Philosophical Transactions of the Royal Society B

Different plasmids exist at different copy numbers per cell, and there is an inverse relationship between a plasmids copy number and its size. Two recent studies quantified this relationship into a scaling law, but both the form and the interpretation of this law are contested. Here, I explore the issues with fitting a single law across plasmid diversity and suggest a consistent synthesis. First, I explore some potential problems with using sequencing-based estimates of copy number. Then, I discuss plasmid copy number through a series of case studies. I argue in favour of interpreting plasmid copy numbers not through a single law, but through the lens of two dominant evolutionary strategies. I suggest that small plasmids which lack active segregation mechanisms have a resulting tradeoff between plasmid inheritance and fitness cost to the host, which is responsible for an inverse relationship between copy number and size. In contrast, larger plasmids with active segregation mechanisms show a much weaker relationship, in line with evidence that their metabolic costs are dominated by the expression of specific genes rather than their size. Where plasmids in the 20-100kb range have higher copy numbers, I argue these probably arise more from selection at the level of the host cell for plasmid-associated phenotypes (e.g. antibiotic resistance) rather than from plasmid-level selection for inheritance.

Conversational AI is being deployed into medical decision support, mental-health triage, and social companionship, where reinforcement of a user's false or delusional belief can cause direct harm. Most deployed safety techniques are evaluated for factual accuracy in isolation; the question of whether they protect against belief-level harm, and whether layered architectures behave additively or synergistically, has not been answered empirically. We compared four configurations of the same underlying model: a bare language model (condition A); an explicit values constraint we call the First Law architecture (condition B); a real-time epistemic verification layer called Aletheia (condition C); and the complete architecture combining all components together (condition D). Across 156 scored responses spanning 39 probe items in four belief-harm domains, condition A only passed 3 of 36 main-battery probes (8.3%; 95% CI 1.8 to 22.5%) under triple-blind human consensus rating demonstrating the core limitations of unmodified LLM deployments. In contrast, the three safety architectures (B-D) passed at least 97% of items (Fisher's exact, P < 0.001 versus A). On a synergy battery designed to test items at the intersection of value- and epistemic-domain failures (16 scored items, AI-rated), only the complete architecture passed every item; single-layer conditions failed on 7 of 16 items (43.8%) where neither values constraint nor verification was individually sufficient. Linear mixed-effects modelling of three-turn emotional escalation gave a slope of -1.00 points per turn for the values-only condition (t = -6.20) and -0.75 points per turn for the verification-only condition (t = -4.65); the complete architecture was flat at {beta} = 0.00. We describe a mechanistic failure of single-layer verification we call bot-validates-kernel-endorses-inference, in which accurate confirmation of a true factual element embedded in a delusional claim transfers epistemic authority to the surrounding false inference. Values alignment and factual verification address different failure modes, and the combined VaaS-Aletheia architecture is what produces stable protection across emotional escalation in conversational settings. The complete architecture evaluated here represents evidence-based specification for safer deployment of AI in high-stakes advisory contexts and serves as a benchmark against which future safety architectures can be compared.

Objective: To develop and demonstrate an agent-based modeling framework for healthcare AI adoption, using ambient clinical documentation as the calibration case. Materials and Methods: We built an agent-based model with 50,000 clinician agents, 500 organization agents, and 4 vendor agents over 104 weeks. Modeled clinicians differed by psychotype, specialty, and friction/benefit thresholds; modeled organizations progressed through deployment phases with governance delays anchored to 8-28 weeks. All cited deployment values were independently re-verified against primary sources, and the model was validated against published data from seven health systems and three benchmarks using formal goodness-of-fit metrics (RMSE, 90% predictive-interval coverage) grouped by reference class. After correcting an organization-initialization artifact, we performed a formal six-parameter re-calibration to align both the early-time trajectory and the steady-state plateau with published data. Six intervention scenarios were compared in paired simulations (n=30 realizations per scenario) using effect sizes with bootstrap intervals, and both the full intervention comparison and the Sobol sensitivity screen were re-run natively under the re-calibrated model. Global sensitivity analysis used Sobol indices (64 base samples; 1,152 parameter sets) across eight parameters. Results: Baseline simulations produced S-curve adoption trajectories with wide variability. The re-calibrated model reproduced both early-time single-site trajectories and the cross-sectional adoption plateau, covering 86% of reference-class-matched anchors at the nominal 90% level, versus 29% for the original configuration. Most intervention scenarios increased adoption; in the original configuration the combined intervention outperformed individual levers, with significant interactions confirmed by 23 factorial analysis. Re-running the analyses natively under the calibrated model both confirmed and revised these conclusions: governance remained the largest single structural lever and non-success absorbing states remained prominent, but intervention effects attenuated sharply, the combined intervention no longer reliably exceeded the best single lever at operating scale, and the leading sensitivity driver shifted from governance delay to clinician friction/edit-rate tolerance. That calibration changes which levers appear influential is itself the central methodological finding. Organizational outcomes clustered into non-success absorbing states (pilot stagnation and failure) alongside success and scaling. Conclusions: Governance delay is an explicit upstream gate in the model, so its influence reflects model architecture and should not be interpreted as a universal real-world priority. The modeled pilot stagnation state is hypothesis-generating rather than an empirical category. Agent-based modeling provides a structured framework for understanding healthcare AI adoption dynamics. The approach supports hypothesis generation and comparative scenario exploration rather than point prediction.

Empirical studies have demonstrated that mutation rates may change with individual condition, such as in the case of stress-induced mutagenesis. This has led to the hypothesis that condition-dependent (or "plastic") mutation rates could be selectively favoured, as the increased production of new mutants in times of maladaptation enhances evolvability, the ability to undergo adaptive evolution. However, while empirical evidence for condition-dependent mutation rates is accumulating, theoretical models studying their evolution are lacking. Here, we employ an individual-based simulation approach to examine the evolution of condition-dependent mutation rates in a changing environment. We find that condition-dependent mutation rates consistently evolve when the environment changes at an intermediate pace. Furthermore, populations with condition-dependent mutation rates are substantially better adapted to their (changing) environment. Finally, the evolutionary dynamics of condition-dependent mutation rates are both accelerated and destabilised when the mutation rate is self-referential (i.e., when mutator loci affect their own mutation rate). We conclude that condition-dependent mutation rates (and thus evolvability) can readily evolve in changing environments. Significance statementMutation provides the raw material for evolution. Mutation rates thus tune evolvability, the ability to undergo adaptive evolution: if mutation rates are too low, evolution is impeded; if mutation rates are too high, adaptive traits cannot be maintained. Using a theoretical model, we explore the evolution of plastic mutation rates that systematically depend on the condition of the organism and its environment. An example is stress-induced mutagenesis in bacteria, which is implicated in the evolution of antibiotic resistance. We show that plastic mutation rates readily evolve, providing "well-timed" variation specifically when organisms are poorly adapted. Such plastic mutation rates thus facilitate better adaptation to changing environments, and their evolution provides an example of evolvability itself evolving.

BackgroundMobile genetic elements (MGEs), including viruses, plasmids, and transposons, are major drivers of evolution in bacteria and archaea. Host-parasite conflicts drive the emergence of a broad variety of defense and counter-defense systems. Recent advances in metagenomics and functional annotation have shown that many defense systems are located on MGEs. The fact that MGEs are, essentially, genomic parasites raises an intriguing question: why do these parasites carry defense systems at high prevalence, often even higher than the host chromosome? ResultsWe developed a simple mathematical model to investigate the factors that promote evolution of defense systems in MGEs and the ecological implications of MGE-encoded defense. Our analysis points to the strength of inter-MGE interference as a key determinant of the evolution of defense systems in MGEs. We identify two qualitatively distinct regimes, depending on the basic reproductive number in mixed coinfections. Weakly interfering MGEs tend to carry low-cost defense systems that enhance the survival of their hosts upon exposure to more damaging MGEs. Although these systems can be occasionally transferred to the host, they typically remain in MGEs. In contrast, strongly interfering MGEs, such as plasmids from the same incompatibility group, can carry high-cost defense systems that are detrimental to the host and the population as a whole, but help their carriers spread by actively replacing their competitors. ConclusionsAnalysis of our model shows that the key determinant of the evolution and spread of defense systems in MGEs is the strength of cross-MGE interference. Weakly interfering MGEs would serve as MGE banks, typically carrying low-cost defense systems that can benefit the host by protecting it from more damaging MGEs. In contrast, strongly interfering MGEs would carry costly defense systems that mediate inter-MGE conflicts but are deleterious to the host. These MGEs could serve as proving grounds for emerging defense systems, which might eventually become cost-effective once optimized by selection.

Horizontal gene transfer (HGT) is a defining feature of plasmid biology, enabling plasmids to spread between bacterial cells and driving the global dissemination of antibiotic resistance and other adaptive traits. HGT and vertical gene transfer (VGT) have long been assumed to be subject to an evolutionary tradeoff, where improvements in one come at the expense of the other. Yet whether this tradeoff reliably constrains plasmid evolution remains unclear. Through the first cross-literature synthesis examining both transmission traits across 16 studies and 245 plasmid-host pairs, we find mixed empirical evidence: patterns consistent with a tradeoff alongside outcomes that a strict tradeoff should make impossible. We propose that this contradiction is resolved by recognizing that a tradeoff is ensured only when plasmid-host pairs are well-adapted to one another. Because HGT introduces plasmids into novel hosts where this adaptation is disrupted, it systematically creates the very conditions under which near-term evolution need not be bound by a tradeoff. We support this framework empirically by documenting the first mutation that simultaneously improves both transmission modes, arising from a non-coevolved plasmid-host pair. These findings reveal a fundamental irony: the defining feature of plasmid transmission is the very mechanism that relaxes the evolutionary constraints of its own tradeoff.

The Biodiversity-Ecosystem Functioning (BEF) literature has shown species diversity to be essential for ecosystem functioning and services. Yet although acquiring information through interspecific networks can impact ecosystem functioning, it is unclear how it is modulated by species diversity. Eliciting vocal responses using predator models across a latitudinal gradient, we first show that the species diversity of birds increases public information about predation both in the low-cost system of mobbing and in the higher-cost system of alarm calls. A similar result was also found across a fragment area gradient for mobbing; this system was then used to test how species diversity affects interspecific information flow in mobbing communities. We set up two BEF playback experiments, manipulating the species richness level of the playback sound files by varying the number of species producing mobbing calls (one, two, four, eight species). In an experiment in which the call rate across treatments was held constant, and only heterospecific responses were counted, increasing species richness of the sound files increased the number of species and individuals responding, the number of calls produced and their frequency range, and decreased latency to call. An experiment in which call rate increased with the addition of species in each treatment showed a similar, but stronger pattern. There was little evidence that the signals of one particular species changed responses. This supports the hypothesis that the species diversity of a community is a key component influencing the quantity and quality of information flow inside it.

Wildlife crossing infrastructure is promoted to restore connectivity for fragmented populations, but its effectiveness at enabling natural recolonisation remains untested. We tested this using a spatially explicit agent-based model parameterised with GPS telemetry data from bobcats (Lynx rufus) in New Jersey, USA. By integrating movement behaviour, stochastic demography, habitat suitability, and traffic-dependent mortality risk, we simulated 50-year recolonisation dynamics across a highly urbanised landscape. Despite extensive unoccupied suitable habitat, natural recolonisation completely failed across all scenarios, with vehicle-induced mortality during dispersal acting as the primary limiting factor and turning the matrix into a demographic sink. Even an idealised mitigation scenario in which mortality at high-mortality crossings was reduced to zero failed to produce a self-sustaining population. Although dispersal increased, individuals at the recolonisation front remained too sparse to overcome the mate-finding Allee effect. Sensitivity analysis confirmed that the recolonisation-failure result is robust to {+/-}50% variation in per-crossing mortality and {+/-}25% variation in disperser survival. Restoring structural connectivity is not, in itself, a sufficient intervention for recovering low-density carnivore populations facing a high-mortality matrix. Instead disperser survival and local density at the recolonisation front are the rate-limiting determinants. In such systems translocation rather than crossing-structure investment is more likely to result in recolonisation success.

There are some disputed hypotheses for the recurrent observations of insular gigantism and dwarfism, like the island rule: small organisms would become larger on islands, while large organisms would become smaller. But, why is the latter? In addition, not all the observations fit this rule. Here I propose a causal model. Following the Island Biogeography Theory (IBT), insular aspects influence the census N. Observations suggest that variation in N is associated with variation in effective population size (Ne). The body size of insular colonisers might change, following Damuths law, as Ne can decrease at a differential rate from the island area A, resulting in a distinctive effective density [Formula]. Interestingly, a prediction of the drift-barrier hypothesis is that Ne is affecting mutation rates. Consequently, body mass, genome size and {micro} may be predicted to some extent by island area, as they are influenced by De and Ne. Falsification of the latter hypothesis is feasible by determining changes in genomic features of insular species. We now have the opportunity to interrogate the extensive data available. Here I ask: (i) How is decreasing island area predicting average body sizes? (ii) To which levels does this prediction apply (species, cells, genomes)? (iii) How well does the model fare on predicting {micro} over paradigmatic case studies? The resolution of these questions may provide a more reliable diagnosis of the evolutionary causes for somatic size variation. Significance statementNaturalists have long reported that insular species tend to become unusually large or small compared to their mainland relatives. Despite the familiarity of this "island rule", there is still no broad mechanistic explanation for why these changes occur so consistently across different groups of organisms. This work proposes that an important neutral factor can be the change in effective density of isolated populations. By combining the expectations of Damuths law, the IBT model, and the nearly-neutral theory it offers unified predictions on how sudden constraints in island area can influence not only the evolution of body size, but also the direction of changes in genome size and evolutionary rates.

Plasmids benefit bacterial communities by storing auxiliary genes that address environmental challenges such as antibiotics. Subsequent plasmid loss can also be advantageous if plasmid benefits are temporary but costs are permanent. However, unless positive selection is sustained, plasmid loss can proceed to extinction, with access to plasmid-derived benefits permanently lost. In principle, horizontal transmission can maintain a plasmid in a population, but if the plasmid cost is too high, the host can become uncompetitive. We examine how survival of costly but occasionally beneficial plasmids is possible in a bacterial population. Using population models, we demonstrate that plasmid-dependent phages can, counterintuitively, solve this plasmid survival problem for their bacterial hosts. Phage predation pins the plasmid at low but nonzero abundance, such that the plasmid cost is effectively neutralized at the population level, dramatically lengthening the persistence time of the plasmid. When conditions change and the costly plasmid becomes beneficial, it spreads across the host population and switches to a vertical-transmission lifestyle until benefits again subside.

The climatic oscillations of the Quaternary have likely affected the demographic history of many species, and PSMC (Pairwise Sequentially Markovian Coalescent) has been widely used to investigate these histories. However, it is increasingly acknowledged that PSMC trajectories are difficult to interpret. First, they are influenced by connectivity changes, even without population size changes. Second, most PSMC curves exhibit a few humps when tens of cycles occurred during the Pleistocene. Finally, responses to ancient habitat change have been shown to be species-specific. To address these issues, we simulated structured populations where connectivity (or population size and connectivity) varied according to successive interglacial and glacial periods during the last 2.6 million years. We computed the IICR (Inverse Instantaneous Coalescence Rate), the function that PSMC estimates, and ran PSMC. We further varied the generation length and assumed that some species were positively or negatively affected by glacials. We found that the IICR carries information regarding the demographic oscillations, but that PSMC fails to recover it for times older than 300 ky. For the last 200 ky, PSMC was often able to reproduce qualitatively the demographic oscillations. We also tested SNIF (Structured Non-stationary Inferential Framework), which produced good results using the IICR curve as an input but not when using the PSMC curve. Altogether, our study suggests that the humps older than 300 ky in PSMC histories are unlikely to represent trends of population size or connectivity. However, improving the estimation of the IICR could potentially help reconstruct some of these past demographic changes.

Many proposed mechanisms for the evolution of cooperation among unrelated individuals rely on relatively demanding cognitive abilities that are not widespread across taxa. In contrast, individual heterogeneity is a pervasive feature of animal groups, encompassing differences in personality as well as physical and cognitive traits. Such heterogeneity can promote the evolution of cooperation, yet its role has received comparatively little attention, particularly as a source of variation giving rise to social organization such as leadership. A specific form of leadership can emerge under unstable environmental conditions, when some individuals become better suited than others to initiate action and influence the behavior of their peers. Unlike fixed dominance hierarchies, emergent leadership can rapidly adjust to changing environmental conditions, thereby reshaping group organization. Because it does not require the maintenance of stable hierarchies, this form of leadership can arise even in species that do not have the cognitive capabilities to sustain complex social structures. In this work, we investigate the combined effects of individual heterogeneity and emergent leadership on the evolution of cooperation using an evolutionary game-theoretic model in which individuals may assume the roles of leaders or followers according to their strength, representing individual differences in suitability to prevailing environmental conditions. We examine different levels of population heterogeneity together with increasingly complex strategy sets requiring progressively greater informational requirements, allowing individuals to condition cooperation on their own strength, leadership role, or both. Our results show that the interplay between leadership and heterogeneity promotes the evolution of cooperation, particularly when only a small fraction of individuals act as leaders. Under these circumstances, cooperation evolves even when individuals employ the simplest possible strategies. Under harsher ecological conditions, cooperation can be sustained by more sophisticated strategies, specifically by conditional strategies that prescribe cooperation when individuals are strong or leading and defect when acting independently. Author summaryIn this study, we propose that emergent leadership mediated by individual diversity can boost the evolution of cooperation in animal groups. Building on growing evidence on the heterogeneity of animal capabilities and personalities, we focus on the fleeting leadership that emerges in animal groups when facing rapidly changing environmental conditions. We suggest that this type of leadership that emerges from individual differences in strength--a generic quality encompassing those characteristics that make an individual more fit to lead in a given situation--does not require complex cognitive capabilities from the animals and represents a valid alternative to more demanding strategies proposed in the past to explain the evolution of cooperation. Using an evolutionary game theory model, we show that if a population includes a few strong players, these can become influential leaders and guide the actions of their peers to achieve cooperation. Although the naive strategy of always cooperating is sufficient for cooperation to evolve, the introduction of more complex strategies leads players to cooperate only when they are more likely to be recognized as influential leaders. These strategies are more effective in promoting cooperation under unfavorable ecological conditions and are also more robust against exploitation by defectors.

Gene drives, genetic constructs that can spread deleterious alleles in wild populations, have the potential to address some of the major pressing challenges of the Anthropocene such as invasive species, spread of disease vectors, and agricultural pests. However, responsible and effective deployment of gene drive requires taking into account the complex nature of real-world population connectivity networks. In particular, it is unclear how the topological position of the deployment site affects the spread process and its final outcome. Here we develop a framework for modeling gene drive spread in population connectivity networks, and study the eco-evolutionary dynamics of gene drive spread under complex population structures. We investigated the relationship between the position of the deployment site in the topology of the network and whether the gene drive is eventually lost, fixed, or maintained at an intermediate frequency. We identified network centrality measures of deployment sites that are highly correlated with the outcome of deployment for different gene drive designs and across diverse network topologies. We also show that there is a trade-off between the time-to-fixation and the final outcome, implying that multiple centrality measures of the deployment site would need to be considered when aiming to achieve rapid and successful population control using gene drives.

Most theory and empirical research on range expansion assumes populations spread into empty landscapes with abundant resources, however expanding populations are likely to compete with residents. In mathematical models, interspecific competition can lead to pushed wave dynamics, where expansions are driven mainly by individuals dispersing from the core, leading to steeper wavefronts and increased genetic diversity at the edge. These predictions are yet to be tested empirically, and the role of interspecific competition in mediating evolution during range expansion is unclear. We used an experimental system with two duckweed species to ask if interspecific competition leads to pushed-like dynamics and to assess how competition alters evolution during range expansion. We found that competition with a resident reduced expansion speed and absolute variance among replicate expansions, suggesting competition makes expansion speed more predictable. Interspecific competition also changed the relative frequencies of genotypes at the leading edge. While competition was associated with some features of pushed waves, genotype diversity did not vary between treatments. Our results demonstrate that demographic and evolutionary patterns associated with pushed waves may not be universal, and that incorporating selective pressures into future research on eco-evolutionary dynamics of range expansion is key to understanding spreading populations in nature.

Background and AimsUnderstanding how reproductive barriers combine to restrict gene flow remains a central challenge in speciation research. Although reproductive isolation is inherently a composite process, most empirical studies have focused on individual barriers in isolation, limiting our ability to capture their joint effects particularly in systems undergoing evolutionary transitions such as shifts in mating system. MethodsHere, we provide a comprehensive, life-cycle-wide quantification of reproductive isolation between two closely related species of the Erysimum incanum complex that differ strikingly in mating system: the predominantly selfing E. incanum and the outcrossing E. wilczekianum. Key ResultsBy integrating ecological, phenological, behavioural, and post-pollination components, we show that total reproductive isolation is nearly complete (T{approx}0.999), but overwhelmingly driven by pre-pollination barriers. Ecogeographical differentiation and, most prominently, pollinator-mediated isolation dominate, with pollinators exhibiting a strong bias toward E. wilczekianum. Floral traits linked to mating system divergence, particularly flower size, emerge as key drivers of assortative mating, supporting their role as "magic traits" coupling ecological divergence with reproductive isolation. In contrast, post-pollination barriers are weaker but strongly asymmetric. Hybrid seed formation is largely prevented when E. wilczekianum acts as the maternal parent, consistent with expectations from mating system differences, whereas reciprocal crosses are relatively successful. Despite reduced germination, hybrids display enhanced growth and no evidence of hybrid breakdown, suggesting that intrinsic incompatibilities remain incomplete. ConclusionsThese findings reveal that mating system divergence restructures the entire architecture of reproductive isolation rather than acting as a single barrier. More broadly, our results highlight that early-stage speciation can be driven by coordinated shifts in ecological and reproductive traits, emphasizing the need for integrative approaches to fully understand how barriers interact to generate species boundaries.

Cooperatively breeding species are disproportionately found in extreme and unpredictable climates globally, suggesting that cooperation is beneficial to persistence in climatically challenging conditions. Notably, other dimensions of sociality, like group living and tendency to engage in affiliative social behaviors, offer fitness-related benefits that could make them similarly advantageous in such climates. Here, I present a phylogenetic analysis of Primates aimed at testing whether these two dimensions of sociality--average group size and average percent time spent social grooming--are predicted by climatic challenges in species environments. Results show that time spent grooming is highest in extreme and unpredictable climates, with how dry conditions are explaining the greatest amount of variation. Thus, climate may influence the evolution and/or persistence of social grooming. While multiple mechanisms could mediate this association, subsequent analyses point to the benefits of social affiliation in environments where groupmates have highly competitive dynamics as one potential explanation.

Understanding the environmental conditions that drive selection for increased horizontal plasmid transfer is crucial for predicting the spread of plasmid-encoded antibiotic resistance. In natural systems, plasmids exhibit diverse lifestyles, ranging from "host-centric" strategies, which favor vertical gene transfer (VGT) from mother to daughter cell at the expense of horizontal mobility, to "parasitic" strategies, which favor horizontal gene transfer (HGT) by conjugation at the expense of host fitness. However, laboratory evolution experiments are biased towards host-centric evolution, highlighting a gap in our ability to consistently select for horizontal mobility. To understand this experimental bias, we developed a mathematical model to explore the invasion of pleiotropic transfer mutations. Using local linear stability analysis, we derived an invasion criterion establishing that, for a given pleiotropic cost, the availability of plasmid-free cells determines whether increased transfer is selected. We expanded this model to better represent our previous evolution experiment, in which selection for a host-centric mutant occurred despite the addition of plasmid-free cells and periodic selection for transconjugants. We found that standard batch culture protocols inherently impose strong selective pressure on VGT, heavily limiting the laboratory observation of increases in HGT. We experimentally and theoretically demonstrated that a simple protocol modification--minimizing excess growth by eliminating batch culture passages--effectively tips selection towards HGT. Finally, we performed a parameter sweep to predict the invasion success of hypothetical mutants across HGT-VGT phenotypic space. Our predictive framework can be used to further explore the evolution of plasmid transfer under conditions more representative of natural environments where medically and environmentally relevant plasmids evolve.

We develop a novel individual-based population dynamics model of academic career progression, using 15 years of data from over 1,000 academics from one university. Our model improves on previous models, which, by homogenising career progression, may underestimate the costs of being female. We find multiple effects that compound to slow career progression for women. Women are hired at lower ranks than men, then face the sticky floor problem of getting stuck at the bottom for longer. Further, individuals in STEM fields are promoted more quickly; this disproportionately affects women who are more prevalent in non-STEM fields. Our model reveals age is more complicated than others have found with ODE-based models. Women are older when hired, and promotions favour the young; hence age costs women more. Finally, the probability of attrition rises with years spent at the same rank, regardless of gender. Since women are promoted slower, they experience higher attrition rates. We also deploy our model to test possible interventions. We find just hiring more women will not work. A more nuanced set of interventions is required. Gender parity will only be achieved at the highest ranks if hiring rates are jointly equalised across gender, academic rank, and discipline.

Cooperation is foundational to complex sociality, yet presents profound evolutionary dilemmas - costs and benefits are rarely distributed evenly and the decision to collaborate or defect can involve a complex contextual calculus. These challenges are compounded when cooperation scales from pairs to groups. Group-level cooperation is fundamental to many species success, but how is it sustained and regulated in nature? One promising route to addressing this question is to examine how individuals reorganise their affiliative interactions in anticipation of group-level cooperation. We examine such pre-cooperative reorganisation using long-term data (2013-2018) from three neighbouring groups of wild chimpanzees at the Tai National Park, Cote dIvoire, who routinely cooperate as a collective to defend their territory against other groups. We found that chimpanzees adjusted the distribution of their social contacts in anticipation of risky and proactive territorial defence by forming more broadly connected, yet more diffuse, affiliative networks. Specifically, adult chimpanzees groomed and played with more group members on days of proactive territorial defence, and this pattern was temporally-sensitive, with increased affiliation occurring before, rather than after, the cooperative act. Chimpanzees accessed a broader range of partners through increased interaction efficiency by switching between more partners with shorter interactions per partner. This pattern suggests a shared evolutionary basis of dynamic social readjustment in preparation for group-level social dilemmas in hominids, potentially providing the foundation for the formalized systems of affiliation found in human societies.

As the worlds oceans change in response to climate change, phytoplankton communities will adapt to warmer, more stratified surface waters via plasticity, evolution, and range shifts. Current global ocean models assume that size structured phytoplankton communities have fixed trait relationships, and as a result generally predict that smaller size classes will become more dominant globally. However, this general expectation fails to consider how intra-species trait tradeoffs may operate orthogonally from large-scale inter-species tradeoffs--allowing for alternative evolutionary pathways given the limits and/or possibilities available to ancestral populations. To identify evolutionary pathways phytoplankton populations might take, we develop a novel modeling framework that combines a trait-based phytoplankton quota model with stochastic evolution (ecoTRACE). EcoTRACE explicitly decouples key phytoplankton traits from interspecific allometric relationships, allowing for novel phenotypes to emerge. We validated ecoTRACE against a long-term artificial size selection experiment on Dunaliella tertiolecta. We show that ecoTRACE captures multi-dimensional evolved phenotypes that quota models based on interspecific relationships fail to reproduce. Under fluctuating multi-stressor growth, model populations evolve phenotypic plasticity that deviates from predicted interspecific allometric relationships. EcoTRACE provides a framework for generating hypotheses as to the evolutionary trajectories that phytoplankton will experience in a warmer, more variable ocean.