American Naturalist最新文献

AbstractSexual selection is widely hypothesized to facilitate speciation and phenotypic evolution, but evidence from comparative studies has been mixed. Many previous studies have relied on proxy variables to quantify the intensity of sexual selection, raising the possibility that inconclusive results may reflect, in part, the imperfect measurement of this evolutionary process. Here, we test the relationship between phylogenetic speciation rates and indices of the opportunity for sexual selection drawn from populations of 82 vertebrate taxa. These indices provide a much more direct assessment of sexual selection intensity than proxy traits and allow straightforward comparisons among distantly related clades. We find no correlation between the opportunity for sexual selection and speciation rate, and this result is consistent across many complementary analyses. In addition, widely used proxy variables-sexual dimorphism and dichromatism-are not correlated with the indices employed here. Moreover, we find that the opportunity for sexual selection has low phylogenetic signal and that intraspecific variability in selection indices for many species approaches the range of variation observed across all vertebrates as a whole. Our results potentially reconcile a major paradox in speciation biology at the interface between microevolution and macroevolution: sexual selection can be important for speciation, yet the evolutionary lability of the process over deeper timescales restricts its impact on broad-scale patterns of biodiversity.

AbstractThe population dynamics of autopolyploids-organisms with more than two genome copies of a single species-and their diploid progenitors have been extensively studied. The acquisition of multiple genome copies is heavily influenced by stochasticity, which strongly suggests the efficacy of a probabilistic approach to examine the long-term dynamics of a population with multiple cytotypes. Yet our current understanding of the dynamics of autopolyploid populations has not incorporated stochastic population dynamics and coexistence theory. To investigate the factors contributing to the probability and stability of coexisting cytotypes, we designed a new population dynamics model that incorporates demographic and environmental stochasticities to simulate the formation, establishment, and persistence of diploids, triploids, and autotetraploids in the face of gene flow among cytotypes. We found that increased selfing rates and pronounced reproductive isolation promote coexistence of multiple cytotypes. In stressful environments and with strong competitive effects among cytotypes, these dynamics are more complex; our stochastic modeling approach reveals the resulting intricacies that give autotetraploids competitive advantage over their diploid progenitors. Our work is foundational for a better understanding of the dynamics of coexistence of multiple cytotypes.

AbstractMany animals exhibit ontogenetic niche shifts as they grow, which strongly affects population dynamics. However, such niche shifts can be constrained by the physical environment that the population occupies. To study this, we develop a physiologically structured population model parameterized for brown trout and vary the availability of a stream used as an exclusive juvenile nursery habitat. We find fewer but large, fast-growing adults in lakes with small streams and more but smaller, slow-growing adults in lakes with large streams. We show that the mechanism behind this pattern is a reduced ability of cannibals to control juvenile survival in the lake with increasing stream availability. Juveniles emerging from the stream at larger sizes intensify competition with the lake-dwelling adults, leading to slower individual growth. These results are similar for other sources of size-dependent juvenile mortality in the lake. Field data from brown trout lakes across a stream size gradient show the same pattern: reduced trout growth and fewer large individuals in lakes with larger tributary streams. We show how ontogenetic niche shifts and stage-specific habitat availability affect population structure and dynamics through size-dependent mortality and competition. Our results provide an important foundation that may help design effective conservation and restoration strategies.

AbstractTheoretical studies have shown that stochasticity can affect the dynamics of ecosystems in counterintuitive ways. However, without knowing the equations governing the dynamics of populations or ecosystems, it is difficult to ascertain the role of stochasticity in real datasets. Therefore, the inverse problem of inferring the governing stochastic equations from datasets is important. Here, we present an equation discovery methodology that takes time series data of state variables as input and outputs a stochastic differential equation. We achieve this by combining traditional approaches from stochastic calculus with the equation discovery techniques. We demonstrate the generality of the method via several applications. First, we deliberately choose various stochastic models with fundamentally different governing equations, yet they produce nearly identical steady-state distributions. We show that we can recover the correct underlying equations, and thus infer the structure of their stability, accurately from the analysis of time series data alone. We demonstrate our method on two real-world datasets-fish schooling and single-cell migration-that have vastly different spatiotemporal scales and dynamics. We illustrate various limitations and potential pitfalls of the method and how to overcome them via diagnostic measures. Finally, we provide our open-source code via a package named PyDaDDy (Python Library for Data-Driven Dynamics).

AbstractAlthough prey foraging in mixed-species groups benefit from a reduced risk of predation, whether heterospecific groupmates move together in the landscape, and more generally to what extent mixed-species groups remain cohesive over time and space, remains unknown. Here, we used GPS collars with video cameras to investigate the movements of plains zebras (Equus quagga) in mixed-species groups. Blue wildebeest (Connochaetes taurinus), impalas (Aepyceros melampus), and giraffes (Giraffa camelopardalis) commonly form mixed-species groups with zebras in savanna ecosystems. We found that zebras adjust their movement decisions solely on the basis of the presence of giraffes, being more likely to move in zebra-giraffe herds, and this was correlated with a higher cohesion of such groups. Additionally, zebras moving with giraffes spent more time grazing, suggesting that zebras benefit from foraging in the proximity of giraffes. Our results provide new insights into animal movements in mixed-species groups, contributing to a better consideration of mutualism in movement ecology.

AbstractA vertical separation in light and nutrient availability is observed in many terrestrial and aquatic ecosystems. In lakes and oceans, the opposing vertical gradients of light and nutrients typically observed are believed to promote phagomixotrophy, a generalist strategy that combines resource acquisition through photoautotrophic and phagoheterotrophic pathways. While phagomixotrophy is widespread, it is not well understood how this strategy performs against pure specialist strategies in a resource competition context. We simulate the dynamics of three competitors (pure photoautotroph, phagomixotroph, pure phagoheterotroph) and bacterial prey over the vertical dimension of a water column to investigate what conditions of resource availability favor mixotrophy and how the presence of the phagomixotroph alters community dynamics. Since mixotrophs can be more or less photoautotrophic, we incorporated this variability into our model. Under weak vertical mixing, mixotrophs persist under most light and nutrient conditions and negatively affect specialists. Mixotrophs can even be dominant competitors when they display an optimal degree of phototrophy, which is positively related to water transparency and negatively related to nutrient supply. The model indicates that the spatial organization of nanophytoplankton communities in water columns could arise through vertical niche partitioning of multiple resource acquisition strategies and that phagomixotrophy can promote overall community production.

AbstractIt is increasingly evident that most interactions are not static and move along a continuum ranging from pure mutualism (i.e., in which each species in the interaction has a net benefit in the long term) to pure antagonism (i.e., in which each species in the interaction has a net damage in the long term). Despite numerous experimental and theoretical works on this concept, predicting interaction outcomes within an ecological community continues to pose a significant challenge. This article aims to tackle this challenge by presenting a theoretical methodology for predicting the interaction outcomes within the common mutualism-antagonism modeling framework. Specifically, my main finding is to describe the influence of the population abundance of the species, the interaction effects, and the ecological context on the interaction outcomes and to quantify their relative contribution. I found that the interaction outcomes depend on the number of interacting species. In particular, when the number of interacting species increases, the trend is to skip situations where all species benefit from the interactions.

AbstractFor as long as ecology has existed, ecologists have struggled to reconcile natural history and mathematical models. This article revisits Gause's 1934 book, The Struggle for Existence, which effectively bridged their divide in his time by integrating insights from the then-separate natural history niche theory and the demographic Lotka-Volterra model. Gause's integration was based on a compelling verbal argument in which he reinterpreted the competition coefficient in terms of the niche concept. This interpretation was highly influential and was later embedded in models of modern coexistence theory. The discussion will compare Gause's verbal integration with current modeling-based approaches. While uncommon today, it will be argued that Gause's original approach carries unique advantages and remains relevant to contemporary ecology.

AbstractRecent developments in competition theory-namely, modern coexistence theory (MCT)-have aided empiricists in formulating tests of species persistence, coexistence, and evolution from simple to complex community settings. However, the parameters used to predict competitive outcomes, such as interaction coefficients, invasion growth rates, and stabilizing differences, remain biologically opaque, making findings difficult to generalize across ecological settings. This article is structured around five goals toward clarifying MCT by first making a case for the modern-day utility of MacArthur's consumer-resource model, a model with surprising complexity and depth: (i) to describe the model in uniquely accessible language, deciphering the mathematics toward cultivating deeper biological intuition about competition's inner workings regardless of what empirical toolkit one uses; (ii) to provide translation between biological mechanisms from MacArthur's model and parameters used to predict coexistence in MCT; (iii) to make explicit important but understated assumptions of MacArthur's model in plain terms; (iv) to provide empirical recommendations; and (v) to examine how key ecological concepts (e.g., r/K-selection) can be understood with renewed clarity through MacArthur's lens. We end by highlighting opportunities to explore mechanisms in tandem with MCT and to compare and translate results across ecological currencies toward a more unified ecological science.

AbstractThe metapopulation concept offers significant explanatory power in ecology and evolutionary biology. Metapopulations, a set of spatially distributed populations linked by dispersal, and their community and ecosystem level analogs, metacommunity and meta-ecosystem models, tend to be more stable regionally than locally. This fact is largely attributable to the interplay of spatiotemporal heterogeneity and dispersal (the inflationary effect). We highlight this underappreciated (but essential) role of spatiotemporal heterogeneity in metapopulation biology, present a novel expression for quantifying and defining the inflationary effect, and provide a mechanistic interpretation of how it arises and impacts population growth and abundance. We illustrate the effect with examples from infectious disease dynamics, including the hypothesis that policy decisions made during the COVID-19 pandemic generated spatiotemporal heterogeneity that enhanced the spread of disease. We finish by noting how spatiotemporal heterogeneity generates emergent population processes at large scales across many topics in the history of ecology, as diverse as natural enemy-victim dynamics, species coexistence, and conservation biology. Embracing the complexity of spatiotemporal heterogeneity is vital for future research on the persistence of populations.