Spatial autocorrelation of ecological phenomena

Ecological variables often fluctuate synchronously over wide geographical areas, a phenomenon known as spatial autocorrelation or spatial synchrony. Development of statistical approaches designed to test for spatial autocorrelation combined with the increasing accessibility of long-term, large-scale ecological datasets are now making it possible to document the patterns and understand the causes of spatial synchrony at scales that were previously intractable. These developments promise to foster significant future advances in understanding population regulation, metapopulation dynamics and other areas of population ecology.     

Ecological hierarchies and self-organisation – Pattern analysis,modelling and process integration across scales

A continuing discussion in applied and theoretical ecology focuses on the relationship of different organisational levels and on how ecological systems interact across scales. We address principal approaches to cope with complex across-level issues in ecology by applying elements of hierarchy theory and the theory of complex adaptive systems. A top-down approach, often characterised by the use of statistical techniques, can be applied to analyse large-scale dynamics and identify constraints exerted on lower levels. Current developments are illustrated with examples from the analysis of within-community spatial patterns and large-scale vegetation patterns. A bottom-up approach allows one to elucidate how interactions of individuals shape dynamics at higher levels in a self-organisation process; e.g., population development and community composition. This may be facilitated by various modelling tools, which provide the distinction between focal levels and resulting properties. For instance, resilience in grassland communities has been analysed with a cellular automaton approach, and the driving forces in rodent population oscillations have been identified with an agent-based model. Both modelling tools illustrate the principles of analysing higher level processes by representing the interactions of basic components.The focus of most ecological investigations on either top-down or bottom-up approaches may not be appropriate, if strong cross-scale relationships predominate. Here, we propose an ‘across-scale-approach’, closely interweaving the inherent potentials of both approaches. This combination of analytical and synthesising approaches will enable ecologists to establish a more coherent access to cross-level interactions in ecological systems.     

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

A number of important questions in ecology involve the possibility of interactions or "coupling" among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator-prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.     

What's your move? Movement as a link between personality and spatial dynamics in animal populations

Recent studies have established the ecological and evolutionary importance of animal personalities. Individual differences in movement and space‐use, fundamental to many personality traits (e.g. activity, boldness and exploratory behaviour) have been documented across many species and contexts, for instance personality‐dependent dispersal syndromes. Yet, insights from the concurrently developing movement ecology paradigm are rarely considered and recent evidence for other personality‐dependent movements and space‐use lack a general unifying framework. We propose a conceptual framework for personality‐dependent spatial ecology. We link expectations derived from the movement ecology paradigm with behavioural reaction‐norms to offer specific predictions on the interactions between environmental factors, such as resource distribution or landscape structure, and intrinsic behavioural variation. We consider how environmental heterogeneity and individual consistency in movements that carry‐over across spatial scales can lead to personality‐dependent: (1) foraging search performance; (2) habitat preference; (3) home range utilization patterns; (4) social network structure and (5) emergence of assortative population structure with spatial clusters of personalities. We support our conceptual model with spatially explicit simulations of behavioural variation in space‐use, demonstrating the emergence of complex population‐level patterns from differences in simple individual‐level behaviours. Consideration of consistent individual variation in space‐use will facilitate mechanistic understanding of processes that drive social, spatial, ecological and evolutionary dynamics in heterogeneous environments.     

Towards a behavioral ecology of ecological landscapes

Recent developments in landscape-level ecological modeling rest upon poorly understood behavioral phenomena. Surprisingly, these phenomena include animal movement and habitat selection, two areas with a long history of study in behavioral ecology. A major problem in applying traditional behavioral ecology to landscape-level ecological problems is that ecologists and behaviorists work at very different spatial scales. Thus a behavioral ecology of ecological landscapes would strive to overcome this inopportune differential in spatial scales. Such a landscape-conscious behavioral undertaking would not only establish more firmly the link between behavior and ecological systems, but also catalyze the study of basic biological phenomena of Interest to behaviorists and ecologists alike.     

Lost in space? Searching for directions in the spatial modelling of individuals,populations and species ranges

The workshop ‘Spatial models in animal ecology, management and conservation’ held at Silwood Park (UK), 9–11 March 2010, aimed to synthesize recent progress in modelling the spatial dynamics of individuals, populations and species ranges and to provide directions for research. It brought together marine and terrestrial researchers working on spatial models at different levels of organization, using empirical as well as theory-driven approaches. Different approaches, temporal and spatial scales, and practical constraints predominate at different levels of organization and in different environments. However, there are theoretical concepts and specific methods that can fruitfully be transferred across levels and systems, including: habitat suitability characterization, movement rules, and ways of estimating uncertainty.     

Temporal ecology in the Anthropocene

Two fundamental axes – space and time – shape ecological systems. Over the last 30 years spatial ecology has developed as an integrative, multidisciplinary science that has improved our understanding of the ecological consequences of habitat fragmentation and loss. We argue that accelerating climate change – the effective manipulation of time by humans – has generated a current need to build an equivalent framework for temporal ecology. Climate change has at once pressed ecologists to understand and predict ecological dynamics in non‐stationary environments, while also challenged fundamental assumptions of many concepts, models and approaches. However, similarities between space and time, especially related issues of scaling, provide an outline for improving ecological models and forecasting of temporal dynamics, while the unique attributes of time, particularly its emphasis on events and its singular direction, highlight where new approaches are needed. We emphasise how a renewed, interdisciplinary focus on time would coalesce related concepts, help develop new theories and methods and guide further data collection. The next challenge will be to unite predictive frameworks from spatial and temporal ecology to build robust forecasts of when and where environmental change will pose the largest threats to species and ecosystems, as well as identifying the best opportunities for conservation.     

Behavioural ecology at the spatial–social interface

Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the ‘spatial–social interface’ as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial–social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial–social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial–social interface.     

Theoretical contributions to biological control success

Ecologists have long tried, with little success, to develop ecological theory for biological control. Biological control illustrates how science often follows, rather than precedes, technological advances. A scientific theory of biological control remains a worthy and achievable goal. We need to (1) combine deductions from mathematical models with rigorous empiricism measuring and modeling the effects of abiotic and biotic environmental drivers on demography and population dynamics of real biological control systems in the field, (2) use a wider range of model systems to explore how population structure, movement, spatial heterogeneity, and external environmental conditions influence population and community dynamics, and (3) combine deductive and inductive approaches to address the day-to-day concerns of biocontrol scientists including how to rear and release a control organism, suppress the target organism, and minimize harm to non-target organisms. Further progress will require more fundamental research in population and community ecology directly relevant to biological control.     

宏观生态学研究的特点与方法

宏观生态学是研究生态系统以上层次的生态学,研究对象为大尺度复杂系统,研究内容和方法都具有不同于传统生态学的明显特点。重视对空间异质性的研究,重视人类的生态作用,注意运用等级结构理论,其研究结果常常是非实验性和非稳定性的。遥感和地理信息系统是空间数据采集和管理、分析的主要手段,景观分析和景观模型是宏观生态研究的重要方法,定位观测试验的网络研究则是实现宏观整体研究的必由之路。     

Unifying population and landscape ecology with spatial capture–recapture

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio‐temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population state and structure. Spatial capture–recapture (SCR) represents an individual‐based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and they have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g. camera trapping, non‐invasive DNA sampling). We describe ways in which SCR methods stand to advance the study of animal population ecology.     

Behavioral states help translate dispersal movements into spatial distribution patterns of floaters

Within the field of spatial ecology, it is important to study animal movements in order to better understand population dynamics. Dispersal is a nonlinear process through which different behavioral mechanisms could affect movement patterns. One of the most common approaches to analyzing the trajectories of organisms within patches is to use random-walk models to describe movement features. These models express individual movements within a specific area in terms of random-walk parameters in an effort to relate movement patterns to the distributions of organisms in space. However, only using the movement trajectories of individuals to predict the spatial spread of animal populations may not fit the complex distribution of individuals across heterogeneous environments. When we empirically tested the results from a random-walk model (a residence index) used to predict the spatial equilibrium distribution of individuals, we found that the index severely underestimated the spatial spread of dispersing individuals. We believe this is because random-walk models only account for the effects of environmental conditions on individual movements, completely overlooking the crucial influence of behavior changes over time. In the future, both aspects should be accounted for when predicting general rules of (meta)population abundance, distribution, and dynamics from patterns of animal movements.     

Movement‐mediated community assembly and coexistence

Organismal movement is ubiquitous and facilitates important ecological mechanisms that drive community and metacommunity composition and hence biodiversity. In most existing ecological theories and models in biodiversity research, movement is represented simplistically, ignoring the behavioural basis of movement and consequently the variation in behaviour at species and individual levels. However, as human endeavours modify climate and land use, the behavioural processes of organisms in response to these changes, including movement, become critical to understanding the resulting biodiversity loss. Here, we draw together research from different subdisciplines in ecology to understand the impact of individual‐level movement processes on community‐level patterns in species composition and coexistence. We join the movement ecology framework with the key concepts from metacommunity theory, community assembly and modern coexistence theory using the idea of micro–macro links, where various aspects of emergent movement behaviour scale up to local and regional patterns in species mobility and mobile‐link‐generated patterns in abiotic and biotic environmental conditions. These in turn influence both individual movement and, at ecological timescales, mechanisms such as dispersal limitation, environmental filtering, and niche partitioning. We conclude by highlighting challenges to and promising future avenues for data generation, data analysis and complementary modelling approaches and provide a brief outlook on how a new behaviour‐based view on movement becomes important in understanding the responses of communities under ongoing environmental change.     

Recent advances in ecological stoichiometry: insights for population and community ecology

Conventional theories of population and community dynamics are based on a single currency such as number of individuals, biomass, carbon or energy. However, organisms are constructed of multiple elements and often require them (in particular carbon, phosphorus and nitrogen) in different ratios than provided by their resources; this mismatch may constrain the net transfer of energy and elements through trophic levels. Ecological stoichiometry, the study of the balance of elements in ecological processes, offers a framework for exploring ecological effects of such constraints. We review recent theoretical and empirical studies that have considered how stoichiometry may affect population and community dynamics. These studies show that stoichiometric constraints can affect several properties of populations (e.g. stability, oscillations, consumer extinction) and communities (e.g. coexistence of competitors, competitive interactions between different guilds). We highlight gaps in general knowledge and focus on areas of population and community ecology where incorporation of stoichiometric constraints may be particularly fruitful, such as studies of demographic bottlenecks, spatial processes, and multi-species interactions. Finally, we suggest promising directions for new research by recommending potential study systems (terrestrial insects, detritivory-based webs, soil communities) to improve our understanding of populations and communities. Our conclusion is that a better integration of stoichiometric principles and other theoretical approaches in ecology may allow for a richer understanding of both population and community structure and dynamics.     

State-space models of individual animal movement

Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.     

Linking community and ecosystem dynamics through spatial ecology

Classical approaches to food webs focus on patterns and processes occurring at the community level rather than at the broader ecosystem scale, and often ignore spatial aspects of the dynamics. However, recent research suggests that spatial processes influence both food web and ecosystem dynamics, and has led to the idea of 'metaecosystems'. However, these processes have been tackled separately by 'food web metacommunity' ecology, which focuses on the movement of traits, and 'landscape ecosystem' ecology, which focuses on the movement of materials among ecosystems. Here, we argue that this conceptual gap must be bridged to fully understand ecosystem dynamics because many natural cases demonstrate the existence of interactions between the movements of traits and materials. This unification of concepts can be achieved under the metaecosystem framework, and we present two models that highlight how this framework yields novel insights. We then discuss patches, limiting factors and spatial explicitness as key issues to advance metaecosystem theory. We point out future avenues for research on metaecosystem theory and their potential for application to biological conservation.     

Self-structuring in spatial evolutionary ecology

Spatial self-structuring has been a focus of recent interest among evolutionary ecologists. We review recent developments in the study of the interplay between spatial self-structuring and evolution. We first discuss the relative merits of the various theoretical approaches to spatial modelling in ecology. Second, we synthesize the main theoretical studies of the evolution of cooperation in spatially structured populations. We show that population viscosity is generally beneficial to cooperation, because cooperators can reap additional benefits from being clustered. A similar mechanism can explain the evolution of honest communication and of reduced virulence in host–parasite interactions. We also discuss some recent innovative empirical results that test these theories. Third, we show the relevance of these results to the general field of evolutionary ecology. An important conclusion is that kin selection is the main process that drives evolution of cooperation in viscous populations. Many results of kin selection theory can be recovered as emergent properties of spatial ecological dynamics. We discuss the implications of these results for the study of multilevel selection and evolutionary transitions. We conclude by sketching some perspectives for future research, with a particular emphasis on the topics of evolutionary branching, criticality, spatial fluctuations and experimental tests of theoretical predictions.     

Complex structural effects of two hemispheric climatic oscillators on the regional spatio-temporal expansion of a threatened bird

Links between climatic conditions in the eastern equatorial Pacific and extratropical ecological processes remain unexplored. The analysis of a 20‐year time series of spatial and numeric dynamics of a threatened Mediterranean bird suggests, however, that such couplings can be remarkably complex. By providing a new ecological time‐series modelling approach, we were able to dissect the joint effects of the El Niño/Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), regional weather, population density and stochastic variability on the expansion dynamics of the White‐headed duck (Oxyura leococephala) in Spain. Our results suggest that the spatial and numeric dynamics of ducks between peak brood emergence and wintering were simultaneously affected by different climatic phenomena during different phases of their global cycles, involving time lags in the numeric dynamics. Strikingly, our results point to both the NAO and the ENSO as potentially major factors simultaneously forcing ecological processes in the Northern Hemisphere, and suggest a new pathway for non‐additive effects of climate in ecology.     

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.