Inferring community assembly processes from macroscopic patterns using dynamic eco‐evolutionary models and Approximate Bayesian Computation (ABC)

Reading Yule in light of the history and present of macroevolution

Yule's 1925 paper introducing the branching model that bears his name was a landmark contribution to the biodiversity sciences. In his paper, Yule developed stochastic models to explain the observed distribution of species across genera and to test hypotheses about the relationship between clade age, diversity and geographic range. Here, we discuss the intellectual context in which Yule produced this work, highlight Yule's key mathematical and conceptual contributions using both his and more modern derivations and critically examine some of the assumptions of his work through a modern lens. We then document the strange trajectory of his work through the history of macroevolutionary thought and discuss how the fundamental challenges he grappled with-such as defining higher taxa, linking microevolutionary population dynamics to macroevolutionary rates, and accounting for inconsistent taxonomic practices-remain with us a century later.This article is part of the theme issue '"A mathematical theory of evolution": phylogenetic models dating back 100 years'.

The paradox of predictability provides a bridge between micro- and macroevolution

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence is often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding of how evolution works within and across timescales.

Evolutionary plant-pollinator responses to anthropogenic land-use change: impacts on ecosystem services

Agricultural intensification at field and landscape scales, including increased use of agrochemicals and loss of semi-natural habitats, is a major driver of insect declines and other community changes. Efforts to understand and mitigate these effects have traditionally focused on ecological responses. At the same time, adaptations to pesticide use and habitat fragmentation in both insects and flowering plants show the potential for rapid evolution. Yet we lack an understanding of how such evolutionary responses may propagate within and between trophic levels with ensuing consequences for conservation of species and ecological functions in agroecosystems. Here, we review the literature on the consequences of agricultural intensification on plant and animal evolutionary responses and interactions. We present a novel conceptualization of evolutionary change induced by agricultural intensification at field and landscape scales and emphasize direct and indirect effects of rapid evolution on ecosystem services. We exemplify by focusing on economically and ecologically important interactions between plants and pollinators. We showcase available eco-evolutionary theory and plant-pollinator modelling that can improve predictions of how agricultural intensification affects interaction networks, and highlight available genetic and trait-focused methodological approaches. Specifically, we focus on how spatial genetic structure affects the probability of propagated responses, and how the structure of interaction networks modulates effects of evolutionary change in individual species. Thereby, we highlight how combined trait-based eco-evolutionary modelling, functionally explicit quantitative genetics, and genomic analyses may shed light on conditions where evolutionary responses impact important ecosystem services.

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

Archetype models upscale understanding of natural pest control response to land-use change

Control of crop pests by shifting host plant availability and natural enemy activity at landscape scales has great potential to enhance the sustainability of agriculture. However, mainstreaming natural pest control requires improved understanding of how its benefits can be realized across a variety of agroecological contexts. Empirical studies suggest significant but highly variable responses of natural pest control to land-use change. Current ecological models are either too specific to provide insight across agroecosystems or too generic to guide management with actionable predictions. We suggest obtaining the full benefit of available empirical, theoretical, and methodological knowledge by combining trait-mediated understanding from correlative studies with the explicit representation of causal relationships achieved by mechanistic modeling. To link these frameworks, we adapt the concept of archetypes, or context-specific generalizations, from sustainability science. Similar responses of natural pest control to land-use gradients across cases that share key attributes, such as functional traits of focal organisms, indicate general processes that drive system behavior in a context-sensitive manner. Based on such observations of natural pest control, a systematic definition of archetypes can provide the basis for mechanistic models of intermediate generality that cover all major agroecosystems worldwide. Example applications demonstrate the potential for upscaling understanding and improving predictions of natural pest control, based on knowledge transfer and scientific synthesis. A broader application of this mechanistic archetype approach promises to enhance ecology's contribution to natural resource management across diverse regions and social-ecological contexts.

Variations in Functional Richness and Assembly Mechanisms of the Subtropical Evergreen Broadleaved Forest Communities along Geographical and Environmental Gradients

Linking functional trait space and environmental conditions can help to understand how species fill the functional trait space when species increase along environmental gradients. Here, we examined the variations in functional richness (FRic) and their correlations with key environmental variables in forest communities along latitudinal, longitudinal, and elevational gradients, by measuring seven functional traits of woody plants in 250 forest plots of 0.04 ha across five locations in the subtropical evergreen broadleaved forests (SEBLF) of China. On this basis, we explored whether environmental filtering constrained the functional volume by using a null model approach. Results showed that FRic decreased with increasing elevation and latitude, while it increased with increasing longitude, mirroring the geographical gradients in species richness. FRic was significantly related to precipitation of driest quarter, soil pH, and total phosphorus. Negative SES.FRic was prevalent (83.2% of the communities) in most SEBLF communities and was negatively related to mean diurnal range. Our study suggested that the geographical variation in the functional space occupied by SEBLF communities was affected mainly by climate and soil conditions. The results of the null model revealed that niche packing was dominant in SEBLF communities, highlighting the importance of environmental filtering in defining functional volume within SEBLF communities.

A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community-scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.

Ecological opportunity and adaptive radiations reveal eco-evolutionary perspectives on community structure in competitive communities

It is well known that ecological and evolutionary processes act in concert while shaping biological communities. Diversification can, for example, arise through ecological opportunity and adaptive radiations and competition play an essential role in such diversification. Eco-evolutionary components of competition are thus important for our understanding of community assembly. Such understanding in turn facilitates interpretation of trait- and phylogenetic community patterns in the light of the processes that shape them. Here, I investigate the link between competition, diversification, and trait- and phylogenetic- community patterns using a trait-based model of adaptive radiations. I evaluate the paradigm that competition is an ecological process that drives large trait- and phylogenetic community distances through limiting similarity. Contrary to the common view, I identify low or in some cases counterintuitive relationships between competition and mean phylogenetic distances due to diversification late in evolutionary time and peripheral parts of niche space when competition is weak. Community patterns as a function of competition also change as diversification progresses as the relationship between competition and trait similarity among species can flip from positive to negative with time. The results thus provide novel perspectives on community assembly and emphasize the importance of acknowledging eco-evolutionary processes when interpreting community data.

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species' abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth's Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth's biodiversity.

Ecological Dynamics: Integrating Empirical, Statistical, and Analytical Methods

Understanding ecological processes and predicting long-term dynamics are ongoing challenges in ecology. To address these challenges, we suggest an approach combining mathematical analyses and Bayesian hierarchical statistical modeling with diverse data sources. Novel mathematical analysis of ecological dynamics permits a process-based understanding of conditions under which systems approach equilibrium, experience large oscillations, or persist in transient states. This understanding is improved by combining ecological models with empirical observations from a variety of sources. Bayesian hierarchical models explicitly couple process-based models and data, yielding probabilistic quantification of model parameters, system characteristics, and associated uncertainties. We outline relevant tools from dynamical analysis and hierarchical modeling and argue for their integration, demonstrating the value of this synthetic approach through a simple predator-prey example.

Ecological opportunity and upward prey-predator radiation cascades

A general goal in community ecology and evolutionary biology is to understand how diversity has arisen. In our attempts to reach such goals we become increasingly aware of interacting ecological and evolutionary processes shaping biodiversity. Ecological opportunity and adaptive radiations can, for example, drive diversification in competitive communities but little is known about how such processes propagate through trophic levels in adaptive radiation cascades. I use an eco-evolutionary model of trait-based ecological interactions and micro-evolutionary processes to investigate the macro-evolutionary aspects of predator diversification in such cascades. Prey diversification facilitates predator radiation through predator feeding opportunity and disruptive selection. Predator radiation, however, often disconnects from the prey radiation as the diversification progresses. Only when predators have an intermediate niche width, high predatory efficiency, and high evolutionary potential can radiation cascades be maintained over macro-evolutionary time scales. These results provide expectations for predator response to prey divergence and insight into eco-evolutionary feedbacks between trophic levels. Such expectations are crucial for future studies that aim for a better understanding of how diversity is generated and maintained in complex communities.

Identifying models of trait-mediated community assembly using random forests and approximate Bayesian computation

Ecologists often use dispersion metrics and statistical hypothesis testing to infer processes of community formation such as environmental filtering, competitive exclusion, and neutral species assembly. These metrics have limited power in inferring assembly models because they rely on often-violated assumptions. Here, we adapt a model of phenotypic similarity and repulsion to simulate the process of community assembly via environmental filtering and competitive exclusion, all while parameterizing the strength of the respective ecological processes. We then use random forests and approximate Bayesian computation to distinguish between these models given the simulated data. We find that our approach is more accurate than using dispersion metrics and accounts for uncertainty in model selection. We also demonstrate that the parameter determining the strength of the assembly processes can be accurately estimated. This approach is available in the R package CAMI; Community Assembly Model Inference. We demonstrate the effectiveness of CAMI using an example of plant communities living on lava flow islands.