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In the Anthropocene, intensifying ecological disturbances pose significant challenges to our predictive capabilities for ecosystem responses. Macroecology—which focuses on emergent statistical patterns in ecological systems—unveils consistent regularities in the organization of biodiversity and ecosystems. These regularities appear in terms of abundance, body size, geographical range, species interaction networks, or the flux of matter and energy. This paper argues for moving beyond qualitative resilience metaphors, such as the 'ball and cup', towards a more quantitative macroecological framework. We suggest a conceptual and theoretical basis for ecological resilience that integrates macroecology with a stochastic diffusion approximation constrained by principles of biological symmetry. This approach provides an alternative novel framework for studying ecological resilience in the Anthropocene. We demonstrate how our framework can effectively quantify the impacts of major disturbances and their extensive ecological ramifications. We further show how biological scaling insights can help quantify the consequences of major disturbances, emphasizing their cascading ecological impacts. The nature of these impacts prompts a re-evaluation of our understanding of resilience. Emphasis on regularities of ecological assemblages can help illuminate resilience dynamics and offer a novel basis to predict and manage the impacts of disturbance in the Anthropocene more efficiently. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'. [ABSTRACT FROM AUTHOR]

We develop a spatially realistic model of mutualistic metacommunities that exploits the joint structure of spatial and interaction networks. Assuming that all species have the same colonisation and extinction parameters, this model exhibits a sharp transition between stable non-null equilibrium states and a global extinction state. This behaviour allows defining a threshold on colonisation/extinction parameters for the long-term metacommunity persistence. This threshold, the 'metacommunity capacity', extends the metapopulation capacity concept and can be calculated from the spatial and interaction networks without needing to simulate the whole dynamics. In several applications we illustrate how the joint structure of the spatial and the interaction networks affects metacommunity capacity. It results that a weakly modular spatial network and a power-law degree distribution of the interaction network provide the most favourable configuration for the long-term persistence of a mutualistic metacommunity. Our model that encodes several explicit ecological assumptions should pave the way for a larger exploration of spatially realistic metacommunity models involving multiple interaction types. [ABSTRACT FROM AUTHOR]

Alpha-diversity indices are an essential tool for describing and comparing biodiversity. Microbial ecologists apply indices originally intended for, or adopted by, macroecology to address questions relating to taxonomy (conserved marker) and function (metagenome-based data). In this Perspective piece, I begin by discussing the nature and mathematical quirks important for interpreting routinely employed alpha-diversity indices. Secondly, I propose a metagenomic alpha-diversity index (MD) that measures the (dis)similarity of protein-encoding genes within a community. MD has defined limits, whereby a community comprised mostly of similar, poorly diverse protein-encoding genes pulls the index to the lower limit, while a community rich in divergent homologs and unique genes drives it toward the upper limit. With data acquired from an in silico and three in situ metagenome studies, I derive MD and typical alpha-diversity indices applied to taxonomic (ribosomal rRNA) and functional (all protein-encoding) genes, and discuss their relationships with each other. Not all alpha-diversity indices detect biological trends, and taxonomic does not necessarily follow functional biodiversity. Throughout, I explain that protein Richness and MD provide complementary and easily interpreted information, while probability-based indices do not. Finally, considerations regarding the unique nature of microbial metagenomic data and its relevance for describing functional biodiversity are discussed. [ABSTRACT FROM AUTHOR]

The article explores the field of cognitive movement ecology, which focuses on understanding the cognitive processes involved in animal movement. It acknowledges the challenges of studying cognition in wild animals but highlights recent research that has shed light on the importance of perception, spatial memory, and learning in animal movement. The article presents a collection of original research papers that use theoretical simulations and empirical studies to analyze movement data and gain insights into the cognitive abilities of animals. The authors emphasize the need for further empirical investigation and discuss the relevance of cognitive movement ecology in the context of environmental changes. [Extracted from the article]

A two-patch logistic metapopulation model is investigated both analytically and numerically focusing on the impact of dispersal on population dynamics. First, the dependence of the global dynamics on the stability type of the full extinction equilibrium point is tackled. Then, the behaviour of the total population with respect to the dispersal is studied analytically. Our findings demonstrate that diffusion plays a crucial role in the preservation of both subpopulations and the full metapopulation under the presence of stochastic perturbations. At low diffusion, the origin is a repulsor, causing the orbits to flow nearly parallel to the axes, risking stochastic extinctions. Higher diffusion turns the repeller into a saddle point. Orbits then quickly converge to the saddle's unstable manifold, reducing extinction chances. This change in the vector field enhances metapopulation robustness. On the other hand, the well-known fact that asymmetric conditions on the patches is beneficial for the total population is further investigated. This phenomenon has been studied in previous works for large enough or small enough values of the dispersal. In this work, we complete the theory for all values of the dispersal. In particular, we derive analytically a formula for the optimal value of the dispersal that maximizes the total population. [ABSTRACT FROM AUTHOR]

Global change is rapidly and fundamentally altering many of the processes regulating the flux of energy throughout ecosystems, and although researchers now understand the effect of temperature on key rates (such as aquatic primary productivity), the theoretical foundation needed to generate forecasts of biomass dynamics and extinction risk remains underdeveloped. We develop new theory that describes the interconnected effects of nutrients and temperature on phytoplankton populations and show that the thermal response of equilibrium biomass (i.e. carrying capacity) always peaks at a lower temperature than for productivity (i.e. growth rate). This mismatch is driven by differences in the thermal responses of growth, death, and per‐capita impact on the nutrient pool, making our results highly general and applicable to widely used population models beyond phytoplankton. We further show that non‐equilibrium dynamics depend on the pace of environmental change relative to underlying vital rates and that populations respond to variable environments differently at high versus low temperatures due to thermal asymmetries. [ABSTRACT FROM AUTHOR]

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just 1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data. [ABSTRACT FROM AUTHOR]

The structure and diversity of microbial communities are intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g. genus vs. family), an empirical pattern previously evaluated in the context of the Diversity Begets Diversity (DBD) hypothesis (Madi et al., 2020). We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members, demonstrating that the DBD can be sufficiently explained using the SLM as a null model of ecology. Contrastingly, only by including correlations between the abundances of community members (e.g. as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.

Economic and ecological models can be extremely complex, with a large number of agents/species each featuring multiple interacting dynamical quantities. In an attempt to understand the generic stability properties of such systems, we define and study an interesting new matrix ensemble with extensive correlations, generalising the elliptic ensemble. We determine analytically the boundary of its eigenvalue spectrum in the complex plane, as a function of the correlations determined by the model at hand. We solve numerically our equations in several cases of interest, and show that the resulting spectra can take a surprisingly wide variety of shapes.

May and Leonard (SIAM J Appl Math 29:243–253, 1975) introduced a three-species Lotka–Volterra type population model that exhibits heteroclinic cycling. Rather than producing a periodic limit cycle, the trajectory takes longer and longer to complete each “cycle”, passing closer and closer to unstable fixed points in which one population dominates and the others approach zero. Aperiodic heteroclinic dynamics have subsequently been studied in ecological systems (side-blotched lizards; colicinogenic Escherichia coli), in the immune system, in neural information processing models (“winnerless competition”), and in models of neural central pattern generators. Yet as May and Leonard observed “Biologically, the behavior (produced by the model) is nonsense. Once it is conceded that the variables represent animals, and therefore cannot fall below unity, it is clear that the system will, after a few cycles, converge on some single population, extinguishing the other two.” Here, we explore different ways of introducing discrete stochastic dynamics based on May and Leonard’s ODE model, with application to ecological population dynamics, and to a neuromotor central pattern generator system. We study examples of several quantitatively distinct asymptotic behaviors, including total extinction of all species, extinction to a single species, and persistent cyclic dominance with finite mean cycle length. [ABSTRACT FROM AUTHOR]

Mathematical models serve many purposes in biology. Each and every model is a necessary simplification of reality, and, as simplifications, these models are also wrong by definition. And yet there are many ways to be wrong, and some of these are a much greater concern than others. For example, a paradoxical model‐based prediction may simply be puzzling whereas unphysical variables (e.g. negative amounts of time or temperatures below absolute zero) and nonbiological variables (e.g. negative abundances or negative feeding rates) should be avoided altogether.Here I analyse a discrete‐time model of annual‐plant population dynamics and three phenomenological models for density‐dependent fecundity. These phenomenological models are generally interchanged solely on the basis of their statistical fit to data. On the other hand, I highlight ways in which their phenomenological basis hampers our ability to capture known aspects of annual‐plant biology.I then demonstrate how to specify a more flexible, biologically appropriate model and illustrate this model's behaviour and interpretation in single‐species and multi‐species contexts. By constructing this generative model for annual‐plant population dynamics, I can also demonstrate how and why emergent phenomena, such as negative density dependence, emerge.Although my focus is on a model applied to annual plants, the biological implications extend to modelling any species with a discrete life cycle and nonoverlapping generations. More broadly, my exploration here showcases that there are many more criteria with which we could, and arguably should, ground‐truth mathematical models across biology. [ABSTRACT FROM AUTHOR]

Bacteriocins, toxic peptides involved in the competition between bacterial strains, are extremely diverse. Previous work on bacteriocin dynamics has highlighted the role of non-transitive 'rock–paper–scissors' competition in maintaining the coexistence of different bacteriocin profiles. The focus to date has primarily been on bacteriocin interactions at the within-host scale (i.e. within a single bacterial population). Yet in species such as Streptococcus pneumoniae, with relatively short periods of colonization and limited within-host diversity, ecological outcomes are also shaped by processes at the epidemiological (between-host) scale. Here, we first investigate bacteriocin dynamics and diversity in epidemiological models. We find that in these models, bacteriocin diversity is more readily maintained than in within-host models, and with more possible combinations of coexisting bacteriocin profiles. Indeed, maintenance of diversity in epidemiological models does not require rock–paper–scissors dynamics; it can also occur through a competition–colonization trade-off. Second, we investigate the link between bacteriocin diversity and diversity at antibiotic resistance loci. Previous work has proposed that bacterial duration of colonization modulates the fitness of antibiotic resistance. Due to their inhibitory effects, bacteriocins are a plausible candidate for playing a role in the duration of colonization episodes. We extend the epidemiological model of bacteriocin dynamics to incorporate an antibiotic resistance locus and demonstrate that bacteriocin diversity can indeed maintain the coexistence of antibiotic-sensitive and -resistant strains. [ABSTRACT FROM AUTHOR]

Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach-inferring which processes and ecological interactions drive observed dynamics-remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.

Abstract Ecosystems are subject to a multitude of anthropogenic environmental changes. Experimental research in the field of multiple stressors has typically involved varying the number of stressors, here termed stressor richness, but without controlling for total stressor intensity. Mistaking stressor intensity effects for stressor richness effects can misinform management decisions when there is a trade‐off between mitigating these two factors. We incorporate multiple stressors into three community models and show that, at a fixed total stressor intensity, increasing stressor richness aggravates joint stressor effects on ecosystem functioning, but reduces effects on species persistence and composition. In addition, stressor richness weakens the positive selection and negative complementarity effects on ecosystem function. We identify the among‐species variation of stressor effects on traits as a key determinant of the resulting community‐level stressor effects. Taken together, our results unravel the mechanisms linking multiple environmental changes to biodiversity and ecosystem function.

All animals face the challenge of finding nutritious resources in a changing environment. To maximize lifetime fitness, the exploratory behavior has to be flexible, but which behavioral elements adapt and what triggers those changes remain elusive. Using experiments and modeling, we characterized extensively how Drosophila larvae foraging adapts to different food quality and distribution and how the foraging genetic background influences this adaptation. Our work shows that different food properties modulated specific motor programs. Food quality controls the traveled distance by modulating crawling speed and frequency of pauses and turns. Food distribution, and in particular the food–no food interface, controls turning behavior, stimulating turns toward the food when reaching the patch border and increasing the proportion of time spent within patches of food. Finally, the polymorphism in the foraging gene (rover–sitter) of the larvae adjusts the magnitude of the behavioral response to different food conditions. This study defines several levels of control of foraging and provides the basis for the systematic identification of the neuronal circuits and mechanisms controlling each behavioral response.

Huanglongbing (HLB) is a disease of citrus trees that devastates production areas. Current strategies focus on control of the psyllid Diaphorina citri, the main insect vector that transmits the disease. Although insecticides are a common control method, continuous use threatens the environment and human health, and does not eliminate inoculum sources outside the commercial areas. Several strategies have been proposed to manage HLB in these non-commercial areas: i) biological control with the parasitoid wasp Tamarixia radiata together with ii) eradication of inoculum sources. Here, we developed a computational model to simulate a landscape composed of commercial and non-commercial areas, which were managed differently. Inside the commercial area (farm), we simulated the effect of chemical control on insect populations; outside, we simulated the effect of external management comprised of biological control with T. radiata and elimination of infected trees. Our model indicated that strategies applied externally are essential to flatten the HLB curve, since we demonstrated that the total number of infected trees is more sensitive to these strategies. When combined with chemical control inside the commercial areas, external management in non-commercial areas resulted in an additional reduction of more than 10% in the total number of infected trees compared to a scenario without external management. Although chemical control is not able to protect farm borders, an approach that combines chemical control and external management was able to protect most trees within the commercial area, including the border. Our model opens broad prospects for future studies and alerts growers to the need to consider using external management to aid in controlling HLB. [ABSTRACT FROM AUTHOR]

Parasites kill hosts but also can indirectly increase the abundance of their resources. Given this resource feedback, how much will parasites decrease host density? Can they increase host density? Seeking answers, we integrate trait measurements, a resource–host–parasite model and experimental epidemics with plankton. This combination reveals how parasites may decrease or increase host density. This spectrum of outcomes reflects tension between parasite‐driven mortality (a density‐mediated effect) and foraging depression upon contact with parasite propagules (a trait‐mediated one).In the model, mortality rises when higher susceptibility to infection increases infection prevalence. These large epidemics release resources while suppressing hosts (creating a trophic cascade). In contrast, when hosts are less susceptible and parasites depress host foraging, a resource feedback can elevate host density during epidemics (creating a hydra effect), particularly at higher carrying capacity of resources. This combination creates the hydra effect because it elevates primary production relative to per‐host consumption of resources (two key determinants of host density).We test these predictions qualitatively with trait measurements and a mesocosm experiment. Clonal lines of zooplankton hosts differed in their foraging depression and susceptibility. Then, with these different host genotypes, we created epidemics in mesocosms supplied with either low or high nutrients (to manipulate carrying capacity). Hydra effects and trophic cascades both arose and in the trait–nutrient combinations predicted by the model.Hence, we show how tension between trait‐ and density‐mediated effects of parasites can govern the fate of host density during epidemics—from trophic cascades to hydra effects. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

Transients in ecology are extremely important since they determine how equilibria are approached. The debate on the dynamic stability of ecosystems has been largely focused on equilibrium states. However, since ecosystems are constantly changing due to climate conditions or to perturbations driven by the climate crisis or anthropogenic actions (habitat destruction, deforestation, or defaunation), it is important to study how dynamics can proceed till equilibria. This article investigates the dynamics and transient phenomena in small food chains using mathematical models. We are interested in the impact of habitat loss in ecosystems with vegetation undergoing facilitation. We provide a dynamical study of a small food chain system given by three trophic levels: primary producers, i.e., vegetation, herbivores, and predators. Our models reveal how habitat loss pushes vegetation towards tipping points, how the presence of herbivores in small habitats could promote ecosystem's extinction (ecological meltdown), or how the loss of predators produce a cascade effect (trophic downgrading). Mathematically, these systems exhibit many of the possible local bifurcations: saddle-node, transcritical, Andronov–Hopf, together with a global bifurcation given by a heteroclinic bifurcation. The associated transients are discussed, from the ghost dynamics to the critical slowing down tied to the local and global bifurcations. Our work highlights how the increase of ecological complexity (trophic levels) can imply more complex transitions. This article shows how the pernicious effects of perturbations (i.e., habitat loss or hunting pressure) on ecosystems could not be immediate, producing extinction delays. These theoretical results suggest the possibility that some ecosystems could be currently trapped into the (extinction) ghost of their stable past. [ABSTRACT FROM AUTHOR]

Ecosystems are subject to a multitude of anthropogenic environmental changes. Experimental research in the field of multiple stressors has typically involved varying the number of stressors, here termed stressor richness, but without controlling for total stressor intensity. Mistaking stressor intensity effects for stressor richness effects can misinform management decisions when there is a trade‐off between mitigating these two factors. We incorporate multiple stressors into three community models and show that, at a fixed total stressor intensity, increasing stressor richness aggravates joint stressor effects on ecosystem functioning, but reduces effects on species persistence and composition. In addition, stressor richness weakens the positive selection and negative complementarity effects on ecosystem function. We identify the among‐species variation of stressor effects on traits as a key determinant of the resulting community‐level stressor effects. Taken together, our results unravel the mechanisms linking multiple environmental changes to biodiversity and ecosystem function. [ABSTRACT FROM AUTHOR]

The multiple ecological crisis that we are facing forces us to ponder the transition toward sustainable agricultural systems. Two key uncertainties need to be unveiled in addressing this problem; first, we need to identify the general features of alternative models that make them sustainable, and second, we need to explore how to build them from the (flawed) existing systems. In this work we explore these two questions using an ethnoecological and theoretical approach. In the exploration of alternative models, we evaluate an ancestral farming system, the conuco, characterized by, (i) the use of the ecological succession to constantly renew its properties, (ii) the increase of its biodiversity over time (in the horizontal and vertical components), and (iii) the self-regulation of the associated populations. Next, we characterize the topology of ecological networks of agroecosystems along the transition from a monoculture to a conuco-like agroecological system. We use topologies obtained from field information of conventional and agroecological systems as starting and arrival points. To model the dynamics of the systems and numerically simulate the transitions, we use a model based on Generalized Lotka-Volterra equations, where all types of population interactions are represented, with outcomes based on a density-dependent conditionality. The results highlight the relevance of increasing the connectance and diminishing the degree centrality of the conventional systems networks to promote their sustainability. Finally, we propose that the transitions between the monoculture and the agroecological systems could be figuratively interpreted as a cusp catastrophe, where the two systems are understood as alternative stable states and the path from one to the other cannot be reverted by just reversing the values of the control parameter. That is, once a system is in either of these states there is a tendency to stay and a resistance to move away from it. This implies that in the process of transition from a monoculture to a multi-diverse system, it is prudent not to despair if there are no immediate improvements in the performance of the system because once a certain point is reached, the system may experience an abrupt improvement.

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible "normal forms" that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points. [ABSTRACT FROM AUTHOR]

There is a great need to understand how and why biodiversity, which we define as the variety of organisms found in a given place, changes over time. Current estimates suggest strikingly slow change in traditional measures of biodiversity. These estimates seem to contradict rapid shifts in the abundance of individual species and have led to a rethinking of the mechanisms shaping biodiversity. Conceptual models emphasise the role of competition among species or, more recently, selection on species identity (i.e. selection that favours some species at the expense of others). However, it is difficult to quantify how these mechanisms contribute to biodiversity change. To illustrate this point, we present cases where strong competition or selection on species identity leads to no biodiversity change. In view of this disconnect, we develop a new approach to studying biodiversity change using the Price equation. We show that biodiversity change responds to selection on species' rarity, rather than to either competition or selection on species identity. We then show how this insight can be used to quantify the effects of the mechanisms previously thought to influence biodiversity: (1) selection, (2) (ecological) drift, (3) immigration and (4) speciation. Our results suggest the connection between species' fates and their rarity is fundamental to understanding biodiversity change. [ABSTRACT FROM AUTHOR]

Understanding how multiple co‐occurring environmental stressors combine to affect biodiversity and ecosystem services is an on‐going grand challenge for ecology. Currently, progress has been made through accumulating large numbers of smaller‐scale empirical studies that are then investigated by meta‐analyses to detect general patterns. There is particular interest in detecting, understanding and predicting 'ecological surprises' where stressors interact in a non‐additive (e.g. antagonistic or synergistic) manner, but so far few general results have emerged. However, the ability of the statistical tools to recover non‐additive interactions in the face of data uncertainty is unstudied, so crucially, we do not know how well the empirical results reflect the true stressor interactions. Here, we investigate the performance of the commonly implemented additive null model. A meta‐analysis of a large (545 interactions) empirical dataset for the effects of pairs of stressors on freshwater communities reveals additive interactions dominate individual studies, whereas pooling the data leads to an antagonistic summary interaction class. However, analyses of simulated data from food chain models, where the underlying interactions are known, suggest both sets of results may be due to observation error within the data. Specifically, we show that the additive null model is highly sensitive to observation error, with non‐additive interactions being reliably detected at only unrealistically low levels of data uncertainty. Similarly, plausible levels of observation error lead to meta‐analyses reporting antagonistic summary interaction classifications even when synergies co‐dominate. Therefore, while our empirical results broadly agree with those of previous freshwater meta‐analyses, we conclude these patterns may be driven by statistical sampling rather than any ecological mechanisms. Further investigation of candidate null models used to define stressor‐pair interactions is essential, and once any artefacts are accounted for, the so‐called 'ecological surprises' may be more frequent than was previously assumed. [ABSTRACT FROM AUTHOR]

Metapopulation and source–sink dynamics are increasingly considered within spatially explicit management of wildlife populations, yet the application of these concepts has generally been limited to comparisons of the performance (e.g., demographic rates or dispersal) inside vs. outside protected areas, and at spatial scales that do not encompass an entire metapopulation. In the present study, a spatially explicit, size‐structured matrix model was applied to simulate the dynamics of an Eastern oyster (Crassostrea virginica) metapopulation in the second largest estuary in the United States—the Albemarle‐Pamlico Estuarine System in North Carolina. The model integrated larval dispersal simulations with empirical measures of oyster demographic rates to simulate the dynamics of the entire oyster metapopulation consisting of 646 reefs and five reef types: (1) restored subtidal reefs closed to harvest (i.e., sanctuaries or protected areas; n = 14), (2) restored subtidal reefs open to harvest (n = 53), (3) natural subtidal reefs open to harvest (n = 301), (4) natural intertidal reefs open to harvest (n = 129), and (5) oyster reefs on manmade, hard structures such as seawalls (n = 149). Key findings included (1) an overall stable, yet slightly declining oyster metapopulation, (2) variable reef type‐specific population trajectories, largely dependent on spatiotemporal variation in larval recruitment, (3) a greater relative importance of inter‐reef larval connectivity on metapopulation dynamics than local larval retention processes, and (4) spatiotemporal variation in the source–sink status of reef subpopulations wherein subtidal sanctuaries and reefs located in the northeastern portion of the estuary were frequent sources. From a management perspective, continued protection of oyster sanctuaries is warranted. Sanctuaries represented only 6.2% of the total reef area, however, they harbored 19% (± 2%) of all oysters and produced 25% (± 6%) of all larvae settling within the metapopulation. Additional management priorities should focus on restoration or conservation of subpopulations that serve as frequent source subpopulations (including those with poor demographic rates, but high connectivity potential), and management of harvest from sink subpopulations. The application of a source–sink framework and similar integrated modeling approach could inform management of oysters in other systems, as well as other species that exhibit similar metapopulation characteristics. [ABSTRACT FROM AUTHOR]

In a time of rapid habitat destruction threatening the existence of many species, restoration of degraded habitats plays a crucial role in hampering biodiversity decline and recovering ecosystem services.The goal of this study is to advance the understanding of the consequences of habitat restoration on metacommunities, which is of upmost importance for designing successful restoration projects.We approach habitat restoration from a theoretical perspective by analysing spatially explicit metacommunity models which have previously been essential to understanding the effects of habitat loss and fragmentation. We investigate the efficiency of various restoration strategies on metacommunities involving interactions ranging from pairwise competition, predation and mutualism to more complex three‐trophic modules. Our novel approach for measuring the restoration efficiency enables direct comparison of the responses of species in different metacommunities.We show that species recovery is affected by the amount of habitat destroyed, and the restoration strategy. When habitat is restored by randomly selecting destroyed sites, species recovery becomes less efficient and more uncertain with increasing amount of previously destroyed habitat. However, when the destroyed sites are restored in clusters, minimising the effects of fragmentation, species recovery and the certainty of success are substantially improved. Furthermore, we demonstrate that the community structure and the types of interactions involved determine the most efficient restoration approach.Our findings highlight the importance of carefully planning the restoration process, especially in landscapes where a large proportion of habitat has been destroyed, and with species at the brink of extinction. Our results may be used as guidelines for designing habitat restoration projects. [ABSTRACT FROM AUTHOR]

Functional, physiological traits are the underlying drivers of niche differentiation. A common framework related to niches occupied by terrestrial prokaryotes is based on copiotrophy or oligotrophy, where resource investment is primarily in either rapid growth or stress tolerance, respectively. A quantitative trait-based approach sought relationships between taxa, traits and niche in terrestrial prokaryotes. With 175 taxa from 11 Phyla and 35 Families (n = 5 per Family), traits were considered as discrete counts of shared genome-encoded proteins. Trait composition strongly supported non-random functional distributions as preferential clustering of related taxa via unweighted pair-group method with arithmetic mean. Trait similarity between taxa increased as taxonomic rank decreased. A suite of Random Forest models identified traits significantly enriched or depleted in taxonomic groups. These traits conveyed functions related to rapid growth, nutrient acquisition and stress tolerance consistent with their presence in copiotroph-oligotroph niches. Hierarchical clustering of traits identified a clade of competitive, copiotrophic Families resilient to oxidative stress versus glycosyltransferase-enriched oligotrophic Families resistant to antimicrobials and environmental stress. However, the formation of five clades suggested a more nuanced view to describe niche differentiation in terrestrial systems is necessary. We suggest considering traits involved in both resource investment and acquisition when predicting niche. [ABSTRACT FROM AUTHOR]

Disturbance is a key factor shaping ecological communities, but little is understood about how the effects of disturbance processes accumulate over time. When disturbance regimes change, historical processes may influence future community structure, for example, by altering invasibility compared to communities with stable regimes. Here, we use an annual plant model to investigate how the history of disturbance alters invasion success. In particular, we show how two communities can have different outcomes from species introduction, solely due to past differences in disturbance regimes that generated different biotic legacies. We demonstrate that historical differences can enhance or suppress the persistence of introduced species, and that biotic legacies generated by stable disturbance history decay over time, though legacies can persist for unexpectedly long durations. This establishes a formal theoretical foundation for disturbance legacies having profound effects on communities, and highlights the value of further research on the biotic legacies of disturbance. [ABSTRACT FROM AUTHOR]

Abstract Intergroup violence is assumed to play a key role in establishing and maintaining gang competitive dominance. However, it is not clear how competitive ability, gang size and reciprocal violence interact. Does competitive dominance lead to larger gangs, or allow them to remain small? Does competitive dominance lead gangs to mount more attacks against rivals, or expose them to more attacks? We explore a model developed in theoretical ecology to understand communities arranged in strict competitive hierarchies. The model is extended to generate expectations about gang size distributions and the directionality of gang violence. Model expectations are explored with twenty-three years of data on gang homicides from Los Angeles. Gangs may mitigate competitive pressure by quickly finding gaps in the spatial coverage of superior competitors. Competitively superior gangs can be larger or smaller than competitively inferior gangs and a disproportionate source or target of directional violence, depending upon where exactly they fall in the competitive hierarchy. A model specifying the mechanism of competitive dominance is needed to correctly interpret gang size and violence patterns.

Inferring the processes underlying the emergence of observed patterns is a key challenge in theoretical ecology. Much effort has been made in the past decades to collect extensive and detailed information about the spatial distribution of tropical rainforests, as demonstrated, e.g. in the 50 ha tropical forest plot on Barro Colorado Island, Panama. These kinds of plots have been crucial to shed light on diverse qualitative features, emerging both at the single-species or the community level, like the spatial aggregation or clustering at short scales. Here, we build on the progress made in the study of the density correlation functions applied to biological systems, focusing on the importance of accurately defining the borders of the set of trees, and removing the induced biases. We also pinpoint the importance of combining the study of correlations with the scale dependence of fluctuations in density, which are linked to the well-known empirical Taylor’s power law. Density correlations and fluctuations, in conjunction, provide a unique opportunity to interpret the behaviours and, possibly, to allow comparisons between data and models. We also study such quantities in models of spatial patterns and, in particular, we find that a spatially explicit neutral model generates patterns with many qualitative features in common with the empirical ones.

The structure and diversity of microbial communities are intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g. genus vs. family), an empirical pattern previously evaluated in the context of the Diversity Begets Diversity (DBD) hypothesis (Madi et al., 2020). We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members, demonstrating that the DBD can be sufficiently explained using the SLM as a null model of ecology. Contrastingly, only by including correlations between the abundances of community members (e.g. as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions., Competing Interests: WS, JG No competing interests declared, (© 2023, Shoemaker and Grilli.)

Higher temperatures are expected to reduce species coexistence by increasing energetic demands. However, flexible foraging behaviour could balance this effect by allowing predators to target specific prey species to maximize their energy intake, according to principles of optimal foraging theory. Here we test these assumptions using a large dataset comprising 2,487 stomach contents from six fish species with different feeding strategies, sampled across environments with varying prey availability over 12 years in Kiel Bay (Baltic Sea). Our results show that foraging shifts from trait- to density-dependent prey selectivity in warmer and more productive environments. This behavioural change leads to lower consumption efficiency at higher temperature as fish select more abundant but less energetically rewarding prey, thereby undermining species persistence and biodiversity. By integrating this behaviour into dynamic food web models, our study reveals that flexible foraging leads to lower species coexistence and biodiversity in communities under global warming., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)

In mosaic ecosystems, multiple land types coexist as alternative stable states exhibiting distinct spatial patterns. Forest‐grassland mosaics are ecologically valuable, due to their high species richness. However, anthropogenic disturbances threaten these ecosystems. Designating protected areas is one approach to preserving natural mosaics. Such work must account for climate change, yet there are few spatially explicit models of mosaics under climate change that can predict its effects. We construct a spatially explicit simulation model for a natural forest‐grassland mosaic, parameterized for Southern Brazil. Using this model, we investigate how the spatial structure of these systems is altered under climate change and other disturbance regimes. By including local spatial interactions and fire‐mediated forest recruitment, our model reproduces important spatial features of protected real‐world mosaics, including the number of forest patches and overall forest cover. Multiple concurrent changes in environmental conditions have greater impacts on tree cover and spatial structure in simulated mosaics than single changes. This sensitivity reflects the narrow range of conditions under which simulated mosaics persist and emphasizes their vulnerability. Our model predicts that, in protected mosaics, climate change impacts on the fire‐mediated threshold to recruitment will likely result in substantial increases in forest cover under Representative Concentration Pathway (RCP) 8.5, with potential for mosaic loss over a broad range of initial forest cover levels. Forest cover trajectories are similar until 2150, when cover increases under RCP 8.5 outpace those under RCP 2.6. Mosaics that persist under RCP 8.5 may experience structural alterations at the patch and landscape level. Our simple model predicts several realistic aspects of spatial structure as well as plausible responses to likely regional climate shifts. Hence, further model development could provide a useful tool when building strategies for protecting these ecosystems, by informing site selection for conservation areas that will be favourable to forest‐grassland mosaics under future climates. [ABSTRACT FROM AUTHOR]

Conspecific negative density dependence is ubiquitous and has long been recognized as an important factor favoring the coexistence of competing species at local scale. By contrast, a positive density-dependent growth rate is thought to favor species exclusion by inhibiting the growth of less competitive species. Yet, such conspecific positive density dependence often reduces extrinsic mortality (e.g. reduced predation), which favors species exclusion in the first place. Here, using a combination of analytical derivations and numerical simulations, I show that this form of positive density dependence can favor the existence of equilibrium points characterized by species coexistence. Those equilibria are not globally stable, but allow the maintenance of species-rich communities in multispecies simulations. Therefore, conspecific positive density dependence does not necessarily favor species exclusion. On the contrary, some forms of conspecific positive density dependence may even help maintain species richness in natural communities. These results should stimulate further investigations into the precise mechanisms underlying density dependence.

The grand ambition of theorists studying ecology and evolution is to discover the logical and mathematical rules driving the world's biodiversity at every level from genetic diversity within species to differences between populations, communities, and ecosystems. This ambition has been difficult to realize in great part because of the complexity of biodiversity. Theoretical work has led to a complex web of theories, each having non-obvious consequences for other theories. Case in point, the recent realization that genetic diversity involves a great deal of temporal and spatial stochasticity forces theoretical population genetics to consider abiotic and biotic factors generally reserved to ecosystem ecology. This interconnectedness may require theoretical scientists to adopt new techniques adapted to reason about large sets of theories. Mathematicians have solved this problem by using formal languages based on logic to manage theorems. However, theories in ecology and evolution are not mathematical theorems, they involve uncertainty. Recent work in Artificial Intelligence in bridging logic and probability theory offers the opportunity to build rich knowledge bases that combine logic's ability to represent complex mathematical ideas with probability theory's ability to model uncertainty. We describe these hybrid languages and explore how they could be used to build a unified knowledge base of theories for ecology and evolution.

The environment affects population dynamics through multiple drivers. Here we explore a simplified version of such influence in a three-species food chain, making use of the Hastings–Powell model. This represents an idealized resource–consumer–predator chain, or equivalently, a vegetation–host–parasitoid system. By stochastically perturbing the value of some parameters in this dynamical system, we observe dramatic modifications in the system behavior. In particular, we show the emergence of on–off intermittency, i.e., an irregular alternation between stable phases and sudden bursts in population size, which hints towards a possible conceptual description of population outbursts grounded into an environment-driven mechanism.

A tangled web of vicious circles, driven by cultural issues, has prevented ecology from growing strong theoretical roots. Now this hinders development of effective conservation policies. To overcome these barriers in view of urgent societal needs, we propose a global network of postgraduate theoretical training programs. [ABSTRACT FROM AUTHOR]

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors. [ABSTRACT FROM AUTHOR]

Whether a species can be defined as 'endemic' or not is very controversial in theoretical terms, because the concepts of 'endemism' and 'area of endemism' remain much debated among scientists. We propose that it is necessary to consider the body size scale of an organism and the breadth of its distributional range if we want to define a given species as 'endemic' or not. Thus, for instance, Madagascar can be an appropriate area of endemism for animals as large and vagile as the fossa (Cryptoprocta ferox), but it is much too large to be considered an ecologically appropriate 'area of endemism' for small-sized frogs with limited dispersal abilities. Instead, for these small species it is ecologically more appropriate to consider given forest regions within Madagascar as 'areas of endemism'. Therefore, we propose a five-step approach in order to define whether a given species can be considered endemic or not within a set of potential candidate species, and we offer a suite of practical examples (African squirrels, African Artiodactyla and a family of freshwater turtles from the Americas) to elucidate the designated concept. We define the new concept presented herein as 'scale-dependent functional endemism'. The new concept has the benefits of including the 'ecological characteristics' of the target species in the concept of 'endemism' and being easily repeated, because it is based on rather objective criteria. [ABSTRACT FROM AUTHOR]

Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems. [ABSTRACT FROM AUTHOR]