Evolutionary history of ponerine ants highlights how the timing of dispersal events shapes modern biodiversity

Domain: earth_science
Published: 2025-09-16
Editorial outcome: minor_revision

Abstract
Disentangling the drivers of global biodiversity patterns is a cornerstone of biogeography that remains elusive for many diverse biological groups. Here we present a complete species-level phylogeny of the ant subfamily Ponerinae based on new genomic sequencing and taxonomic grafting. We combine results with a large-scale geographic database to explore the contribution of three main mechanisms in shaping global ponerine biodiversity patterns: time for accumulation, differences in diversification rate, and asymmetric dispersal. We show that extant ponerine ants originated in Gondwana, spread eastward across tropical bioregions, and more recently colonized temperate areas. The relative timing of colonization events was identified as the prominent driver of present-day biodiversity patterns, supporting the time for accumulation hypothesis. Conversely, differences in diversification rates and asymmetrical dispersal histories mitigated the heterogeneity in biodiversity by fueling accumulation of lineages in the least diverse bioregions. These findings suggest that tropical niche conservatism played a major role in shaping the biogeographic and evolutionary history of Ponerinae. Overall, we emphasize the importance of considering the relative timing of past dispersal events and variations in diversification rates over evolutionary time to gain a deeper understanding of Earth’s biodiversity patterns.

Introduction
Unraveling the drivers of the distribution of biodiversity patterns worldwide has been a core task of biogeographers since the dawn of the discipline. Multiple non-exclusive mechanisms have been proposed to explain the heterogeneous patterns of biodiversity at a large spatial scale1. Three overarching hypotheses offer predictions that can be put to scrutiny by using phylogenetic and historical biogeographic approaches to reconstruct diversification dynamics across space and time. The Diversification Rate Hypothesis (DRH) states that differences in net diversification rates across bioregions are responsible for the current imbalance in diversity patterns2,3. The Time-for-Accumulation Hypothesis (TfAH) posits that the timing of colonization of bioregions is crucial to explain biodiversity patterns, as more time to diversify leads to the accumulation of more local richness over time4. Finally, the Asymmetric Dispersal Hypothesis (ADH) relies on a pervasive imbalance in lineage exchanges across bioregions over time to generate macroevolutionary sources and sinks of lineages and explain biodiversity heterogeneity5,6.

The task of disentangling the importance of these three mechanisms for generating present-day biodiversity patterns has been reinvigorated by the emergence of global species-level occurrence databases and genome-scale sequencing data used to reconstruct comprehensive phylogenies for macroevolutionary and historical biogeographic analyses7. However, that new power has been applied primarily to large organisms: vertebrates and plants8–10. Invertebrates, especially terrestrial arthropods, are a major biodiversity component that has been historically overlooked relative to its importance11,12. In terrestrial systems, ants are dominant invertebrates and ecosystem engineers whose diversification dynamics play a major role in the evolution of terrestrial life in general13,14. In this study, we aim to disentangle the importance of the different mechanisms at play for generating present-day biodiversity patterns in a cosmopolitan and ecologically significant insect group, the ponerine ants.

Ponerinae rank as the third most diverse subfamily of ants with 50 described genera and ca. 1400 extant species, including the largest known ant species15. The subfamily is superseded in diversity only by Myrmicinae (ca. 7200 sp.) and Formicinae (ca. 3300 sp.), and dwarfs all other closely related subfamilies in the poneroid clade that each record fewer than 200 described extant species16. A ubiquitous and abundant predators of small invertebrates, ponerine ants are a key component of trophic webs, especially in tropical ecosystems17. Ponerines are also widely distributed across all biogeographic regions except Antarctica and constitute an important presence in the fossil record18. Therefore, the group is well-suited to exploring diversity dynamics across large scales of space and time.

Ant species richness is centered in the tropics with hotspots located in the Amazon basin, Mesoamerica, the Brazilian Atlantic Forest, the African Equatorial forest, and Southeast Asia. Outside of the tropical belt, the Mediterranean basin, Eastern Australia, and Madagascar appear as secondary hotspots19. Economo et al.20 examined two hypotheses to explain latitudinal diversity patterns across ants, the aforementioned DRH and the Tropical Conservatism Hypothesis (TCH), which assumes that tropical ecosystems have remained stable over longer evolutionary timeframes, allowing for more lineages to accumulate due to longer diversification times. This study included phylogenetic data from 90 ponerine taxa, while the remaining species were added to the phylogeny by random grafting. The inferred diversification dynamics indicated an absence of signal for the DRH but are consistent with TCH and time for accumulation being the most important factor in driving latitudinal diversity in ants20. Whether these patterns will also hold true on a finer scale, such as the subfamily level, and within a more robust phylogenetic framework, remains to be tested.

This strong latitudinal gradient observed in ants hints at a tropical origin for most ant subfamilies, including the Ponerinae. A previous attempt to reconstruct the ancestral ranges of ants led to strong support for a Neotropical origin of ponerine ants21. This result, based on a reduced sample of 27 ponerine species, predicted an emergence of Ponerinae in the Neotropics during the Paleocene (65-55 Mya). However, this age estimate, among the youngest for Ponerinae origin22, was later contradicted by the identification of older fossils found in Kachin amber from Myanmar (Late Cretaceous: 98.79 ± 0.62 My)23. Beyond this uncertainty in the origin of Ponerinae, the details of their current diversity patterns, diversification dynamics, and dispersal history across the world are poorly known. While Moreau and Bell21 identified the Neotropics as an important center for early diversification of ants as a whole, acting as both a museum and a cradle for ant diversity, it is unclear whether this pattern holds for ponerine ants.

Here, we produce a complete species-level phylogeny of ponerine ants, based on new genomic sequencing and taxonomic grafting, and leverage comprehensive online occurrence databases to reconstruct the diversification dynamics and dispersal history of the extant taxa in the subfamily to investigate the contribution of the three hypotheses (DRH, TfAH, and ADH) as drivers of biogeographic and diversification patterns in Ponerinae. We model ancestral range estimates to trace the origins of the clade and its major lineages. We map current species richness and compare net diversification rates across bioregions along geological time. Finally, we investigate the main drivers of the richness of bioregional radiations and quantify the immigration age of lineages. Altogether, our study paints a complex biogeographic and evolutionary history for an ecologically significant group of insects, contributing to the much-needed invertebrate perspective on global biodiversity dynamics11,12.

Results
A new comprehensive species-level time-calibrated phylogeny
We produced the most comprehensive species-level time-calibrated phylogeny of Ponerinae, including 789 phylogenetically-informed taxa (Supplementary Figs. S3 and S5), complemented with 745 taxonomically-informed taxa to reach an exhaustive coverage of described ponerine species to date (Fig. 1 and Supplementary Fig. S5). We obtained a median root age estimate of 122.8 My with a 95% Highest Posterior Density (HPD) interval ranging from 96.6 My to 150.8 My across the 1000 backbone posterior trees processed with TreePL (Supplementary Tables S5–S8). This estimate falls within the upper range of previous estimates and dates the emergence of the extant ponerine ant clade during the Early Cretaceous, coinciding with the timing of origins of other ant subfamilies22. We retrieved monophyly for all previously described tribes and genus-groups24. Within genus groups, a few cases of paraphyletic genera were discovered, providing a phylogenetic framework for future taxonomic revisions (Supplementary Figs. S3 and S5, S6).

Gondwanan origin of Ponerinae
The distribution of ponerine diversity across the globe presents a striking latitudinal gradient with only 95 (6.2%) extant lineages found in the entire Holarctic (Fig. 2A and Supplementary Fig. S8A). However, the bulk of ponerine diversity shows a balanced distribution across the tropics with 397 taxa found in the Neotropics, 452 in the Afrotropics, 380 in Indomalaya, and a relatively lower record of 236 taxa in Australasia (Fig. 2A and Supplementary Fig. S8B).

We found very high agreement for ancestral range estimates between the different divergence dating hypotheses, with Cohen’s Kappa indices higher than 0.90 (MCC vs. Youngest = 0.971; MCC vs. Oldest = 0.907), reflected by the high similarity in ancestral ranges mapped on the respective phylogenies (Fig. 1 and Supplementary Fig. S9). The most likely ancestral range for Ponerinae estimated on the grafted Maximum Clade Credibility (MCC) tree with the best fitted DEC+J model (mean Akaike’s weight = 99.95%; Supplementary Tables S1 and S2) encompassed both the Afrotropics and the Neotropics (scaled marginal likelihood (ML) = 0.482). Lower support values for Afrotropics-Indomalaya (ML = 0.235) and Afrotropics alone (ML = 0.221) were also found. Alternative time-calibrated grafted trees, including the most extreme youngest/oldest hypotheses, led to congruent results (Supplementary Tables S4 and S6–S8). Altogether, those ancestral ranges point toward an origin of ponerine ants in Gondwana, at a time when the African, South American, and Indian plates were part of the Gondwanan supercontinent. By contrast, the seven genus-groups, which emerged after the split of Gondwana ca. 120–100 Mya25 in the MCC tree, are estimated to originate from unique areas distributed across three bioregions in the Tropics: Odontomachus group and Pachycondyla group in the Neotropics; Plectroctena group, Hypoponera group, and Platythyrea group in the Afrotropics; and Ponera group and Harpegnathos group in Indomalaya (Supplementary Tables S5–S8). Most lineages were found within these three tropical bioregions (i.e., Afrotropics, Neotropics, and Indomalaya) across the evolutionary history of ponerine ants (Fig. 1 and Supplementary Table S9).

Asymmetry in dispersal dynamics
We reconstructed the dispersal network of ponerine ants across bioregions based on the aggregation of 1000 Biogeographic Stochastic Mapping (BSM) simulations (Fig. 2B; An animated figure showing the overall dispersal and diversification dynamics of ponerine ants through time is available in the online version). Overall, the most important dispersal routes occurred between tropical regions from the Neotropics to Australasia, going through the Afrotropics and Indomalaya. Extensions towards temperate zones occurred mostly from the Neotropics to the Nearctic, from the Afrotropics to the Western Palearctic, and from Indomalaya to the Eastern Palearctic, strikingly with almost no dispersal recorded between temperate bioregions (Fig. 2B). The Afrotropics were the most important source of dispersal events in early times (Late Cretaceous to Oligocene), while Indomalaya emerged as the most important source since the Miocene. The Neotropics remained an important source throughout the history of Ponerinae but received relatively few new lineages since the Miocene (Fig. 3A, B and Supplementary Fig. S10). Ponerinae likely reached Australasia for the first time around 60 Mya with a first wave of colonization from the Afrotropics, followed by a resurgence during the Miocene when the Australian plate collided with Southeast Asia creating temporary land bridges with Indomalaya26 (Fig. 3C, D and Supplementary Fig. S10). The Eastern Palearctic was colonized ca. 40–30 Mya from Indomalaya. The Nearctic was reached for the first time 30–20 Mya from South America through the Caribbean plate. Finally, our analyses indicate arrival of extant ponerines in the western Palearctic ca. 20–15 Mya, after the African plate closed on the European plate27. Across all Ponerinae history, the Neotropics (N) and Afrotropics (A) appeared as macroevolutionary sources of lineages, recording on average more departing lineages (Fig. 4A; N: 53.8 ± 3.4 taxa out; A: 67.0 ± 4.8 taxa out) than newcomers (Fig. 4A; N: 22.4 ± 2.6 taxa in; A: 31.6 ± 3.1 taxa in). Indomalaya (100.7 ± 5.2 taxa in vs. 168.1 ± 7.2 taxa out) displayed a more neutral balance, while Australasia (115.4 ± 3.9 taxa in vs. 54.2 ± 5.7 taxa out), and even more clearly the temperate bioregions (Nearctic: 18.5 ± 0.7 taxa in vs. 5.0 ± 1.6 taxa out; Western Palearctic: 17.0 ± 0.4 taxa in vs. 3.1 ± 1.6 taxa out; Eastern Palearctic: 58.5 ± 1.5 taxa in vs. 12.9 ± 3.9 taxa out) acted as macroevolutionary sinks (Fig. 4A). Overall, we reconstructed a longitudinal spatiotemporal asymmetry of dispersal from a Western origin toward the Eastern continental areas with a hierarchical order of source bioregions providing on average more lineages than they received from others such as the Neotropics (N) > Afrotropics (A) > Indomalaya (I) > Australasia (U) (Fig. 2B; N→A = 11 > A→N = 8; N→I = 16 > I→N = 8; N→U = 8 > U→N = 1; A→I = 37 > I→A = 13; A→U = 14 > U→A = 5; I→U = 89 > U→I = 41). Similarly, we found a clear latitudinal asymmetry from a tropical origin towards the temperate areas, with all shared dispersal routes recording more dispersal towards higher latitudes than towards tropical areas (Fig. 2B). These asymmetrical dispersal dynamics were also found under the two alternative dating hypotheses (Supplementary Fig. S23A, B). Even though the absolute timing of events estimated differed, the conclusions regarding the primary direction of dispersal flux and the status of bioregions as macroevolutionary sources and sinks were invariant to the divergence ages selected (Supplementary Figs. S11–S13).

Diversification dynamics across space and time
We recorded the location of in situ diversification events observed across the 1000 BSM simulations. The Neotropics arose as the primary location for in situ diversification during the Late Cretaceous comprising the majority of the few in situ speciation events recorded during this epoch (Fig. 3E, F). During the Paleocene and Eocene (66–34 My), the Afrotropics emerged progressively as the new major area of in situ speciation until the present-day balanced picture between Neotropics, Afrotropics, and Indomalaya set up during the Oligocene (Fig. 3E, F). Across Ponerinae history, the Afrotropics (420.9 ± 3.3 events = 34.9%) were the major source of in situ diversification, followed by the Neotropics (377.7 events ± 2.9 events = 31.3%), Indomalaya (281.8 ± 7.4 events = 23.4%), and Australasia (120.0 ± 5.0 events = 9.9%) (Fig. 4B). Strikingly, the temperate bioregions recorded very few in situ diversifications (6.3 ± 2.9 events = 0.5%), hinting that temperate biodiversity of ponerine ants originated mostly from dispersal events from the Tropics rather than regional diversification. This result was also found when assuming different divergence dating hypotheses (Supplementary Figs. S11 and S13).

Complementary to empirical counts of speciation events, we modeled changes in diversification rates along branches in the BAMM framework. Next, we mapped current net diversification rates in space computing mean tip rates among local taxa. We observed a longitudinal gradient of current diversification rates in ponerine ants increasing from West to East, with higher rates found in Indomalaya and Australasia (Fig. 2C and Supplementary Fig. 23C, D). Mean rates in the New World (Nearctic and Neotropics) and Afrotropics were found to be marginally lower than what can be expected from a random spatial distribution of taxa (Supplementary Fig. S20A). Rates in the northern temperate regions (Latitude > 30° N) appeared lower than average, yet, due to the relatively low taxon richness were not significant compared to a random spatial distribution of taxa (Supplementary Fig. S20B).

Diversification rates hypothesis is not supported at any time point in the evolution of Ponerinae
We recovered on average 12.1 ± 4.1 regime shifts in diversification dynamics along ponerine ant history from BAMM applied on the MCC tree. Those regime shifts corresponded to seven major taxonomic groups (Supplementary Fig. S14 and Supplementary Table S10). While we found differences in absolute net diversification rates estimated from the MCC tree and the youngest and oldest divergence dating hypotheses due to differences in the timing of speciation events, the relative distribution of rates across shared clades are significantly correlated between the hypotheses (Spearman’s rank correlation: MCC vs. Youngest: t = 13.7, p < 0.001, ρ = 0.619, 95% CI = 0.544–0.684; MCC vs. Oldest: t = 37.4, p < 0.001, ρ = 0.904, 95% CI = 0.882–0.922; Supplementary Figs. S14 and S15). We formally tested for differences in diversification rates along a latitudinal gradient in the Tropics and Temperate regions and a longitudinal gradient between the New World and the Old World with two-sided STRAPP tests28. In current time, Old World taxa did not present significantly higher mean net diversification rates than New World taxa (STRAPP test: Δ Mann–Whitney–Wilcoxon’s U 5% = -26230, p = 0.376, R = 0.1070, R = 0.0982). Notably, when testing for differences at earlier time steps, we found no period during Ponerinae evolutionary history with a significant difference in diversification rates between the New World and the Old World (Fig. 5A and Supplementary Fig. S18A). Similarly, we found no significant differences in net diversification rates between temperate and tropical taxa either in current time (STRAPP test: Δ Mann–Whitney–Wilcoxon’s U 5% = -9431, p = 0.474, R = 0.1071, R = 0.1077), or along any evolutionary time-step (Fig. 5B and Supplementary Fig. S18B). Furthermore, no overall difference of diversification rates across the seven bioregions used for historical biogeographic inferences was found in the present-day (STRAPP test: Δ Kruskal−Wallis’s Khi² 5% = -53.58, p = 0.594), nor along evolutionary times (Supplementary Fig. S18C). The youngest and oldest divergence dating hypotheses, while yielding different absolute net diversification rates in line with their different timeframes (Supplementary Fig. S23C, D), led to similar conclusions, with no differences in net diversification rates detected between bioregions, Old World vs. New World, and Tropics vs. Temperate areas along evolutionary times (Supplementary Figs. S17 and S19).

The New World is older than the Old World for Ponerinae
To explore more thoroughly the main factors shaping differences in diversification rates across bioregions, we investigated the diversification dynamics of Regional In Situ Radiations (RISR). Neotropical and Afrotropical radiations were revealed to be on average more diverse and older but showed no significant differences with Indomalaya in diversification dynamics in terms of crown rates, current rates, or time-variation in rates (Fig. 6). On the other end of the spectrum, Australasian radiations appeared younger and are experiencing currently higher rates of diversification induced by significant rate increases over time (Fig. 6).

The older ages of Neotropical radiations and younger ages of Australian radiations were also reflected in the spatial distribution of taxon immigration ages. We found striking latitudinal and longitudinal gradients of immigration ages, with the temperate regions displaying significantly younger taxa (Fig. 2D and Supplementary Fig. S21A). Meanwhile, within the tropical belt, immigration ages decreased from the New World in the Western hemisphere toward the Old World in the Eastern hemisphere, with the oldest communities found in the Neotropics, intermediate-aged communities in the Afrotropics, and younger tropical communities in Indomalaya and Australasia (Fig. 2D and Supplementary Fig. S21B). These striking gradients of taxon immigration ages were also found under the alternative divergence dating hypotheses. While the absolute ages varied in accordance with the timeframe of each hypothesis, the relative patterns remained robust. Especially the Neotropics and Afrotropics appeared to host taxa with the oldest immigration ages, while Indomalaya, Australasia, and the temperate bioregions exhibited younger mean immigration ages (Supplementary Fig. S23E, F).

Lastly, we found contrasting origins for taxa between bioregions. Neotropical and Afrotropical ponerine diversity is currently composed of 97% and 95% of taxa originating from in situ speciation events as opposed to dispersal, illustrating their constant high proportion of autochthonous lineages in the regional ponerine fauna through evolutionary time (Fig. 7). Other tropical bioregions currently display high levels of autochthonous fauna (82–84%) that have increased since the Cretaceous origins for Indomalaya, and more recently during the Cenozoic in Australasia (Fig. 7). Temperate regions have also witnessed increases in their proportions of autochthonous fauna since their respective colonizations. However, in sharp contrast with the tropics, they still currently record low levels of autochthonous taxa with 44% to 69% of their current diversity being of exotic origin (Fig. 7). These contrasting origins were retrieved similarly for the two alternative dating hypotheses, albeit with different absolute timing along evolutionary times (Supplementary Fig. S22).

Methods
Taxon sampling
Nonewspecimenswerecollectedspecificallyforthisstudy.Allspecimensusedwereobtainedduringpriorresearchprojectsandwerecollectedinfullcompliancewiththerelevantpermitsandregulations.Access and sampling of museum specimens was granted from the respective curators of the collections, including the authors, as listed under “contact persons” in Supplementary Data S1. We selected a total of 789 ponerine taxa plus 3 outgroups for phylogenetic analysis. Among these samples, 136 were sequenced previously51–55; 656 were newly sequenced for this study. We aimed to include Ultra Conserved Elements (UCE) data for as many unique species-level taxa as possible. In total we were able to include 528 (41.5 %) of the 1273 described species, and an additional batch of 261 expert-identified morphospecies suspected to represent independent taxa. Worker specimens have been favored for sampling, but queen and males have been sporadically used if no worker was available. Specimens have collecting year ranges from 1999 to 2023. Information on taxon sampling with NCBI accession numbers and references is presented in Supplementary Data S1 and S2. Our taxon sampling covers all geographic regions where the subfamily occurs, with taxonomic coverage ranging from 30.9% in the Eastern Palearctic to 87.2% in the Nearctic. This sampling scale is considerably greater than in previous studies with a similar or larger ant focus, and multiplies the taxonomic coverage of sequenced species several fold20,21,56. Such resources open the door to reliable investigations of diversification dynamics at large taxonomic and temporal scales.

UCE data generation and processing
Samples were extracted using Qiagen DNeasy Blood and Tissue kits following manufacturer’s protocols, with the following modifications: samples were incubated overnight for up to 16 hours in digestion buffer; AE buffer was heated to 60C prior to DNA elution; and two rounds of elution were performed whereby 50–100 μL of AE buffer was added to each column and incubated at room temperature for 3 min prior to centrifugation. Extracted DNAs were then quantified using a Qubit fluorometer. Up to 50 ng of extracted DNA was then sheared to an approximate fragment distribution of 400–600 bp using a Qsonica Q800R sonicator. The sonicated DNA was then processed in-house, or sent to either Rapid Genomics (now LGC Bioresearch Technologies) in Gainesville, FL, or the genomic score facility at the University of Utah for library preparation, UCE enrichment, pooling, and sequencing. For the in-house work, libraries were prepared following the methods reported in Branstetter et al.51. In brief, libraries were prepared using Kapa HyperPrep kits and custom, dual-indexing adapters described in Glenn et al.57. Libraries were pooled into batches of up to 12 samples and then enriched using the HymV2 ant-specific probe set, synthesized by Arbor Biosciences. Enrichment was performed following Arbor Biosciences v4 protocol for day 1 and an older UCE enrichment protocol for day 2 (enrichment protocol v1.5 available at ultraconserved.org; based on Blumenstiel et al.58). Following enrichment, pools were quantified using Qubit and qPCR and then pooled together into a final pool containing up to 120 samples. The final pool was then sent to either Novogene or the University of Utah for sequencing on Illumina HiSeq X or NovaSeq. Rapid Genomics and the University of Utah followed similar procedures, except that they sequenced 2–3 million PE reads for UCE-enriched libraries and also ran additional sequencing on unenriched libraries for 1–2 million reads (whole genome sequence data).

We combined UCE and Whole-Genome Sequencing (WGS) reads produced for this study with reads available from the NCBI Sequence Read Archive (See “Data availability” in the main text for accession codes). Most data processing steps were performed using Phyluce v1.759. Reads were trimmed using Illumiprocessor v2.060 and assembled using SPAdes v3.14.161. UCEs were extracted using the HymV2 ant-specific probe file and default settings51. UCE contigs were assembled using MAFFT v7.130b, as implemented in Phyluce. Alignments were trimmed with (1) TrimAl v1.4.rev1562, as implemented in Phyluce, and (2) Spruceup v2022.2.463. Several iterative rounds of Spruceup were performed, with the final analysis implementing the lognormal criterion, a global cutoff of 0.93, and several manual cutoffs for taxa with noticeably longer branch lengths (Hypoponera_MG024_MAMI0635_CASENT0175229: 0.05; Leptogenys_diminuta_D0273: 0.05; Neoponera_magnifica2_EX2401: 0.04; Odontomachus_rufithorax_EX2952: 0.05; Ponera_selenophora_EX2971: 0.03; Pseudoneoponera_AU_A_EX2579: 0.07). The trimmed alignments were then split using AMAS64 and filtered to remove gap-only columns using a custom script (https://github.com/marekborowiec/remove_empty_columns). The alignments were then processed to retain only those that included at least 75% of all samples. This resulted in a final dataset that comprised 2365 UCE loci and 1,055,001 bp of sequenced data, of which 542,612 bp were informative. The dataset included only 17.2% missing data.

Phylogenetic inferences
We concatenated the filtered loci and inferred phylogenetic trees in a Maximum Likelihood (ML) framework using IQ-Tree v2.265. Three different partitioning schemes were evaluated: unpartitioned, by locus with merging, and Sliding-Window Site Characteristics based on Entropy (SWSC-EN) with merging. For the unpartitioned analysis, we followed recommendations from Abadi et al.66 and selected a unique parameter-rich substitution model, which encompasses simpler models as special cases, and yields similar performances for phylogenetic inferences, while avoiding including a time-consuming model selection step in our computationally intensive workflow. Therefore, we used a General Time Reversible (GTR) model of sequence evolution with Gamma rate variation across sites (G4) and empirical base frequencies (F) as a substitution model. For the by locus analysis and SWSC-EN analyses, we set up analyses that combined partition merging, full model selection, and tree inference. For the by-locus analysis, we used as input the concatenated alignment and a by locus partition file into IQ-Tree v2. The best partitioning scheme was inferred using ModelFinder267. We used the rclusterf algorithm limited to testing 10% of possible dataset combinations and using only the GTR+F+G4 model of sequence evolution. We used the corrected Akaike Information Criterion (AICc) as criterion for selection. After merging, best-fit substitution models for each partition were inferred using the “-m MFP” command in IQ-Tree. For the SWSC-EN analysis, we used the program CURE68 to separate loci into right and left flanks and a core region using the Sliding Window Site Characteristics based on Entropy69. We then used the same merging, model selection, and tree inference approach as done for the by locus analysis going from 6715 partitions down to 2565 after merging. In all cases, support values were assessed using 1000 replicates of ultrafast bootstraps (UFBS)70 and SH-like approximate likelihood ratio test71. The three partitioning schemes led to identical topologies, except for five clades (unpartitioned: 99.4% identity) and one clade (by locus: 99.9% identity), respectively, differing from the SWSC-EN topology due to uncertain placement of single isolated rogue taxa (Supplementary Fig. S3 and S4). The SWSC-EN with merging scheme had the highest likelihood (unpartitioned: -37.82 × 106, by locus with merging: -37.30 × 106, SWSC-EN with merging: -37.08 × 106) and the best overall support values (mean UFBS: unpartitioned = 97.48%; by locus with merging = 97.84%; SWSC-EN with merging = 97.95%). Moreover, the complexity of the SWSC-EN scheme encompasses the unpartitioned and by locus schemes in allowing different evolutionary dynamics to occur in the ultra-conserved and variation section of each locus. Therefore, the topology from the SWSC-EN analysis was selected for downstream analyses. The ML phylogram with 789 ponerine ants is shown in Supplementary Fig. S3 and available in Supplementary Data 8.1. Configuration files, analysis output, and custom scripts used for phylogenetic inferences can be found in the dedicated part of the Zenodo archive at https://doi.org/10.5281/zenodo.14216556.

Divergence dating
We used a two-step approach to make the generation of time-calibrated phylogenies computationally feasible. First, we applied Bayesian time tree inference in MCMCTree implemented in PAML v4.10.724,25 using node calibrations on a backbone tree with reduced taxon sampling. We then ran a penalized likelihood approach26 in TreePL v1.027 using secondary calibrations from the backbone tree to infer ages of all nodes.

We selected fossil calibrations following three guiding principles: (1) fossils had to be confidently assignable to total groups; (2) in the absence of total-evidence phylogenetic placement, we conservatively assumed that fossils could belong to either stem or crown groups; and (3) when multiple fossils of similar age could inform a set of nested nodes, we calibrated only the shallowest such node to avoid redundant constraints. These principles were applied in conjunction with best practices outlined by Parham et al.72 for fossil selection and have been applied successfully in divergence dating of ants and other Hymenoptera73–75. Following this approach, we identified seven fossil calibrations appropriate for our analyses, prioritizing those with clear morphological justification, sufficient preservation, and unambiguous taxonomic placement.

For the first step, we produced a reduced dataset for MCMCTree analyses by trimming taxon sampling to 69 taxa spanning all clades that could be dated using our seven fossil calibrations. We set Cauchy distribution priors with a 0.1 offset, scale parameter of 1, and soft minimum bounds on nodes calibrated by fossils. Details about fossils, nodes, and age constraints used can be found in Supplementary Data S3. To explicitly account for the large uncertainty surrounding divergence estimates in ants, we set a normal distribution for the required root calibration prior based on ranges inferred for this node in seven recent divergence dating studies of ants reviewed by Borowiec et al.22. This normal distribution was centered around 113 Ma, which is the average of mean Highest Posterior Densities (HPD) or confidence intervals from these studies, and with 95% of density falling between 69 and 149 Ma, corresponding to the lowest and highest-end estimates from those dating studies.

In addition to reducing taxon sampling, we reduced the molecular dataset to 100 UCE loci selected based on the properties of their individual locus trees. We sorted the loci based on the weighted average of three properties that have been shown to identify loci suitable for divergence dating analyses76. The most important property considered was “clocklikeness” (weight 0.5), here defined as the minimum coefficient of variation of root to tip distances, followed by average locus tree branch length (weight 0.3), and Robinson-Foulds distances to the 792-taxon ML tree inferred by concatenated analysis (weight 0.2), evaluated using kinda_date74 (https://github.com/marekborowiec/kinda_date). These properties were computed on locus trees with complete taxon sampling. We ran the MCMCTree analyses using fixed concatenated topology and likelihood approximation77 for four independent chains for 10,000,000 generations each, sampling every 1000th generation and discarding samples from the first 1,000,000 (10%) generations as burn-in. We combined the output of the four chains and confirmed that the analyses converged by visually examining the posterior traces of each parameter in Tracer v1.7.178 and ensuring effective sample size (ESS) for each parameter was above 300. We also compared the results to a prior-only run to confirm that the posterior was informed by the data.

To account for and ensure the robustness of our downstream analyses to dating uncertainty, we randomly selected 1000 reduced 69-taxon trees from the 40,004 total posterior samples from MCMCTree. We extracted node ages and created a TreePL config file with constraints extracted from each of those reduced 69-taxon trees. This ensures each calibrated node has min and max calibration ages equal to the age of the corresponding node in each input tree.

We used the 792-taxon ML tree as input, set the number of sites to 1,055,001, as in the full alignment that tree was inferred under, and turned on the “thorough” setting. We used random-sampled cross-validation to find the optimal smoothing value for one of the randomly selected trees from a range of 1000 down to 0.0000001. The best value turned out to be 0.01. We used this value for all the configuration files and inferred 1000 dated backbone trees using TreePL.

We obtained a single backbone tree with node age confidence intervals using TreeAnnotator v1.1079 to summarize the 1000 trees into a Maximum Clade Credibility tree (subsequently, MCC tree) with mean node heights. We also extracted the two most extreme divergence dating hypotheses with the lowest and highest sum of estimated node ages to use for sensitivity analyses (subsequently, youngest and oldest hypotheses). The time-calibrated MCC tree with 789 ponerine ants is shown in Supplementary Fig. S5 and available in Supplementary Data 8.2. Posterior time-calibrated trees are available in Supplementary Data 8.3. Configuration files, analysis output, and custom scripts used for divergence dating analyses can be found in GitHub at https://github.com/marekborowiec/ponerine_phylogeny.

Tree grafting
Analyses of diversity dynamics have increased power and lower estimation bias on imputed exhaustive phylogenies relative to analyses that uses sampling fractions to account for missing taxa80. Moreover, a grafting approach accounts for the location of missing taxa across clades, while avoiding potentially incorrect likelihood behaviors and identifiability issues associated with clade-specific sampling fractions81–83. Therefore, we followed the recommended approach for accounting for non-random incomplete taxon sampling in diversification studies (http://bamm-project.org/advanced.html; Section 9.1; accessed on April 16th, 2025). We generated a distribution of fully sampled phylogenies encompassing all known ponerine ant species by using phylogenetically informed imputations of missing taxa on the UCE-based time-calibrated backbone phylogenies obtained from TreePL.

We used all taxonomic and biogeographic information available in the literature to define, for each of the 745 missing taxa, a target clade within which to graft the missing taxa, including its stem branch (Supplementary Data S4). When relevant, we also identified “excluded subclades” within the target clade to exclude from potential grafting locations. For instance, we prevented missing taxa distributed in one bioregion from being grafted in a subclade known to be restricted to another distinct bioregion, limiting overestimation of dispersal events that would arise from a purely random grafting in widely spread target clade. Within each identified target clade, we fitted a birth-death model with the sampling fraction adjusted to the number of missing taxa to graft within. Then, we simulated ages of missing taxa with the corsim function from the TreeSim R package84, extracted the set of branches present at the given age, and randomly selected the grafting branch while excluding the branches belonging to the excluded subclade(s) from the set of options. This imputation procedure is conservative, because it minimizes among-lineage diversification rate heterogeneity within any target clades85. Imputations were run in a stepwise nested fashion, starting with nested target clades. We ran an independent random imputation of all missing taxa on each of the 1000 backbone time-calibrated phylogenies to generate a set of exhaustive ponerine phylogenies for sensitivity analyses; these account simultaneously for uncertainty in divergence age estimates and informed imputations of missing taxa. This distribution includes the youngest and oldest divergence dating hypotheses used for sensitivity analyses downstream. We also ran a random imputation on the MCC tree obtained from summarizing TreePL outputs across all posterior time-calibrated trees to produce a grafted MCC tree used for reporting results of downstream analyses. The grafted MCC tree with 1534 ponerine ants is shown in Supplementary Fig. S6 and available in Supplementary Data 8.4. Posterior grafted trees are available in Supplementary Data 8.5. The overall workflow used to produce phylogenies is visually summarized in Supplementary Fig. S1.

Biogeographic data
Data sources and curation. All available georeferenced occurrence data for ponerine ants were retrieved from AntWeb (62,301 records; https://www.antweb.org/, accessed on 30/05/2024) and AntMaps86 (93,557 records; GABI v1.0). Coordinates were screened with CoordinateCleaner87 to detect common errors in biodiversity datasets, including locations in bodies of water, within biodiversity institutions, in centroids of political entities, and outliers based on distance to other occurrences of the same taxa. Four thousand one hundred and forty-three records were flagged and manually curated in the process. We also detected mismatches between coordinates and assigned geographic administrative entities at several hierarchical levels (adm2, adm1, and countries from geoBoundaries88, http://www.geoboundaries.org, accessed on 15/02/2024; bentity2 from AntMaps86, https://antmaps.org/) and solved conflicts for those 15,244 flagged records. Additionally, we designed a custom screening procedure to detect bioregional outliers. For each taxon, we flagged all records falling in a bioregion represented by less than 20% of records, highlighting potential cases of erroneous outliers likely to affect our ancestral range estimates. Two hundred and fifty-six dubious records belonging to 101 taxa were removed, while documented cases of range expansion across bioregions were conserved. Finally, we retrieved coordinates for 50,114 records missing GPS data based on their assignment to administrative entities. For each record, we randomly drew coordinates within the smallest assigned entity and recorded uncertainty as the area of the assigned entity. Such uncertainty has no effect on our downstream analyses as they are carried out at a bioregional level encompassing all geographic administrative entities used in this process. Finally, we cleaned records for duplicates to obtain a final dataset of 38,128 curated unique taxon-location georeferenced records based on one degree-grid cells, covering all 1534 ponerine taxa included in our study (all 1273 described species and 261 UCE-sequenced morphospecies). The map of curated occurrences is shown in Supplementary Fig. S7. The overall workflow for curation of biogeographic data is visually summarized in Supplementary Fig. S2. The exhaustive database of 149,541 curated geolocalized occurrences of ponerine ants is available in Supplementary Data S5.

Mapping current species richness. We estimated the range of taxa based on georeferenced occurrences, excluding those retrieved from administrative entities. First, we drew alpha-hull shapes around occurrences with an alpha radius = 1000 km, allowing us to retrieve disjunct ranges in case occurrences formed distant clusters. Next, we applied a taxon-specific buffer around alpha-hulls based on the density of sampling for each taxon using a minimal buffer of one arc-degree, and a maximum buffer estimated as the 80% quantile of minimal pairwise distances between occurrences as a way to account for uncertainty in the sampling of the actual species range89. Finally, we rasterized and stacked all buffered alpha-hulls representing taxon ranges and computed local species richness as the sum of taxon presence recorded in each quarter-degree cell to obtain a map of worldwide ponerine species richness.

Historical biogeography
Bioregional scheme. Each occurrence was attributed to a bioregion based on the bioregion scheme used in AntWeb (https://www.antweb.org/). We lumped Madagascar with the Afrotropics and split the Palearctic between Western and Eastern Palearctic to ensure a more homogeneous split of terrestrial areas. The latter also allowed discrimination of Nearctic/Palearctic dispersal through the Bering land bridge in the Northern Pacific or the Thule land bridge in the Northern Atlantic. We also classified India and the Burmese terrane under a unique Indo Malayan bioregion, even though those territories have been allegedly separated over geological time90. The vast majority of ponerines found in the present-day Burmese area and in the Indian subcontinent are also encroaching on the surrounding South-Asian regions, limiting vastly the informative power of historical biogeographic inferences treating those areas as separate entities. This treatment also ensures a more balanced area division of terrestrial lands. Recent investigations of current native ant bioregionalization have identified sets of different bioregions91,92 that remain largely concomitant with the broad delineation we used. Such delineations are based on the current distribution of taxa but are less suitable for use in historical biogeographic analyses accounting for continental drift and investing taxon distributions over evolutionary time. Thus, we favored the use of our larger bioregion scheme in line with past and recent endeavors in zooregionalization and historical biogeography31,93–97. Binary tables of presence/absence of the 1534 ponerine ant taxa across bioregions used in this study are available in Supplementary Data S6.

Ancestral range estimates with BioGeoBEARS. To study support for the Time-for-Accumulation Hypothesis (TfAH), we estimated the ancestral ranges of ponerine ants in our exhaustive phylogeny. We ran a set of time-stratified historical biogeographic models within the BioGeoBEARS framework accounting for different combinations of biogeographic events such as range expansion and contraction, vicariance, subset speciation, and jump-dispersal, with DIVALIKE, DIVALIKE+J, DEC, and DEC+J models98. Such a time-stratified approach accounts for changes in bioregion connectivity by providing adjacency matrices (Supplementary Data S7) associated with each geological epoch and preventing lineages from harboring ranges encompassing non-adjacent bioregions at any given point in time. Although we acknowledge that +J models may yield unrealistically low regional extinction rates and tend to limit the residence times of widespread ranges99, they also allow long-distance jump-dispersal events concomitant with speciation (i.e., founder effect) to occur, even between non-adjacent areas. This releases an important, and likely unrealistic, constraint of other models with the j parameter set to 0 that prevents any dispersal to occur between non-adjacent areas. Therefore, we chose to rely on likelihood-based statistical comparison to select the best-fitting model for our analyses100 and ran a whole set of models as listed above, without a priori deciding which one should be favored.

The DEC+J model was selected as the best model across both the grafted MCC tree and all 1000 grafted time-calibrated phylogenies, accounting for uncertainty in both divergence dating and taxon grafting, including the youngest and oldest divergence dating hypotheses (Mean Akaike’s weight = 99.95%; Supplementary Tables S1–S4). Similarly, ancestral range estimates based on posterior marginal likelihoods using those fitted DEC+J models appeared congruent across all phylogenies (Supplementary Tables S5–S8). Therefore, we conducted all downstream analyses on the grafted MCC tree under the DEC+J model. In parallel, we performed sensitivity analyses by conducting the same analytical pipeline on both the youngest and oldest divergence dating hypotheses in order to fully assess the robustness of our conclusions to alternative divergence age estimates. We also employed the Cohen’s Kappa index for classification similarity (Cohen, 1960) to quantify agreement in the most likely ancestral range estimated for shared nodes between the different divergence dating hypotheses (i.e., MCC, Youngest, and Oldest).

Biogeographic stochastic mapping (BSM). We applied Biogeographic Stochastic Mapping101 (BSM) using the best-fitted model for historical biogeography (DEC+J) to simulate 1000 possible biogeographic histories for ponerine ants on the grafted MCC tree. BSM draws random ranges at each internal node based on the estimated posterior marginal probabilities, then simulates range evolution on each branch using estimated state transition parameters until it finds a biogeographic history compatible with allowed ranges. From BSM simulations, we retrieved the posterior frequencies of anagenetic and cladogenetic events and recorded the sources and destinations of dispersal events in time. In particular, we quantified the counts and relative frequencies of events along 10 My sliding windows to compare dispersal dynamics in time across bioregions and investigate support for the Asymmetrical Dispersal Hypothesis (ADH). In the case of ranges encompassing multiple bioregions, the lineages were equally divided among the bioregions to contribute to respective bioregional counts.

Mapping biogeographic and diversification dynamics through time. To visualize the overall dynamic of ponerine ants in space and time, we constructed paleomaps of bioregions based on a geological model of continental plate drift102 implemented in the rgplates R package103. We then overlaid the network of dispersal events across moving bioregions at each time step (1 My), with edge width scaled by event counts recorded within time-windows of 5 My, and vertex size scaled by regional species richness quantified as number of lineages present in each region at each time step, aggregated across the 1000 BSM simulations. The final animation of ponerine ant biogeographic and diversification dynamics is available in Supplementary Movie 1.

Diversification dynamics
Empirical in situ speciation rates. Complementary to frequencies of dispersal events, we also recorded the source of cladogenetic events and quantified the frequencies and rates of in situ speciation as the number of range inheritance events within time-sliding windows. As with dispersal events, ranges encompassing multiple bioregions contributed equally to respective bioregional counts.

BAMM diversification rates. Besides empirical speciation rates estimated from in situ speciation event counts on time-calibrated phylogenies, we also used BAMM v2.5.0 to estimate diversification rates among lineages using a mechanistic approach modeling rate shifts104. Several independent runs with four reversible-jump MCMC on 107 generations were carried out for 1, 5, 10, and 20 expected number of shifts, with all other priors set to default using the R package BAMMtools v2.1.1228. We combined the output of the four chains in each run and confirmed that all analyses converged by visually examining the posterior traces of parameters (Supplementary Fig. S16) and ensuring effective sample size (ESS) for each parameter was above 200. We sampled 1000 posterior generations after discarding burn-in and selected the run with 10 expected shifts for downstream analyses since its prior distribution was the most similar to the posterior. From this, we obtained 1000 evolutionary scenarios from which we retrieved median diversification rates along branches (Supplementary Figs. S14 and S15). We compared median rates estimated for the grafted MCC tree and the alternative divergence dating hypotheses using Spearman’s sum-rank correlation tests across branches associated with shared clades. Estimated rates were aggregated per bioregion membership, as estimated from the BSM simulations, to compute rates through time evolution in relation to bioregions31. For the MCC tree, we recovered on average 12.1 ± 4.1 regime shifts in diversification dynamics along ponerine ant history, of which seven concerned major radiations (see Supplementary Table S10 for regime shifts metadata from the Maximum Shift Credibility (MSC) configuration).

BAMM, and other similar methods used to estimate time-varying diversification rates and identify regime shifts suffer from identifiability issues105. To limit potential issues, we focused our analyses on net diversification rates rather than on the absolute estimation of speciation and extinction rates individually. As a Bayesian approach, BAMM samples a large number of alternative diversification histories that can lead to similar likelihood, effectively accounting for a portion of the uncertainty regarding the identification of the “real” diversification history. Such uncertainty was carried out down to our tests for diversification rate differences as we performed STRAPP tests across posterior BAMM samples (see Section 9.1 below). Additionally, we combined BAMM analyses with a different approach based on Regional In-Situ Radiations (see Section 9.2 below) in order to cross-validate our results and address identifiability issues to the best possible extent.

Mapping current diversification rates. To visualize the spatial distribution of current diversification rates, we assigned taxon-specific rates estimated from BAMM to each respective taxon range, as estimated from buffered alpha-hulls, and extracted the mean current rates recorded among taxa found in each quarter-degree grid cell. To limit artifactual outlier values, we recorded mean rates only for grid cells with more than two taxa. Additionally, we extracted mean rates along latitudinal and longitudinal bands of one arc-degree based on taxa with a range overlapping each band. We extracted such geographic gradients across all 1000 BAMM posterior samples and generated a null distribution of mean rates along gradients by permuting species identities among range data. We identified latitudinal and longitudinal bands where the mean current diversification rates are lower/higher than expected if species were distributed randomly.

Diversification-dispersal relationships
BAMM rates across bioregions: STRAPP tests. We carried out STRAPP tests using the traitDependentBAMM function in the R package BAMMtools v2.1.1228 to investigate differences in current diversification rates across bioregions and formally test for support for the Diversification Rate Hypothesis (DRH). For greater statistical power, we focused on differences between temperate and tropical bioregions along a latitudinal gradient and between the Old World and the New World along a longitudinal gradient (Fig. 5), but also report results considering the seven bioregions altogether (Supplementary Information Fig. S10). Such a test permutes tip rates across taxa in the posterior samples from a BAMM analysis using a block-design that accounts for the non-independence of tip rates data via permutations restricted among macroevolutionary regimes106. Then, statistics from two-sided Wilcoxon tests (for pairwise gradient tests) and Kruskal-Wallis tests (across all bioregions) are compared to the value obtained from the observed data to assess significance of rate differences against random rate distribution across regimes.

Additionally, we designed a modified version of the STRAPP test that allows us to test for diversification rate differences through evolutionary time, and not only between present-day rates, as those are unlikely to be informative about long-term past dynamics shaping biodiversity32,107. We used bioregion membership estimates from the BSM simulations and cut off the phylogenies mapped with biogeographic histories every 0.1 My time step to be able to carry out STRAPP tests across lineages present at any given point in time. As a result, we obtained p-values for rate differences along evolutionary time and could identify timeframes of significance.

RISR analyses. To identify the main factors explaining differences in diversification rates across bioregions, we investigated the diversification dynamics of Regional In Situ Radiations (RISR). RISR are independent clades that diversify almost exclusively within a unique bioregion, allowing for statistical tests between evolutionary independent units31. First, we identified RISR as clades whose lineages spent more than 75% of cumulated time along branches in a single bioregion, with a minimum of 10 extant taxa. As such, we identified 9 RISR in Afrotropics, 5 in Australasia, 12 in Indomalaya, and 5 in Neotropics. For each clade, we fitted a time-dependent birth-death model with speciation rates (λ) being an exponential function of time since the crown age (t=0) and extinction rates (μ) being constants similar to macroevolutionary regimes in BAMM104 (Eqs. 1, 2 and 3). Models were fitted with the R package diversitree v0.10-1108. (Eqs. 1, 2, 3 omitted).

We extracted the crown age, the crown net diversification rate (R_crown), the current net diversification rate (R_current), and the time-variation parameter (α) associated with each RISR. We then tested for differences in parameter values across bioregions using two-sided Kruskal–Wallis and pairwise post hoc Wilcoxon tests. Finally, we used a Poisson regression to predict present-day taxonomic richness of RISR across bioregions based on crown ages.

Quantifying lineage origins through time. We designed a new method to estimate and map the age of immigration for lineages within a new bioregion and gather additional support for the Time-for-Accumulation Hypothesis (TfAH). We browsed our 1000 BSM simulations from tips-to-root to retrieve for each present-day taxon, the timing of the range shift when the lineage reached its current bioregion for the first time. For taxa harboring a range encompassing multiple bioregions, the “biogeographic immigration age” is averaged between areas. Taxon immigration ages were then averaged across all simulations. As with current diversification rates, we mapped the spatial distribution of biogeographic immigration ages using taxon ranges as estimated from buffered alpha-hulls and extracted the mean immigration ages of taxa recorded in each quarter-degree grid cell. To limit artifactual outlier values, we recorded mean ages only for grid cells with more than two taxa. Additionally, we computed geographic gradients of mean ages along latitudinal and longitudinal bands of one arc-degree based on taxa with a range overlapping each band. For statistical comparison, we generated a null distribution of mean immigration ages along geographic gradients by permuting species identities among range data.

Finally, we investigated changes through time in the origin of lineages across bioregions. Using the BSM simulations, we recorded at each time step (1 My) the origin of lineages found in each bioregion. The origins were examined by comparing the lineage ranges at the focal time step with the range of their most direct parental node. Autochthonous scores of each lineage were computed as the proportion of unique areas in the lineage range found among the unique areas of its parental range. Then we computed the mean autochthonous score per bioregion by aggregating lineages across their focal time ranges, splitting lineages equally among bioregions in the case of ranges encompassing multiple areas. As such, for each time step and each bioregion, we obtained mean autochthonous scores representing the counts and proportions of lineages present at a given time step whose presence is due to in situ diversification as opposed to dispersal.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present an impressive and comprehensive phylogeny of the ant subfamily Ponerinae—the largest ever assembled for this clade—and use this newly constructed tree to explore its biogeographic history, with a particular focus on dispersal events and diversification patterns. The dataset (in terms of loci) and manuscript (in terms of number of analyses) are of high quality. The authors emphasize why non-vertebrate taxa should be investigated to better understand global biodiversity patterns. However, while the study is engaging, we have several concerns that we believe preclude publication in Nature Communications, at least in its current form. We (the reviewers) are an early career researcher and a senior researcher with complementary skills and knowledge in insect biogeography and diversification analyses. We reviewed the manuscript separately, before exchanging our opinions, and had the same overall sense of the limitations of this work, limitations that primarily relate to the level of novelty and support of the main results. We have summarised our comments below, first with our main points and then with some more specific suggestions, mainly on additional analyses that should be carried out to fully support the hypotheses and conclusions of the manuscript, especially in light of methodological concerns raised in recent studies (e.g., Louca and Pennell, 2020 - Nature).\n\n1) While we acknowledge the considerable amount of work and the production of the most comprehensive phylogenetic hypothesis for Ponerinae, we have concerns about the novelty of the main results of this study. Importantly, the discussion highlights the role of the Time-for-Accumulation Hypothesis (TfAH) in explaining the latitudinal diversity gradient of this ant subfamily. This has already been found in Economo et al. (2018 – Nat. Comm.) at the whole scale of ants. As Economo et al. (2018) is surprisingly not reported in the introduction, the novelty of this study in this respect was unclear to us. We could see that the methodological approaches were different and that here, for instance, biogeographic analyses were carried out, but it was not always clear what each set of analyses brings to the overall narrative and what is completely new and what supports previous knowledge/hypotheses.\n2) Somewhat related to our first point, we were confused by what seemed to us to be a rather strong disconnection between the introduction and the discussion of this manuscript. Overall, we were not well prepared by the introduction to fully grasp what was really at stake—the abstract with a single sentence of context did not help. And the relatively short discussion did not emphasize the novelties enough, whereas some of the ‘key findings’ seem like very general statements or truisms (e.g. line 529, which states that “this study highlights the importance of reconstructing the timing of past biogeographic and evolutionary events”).\n3) “Timing is everything”. This work emphasizes the importance of timing, but we feel that the uncertainties surrounding Dating analyses are not as advanced as they should be. A reliable and credibly dated phylogeny is essential for assessing diversification rate shifts and the biogeographic history of lineages. There are also increasing examples of how the inclusion of fossil taxa can alter datings, diversification and biogeographic analyses. The authors do report and briefly explain why they did not use fossils, but how and to what extent this may affect their main results and conclusions is only touched upon.\n4) Bioregions. The authors used 7 bioregions in several maps and some analyses. But when it comes to testing latitudinal and longitudinal gradients, they only use 2 bioregions (temperate vs tropical and Old World vs New World, respectively). This choice is motivated by statistical reasons (line 264), but there is no critical discussion or sensitivity analyses to explain how harmless this important choice is (or is not).\n5) An important piece of information that is missing from biogeographic analyses is how areas were or were not connected over time (present as supplementary files only on Zenodo). In other words, what were your time-stratified models? This is crucial for ARE and BSM analyses. One of the things we were curious about was how India and the Burmese terrane were treated. Also, we do not think we clearly understood what some of the panels in Fig. 3 were showing. For example, in Fig 3A, if we lookt only at the four rightmost bars, we count more than 800 dispersal events near the Miocene/Pliocene boundary, which is colossal. But since this figure is based on 1000 simulations, we think that ‘only’ 80% of the simulations reported a dispersal event at this time, and that 7 bioregions can be the source of this potential dispersal event (even if Indomalaya seems the most likely). Is this correct? In any case, it also means that some of these ‘contemporaneous’ events actually occur in different clades in these simulations. Furthermore, the events are shown every Myr, but the counts were done in sliding windows of 5 (in the Methods) or 10 (in the caption) Myrs. Is there a possibility of overestimating the number of events because of this sliding window? As you can see from our questions, we may have not have understood well what readers should see and take away from this figure. We appreciate that BSM was used to get an idea of the uncertainty in the biogeographic scenario, but we found the results confusing. Would it be worth contrasting these results with the dispersal events found in the ARE analysis? Finally, the readability of Fig. 2B, which also shows the results of dispersal events from the 1000 simulations, could be improved: as it stands, we mainly see the numbers representing the specific richness rather than the dispersal events, whose directions are sometimes obstructed by these numbers.\n\nIn addition, we highlight below potential limitations in some of the analyses and recommend additional sensitivity analyses and tests to strengthen the manuscript with regard to phylogenetic inferences (and in particular the potential impact of the grafting approach), datings, biogeographic (and in particular the impact of the controversial +J models) and dating analyses.\n\n*Phylogenetic inferences*\nWe are concerned about a few points in the methodology used for phylogenetic inference: the number of outgroups; model selection; the limited discussion of the quality of the phylogenetic analyses; and the grafting approach.\n6) The authors reconstructed their phylogenetic tree using only three outgroup taxa, namely Paraponera clava (Paraponerinae), Proceratium google (Proceratiinae), and Amblyopone australis (Amblyoponinae). However, previous studies, including some of the co-authors of the present paper, have clearly demonstrated that outgroup choice can affect phylogenetic reconstructions, particularly in ants (Borowiec et al., 2019 - MPE; or Spasojević et al., 2021 although in a total-evidence dating framework). In particular, it remains to be seen how the inclusion of formicoid outgroups or even representatives of the Leptanillinae might affect their results, if at all.\n7) The authors used a GTR+F+G4 model in their unpartitioned analyses without justifying this choice. Given that IQ-TREE was used, why not perform a ModelFinder analysis to determine the best model for the unpartitioned analysis (especially since that the authors used it to determine the partitioning scheme and the best model in the partitioned analyses)? A rationale for model selection would strengthen the methodological transparency and reproducibility of the study.\n8) There is little discussion of the quality of the phylogenetic analyses, no formal statistical tests are performed, and the uncertainty that short internodes introduce into the results is not addressed, particularly in the ‘Odontomachus-group’ (see Supplementary Figure S3).\n9) The authors rely heavily on a grafting approach to supplement their otherwise strong initial sampling. While this practice is common in similar analyses (e.g., Rabosky et al., 2018 - Nature; Economo et al., 2018 - Nat. Commun.) and we do not object to its use, it is important to acknowledge its potential implications. Previous studies have shown that inferring clade-specific evolutionary histories from both grafted and non-grafted phylogenies can have a significant impact on diversification analyses. Given the strong claims presented in this study, we believe this effect warrants further investigation and discussion. At a minimum, the full set of biogeographic and diversification analyses should also be performed on the non-grafted tree, and the results compared to assess the robustness of the conclusions.\n10) Since the authors evaluated three different partitioning schemes—unpartitioned, by locus with merging, and Sliding Window Site Characteristics (SWSC) based on entropy with merging—it would be beneficial to include the detailed topologies in the Supplementary Material along with a discussion of their differences. Simply stating that the SWSC analysis yielded the “highest likelihood” to justify prioritizing it over the others does not seem sufficient. The authors should compare the three topologies obtained under different partitioning schemes, and the grafted vs. non-grafted topologies to assess how differences in partitioning and grafting affect divergence time estimates, as these variations are likely to have significant downstream effects on all subsequent analyses.\n\n*Divergence dating*\n11) We wonder why some fossils were not used as additional calibrations. We agree that some fossils are dubious, poorly preserved, and cannot be reliably used as calibration points, but ants have one of the best fossil records of the order Hymenoptera, and it is a pity not to take advantage of this. Calibrations strangely overlooked in this manuscript include (not exhaustive):\n- a) Platythyrea dlusskyi Aria et al. 2011 from Ypresian Oise amber, which can be used to calibrate the Platythyreini (Aria et al. 2011 - Zootaxa). Given its age and the 95% HDP of the node Platythyreini it is likely that it will push towards slightly older age the node or at least help in reducing the 50 Ma interval of the HDP. It has been used in other studies (e.g., Borowiec et al 2019 - MPE)\n- b) Pachycondyla(s) from Baltic amber can be used to calibrate either Pachy + Neo or the crown Pachy, depending on the interpretation you make of their placement, but in both cases they will likely impact the dating (e.g., Dlussky 2009 - Paleontol. J.).\n- c) Brachyponera from Zhangpu amber (Wang et al. 2021 - Sci. Adv., see supp material) can be used to calibrate Brachyponera, likely contributing to better estimate the origin of this genus.\nOverall, the authors should say a bit more about their rationale for the selection of fossil calibrations in general. Parham et al. (2012 - Syst. Biol.) is a good starting point (we've seen the supplementary file on Zenodo), but it would be helpful to read a bit more about their overall thinking. This might include the otherwise plausible fossils not used, and the reasons for their exclusion.\n12) The authors chose to calibrate their tree using MCMCTree applied to a reduced set of taxa, and then scaled it to the full dataset with TreePL. Given the small size of their subsampled dataset and the ongoing advances in dating analyses—particularly regarding the incorporation of fossil data—we wonder why they did not opt for the fossilized birth-death (FBD) process (Stadler 2010 - J. Theor. Biol.; Heath et al. 2014 - PNAS).\nFBD-based methods can overcome several limitations associated with node calibration approaches, allowing for the integration of a wider range of fossil data. Given the extensive fossil record of ants and their relatively well-established taxonomic classifications, this method is likely to yield robust results. Alternatively, if the authors are concerned about gaps in the fossil record in certain geological periods, or skeptical about the classical FBD approach, they could use a sequential sampling framework using a birth–death skyline tree prior (BDSS; Stadler et al. 2013 - PNAS; Culshaw et al. 2019 - Evolution). This method allows fossil sampling proportions to be modeled as time-heterogeneous, incorporating time slices before and after the oldest fossil sample using tools such as the TreeSlicer function in the skylinetools package (https://github.com/laduplessis/skylinetools). We strongly encourage the authors to investigate how their methodological choices affect divergence time estimates.\n13) Lastly, as emphasized in our point 9), the authors should investigate how divergence time estimates vary between the three different partitioning schemes and provide a thorough discussion of their results. It is well known that dating estimates can vary significantly depending on the methodology, priors, and calibration choices. For a manuscript aiming to elucidate the global diversification and biogeographic history of a clade, the current analyses appear insufficiently comprehensive. Extending these comparisons would strengthen the conclusions of the study and provide a more robust framework for understanding the evolutionary timeline of the Ponerinae, while also serving as a textbook example for future work.\n\n*Historical Biogeography*\n14) The authors use BioGeoBEARS 1.1.3 (Matzke 2013 - FoB, 2018) for their biogeographic analyses, and specifically use the DEC+J model (Matzke 2014 - Syst. Biol.). However, they do not provide a clear rationale for selecting DEC+J over the DEC model, other than an AICc comparison. While the authors state that DEC+J was selected as the best-fitting model, it is important to acknowledge the ongoing debate regarding statistical validity and model selection process in BioGeoBEARS (Ree and Sanmartín, 2018 - Syst. Biol.). Additionally, the DEC+J model has been criticized for often yielding null or unrealistically low extinction rates (Sanmartín and Meseguer, 2016 - Front. Genet.) and for favoring direct dispersal over widespread ancestral ranges—an assumption that may not be appropriate for reconstructing the evolutionary history of Ponerinae (Rolland and Condamine, 2019 - J. Biogeogr.). We believe that these concerns can be easily addressed by re-running the analyses using the DEC model. Given the authors’ established analytical pipeline, this should be feasible within a reasonable timeframe. This would not only strengthen the findings of the paper if the results are consistent with the initial analyses, but would also provide valuable insights and discussion in the case of discrepancies.\n\n*Diversification dynamics*\n15) The authors refer directly to BAMM in their diversification analyses. BAMM is indeed a powerful tool for inferring extinction and origination rates and can be easily integrated with STRAPP and other downstream analyses. While the BAMM approach can assess diversification rate heterogeneity on highly incomplete and phylogenetically non-random datasets, it has been criticized for identifiability issues (Louca & Pennell, 2020 - Nature). These concerns are significant and have far-reaching implications, as they affect all widely used methods for studying diversification, including those used in this study. We think that a section in the supplementary material discussing how the authors have addressed these issues is mandatory.\n16) As with other sections, we feel that the manuscript lacks sufficient sensitivity analyses. We strongly recommend that the authors perform their BAMM analyses on the non-grafted tree as well as on the different topologies obtained under the different partitioning schemes. The results should then be compared and discussed. Given the known sensitivity of BAMM to taxon sampling and tree structure, we would expect the outcomes of these analyses to differ.\n17) Why was BAMM chosen over alternative approaches such as ClaDS2 (Maliet et al. 2019 - NEE), LSBDS (Höhna et al. 2019 - bioRxiv), PESTO (Kopperud & Höhna 2023 - https://github.com/kopperud/Pesto.jl), or MTBD (Barido-Sottani et al. 2020 - Syst. Biol.)? The choice of one method over another—while potentially justified—can have a significant impact on estimates of branch-specific diversification rates (Martínez-Gómez et al. 2024 - Evol. Lett.). While we do not expect the authors to repeat all their analyses using multiple methods (as we already suggest several additional analyses), we encourage them to explore ways to cross-validate their results.\nAdditional visualizations of BAMM outputs would also be beneficial. At the very least, a tree displaying BAMM-derived rates should be included in the supplementary material, along with a figure assessing MCMC convergence for the BAMM run.\nWe are aware that we have suggested a number of possible additional analyses here, but these are mostly aimed at strengthening the manuscript and better exploring the limits and sensitivity of their results. Finally, we would like to congratulate the authors on their work, which already includes several analyses aimed at better understanding the distribution pattern of Ponerinae.\n\nMinor comments:\n· L 98: Kachin amber from Myanmar (Late Cretaceous: 94-100 My)\nRadiometric data established an early Cenomanian age (98.79 ± 0.62 Ma) for Kachin amber, based on zircons from volcanic clasts found within the amber-bearing sediments (Shi et al. 2012 - Cret. Res.). Some ammonites found in the amber-bearing bed and within amber corroborate a late Albian–early Cenomanian age (Cruickshank & Ko 2003 - J. Asian Earth Sci.).\n· L 197: “we designed a custom screening procedure to detect bioregional outliers and removed dubious records falling in rarely documented bioregions”\nHow many dubious records and bioregions are we talking about?\n· L 305-307: This estimate falls within the range of previous estimates and dates the emergence of the extant ponerine ant clade during the Early Cretaceous, coinciding with the timing of origins of other ant subfamilies.\nThis sentence is not correct. The range is not in agreement with the ages from a few recent and older studies: 73 (61–84) in Borowiec et al. (2019 - MPE) or 56–60 in Moreau and Bell (2013 - Evolution).\n· L 427: How many diversification shifts were identified in your BAMM analysis, and where are they located on the phylogenetic tree?\n· L. 443: Is the RNW value correct? (seems wrong when compared to Fig. 5A)\n· The authors seem to equate in-situ diversification with endemism, which are two very different concepts. A few sentences of the manuscript should be rephrased accordingly.", "reviewer_2": "Dear Editor,\n\nIn their manuscript \"Timing is everything: Evolution of ponerine ants highlights how dispersal history shapes modern biodiversity\" Doré et al. present the results of a thorough phylogeny-based analysis of the global historical biogeography of ants in the subfamily Ponerinae.\n\nFor their study, the authors first reconstructed phylogenetic relationships and divergence times among 789 ponerine species and morphospecies using phylogenomic data. Then, they grafted missing species on the tree based on taxonomic background data, to obtain a \"full\" 1534-tip tree for ponerine ants. The main analyses are based on the best full dated tree as well as 1000 posterior \"full\" trees sampled for estimating the effects of phylogenetic uncertainty. The authors then estimated ancestral geographic ranges across the fossil-calibrated trees based on an extensive global database of the distributions of extant ponerine species. These ancestral reconstructions are combined with phylogeny-based inferences of diversification rates in past and present lineages, to infer support for three different hypotheses on the distribution of species diversity observed in the group today. The main conclusion is that the latitudinal gradient seen in the diversity of extant ponerine ants is mainly the result of a tendency to remain in the ancestral tropical habitat (tropical niche conservatism), while asymmetric dispersal and differences in diversification rates have a minor role. The results are discussed in relation to diversity patterns found in other taxa.\n\nI find the study highly interesting and impressive. The breadth of phylogenetic and statistical methods covered by the work is breathtaking, but Supplementary figures S1 and S2 do a good job in explaining how the phylogenetic and biogeographic datasets used in the different analyses were compiled.\n\nI might have commented on the restriction to net diversification rates and the existence of megathermal forests in the northern Hemisphere during the early Cenozoic, but both of these issues are in fact dealt with in the end of the Discussion. These challenges are by no means restricted to this study, and I find the caveats given in the text sufficient.\n\nI cannot claim to be a specialist in all aspects of the highly diverse statistical analyses, but the methods used in the parts that I have experience with seem to be up to date, logical and justified. In some cases, there would be alternative methods available, but these are not necessarily better than the ones used here. The combined analyses and plots of results from different statistical methods are highly innovative. To me it seems that such approaches might be of use also in fields not directly related to biogeography.\n\nBased on the importance of the results and high quality of the data and analyses of the study, I recommend publication of the manuscript as-is. The supplementary information and scripts and data files in databases appear to contain all information necessary for replicating the analyses and results. The manuscript package is so carefully prepared that I struggled to find even small mistakes or typos from the text, these are listed below.\n\nMinor comments:\nR40: drivers -> driver?\nR59: \"by using\"?\nR67: Split paragraph before \"The\"?\nR113: \"that\" -> \", which\"?\nR165: 2 x \"in\"\nFigure 3: Is \"Sources\" the correct word in (E) and (F)?\nR579: \"Ponerine\" -> \"Ponerines\"\n- The \"Wilcox test\" is mentioned in the text and supplementary methods, I believe this should be spelled out as \"Wilcoxon test\" when used in text?\n- In the Supplementary information, one sentence seems to be essentially repeated twice in the Phylogenetic inference section: \"Among the three partitioning schemes, the SWSC-En with merging scheme had the highest likelihood and was selected as the best result for downstream analyses. Among the three partitioning schemes, the SWSC with merging scheme had the highest log-likelihood and was selected as the best result for downstream analyses (unpartitioned: -37.82 × 106, by locus with merging: -37.30 × 106, SWSC-EN with merging: -37.08 × 106).\"\n- In the Reporting Summary, the Reporting on sex section contains the sentence \"Sex-based analyses are not required for this type of phylogenomic study as no sex-linked loci were studied.\" Here it could be specified that ants (and other hymenopterans) have a haplodiploid sex determination system, so sex-linked loci technically do not exist.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to reviewers\nTiming is everything: Evolution of ponerine ants highlights\nhow dispersal history shapes modern biodiversity\nNCOMMS-25-04417\nWe thank the reviewers for their detailed and thoughtful critiques of our manuscript. Their\ncomments reflect a deep and comprehensive understanding of both the current literature and\nthe available methodological frameworks for macroevolutionary research. We are grateful for\nthe opportunity those concerns gave us to further clarify our analytical choices and to\nstrengthen the transparency, robustness, and reproducibility of our study.\nWhile we have incorporated a number of constructive revisions, we respectfully disagree with\nseveral of the proposed methodological additions. In these instances, we explain in detail why\nwe believe that implementing the suggested alternatives would not improve, and in some\ncases would compromise, the interpretability, or coherence of our results. Our primary\nobjective in this study was to reconstruct the macroevolutionary history of extant ponerine ants\nusing the most complete and reliable data available to provide an invertebrate perspective on\nmacroevolutionary processes shaping biodiversity. To that end, we deliberately avoided\nanalytical choices that might introduce unnecessary sources of noise or bias, such as the\ninclusion of insufficiently documented fossil data, or the use of non-grafted trees in\ndiversification analyses. Instead, we prioritized methodological consistency,\nreproducibility, and scalability.\nOur current selection of methods was made with careful consideration and guided by a goal\nof producing a coherent and scalable analytical pipeline. While we agree that sensitivity\nanalyses are an essential aspect of ensuring robustness in scientific research, some of the\nsuggestions are, in our view, out of our manuscript’s scope and unrealistic in terms of time,\ncomputational cost, and carbon footprint induced for an already complex and computationally\ndemanding analytic pipeline. We note the stark contrast between these requests and the more\nmoderated opinion expressed by Reviewer #2, who recommended publication of the\nmanuscript in its current form and prudently noted that \"in some cases, there would be\nalternative methods available, but these are not necessarily better than the ones used here.\"\nWe already carried out an extensive set of robustness checks regarding the effects of\ndivergence dating uncertainty on biogeographic model selection and estimation of ancestral\nranges, well beyond what is found in other recent high-impact macroevolutionary studies. We\nnonetheless attempted to implement all additional analyses suggested by the reviewers\nthat we deemed feasible. Among other things, we have now incorporated:\n- A comprehensive sensitivity analysis that provides results for the whole analytic\npipeline using two extreme hypotheses for divergence dating (see details in reply to\nPoint #13). These alternative hypotheses lead to highly similar results compared to the\nMaximum Credibility Clade tree with median ages and now provide even clearer\nsupport for the robustness of our conclusions (See Supplementary Figures S9, S11,\nS12, S13, S15, S17, S19, S22 & S23, and Supplementary Tables S2, S3, S6 & S7).\n- A test for correlations of net diversification rates estimated from BAMM between the\ndivergence dating hypotheses that revealed significant positive relationships.\n- A test for agreement in ancestral range estimates between the divergence dating\nhypotheses that revealed high levels of agreement between the estimates.\n- The topology inferred from alternative partitioning schemes (Supplementary Figure\nS4), and a brief comment on their high similarity with the partitioning scheme selected\n(See details in Point #10).\n- A new section in the Supplementary Information entitled “Supplementary Discussion\non Phylogenetic inferences” that provides a detailed commentary on the overall quality\nof the inferred tree topology and its associated statistical support, as well as\ndocumented comparison between our new phylogenetic results and previous\nphylogenetic studies on Ponerinae (See details in Point #8).\n- A revised abstract and introduction that emphasize the major conceptual differences\nbetween our study and the one of Economo et al. 2018, improving the clarity of the\nscientific context of our study without overstating novelty (See details in Point #1 and\nPoint #2).\n- A new paragraph dedicated to acknowledging the possible biases and limits of the\nDEC+J vs. the DEC model, while emphasizing our rationale for model selection (See\ndetails in Point #14).\n- A new paragraph dedicated to discussing the limits and theoretical challenges of\nBAMM, while clarifying our rationale for method selection (See details in Point #15).\n- A plot of BAMM traces illustrating the convergence of chains across runs\n(Supplementary Figure S16)\n- A phylogeny mapping net diversification rates estimated from BAMM for the MCC tree\n(Supplementary Figure S14) and for the two alternative divergence dating\nhypotheses used for sensitivity analyses (Supplementary Figure S15).\n- An additional test for differences in diversification rates across all bioregions\n(Supplementary Figures S18C & 19E-F; See details in Point #4)\nWe wish to thank all reviewers for the time and care put into reviewing our work. We believe\nthat, thanks to the reviewers’ suggestions, the revised manuscript now offers an even more\nrobust and transparent blueprint for macroevolutionary analyses that will hopefully guide\nmany future works.\nAll changes compared to the initial submission are temporarily highlighted in blue in the\nMain text and Supplementary Information files.\nBelow, we provide point-by-point responses to all concerns raised by the reviewers, also\ncolored in blue, with references to the associated lines in the Main text.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors present an impressive and comprehensive phylogeny of the ant subfamily\nPonerinae—the largest ever assembled for this clade—and use this newly constructed tree to\nexplore its biogeographic history, with a particular focus on dispersal events and diversification\npatterns. The dataset (in terms of loci) and manuscript (in terms of number of analyses) are of\nhigh quality. The authors emphasize why non-vertebrate taxa should be investigated to better\nunderstand global biodiversity patterns. However, while the study is engaging, we have\nseveral concerns that we believe preclude publication in Nature Communications, at least in\nits current form. We (the reviewers) are an early career researcher and a senior researcher\nwith complementary skills and knowledge in insect biogeography and diversification analyses.\nWe reviewed the manuscript separately, before exchanging our opinions, and had the same\noverall sense of the limitations of this work, limitations that primarily relate to the level of\nnovelty and support of the main results. We have summarised our comments below, first with\nour main points and then with some more specific suggestions, mainly on additional analyses\nthat should be carried out to fully support the hypotheses and conclusions of the manuscript,\nespecially in light of methodological concerns raised in recent studies (e.g., Louca and\nPennell, 2020 - Nature).\n1) While we acknowledge the considerable amount of work and the production of the most\ncomprehensive phylogenetic hypothesis for Ponerinae, we have concerns about the novelty\nof the main results of this study. Importantly, the discussion highlights the role of the Time-for-\nAccumulation Hypothesis (TfAH) in explaining the latitudinal diversity gradient of this ant\nsubfamily. This has already been found in Economo et al. (2018 – Nat. Comm.) at the whole\nscale of ants. As Economo et al. (2018) is surprisingly not reported in the introduction, the\nnovelty of this study in this respect was unclear to us. We could see that the methodological\napproaches were different and that here, for instance, biogeographic analyses were carried\nout, but it was not always clear what each set of analyses brings to the overall narrative and\nwhat is completely new and what supports previous knowledge/hypotheses.\nOur study is conceptually different from Economo et al. 2018 in many aspects. First, we\ngenerated and assembled a comprehensive phylogenomic data set for Ponerinae de novo.\nOur taxon coverage for Ponerinae is 8x higher in terms of taxonomic sampling (90 vs. 789\nponerine taxa) and more than 200x more extensive in terms of molecular data (11 loci vs.\n2,365 UCE loci) than in Economo et al. 2018. Thus, our data set presents a much more robust\nphylogenetic backdrop for our macroevolutionary analyses. Second, our study focuses on\nhistorical biogeographic inferences and examination of source-sink dynamics in addition to\nanalyzing diversification dynamics and testing the Time-for-Accumulation Hypothesis (TfAH),\nDiversification Rate Hypothesis (DRH) and Asymmetric Dispersal Hypothesis (ADH). By\ncontrast, Economo et al. 2018 were singularly focused on testing two hypotheses, the\nDiversification Rate Hypothesis (DRH) and the Tropical Conservatism Hypothesis (TCH,\nbased on a Mk model for tropicality vs. non-tropicality), to explain the latitudinal gradient of\ndiversity in ants. They did not strictly address the Time-for-Accumulation Hypothesis (TfAH),\nalthough their interpretation of the Tropical Conservatism Hypothesis is conceptually similar\nto the former.\nEconomo et al. 2018 did come to similar conclusions to our study regarding the absence of\nsignal for the DRH and the importance of time for diversification in the tropics (TCH/TfAH), as\nalready highlighted in the second paragraph of the discussion, although they did not produce\na test for rate differences over evolutionary times as we do here. We believe this\ncongruence in results at a higher taxonomic level (all ants) than our study (Ponerinae)\nstrengthens our conclusions. Meanwhile our study still presents significant novel insights\ninto time-stratified, regional diversification dynamics of Ponerinae, as well as a detailed\noverview of the spatio-temporal dispersal dynamics that Economo et al. 2018 could not\ndeliver with their data.\nWe agree that Economo et al. 2018’s results should be mentioned earlier in the manuscript\nand have now included an entire new segment in the Introduction dedicated to present their\nresults (L. 92-101), in complement to our references in the Discussion (L. 614 & 641). We\nbelieve this improves the clarity of the scientific context of this study without overstating\nnovelty.\nReference:\nEconomo, E.P., Narula, N., Friedman, N.R., Weiser, M.D., & Guénard, B. (2018).\nMacroecology and macroevolution of the latitudinal diversity gradient in ants. Nature\ncommunications, 9(1), 1778. https://doi.org/10.1038/s41467-018-04218-4\n2) Somewhat related to our first point, we were confused by what seemed to us to be a rather\nstrong disconnection between the introduction and the discussion of this manuscript. Overall,\nwe were not well prepared by the introduction to fully grasp what was really at stake—the\nabstract with a single sentence of context did not help. And the relatively short discussion did\nnot emphasize the novelties enough, whereas some of the ‘key findings’ seem like very\ngeneral statements or truisms (e.g. line 529, which states that “this study highlights the\nimportance of reconstructing the timing of past biogeographic and evolutionary events”).\nWe have revised the abstract (L.36 & 39-40) and introduction (L.68-71 & 117-118) to better\narticulate the three central macroevolutionary hypotheses investigated in the study (TfAH,\nDRH, ADH), thereby connecting better to the discussion where the relative contribution of\nthese hypotheses to ponerine diversification is discussed in the context of previous literature\n(L. 619-620, 639-640, 716-720).\nConcerning the cited statement in (former) line 529 (now L. 602-604) of the discussion: This\nis far from a truism since previous studies, such as Economo et al. 2018, did not perform any\nhistorical biogeographic reconstructions per se, nor looked at differences in diversification\nrates over time (previous studies compare only current rates) to produce their conclusions.\nWe have modified this specific statement in the discussion to highlight the novelty of this\nconclusion (“this study highlights the importance of reconstructing the relative timing of past\nbiogeographic and evolutionary events and considering diversification rates over time in\ncomplement to current diversification rates to better understand patterns of biodiversity on\nEarth”.) and also made changes to the abstract (L. 44-46) to emphasize this key finding.\n3) “Timing is everything”. This work emphasizes the importance of timing, but we feel that the\nuncertainties surrounding Dating analyses are not as advanced as they should be. A reliable\nand credibly dated phylogeny is essential for assessing diversification rate shifts and the\nbiogeographic history of lineages. There are also increasing examples of how the inclusion of\nfossil taxa can alter dating, diversification and biogeographic analyses. The authors do report\nand briefly explain why they did not use fossils, but how and to what extent this may affect\ntheir main results and conclusions is only touched upon.\nWe agree with the reviewers that relying on a robust time-calibrated phylogenetic hypothesis\nis essential to perform the type of extensive diversification and biogeographic assessment we\nare aiming for in this study. For this reason, we took special care in accounting for the\nuncertainties in divergence dating (and taxonomic grafting) down to the selection of\nbiogeographic models and estimates of ancestral ranges to show the robustness of our results\nto these sources of uncertainty (Supplementary Information Tables S4 & S8). Specifically,\nwe did use multiple fossil calibrations, and we argue we used all that could have been included\nfollowing best practices outlined in the literature (see our response to Point #11 below under\n*Divergence dating* heading). Our analyses also go beyond other published work to\naddress sensitivity to timing: Divergence dating analysis includes a broad root prior design\nto span estimates from the range obtained in multiple published dating studies in ants and our\nselection of biogeographic models and estimates of ancestral ranges and are based on\nuncertainty from 1,000 posterior trees from the MCMC Bayesian analysis that span a wide\nrange of dates. For example, these trees span 42 Ma of time at the initial split within Ponerinae.\nThis represents, to our knowledge, the most advanced sensitivity study carried out for\nmacroevolutionary analyses of such scale. The most recent high-impact studies published\nin Nature Communications and other similar journals did not perform such time-consuming\nanalyses and typically proceed with a single phylogenetic hypothesis and divergence dating\nestimates (Chazot et al., 2021; Kawahara et al., 2023; Wien et al., 2023) likely for time-\nlimitation reasons. Indeed, we are computationally not able to carry on the multiplication\nof sensitivity analyses further down our analytic pipeline in a reasonable time, knowing\nthat the Biogeographic Stochastic Mapping, BAMM diversification analyses, STRAPP\ntests over evolutionary time, RISR-focused models, latitudinal and longitudinal permutation tests,\nand investigation of lineage origins over time for a single phylogenetic hypothesis are already\ntaking over a week of CPU-time.\nHowever, we did our best to address the reviewers’ concerns about the possible effect of\ndating uncertainty on our main conclusions. We further elaborate on our careful selection of\nfossils and age priors used for time-calibration (see Point #11). We showed that our main\nconclusions regarding the preponderance of colonization timing (TfAH) over\ndifferences in diversification rates (DRH) and asymmetry in dispersal flux (ADH) are\nfirmly holding independently from the divergence dating hypothesis selected (see Point\n#13; new Supplementary Figures S9, S11, S12, S13, S15, S17, S19, S22 & S23, and\nSupplementary Tables S2, S3, S6 & S7; and additions to the Main text: L. 192-194, 203-205,\n251, 255-260, 281-283, 361-364, 369-370, 432-437, 487-492, 505-510, 538-543, 553-555,\n623-626 & 663). We believe that thanks to the addition of those new extensive sensitivity\nanalyses, in complement to the already existing ones, we now provide a transparent robust\nframework for our study that will likely contribute to establishing a new gold standard for this\ntype of ambitious macroevolutionary analyses.\nReferences:\nChazot, N., Condamine, F. L., Dudas, G., Peña, C., Kodandaramaiah, U., Matos-Maraví, P.,\n... & Wahlberg, N. (2021). Conserved ancestral tropical niche but different continental\nhistories explain the latitudinal diversity gradient in brush-footed butterflies. Nature\ncommunications, 12(1), 5717. https://doi.org/10.1038/s41467-021-25906-8\nReginato, M., Almeda, F., Michelangeli, F. A., Goldenberg, R., Fritsch, P. W., Stone, R. D., &\nPenneys, D. S. (2022). Historical biogeography of the Melastomataceae. In Systematics,\nevolution, and ecology of melastomataceae (pp. 87-105). Cham: Springer International\nPublishing. https://doi.org/10.1007/978-3-030-99742-7_4\nKawahara, A. Y., Storer, C., Carvalho, A. P. S., Plotkin, D. M., Condamine, F. L., Braga, M.\nP., ... & Lohman, D. J. (2023). A global phylogeny of butterflies reveals their evolutionary\nhistory, ancestral hosts and biogeographic origins. Nature ecology & evolution, 7(6), 903-\n913. https://doi.org/10.1038/s41559-023-02041-9\nWeil, S. S., Gallien, L., Nicolaï, M. P., Lavergne, S., Börger, L., & Allen, W. L. (2023). Body\nsize and life history shape the historical biogeography of tetrapods. Nature Ecology &\nEvolution, 7(9), 1467-1479. https://doi.org/10.1038/s41559-023-02150-5\n4) Bioregions. The authors used 7 bioregions in several maps and some analyses. But when\nit comes to testing latitudinal and longitudinal gradients, they only use 2 bioregions (temperate\nvs tropical and Old World vs New World, respectively). This choice is motivated by statistical\nreasons (line 264), but there is no critical discussion or sensitivity analyses to explain how\nharmless this important choice is (or is not).\nThe choice of using pairwise comparison between Tropical vs. Temperate and Old World vs.\nNew World is tailored to the question of the existence of a latitudinal and longitudinal\ngradient in diversification rates. It is also indeed motivated by statistical reasons as the\nrestricted number of diversification regimes represented in each of the seven bioregions used\nfor historical biogeographic inference is limiting the power of STRAPP tests based on\npermutations of rates across regimes.Nonetheless, following the reviewers’ concerns, we now\nadd a complementary analysis encompassing our seven bioregions, showing that not only are\ndiversification rates across bioregions not significantly different in the present-day (STRAPP\ntest: Δ Kruskal−Wallis's Khi² 5% = -53.58, p = 0.594), but they have not been different along\nevolutionary times (see Supplementary Information Figure S18C). Values obtained for the\ntest across all bioregions appear highly similar to what is obtained when testing the longitudinal\ngradient between Old World and New World because most of the (non-significant) rate\ndifferences occur on that geographic axis (Supplementary Information Figure S18A). This\ncomplementary analysis strengthens our conclusion that the RDH is not supported by\nour data, beyond addressing this question from a latitudinal/longitudinal gradient perspective\nwith pairs of Tropical vs. Temperate and West (New World) vs. East (Old World) areas (L.\n502-505).\nWe modified the dedicated Methods section in the main text (L. 298) and Supplementary\nMethods (9.1 BAMM rates across bioregions: STRAPP tests) to describe the different tests\n(i.e., long/lat gradients on pairwise areas, and overall tests across the seven bioregions).\n5) An important piece of information that is missing from biogeographic analyses is how areas\nwere or were not connected over time (present as supplementary files only on Zenodo). In\nother words, what were your time-stratified models? This is crucial for ARE and BSM analyses.\nOne of the things we were curious about was how India and the Burmese terrane were treated.\nAs the reviewers noted, this piece of information is available in the Supplementary Data under\nthe Supplementary Table S7 with all associated references used to define the connectivity\nover time of the bioregions used in our study. We now also provide this information as a\nSupplementary Table (Table S7 in ‘Supplementary Data S1-S7.xlsx’) directly accessible\nfrom the online version of the article.\nBecause our downstream analyses rely on comparing in situ diversification dynamics and\ndispersal pathways between bioregions, we opted to use a geographic classification with\nbioregions of comparable areas to limit bias due to size differences. Moreover, the vast\nmajority of ponerines found in the present-day Burmese area and in the Indian subcontinent\nare also encroaching on the surrounding South-Asian regions, thereby limiting greatly the\ninformative power of historical biogeographic inferences treating those areas as separate\nentities. Therefore, we classified India and the Burmese terrane under a joint IndoMalayan\nbioregion, even though those territories had a partly independent geological history. For similar\nreasons, we decided to keep Madagascar as part of Afrotropics. These choices are in-line\nwith both historical and recent studies of similar scope aiming to produce bioregional\nschemes at global scale, or to reconstruct the biogeographic history of cosmopolitan diverse\nclades (Wallace, 1876; Udvardy, 1975; Rueda et al., 2013; Chazot et al., 2021; Reginato et\nal., 2022; Kawahara et al., 2023).\nFollowing the reviewers’ legitimate concerns, we now elaborate to a wider extent on this\nrationale in the Supplementary Methods (see Section 7.1 on Bioregion scheme).\nReferences:\nChazot, N., Condamine, F. L., Dudas, G., Peña, C., Kodandaramaiah, U., Matos-Maraví, P.,\n... & Wahlberg, N. (2021). Conserved ancestral tropical niche but different continental\nhistories explain the latitudinal diversity gradient in brush-footed butterflies. Nature\ncommunications, 12(1), 5717. https://doi.org/10.1038/s41467-021-25906-8\nReginato, M., Almeda, F., Michelangeli, F. A., Goldenberg, R., Fritsch, P. W., Stone, R. D., &\nPenneys, D. S. (2022). Historical biogeography of the Melastomataceae. In Systematics,\nevolution, and ecology of melastomataceae (pp. 87-105). Cham: Springer International\nPublishing.\nRueda, M., Rodríguez, M. Á. & Hawkins, B. A. Identifying global zoogeographical regions:\nLessons from Wallace. J. Biogeogr. 40, 2215–2225 (2013).\nhttps://doi.org/10.1111/jbi.12214\nKawahara, A. Y., Storer, C., Carvalho, A. P. S., Plotkin, D. M., Condamine, F. L., Braga, M.\nP., ... & Lohman, D. J. (2023). A global phylogeny of butterflies reveals their evolutionary\nhistory, ancestral hosts and biogeographic origins. Nature ecology & evolution, 7(6), 903-\n913. https://doi.org/10.1038/s41559-023-02041-9\nUdvardy, M. D. F. A classification of the biogeographical provinces of the world. Morges\n(Switzerland): International Union of Conservation of Nature and Natural Resources.\nIUCN Occasional Paper no. 18 (IUCN, 1975).\nWallace, A.R. (1876) The geographical distribution of animals, Vols 1 & 2. Harper & Brothers,\nNew York.\nAlso, we do not think we clearly understood what some of the panels in Fig. 3 were showing.\nFor example, in Fig 3A, if we look only at the four rightmost bars, we count more than 800\ndispersal events near the Miocene/Pliocene boundary, which is colossal. But since this figure\nis based on 1000 simulations, we think that ‘only’ 80% of the simulations reported a dispersal\nevent at this time, and that 7 bioregions can be the source of this potential dispersal event\n(even if Indomalaya seems the most likely). Is this correct? In any case, it also means that\nsome of these ‘contemporaneous’ events actually occur in different clades in these\nsimulations. Furthermore, the events are shown every Myr, but the counts were done in sliding\nwindows of 5 (in the Methods) or 10 (in the caption) Myrs. Is there a possibility of\noverestimating the number of events because of this sliding window? As you can see from our\nquestions, we may have not have understood well what readers should see and take away\nfrom this figure.\nThe counts of events are averaged across the 1,000 BSM simulations and summed within\nsliding windows of 10 My. Thus, the rightmost bar on Fig. 3A at time = 5 Mya indicates that\nthere was an estimated average number of 285 dispersal events occurring within a time-\nwindow of [0-10 Mya] for a single biogeographic history. Most of those events (135 on average)\nhad IndoMalaya as the source of the dispersal. As discussed in the Main text (Sections\n‘Asymmetry in dispersal dynamics’ and ‘Diversification dynamics across space and\ntime‘), these (left-side) panels are essential for the reader to gain a sense of the quantity of\nevents involved, but more importantly, of the relative occurrence of dispersals over time, which\nthe proportions of events (right-side panels) are not offering. We complemented the caption\nof Fig. 3 to clarify this point (L.442-446), which is not straightforward, as rightly mentioned by\nthe reviewers.\nWe corrected the width of the time-windows reported in the Supplementary Methods (Section\n7.3 Biogeographic Stochastic Mapping (BSM)). We did use time-windows of 10 My to\ngenerate results shown in Fig. 3. However, the mistake arose from the fact we used time-\nwindows of 5 My for the animated version (Section 7.4 Map biogeographic and\ndiversification dynamics through time). We added this distinction to the related sections.\nWe appreciate that BSM was used to get an idea of the uncertainty in the biogeographic\nscenario, but we found the results confusing. Would it be worth contrasting these results with\nthe dispersal events found in the ARE analysis?\nARE do not provide direct estimates of dispersal events but only estimates the most likely\nranges observed at ancestral nodes as scaled marginal likelihoods. Mapping the occurrence\nof dispersal events requires the use of BSM to simulate stochastically those dispersal events\nbased on ARE and transition parameters from the best fitting model for biogeographic\ninferences as it is the standard in such analyses (Li et al., 2022; Kawahara et al., 2022; Weil\net al., 2022).\nReferences:\nLi, X. et al. A diversification relay race from Caribbean-Mesoamerica to the Andes: historical\nbiogeography of Xylophanes hawkmoths. Proc. R. Soc. B Biol. Sci. 289, 20212435\n(2022). https://doi.org/10.1098/rspb.2021.2435\nKawahara, A. Y., Storer, C., Carvalho, A. P. S., Plotkin, D. M., Condamine, F. L., Braga, M.\nP., ... & Lohman, D. J. (2023). A global phylogeny of butterflies reveals their evolutionary\nhistory, ancestral hosts and biogeographic origins. Nature ecology & evolution, 7(6), 903-\n913. https://doi.org/10.1038/s41559-023-02041-9\nWeil, SS., Gallien, L., Nicolaï, M.P.J. et al. Body size and life history shape the historical\nbiogeography of tetrapods. Nat Ecol Evol 7, 1467–1479 (2023).\nhttps://doi.org/10.1038/s41559-023-02150-5\nFinally, the readability of Fig. 2B, which also shows the results of dispersal events from the\n1000 simulations, could be improved: as it stands, we mainly see the numbers representing\nthe specific richness rather than the dispersal events, whose directions are sometimes\nobstructed by these numbers.\nWe improved the clarity of the directions of dispersal events (which are also apparent from the\ncolor of the arrows) by moving the tips of the arrows to the border of the circle depicting\nregional richness, and providing the estimated number of events directly on the tip of each\narrow, in Fig. 2B for the dispersal history inferred from the MCC tree, and in the new\nSupplementary Fig. S23A-B for the two alternative divergence hypotheses.\nIn addition, we highlight below potential limitations in some of the analyses and recommend\nadditional sensitivity analyses and tests to strengthen the manuscript with regard to\nphylogenetic inferences (and in particular the potential impact of the grafting approach),\ndatings, biogeographic (and in particular the impact of the controversial +J models) and dating\nanalyses.\nSee our detailed answers below.\n*Phylogenetic inferences*\nWe are concerned about a few points in the methodology used for phylogenetic inference: the\nnumber of outgroups; model selection; the limited discussion of the quality of the phylogenetic\nanalyses; and the grafting approach.\n6) The authors reconstructed their phylogenetic tree using only three outgroup taxa, namely\nParaponera clava (Paraponerinae), Proceratium google (Proceratiinae), and Amblyopone\naustralis (Amblyoponinae). However, previous studies, including some of the co-authors of the\npresent paper, have clearly demonstrated that outgroup choice can affect phylogenetic\nreconstructions, particularly in ants (Borowiec et al., 2019 - MPE; or Spasojević et al., 2021\nalthough in a total-evidence dating framework). In particular, it remains to be seen how the\ninclusion of formicoid outgroups or even representatives of the Leptanillinae might affect their\nresults, if at all.\nAll molecular phylogenetic studies, going back to 2006, have consistently provided a strong\nsupport for the monophyly of Ponerinae and the reciprocal monophyly of two subclades\n(Platythyrea versus all others) at the base of the Ponerinae tree (Brady et al., 2006; Moreau\net al., 2006; Schmidt, 2013; Branstetter et al., 2017; Borowiec et al., 2019, 2025). The\nuncertainties in ant phylogeny lie in older splits between subfamilies of poneroids and\nformicoids. As pointed out by the reviewers, these uncertainties, which were the focus of a\nprevious study conducted by some of the authors (Borowiec et al., 2019), do not pertain to the\ninternal topology of Ponerinae. Therefore, the addition of more outgroups is extremely\nunlikely to affect any of our analyses and results, which focus uniquely on this stable\nportion of the phylogenetic tree.\nConversely, we chose to follow the most recent best practices in phylogenomics outlined in a\nrecent study from DeSalle et al., 2023. Here, the authors showed that the former standard\npractice of using multiple outgroups often causes random rooting that generates spurious\nincongruences in phylogenetic inferences. Instead, they recommended using a single (most\nclosely) related relative as the outgroup. In our case, we decided to limit our selection of\noutgroups to three taxa as adding more outgroups in an already computational-heavy\ndataset would not be helpful to achieve our aims and would not provide any additional\ninsights on the internal evolutionary dynamics of Ponerinae, which is the focus of our study.\nReferences:\nBorowiec, M. L.; Rabeling, C.; Brady, S. G.; Fisher, B. L.; Schultz, T. R.; Ward, P. S. 2019.\nCompositional heterogeneity and outgroup choice influence the internal phylogeny of the\nants. Molecular Phylogenetics and Evolution 134:111-121.\nhttps://doi.org/10.1016/j.ympev.2019.01.024\nBorowiec, M. L., Zhang, Y. M., Neves, K., Ramalho, M. O., Fisher, B. L., Lucky, A., Moreau,\nC. S. 2025. Evaluating UCE data adequacy and integrating uncertainty in a\ncomprehensive phylogeny of ants. Systematic Biology, syaf001,\nhttps://doi.org/10.1093/sysbio/syaf001\nBrady, S. G.; Schultz, T. R.; Fisher, B. L.; Ward, P. S. 2006. Evaluating alternative hypotheses\nfor the early evolution and diversification of ants. Proceedings of the National Academy\nof Sciences of the United States of America 103:18172-18177.\nBranstetter, M. G.; Longino, J. T.; Ward, P. S.; Faircloth, B. C. 2017. Enriching the ant tree of\nlife: enhanced UCE bait set for genome-scale phylogenetics of ants and other\nHymenoptera. Methods in Ecology and Evolution 8:768-776.\nhttps://doi.org/10.1111/2041-210X.12742\nDeSalle, R., Narechania, A., & Tessler, M. 2023. Multiple outgroups can cause random rooting\nin phylogenomics. Molecular Phylogenetics and Evolution, 184, 107806.\nhttps://doi.org/10.1016/j.ympev.2023.107806\nMoreau, C. S.; Bell, C. D.; Vila, R.; Archibald, S. B.; Pierce, N. E. 2006. Phylogeny of the ants:\ndiversification in the age of angiosperms. Science (Washington, D. C.) 312:101-104.\nSchmidt, C. A. 2013. Molecular phylogenetics of ponerine ants (Hymenoptera: Formicidae:\nPonerinae). Zootaxa 3647:201-250.\n7) The authors used a GTR+F+G4 model in their unpartitioned analyses without justifying this\nchoice. Given that IQ-TREE was used, why not perform a ModelFinder analysis to determine\nthe best model for the unpartitioned analysis (especially since that the authors used it to\ndetermine the partitioning scheme and the best model in the partitioned analyses)? A rationale\nfor model selection would strengthen the methodological transparency and reproducibility of\nthe study.\nWe selected GTR+F+G4 for the unpartitioned analysis for several reasons. One practical\nreason is that performing model selection on unpartitioned, genome-scale datasets using\nModelFinder in IQ-Tree takes an incredibly long time due to the size of the dataset and the\nnumber of models available to test. We chose here to select analyses that would be\nefficient while not jeopardizing quality. For similar reasons, all tree inference programs\nemploy heuristics to search only part of tree space rather than to evaluate all possible\ntopologies, because the latter is impractical. Similarly, when the SWSC-EN method was first\npublished, it would output an input file for ParitionFinder2 that restricted the merging model to\nGTR+G only. This was done to make the analyses feasible and because of point two below.\nSecond, GTR is a general, flexible substitution model that allows to parametrize all types of\nsubstitutions and encompasses simpler models as special cases. Given the large size of our\nmolecular dataset, there is no risk of overparameterization associated with poor parameter\nestimation. In this context, directly applying a complex model like GTR+F+G4, for an analysis\nwith an unpartitioned scheme, is justified and avoids unnecessary model selection steps,\nespecially since it would converge on simpler models if the data supported it. This choice is\nfully in-line with the recommendations of Abadi et al., 2019, who showed in a test study that,\nacross their simulated datasets, the proportion of correctly inferred tree topologies for the\nparameter-rich GTR+I+G model is highly similar to the ones obtained with a model selection\nprocess. They concluded that “there is no clear benefit for performing a model selection\nstep for phylogeny inference, and a fixed parameter-rich model can be used instead”.\nFinally, we analyzed the unpartitioned matrix primarily to see how partitioning could improve\nmodel fit and potentially the phylogenetic results. The two additional partitioning schemes that\nwe employed, by-locus + merging and SWSC-EN + merging, both did model merging using\nGTR+G for merging, followed by full model selection on the final partitioning scheme. Given\nthat we performed a merging step, it is completely feasible that ModelFinder could have\nmerged all of the initial by-locus and SWSC partitions back into a single partition, but this did\nnot occur. Thus, there is no evidence that the unpartitioned analysis is in any way the\nideal partitioning scheme and that is why, in complement with the comparison of likelihoods\nand node support values (see Point #10), we selected the phylogeny obtained from the\nSWSC-EN + merging scheme as the best tree to conduct downstream analyses.\nWe now clearly explain this rationale in the dedicated section of the Supplementary Methods\n(Section 3. Phylogenetic Inferences), improving methodological transparency and ensuring\nperfect reproducibility.\nReferences:\nAbadi, S., Azouri, D., Pupko, T. et al. Model selection may not be a mandatory step for\nphylogeny reconstruction. Nat Commun 10, 934 (2019). https://doi.org/10.1038/s41467-\n019-08822-w\n8) There is little discussion of the quality of the phylogenetic analyses, no formal statistical\ntests are performed, and the uncertainty that short internodes introduce into the results is not\naddressed, particularly in the ‘Odontomachus-group’ (see Supplementary Figure S3).\nAs indicated in our Methods and Supplementary Methods (Sections Phylogenetic Inferences;\nL. 171-172 in the Main text), we performed standard statistical assessments of node support\nusing 1,000 replicates of ultrafast bootstraps (UFBS) and SH-like approximate likelihood ratio\ntests (SH-aLRT) across all nodes. As noted by the reviewers, the UFBS scores are reported\nexhaustively in Supplementary Figure S3, and now also in new Supplementary Figure S4.\nIn response to the reviewers’ suggestion, we have now added a more detailed commentary\non the overall quality of the inferred tree topology and its associated statistical support\nbased on both UFBS scores and SH-aLRT. In particular, we discuss the limited sources of\nuncertainty introduced by several short internodes, especially in the deeper relationships\nwithin the ‘Odontomachus group’. In addition, we also provide a documented comparison\nbetween our new phylogenetic results and previous phylogenetic studies on Ponerinae,\nhighlighting both areas of concordance and novelty in this scientific historical context.\nAll these points are addressed in the newly added section of our Supplementary Information\nentitled “Supplementary Discussion on Phylogenetic inferences”.\n9) The authors rely heavily on a grafting approach to supplement their otherwise strong initial\nsampling. While this practice is common in similar analyses (e.g., Rabosky et al., 2018 -\nNature; Economo et al., 2018 - Nat. Commun.) and we do not object to its use, it is important\nto acknowledge its potential implications. Previous studies have shown that inferring clade-\nspecific evolutionary histories from both grafted and non-grafted phylogenies can have a\nsignificant impact on diversification analyses. Given the strong claims presented in this study,\nwe believe this effect warrants further investigation and discussion. At a minimum, the full set\nof biogeographic and diversification analyses should also be performed on the non-grafted\ntree, and the results compared to assess the robustness of the conclusions.\nWe thank the reviewers for highlighting this important methodological choice in our analytical\nworkflow that led us to elaborate in even more depth the rationale behind this choice. We\nargued here that we carefully selected the best suited approach for our type of data and\nobjectives, that we already included uncertainty in grafting in our downstream analyses\nto the maximum extent possible for a reasonable computation-time, and that the use of\nalternative approaches based on non-grafted trees would only lead to biased results of lesser\nquality than what we have already done and would be improper as sensitivity analyses aiming\nto increase the robustness of our results.\nWe decided to rely on a grafting approach to supplement our initial sampling because\ndownstream analyses of diversity dynamics based on imputed exhaustive phylogenies have\nincreased power and lower estimation bias for identifying regime shifts relative to analyses\nthat use sampling fractions to account for missing taxa (Chang et al., 2020). Moreover,\nperforming diversification analyses on non-grafted trees would imply a choice between two\nequally problematic approaches. (1) We could use a global sampling fraction to account for\nthe missing taxa. This is highly likely to lead to biased results as the location of missing grafted\ntaxa in our phylogeny is widely heterogeneous across clades (see Supplementary Figure\nS6) and thus will not be reflected with the use of a global sampling fraction parameter\nassuming a homogeneous distribution of missing taxa (Cusimano & Renner, 2010; Höhna et\nal., 2011). (2) Alternatively, we could use clade-specific sampling fractions to account for the\nappropriate number of missing taxa within each subclade. However, this approach has been\npointed to lead to potentially incorrect likelihood calculation behavior (Beaulieu, 2020 -\nVignette in hisse R package: “The problem with clade-specific sampling fractions”, Moore et\nal., 2016) and to have low power to appropriately estimate diversification models and\nparameters when they fall below 50% (FitzJohn et al. 2009), which would be our case for some\nof our clades . Therefore, we followed the recommended approach by the authors of the\nBAMM software (http://bamm-project.org/advanced.html; accessed on April 16th, 2025.\nSection 9.1 Accounting for non-random incomplete taxon sampling in diversification studies)\nthat consists in using taxonomically informed grafted trees with missing speciation events\nestimated from birth-death models fit on incomplete subclades such as in the TACT method\n(Chang et al., 2020) and as described in our own Supplementary Methods (Section 5. Tree\ngrafting). We now elaborate more thoroughly on this rationale in the dedicated section of the\nSupplementary Methods to enhance transparency in our choices of analytic pipeline.\nReferences:\nBeaulieu, J. M. (2020). Vignette in hisse R package. The problem with clade-specific\nsampling fractions. https://cran.r-project.org/web/packages/hisse/vignettes/Clade-specific-\nsampling.pdf [Accessed on 17 April 2025].\nChang, J., Rabosky, D. L., & Alfaro, M. E. (2020). Estimating diversification rates on\nincompletely sampled phylogenies: theoretical concerns and practical solutions.\nSystematic Biology, 69(3), 602-611. https://doi.org/10.1093/sysbio/syz081\nCusimano N., Renner S.S. 2010. Slowdowns in diversification rates from real phylogenies may\nnot be real. Syst. Biol. 59:458–464. https://doi.org/10.1093/sysbio/syq032\nFitzJohn R.G., Maddison W.P., Otto S.P. 2009. Estimating trait-dependent speciation and\nextinction rates from incompletely resolved phylogenies. Syst. Biol. 58:595–611.\nhttps://doi.org/10.1093/sysbio/syp067\nHöhna S., Stadler T., Ronquist F., Britton T. 2011. Inferring speciation and extinction rates\nunder different sampling schemes. Mol. Biol. Evol. 28:2577–2589.\nhttps://doi.org/10.1093/molbev/msr095\nMoore, B.R., S. Hohna, M.R. May, B. Rannala, and J.P. Huelsenbeck. 2016. Critically\nevaluating the theory and performance of Bayesian analysis of macroevolutionary\nmixtures. Proceedings of the National Academy of Sciences, USA, 113:9569-9574.\nhttps://doi.org/10.1073/pnas.1518659113\n10) Since the authors evaluated three different partitioning schemes—unpartitioned, by locus\nwith merging, and Sliding Window Site Characteristics (SWSC) based on entropy with\nmerging—it would be beneficial to include the detailed topologies in the Supplementary\nMaterial along with a discussion of their differences. Simply stating that the SWSC analysis\nyielded the “highest likelihood” to justify prioritizing it over the others does not seem sufficient.\nThe authors should compare the three topologies obtained under different partitioning\nschemes, and the grafted vs. non-grafted topologies to assess how differences in partitioning\nand grafting affect divergence time estimates, as these variations are likely to have significant\ndownstream effects on all subsequent analyses.\nWe believe that SWSC-EN with model selection and partition merging is the most\nsophisticated and appropriate way to partition UCE data. It handles the expected different\nevolutionary dynamics occurring in the ultra-conserved and variation section of each locus\nand because it starts with a scheme that has each locus split into three distinct partitions and\nworks its way down towards progressively more merged schemes (Tagliacollo & Lanfear,\n2018). This way, unpartitioned and partitioned by-locus schemes are nested within the\nSWCS-EN model. In other words, the partition merging step of the SWSC-EN approach would\nlead to an unpartitioned or a partitioned by-locus scheme, if one of these received more\nstatistical support for a given dataset. Thus, beyond the higher likelihood, there is a strong\nstatistical rationale to select the SWSC-EN approach over the unpartitioned scheme or\nthe by-locus scheme for downstream analyses. Moreover, the SWSC-EN scheme also\nprovided the highest node support values with mean UFBS scores of 97.95% vs. 97.48% for\n‘unpartitioned’, and 97.84% for ‘by locus with merging. For clarity, we now detail the rationale\nfor the selection of the best partitioning scheme (See sections in Phylogenetic Inferences in\nMain text (L. 172-177) and Supplementary Methods), provide visuals of the phylogenies\nresulting from other partitioning schemes and report on the very limited variation that\noccurs between them. Indeed, the three partitioning schemes led to highly similar topologies,\nwith respectively five clades (unpartitioned: 99.4% identity) and one clade (by locus: 99.9%\nidentity) differing from the SWSC-EN topology due to uncertain placement of single isolated\nrogue taxa (see Supplementary Figure S4). We are highly confident that these minor\ndiscrepancies cannot affect our overall conclusions to any detectable degree, comforting our\nchoice to focus our time-consuming downstream analyses on the best partitioning scheme.\nFor a comment on grafted vs. non-grafted topologies, please refer to our reply to Point #9\nabove.\nReference:\nTagliacollo, V. A., & Lanfear, R. (2018). Estimating improved partitioning schemes for\nultraconserved elements. Molecular biology and evolution, 35(7), 1798-1811.\nhttps://doi.org/10.1093/molbev/msy069\n*Divergence dating*\n11) We wonder why some fossils were not used as additional calibrations. We agree that some\nfossils are dubious, poorly preserved, and cannot be reliably used as calibration points, but\nants have one of the best fossil records of the order Hymenoptera, and it is a pity not to take\nadvantage of this. Calibrations strangely overlooked in this manuscript include (not\nexhaustive):\n- a) Platythyrea dlusskyi Aria et al. 2011 from Ypresian Oise amber, which can be used to\ncalibrate the Platythyreini (Aria et al. 2011 - Zootaxa). Given its age and the 95% HDP of the\nnode Platythyreini it is likely that it will push towards slightly older age the node or at least help\nin reducing the 50 Ma interval of the HDP. It has been used in other studies (e.g., Borowiec et\nal 2019 - MPE)\n- b) Pachycondyla(s) from Baltic amber can be used to calibrate either Pachy + Neo or the\ncrown Pachy, depending on the interpretation you make of their placement, but in both cases\nthey will likely impact the dating (e.g., Dlussky 2009 - Paleontol. J.).\n- c) Brachyponera from Zhangpu amber (Wang et al. 2021 - Sci. Adv., see supp material) can\nbe used to calibrate Brachyponera, likely contributing to better estimate the origin of this\ngenus.\nOverall, the authors should say a bit more about their rationale for the selection of fossil\ncalibrations in general. Parham et al. (2012 - Syst. Biol.) is a good starting point (we've seen\nthe supplementary file on Zenodo), but it would be helpful to read a bit more about their overall\nthinking. This might include the otherwise plausible fossils not used, and the reasons for their\nexclusion.\nWe thank the reviewers for the attention they have given our divergence dating approaches.\nWe have altered the text in Supplementary Methods to make our reasoning more explicit\n(see Section 4 - Divergence dating). Our guiding principles for choosing fossil node\ncalibrations were:\n1) Only using fossils that could be confidently assigned to crown groups.\n2) We conservatively assumed that each fossil could belong to either the crown or stem group\nof a clade in which it has been placed in the literature, except for those that had been explicitly\nplaced using total-evidence tip dating (Ronquist et al., 2012).\n3) When multiple fossils of the same age informed a set of nested nodes, we chose to calibrate\nonly the shallowest node, assuming the fossil to bracket the minimum age for that node.\nThese are common guiding principles in node calibration divergence dating analyses,\nsuccessfully applied in ants and Hymenoptera by the authors and others before (e.g, Peters\net al. 2017, Blaimer et al. 2023, Borowiec et al. 2025, and others). This results in seven fossils\nidentified as relevant for this study. The importance of the first and second guiding principles\nis well-articulated in the reviewer-cited Parham et al. 2012. The third principle is simply to\navoid redundant constraints on dependent events. Consider a tree (A,(B,C)) calibrated with\nBaltic Amber fossils at both the MRCAs of BC and ABC. In this case, these nodes are nested,\nAB is by definition younger than the ancestor of ABC, and so it makes no sense for both to\nhave the same minimum age.\nWith these in mind, we can address the individual fossils mentioned by the reviewers. We\nactually considered the fossil Platythyrea. However, because it is impossible to deduce\nwhether this fossil is within the crown group of this genus, it would have to calibrate the split\nbetween Platythyreini and Ponerini (i.e., the rest of Ponerinae). For this initial split, however,\nwe chose a much older (minimum 94 Ma) undescribed species of Ponerinae that was explicitly\nplaced as sister to Ponerini using many morphological characters obtained for total-evidence\ndating by Boudinot et al. 2022.\nThe multiple species of fossil Pachycondyla have all been described prior to the recognition\nthe genus was polyphyletic, leading to its split into 19 different genera in 2014 (Schmidt and\nShattuck, 2014). It is beyond the scope of this paper to comprehensively re-evaluate these\nfossils. Some of these had apparently been examined by the authors of the generic revision,\nwith the outcome being the following statement (Schmidt and Shattuck, 2014, p. 155):\n“We have not managed to discern the proper taxonomic placement for a handful of ponerine\nspecies previously associated with Pachycondyla. In some cases, this uncertainty stems from\nour failure to examine specimens of the species in question (especially when the original\ndescriptions are ambiguous), while in other cases the morphological traits of the species leave\ndoubts about its affinities. We leave these species as incertae sedis within Pachycondyla while\nacknowledging this placement is undoubtedly incorrect. Exact placement will require detailed\nexamination of relevant material of these taxa.”\nThe fossil Brachyponera from Wang et al. 2021 was not included because it contradicts some\nof the recommendations of Parham et al. 2012: It has not been officially described as a species\nand is mentioned in the supplement without any identifying characters and no photograph\navailable for examination. We opted instead to calibrate the next node down with a much more\ncompletely described and unmistakable fossil of the same age, Odontoponera\npseudotransversa (Perrichot, 2022).\nWe were aware of and considered in this study fossil data from Jouault et al. (2025) and\nJouault et al. (2024). We chose not to use fossils reported only in supplementary data files,\nespecially when they lacked formal taxonomic descriptions, accessible supporting images, or\nclear character justification. To do otherwise would contradict best practices outlined by\nParham et al. 2012.\nWe believe that our selection of fossil calibrations is as complete as possible when\nfollowing best practices for node calibration and our rationale above, supported by the\ncited literature. Combined with root prior spanning 80 Ma around the dates recovered in the\nliterature (see Supplementary Information Section 4 - Divergence Dating) and 1000\nposterior trees used in downstream analyses, our conclusions are more robust to\nuncertainty in dating analyses than the literature standard in macroevolutionary\nstudies, which often consider only a single “best” tree.\nWe thank the reviewers for the prompt to explain our rationale, which we do by adding the\nfollowing to the Supplementary Information (in Section 4. Divergence dating):\n“We selected fossil calibrations following three guiding principles: (1) fossils had to be\nconfidently assignable to total groups; (2) in the absence of total-evidence phylogenetic\nplacement, we conservatively assumed that fossils could belong to either stem or crown\ngroups; and (3) when multiple fossils of similar age could inform a set of nested nodes, we\ncalibrated only the shallowest such node to avoid redundant constraints. These principles\nwere applied in conjunction with best practices outlined by Parham et al. (2012) for fossil\nselection and have been applied successfully in divergence dating of ants and other\nHymenoptera (e.g., Peters et al. 2017, Borowiec et al. 2025, Blaimer et al. 2023). Following\nthis approach, we identified seven fossil calibrations appropriate for our analyses, prioritizing\nthose with clear morphological justification, sufficient preservation, and unambiguous\ntaxonomic placement.”\nTo further highlight explicitly accounting for uncertainty in our analyses, we modified the\nfollowing existing statements in Supplementary Information:\n“We also set a normal distribution for the required root calibration prior based on ranges\ninferred for this node in seven recent divergence dating studies of ants reviewed by Borowiec\net al.”\nWas modified to:\n“To explicitly account for the large uncertainty surrounding divergence estimates in ants, we\nset a normal distribution for the required root calibration prior based on ranges inferred for this\nnode in seven recent divergence dating studies of ants reviewed by Borowiec et al.”\nWe also modified:\n“We randomly selected 1,000 reduced 69-taxon trees from the 40,004 total posterior samples\nfrom MCMCTree.”\nTo read:\n“To account for and ensure the robustness of our downstream analyses to dating uncertainty,\nwe randomly selected 1,000 reduced 69-taxon trees from the 40,004 total posterior samples\nfrom MCMCTree.”\nReferences:\nBlaimer, B. B., Santos, B. F., Cruaud, A., Gates, M. W., Kula, R. R., Mikó, I., ... & Buffington,\nM. L. (2023). Key innovations and the diversification of Hymenoptera. Nature\nCommunications, 14(1), 1212. https://doi.org/10.1038/s41467-023-36868-4\nBorowiec, M. L., Zhang, Y. M., Neves, K., Ramalho, M. O., Fisher, B. L., Lucky, A., & Moreau,\nC. S. (2025). Evaluating UCE data adequacy and integrating uncertainty in a\ncomprehensive phylogeny of ants. Systematic Biology, syaf001.\nhttps://doi.org/10.1093/sysbio/syaf001\nBoudinot, B. E., Khouri, Z., Richter, A., Griebenow, Z. H., van de Kamp, T., Perrichot, V., &\nBarden, P. (2022). Evolution and systematics of the Aculeata and kin (Hymenoptera), with\nemphasis on the ants (Formicoidea:†@@@ idae fam. nov., Formicidae). BioRxiv, 2022-\n02. https://www.biorxiv.org/content/10.1101/2022.02.20.480183v1.full.pdf\nJouault, C., Condamine, F. L., Legendre, F., & Perrichot, V. (2024). The Angiosperm\nTerrestrial Revolution buffered ants against extinction. Proceedings of the National\nAcademy of Sciences, 121(13), e2317795121. https://doi.org/10.1073/pnas.2317795121\nJouault, C., Oyama, N., Álvarez-Parra, S., Huang, D., Perrichot, V., Condamine, F. L., &\nLegendre, F. (2025). The radiation of Hymenoptera illuminated by Bayesian inferences\nfrom the fossil record. Current Biology. https://doi.org/10.1016/j.cub.2025.03.002\nParham, J. F., Donoghue, P. C., Bell, C. J., Calway, T. D., Head, J. J., Holroyd, P. A., ... &\nBenton, M. J. (2012). Best practices for justifying fossil calibrations. Systematic Biology,\n61(2), 346-359. https://doi.org/10.1093/sysbio/syr107\nPerrichot, V. (2023). The first fossil occurrence of Odontoponera (Formicidae: Ponerinae),\nfrom Middle Miocene Zhangpu amber. Palaeoworld, 32(4), 693-698.\nhttps://doi.org/10.1016/j.palwor.2022.09.007\nPeters, R. S., Krogmann, L., Mayer, C., Donath, A., Gunkel, S., Meusemann, K., ... & Niehuis,\nO. (2017). Evolutionary history of the Hymenoptera. Current biology, 27(7), 1013-1018.\nhttps://doi.org/10.1016/j.cub.2017.01.027\nRonquist, F., Klopfstein, S., Vilhelmsen, L., Schulmeister, S., Murray, D. L., & Rasnitsyn, A. P.\n(2012). A total-evidence approach to dating with fossils, applied to the early radiation of the\nHymenoptera. Systematic biology, 61(6), 973-999. https://doi.org/10.1093/sysbio/sys058\nSchmidt, C. A., & Shattuck, S. O. (2014). The higher classification of the ant subfamily\nPonerinae (Hymenoptera: Formicidae), with a review of ponerine ecology and behavior.\nZootaxa, 3817(1), 1-242. http://dx.doi.org/10.11646/zootaxa.3817.1.1\n12) The authors chose to calibrate their tree using MCMCTree applied to a reduced set of\ntaxa, and then scaled it to the full dataset with TreePL. Given the small size of their\nsubsampled dataset and the ongoing advances in dating analyses—particularly regarding the\nincorporation of fossil data—we wonder why they did not opt for the fossilized birth-death\n(FBD) process (Stadler 2010 - J. Theor. Biol.; Heath et al. 2014 - PNAS).\nFBD-based methods can overcome several limitations associated with node calibration\napproaches, allowing for the integration of a wider range of fossil data. Given the extensive\nfossil record of ants and their relatively well-established taxonomic classifications, this method\nis likely to yield robust results. Alternatively, if the authors are concerned about gaps in the\nfossil record in certain geological periods, or skeptical about the classical FBD approach, they\ncould use a sequential sampling framework using a birth–death skyline tree prior (BDSS;\nStadler et al. 2013 - PNAS; Culshaw et al. 2019 - Evolution). This method allows fossil\nsampling proportions to be modeled as time-heterogeneous, incorporating time slices before\nand after the oldest fossil sample using tools such as the TreeSlicer function in the skylinetools\npackage (https://github.com/laduplessis/skylinetools). We strongly encourage the authors to\ninvestigate how their methodological choices affect divergence time estimates.\nWe thank the authors for this suggestion and would like to respond by explaining our rationale\nfor not using fossilized birth-death (FBD) methods. We believe that there are a number of\ntrade-offs using classical node calibrations versus FBD-based methods, and that the goals\nof our study were already fully accomplished by node dating.\nFirst, the use of FBD is much more computationally intensive. We know from experience\nthat in order to apply the FBD framework we would have had to decrease the number of loci\nused dramatically, even in reduced dataset, perhaps to the order of 5-10 loci for the analysis\nto converge in a reasonable time. Furthermore, our reduced dataset would have to include\nmuch more taxa to accommodate all the nodes that need to be constrained to place additional\npotential fossils. Because we perceive the completeness of our data in terms of taxa\nrepresented by genomic data as a major strength and innovation of this study (nearly half of\nall species represented by UCE data), we decided it would have been undesirable to reduce\nthe dataset to a tiny fraction of loci just to use FBD.\nFurthermore, while FBD can theoretically offer greater accuracy, it often has low precision\nrelative to node dating (e.g., Heath et al. 2014, Borowiec 2019). Since absolute time\ndivergence estimates vary so widely in the ant systematic literature (Borowiec et al. 2020,\nBorowiec et al. 2025), we focused primarily on the robustness of our analyses regarding\ndivergence time ranges from classical node calibrations. As mentioned above, we were able\nto inject considerable uncertainty into our dating by applying broad root prior encompassing\nthe ranges from published studies. This way, we are already generating a wide range of\nscenarios that could have been produced by FBD without using explicit morphological\nplacement of fossils that may sometimes introduce additional bias. We believe this makes our\nconclusions robust and less dependent on the many assumptions inherently present in\ndivergence dating, whether using FBD or node dating.\nWe are not particularly concerned about the limitations of FBD-based approaches and believe\nthat it would have been advantageous under certain circumstances. A major advantage of\nFBD is the possibility of explicit placement of fossils using morphological data in a total-\nevidence framework (cf. Ronquist et al. 2012, Boudinot et al. 2022). If a comprehensive\nmorphological matrix from all extant and fossil ponerines was available for this project, we\nwould have chosen FBD. As it is, we generated great amounts of molecular data for this\nproject and decided to prioritize information therein.\nWe hope the reviewers appreciate this rationale and realize that it would have been both\ncomputationally challenging to carry out FBD for our dataset and an enormous burden to\nrepeat all our downstream analyses for potentially limited improvement (see for instance\nBeaulieu & O’Meara 2023).\nReferences:\nBeaulieu, J. M., & O’Meara, B. C. (2023). Fossils do not substantially improve, and may even\nharm, estimates of diversification rate heterogeneity. Systematic Biology, 72(1), 50-61.\nhttps://doi.org/10.1093/sysbio/syac049\nBorowiec, M. L. (2019). Convergent evolution of the army ant syndrome and congruence in\nbig-data phylogenetics. Systematic biology, 68(4), 642-656.\nhttps://doi.org/10.1093/sysbio/syy088\nBorowiec, M. L., Moreau, C. S., & Rabeling, C. (2020). Ants: phylogeny and classification.\nEncyclopedia of social insects, 52-69. https://doi.org/10.1007/978-3-319-90306-4_155-1\nBorowiec, M. L., Zhang, Y. M., Neves, K., Ramalho, M. O., Fisher, B. L., Lucky, A., & Moreau,\nC. S. (2025). Evaluating UCE data adequacy and integrating uncertainty in a\ncomprehensive phylogeny of ants. Systematic Biology, syaf001.\nhttps://doi.org/10.1093/sysbio/syaf001\nBoudinot, B. E., Khouri, Z., Richter, A., Griebenow, Z. H., van de Kamp, T., Perrichot, V., &\nBarden, P. (2022). Evolution and systematics of the Aculeata and kin (Hymenoptera), with\nemphasis on the ants (Formicoidea:†@@@ idae fam. nov., Formicidae). BioRxiv, 2022-\n02. https://www.biorxiv.org/content/10.1101/2022.02.20.480183v1.full.pdf\nHeath, T. A., Huelsenbeck, J. P., & Stadler, T. (2014). The fossilized birth–death process for\ncoherent calibration of divergence-time estimates. Proceedings of the National Academy\nof Sciences, 111(29), E2957-E2966. https://doi.org/10.1073/pnas.1319091111\nRonquist, F., Klopfstein, S., Vilhelmsen, L., Schulmeister, S., Murray, D. L., & Rasnitsyn, A. P.\n(2012). A total-evidence approach to dating with fossils, applied to the early radiation of the\nHymenoptera. Systematic biology, 61(6), 973-999. https://doi.org/10.1093/sysbio/sys058\n13) Lastly, as emphasized in our point 9), the authors should investigate how divergence time\nestimates vary between the three different partitioning schemes and provide a thorough\ndiscussion of their results. It is well known that dating estimates can vary significantly\ndepending on the methodology, priors, and calibration choices. For a manuscript aiming to\nelucidate the global diversification and biogeographic history of a clade, the current analyses\nappear insufficiently comprehensive. Extending these comparisons would strengthen the\nconclusions of the study and provide a more robust framework for understanding the\nevolutionary timeline of the Ponerinae, while also serving as a textbook example for future\nwork.\nAs outlined in our response to Point #3, we believe that we have taken a special and, to our\nknowledge, unprecedented care in addressing uncertainties in divergence dating (and\ntaxonomic grafting) down to the selection of biogeographic models and estimates of ancestral\nranges (Supplementary Information Tables S4 & S8). While we appreciate the suggestion\nto extend sensitivity analyses further, the reviewers should notice that we are already\ncomputationally not able to carry on the multiplication of sensitivity analyses further\ndown our analytic pipeline in a reasonable time, knowing that the Biogeographic Stochastic\nMapping, Dispersal events summaries across time sliding-windows, BAMM diversification\nanalyses, STRAPP tests over evolutionary time, RISR investigations, and investigation of\nlineage origins over time already require over a week of CPU-time for a single\nphylogenetic hypothesis.\nNonetheless, we fully acknowledge the importance of testing the robustness of our main\nconclusions to dating uncertainty and have worked to address this to the greatest extent\npossible while keeping the analytic pipeline under a feasible, albeit already massive\ncomputational and time burden. To this end, we performed the entire pipeline of\ndownstream analyses using the two most extreme hypotheses for divergence dating\nsupported by our data (i.e., using the youngest and oldest divergence age estimates found\namong our posterior samples). We tested for correlations between the net diversification rates\nestimated from BAMM for each hypothesis and found significant positive relationships\n(Spearman’s rank correlation: MCC vs. Youngest: ρ = 0.619, t = 13.7, df = 303, p < 0.001;\nMCC vs. Oldest: ρ = 0.904, t = 37.4, df = 313, p < 0.001; Supplementary Figs. S14 & S15).\nIn complement, we also found high agreements in the ancestral range estimates across the\ndivergence dating hypotheses with Cohen’s Kappa indices higher than 0.90 (MCC vs.\nYoungest = 0.971; MCC vs. Oldest = 0.907) reflected by the high similarity in ancestral ranges\nmapped on the respective phylogenies (Fig. 1; Supplementary Fig. S9).\nWe now show that our main conclusions regarding the prevalence of colonization timing\n(TfAH) over differences in diversification rates (DRH) and asymmetry in dispersal flux\n(ADH) in explaining present-day biodiversity patterns in ponerine ants are robust\nregardless of the hypothesis for divergence dating selected (see new Supplementary\nFigures S9, S11, S12, S13, S15, S17, S19, S22 & S23, and Supplementary Tables S2, S3,\nS6 & S7; and additions to the Main text: L. 192-194, 203-205, 251, 255-260, 281-283, 361-\n364, 369-370, 432-437, 487-492, 505-510, 538-543, 553-555, 623-626 & 663). Those\nsensitivity analyses also emphasize that the relative timing of colonization events across\nbioregions is the key factor, independently from the absolute estimated timing of events.\nWe are confident that, thanks to the addition of those new sensitivity analyses in complement\nto the original ones, we now provide a fully transparent, robust, and extensive analytical\nframework that will likely contribute to establishing a new standard for future large-scale\nmacroevolutionary studies.\n*Historical Biogeography*\n14) The authors use BioGeoBEARS 1.1.3 (Matzke 2013 - FoB, 2018) for their biogeographic\nanalyses, and specifically use the DEC+J model (Matzke 2014 - Syst. Biol.). However, they\ndo not provide a clear rationale for selecting DEC+J over the DEC model, other than an AICc\ncomparison. While the authors state that DEC+J was selected as the best-fitting model, it is\nimportant to acknowledge the ongoing debate regarding statistical validity and model selection\nprocess in BioGeoBEARS (Ree and Sanmartín, 2018 - Syst. Biol.). Additionally, the DEC+J\nmodel has been criticized for often yielding null or unrealistically low extinction rates\n(Sanmartín and Meseguer, 2016 - Front. Genet.) and for favoring direct dispersal over\nwidespread ancestral ranges—an assumption that may not be appropriate for reconstructing\nthe evolutionary history of Ponerinae (Rolland and Condamine, 2019 - J. Biogeogr.). We\nbelieve that these concerns can be easily addressed by re-running the analyses using the\nDEC model. Given the authors’ established analytical pipeline, this should be feasible within\na reasonable timeframe. This would not only strengthen the findings of the paper if the results\nare consistent with the initial analyses, but would also provide valuable insights and discussion\nin the case of discrepancies.\nWe are aware of the debates in the community surrounding the use of the +J models including\njump dispersal at speciation for biogeographic inferences. We do not think it is the purpose of\nour study to reopen this discussion, but we agree with the reviewers that potential bias\nassociated with the use of specific models should be acknowledged. To our view, the\nissue of model selection has been convincingly addressed by the author of the BioGeoBEARS\nmeta-model himself in his reply to Ree and Sanmartín’s concerns (Matzke, 2022). Here, he\nexposed how the likelihood-based comparison framework commonly accepted as the\nstandard in modern statistics (Burnham, & Anderson, 2002), including comparison of\nmacroevolutionary models of trait evolution, SSE models, and partitioning scheme for\nphylogenetic inferences (see Point #10) is a valid approach to model selection (Harmon,\n2019). DEC and DEC+J are effectively both special cases of a ClaSSE model relying on a\nsimilar framework for likelihood definition. Moreover, the DEC model is nested within the\nDEC+J model which implies that direct comparison of model fit and parameter estimates\nare valid and easily accessible. The selection of biogeographic models based on AICc\ncomparison, especially within the BioGeoBEARS framework, is currently the common best\npractice used by high-impact studies involving biogeographic analyses of similar scope (Klein\net al., 2021; Zhao et al., 2022; Weil et al., 2023; Leong et al., 2025). In our case, the statistical\nsupport for DEC+J over DEC is overwhelming with 1000 out of 1000 of our posterior trees\naccounting for both uncertainty in divergence dating and in taxon grafting leading to the\nselection of the DEC+J model over DEC, DIVALIKE, and DIVALIKE+J models using AICc as\na selection criterion (Supplementary Information Table S2).\nAs stated by the reviewers, an important point of contention against the use of DEC+J models\nis the fact that it often yields unrealistically low extinction rates and limited residence times in\nwidespread areas by allowing jump-dispersal at speciation ((A) → (A),(B)) as an alternative\nmechanism to range expansion ((A) → (AB)) followed by a vicariance ((AB) → (A),(B)) or a\nsympatric subset speciation event ((AB) → (AB),(B)) that both require a temporary widespread\nstate to model a cladogenetic range transition (Sanmartín and Meseguer, 2016). While these\ncriticisms are valid, the use of a DEC model that does not allow jump-dispersal events to\noccur may also entail equally, if not even more, problematic assumptions in the context\nof a time-stratified model. Indeed, under the DEC model, in a time-stratified context using\nadjacency matrices to control for the validity of widespread ranges (AB) during geological\nperiods based on connectivity between terrestrial landmasses, no transition (A → B) is possible\nbetween non-adjacent areas because all succession of events modeling such transition\nrequire an invalid temporary wide-spread state (AB). Therefore, a time-stratified DEC model\nis mechanically denying the possibility of long-dispersal events followed by isolation leading\nto speciation (i.e., a founder event) between two non-adjacent areas. Conversely, under a\ntime-stratified DEC+J model, such events, especially on a geological timescale, are exactly\naccounted for by jump-dispersals that describe a range transition associated with speciation\n((A) → (A),(B)). Long-distance dispersal events across non-adjacent areas are to be expected\nfor ants which managed for instance to colonize remote Pacific islands (e.g., Lucky & Sarnat,\n2010). Using dispersal multiplier matrices (+W models) would not solve this issue as they only\nmodulate dispersal parameter values, but do not affect the state space, and in particular do\nnot make widespread ranges available for transitions between non-adjacent areas in DEC\nmodels.\nOverall, we recognize that there may be potentially problematic biases with the DEC+J model,\nbut we stress here that different problematic assumptions are also associated with DEC\nmodels. We now explicitly acknowledge the possible biases of the DEC+J model in the\nSupplementary Methods (Section 7.2 Ancestral Range Estimates with BioGeoBEARS).\nHowever, we believe our current model selection framework is grounded on compelling\nstatistical support and aligns with the current best practice in biogeographic inferences\nand statistics in general. Given the unambiguous and robust statistical support for the\nDEC+J model over the DEC model found with our dataset, we believe it is appropriate to\nproceed with the DEC+J model in our analytical pipeline. While we understand the concerns\nsurrounding the use of DEC+J, we feel that disregarding the result of our carefully designed\nmodel selection process, including time-consuming sensitivity analyses that go already\nbeyond any practice in the field, would undermine the evidence-based approach we aim to\npromote in this study.\nReferences:\nBurnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A\npractical information-theoretic approach (2nd ed.). Springer-Verlag.\nhttps://doi.org/10.1007/b97636\nHarmon, L. (2019). Chapter 2: Fitting Statistical Models to Data in Phylogenetic Comparative\nMethods. https://lukejharmon.github.io/pcm/\nKlein, C. G., Pisani, D., Field, D. J., Lakin, R., Wills, M. A., & Longrich, N. R. (2021). Evolution\nand dispersal of snakes across the Cretaceous-Paleogene mass extinction. Nature\nCommunications, 12(1), 5335. https://doi.org/10.1038/s41467-021-25136-y\nLeong, J. V., Matos Maraví, P., Núñez, R., Nunes, R., Liang, W., Braby, M. F., ... & Lohman,\nD. J. (2025). Around the World in 26 Million Years: Diversification and Biogeography of\n‐\nPantropical Grass Yellow Eurema Butterflies (Pieridae: Coliadinae). Journal of\nBiogeography. https://doi.org/10.1111/jbi.15107\n‐\nLucky, A., & Sarnat, E. M. (2010). Biogeography and diversification of the Pacific ant genus\nLordomyrma Emery. Journal of Biogeography, 37(4), 624-634.\nhttps://doi.org/10.1111/j.1365-2699.2009.02242.x\nMatzke, N. J. (2022). Statistical comparison of DEC and DEC+ J is identical to comparison of\ntwo ClaSSE submodels, and is therefore valid. Journal of Biogeography, 49(10), 1805-\n1824. https://doi.org/10.1111/jbi.14346\nSanmartín, I., & Meseguer, A. S. (2016). Extinction in phylogenetics and biogeography: from\ntimetrees to patterns of biotic assemblage. Frontiers in genetics, 7, 35.\nhttps://doi.org/10.3389/fgene.2016.00035\nWeil, SS., Gallien, L., Nicolaï, M.P.J. et al. Body size and life history shape the historical\nbiogeography of tetrapods. Nat Ecol Evol 7, 1467–1479 (2023).\nhttps://doi.org/10.1038/s41559-023-02150-5\nZhao, J. L., Yu, X. Q., Kress, W. J., Wang, Y. L., Xia, Y. M., & Li, Q. J. (2022). Historical\nbiogeography of the gingers and its implications for shifts in tropical rain forest habitats.\nJournal of Biogeography, 49(7), 1339-1351. https://doi.org/10.1111/jbi.14386\n*Diversification dynamics*\n15) The authors refer directly to BAMM in their diversification analyses. BAMM is indeed a\npowerful tool for inferring extinction and origination rates and can be easily integrated with\nSTRAPP and other downstream analyses. While the BAMM approach can assess\ndiversification rate heterogeneity on highly incomplete and phylogenetically non-random\ndatasets, it has been criticized for identifiability issues (Louca & Pennell, 2020 - Nature). These\nconcerns are significant and have far-reaching implications, as they affect all widely used\nmethods for studying diversification, including those used in this study. We think that a section\nin the supplementary material discussing how the authors have addressed these issues is\nmandatory.\nWe thank the reviewers for raising this important point. We fully acknowledge the identifiability\nissues discussed by Louca & Pennell (2020), who showed that under generalized birth–death\nmodels, multiple distinct diversification scenarios with time-varying speciation and extinction\nrates can yield identical likelihoods for a given extant timetree. These concerns have indeed\nmajor implications for the interpretation of diversification analyses and merit transparent\ndiscussion. We present here, and in the dedicated section of Supplementary Information\n(Section 8.2 BAMM diversification rates), the different steps taken in our analytic framework\nin order to mitigate potential detrimental effects of those identifiability issues on our\nconclusions.\nAs a preamble, it is worth noting, as the reviewers did, that these identifiability challenges\nare not specific to BAMM, but apply equally to all currently available diversification methods\nthat model time-varying speciation and extinction rates from extant phylogenies, including\nalternative approaches such as ClaDS2, LSBDS, PESTO, or MTBD. To our knowledge, all\nsuch methods operate within the same general modeling framework of generalized birth–\ndeath models and are thus subject to the same theoretical constraints.\n(1) We focused our analyses on net diversification rates rather than on the absolute estimation\nof speciation and extinction rates individually. While net diversification is also affected by\nidentifiability issues, it is generally more robust and interpretable than attempting to\ndisentangle speciation and extinction trends independently (as noted by Louca & Pennell).\n(2) We relied on a Bayesian framework (BAMM) to estimate changes in diversification rates\nover time and clades. Even though not perfect, the sampling approach of Bayesian methods\nallows theoretically to sample multiple of the infinite congruent diversification histories that can\nlead to similar likelihood, effectively accounting for a portion of the uncertainty regarding\nthe identification of the “best” fitting diversification history. We carried on this uncertainty\ndown to our tests for diversification rate differences as we performed STRAPP tests that are\nespecially designed to be run across posterior samples from a BAMM encompassing a\ndiversity of alternative diversification histories.\n(3) The set of diversification histories that yield similarly maximized likelihood is called a\n“congruence class”. Such a congruence class contains exactly one model with a complete\nsampling fraction (ρ = 1) and absence of extinctions (μ = 0). This model presents speciation\nrates equal to the ‘pulled speciation rates’ defined by Louca & Pennell as the key parameter\nidentifiable from time-varying BD models and suggested to be used for further interpretation.\nSince we used grafted trees (effectively having a complete sampling fraction), and obtained\nextinction estimates that were relatively low compared to speciation rates (mean μ = 0.041;\nmean λ = 0.148), the net diversification rates we used for downstream analyses are actually\nsomewhat close to the ‘pulled speciation rates’ defined by Louca & Pennell (and arguably\nlargely ignored by the community of macroevolutionists ever since).\n(4) As exposed in more details in our reply to Point #17, we combined different approaches\n(BAMM vs Regional In-Situ Radiation analyses vs GeoSSE) in an attempt to cross\nvalidate our results following the framework introduced by Chazot et al., 2021.\nWe believe the diversification analyses we present in this study, especially when paired with\nalternative approaches and conservative interpretation limited to net diversification rates,\nremain informative and methodologically sound despite the challenges raised by Louca &\nPennell. Following the reviewer’s recommendation, we have now included a dedicated\nparagraph in the Supplementary Methods (Section 8.2 BAMM diversification rates), where\nwe explicitly discuss these theoretical limitations and clarify the rationale for our\nmethodological choices accordingly.\nReferences:\nChazot, N., Condamine, F. L., Dudas, G., Peña, C., Kodandaramaiah, U., Matos-Maraví, P.,\n... & Wahlberg, N. (2021). Conserved ancestral tropical niche but different continental\nhistories explain the latitudinal diversity gradient in brush-footed butterflies. Nature\ncommunications, 12(1), 5717. https://doi.org/10.1038/s41467-021-25906-8\nLouca, S., & Pennell, M. W. (2020). Extant timetrees are consistent with a myriad of\ndiversification histories. Nature, 580(7804), 502-505. https://doi.org/10.1038/s41586-020-\n2176-1\n16) As with other sections, we feel that the manuscript lacks sufficient sensitivity analyses. We\nstrongly recommend that the authors perform their BAMM analyses on the non-grafted tree\nas well as on the different topologies obtained under the different partitioning schemes. The\nresults should then be compared and discussed. Given the known sensitivity of BAMM to\ntaxon sampling and tree structure, we would expect the outcomes of these analyses to differ.\nPlease refer to our answers to Points #9 and #10 regarding the selection of partition\nschemes and the use of non-grafted trees.\nAdditionally, we note that our grafted trees are more suited to tackle the identifiability issues\nhighlighted by Louca & Pennell (2020) since they present a complete sampling fraction (ρ =\n1) that may yield speciation rates closer to the ‘pulled speciation rates’ defined by Louca &\nPennell as the key parameter identifiable from time-varying BD models and suggested to be\nused for further interpretation (Please, refer to our reply to Point #15 for context).\n17) Why was BAMM chosen over alternative approaches such as ClaDS2 (Maliet et al. 2019\n- NEE), LSBDS (Höhna et al. 2019 - bioRxiv), PESTO (Kopperud & Höhna 2023 -\nhttps://github.com/kopperud/Pesto.jl), or MTBD (Barido-Sottani et al. 2020 - Syst. Biol.)? The\nchoice of one method over another—while potentially justified—can have a significant impact\non estimates of branch-specific diversification rates (Martínez-Gómez et al. 2024 - Evol. Lett.).\nWhile we do not expect the authors to repeat all their analyses using multiple methods (as we\nalready suggest several additional analyses), we encourage them to explore ways to cross-\nvalidate their results.\nWe favored the use of BAMM to carry out diversification analyses as, as noted by the\nreviewers, it is easily integrated with the STRAPP tests we performed in downstream\nanalyses. This choice is motivated by the adequation of BAMM outputs with the type of\ntests and analyses we aimed to perform for this study. Beyond facilitating the use of\nSTRAPP tests, BAMM also allowed us to carry out the uncertainty of diversification histories\nin downstream analyses such as the estimation of the evolution of rates through times (Fig.\n5), and the mapping of longitudinal and latitudinal gradients of diversification rates\n(Supplementary Figure S9). We are aware of the existence of the alternative approaches\nlisted by the reviewers, but as noted by Reviewer #2: “there would be alternative methods\navailable, but these are not necessarily better than the ones used here.”. It is beyond the\nscope of this study to compare the multiple methods existing to produce the type of\ndiversification analyses included as a key step in our analytic pipeline, especially in a\ncontext of a study dealing with large datasets incurring thousands of CPU-hours of\ncomputation. To our knowledge, the standard for recent studies with similar scope is to rely\non BAMM for diversification analyses and sometimes DR statistics to quickly estimate tip-rates\n(Jetz et al., 2012) without exploring many alternatives (Cooney & Thomas, 2021; Chazot et\nal., 2021; Kawahara et al., 2023; Thompson et al., 2024). However, we do agree with the\nreviewers that we should aim to set the bar higher and improve those standards by\nencouraging sensitivity analyses, when those can be achieved without an excessive time and\ncomputational burden.\nFor this reason, we indeed explored ways to cross-validate our results using an\nalternative approach to BAMM that did not require much more computational time.\nFollowing the framework introduced by Chazot et al., 2021, we performed an entire set of\nanalyses based on Regional In-situ Radiation (RISR) that aimed to compare diversification\ndynamics identified within clades largely restricted to a single bioregion using the R package\ndiversitree (See Supplementary Information - Section 9.2 RISR analyses). Those analyses\nindeed showed that slightly significant diversification dynamics could be identified across\nbioregions, which the STRAPP tests conducted on BAMM posterior samples could not detect.\nFor instance, we showed that the recent Australasian radiations are experiencing currently\nhigher rates of diversification induced by significant rate increases over time (Fig. 6). This led\nto a key point of our conclusion, highlighting that if differences in rates could exist between\nbioregions, they tend to mitigate heterogeneity in diversity patterns due to differences in timing\nof colonizations (TfAH) rather than being their origin as postulated in the DRH (Table 1; L.\n663-673).\nAdditionally, we also explored GeoSSE and GeoHiSSE approaches (not reported in the\nmanuscript) as alternative ways to investigate differences in diversification rates across\nbioregions. Although these approaches do not allow us to test for differences at different time-\nstep as our modified STRAPP analyses do, we perceived them as a useful additional layer of\nsensitivity testing to add to our study. However, we had to limit tests to the comparison of the\nNew World vs. Old World (i.e., longitudinal gradient) to limit parameter inflation. We obtained\nhigher support for a Character InDependent model with two regimes (CID-2) than for a\nGeoSSE model. Moreover, the highest support was found for a Character InDependent model\nwith four regimes (CID-4: the null model against which GeoHiSSE is compared) supporting\nour conclusions that no difference of rates across bioregions (here, New World vs. Old World)\nare detected (See Table R1 below). However, we did not report those results in the\nmanuscript because we detected unrealistic parameter estimates in our models with\nabnormally high vicariance rates (> 100 events per lineage per My) contrasted with abnormally\nlow range shift rates (< 0.002 events per lineage per My), while multiple models yielding null\nspeciation or extinction rates in some regimes. We report here those ill-fitted results to\nillustrate to the reviewers that we indeed explored multiple ways to cross-validate our\nresults with a wealth of complementary approaches and strategies.\nFollowing the reviewers’ suggestions, we now more clearly emphasize the rationale behind\nour use of RISR analyses as a means to cross-validate results from BAMM using an\nindependent approach in the Supplementary Methods (See Section 8.2 BAMM\ndiversification rates). We believe this clarification highlights our efforts to assess the\nrobustness of our conclusions in meaningful ways.\nTable R1: GeoSSE model summaries\nHidde Div. Nb Δ Akaike'\nRan\nModel Type n regime param logLk AICc AIC s\nk\nstates s s c weights\n1030\n1028\n1018\nGeoHiSS 1015\nIndependen 1014\nReference:\nChazot, N., Condamine, F. L., Dudas, G., Peña, C., Kodandaramaiah, U., Matos-Maraví, P.,\n... & Wahlberg, N. (2021). Conserved ancestral tropical niche but different continental\nhistories explain the latitudinal diversity gradient in brush-footed butterflies. Nature\ncommunications, 12(1), 5717. https://doi.org/10.1038/s41467-021-25906-8\nCooney, C. R., & Thomas, G. H. (2021). Heterogeneous relationships between rates of\nspeciation and body size evolution across vertebrate clades. Nature Ecology & Evolution,\n5(1), 101-110. https://doi.org/10.1038/s41559-020-01321-y\nJetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds\nin space and time. Nature 491, 444–448 (2012). https://doi.org/10.1038/nature11631\nKawahara, A. Y., Storer, C., Carvalho, A. P. S., Plotkin, D. M., Condamine, F. L., Braga, M.\nP., ... & Lohman, D. J. (2023). A global phylogeny of butterflies reveals their evolutionary\nhistory, ancestral hosts and biogeographic origins. Nature ecology & evolution, 7(6), 903-\n913. https://doi.org/10.1038/s41559-023-02041-9\nThompson, J. B., Hernández-Hernández, T., Keeling, G., Vásquez-Cruz, M., & Priest, N. K.\n(2024). Identifying the multiple drivers of cactus diversification. Nature Communications,\n15(1), 7282. https://doi.org/10.1038/s41467-024-51666-2\nAdditional visualizations of BAMM outputs would also be beneficial. At the very least, a tree\ndisplaying BAMM-derived rates should be included in the supplementary material, along with\na figure assessing MCMC convergence for the BAMM run.\nWe now include a tree displaying BAMM rates and regime shifts identified on the MCC tree in\nSupplementary Figure S14. We report the details of the major regime shifts identified from\nthe Maximum Shift Credibility (MSC) configuration in Supplementary Table S10 and in the\nMain text (L. 484-487). We also provide time-calibrated phylogenies displaying the median net\ndiversification rates as estimated for the two alternative divergence dating hypotheses used\nfor sensitivity analyses (Supplementary Figure S15).\nAdditionally, we now provide a figure assessing the MCMC convergence of the BAMM run in\nSupplementary Figure S16 as suggested by the reviewers.\nWe are aware that we have suggested a number of possible additional analyses here, but\nthese are mostly aimed at strengthening the manuscript and better exploring the limits and\nsensitivity of their results. Finally, we would like to congratulate the authors on their work,\nwhich already includes several analyses aimed at better understanding the distribution pattern\nof Ponerinae.\nWe have done our best to include all possible relevant alternative analyses suggested by the\nreviewers, within the limit of our time and computational resources. We believe these\nadditions, combined with the clarification of key methodological choices, have resulted in what\nis, to our knowledge, the most thorough and careful examination to date of the limits and\nsensitivity of analyses of biogeographic and diversification dynamics for a diverse,\ncosmopolitan taxonomic group. We hope that we have respectfully and convincingly\nexplained why certain proposed alternative methods were ultimately not adopted because: (1)\nthey would lessen the careful rationale that guided our selection of the most appropriate\napproach for each analytic step, (2) they would require computational efforts far beyond\nfeasible limits, that are not in line with the standards in recent analyses of similar scale and\nscope. As stated in the preamble, we sincerely believe that, thanks to the reviewers’ insightful\nsuggestions, these additions have strengthened our study. We feel that the revised manuscript\nnow offers an even more robust and transparent blueprint for macroevolutionary\nanalyses that will hopefully guide many future works.\nMinor comments:\n· L 98: Kachin amber from Myanmar (Late Cretaceous: 94-100 My)\nRadiometric data established an early Cenomanian age (98.79 ± 0.62 Ma) for Kachin amber,\nbased on zircons from volcanic clasts found within the amber-bearing sediments (Shi et al.\n2012 - Cret. Res.). Some ammonites found in the amber-bearing bed and within amber\ncorroborate a late Albian–early Cenomanian age (Cruickshank & Ko 2003 - J. Asian Earth\nSci.).\nWe replaced the estimated age for the Kachin amber with the estimates given in Shi et al.\n2012 - Cret. Res. as suggested by the reviewers (L. 108, 683 & 703).\n· L 197: “we designed a custom screening procedure to detect bioregional outliers and\nremoved dubious records falling in rarely documented bioregions”\nHow many dubious records and bioregions are we talking about?\nDuring this curation step, 256 occurrences belonging to 101 taxa have been removed. They\nwere first flagged because they were falling into a bioregion that represented less than 20%\nof the records and removed because they were assessed as dubious according to our\nexpertise and available literature. We added those numbers in the Main text (L. 220) and in\nthe dedicated section of the Supplementary Methods (Section 6.1 Data sources and\ncuration).\n· L 305-307: This estimate falls within the range of previous estimates and dates the\nemergence of the extant ponerine ant clade during the Early Cretaceous, coinciding with the\ntiming of origins of other ant subfamilies.\nThis sentence is not correct. The range is not in agreement with the ages from a few recent\nand older studies: 73 (61–84) in Borowiec et al. (2019 - MPE) or 56–60 in Moreau and Bell\n(2013 - Evolution).\nWe added “upper” as in “This estimate falls within the upper range of previous estimates and\ndates the emergence of the extant ponerine ant clade during the Early Cretaceous, coinciding\nwith the timing of origins of other ant subfamilies.” (L. 339). We mean here that our estimates\nfall in the upper range of all the estimates, not within the range of each study, which is\nimpossible as those range estimates contradict each other (Ex: Moreau and Bell, 2013: 54–\n66 vs. Economo et al., 2018: 106–130)\nReferences:\nEconomo, E.P., Narula, N., Friedman, N.R., Weiser, M.D., & Guénard, B. (2018).\nMacroecology and macroevolution of the latitudinal diversity gradient in ants. Nature\ncommunications, 9(1), 1778. https://doi.org/10.1038/s41467-018-04218-4\nMoreau, C. S., & Bell, C. D. (2013). Testing the museum versus cradle tropical biological\ndiversity hypothesis: phylogeny, diversification, and ancestral biogeographic range\nevolution of the ants. Evolution, 67(8), 2240-2257. https://doi.org/10.1111/evo.12105\n· L 427: How many diversification shifts were identified in your BAMM analysis, and where are\nthey located on the phylogenetic tree?\nWe recovered on average 12.1 ± 4.1 regime shifts in diversification dynamics along ponerine\nant history whose seven concerned major radiations. We now report on the number of regime\nshifts recovered in the BAMM posterior samples in the Main text (L. 484-487) and in the\ndedicated section in Supplementary Methods (Section 8.2 BAMM diversification rates) and\nprovide a Supplementary Table S10 summarizing metadata of regime shifts from the\nMaximum Shift Credibility (MSC) configuration.\n· L. 443: Is the RNW value correct? (seems wrong when compared to Fig. 5A)\nThanks for spotting the typo! We corrected the current net diversification rates to 0.0982\n(instead of 0.1082) as shown in Fig. 5A. The results of the test and the conclusion remain\nunchanged (L. 496).\n· The authors seem to equate in-situ diversification with endemism, which are two very\ndifferent concepts. A few sentences of the manuscript should be rephrased accordingly.\nAll mentions of endemism have been replaced with the term autochthonous lineages, which\nconveys more precisely the idea of “originating from the same location/area/bioregion” (L. 321,\n544-553 in Main text; and in Section 9.3. Quantifying lineage origins through time of\nSupplementary Methods). Additionally, in the caption of Fig. 7, we can now read: “The opaque\nplain lines show the average proportions of autochthonous lineages recorded across\nsimulations” instead of “The opaque plain lines show the average proportions of endemicity\nrecorded across simulations”.\nReviewer #2 (Remarks to the Author):\nDear Editor,\nIn their manuscript \"Timing is everything: Evolution of ponerine ants highlights how dispersal\nhistory shapes modern biodiversity\" Doré et al. present the results of a thorough phylogeny-\nbased analysis of the global historical biogeography of ants in the subfamily Ponerinae.\nFor their study, the authors first reconstructed phylogenetic relationships and divergence times\namong 789 ponerine species and morphospecies using phylogenomic data. Then, they\ngrafted missing species on the tree based on taxonomic background data, to obtain a \"full\"\n1534-tip tree for ponerine ants. The main analyses are based on the best full dated tree as\nwell as 1000 posterior \"full\" trees sampled for estimating the effects of phylogenetic\nuncertainty. The authors then estimated ancestral geographic ranges across the fossil-\ncalibrated trees based on an extensive global database of the distributions of extant ponerine\nspecies. These ancestral reconstructions are combined with phylogeny-based inferences of\ndiversification rates in past and present lineages, to infer support for three different hypotheses\non the distribution of species diversity observed in the group today. The main conclusion is\nthat the latitudinal gradient seen in the diversity of extant ponerine ants is mainly the result of\na tendency to remain in the ancestral tropical habitat (tropical niche conservatism), while\nasymmetric dispersal and differences in diversification rates have a minor role. The results are\ndiscussed in relation to diversity patterns found in other taxa.\nI find the study highly interesting and impressive. The breadth of phylogenetic and statistical\nmethods covered by the work is breathtaking, but Supplementary figures S1 and S2 do a good\njob in explaining how the phylogenetic and biogeographic datasets used in the different\nanalyses were compiled.\nI might have commented on the restriction to net diversification rates and the existence of\nmegathermal forests in the northern Hemisphere during the early Cenozoic, but both of these\nissues are in fact dealt with in the end of the Discussion. These challenges are by no means\nrestricted to this study, and I find the caveats given in the text sufficient.\nI cannot claim to be a specialist in all aspects of the highly diverse statistical analyses, but the\nmethods used in the parts that I have experience with seem to be up to date, logical and\njustified. In some cases, there would be alternative methods available, but these are not\nnecessarily better than the ones used here. The combined analyses and plots of results from\ndifferent statistical methods are highly innovative. To me it seems that such approaches might\nbe of use also in fields not directly related to biogeography.\nBased on the importance of the results and high quality of the data and analyses of the study,\nI recommend publication of the manuscript as-is. The supplementary information and scripts\nand data files in databases appear to contain all information necessary for replicating the\nanalyses and results. The manuscript package is so carefully prepared that I struggled to find\neven small mistakes or typos from the text, these are listed below.\nWe thank reviewer #2 for their overall positive review of our manuscript. We particularly\nappreciate their recognition of the methodological breadth and the innovative nature of our\nanalytical framework. Below, we provide detailed, point-by-point responses to all the minor\ncorrections and suggestions raised by the reviewer.\nMinor comments:\nWe accepted all the following orthographic and grammar suggestions in the new revised\nversion of the manuscript.\nR40: drivers -> driver?\nR59: \"by using\"?\nR67: Split paragraph before \"The\"?\nR113: \"that\" -> \", which\"?\nR165: 2 x \"in\"\nFigure 3: Is \"Sources\" the correct word in (E) and (F)?\nWe changed “Sources” for “Location” in panels E and F of Fig. 3 and new Supplementary\nFig. S12 to avoid possible confusion with the dispersal events shown in panels A and B.\nR579: \"Ponerine\" -> \"Ponerines\"\n- The \"Wilcox test\" is mentioned in the text and supplementary methods, I believe this should\nbe spelled out as \"Wilcoxon test\" when used in text?\nWe corrected the name of the tests in both the Main text (L. 314 & 569) and the Supplementary\nMethods.\n- In the Supplementary information, one sentence seems to be essentially repeated twice in\nthe Phylogenetic inference section: \"Among the three partitioning schemes, the SWSC-En\nwith merging scheme had the highest likelihood and was selected as the best result for\ndownstream analyses. Among the three partitioning schemes, the SWSC with merging\nscheme had the highest log-likelihood and was selected as the best result for downstream\nanalyses (unpartitioned: -37.82 × 106, by locus with merging: -37.30 × 106, SWSC-EN with\nmerging: -37.08 × 106).\"\nThank you for spotting this mistake. We removed the duplicated sentence in the associated\nsection.\n- In the Reporting Summary, the Reporting on sex section contains the sentence \"Sex-based\nanalyses are not required for this type of phylogenomic study as no sex-linked loci were\nstudied.\" Here it could be specified that ants (and other hymenopterans) have a haplodiploid\nsex determination system, so sex-linked loci technically do not exist.\nWe updated the Reporting Summary to the following: “Sex-based analyses are not required\nfor this type of phylogenomic study as ants (and other hymenopterans) have a haplodiploid\nsex determination system, therefore sex-linked loci do not exist in ants.”\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nPlease, see our replies to the shared review in the comments from Reviewer #1."}, {"round": 2, "reviewer_comments": {"reviewer_1": "As emphasised in the initial reviews, the authors present an impressive and comprehensive phylogeny of the ant subfamily Ponerinae, which they use for macroevolutionary analyses. The dataset and manuscript were already of a very high standard, and the authors have improved them further in this revised version.\n\nWe were aware that we had suggested many possible avenues and did not necessarily expect the authors to pursue all of them, given that our perspectives, preferences and opinions may differ. Our main motivation was to further increase clarity in the rationale and methodological choices, and support for the results and conclusions.\n\nI would like to commend the authors for all their hard work and attention to detail in revising this version. In their comprehensive reply letter, the authors convincingly argued and explained their choices. They responded to all our comments and suggestions carefully, which I really appreciated, even when they disagreed with us or were unable to follow some of our suggestions. I am also grateful that the authors seized several opportunities to create an even more robust and compelling piece of work.\n\nI have no further comments and would like to congratulate the authors on their work. Well done!", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter