Ryosuke Iritani

Anthropogenic activity threatens biodiversity through climate change, habitat fragmentation, and increasing frequency and scale of disturbance. Various theoretical studies have sought to shed light on how these factors could promote or hinder the coexistence of species. However, our understanding of the relative importance of, and interactions between, these factors remains limited. In this study, we employ a theoretical approach integrating three commonly cited coexistence mechanisms – the competition–colonisation trade‐off, the intermediate disturbance hypothesis, and habitat fragmentation – into a unified model. We implement a novel method to integrate habitat structure into a system of differential equations, to create a simple and flexible model that can be used to investigate coexistence of multiple species arranged in a competitive hierarchy under different disturbance and habitat structure scenarios. Using this model, we find that considering interactions between different mechanisms is crucial for explaining the coexistence of species. Biodiversity patterns alternative to the uni‐peak curve predicted by the intermediate disturbance hypothesis (e.g. bimodal) emerge along disturbance gradients as habitat fragmentation increases. Furthermore, habitat loss outweighs spatial isolation effects in highly disturbed scenarios, yet isolation can shape species coexistence under low disturbance. These findings underscore the need to integrate spatial and temporal mechanisms in biodiversity management.

Both theory and experiment indicate that biodiversity may increase productivity. However, theory tends to focus on long‐term equilibria, while experiments are most concerned with short‐term effects. An implication of this is that existing theoretical predictions may not match results of experiments. The purpose of our paper is to explore diversity–productivity mechanisms that can work on both short and long‐term trends. We start from short term theory on population growth and analyze the conditions under which positive diversity–productivity relationships emerge. Consistent with contemporary predictions, we show that species diversity can directly increase productivity by increasing the importance of niche differences. The case is not so simple for selection. Previous authors have suggested that biodiversity shapes productivity through a form of selection: species diversity facilitates the increase in abundance of successful species relative to others leading to greater overall productivity. This mechanism is thought to be analogous to natural selection in evolution, but focuses on different species rather than different genotypes. We show that this analogy is incomplete. When the formal definition of selection is applied, selection does not affect absolute productivity. Instead, selection affects the rate of change of productivity over time. As a result, measuring the effect of selection requires measurements from a minimum of three different time points so that multiple measures of productivity can be contrasted. We illustrate how to measure selection in competition experiments. These results demonstrate that diversity can influence productivity through a broader range of mechanisms than those that have been emphasized in ecology. Our results show that this disconnect between theory and experiment can be reconciled by moving past individual observations of productivity and modeling how productivity changes over time.

Beta diversity is a fundamental component of regional biodiversity that accumulates from compositional differences between local species assemblages. Although beta diversity is increasingly applied across spatial scales, its scale dependence remains incompletely synthesized, particularly concerning variation in spatial scale used to define alpha diversity (grain size). Using tree community data from Barro Colorado Island, we find that both additive and multiplicative beta diversity are consistently higher for unequal than equal grain divisions across different spatial extents and numbers of grains. These results can be explained by Jensen’s inequality based on the concave-down relationship of alpha diversity with area that in turn increases beta diversity through the scaling relationship between alpha and gamma diversity. Combined with our additional analysis showing that this prediction is robust to more general models of species richness, including species–area and species–sampling-effort curves, these results reveal a previously unrecognized, predictable range of beta diversity variation.

Natural populations exhibit complex class structures that fundamentally shape evolutionary dynamics. Yet predicting adaptation in such systems remains a central challenge in ecology because ecological realism inherently generates high-dimensional models that obscure biological interpretation and hinder general evolutionary prediction. Here, we overcome this long-standing obstacle by developing structural evolutionary invasion analysis, a unifying theoretical framework integrating invasion determinants with the Projected Next-Generation Matrix (PNGM). The invasion determinant provides a general, closed-form algebraic condition for invasion, while the PNGM structurally compresses life-cycle graphs by eliminating non-focal classes. Crucially, this compression formally derives timescale separation, rigorously preserves Fisher’s reproductive values, and guarantees that the location, convergence, and evolutionary stability of equilibria remain identical to those of the full ecological system. By applying the framework to examples spanning evolutionary epidemiology, stage structure, and inclusive fitness, we show how to capture the direct impact of ecological complexity on evolutionary outcomes while retaining analytical tractability. Our results establish a general principle linking ecological structure to evolutionary prediction, enabling ecologists to analyze adaptation and eco-evolutionary dynamics in heterogeneous populations.

Climate change imposes multiple interacting stressors on ocean ecosystems. The Southern Ocean is among the regions where these impacts are particularly pronounced. Phytoplankton, which perform the bulk of primary production, both maintain the Antarctic marine food web and contribute to the role of the Southern Ocean as the largest oceanic sink for atmospheric CO ₂ . Previous observational and modeling studies have revealed certain drivers–phytoplankton relationships and highlighted the important confounding factors. In this study, we quantified how climate and environmental drivers interact and influence phytoplankton biomass over a 20-year period, across the entire Southern Ocean, within a multi-factor framework. Specifically, we examined the relationships among atmospheric CO ₂ , surface water pH, sea ice concentration, sea surface temperature, mixed layer depth, dissolved iron concentration, and chlorophyll a concentration. To perform this analysis, we introduced and applied two novel methodological approaches. Firstly, to address seasonal autocorrelation, we combined seasonal trend decomposition using LOESS (STL) with piecewise structural equation modeling (SEM). This approach allowed for robust estimation of long-term driver magnitudes and causal pathways, even in the presence of limited sample sizes and complex data structures. Secondly, given the highly interconnected nature of environmental drivers, we employed a data-driven SEM approach to objectively identify causal structures and avoid biases associated with a priori pathway selection. Our results show a clear signal of ocean acidification driven by increasing CO ₂ , iron limitation of phytoplankton biomass during spring, and seasonal differences in both the magnitude and structure of the causal pathways, demonstrating the dynamic nature of seasonal processes in the Southern Ocean. This study presents the first Southern Ocean-wide, multi-factor assessment of climate and environmental impacts on phytoplankton biomass over a two-decade period. It introduces a methodological framework mitigates seasonal autocorrelation to accurately capture long-term seasonal trends, and demonstrates the utility of data-driven causal discovery in resolving complex ecological interactions.

A fundamental problem in ecology is understanding the changes in species composition among sites (i.e. beta‐diversity). It is unclear how spatial heterogeneity in species occupancy across sites shapes patterns of beta‐diversity. To address this question, we develop probabilistic models that consider two spatial or temporal sites, where presence probabilities vary both among species and between the sites. We derive analytical and approximate formulae for the expectation of pairwise beta‐diversity. Using a graphical tool, stochastic incidence plots (SIPs), which depict the presence probabilities in two sites along species labels, we develop a means to conceptualize the heterogeneity in presence probabilities: the steepness or unevenness of SIPs reflects species‐level heterogeneity, while the degree of overlap between SIPs indicates site‐level heterogeneity. We find that when SIPs completely overlap (i.e. two sites have the same presence probability for each species), flat SIPs ‐ with all species having the same presence probability ‐ maximize the expected beta‐diversity. We refer to this prediction as the ‘transfer principle for beta'. Second, using SIPs and the probabilistic method in a two‐species scenario, we demonstrate that beta‐diversity is lower when SIPs are parallel compared to when they are anti‐parallel. We also find that this prediction is consistent with the well‐known checkerboard pattern in incidence matrices. Finally, we apply the method to the species distribution models for five woodpecker species in Switzerland, showing that their spatial distributions will change significantly. Overall, this work improves our understanding of how pairwise beta‐diversity responds to occupancy heterogeneity.

Interspecific pollen transfer (IPT), the pollen movement between plant species via shared pollinators, reduces the reproductive success of pollen‐recipient plants due to hybridization with heterospecific pollen grains. As a result, IPT hinders coexistence of sympatric, co‐flowering species by reducing their reproductive success. IPT likely exerts selective pressure on plant reproductive systems, particularly on sex allocation (resource investment to pollen versus ovules). However, the influence of IPT on the evolutionary dynamics of sex allocation and associated ecological processes remains poorly understood. Here, we explore how the female costs incurred by IPT affect the co‐evolution and coexistence dynamics of two plant species. To address this, we construct a mathematical model capturing interactions between two plant species through pollen transfer and resource competition, and reduced fertilization caused by IPT. Our analysis focuses on a scenario where an invasive species with female‐biased sex allocation enters a habitat occupied by a resident species with evolutionarily stable, equal sex allocation (Fisherian sex allocation). Using adaptive dynamics theory, we demonstrated that, irrespective of IPT strength, natural selection drives both species toward equal sex allocation, consistent with the Fisherian sex allocation theory. We present two key predictions of eco‐evolutionary outcomes. First, when the impacts of IPT are comparable for both species, the resulting eco‐evolutionary dynamics lead to their stable coexistence. In contrast, if IPT from the invasive species is disproportionately stronger than IPT from the resident species, the invasive species' evolutionary shift from female‐biased to equal sex allocation drives the resident species to extinction. Since local mate competition in small populations can favour female‐biased sex allocation, our results suggest that invasive species experiencing reduced local mate competition may drive resident species to extinction. Our findings underscore the intricate role of IPT‐driven sex allocation evolution in shaping coexistence of closely related plant species.

Parasitoid wasps exhibit remarkable diversity in life-history traits and are categorized into two major groups based on the timing of emergence: those that begin consuming host tissues immediately after hatching (“idiobiont”) and those that can delay the emergence depending on host maturation (“koinobiont”). Although delayed emergence allows parasitoids to exploit unparasitized, young hosts, it incurs a mortality cost during the waiting period. While numerous empirical studies have examined the adaptive significance of this trait, how host stage-structure and ecological factors jointly shape the evolution of emergence timing remains poorly understood in a formalized context. Here, we develop a stage-structured mathematical model to analyze the evolutionary dynamics of delayed emergence and its consequences for host exploitation. By explicitly incorporating host developmental stages, our model explores the joint evolution of two traits: the preference for young versus old host, and the timing of emergence. Our results reveal that the optimal strategy is determined by the relative reproductive value of host stages, which depends on the balance between host growth and mortality. We demonstrate that delayed emergence evolves when the future value of a growing host outweighs the immediate cost of mortality, thereby driving the divergence between idiobiont and koinobiont strategies. These findings provide a theoretical framework that unifies the diversity of parasitoid life histories. Significance Statement: Parasitoids exhibit striking diversity in life-history traits such as body size, fecundity, and clutch size, reflecting adaptations to host exploitation. A key behavioral distinction is whether parasitoids consume the host immediately (idiobionts) or can delay emergence while the host continues to grow (koinobionts). Many empirical studies have suggested and tested some hypothesis. On the other hand, the adaptive significance of delayed emergence have not been formulated yet, so the quantitative relationship between selection pressure and life history traits is yet to be better understood. Here, we develop a mathematical model that integrates host growth with parasitoid emergence timing. Our analysis supports the adaptive significance of delayed emergence and shows how delayed emergence can promote diversification of parasitoid life-history strategies. These results suggest that delayed emergence is a key driver of idiobiont–koinobiont dichotomy, providing new insights into the evolutionary basis of parasitoid–host interactions and aligning directly with the central aims of behavioral ecology.

Interspecific pollen transfer (IPT), the movement of pollen grains between different plant species by sharing pollinators, incurs costs (fitness reduction) for seed production. IPT thereby reduces the reproductive success of co-flowering plants sharing pollinators, thus preventing their coexistence. However, the impact of IPT on the evolutionary dynamics and evolution-mediated ecological dynamics of sex allocation resource investment to pollen versus ovules) is poorly understood. Here, we investigate the consequences of the female costs incurred by IPT for the co-evolution and coexistence of two plants, by using a mathematical model where two plant species interact with each other via resource competition, pollen movements within and between species, and reduced fertilization due to IPT. The ecological situation we consider here is that an invasive species with female-biased sex allocation immigrates into a habitat of a resident species whose sex allocation is evolutionarily maintained at Fisherian sex allocation (FSA). By using adaptive dynamics theory, we found that regardless of the strength of IPT, natural selection favours the equal allocation to pollen grains and ovules (FSA) for both species. If the mutual impact of IPT on two species is similar in magnitude, we find that the eco-evolutionary dynamics can lead to their stable coexistence. In contrast, when only the invasive species negatively impacts the resident species through IPT, the evolution in invasive species from female-biased sex allocation to FSA causes the extinction of the resident species. Given that local mate competition in small populations is expected to result in female-based sex allocation, our finding suggests that if invasive species are relaxed from local mate competition, they may drive the resident species to extinction. Our study highlights the importance and complexity of the evolution of biased sex allocation driven by IPT to understand the coexistence of closely related plant species.

Experiments suggest that increasing species’ diversity increases productivity over short observation periods. At equilibrium, theory predicts that diversity may increases productivity, such as in communities of coexisting competitors. This suggests a disconnect with theory focusing on long term trends and experiments observing short-term population growth. To resolve this gap, we start from short term models of population growth and analyze the conditions under which diversity productivity relationships emerge. Using this approach, we show that selection (as it is formally defined in dynamic models) does not affect productivity. Instead, selection changes productivity over time. In contrast, niche complementarity can change productivity directly. We illustrate how to measure each of the terms in competition experiments. These results highlight how the effects of diversity on productivity can be obscured by our choice of response variable.