A document by Sean Eng Howe Pang. Click on the document to view its contents.

The Demographic Buffering Hypothesis (DBH) predicts that natural selection reduces the temporal fluctuations in demographic processes (such as survival, development, and reproduction), due to their negative impacts on population dynamics. However, a comprehensive approach that allows for the examination of demographic buffering patterns across multiple species is still lacking. Here, we propose a three-step framework aimed at quantifying demographic buffering. Firstly, we categorize species along a continuum of variance based on the sums of stochastic elasticities. Secondly, we examine the linear selection gradients, followed by the examination of nonlinear selection gradients as the third step. With these three steps, our framework overcomes existing limitations of conventional approaches to quantify demographic buffering, allows for multi-species comparisons, and offers insight into the evolutionary forces that shape demographic buffering. We apply this framework to mammal species and discuss both the advantages and potential of our framework.

The Demographic Buffering Hypothesis (DBH) predicts that natural selection reduces the temporal fluctuations in demographic processes (such as survival, development, and reproduction), due to their negative impacts on population dynamics. However, a comprehensive approach that allows for the examination of demographic buffering patterns across multiple species is still lacking. Here, we propose a three-step framework aimed at identifying and quantifying demographic buffering. Firstly, we categorize species along a continuum of variance based on their stochastic elasticities. Secondly, we examine the linear selection gradients, followed by the examination of nonlinear selection gradients as the third step. With these three steps, our framework overcomes existing limitations of conventional approaches to identify and quantify demographic buffering, allows for multi-species comparisons, and offers insight into the evolutionary forces that shape demographic buffering. We apply this framework to mammal species and discuss both the advantages and potential of our framework.

Environmental stochasticity is a key determinant of population viability. Decades of work exploring how environmental stochasticity influences population dynamics have highlighted the ability of some natural populations to limit the negative effects of environmental stochasticity, one of these strategies being demographic buffering. Whilst various methods exist to quantify demographic buffering, we still do not know which environment factors and demographic characteristics are most responsible for the demographic buffering observed in natural populations. Here, we introduce a framework to quantify the relative effects of three key drivers of demographic buffering: environment components (e.g., temporal autocorrelation and variance), population structure, and demographic rates (e.g., progression and fertility). Using Integral Projection Models, we explore how these drivers impact the demographic buffering abilities of three plant species with different life histories and demonstrate how our approach successfully characterises a population’s capacity to demographically buffer against environmental stochasticity in a changing world.

Anthropogenic impacts are typically detrimental to tropical coral reefs, but the effect of increasing environmental stress and variability on the size structure of coral communities remains poorly understood. This limits our ability to effectively conserve coral reef ecosystems because size specific dynamics are rarely incorporated. Our aim is to quantify variation in the size structure of coral populations across 20 sites along a tropical-to-subtropical environmental gradient on the east coast of Australia (~23°S to 30°S), to determine how size structure changes with a gradient of sea surface temperature, turbidity, productivity and light levels. We use two approaches: 1) linear regression with summary statistics (such as median size) as response variables, a method frequently favoured by ecologists; and 2) compositional functional regression, a novel method using entire size-frequency distributions as response variables. We then predict coral population size structure with increasing environmental stress and variability. Together, we find fewer but larger coral colonies in marginal reefs than in tropical reefs, where environmental conditions are more variable and stressful for tropical corals. Our model predicts that coral populations may become gradually dominated by larger colonies (> 148 cm2) with increasing environmental stress. Fewer but bigger corals suggest low survival of smaller corals, slow growth, and / or poor recruitment. This finding is concerning for the future of coral reefs as it implies populations may have low recovery potential from disturbances. We highlight the importance of continuously monitoring changes to population structure over biogeographic scales.

Beyond the Fast-Slow Continuum of Life Histories

In this paper, we revisit the long-standing debate of whether there is a pattern in the evolution of organisms towards greater complexity, and how this hypothesis could be tested using an interdisciplinary lens. We argue that this debate remains alive today due to the lack of a quantitative measure of complexity that is related to the teleonomic (i.e. goal-directed) nature of living systems. Further, we argue that such a biological measure of complexity can indeed be found in the vast literature produced within life history theory. We propose that an ideal method to quantify this complexity lays within life history strategies (i.e., schedules of survival and reproduction across an organism's life cycle), as it is precisely these strategies that are under selection to optimise the organism's fitness. In this context, we set an agenda for future steps: (1) how this complexity can be measured mathematically, and (2) how we can engage in a comparative analysis of this complexity across species to investigate the evolutionary forces driving increases or for that matter decreases in teleonomic complexity.

A document by Rob Salguero-Gómez. Click on the document to view its contents.

1 IntroductionUnderstanding, quantifying, and predicting the ability of organisms to adapt to changing environments is at the core of eco-evolutionary research[1,2]. In the face of unprecedented environmental change, natural populations, especially those with limited mobility, can avoid extinction via phenotypic plasticity and/or adaptive evolution [3]. However, our understanding of the interplay between selection and plasticity in changing environments is surprisingly limited[4–8]. This limitation is not trivial, for plasticity can itself evolve[9], can be adaptive or nonadaptive[10], and has seemingly contradictory effects on adaptive evolution[11], on which we focus here. For decades, researchers have theorized whether plasticity facilitates or hinders adaptive evolution[9,12]; the evidence is contradictory and general patterns have not emerged [5,10,11,13,14].The primary conflicting hypotheses for whether plasticity facilitates or hinders adaptive evolution are:(H1) plasticity weakens directional selection by masking genotypic variation (Bogert Effect [15]), thus slowing the rate of genetic change[5,16–18] vs.(H2) plasticity facilitates evolution by allowing the population to persist under environmental change long enough for genetic change to occur[19–22] (Plasticity-First Hypothesis [21] orBaldwin Effect [19]).This debate remains unresolved, for even when theoretical predictions agree with empirical findings[5,10,11,13,14,23], we lack a general framework to ascertain the context-dependency of the prevalent mechanism. Here, we introduce a framework based on environmental change context, to outline clear null hypotheses for when and how plasticity interacts with directional evolution. We place the plasticity facilitates vs. hinders selection debate on two ends of a continuum, and specify the properties of environmental change–rate of mean change , variability , andtemporal autocorrelation –that influence how plasticity impacts adaptive evolution.The type of environmental change a population experiences can alter its likelihood of adaptation and, ultimately, persistence[24–27]. Studies of demographic[28], genetic[29], and evolutionary rescue[30], show that rate of mean change, variability, and temporal autocorrelation of a population’s selective environment impact population persistence[24,25,29,31–35]. However, because different types of environmental change can have contradictory effects on plasticity and evolution[34,36–38], elucidating these dynamics is not trivial. Consequently, there is an urgent need to place this discussion on the environmental stage in a generalizable way that will allow ecologists and evolutionary biologists to better contextualize, mechanistically understand, predict, and compare their findings.Moving optimum theory links environmental change to the resulting evolutionary responses. Three decades of research on this theory shows that, when a population is confronted with an environment that changes directionally, there is a critical rate of changethat must be matched by change in the mean phenotype of the population, such that the mean remains close to the theoretical phenotypic optimum . In this context, a phenotypic lag between the mean phenotype and the optimum phenotype typically emerges which, if too large, makes extinction certain [39–41]. Evolutionary (e.g. , selection, genetic variation) and ecological processes (e.g. , within-generation life history, plasticity and population dynamics) together influence the limit of how far a population can lag without going extinct. The contribution of plasticity to population persistence and adaptation is largely determined by this phenotypic lag: how much of the short- or long-term lag can be compensated for or even hindered by plasticity?We argue that hypotheses such as the Bogert Effect and the Plasticity-First Hypothesis / Baldwin Effect are not mutually exclusive. Rather, plasticity may facilitate or hinder adaptive evolution depending on the properties of environmental change. To assess the impact of plasticity on the ability of a population to evolutionarily track a changing environmental optimum, we specify the links among the type of environmental change, plasticity, and adaptive evolution by considering several fundamental processes. Thus, we utilize both theoretical and experimental studies to:Assess how three key components of environmental change (rate of mean change , variability , and temporal autocorrelation ) each alter the mechanisms behind phenotypic tracking of a moving optimum ([i] Genetic variation, heritability, and selection , and [ii] life history, plasticity andpopulation dynamics ).Introduce a unified framework of testable hypotheses detailing how those three components of environmental change can influence the relative benefit of plasticity to adaptive evolution.

The popularity of trait-based approaches continues to rise despite challenges in identifying strong links between traits and organism performance. Here, we summarise evidence demonstrating that not all traits appear to be functional, and discuss how life history theory and demography can help elucidate which, how, where, and when traits gain functionality

Phenotypic plasticity can mask population genetic differentiation, reducing the predictability of trait-environment relationships. In short-lived plants, reproductive traits may be more genetically determined due to their direct impact on fitness, whereas vegetative traits may show higher plasticity to buffer short-term perturbations. Combining a multi-treatment greenhouse experiment with global field observations for the short-lived Plantago lanceolata, we 1) disentangled the genetic and plastic responses of functional traits to a set of environmental drivers and 2) assessed the utility of trait-environment relationshisps inferred from observational data for predicting genetic differentiation. Reproductive traits showed distinct genetic differentiation that was highly predictable from observational data, but only when correcting traits for differences in their (labile) biomass component. Vegetative traits showed higher plasticity and contrasting genetic and plastic responses, leading to unpredictable trait patterns. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related with fitness.

A document by Rob Salguero-Gómez. Click on the document to view its contents.