1. Ecological data are increasingly collected by networks of collaborators using replicated designs and methods, which can significantly improve the quality and quantity of data collected throughout the ecological niche and geographic range of a species. The coordinated generation and management of data is critical to producing coherent datasets (Standard Data Products) across multiple sites that can be used by different researchers, over extended time periods and for multiple purposes. 2. Here, we describe and use a Quality Assurance framework for the design, collection and production of reproducible Standard Data Products for population ecology. We identified six critical project elements of a Quality Assurance framework (QA1-6) to produce ecological Standard Data Products with high immediate and future value. 3. We applied the Quality Assurance framework to the Plantpopnet project as a case-study. Plantpopnet is a coordinated distributed system for demography and population macroecology which uses the model species Plantago lanceolata. We mapped Plantpopnet activities to the Quality Assurance Framework as: QA1) Measurable objectives: research project objectives with data requirements, QA2) Process control: governance policies, QA3) Project specific procedures: model organism selection and data collection protocol, QA4) Supporting production of high quality data: recruitment, retention and engagement of participants, QA5) Data management: data management plan and reproducible data cleaning workflow, QA6) Production and management of outputs: Standard Data Products and papers. The framework allows for flexibility and adaptation to changing circumstances. 4. Explicit use of Quality Assurance, project and data management tools together with standardised ecological methods enabled the design, collection, maintenance and sustainability of high-quality data products. We provide a Quality Assurance framework together with governance documents, code and data for a reproducible Standard Data Product. This framework can be applied to the goals of the Plantpopnet project as well as facilitate future research and applications of coordinated distributed ecology projects more generally.

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

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

Effective conservation of biodiversity depends on the successful management of wildlife populations and their habitats. Successful management, in turn, depends on our ability to understand and accurately forecast how populations and communities respond to human-induced changes in their environments. However, quantifying how these stressors impact population dynamics remains challenging. Another significant hurdle at this interface is determining which quantitative approach(es) are most appropriate given data constraints and the intended purpose. Here, we provide a cross-taxa overview of key methodological approaches (e.g., matrix population models) and model elements (e.g., energetics) that are currently used to model the effects of anthropogenic disturbance on wildlife populations. Specifically, we discuss how these modelling approaches differ in their key assumptions, aspects of their structure and complexity, the questions they are best poised to address, and their data requirements. Our hope is to help overcome some of the methodological biases that might persist across taxonomic specialisations, identify new opportunities to address existing modelling challenges, and improve our understanding of the direct and indirect impacts of anthropogenic disturbance. We guide users through the identification of appropriate model configurations for different management purposes, while also suggesting key priorities for model development and integration.

Environmental stochasticity impacts population dynamics and their viability. As such, understanding how organisms cope with this variability is crucial. Here, we investigate demographic buffering, the ability of populations to maintain stable growth despite environmental fluctuations. We integrate well-established stochastic and deterministic approaches to investigate characteristics of demographic buffering, analysing stochastic elasticities and self-second derivatives of deterministic population growth rate. We test the hypothesis that buffered species exhibit low stochastic elasticity to temporal variability and signs of concave selection (i.e. negative second derivatives of population growth rate with respect to demographic processes), reducing variance in key demographic processes. Analysing 43 natural populations of 37 mammal species, we find limited support for this hypothesis. Indeed, while primates often show low stochastic elasticity, concave selection is less prevalent than expected. Our findings highlight the complex and dynamic relationship between demographic processes, environmental variability, and selection pressures in determining population persistence.

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.

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.