Till Ramm, Jaimi A Gray, Christy A Hipsley, Scott Hocknull, Jane Melville, Johannes Müller
Comparisons of extant and extinct biodiversity are often dependent on objective morphology-based identifications of fossils and assume a well-established and comparable taxonomy for both fossil and modern taxa. However, since many modern (cryptic) species are delimitated mainly via external morphology and/or molecular data, it is often unclear to what degree fossilized (osteological) remains allow classification to a similar level. When intraspecific morphological variation in extant taxa is poorly known, the definition of extinct species as well as the referral of fossils to extant species can be heavily biased, particularly if fossils are represented by incomplete isolated skeletal elements. This problem is especially pronounced in squamates (lizards and snakes) owing to a lack of osteological comparative knowledge for many lower taxonomic groups, concomitant with a recent increase of molecular studies revealing great cryptic diversity. Here, we apply a quantitative approach using 3D geometric morphometrics on 238 individuals of 14 genera of extant Australian and Papua New Guinean agamid lizards to test the value of 2 isolated skull bones (frontals and maxillae) for inferring taxonomic and ecological affinities. We further test for the consistency of intra- and interspecific morphological variability of these elements as a proxy for extinct taxonomic richness. We show that both bones are diagnostic at the generic level, and both can infer microhabitat and are of paleoecological utility. However, species-level diversity is likely underestimated by both elements, with ~30-40% of species pairs showing no significant differences in shape. Mean intraspecific morphological variability is largely consistent across species and bones and thus a useful proxy for extinct species diversity. Reducing sample size and landmark completeness to approximate fossil specimens led to decreased classification accuracy and increased variance of morphological disparity, raising further doubts on the transferability of modern species borders to the fossil record of agamids. Our results highlight the need to establish appropriate levels of morphology-based taxonomic or ecological groupings prior to comparing extant and extinct biodiversity.
{"title":"Are Modern Cryptic Species Detectable in the Fossil Record? A Case Study on Agamid Lizards.","authors":"Till Ramm, Jaimi A Gray, Christy A Hipsley, Scott Hocknull, Jane Melville, Johannes Müller","doi":"10.1093/sysbio/syae067","DOIUrl":"10.1093/sysbio/syae067","url":null,"abstract":"<p><p>Comparisons of extant and extinct biodiversity are often dependent on objective morphology-based identifications of fossils and assume a well-established and comparable taxonomy for both fossil and modern taxa. However, since many modern (cryptic) species are delimitated mainly via external morphology and/or molecular data, it is often unclear to what degree fossilized (osteological) remains allow classification to a similar level. When intraspecific morphological variation in extant taxa is poorly known, the definition of extinct species as well as the referral of fossils to extant species can be heavily biased, particularly if fossils are represented by incomplete isolated skeletal elements. This problem is especially pronounced in squamates (lizards and snakes) owing to a lack of osteological comparative knowledge for many lower taxonomic groups, concomitant with a recent increase of molecular studies revealing great cryptic diversity. Here, we apply a quantitative approach using 3D geometric morphometrics on 238 individuals of 14 genera of extant Australian and Papua New Guinean agamid lizards to test the value of 2 isolated skull bones (frontals and maxillae) for inferring taxonomic and ecological affinities. We further test for the consistency of intra- and interspecific morphological variability of these elements as a proxy for extinct taxonomic richness. We show that both bones are diagnostic at the generic level, and both can infer microhabitat and are of paleoecological utility. However, species-level diversity is likely underestimated by both elements, with ~30-40% of species pairs showing no significant differences in shape. Mean intraspecific morphological variability is largely consistent across species and bones and thus a useful proxy for extinct species diversity. Reducing sample size and landmark completeness to approximate fossil specimens led to decreased classification accuracy and increased variance of morphological disparity, raising further doubts on the transferability of modern species borders to the fossil record of agamids. Our results highlight the need to establish appropriate levels of morphology-based taxonomic or ecological groupings prior to comparing extant and extinct biodiversity.</p>","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":" ","pages":"373-394"},"PeriodicalIF":6.1,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142688897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maximum Likelihood (ML) based phylogenetic inference constitutes a challenging optimization problem. Given a set of aligned input sequences, phylogenetic inference tools strive to determine the tree topology, the branch-lengths, and the evolutionary model parameters that maximize the phylogenetic likelihood function. However, there exist compelling reasons to not push optimization to its limits, by means of early, yet adequate stopping criteria. Since input sequences are typically subject to stochastic and systematic noise, caution is warranted to prevent over-optimization and the risk of overfitting the model to noisy data. To address this, we integrate the Kishino-Hasegawa (KH) test into RAxML-NG as a reliable and fast-to-compute Early Stopping criterion to effectively limit excessive and compute-intensive over-optimization. Initially, we introduce a simplified heuristic tree search strategy in RAxML-NG (sRAxML-NG) as an underlying method for Early Stopping. Subsequently, we use the KH test in combination with sRAxML-NG, to statistically assess the significance of differences between intermediate trees prior to and after major optimization steps. The tree search terminates early when improvements are statistically insignificant. We also propose an extension to the standard KH test that allows to correct for multiple testing, which maintains accuracy while achieving even higher speedups. For benchmarking we use 300 large representative empirical datasets from TreeBASE. For 98% of the DNA datasets, all Early Stopping methods we introduce infer trees that are statistically equivalent to those inferred from RAxML-NG v1.2. For AA datasets, the fraction of datasets where sRAxML-NG, KH, and the KH-multiple testing versions infer statistically equivalent trees is 96%, 95%, and 92%, respectively. In conjuction with sRAxML-NG, the average speedup achieved by the KH-multiple testing version is 5x for DNA and 3.9x for protein datasets compared to RAxML-NG v1.2. We implemented our stopping criteria in RAxML-NG, which is available under GNU GPL at https://github.com/togkousa/raxml-ng/tree/stopping-criteria.
{"title":"Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization","authors":"Anastasis Togkousidis, Alexandros Stamatakis, Olivier Gascuel","doi":"10.1093/sysbio/syaf043","DOIUrl":"https://doi.org/10.1093/sysbio/syaf043","url":null,"abstract":"Maximum Likelihood (ML) based phylogenetic inference constitutes a challenging optimization problem. Given a set of aligned input sequences, phylogenetic inference tools strive to determine the tree topology, the branch-lengths, and the evolutionary model parameters that maximize the phylogenetic likelihood function. However, there exist compelling reasons to not push optimization to its limits, by means of early, yet adequate stopping criteria. Since input sequences are typically subject to stochastic and systematic noise, caution is warranted to prevent over-optimization and the risk of overfitting the model to noisy data. To address this, we integrate the Kishino-Hasegawa (KH) test into RAxML-NG as a reliable and fast-to-compute Early Stopping criterion to effectively limit excessive and compute-intensive over-optimization. Initially, we introduce a simplified heuristic tree search strategy in RAxML-NG (sRAxML-NG) as an underlying method for Early Stopping. Subsequently, we use the KH test in combination with sRAxML-NG, to statistically assess the significance of differences between intermediate trees prior to and after major optimization steps. The tree search terminates early when improvements are statistically insignificant. We also propose an extension to the standard KH test that allows to correct for multiple testing, which maintains accuracy while achieving even higher speedups. For benchmarking we use 300 large representative empirical datasets from TreeBASE. For 98% of the DNA datasets, all Early Stopping methods we introduce infer trees that are statistically equivalent to those inferred from RAxML-NG v1.2. For AA datasets, the fraction of datasets where sRAxML-NG, KH, and the KH-multiple testing versions infer statistically equivalent trees is 96%, 95%, and 92%, respectively. In conjuction with sRAxML-NG, the average speedup achieved by the KH-multiple testing version is 5x for DNA and 3.9x for protein datasets compared to RAxML-NG v1.2. We implemented our stopping criteria in RAxML-NG, which is available under GNU GPL at https://github.com/togkousa/raxml-ng/tree/stopping-criteria.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"12 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Studying rates of species diversification is one of the key themes in macroevolution. In particular, we are interested in if some clades in a phylogeny diversify more rapidly/slowly than others due to branch-specific diversification rates. A common approach in neontological studies is to use a phylogenetic birth-death process to model species diversification. Specifically, the birth-death-shift process is used to model branch-specific shifts in the tempo of diversification. Here, we present Pesto, a new method and software that estimates branch-specific diversification rates using an empirical Bayes approach. Pesto does not rely on Markov chain Monte Carlo simulations and instead deterministically computes the posterior mean branch-specific diversification rates using only two traversals of the tree. This method is blazingly fast: the birth-death-shift model can be fitted to large phylogenies (>20k taxa) in minutes on a personal computer while also providing branch-specific inference of diversification rate shift events. Thus, we can robustly infer branch-specific diversification rates and the number of diversification rate shift events for large-scale phylogenies, as well as exploring the characteristics of the birth-death-shift model through complex and large-scale simulations. Here, we first describe the method and the software implementation Pesto and explore its behavior using trees simulated under the birth-death-shift model. Then, we explore the behavior of inferring significant branch-specific diversification rate shifts using both Bayes factors and effect sizes. We find few to no false positive inferences of diversification rate shift events but many false negatives (reduced power). The most difficult parameter to estimate is the rate at which diversification rate shifts occur. Despite this, branch-specific diversification rate estimates are precise and nearly unbiased.
{"title":"Phylogenetic Estimation of Branch-specific Shifts in the Tempo of Origination","authors":"Bjørn T Kopperud, Sebastian Höhna","doi":"10.1093/sysbio/syaf041","DOIUrl":"https://doi.org/10.1093/sysbio/syaf041","url":null,"abstract":"Studying rates of species diversification is one of the key themes in macroevolution. In particular, we are interested in if some clades in a phylogeny diversify more rapidly/slowly than others due to branch-specific diversification rates. A common approach in neontological studies is to use a phylogenetic birth-death process to model species diversification. Specifically, the birth-death-shift process is used to model branch-specific shifts in the tempo of diversification. Here, we present Pesto, a new method and software that estimates branch-specific diversification rates using an empirical Bayes approach. Pesto does not rely on Markov chain Monte Carlo simulations and instead deterministically computes the posterior mean branch-specific diversification rates using only two traversals of the tree. This method is blazingly fast: the birth-death-shift model can be fitted to large phylogenies (&gt;20k taxa) in minutes on a personal computer while also providing branch-specific inference of diversification rate shift events. Thus, we can robustly infer branch-specific diversification rates and the number of diversification rate shift events for large-scale phylogenies, as well as exploring the characteristics of the birth-death-shift model through complex and large-scale simulations. Here, we first describe the method and the software implementation Pesto and explore its behavior using trees simulated under the birth-death-shift model. Then, we explore the behavior of inferring significant branch-specific diversification rate shifts using both Bayes factors and effect sizes. We find few to no false positive inferences of diversification rate shift events but many false negatives (reduced power). The most difficult parameter to estimate is the rate at which diversification rate shifts occur. Despite this, branch-specific diversification rate estimates are precise and nearly unbiased.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"146 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Analysis of genomic data in the past two decades has highlighted the prevalence of introgression as an important evolutionary force in both plants and animals. The genus Drosophila has received much attention recently, with an analysis of genomic sequence data revealing widespread introgression across the species phylogeny for the genus. However, the methods used in the study are based on data summaries for species triplets and are unable to infer gene flow between sister lineages or to identify the direction of gene flow. Hence, we reanalyze a subset of the data using the Bayesian program bpp, which is a full-likelihood implementation of the multispecies coalescent model and can provide more powerful inference of gene flow between species, including its direction, timing, and strength. While our analysis supports the presence of gene flow in the species group, the results differ from the previous study: we infer gene flow between sister lineages undetected previously whereas most gene-flow events inferred in the previous study are rejected in our tests. To verify our conclusions, we performed simulations to examine the properties of Bayesian and summary methods. Bpp was found to have high power to detect gene flow, high accuracy in estimated rates of gene flow, and robustness under misspecification of the mode of gene flow. In contrast, summary methods had low power and produced biased estimates of introgression probability. Our results highlight an urgent need for improving the statistical properties of summary methods and the computational efficiency of likelihood methods for inferring gene flow using genomic sequence data.
{"title":"Inference of Cross-Species Gene Flow Using Genomic Data Depends on the Methods: Case Study of Gene Flow in Drosophila.","authors":"Jiayi Ji,Thomas Roberts,Tomáš Flouri,Ziheng Yang","doi":"10.1093/sysbio/syaf019","DOIUrl":"https://doi.org/10.1093/sysbio/syaf019","url":null,"abstract":"Analysis of genomic data in the past two decades has highlighted the prevalence of introgression as an important evolutionary force in both plants and animals. The genus Drosophila has received much attention recently, with an analysis of genomic sequence data revealing widespread introgression across the species phylogeny for the genus. However, the methods used in the study are based on data summaries for species triplets and are unable to infer gene flow between sister lineages or to identify the direction of gene flow. Hence, we reanalyze a subset of the data using the Bayesian program bpp, which is a full-likelihood implementation of the multispecies coalescent model and can provide more powerful inference of gene flow between species, including its direction, timing, and strength. While our analysis supports the presence of gene flow in the species group, the results differ from the previous study: we infer gene flow between sister lineages undetected previously whereas most gene-flow events inferred in the previous study are rejected in our tests. To verify our conclusions, we performed simulations to examine the properties of Bayesian and summary methods. Bpp was found to have high power to detect gene flow, high accuracy in estimated rates of gene flow, and robustness under misspecification of the mode of gene flow. In contrast, summary methods had low power and produced biased estimates of introgression probability. Our results highlight an urgent need for improving the statistical properties of summary methods and the computational efficiency of likelihood methods for inferring gene flow using genomic sequence data.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"12 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Harald Letsch,Carola Greve,Anna K Hundsdoerfer,Iker Irisarri,Jenna M Moore,Marianne Espeland,Stefan Wanke,Umilaela Arifin,Mozes P K Blom,Carolina Corrales,Alexander Donath,Uwe Fritz,Gunther Köhler,Patrick Kück,Sarah Lemer,Ximo Mengual,Nancy Mercado Salas,Karen Meusemann,Anja Palandačić,Christian Printzen,Julia D Sigwart,Karina L Silva-Brandão,Marianna Simões,Madlen Stange,Alexander Suh,Nikolaus Szucsich,Ekin Tilic,Till Töpfer,Astrid Böhne,Axel Janke,Steffen Pauls
Name-bearing type specimens have a fundamental role in characterising biodiversity, as these objects represent the physical link between a scientific name and the biological organism. Type specimens are usually deposited in natural history collections, which provide key infrastructure for research on essential biological structures and processes, while preserving records of biodiversity for future generations. Modern systematics increasingly depends on genetic and genomic data to differentiate and characterise species. While the results of genome sequencing are often connected to a physical voucher specimen, they are rarely derived from the ultimate taxonomic reference for a species, i.e., the name-bearing type specimens. This is a known but under-appreciated problem for ensuring the replicability of findings, especially those that affect the interpretation of biodiversity distributions and phylogenetic relationships. Destructive sampling of museum specimens, particularly of type material, often carries a high risk of sequencing failure, and thus the cost of damage to the specimen may outweigh the resulting benefit. Both taxonomic work and genome sequencing require specialist skills and there are often communication gaps between the respective experts. A new, harmonised approach, maximising information extraction while minimising risk to type specimens, is a critical step forward toward linking disciplines across biodiversity research and promoting a better taxonomic and systematic understanding of eukaryotic diversity. The genetic make-up of a type specimen is a fundamental part of its biological information, which can and should be made freely and digitally available through type genomics. Here we describe guidelines for the use of nomenclatural types in genome sequencing approaches considering different kinds of types in different stages of preservation and different data types.
{"title":"Type genomics: a Framework for integrating Genomic Data into Biodiversity and Taxonomic research.","authors":"Harald Letsch,Carola Greve,Anna K Hundsdoerfer,Iker Irisarri,Jenna M Moore,Marianne Espeland,Stefan Wanke,Umilaela Arifin,Mozes P K Blom,Carolina Corrales,Alexander Donath,Uwe Fritz,Gunther Köhler,Patrick Kück,Sarah Lemer,Ximo Mengual,Nancy Mercado Salas,Karen Meusemann,Anja Palandačić,Christian Printzen,Julia D Sigwart,Karina L Silva-Brandão,Marianna Simões,Madlen Stange,Alexander Suh,Nikolaus Szucsich,Ekin Tilic,Till Töpfer,Astrid Böhne,Axel Janke,Steffen Pauls","doi":"10.1093/sysbio/syaf040","DOIUrl":"https://doi.org/10.1093/sysbio/syaf040","url":null,"abstract":"Name-bearing type specimens have a fundamental role in characterising biodiversity, as these objects represent the physical link between a scientific name and the biological organism. Type specimens are usually deposited in natural history collections, which provide key infrastructure for research on essential biological structures and processes, while preserving records of biodiversity for future generations. Modern systematics increasingly depends on genetic and genomic data to differentiate and characterise species. While the results of genome sequencing are often connected to a physical voucher specimen, they are rarely derived from the ultimate taxonomic reference for a species, i.e., the name-bearing type specimens. This is a known but under-appreciated problem for ensuring the replicability of findings, especially those that affect the interpretation of biodiversity distributions and phylogenetic relationships. Destructive sampling of museum specimens, particularly of type material, often carries a high risk of sequencing failure, and thus the cost of damage to the specimen may outweigh the resulting benefit. Both taxonomic work and genome sequencing require specialist skills and there are often communication gaps between the respective experts. A new, harmonised approach, maximising information extraction while minimising risk to type specimens, is a critical step forward toward linking disciplines across biodiversity research and promoting a better taxonomic and systematic understanding of eukaryotic diversity. The genetic make-up of a type specimen is a fundamental part of its biological information, which can and should be made freely and digitally available through type genomics. Here we describe guidelines for the use of nomenclatural types in genome sequencing approaches considering different kinds of types in different stages of preservation and different data types.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"55 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Models used in likelihood-based morphological phylogenetics often adapt molecular phylogenetics models to the specificities of morphological data. Such is the case for the widely used Mkv model—which introduces an acquisition bias correction for sampling only characters that are observed to be variable—and for models of among-character rate variation (ACRV), routinely applied by researchers to relax the equal-rates assumption of Mkv. However, the interaction between variable character acquisition bias and ACRV has never been explored before. We demonstrate that there are two distinct approaches to condition the likelihood on variable characters when there is ACRV, and we call them joint and marginal acquisition bias. Far from being just a trivial mathematical detail, we show that the way in which the variable character conditional likelihood is calculated results in different assumptions about how rate variation is distributed in morphological datasets. Simulations demonstrate that tree length and amount of ACRV in the data are systematically biased when conditioning on variable characters differently from how the data was simulated. Moreover, an empirical case study with extant and extinct taxa reveals a potential impact not only on the estimation of branch lengths, but also of phylogenetic relationships. We recommend the use of the marginal acquisition bias approach for morphological datasets modeled with ACRV. Finally, we urge developers of phylogenetic software to clarify which acquisition bias correction is implemented for both estimation and simulation, and we discuss the implications of our findings on modeling variable characters for the future of morphological phylogenetics.
{"title":"On the Mkv Model with Among-Character Rate Variation","authors":"Alessio Capobianco, Sebastian Höhna","doi":"10.1093/sysbio/syaf038","DOIUrl":"https://doi.org/10.1093/sysbio/syaf038","url":null,"abstract":"Models used in likelihood-based morphological phylogenetics often adapt molecular phylogenetics models to the specificities of morphological data. Such is the case for the widely used Mkv model—which introduces an acquisition bias correction for sampling only characters that are observed to be variable—and for models of among-character rate variation (ACRV), routinely applied by researchers to relax the equal-rates assumption of Mkv. However, the interaction between variable character acquisition bias and ACRV has never been explored before. We demonstrate that there are two distinct approaches to condition the likelihood on variable characters when there is ACRV, and we call them joint and marginal acquisition bias. Far from being just a trivial mathematical detail, we show that the way in which the variable character conditional likelihood is calculated results in different assumptions about how rate variation is distributed in morphological datasets. Simulations demonstrate that tree length and amount of ACRV in the data are systematically biased when conditioning on variable characters differently from how the data was simulated. Moreover, an empirical case study with extant and extinct taxa reveals a potential impact not only on the estimation of branch lengths, but also of phylogenetic relationships. We recommend the use of the marginal acquisition bias approach for morphological datasets modeled with ACRV. Finally, we urge developers of phylogenetic software to clarify which acquisition bias correction is implemented for both estimation and simulation, and we discuss the implications of our findings on modeling variable characters for the future of morphological phylogenetics.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"33 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael B J Kelly, Shahan Derkarabetian, Donald James McLean, Ryan Shofner, Cristian J Grismado, Charles R Haddad, Gerasimos Cassis, Gonzalo Giribet, Marie E Herberstein, Jonas O Wolff
Batesian mimicry is an impressive example of convergent evolution driven by predation. However, the observation that many mimics only superficially resemble their models despite strong selective pressures is an apparent paradox. Here, we tested the ‘perfecting hypothesis’, that posits that inaccurate mimicry may represent a transitional stage at the macro-evolutionary scale by performing the hereto largest phylogenetic analysis (in terms of the number of taxa and genetic data) of ant-mimicking spiders across two speciose but independent clades, the jumping spider tribe Myrmarachnini (Salticidae) and the sac spider sub-family Castianeirinae (Corinnidae). We found that accurate ant mimicry evolved in a gradual process in both clades, by an integration of compound traits contributing to the ant-like habitus with each trait evolving at different speeds. Accurate states were highly unstable at the macro-evolutionary scale likely because strong expression of some of these traits comes with high fitness costs. Instead, the inferred global optimum of mimicry expression was at an inaccurate state. This result reverses the onus of explanation from inaccurate mimicry to explaining the exceptional evolution and maintenance of accurate mimicry and highlights that the evolution of Batesian mimicry is ruled by multiple conflicting selective pressures.
{"title":"Batesian Mimicry Converges Towards Inaccuracy in Myrmecomorphic Spiders","authors":"Michael B J Kelly, Shahan Derkarabetian, Donald James McLean, Ryan Shofner, Cristian J Grismado, Charles R Haddad, Gerasimos Cassis, Gonzalo Giribet, Marie E Herberstein, Jonas O Wolff","doi":"10.1093/sysbio/syaf037","DOIUrl":"https://doi.org/10.1093/sysbio/syaf037","url":null,"abstract":"Batesian mimicry is an impressive example of convergent evolution driven by predation. However, the observation that many mimics only superficially resemble their models despite strong selective pressures is an apparent paradox. Here, we tested the ‘perfecting hypothesis’, that posits that inaccurate mimicry may represent a transitional stage at the macro-evolutionary scale by performing the hereto largest phylogenetic analysis (in terms of the number of taxa and genetic data) of ant-mimicking spiders across two speciose but independent clades, the jumping spider tribe Myrmarachnini (Salticidae) and the sac spider sub-family Castianeirinae (Corinnidae). We found that accurate ant mimicry evolved in a gradual process in both clades, by an integration of compound traits contributing to the ant-like habitus with each trait evolving at different speeds. Accurate states were highly unstable at the macro-evolutionary scale likely because strong expression of some of these traits comes with high fitness costs. Instead, the inferred global optimum of mimicry expression was at an inaccurate state. This result reverses the onus of explanation from inaccurate mimicry to explaining the exceptional evolution and maintenance of accurate mimicry and highlights that the evolution of Batesian mimicry is ruled by multiple conflicting selective pressures.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"18 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144097490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Phylogenies contain a wealth of information about the evolutionary history and process that gave rise to the diversity of life. This information can be extracted by fitting phylogenetic models to trees. However, many realistic phylogenetic models lack tractable likelihood functions, prohibiting their use with standard inference methods. We present phyddle, pipeline-based software for performing phylogenetic modeling tasks on trees using likelihood-free deep learning approaches. phyddle has a flexible command-line interface, making it easy to integrate deep learning approaches for phylogenetics into research workflows. phyddle coordinates modeling tasks through five pipeline analysis steps (Simulate, Format, Train, Estimate, and Plot) that transform raw phylogenetic datasets as input into numerical and visual model-based output. We conduct three experiments to compare the accuracy of likelihood-based inferences against deep learning-based inferences obtained through phyddle. Benchmarks show that phyddle accurately performs the inference tasks for which it was designed, such as estimating macroevolutionary parameters, selecting among continuous trait evolution models, and passing coverage tests for epidemiological models, even for models that lack tractable likelihoods. Learn more about phyddle at https://phyddle.org.
{"title":"phyddle: software for exploring phylogenetic models with deep learning","authors":"Michael J Landis, Ammon Thompson","doi":"10.1093/sysbio/syaf036","DOIUrl":"https://doi.org/10.1093/sysbio/syaf036","url":null,"abstract":"Phylogenies contain a wealth of information about the evolutionary history and process that gave rise to the diversity of life. This information can be extracted by fitting phylogenetic models to trees. However, many realistic phylogenetic models lack tractable likelihood functions, prohibiting their use with standard inference methods. We present phyddle, pipeline-based software for performing phylogenetic modeling tasks on trees using likelihood-free deep learning approaches. phyddle has a flexible command-line interface, making it easy to integrate deep learning approaches for phylogenetics into research workflows. phyddle coordinates modeling tasks through five pipeline analysis steps (Simulate, Format, Train, Estimate, and Plot) that transform raw phylogenetic datasets as input into numerical and visual model-based output. We conduct three experiments to compare the accuracy of likelihood-based inferences against deep learning-based inferences obtained through phyddle. Benchmarks show that phyddle accurately performs the inference tasks for which it was designed, such as estimating macroevolutionary parameters, selecting among continuous trait evolution models, and passing coverage tests for epidemiological models, even for models that lack tractable likelihoods. Learn more about phyddle at https://phyddle.org.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"13 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jennifer R Hodge,Danielle S Adams,Keiffer L Williams,Laura R V Alencar,Benjamin Camper,Olivier Larouche,Mason A Thurman,Katerina Zapfe,Samantha A Price
Understanding the ecological drivers and limitations of adaptive convergence is a fundamental challenge. Here, we explore how adaptive convergence of planktivorous fishes has been influenced by multiple ecological factors, evolutionary history, and chance. Using ecomorphological data for over 1600 marine species, we integrate pattern-based metrics of convergence with evolutionary model fitting to test whether phenotypic similarities among specialist planktivores exceed expectations under null models and whether ecology, evolutionary history, or their combined effects best explain trait evolution. We find that planktivores are significantly more similar in phenotype than expected. Traits with functional relevance for prey detection and capture, such as eye diameter and lower jaw length, are strongly convergent, while general body size and shape are constrained by deep divisions between clades where the effects of evolutionary history are most pronounced. Since not all traits undergo strong selection toward a convergent ecomorph, their evolutionary trajectories have not entirely overcome ancestral differences in the multivariate trait space, resulting in a specific form of convergence termed conservatism. We show how adaptive responses to feeding ecology intertwine with other ecological pressures (i.e., light environment) and historical contingency to shape fish phenotype evolution over deep time, offering key insights into the generality of phenotypic evolution.
{"title":"Unravelling the Effects of Ecology and Evolutionary History in the Phenotypic Convergence of Fishes.","authors":"Jennifer R Hodge,Danielle S Adams,Keiffer L Williams,Laura R V Alencar,Benjamin Camper,Olivier Larouche,Mason A Thurman,Katerina Zapfe,Samantha A Price","doi":"10.1093/sysbio/syaf034","DOIUrl":"https://doi.org/10.1093/sysbio/syaf034","url":null,"abstract":"Understanding the ecological drivers and limitations of adaptive convergence is a fundamental challenge. Here, we explore how adaptive convergence of planktivorous fishes has been influenced by multiple ecological factors, evolutionary history, and chance. Using ecomorphological data for over 1600 marine species, we integrate pattern-based metrics of convergence with evolutionary model fitting to test whether phenotypic similarities among specialist planktivores exceed expectations under null models and whether ecology, evolutionary history, or their combined effects best explain trait evolution. We find that planktivores are significantly more similar in phenotype than expected. Traits with functional relevance for prey detection and capture, such as eye diameter and lower jaw length, are strongly convergent, while general body size and shape are constrained by deep divisions between clades where the effects of evolutionary history are most pronounced. Since not all traits undergo strong selection toward a convergent ecomorph, their evolutionary trajectories have not entirely overcome ancestral differences in the multivariate trait space, resulting in a specific form of convergence termed conservatism. We show how adaptive responses to feeding ecology intertwine with other ecological pressures (i.e., light environment) and historical contingency to shape fish phenotype evolution over deep time, offering key insights into the generality of phenotypic evolution.","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"1 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fernando Alda,S Elizabeth Alter,Naoko P Kurata,Prosanta Chakrabarty,Melanie L J Stiassny
Understanding the drivers of diversification is a central goal in evolutionary biology but can be challenging when lineages radiate quickly and/or hybridize frequently. Cichlids in the tribe Lamprologini, an exceptionally diverse clade found in the Congo basin, exemplify these issues: their evolutionary history has been difficult to untangle with previous datasets, particularly with regard to river-dwelling lineages in the genus Lamprologus. This clade notably includes the only known blind and depigmented cichlid, L. lethops. Here, we reconstructed the evolutionary, population, and biogeographic history of a Lamprologus clade from the Congo River by leveraging genomic data and sampling over 50 lamprologine species from the entire Lake Tanganyika radiation. This study provides the most comprehensive species-level coverage to date of the riverine taxa within this lacustrine-origin clade. We found that in the mid-late Pliocene, two lineages of Lake Tanganyika lamprologines independently colonized the Congo River, where they subsequently hybridized and diversified, forming the current monophyletic group of riverine Lamprologus. Our estimates for divergence time and introgression align with the region's geological history and suggest rapid speciation in Lamprologus species from the Congo River marked by rapids-driven vicariance and water level fluctuations, and repeated episodes of secondary contact and reticulation. As a result of our analyses, we propose the taxonomic restriction of the genus Lamprologus to Congo River taxa only. The complex evolutionary history of this group-characterized by introgressive hybridization followed by a rapid series of isolation and reconnection-illustrates the multifaceted dynamics of speciation that have shaped the rich biodiversity of this region. [African cichlids; Congo River; diversification; hybridization; Lamprologini; phylogenomics; UCEs; ultraconserved elements].
{"title":"Phylogenomic and Population Genomic Analyses of Ultraconserved Elements Reveal Deep Coalescence and Introgression Shaped Diversification Patterns in Lamprologine Cichlids of the Congo River.","authors":"Fernando Alda,S Elizabeth Alter,Naoko P Kurata,Prosanta Chakrabarty,Melanie L J Stiassny","doi":"10.1093/sysbio/syaf032","DOIUrl":"https://doi.org/10.1093/sysbio/syaf032","url":null,"abstract":"Understanding the drivers of diversification is a central goal in evolutionary biology but can be challenging when lineages radiate quickly and/or hybridize frequently. Cichlids in the tribe Lamprologini, an exceptionally diverse clade found in the Congo basin, exemplify these issues: their evolutionary history has been difficult to untangle with previous datasets, particularly with regard to river-dwelling lineages in the genus Lamprologus. This clade notably includes the only known blind and depigmented cichlid, L. lethops. Here, we reconstructed the evolutionary, population, and biogeographic history of a Lamprologus clade from the Congo River by leveraging genomic data and sampling over 50 lamprologine species from the entire Lake Tanganyika radiation. This study provides the most comprehensive species-level coverage to date of the riverine taxa within this lacustrine-origin clade. We found that in the mid-late Pliocene, two lineages of Lake Tanganyika lamprologines independently colonized the Congo River, where they subsequently hybridized and diversified, forming the current monophyletic group of riverine Lamprologus. Our estimates for divergence time and introgression align with the region's geological history and suggest rapid speciation in Lamprologus species from the Congo River marked by rapids-driven vicariance and water level fluctuations, and repeated episodes of secondary contact and reticulation. As a result of our analyses, we propose the taxonomic restriction of the genus Lamprologus to Congo River taxa only. The complex evolutionary history of this group-characterized by introgressive hybridization followed by a rapid series of isolation and reconnection-illustrates the multifaceted dynamics of speciation that have shaped the rich biodiversity of this region. [African cichlids; Congo River; diversification; hybridization; Lamprologini; phylogenomics; UCEs; ultraconserved elements].","PeriodicalId":22120,"journal":{"name":"Systematic Biology","volume":"44 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号