Disentangling the importance of ecological niches from stochastic processes across scales

Deterministic theories in community ecology suggest that local, niche-based processes, such as environmental filtering, biotic interactions and interspecific trade-offs largely determine patterns of species diversity and composition. In contrast, more stochastic theories emphasize the importance of chance colonization, random extinction and ecological drift. The schisms between deterministic and stochastic perspectives, which date back to the earliest days of ecology, continue to fuel contemporary debates (e.g. niches versus neutrality). As illustrated by the pioneering studies of Robert H. MacArthur and co-workers, resolution to these debates requires consideration of how the importance of local processes changes across scales. Here, we develop a framework for disentangling the relative importance of deterministic and stochastic processes in generating site-to-site variation in species composition (β-diversity) along ecological gradients (disturbance, productivity and biotic interactions) and among biogeographic regions that differ in the size of the regional species pool. We illustrate how to discern the importance of deterministic processes using null-model approaches that explicitly account for local and regional factors that inherently create stochastic turnover. By embracing processes across scales, we can build a more synthetic framework for understanding how niches structure patterns of biodiversity in the face of stochastic processes that emerge from local and biogeographic factors.     

Understanding ecological change across large spatial,temporal and taxonomic scales: integrating data and methods in light of theory

The difficulty of integrating multiple theories, data and methods has slowed progress towards making unified inferences of ecological change generalizable across large spatial, temporal and taxonomic scales. However, recent progress towards a theoretical synthesis now provides a guiding framework for organizing and integrating all primary data and methods for spatiotemporal assemblage‐level inference in ecology. In this paper, we describe how recent theoretical developments can provide an organizing paradigm for linking advances in data collection and methodological frameworks across disparate ecological sub‐disciplines and across large spatial and temporal scales. First, we summarize the set of fundamental processes that determine change in multispecies assemblages across spatial and temporal scales by reviewing recent theoretical syntheses of community ecology. Second, we review recent advances in data and methods across the main sub‐disciplines concerned with ecological inference across large spatial, temporal and taxonomic scales, and organize them based on the primary fundamental processes they include, rather than the spatiotemporal scale of their inferences. Finally, we highlight how iteratively focusing on only one fundamental process at a time, but combining all relevant spatiotemporal data and methods, may reduce the conceptual challenges to integration among ecological sub‐disciplines. Moreover, we discuss a number of avenues for decreasing the practical barriers to integration among data and methods. We aim to reconcile the recent convergence of decades of thinking in community ecology and macroecology theory with the rapid progress in spatiotemporal approaches for assemblage‐level inference, at a time where a robust understanding of spatiotemporal change in ecological assemblages is more crucial than ever to conserve biodiversity.     

Extending eco-evolutionary theory with oligomorphic dynamics

Understanding the interplay between ecological processes and the evolutionary dynamics of quantitative traits in natural systems remains a major challenge. Two main theoretical frameworks are used to address this question, adaptive dynamics and quantitative genetics, both of which have strengths and limitations and are often used by distinct research communities to address different questions. In order to make progress, new theoretical developments are needed that integrate these approaches and strengthen the link to empirical data. Here, we discuss a novel theoretical framework that bridges the gap between quantitative genetics and adaptive dynamics approaches. ‘Oligomorphic dynamics’ can be used to analyse eco-evolutionary dynamics across different time scales and extends quantitative genetics theory to account for multimodal trait distributions, the dynamical nature of genetic variance, the potential for disruptive selection due to ecological feedbacks, and the non-normal or skewed trait distributions encountered in nature. Oligomorphic dynamics explicitly takes into account the effect of environmental feedback, such as frequency- and density-dependent selection, on the dynamics of multi-modal trait distributions and we argue it has the potential to facilitate a much tighter integration between eco-evolutionary theory and empirical data.     

Dynamic disequilibrium of the terrestrial carbon cycle under global change

In this review, we propose a new framework, dynamic disequilibrium of the carbon cycles, to assess future land carbon-sink dynamics. The framework recognizes internal ecosystem processes that drive the carbon cycle toward equilibrium, such as donor pool-dominated transfer; and external forces that create disequilibrium, such as disturbances and global change. Dynamic disequilibrium within one disturbance-recovery episode causes temporal changes in the carbon source and sink at yearly and decadal scales, but has no impacts on longer-term carbon sequestration unless disturbance regimes shift. Such shifts can result in long-term regional carbon loss or gain and be quantified by stochastic statistics for use in prognostic modeling. If the regime shifts result in ecosystem state changes in regions with large carbon reserves at risk, the global carbon cycle might be destabilized.     

SPATIAL DYNAMICS OF A HIMANTHALIA ELONGATA (FUCALES, PHAEOPHYTA) POPULATION

In dense monospecific stands of plants intraspecific competition usually results in self-thinning, the concurrent increase in biomass and decrease in density over time. Self-thinning may also result in a change in the spatial pattern of individuals, but so far the spatial dynamics of marine plants has not been investigated. The brown alga Himanthalia elongata ( L.) S. F. Gray forms dense monospecific stands on many northern temperate rocky shores, and various attributes (including its simple form) facilitated the study of the spatial dynamics of this species .
The spatial pattern of settling zygotes was examined in the laboratory. In the absence of water movement, substratum heterogeneity, and a point source, zygotes usually settled in clumps rather than randomly. Within the clumps zygotes appeared to be regularly distributed at a scale similar to the size of the zygotes themselves. Furthermore, the clumps themselves seemed to be regularly distributed. On the shore, well-established stands of "button-stage" Himanthalia populations were examined during a period of extensive growth and self-thinning. Individual plants were initially highly regular in spatial pattern but became less so over time. The pattern of plants dying during self-thinning was also highly regular and probably reflected existing spatial regularity. However, using a hypothesis of mortality as a random event, I found that smaller plants had a less than average survival potential, while larger plants had a greater than average chance. A consideration of the spatial pattern of plants alive at the end of the study revealed regularity at a scale of 2–7 mm but a random spatial pattern at larger scales, which might indicate a small sphere of influence of competing individuals. The best predictor of mean nearest neighbor distances in the populations was mean plant diameter .     

Dynamics of a neural system with a multiscale architecture

The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales-neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are 'slaved' to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested.     

Riverine landscape dynamics and ecological risk assessment

1. The aim of ecological risk assessments is to evaluate the likelihood that ecosystems are adversely affected by human‐induced disturbance that brings the ecosystem into a new dynamic equilibrium with a simpler structure and lower potential energy. The risk probability depends on the threshold capacity of the system (resistance) and on the capacity of the system to return to a state of equilibrium (resilience). 2. There are two complementary approaches to assessing ecological risks of riverine landscape dynamics. The reductionist approach aims at identifying risk to the ecosystem on the basis of accumulated data on simple stressor–effect relationships. The holistic approach aims at taking the whole ecosystem performance into account, which implies meso‐scale analysis. 3. Landscape patterns and their dynamics represent the physical framework of processes determining the ecosystem's equilibrium. Assessing risks of landscape dynamics to riverine ecosystems implies addressing complex interactions of system components (e.g. population dynamics and biogeochemical cycles) occurring at multiple scales of space and time. 4. One of the most important steps in ecological risk assessment is to establish clear assessment endpoints (e.g. vital ecosystem and landscape attributes). Their formulation must recognise that riverine ecosystems are dynamic, structurally complex and composed of both deterministic and stochastic components. 5. Remote sensing (geo)statistics and geographical information systems are primary tools for quantifying spatial and temporal components of riverine ecosystem and landscape attributes. 6. The difficulty to experiment at the riverine landscape level means that ecological risk management is heavily dependent on models. Current models are targeted towards simulating ecological risk at levels ranging from single species to habitats, food webs and meta‐populations to ecosystems and entire riverine landscapes, with some including socio‐economic considerations.     

Ecosystem stability in space: α, β and γ variability

The past two decades have seen great progress in understanding the mechanisms of ecosystem stability in local ecological systems. There is, however, an urgent need to extend existing knowledge to larger spatial scales to match the scale of management and conservation. Here, we develop a general theoretical framework to study the stability and variability of ecosystems at multiple scales. Analogously to the partitioning of biodiversity, we propose the concepts of alpha, beta and gamma variability. Gamma variability at regional (metacommunity) scale can be partitioned into local alpha variability and spatial beta variability, either multiplicatively or additively. On average, variability decreases from local to regional scales, which creates a negative variability–area relationship. Our partitioning framework suggests that mechanisms of regional ecosystem stability can be understood by investigating the influence of ecological factors on alpha and beta variability. Diversity can provide insurance effects at the various levels of variability, thus generating alpha, beta and gamma diversity–stability relationships. As a consequence, the loss of biodiversity and habitat impairs ecosystem stability at the regional scale. Overall, our framework enables a synthetic understanding of ecosystem stability at multiple scales and has practical implications for landscape management.     

Temporal dynamics of natural enemy–pest interactions in a changing environment

Agroecosystems contain complex networks of interacting organisms and these interaction webs are structured by the relative timing of key biological and ecological events. Recent intensification of land management and global changes in climate threaten to desynchronize the temporal structure of interaction webs and disrupt the provisioning of ecosystem services, such as biological control by natural enemies. It is therefore critical to recognize the central role of temporal dynamics in driving predator–prey interactions in agroecosystems. Specifically, ecological dynamics in crop fields routinely behave as periodic oscillations, or cycles. Familiar examples include phenological cycles, diel activity rhythms, and crop-management cycles. The relative timing and the degree of overlap among ecological cycles determine the nature and magnitude of the ecological interactions among organisms, and ultimately determine whether ecosystem services, such as biological control, can be provided. Additionally, the ecological dynamics in many cropping systems are characterized by a pattern of frequent disturbances due to management actions such as harvest, sowing and pesticide applications. These disturbance cycles cause agroecosystems to be dominated by dispersal and repopulation dynamics. However, they also serve as selective filters that regulate which animals can persist in agroecosystems over larger temporal scales. Here, we review key concepts and examples from the literature on temporal dynamics in ecological systems, and provide a framework to guide biological control strategies for sustainable pest management in a changing world.     

Scale-dependent feedback and regular spatial patterns in young mussel beds

In the past decade, theoretical ecologists have emphasized that local interactions between predators and prey may invoke emergent spatial patterning at larger spatial scales. However, empirical evidence for the occurrence of emergent spatial patterning is scarce, which questions the relevance of the proposed mechanisms to ecological theory. We report on regular spatial patterns in young mussel beds on soft sediments in the Wadden Sea. We propose that scale-dependent feedback, resulting from short-range facilitation by mutual protection from waves and currents and long-range competition for algae, induces spatial self-organization, thereby providing a possible explanation for the observed patterning. The emergent self-organization affects the functioning of mussel bed ecosystems by enhancing productivity and resilience against disturbance. Moreover, self-organization allows mussels to persist at algal concentrations that would not permit survival of mussels in a homogeneous bed. Our results emphasize the importance of self-organization in affecting the emergent properties of natural systems at larger spatial scales.     

A novel framework for disentangling the scale‐dependent influences of abiotic factors on alpine treeline position

Low‐temperature growth limitation largely determines alpine treeline position globally, but treeline elevation also varies locally at a range of scales in response to multiple biotic and abiotic factors. In this study, we conceptualise how variability in treeline elevation is related to abiotic factors that act as thermal modifiers, physiological stressors, or disturbance agents. We then present a novel analytical framework for quantifying how abiotic factors influence treeline elevation at different spatial scales using New Zealand Nothofagus treelines as a case study. We delineated Nothofagus treelines in a GIS, along which we extracted data for treeline elevation and eight abiotic explanatory variables at 54 000 points. Each location was classified at each of five spatial scales based on nested river catchments, ranging from large regional to small hillslope catchments. We used hierarchical linear models to partition the variation in both treeline elevation and the eight abiotic variables by spatial scale, and then quantified the relationships between these at each spatial scale in turn. Nothofagus treeline elevation varied from 800–1740 m a.s.l. across New Zealand. Abiotic factors explained 82% of the variation in treeline elevation at the largest (regional) scale and 44–52% of variation at the four finer scales. Broad‐scale variation in Nothofagus treeline elevation was strongly associated with thermal modifiers, consistent with the idea that treelines coincide with a temperature‐driven, physiological limit. However, much of the finer‐scale variation in treeline elevation was explained by a combination of thermal, physiological stress‐related, and disturbance variables operating at different spatial scales. The conceptual model and analytical methods developed here provide a general framework for understanding treeline variation at different spatial scales.     

Woodstoich III: Integrating tools of nutritional geometry and ecological stoichiometry to advance nutrient budgeting and the prediction of consumer‐driven nutrient recycling

Within the last two decades, ecological stoichiometry (ES) and nutritional geometry (NG, also known as geometric framework for nutrition) have delivered novel insights into core questions of nutritional ecology. These two nutritionally explicit frameworks differ in the ‘nutrient currency’ used and the focus of their past research; behavioural feeding strategies in NG, mainly investigating terrestrial organisms, and trophic ecology in ES, mainly in aquatic settings. However, both NG and ES have developed in explaining patterns across various scales of biological organization. Integrating specific tools of these frameworks could advance the field of nutritional ecology by unifying theoretical and empirical approaches from the organismal to ecosystem level processes. Toward this integration, we identified 1) nutrient/element budgets as a shared concept of both frameworks that encompass nutrient intake, retention, and release, 2) response surface plots of NG as powerful tools to illustrate processes at the organismal level and 3) the concept of consumer‐driven nutrient recycling (CNR) of ES as a useful tool bridging organism and ecosystem scales. We applied response surface plots to element budget data from an ES study to show how this approach can deliver new insights at the organismal level, e.g. by showing the interplay between egestion and excretion depending simultaneously on the consumed amount of carbon and phosphorus based on variation across individuals. By integrating concepts of ES and NG to model microbial uptake and mineralization of nitrogenous wastes reported in a NG study, we also demonstrate that considering biochemically explicit mineralization rates of organic wastes can improve predictions of CNR by reducing over‐ or underestimation of mineralization depending on the quality of the consumer's diet. Our presented tools and approaches can help to bridge the organismal and ecosystem level, advancing the predictive power of studies in nutritional ecology at multiple ecological scales.     

Spatial pattern of distribution of marine invertebrates within a subtidal community: do communities vary more among patches or plots?

Making links between ecological processes and the scales at which they operate is an enduring challenge of community ecology. Our understanding of ecological communities cannot advance if we do not distinguish larger scale processes from smaller ones. Variability at small spatial scales can be important because it carries information about biological interactions, which cannot be explained by environmental heterogeneity alone. Marine fouling communities are shaped by both the supply of larvae and competition for resources among colonizers—these two processes operate on distinctly different scales. Here, we demonstrate how fouling community structure varies with spatial scale in a temperate Australian environment, and we identify the spatial scale that captures the most variability. Community structure was quantified with both univariate (species richness and diversity) and multivariate (similarity in species composition) indices. Variation in community structure was unevenly distributed between the spatial scales that we examined. While variation in community structure within patch was usually greater than among patch, variation among patch was always significant. Opportunistic taxa that rely heavily on rapid colonization of free space spread more evenly among patches during early succession. In contrast, taxa that are strong adult competitors but slow colonizers spread more evenly among patches only during late succession. Our findings show significant patchiness can develop in a habitat showing no systematic environmental spatial variation, and this patchiness can be mediated through different biological factors at different spatial scales.     

Integrating data mining and transmission theory in the ecology of infectious diseases

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.     

Scaling up population dynamics: integrating theory and data

How to scale up from local-scale interactions to regional-scale dynamics is a critical issue in field ecology. We show how to implement a systematic approach to the problem of scaling up, using scale transition theory. Scale transition theory shows that dynamics on larger spatial scales differ from predictions based on the local dynamics alone because of an interaction between local-scale nonlinear dynamics and spatial variation in density or the environment. Based on this theory, a systematic approach to scaling up has four steps: (1) derive a model to translate the effects of local dynamics to the regional scale, and to identify key interactions between nonlinearity and spatial variation, (2) measure local-scale model parameters to determine nonlinearities at local scales, (3) measure spatial variation, and (4) combine nonlinearity and variation measures to obtain the scale transition. We illustrate the approach, with an example from benthic stream ecology of caddisflies living in riffles. By sampling from a simulated system, we show how collecting the appropriate data at local (riffle) scales to measure nonlinearities, combined with measures of spatial variation, leads to the correct inference for dynamics at the larger scale of the stream. The approach provides a way to investigate the mechanisms and consequences of changes in population dynamics with spatial scale using a relatively small amount of field data.     

Variation in reproduction and growth in declining Scots pine populations

Disentangling how variation in reproduction and growth is linked in plants across different ecological scales, and how allocation rules change in response to stress are fundamental aspects of life history theory. Although it is known that reproductive allocation is an allometric process and that environmental conditions can influence demographic traits, patterns of variation in vegetative and reproductive functions across and within individuals of tree species suffering drought-induced decline have rarely been documented. In this study we use Scots pine (Pinus sylvestris L.) as a model species to explore patterns of variation in cone production and growth in two declining populations at the southern edge of its distribution. A Bayesian approach was used to assess how these demographic traits vary as a function of drought effects and competition and covary across different ecological scales. The allometric trajectories relating tree size with cone production and growth differed along gradients of drought impacts and biotic interactions. Although reproduction and growth increased with tree size, cone production reached a maximum at intermediate sized trees and stabilized or decreased at larger sizes. Drought stress effects (defoliation at the tree level and overall decline at the plot level) and competition for resources reduced cone production and growth. Our results also showed differential effects of defoliation on cone production depending on tree size, with stronger effects on larger individuals. After accounting for these effects, much of the variation of demographic traits and correlations among them occurred at small ecological scales across individuals (i.e. within plots) and within individuals across years. This resulted in covariations between demographic traits among nearby individuals and within individuals through time, suggesting a consistent advantage in resource acquisition of some individuals within plots, and trade-offs between growth and cone production within trees across years. In conclusion, this study reports that drought-induced forest decline is associated with lower growth and cone production in Scots pine, which could contribute to explain the long-term impacts of drought in southern populations of this species and, in particular, its low regeneration capacity after severe drought.     

Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but only recently community ecologists realized the need to document their spatial and temporal variation. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time – i.e. a novel approach to interaction distribution modeling. We apply this framework to host–parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co‐occurrence, but not on species interactions. Even where species co‐occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host‐parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modeling and community ecology.     

Pervasive canopy dynamics produce short-term stability in a tropical rain forest landscape

A fundamental property of all forest landscapes is the size frequency distribution of canopy gap disturbances. But characterizing forest structure and changes at large spatial scales has been challenging and most of our understanding is from permanent inventory plots. Here we report the first application of light detection and ranging remote sensing to measurements of canopy disturbance and regeneration in an old-growth tropical rain forest landscape. Pervasive local height changes figure prominently in the dynamics of this forest. Although most canopy gaps recruited to higher positions during 8.5 years, size frequency distributions were similar at two points in time and well-predicted by power-laws. At larger spatial scales (hundreds of ha), height increases and decreases occurred with similar frequency and changes to canopy height that were analysed using a height transition matrix suggest that the distribution of canopy height at the beginning of the study was close to the projected steady-state equilibrium under the recent disturbance regime. Taken together, these findings show how widespread local height changes can produce short-term stability in a tropical rain forest landscape.     

Legacy effects and memory loss: how contingencies moderate the response of rocky intertidal biofilms to present and past extreme events

Understanding how historical processes modulate the response of ecosystems to perturbations is becoming increasingly important. In contrast to the growing interest in projecting biodiversity and ecosystem functioning under future climate scenarios, how legacy effects originating from historical conditions drive change in ecosystems remains largely unexplored. Using experiments in combination with stochastic antecedent modelling, we evaluated how extreme warming, sediment deposition and grazing events modulated the ecological memory of rocky intertidal epilithic microphytobenthos (EMPB). We found memory effects in the non‐clustered scenario of disturbance (60 days apart), where EMPB biomass fluctuated in time, but not under clustered disturbances (15 days apart), where EMPB biomass was consistently low. A massive grazing event impacted on EMPB biomass in a second run of the experiment, also muting ecological memory. Our results provide empirical support to the theoretical expectation that stochastic fluctuations promote ecological memory, but also show that contingencies may lead to memory loss.