not possess any equivalent benefit from multithreading). Two central processing units were allocated to each setting.
When running in singlethreaded mode, StarBeast3 and STACEY performed comparably well. Notably, there was no significant difference in mixing between the “slowest” term (i.e., the term which mixed the slowest on any given MCMC replicate) between the two programs (p > 0.05 in a twosided t test).
However, StarBeast3 outperformed STACEY on both datasets when run in multithreaded mode (Fig. 3). This discrepancy was strongest for the lizard dataset, with StarBeast3 mixing between 1.3 and 9.5 times as fast as STACEY, depending on the parameter, and usually at a statistically significant level. For the simulated dataset, StarBeast3 outperformed in most areas, while STACEY outperformed in others. Most notably, the “slowest” term min mixed 70% and 120% faster for StarBeast3 on both datasets, respectively (p < 0.05).
Overall, StarBeast3 performed at least as well as STACEY, but outperformed when allocated additional threads. The efficiency increase is likely to go up with more threads and more cores (up to a maximum of 1 thread per loci).
Species delimitation on Gecko SNP data using SNAPPER
The Hemidactylus are a genus of geckos, found in tropical regions all over the world. To date, there are 180 known species, with newfound species being described every year^{32}. Leaché et al. collected 46 samples of genomic data at 1087 loci from 10 forest gecko populations in Western Africa^{4,33}. They identified several species among the populations by explicitly generating multiple species assignment hypotheses (illustrated in Fig. 2 of ref. ^{4}), and comparing their marginal likelihoods to that of a baseline null hypothesis, using path sampling in conjunction with SNAPP^{10} (Table 1). This method is known as BFD* and involves one path sampling experiment per hypothesis.
Here, we applied the YSC tree prior in conjunction with SNAPPER (instead of SNAPP). In contrast to BFD*, this approach does not require any explicit hypotheses. Instead, we assigned each of the 46 samples to its own species, thus increasing the number of potential hypotheses to B_{46} ≈ 2.2 × 10^{42} (Bell number B_{46}). As a sensitivity analysis, we explored four varying values for threshold ϵ = (10^{−2}, 10^{−3}, 10^{−4}, 10^{−5}). These results support the lumping of western forest populations into a single species, unlike Leaché et al. (Fig. 2). However, these experiments have also identified an individual from the H. eniangii population who should have been assigned to the western forest species. Visual inspection of the SNP data also supports this grouping (Fig. 4). All four thresholds ϵ generated the same leading hypothesis (Fig. 2), thus providing high confidence in this species delimitation, and also demonstrating the robustness of this method to varying thresholds.
We denote this newly generated hypothesis as H3. In order to test H3 (and also to further validate the tree collapse method), we compared it with other hypotheses proposed by Leaché et al., using path sampling (Table 1). These results confirmed that H3 is indeed the leading hypothesis, because it had the largest marginal likelihood.
Overall, these experiments have exemplified the major pitfall of the Bayes factor delimitation method: its reliance on explicit species assignment hypotheses. Using this method, we can run a single MCMC analysis and test a large number of hypotheses, whereas BFD* requires a path sampling run for each hypothesis under consideration, and each of these path sampling runs are at least as computationally intensive as a single MCMC run. By using SNAPPER instead of SNAPP, a further order of magnitude in performance gain is accumulated^{28}.
Species delimitation on bush baby and Loris genomic data using StarBeast3
The Galagidae, commonly known as the bush babies, and the Lorisidae are closely related families of small nocturnal primates^{34}. Due to their nocturnal habits, bush babies are fairly understudied compared with other primates and their taxonomy is cryptic^{35,36}.
Pozzi et al. compiled a large molecular dataset of the two families and (their outgroups), consisting of 27 genes^{35}. We applied the Yuleskyline collapse tree prior, in conjunction with StarBeast3, to infer species boundaries from this dataset. We used the multispecies relaxed clock model to allow substitution rates to vary across lineages^{9}. As a sensitivity analysis, we explored four varying thresholds ϵ = (10^{−2}, 10^{−3}, 10^{−4}, 10^{−5}). Divergence times were calibrated from fossil records, as described by Pozzi et al., and therefore ϵ is in units of millions of years.
Our resulting phylogeny was in general agreement with that of Pozzi et al. These results contradicted the withstanding taxonomic classifications in three instances (Figs. 2, 5). First, two bush baby species (Galago moholi and Galago senegalensis) were lumped into one (57% posterior support for ϵ = 10^{−2}). Pozzi et al. hypothesised this contradiction arose as a consequence of taxonomic misclassifications of sequences and/or captive animals. Second, two members of Galagoides demidoff were split into two distinct clusters, suggesting that the two individuals might not have belonged to the same species (100% support). This was also reported by Pozzi et al. Finally, two species of the Lorisidae were lumped together (Nycticebus bengalensis and Nycticebus coucang), with 95% support. These three anomalies occurred in the maximalposterior clustering scheme for three of the four thresholds ϵ = (10^{−2}, 10^{−3}, 10^{−4}), thus placing a high level of support in these results, and also demonstrating the robustness of this method to varying ϵ (Fig. 2). In contrast, ϵ = 10^{−5} designated each taxon to its own species (as its maximum a posteriori estimate), which is an intuitive result given that ϵ = 10^{−5} is equal to just 10 years.
User guide for selecting threshold ϵ
The threshold ϵ describes the maximum divergence that can be tolerated before two samples are regarded as separate species. If ϵ is too large (e.g. older than the tree), then all samples will be lumped into one species. Whereas, if ϵ is too small (e.g. younger than the youngest divergence time), then all individuals will be split into their own species. When testing the hypothesis that two samples are from different species, larger values of ϵ make a more conservative model (by only splitting when the samples are extremely divergent). In contrast, when testing the hypothesis that two samples are part of the same species, smaller values of ϵ are more conservative (by only lumping when the samples are extremely similar). Furthermore, the meaning of ϵ is impacted by the units in which the tree height is measured: a tree height in units of years, millenia, millions of years, or expected number of substitutions all lead to different interpretations of the same value of ϵ.
Although selecting ϵ is not always straightforward, researchers often have prior knowledge about certain samples belonging to the same species, and this knowledge can inform the threshold. We therefore recommend users do a preliminary phylogenetic analysis to estimate divergence times between samples to assist with ϵ selection. If two samples are uncontroversially different species (e.g. mice and fish), then ϵ should be less than their divergence time. Whereas, if two samples are known to be the same species (e.g. both Homo sapiens), then ϵ should be above their divergence time. This preliminary exercise should help with finding a sensible range of thresholds to explore.
The threshold itself is expressed in the same units as the tree height. First, consider the case where divergence times are in units of substitutions per site (such as the gecko analysis). The distance between human and chimpanzee genomes, for instance, is around 1.2% based on SNPs and 0.9% based on proteincoding sequences^{37}. In this scenario, ϵ should be much less half of that (ϵ ≪ 0.006 and ϵ ≪ 0.0045, respectively; halved to account for both the human and the chimpanzee lineage). Second, consider the case where divergence times are in time units (such as the primate analysis; millions of years). In this scenario, ϵ can be equated to generations. For example, Galago moholi are estimated to live 35 years in the wild^{38}, and ecological speciation time can potentially take dozens of generations^{39}. This places a lower limit on ϵ (i.e. ϵ ≫ 5 years). Our selection of ϵ = 10^{−5} = 10 years for this dataset was clearly too small, and was consequently met with quite different results than the other three thresholds (Fig. 2).
Overall, we recommend users explore a range of values for ϵ, where the range itself is informed by prior knowledge about the system being studied, or other related systems. Although ϵ has a moderate degree of robustness (see ref. ^{21} and Fig. S3), a sensitivity analysis is still important.
Discussion
The species delimitation methods we have presented are advanced in both their computational efficiencies as well as their biological realism.
First, we amalgamated the birthcollapse model^{21} with the Yuleskyline model^{29} to enable ancestral speciation rates to vary through time as a smooth piecewise function. In this method, speciation rates are integrated out and the model is reported to converge quite efficiently, despite its increase in complexity over the standard Yule model^{40}. Second, we introduced the multispecies relaxed clock model^{9} to the species delimitation problem. This model allows molecular evolution rates to vary across species lineages and is therefore more biologically realistic than the withstanding strict clock model. However, these additional complexities in the model are met with highly efficient proposal kernels^{17,27,31}, and much like the Yuleskyline collapse model, is expected to converge quite efficiently in MCMC. Lastly, we demonstrated how the collapse model can be used for molecular sequence analysis in conjunction with StarBeast3^{27} and for SNP analysis in conjunction with SNAPPER^{28}—each of which are reported to be significantly more efficient than their predecessors. We demonstrated that StarBeast3 outperforms STACEY at achieving convergence during Bayesian MCMC through use of its parallelised gene tree inference (Fig. 3). We showed how the collapse model can implicitly test all possible species delimitation hypotheses at once (through MCMC), as opposed to one hypothesis at a time (through path sampling^{3,4};). Overall, these methods are faster and more advanced than other species delimitation approaches.
We validated these advanced methods and applied them to two biological datasets. First, we examined the geckos (genus: Hemidactylus) studied by Leaché et al.^{4,33}. Several species delimitation hypotheses were informed by population geography—the leading hypotheses identified 4–5 different species^{41}. However, by applying the collapse method to this dataset (without imposing any a priori species assignments), we identified an individual from the H. eniangii population whose genome was more akin to those from the western forest populations (Fig. 4). Our analysis defined 3 species, and the hypothesis was met with high posterior support even across varying collapse model thresholds (Fig. 2). It is not immediately clear whether this is a case of taxonomic misclassification, or whether this gecko represents more migration between the forests than anticipated. Although we assigned each sample to its own potential species, it is possible to limit the number of species by for example assigning species to one of six groups such that each of the seven hypotheses considered in the BFD* analysis can be formed by collapsing the species tree. However, this would not have allowed us to find the best fitting assignment, because the misclassified sequence eng_CA2_20 would not be allowed to cluster with the western forest sequences. Therefore, we recommend assigning each sample to its own species when computationally feasible.
Second, we examined the primates (families: Galagidae and Lorisidae) studied by Pozzi et al.^{35}. We showed that four bush babies should have been lumped into a single species, instead of two (Galago moholi and Galago senegalensis), and we identified a paraphyletic relationship between two members of Galagoides demidoff. Both observations have a moderatetohigh level of posterior support, across a range of collapse thresholds (Fig. 2), and we therefore concur with Pozzi et al. Our analysis also lumped two further Lorisidae species together (Nycticebus bengalensis and Nycticebus coucang) with 95% posterior support, thus providing high confidence that these two individuals were in fact from the same species.
For both datasets considered, the collapse model unveiled anomalies underpinning their taxonomic classifications. It is indeterminate from genomic data alone whether these are trivial labelling errors (at the sequence level or at the animal level) or whether they represent nontrivial biological processes. Either way, automated methods like this one, that make no a priori assumptions about species assignments, can remove some of the burden from the researcher carrying out such analyses.
The methods discussed here can be further advanced by reducing the size of the search space. When ancestral relations among a set of taxa are firmly established, a fixed topology analysis may be sufficient. In this case, the species tree topologies can be fixed at some nondisputed estimate, with only their node heights, and therefore species boundaries, estimated during MCMC. This would reduce the search space and further expedite the analysis. Alternatively, the species boundary hypothesis space can be restricted without the need to fix the topology or generate explicit hypotheses. This can be achieved by introducing monophyletic constraints onto the species tree. Both of these scenarios are readily achieved in BEAST 2 and the collapse tree prior is applicable in either case.
However, the methods discussed in this article come with their limitations. First, the collapse model is reliant on a threshold parameter ϵ, and it is not clear what this threshold should be. Although there is a moderate degree of robustness to this term (Fig. 2, and ^{21}), it would be beneficial to have a method which explicitly estimates the species assignment function without the need for such a heuristic. However, such an improvement may be met with convergence difficulties during Bayesian MCMC. Second, the collapse model is not applicable to ancestral lineages. Lineages which date back before the threshold ϵ (including ancestral samples) are unable to be clustered under the collapse model in its current form. Further, as pointed out by Jones et al.^{21}, the multispecies coalescent model has assumptions such as lack of hybridisation that are likely to be violated and may impact the results of the species delimitation analysis. The method does not correct cluster bias due to sampling selection bias and its behaviour with ring species is unclear.
Conclusion
The collapse model is a phylogenetic tree prior distribution (Fig. 1) used for species delimitation under the multispecies coalescent^{21}. We advanced the work by Jones et al. by formally validating this method through wellcalibrated simulation studies (Fig. 2 and Figs. S1, S2), and we demonstrated that the recently developed StarBeast3^{27} and SNAPPER^{28} inference engines outperformed existing methods at the task of fast Bayesian species delimitation (Fig. 3). Furthermore, we combined the collapse model with our Yuleskyline model^{29} to allow the species tree birth rate to vary as a smooth piecewise function over time. We applied the Yuleskyline collapse model to two biological datasets; gecko SNP data^{4} and primate genomic data^{35}. In either case, we identified species boundaries that contradicted those assigned to individuals in the original datasets (Figs. 4, 5), thus exemplifying the appeal of the method.
The methods presented are implemented in the SPEEDEMON package for BEAST 2 and are suitable for rapidly identifying species on large datasets with over 100 genes or thousands of SNPs. The implementation in BEAST 2 allows adding various other types of data to the species tree, such as morphological features (as recommended by Olave et al.^{42}) and geographical location^{43,44}. Together, SPEEDEMON provides a flexible package for species delimitation catering to a wide range of biological applications.
Methods
Collapse models
Let T be a binary rooted time tree over n taxa with leaf nodes x_{1}, …, x_{n} and internal nodes x_{n+1}, …, x_{2n−1}. Let h_{i} ≥ 0 denote the height of node i, where all leaves are assumed to be extant with height h_{i} = 0. Suppose, we have a distribution over trees f(T∣θ) for some set of parameters θ, such as a Yule or birth–death distribution, where f can be written as the product of internal node height contributions. That is, we can write f(T∣θ) as \(\mathop{\prod }\nolimits_{i = n+1}^{2n1}f({x}_{i} \theta )\). Furthermore, we assume that f(x_{i}∣θ) = 1 if h_{i} = 0, so internal nodes of height zero do not contribute to this tree distribution. To avoid numerical instabilities associated with zeronodeheights, we will assume that nodes below some threshold ϵ do not contribute to the branching/coalescent process, and \(f(T \theta ,\epsilon )={\prod }_{n\le i\le 2n1,{h}_{i}\ge \epsilon }\,f({x}_{i} T,\theta )\), where f(x_{i}∣T, θ) = 0 for h_{i} < ϵ.
Now, let us define the collapse tree prior as the weighted sum of some tree distribution f(T∣θ, ϵ) with a spike density m(x_{i}∣ϵ) on internal nodes heights, where m(x_{i}∣ϵ) = 0 if h_{i} > ϵ and m(x_{i}∣ϵ) = 1/ϵ otherwise (Fig. 1). Let ω be a weight between 0 and 1 that governs the contribution of the components of the mixture. Then, the collapse tree prior f(T∣θ, ϵ, ω) can be written as
where k is the number of internal nodes with node heights less than ϵ. In this study, we fixed ϵ to a small, e.g., 10^{−4} substitutions per site, and sampled the value of ω during MCMC.
When using the birth–death distribution as a tree distribution f(T∣θ, ϵ), we get the birth–death collapse model defined for DISSECT and STACEY^{21,22}. This model is conditioned on an origin height and its parameters θ consist of a birth rate, a death rate, and the origin height. By setting the death rate to zero, the widelyused Yule model is obtained^{40}.
Alternatively, we can use the Yuleskyline model^{29}, which is a pure birth model that conditions on the number of extant species n − k − 1. This model splits up time into epochs and can therefore be naturally extended to the case where nodes are collapsed below ϵ height. The Yuleskyline model integrates out the birth rate skyline (which is assumed to follow a gamma prior), and allows the smoothing of birth rates over epochs, where the birth rate prior at epoch i + 1 is conditional on the birth rate posterior estimate at epoch i. In this model, θ consists of the shape and rate of the gamma prior of the first epoch. This forms the basis for the Yuleskyline collapse (YSC) model.
Another suitable epoch model is the birth–death skyline model^{45}, which allows different birth rates and death rates in each epoch, and can easily be adapted to ignore events in the epoch with height less than ϵ. While the Yule model assumes all species are observed, the birth–death skyline model introduces a sampling proportion parameter ρ. In general, any tree distribution that can be decomposed into contributions of the individual nodes in the tree can be combined with the collapse model, for instance, the multitype tree distribution^{46} allows rate changes at arbitrary locations in the tree.
Prior distributions
For SNAPPER (wellcalibrated simulation studies and Gecko analysis), we used the YSC tree prior with coalescent rates ~ Gamma(α = 100, β = 0.01) and collapse weight ω ~ Beta(α = 1, β = 2) under the prior distribution. The skyline consisted of 4 epochs, where the birth rate of the first epoch was drawn from a Gamma(α = 2, β = b) prior where b ~ Lognormal(μ = − 1.63, σ = 0.2) in the wellcalibrated simulations studies, and b ~ Lognormal(μ = − 4.73, σ = 0.5) when analysing geckos.
For STACEY, we used the strict clock model and the birthcollapse tree prior with collapse weight ω ~ Beta(α = 1, β = 1), birth rate λ ~ Lognormal(μ = − 2.43, σ = 0.5), and origin height \({{{{{{{\mathcal{O}}}}}}}} \sim \,{{\mbox{Lognormal}}}\,(\mu =0.19,\sigma =1)\) under the prior distribution. Species tree branchwise effective population sizes were drawn from an Inversegamma distribution with a shape of 2, and a mean of μ_{N}, where μ_{N} ~ Lognormal(μ = 2.87, σ = 0.5). Nucleotide evolution was assumed to follow an HKY substitution model^{47} with transitiontransversion ratio κ ~ Lognormal(μ = 1, σ = 1.25), nucleotide frequencies f ~ Dirichlet(α = (10, 10, 10, 10)), and substitution rate ν ~ Lognormal(μ = − 0.18, σ = 0.6). Each gene tree was associated with an independent and identically distributed substitution model.
For StarBeast3, we used the same model as STACEY during performance benchmarking (but with effective population sizes estimated instead of integrated out). However for wellcalibrated simulation studies, and for analysing primates, we instead ran StarBeast3 under the multispecies relaxed clock model^{9}, with species branch rates drawn from \(\,{{\mbox{Lognormal}}}\,(\mu =\frac{{S}^{2}}{2},\sigma =S)\) with standard deviation S ~ Gamma(α = 5, β = 0.05). We also used the YSC species tree prior (with 4 epochs) where the first epoch was drawn from a Gamma(α = 2, β = b), where b ~ Lognormal(μ = − 1.88, σ = 1) in the wellcalibrated simulations studies, and b ~ Lognormal(μ = 2.18, σ = 0.5) when analysing the primates. The collapse weight ω ~ Beta(α = 1, β = 1) for the former, and ω ~ Beta(α = 2, β = 1) for the latter.
Further information on the wellcalibrated simulation studies can be found in Fig. S1, S2.
Proposal kernels
We employed the proposal kernels of SNAPPER, STACEY, and StarBeast3 when doing inference under the collapse model. We also introduce one further tree node height operator which increases or decreases the number of clusters in the species tree. This operator is known as ThresholdUniform and works as follows:

Sample B ~ Bernoulli(0.5).

If B = 0, then let x be an internal node from T such that h_{x} ≥ ϵ, and h_{l}, h_{r} < ϵ, where l and r are the children of x. Let the lower limit \({t}_{0}=\max \{{h}_{l},{h}_{r}\}\) and let the upper limit t_{1} = ϵ.

If B = 1, then let x be an internal node from T such that h_{x} < ϵ, and h_{p} ≥ ϵ, where p is the parent of x. Let the lower limit t_{0} = ϵ and let the upper limit t_{1} = t_{p}.

If there are no such eligible nodes for x, then reject the proposal.

Propose a new value for h_{x} as: \({h}_{x}^{\prime} \sim \,{{\mbox{Uniform}}}\,({t}_{0},{t}_{1})\).
This proposal adjusts the height of a species tree internal node from one side of the threshold boundary (at height ϵ) to the other. This operation will either lump two clusters together or split one cluster into two, without affecting the species tree topology.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
BEAST 2 XML files used in this study can be found at https://github.com/jordandouglas/speedemon_SI. This repository contains our wellcalibrated simulation study pipeline, the datasets used for benchmarking, and the Gecko and Primate datasets used as applications.
Code availability
SPEEDEMON is available as an opensource BEAST 2 package with an easytouse graphical user interface. Instructions for downloading and running SPEEDEMON can be found at https://github.com/rbouckaert/speedemon.
References
Simpson, G. G. The species concept. Evolution 5, 285–298 (1951).
Carstens, B. C., Pelletier, T. A., Reid, N. M. & Satler, J. D. How to fail at species delimitation. Mol. Ecol. 22, 4369–4383 (2013).
Fujita, M. K., Leaché, A. D., Burbrink, F. T., McGuire, J. A. & Moritz, C. Coalescentbased species delimitation in an integrative taxonomy. Trends Ecol. Evolution 27, 480–488 (2012).
Leaché, A. D., Fujita, M. K., Minin, V. N. & Bouckaert, R. R. Species delimitation using genomewide SNP data. Syst. Biol. 63, 534–542 (2014).
Yang, Z. The BPP program for species tree estimation and species delimitation. Curr. Zool. 61, 854–865 (2015).
Degnan, J. H. & Rosenberg, N. A. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends Ecol. Evol. 24, 332–340 (2009).
Edwards, S. V. Is a new and general theory of molecular systematics emerging? Evolution 63, 1–19 (2009).
Heled, J. & Drummond, A. J. Bayesian inference of species trees from multilocus data. Mol. Biol. Evol. 27, 570–580 (2010).
Ogilvie, H., Bouckaert, R. & Drummond, A. StarBEAST2 brings faster species tree inference and accurate estimates of substitution rates. Mol. Biol. Evol. 34, 2101–2114 (2017).
Bryant, D., Bouckaert, R., Felsenstein, J., Rosenberg, N. A. & RoyChoudhury, A. Inferring species trees directly from biallelic genetic markers: bypassing gene trees in a full coalescent analysis. Mol. Biol. Evol. 29, 1917–1932 (2012).
Kubatko, L. S., Gibbs, H. L. & Bloomquist, E. W. Inferring specieslevel phylogenies and taxonomic distinctiveness using multilocus data in Sistrurus rattlesnakes. Syst. Biol. 60, 393–409 (2011).
Mendes, F. K. & Hahn, M. W. Gene tree discordance causes apparent substitution rate variation. Syst. Biol. 65, 711–721 (2016).
Ogilvie, H., others & Drummond, A. J. Computational performance and statistical accuracy of *BEAST and comparisons with other methods. Syst. Biol. 65, 381–396 (2016).
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput Biol. 10, e1003537 (2014).
Bouckaert, R. et al. BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).
Green, P. J. & Hastie, D. I. Reversible jump MCMC. Genetics 155, 1391–1403 (2009).
Douglas, J., Zhang, R. & Bouckaert, R. Adaptive dating and fast proposals: revisiting the phylogenetic relaxed clock model. PLoS Comput. Biol. 17, e1008322 (2021).
Fujisawa, T. & Barraclough, T. G. Delimiting species using singlelocus data and the Generalized Mixed Yule Coalescent approach: a revised method and evaluation on simulated data sets. Syst. Biol. 62, 707–724 (2013).
Kapli, P. et al. Multirate Poisson tree processes for singlelocus species delimitation under maximum likelihood and markov chain monte carlo. Bioinformatics 33, 1630–1638 (2017).
Ence, D. D. & Carstens, B. C. SpedeSTEM: a rapid and accurate method for species delimitation. Mol. Ecol. Resour. 11, 473–480 (2011).
Jones, G., Aydin, Z. & Oxelman, B. DISSECT: an assignmentfree bayesian discovery method for species delimitation under the multispecies coalescent. Bioinformatics 31, 991–998 (2015).
Jones, G. Algorithmic improvements to species delimitation and phylogeny estimation under the multispecies coalescent. J. Math. Biol. 74, 447–467 (2017).
Nee, S., May, R. M. & Harvey, P. H. The reconstructed evolutionary process. Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 344, 305–311 (1994).
Mamos, T., Jażdżewski, K., ČiamporováZat’ovičová, Z., Čiampor, F. & Grabowski, M. Fuzzy species borders of glacial survivalists in the carpathian biodiversity hotspot revealed using a multimarker approach. Sci. Rep. 11, 1–23 (2021).
Sklenář, F. et al. Reexamination of species limits in aspergillus section flavipedes using advanced species delimitation methods and description of four new species. Stud. Mycol. 99, 100120 (2021).
TorresHernández, E. et al. A multilocus approach to elucidating the evolutionary history of the clingfish tomicodon petersii (gobiesocidae) in the tropical eastern pacific. Mol. Phylogenet. Evolution 166, 107316 (2022).
Douglas, J., JiménezSilva, C. L. & Bouckaert, R. StarBeast3: adaptive parallelized Bayesian inference under the multispecies coalescent. Syst. Biol. 71, 901–916 (2022).
Stoltz, M. et al. Bayesian inference of species trees using diffusion models. Syst. Biol. 70, 145–161 (2021).
Bouckaert, R. R. An efficient coalescent epoch model for bayesian phylogenetic inference. Syst. Biol. syac015, https://doi.org/10.1093/sysbio/syac015 (2022).
Ashman, L. et al. Diversification across biomes in a continental lizard radiation. Evolution 72, 1553–1569 (2018).
Zhang, R. & Drummond, A. Improving the performance of bayesian phylogenetic inference under relaxed clock models. BMC Evolut. Biol. 20, 1–28 (2020).
Uetz, P. et al. The reptile database (2019) (Retrieved 17 Dec 2021).
Leaché, A. D. & Fujita, M. K. Bayesian species delimitation in west African forest geckos (hemidactylus fasciatus). Proc. R. Soc. B: Biol. Sci. 277, 3071–3077 (2010).
Fleagle, J. G. in Primate Adaptation and Evolution 3rd edn, Ch. 4 (ed. Fleagle, J. G.) 57–88 (Academic Press, 2013). https://www.sciencedirect.com/science/article/pii/B9780123786326000045.
Pozzi, L., Disotell, T. R. & Masters, J. C. A multilocus phylogeny reveals deep lineages within African galagids (primates: Galagidae). BMC Evolut. Biol. 14, 1–18 (2014).
Perelman, P. et al. A molecular phylogeny of living primates. PLoS Genet. 7, e1001342 (2011).
Suntsova, M. V. & Buzdin, A. A. Differences between human and chimpanzee genomes and their implications in gene expression, protein functions and biochemical properties of the two species. BMC Genomics 21, 1–12 (2020).
Dausmann, K. H., Nowack, J., Kobbe, S. & Mzilikazi, N. in Living in a Seasonal World (eds. Ruf, T., Bieber, C., Arnold, W. & Millesi, E.)13–27 (Springer, 2012).
Hendry, A. P., Nosil, P. & Rieseberg, L. H. The speed of ecological speciation. Funct. Ecol. 21, 455 (2007).
Yule, G. U. Ii. a mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F. R. S. Philos. Trans. R. Soc. Lond. Ser. B 213, 21–87 (1925).
Leaché, A. D. & Bouckaert, R. R. Species trees and species delimitation with SNAPP: a tutorial and worked example. In Workshop on Population and Speciation Genomics, Česky` Krumlov (2018).
Olave, M., Solà, E. & Knowles, L. L. Upstream analyses create problems with DNAbased species delimitation. Syst. Biol. 63, 263–271 (2014).
Bouckaert, R. Phylogeography by diffusion on a sphere: whole world phylogeography. PeerJ 4, e2406 (2016).
Lemey, P., Rambaut, A., Drummond, A. J. & Suchard, M. A. Bayesian phylogeography finds its roots. PLoS Comput. Biol. 5, e1000520 (2009).
Stadler, T., Kühnert, D., Bonhoeffer, S. & Drummond, A. J. Birth–death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc. Natl Acad. Sci. 110, 228–233 (2013).
BaridoSottani, J., Vaughan, T. G. & Stadler, T. A multitype birth–death model for Bayesian inference of lineagespecific birth and death rates. Syst. Biol. 69, 973–986 (2020).
Hasegawa, M., Kishino, H. & Yano, T.a Dating of the humanape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evolution 22, 160–174 (1985).
Douglas, J. & Welch, D. PEACH tree: a multiple sequence alignment and tree display tool for epidemiologists. Preprint at https://arxiv.org/abs/2112.07422 (2021).
Douglas, J. UglyTrees: a browserbased multispecies coalescent tree visualiser. Bioinformatics 37, 268–269 (2020).
Acknowledgements
The study was supported by a Marsden grant 18UOA096 from the Royal Society of New Zealand. Software packages were benchmarked using the New Zealand eScience Infrastructure (NeSI) cluster, funded by the New Zealand Ministry of Business, Innovation and Employment.
Author information
Authors and Affiliations
Contributions
J.D and R.B. were both involved in manuscript writing, software development, formal analysis, and project conceptualisation.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Communications Biology thanks Alexandros Stamatakis and the other anonymous reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Luke R. Grinham.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Douglas, J., Bouckaert, R. Quantitatively defining species boundaries with more efficiency and more biological realism. Commun Biol 5, 755 (2022). https://doi.org/10.1038/s4200302203723z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s4200302203723z
This article is cited by
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.