Speciation is the ultimate source of biodiversity, yet because most species arise in spatial isolation (allopatry), it remains unclear how speciation history shapes patterns of sympatric species richness. Here, we examine how the timing of past speciation events influences the maximum sympatric species richness attained across 40 families of passerine birds. Using a phylogenetic model, we infer that the average waiting time for species to assemble in sympatry is remarkably long (~8 million years), occurring over macroevolutionary timescales comparable to the pace of speciation itself. Consequently, we find that the proportion of species in sympatry varies substantially across families, peaking in ancient or small clades comprised of older species, while remaining low in large, rapidly diversifying clades. Our analysis shows that macroevolutionary delays in colonisation are sufficient for speciation history to leave an indelible legacy on present-day assemblages, challenging the view that richness is strictly limited by contemporary environmental capacity.

Understanding island biodiversity requires studying the processes driving its assembly and examining how these processes vary over time, across lineages, and across different islands. The DAISIE (Dynamic Assembly of Island biota through Speciation, Immigration, and Extinction) framework has been developed for this purpose, and has advanced our understanding of island community assembly by estimating, from phylogenetic data, the contribution of the processes of colonization, speciation and extinction to island community assembly. However, the model assumes uniform colonization and diversification rates across lineages, ignoring potential variation in these rates caused by, for example, lineage-specific traits. This assumption thus restricts the model’s capacity to capture complex colonization and diversification dynamics, and may consequently bias inference of colonization and diversification dynamics on islands. An extension of the framework behind these more complex dynamics is therefore desired. However, for this state-dependent colonization and diversification extension, the current computation of the likelihood of the model given phylogenetic data is computationally prohibitive. In this study, we therefore propose an alternative approach to computing the DAISIE likelihood, under the assumption of diversity-independent colonization and diversification. Our novel approach is based on the pruning algorithm that has been used for computing the likelihood of SSE (State-dependent Speciation and Extinction) models, which traces lineage history backward in time from the present day to the root of the tree. We demonstrate that our alternative approach reproduces DAISIE’s predictions under diversity-independent colonization and diversification. This provides an additional layer of support to DAISIE in diversity-independent settings, but in doing so also gives more confidence in the results under diversity-dependence. Furthermore, our alternative approach offers greater computational efficiency. Finally, it provides a flexible framework for incorporating state-dependent dynamics in colonization, speciation, and extinction processes.

In arid landscapes perennials can provide protection for annual plants against environmental stress such as drought. This could lead to population sub-structuring of the annuals, because of position dependent reproductive success, and/or competition for association with perennial shrubs. Although such interactions are well known drivers of plant community dynamics, little is known about the local and temporal effects of perennial presence on the genetic structure of annuals. As a first step to address this, we used a set of microsatellite markers to assess the genetic structure of the annual grass Brachypodium hybridum (L.) P. Beauv. growing underneath and outside the canopy cover of perennials at an arid site in southern Spain over two consecutive years (2018 and 2019). Upon sampling, we observed clear but inconsistent seasonal differences in phenology between individuals underneath or outside canopies. However, we did not find genetic differentiation between sampling location or sampling year (four populations: overall FST = 0.024). An analysis of the overall genetic structure inferred three putative clusters, but these clusters were neither associated with location nor with year of sampling. We conclude that the genetic structure of this B. hybridum population is independent of neighboring perennials and stable over two consecutive years and is not associated with phenological differences. The data further show a high level of homozygosity in the population, and the recurrent presence of identical genotypes, both with respect to location and year, indicating a major role of selfing in B. hybridum reproduction. However, the samples collected outside the canopies in 2019 show a slightly higher value of estimated heterozygosity. This may indicate the effect of immigration from other B. hybridum populations. These results show that neighboring perennial plants do not affect the stable genetic structure of the B. hybridum population within this arid site, but that other factors such as immigration, could eventually result in local and temporal differences. These results also indicate a possible patchy distribution of identical genotypes of the annuals, which should be considered when sampling such populations.

The repeatability of evolution is fundamentally important for understanding the origin and diversification of life as well as for developing evolutionary forecasting tools. Repeatability is limited by stochasticity, here defined as changes that are independent of genotypic fitness effects. Over short timescales, the two main sources of stochasticity of evolutionary change are environmental stochasticity and demographic (life-history) stochasticity. Quantifying the effect of these two sources of stochasticity and how they interact in driving fitness outcomes is crucially important for predicting contemporary evolutionary responses. To gain insights in the effects of stochasticity, five institutes replicated an evolutionary experiment exposing Caenorhabditis elegans to novel rearing conditions. Replication across the institutes led to variation in selective environments, including through divergent microbiomes among institutes. Replication within institutes was done across demographic treatments that influence the potential for population-size dependent fluctuations in allele frequencies (drift) and genetic hitchhiking (draft). We found high among-institute variation in fitness outcomes, which was partially explained by variation in microbiota. Whereas lab-specific effects explained most of the variance in mean fitness, the repeatability of fitness outcomes depended more on demographic heterogeneity. Specifically, population bottlenecks resulted in high among-replicate variation in fitness. When combined, environmental and demographic stochasticity additively reduced repeatability, underlining their additive importance in developing evolutionary forecasting tools. These results further highlight the importance of statistically integrating heterogeneity in experimental evolution to identify factors constraining outcome repeatability and study replicability.

Species diversification is characterized by speciation and extinction, the rates of which can, under some assumptions, be estimated from time-calibrated phylogenies. However, maximum likelihood estimation methods (MLE) for inferring rates are limited to simpler models and can show bias, particularly in small phylogenies. Likelihood-free methods to estimate parameters of diversification models using deep learning have started to emerge, but how robust neural network methods are at handling the intricate nature of phylogenetic data remains an open question. Here we present a new ensemble neural network approach to estimate diversification parameters from phylogenetic trees that leverages different classes of neural networks (dense neural network, graph neural network, and long short-term memory recurrent network) and simultaneously learns from graph representations of phylogenies, their branching times, and their summary statistics. Our best-performing ensemble neural network (which adjusts the graph neural network result using a recurrent neural network) delivers estimates faster than MLE and shows less sensitivity to tree size for constant-rate and diversity-dependent speciation scenarios. It performs well compared with an existing convolutional network approach. However, like MLE, our approach still fails to recover parameters precisely under a protracted birth-death process. Our analysis suggests that the primary limitation to accurate parameter estimation is the amount of information contained within a phylogeny, as indicated by its size and the strength of effects shaping it. In cases where MLE is unavailable, our neural network method provides a promising alternative for estimating phylogenetic tree parameters. If detectable phylogenetic signals are present, our approach delivers results that are comparable to MLE but without inherent biases.

Islands are often regarded as ”natural laboratories” for biology, and this applies also to ecological interactions between species that co-occur on islands. Mutualistic interactions, the association between organisms of two different species in which each benefits, should have important effects on the ability of species to establish, speciate or persist on islands. However, while island biogeography models increasingly incorporate the dynamics of biotic interactions in island ecosystems, current approaches do not consider the complexities introduced by mutualistic relationships, e.g., mutualism-driven immigration, cospeciation between mutualistic partners, dynamic formation and loss of mutualistic links. Here we present a new simulation model of island biogeography that explicitly includes mutualistic interactions and their influence on biodiversity at macroevolutionary scales. With this approach, we aim to better understand the effect of mutualism on insular diversity patterns over macroevolutionary time. Our model shows how mutualism influences species immigration, diversification and persistence through its effects on species-level processes. Strong mutualism is associated with higher species richness and more connected communities, while networks with weak or absent mutualism remain fragmented, with many species failing to establish interactions. By extending classical island biogeography to include ecological interaction networks, our study offers a new theoretical lens on how mutualism can influence biodiversity patterns and network structures in insular systems over evolutionary timescales.

State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states ( λ 1 / λ 0 = 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s D ) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.

Macroevolutionary studies have shown that the shape of phylogenetic trees differs in space, time, and between taxa. It is commonly assumed that these differences in tree shape reflect variability in the underlying ecological and evolutionary processes that produced them, and mechanistic eco-evolutionary models are increasingly used to explore this link. A concern in this context is whether conclusions drawn from such mechanistic models are robust to idiosyncrasies in how eco-evolutionary processes are formalized in the models. Here, we use eight mechanistic macroevolutionary models to study how 52 metrics of phylogenetic tree shape respond to variation in the strength of five fundamental processes: competition, dispersal, environmental filtering, niche conservatism, and speciation. We find that models agree on how some tree metrics respond to changes in these processes, in particular dispersal and speciation. However, no tree metric uniquely correlated with a single process, suggesting that single tree metrics have limited utility as shortcuts for inferring the underlying eco-evolutionary processes. Moreover, while it was possible to infer the underlying processes if the data-generating model was known, inference was not consistent across the different models. We conclude that the relationship between phylogenetic patterns and eco-evolutionary processes in macroevolutionary analysis is likely sensitive to the structural and mechanistic details of how a given process is implemented within models.

Sociability—the propensity of an individual to engage in group activities—is a trait present in all social species. In humans and many animals, sociability varies between individuals yet remains consistent across contexts, qualifying it as a personality trait. Sociability influences health and physiology, but the mechanisms underlying sociability and its inter-individual variation remain poorly understood. The genetically tractable fruit fly, Drosophila melanogaster , is increasingly used to study social behavior and exhibits a wide range of sociability phenotypes. However, previous studies have relied on distinct behavioral paradigms, limiting cross-context comparisons and motivating a more extensive characterization of sociability in this species. Here, we quantified sociability in D. melanogaster using a multidimensional approach encompassing three paradigms that capture engagement in group activities across contexts: (1) preference for communal versus solitary egg-laying, (2) egg-laying latency in a group, and (3) frequency and duration of spontaneous social interactions and interindividual distance. We assessed these behaviors in 105 lines of the Drosophila Genetic Reference Panel and observed substantial variation in responses to conspecific presence across paradigms. Sociability-related behaviors differed between genetically distinct lines, indicating a genetic component. However, the three sociability traits were uncorrelated, demonstrating that sociability in D. melanogaster is multidimensional. These findings suggest that sociability is not governed by a single central mechanism, but instead arises from multiple context-dependent pathways.

New methodologies to infer past evolutionary, ecological and biogeographical processes from molecular phylogenies are rapidly being developed. However, these often employ unfamiliar data structures that may pose a barrier to their use. DAISIE (Dynamic Assembly of Islands through Speciation, Immigration and Extinction) is an island biogeography model that can estimate rates of colonisation, speciation and extinction from molecular phylogenetic data across insular assemblages. The method uses an unconventional phylogenetic data structure: instead of considering a single island lineage, it focuses on multiple independent lineages descending from different colonisation events of the island. While analysing phylogenies from this perspective has plenty of potential, this comes with challenges for the user. Here we describe software DAISIEprep, an R package to aid the extraction of data from one or many phylogenetic trees to generate and visualise data in a format interpretable by macroevolutionary and biogeographical inference models. DAISIEprep includes simple algorithms to extract data on island colonists and account for biogeographical, topological and taxonomic uncertainty. It also allows flexible incorporation of either missing species or entire insular lineages when molecular data are not available. The software enables reproducible and user-friendly data extraction, formatting and visualisation of phylogenetic data from island lineages, and will facilitate addressing questions about island evolution, community ecology and anthropogenic impacts in insular systems. The tools presented here will also be useful for researchers who do not plan to use DAISIE but are interested in how to interpret, visualise and analyse phylogenetic datasets of islands species or island-like environments.

Slowdowns in lineage accumulation are often observed in phylogenies of extant species. One explanation is the presence of ecological limits to diversity and hence to diversification. Previous research has examined whether and how species richness (SR) impacts diversification rates, but rarely considered the evolutionary relatedness (ER) between species, although ER can affect the degree of interaction between species, which likely sets these limits. To understand the influences of ER on species diversification and the interplay between SR and ER, we present a simple birth-death model in which the speciation rate depends on the ER. We use different metrics of ER that operate at different scales, ranging from branch/lineage-specific to clade-wide scales. We find that the scales at which an effect of ER operates yield distinct patterns in various tree statistics. When ER operates across the whole tree, we observe smaller and more balanced trees, with speciation rates distributed more evenly across the tips than in scenarios with lineage-specific ER effects. Importantly, we find that negative SR dependence of speciation masks the impact of ER on some of the tree statistics. Our model allows diverse evolutionary trajectories for producing imbalanced trees, which are commonly observed in empirical phylogenies but have been challenging to replicate with earlier models.

The phylogenetic structure of ecological assemblages carries the signature of ecological processes that influenced and are still influencing their assembly. However, identifying the mechanisms that shape assemblages is not simple, as they can vary geographically. Here, we investigate how the phylogenetic structure of Canidae assemblages across the globe is affected by the abiotic and biotic environment. We first identify phylogenetically clustered and overdispersed assemblages of canids over the planet. Then, we apply Structural Equation Models in these assemblages to identify the effect of six variables (current temperature, Last Glacial Maximum temperature, vegetation cover, human impact, Felidae richness, and a measure of canid body size dissimilarity) on the phylogenetic relatedness of canids. We find that South America and Asia present a high concentration of clustered assemblages, whereas Central America, Europe, and North America show phylogenetically overdispersed assemblages. Temperature from the Last Glacial Maximum is the most important variable in our models, indicating that as LGM temperature increases, assemblages become less overdispersed (clustered). Therefore, Canidae assemblage composition across the world presents patterns of clustering and overdispersion, which mainly follow the environmental gradient, suggesting habitat filtering as the principal force acting on Canidae assemblages. When examining phenotypic distribution patterns, we found that abiotic factors also had a stronger influence than biotic ones, but competition appears to have played an important role in shaping phenotypic diversity within Canidae assemblages, likely promoting body size divergence and niche partitioning among closely related species.

While the ecological roles of colored integument have been extensively studied, what regulates global patterns of color variation remains poorly understood. Here, using a global dataset of 1249 squamates, we evaluate whether and how six key eco-environmental variables and their interactions shaped the evolutionary history of their coloration. We show that only habitat openness consistently associates with brightness evolution, with brighter integuments favored in open habitats, possibly for enhanced heat reflection. Furthermore, brightness evolution rates likely track δ18O (a temperature proxy) changes and increase during global aridification phases, such as those in the Miocene and Pliocene. This trend may be due to the establishment of an arid climate that promoted habitat openness shifts, ultimately inducing adaption to new niches. Our findings suggest that a single environmental variable is associated with color variation in the largest extant tetrapod order.

In archipelagic environments, the successive emergence and submergence of islands induces changes in area, spatial structure and isolation. Here, we aim to understand how such geo-environmental dynamics, by altering immigration, speciation and extinction over time, may influence phylogenetic patterns. We use a neutral, stochastic, individual-based model which simulates a community evolving in an archipelago where four islands emerge and submerge consecutively. We record each birth, death and immigration event, allowing us to build the complete phylogeny at any time, from which we extract the phylogeny of extant species. We show that the rate of lineage accumulation and tree imbalance vary according to a hump-shaped curve, and we show that this is mainly due to variations in area and inter-island connectivity. We highlight that past abrupt changes, such as island emergence, may leave persistent imprints in the rate of lineage accumulation. We show that the spatial configuration of an archipelago modulates these effects: (i) enhancing inter-island connectivity leads to more frequent inter-island speciation events, resulting in a faster accumulation of lineages, and in larger evolutionary radiations, which in turn produce highly imbalanced phylogenies; (ii) increase in mainland connectivity brings ancestral lineages to the islands, which slows down the rate of lineage accumulation and increases the species turnover, allowing for more balanced phylogenies. Accounting for variations in the geo-environmental configuration of an archipelago is important to understand the shape of contemporary phylogenies. However, these effects have to be interpreted in the context of the spatial configuration of an archipelago. The emergence and submergence of islands in an archipelago influences immigration, speciation and extinction rates at both island and archipelago scales Landscape dynamics in an archipelago drives insular species diversification rates and influences phylogenies of island species Inter-island dispersal favors lineage accumulation and generates imbalanced phylogenies Mainland connectivity increases species turnover, slows down lineage accumulation and makes phylogenies more balanced The establishment and size of a phylogenetic island clade may depend on the spatial configuration of the archipelago at the time of colonization

Island biotas often show highly uneven species richness among lineages, influenced by clade age, diversification rates, and/or spatio-ecological limits. However, disentangling these drivers has been challenging due to the lack of comprehensive datasets across multiple lineages in the same geographical arena. The flora of the Canary Islands includes hundreds of plant lineages with contrasting species richness and harbours the highest number of species that evolved their woodiness in-situ (“insular woodiness”). Here, we present a phylogenomic reconstruction for Canary Island angiosperms and show that diversity unevenness in the flora is not driven by lineage age but by trait-dependent diversification and spatio-ecological limits. Our phylogenomic dataset, based on 1,244 newly generated and 501 published DNA sequences for 669 Canary Island species and 771 closely related mainland taxa, allows us to simultaneously study 435 plant lineages (∼50% of total). Applying dynamic stochastic modelling, we find the flora is shaped by high extinction and colonisation rates, maintaining a macroevolutionary equilibrium. Additionally, insular woody lineages exhibit higher diversification rates than the remaining flora. Our results suggest the uneven diversity of a highly dynamic insular region can be explained by the interaction of trait evolution and ecological constraints, providing insights into island biodiversity dynamics. ### Competing Interest Statement The authors have declared no competing interest. NWO, OCENW.KLEIN.498

Phylogenetic trees are increasingly used to infer the processes that shape biodiversity patterns, such as diversification, dispersal and trait evolution. However, collecting many samples and sequencing them for phylogenetic reconstruction is challenging and costly, and achieving high sampling fractions can be logistically impossible for species‐rich groups or regions. When studying isolated environments such as islands, this issue applies to the species that make up the island community, as well as outgroup taxa when sampling from a large pool of mainland relatives is required. In this study, we use simulations of the island biogeography model DAISIE (Dynamic Assembly of Islands through Speciation, Immigration and Extinction) to investigate the best sampling strategy to minimize error in the estimation of the model parameters when there is a constraint on the number of species that can be sampled. We compare three different community‐level sampling strategies for islands: (1) prioritizing species‐rich island lineages; (2) prioritizing species‐poor island lineages; and (3) random selection of island lineages. Furthermore, we explore the effect of the nature of the missing data within each (species‐rich or species‐poor) lineage by testing the impact of incomplete sampling of the oldest and youngest extant species within each lineage and assess the effect of excluding outgroup species or even entire island lineages. Parameter estimates of speciation, colonization and extinction rates of island lineages show slightly larger errors when the unsampled species belong to species‐rich lineages. Within clades, we observed larger errors when the unsampled species were the oldest or outgroup species (i.e. mainland species sampled to determine the stem age of the clade). When sampling is limited by time and/or budget, our study suggests prioritizing sampling of the phylogenetically most distinct species from the most diversified island lineages along with their mainland relatives.

The island species-area relationship (ISAR) describes how larger islands support more species. ISARs of isolated oceanic archipelagos, assembled over millions of years, typically show positive relationships, steep slopes, and species richness equilibrium. However, it remains unclear how quickly such characteristics emerge. We compiled a dataset for fish communities of 79 postglacial peri-Alpine lakes and report an ISAR, formed de novo in less than 15,000 years, that partially mirrors older systems, but has an asymptotic shape. Immigration and speciation, the main ISAR drivers, are primarily associated with area and depth, respectively. Immigration increases with area, while speciation is promoted by greater depth, likely due to species depletion in the source pool and ecological constraints on speciation. This young ISAR has been reshaped by anthropogenic activities, with species introductions erasing its asymptotic shape. We demonstrate that ISARs can develop rapidly after insular habitat formation, offering insights into patterns of biodiversity assembly.