Rudolf Philippe Rohr

Introduction: Treatment decisions in older patients with mature B‐cell lymphoproliferative disorders (MBLD) are frequently guided by chronological age and comorbidity burden, although their ability to predict clinically relevant outcomes remains uncertain. Methods: We conducted a retrospective real‐world study including 172 patients with MBLD between 2016 and 2021, assessing the association of age and comorbidity burden (standardised using the Cumulative Illness Rating Scale for Geriatrics, CIRS‐G score) with treatment intensity, toxicity, and clinical outcomes. Results: Treatment intensity decreased significantly with both age ( p = 0.003) and CIRS‐G score ( p < 0.001). However, neither age nor CIRS‐G score was associated with an increased rate of treatment‐related toxic events. In contrast, older age was associated with worse overall survival and lower response rates (all p < 0.05), whereas CIRS‐G score was not significantly associated with these outcomes. Similar associations between age and outcomes were observed in the DLBCL subgroup. Conclusion: In this real‐world cohort of patients with MBLD, chronological age was more consistently associated with treatment patterns and clinical outcomes than comorbidity burden as measured by the CIRS‐G score. These findings suggest that chronological age alone may not adequately capture the heterogeneity of older patients and support the use of more comprehensive assessments of frailty and functional status to inform treatment decisions.

Reply to Ernst and Spaak: The LV-map accurately estimates species interactions even when functional responses are nonlinear

Given the accumulation of evidence that evolution can affect ecological dynamics, especially under global change scenarios, a key question is how such ecoevolutionary dynamics may change the coexistence of species and biodiversity in general. In the present article, we propose a graphical approach allowing to simultaneously discuss ecological coexistence and phenotype evolution. Our graphical approach allows tackling the two aspects in the same parameter space, allowing direct links between ecological and evolutionary perspectives. While evolution is often thought positive for the resilience of ecological systems, we first highlight it does not usually allow for better coexistence for the system as a whole. Even when focusing on the fate of the species that is evolving, evolution often leads to greater vulnerability. The graphical approach we propose is flexible and can be applied to all interaction types and covers variations in trade-off structures. Using this flexibility, we highlight how evolutionary effects can be positive or negative for coexistence, depending on these two components. Finally, we illustrate how the approach can be applied, using empirical examples derived from the literature. We thereby highlight the critical ingredients needed to inform the graphical approach, its potential use for proposing testable scenarios, but also clarify its limits.

Species and populations in nature fall along a specialist-generalist continuum in habitat preferences and resource use. Despite a rich literature on the evolution of specialization at the population level, most studies focusing on community assembly, species packing and diversification have considered solely niche positions within a community. Yet, empirical evidence suggests that generalism levels in communities respond dynamically to the current biotic environment and species interactions. Here, we jointly consider niche positions and generalism, and ask how different modes of community assembly - from evolutionary assembly to repeated immigration - shape the resulting community. We find that coevolution can lead to diversification, including spontaneous emergence of asymmetric configurations of generalist and specialist phenotypes. We further observe distinct alternative evolutionary endpoints, comprising communities consisting of either many specialists or fewer generalists, some of which are only locally uninvasible. More broadly, we show how the history of local coevolution and dispersal events jointly influence community dynamics and structure. By modeling assembly along a continuum from pure coevolution to pure dispersal, we highlight how high rates of evolution constrain richness while promoting generalism, partly because evolutionary assembly can get stuck at local optima. In contrast, communities shaped by immigration tend to be more species-rich, with diverse and specialist characters. Finally, higher rates of evolution decrease invasion success in mixed-assembly scenarios, a pattern consistent with community monopolization. We discuss how our theoretical findings match observational and experimental evidence for community assembly across scales.

Inferring coexistence metrics, such as niche and fitness differences, in changing environments is key for understanding the mechanism behind species coexistence and predicting its likelihood. However, it first requires estimating the per capita interactions between organisms and their intrinsic growth rates—parameters that are typically measured by isolating organisms from their natural context. Here, we first use weighted multivariate regression on the per capita growth rates of populations to estimate these key ecological parameters directly from time-series data of species-rich communities. Second, we infer niche differences and species resistance, which are two important metrics for understanding species coexistence. Our approach allows these metrics to vary over time and under different environmental conditions. We validate our approach using synthetic data and apply it to both experimental and observational data as a proof of concept. Experimental results show an expected allocative trade-off between grazing resistance and rapid growth in algae. Moreover, coexistence likelihood decreases, and coexistence balance is disturbed under stressful environmental conditions. Observational data suggests variations in intrinsic growth rates and per capita interactions among autotrophic guilds with respect to seasonal patterns. In addition, interactions between cyanobacteria with green algae and chrysophytes might indicate a potential cause for bloom development. Our approach offers a powerful toolbox to gain insight into the mechanisms underlying ecological dynamics, species coexistence, and community structures under varying environments. Such an understanding will help us address important ecological and evolutionary questions, such as explaining biodiversity patterns and solving the problem of cyanobacteria bloom.

Ecologists have documented many characteristics of natural systems that foster ecosystem persistence, and it might be deduced that such strategies are essential for counteracting the negative effect of complexity on local stability that was suggested by R.M. May in his influential work of the 1970s. However, we show that the loss of local stability does not necessarily imply total ecosystem extinction. A more general criterion of ecosystem viability is the long‐term persistence of any number of surviving species—not necessarily all of them. With this approach, we show that persistence increases with complexity, contrary to previous theoretical findings. In particular, positive interactions (mutualistic or prey‐to‐predator) play a crucial role in creating ecological niches, which sustain biodiversity with increasing complexity.

Terrestrial and marine ecosystems exhibit remarkable species diversity and richness. Among the hypotheses put forward to explain this diversity —its origins and the forces sustaining it— one prominent hypothesis posits that pathogens could be pivotal in generating and maintaining species diversity within ecosystems. To explore this hypothesis, we analyzed a host-pathogen system, examining the specific conditions that might facilitate pathogen-driven diversification within the host population, with a focus on both the emergence and persistence of diversity. By applying a fecundity-transmission trade-off in which a non-evolving pathogen influences its evolving host, we observed that pathogens only infrequently drive host diversification, and only around intermediate trade-off conditions. Furthermore, while these rare diversification events result in stable coexistence, evolved diversity is low, consisting of two coexisting host morphs. Because the sole evolution of hosts in response to pathogens offers limited opportunity for diversification, we propose additional mechanisms that could foster higher levels of diversity within the system.

In most species, life history trade-offs generate conflicts between several biological functions because of resource allocation constraints. If under a pathogenic infection, the host can trigger some defense mechanisms against the pathogen, there is usually a fitness cost associated in terms of fecundity reduction. This pattern, known as the fecundity-immunity trade-off, has received much attention, but its implications on host eco-evolutionary dynamics, and particularly on emergent properties such as host abundance and pathogen propagation remain poorly understood. Here, we use an SIR model that incorporates three distinct host immune mechanisms, which are a reduction in pathogen transmission, an increase in host recovery, and a reduction in pathogen-associated host death. Our results indicate that the effect of a fecundity-immunity trade-off depends not only on its strength, but also on the particular immune mechanism considered. We found that, in all three immune mechanisms, for a weak trade-off, the host evolves simultaneously toward high fecundity and high immunity, while for a strong trade-off, high fecundity is selected at the expense of immunity. Intermediate trade-off results in two distinct alternative evolutionary strategies; high fecundity and low immunity, and low fecundity and high immunity. Moreover, we show that disease propagation and host abundance usually show opposite dynamics along the evolutionary trajectories. We here believe that the incorporation of this trade-off in further analysis of host-pathogen interactions may help to better understand the complexity of the ecological and evolutionary dynamics in such systems.

Knowledge about the per capita interactions between organisms and their intrinsic growth rates, and how these vary over environmental gradients, allows understanding and predicting species coexistence and community dynamics. Estimating these crucial ecological parameters requires tedious experimental work, with isolation of organisms from their natural context. Here, we provide a novel approach for inferring these key parameters from time-series data by using weighted multivariate regression on the per capita growth rates of populations. Beyond the validation of our approach on synthetic data, we reveal from experimental data an expected allocative trade-off between grazing resistance and rapid growth in algae. Application of observational data suggests facilitation between cyanobacteria and chrysophyte, indicating a possible explanation for cyanobacteria bloom. Our approach offers a way forward for inferring per capita interactions and intrinsic growth rates directly from natural communities, providing realism, mechanistic understanding of eco-evolutionary dynamics, and key parameters to develop predictive models.

The realization that evolutionary feedbacks need to be considered to fully grasp ecological dynamics has sparked interest in the effect of evolution on community properties like coexistence and productivity. However, little is known about the evolution of community robustness and productivity along diversification processes in species-rich systems. We leverage the recent structural approach to coexistence together with adaptive dynamics to study such properties and their relationships in a general trait-based model of competition on a niche axis. We show that the effects of coevolution on coexistence are two-fold and contrasting depending on the time scale considered. In the short term, evolution of niche differentiation strengthens coexistence, while long-term diversification leads to niche packing and decreased robustness. Moreover, we find that coevolved communities tend to be on average more robust and more productive than non-evolutionary assemblages. We illustrate how our theoretical predictions echo in observed empirical patterns and the implications of our results for empiricists and applied ecologists. We suggest that some of our results such as the improved robustness of Evolutionarily Stable Communities could be tested experimentally in suitable model systems.