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.     

The metacommunity concept: a framework for multi-scale community ecology

The metacommunity concept is an important way to think about linkages between different spatial scales in ecology. Here we review current understanding about this concept. We first investigate issues related to its definition as a set of local communities that are linked by dispersal of multiple potentially interacting species. We then identify four paradigms for metacommunities: the patch‐dynamic view, the species‐sorting view, the mass effects view and the neutral view, that each emphasizes different processes of potential importance in metacommunities. These have somewhat distinct intellectual histories and we discuss elements related to their potential future synthesis. We then use this framework to discuss why the concept is useful in modifying existing ecological thinking and illustrate this with a number of both theoretical and empirical examples. As ecologists strive to understand increasingly complex mechanisms and strive to work across multiple scales of spatio‐temporal organization, concepts like the metacommunity can provide important insights that frequently contrast with those that would be obtained with more conventional approaches based on local communities alone.     

Multidimensional biases,gaps and uncertainties in global plant occurrence information

Plants are a hyperdiverse clade that plays a key role in maintaining ecological and evolutionary processes as well as human livelihoods. Biases, gaps and uncertainties in plant occurrence information remain a central problem in ecology and conservation, but these limitations remain largely unassessed globally. In this synthesis, we propose a conceptual framework for analysing gaps in information coverage, information uncertainties and biases in these metrics along taxonomic, geographical and temporal dimensions, and apply it to all c. 370 000 species of land plants. To this end, we integrated 120 million point‐occurrence records with independent databases on plant taxonomy, distributions and conservation status. We find that different data limitations are prevalent in each dimension. Different metrics of information coverage and uncertainty are largely uncorrelated, and reducing taxonomic, spatial or temporal uncertainty by filtering out records would usually come at great costs to coverage. In light of these multidimensional data limitations, we discuss prospects for global plant ecological and biogeographical research, monitoring and conservation and outline critical next steps towards more effective information usage and mobilisation. Our study provides an empirical baseline for evaluating and improving global floristic knowledge, along with a conceptual framework that can be applied to study other hyperdiverse clades.     

Implications of scale for patterns and processes in stream ecology

Abstract Although the scale-dependence of ecological patterns and processes is recognized by freshwater ecologists, current knowledge of scale effects is rudimentary and non-quantitative. We review issues of spatial and temporal scale in this paper to highlight conceptual problems relating to scale and some potential solutions. We present examples of how the spatial scale of a study influences observed patterns and their interpretation, and discuss how the size of an experimental arena influences the degree to which the dynamics of studied populations are influenced by exchange processes (immigration and emigration). The results of small-scale field experiments in streams will often be strongly influenced by the per capita exchange rates of organisms and differences in exchange rates may explain differences in the perceived effects of stream manipulations across scales. Spatial extent also influences the amount of spatial heterogeneity within a study site or arena, with important consequences for the outcome of predator-prey interactions. We suggest that changes in the availability of prey refuges may help explain why predator manipulations in streams appear to weaken as arena size increases. We also recommend that new techniques for decomposing and quantifying spatial heterogeneity be applied to characterize scale-dependent variation in freshwater systems. Lastly, we discuss the pitfalls of mismatching the temporal scale of experiments and models. Models incorporating spatial heterogeneity and the behaviour of organisms are needed to predict the short-term outcome of perturbations in streams, whereas models predicting long-term dynamics will need to integrate the impacts of episodic disturbance and all life history stages of organisms. In general, we recommend that freshwater ecologists undertake more multi-scale sampling and experimentation to examine patterns and processes at multiple scales, and make greater attempts to match the scales of their observations and experiments to the characteristic scales of the phenomena that they investigate.     

Terrestrial ecosystems from space: a review of earth observation products for macroecology applications

Aim Earth observation (EO) products are a valuable alternative to spectral vegetation indices. We discuss the availability of EO products for analysing patterns in macroecology, particularly related to vegetation, on a range of spatial and temporal scales. Location Global. Methods We discuss four groups of EO products: land cover/cover change, vegetation structure and ecosystem productivity, fire detection, and digital elevation models. We address important practical issues arising from their use, such as assumptions underlying product generation, product accuracy and product transferability between spatial scales. We investigate the potential of EO products for analysing terrestrial ecosystems. Results Land cover, productivity and fire products are generated from long‐term data using standardized algorithms to improve reliability in detecting change of land surfaces. Their global coverage renders them useful for macroecology. Their spatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation and fire, ≥ 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopy structure and productivity products are based on physical approaches and thus are independent of biome‐specific calibrations. Active fire locations are provided in near‐real time, while burnt area products show actual area burnt by fire. EO products can be assimilated into ecosystem models, and their validation information can be employed to calculate uncertainties during subsequent modelling. Main conclusions Owing to their global coverage and long‐term continuity, EO end products can significantly advance the field of macroecology. EO products allow analyses of spatial biodiversity, seasonal dynamics of biomass and productivity, and consequences of disturbances on regional to global scales. Remaining drawbacks include inter‐operability between products from different sensors and accuracy issues due to differences between assumptions and models underlying the generation of different EO products. Our review explains the nature of EO products and how they relate to particular ecological variables across scales to encourage their wider use in ecological applications.     

Historical foundations and future directions in macrosystems ecology

Macrosystems ecology is an effort to understand ecological processes and interactions at the broadest spatial scales and has potential to help solve globally important social and ecological challenges. It is important to understand the intellectual legacies underpinning macrosystems ecology: How the subdiscipline fits within, builds upon, differs from and extends previous theories. We trace the rise of macrosystems ecology with respect to preceding theories and present a new hypothesis that integrates the multiple components of macrosystems theory. The spatio‐temporal anthropogenic rescaling (STAR) hypothesis suggests that human activities are altering the scales of ecological processes, resulting in interactions at novel space–time scale combinations that are diverse and predictable. We articulate four predictions about how human actions are “expanding”, “shrinking”, “speeding up” and “slowing down” ecological processes and interactions, and thereby generating new scaling relationships for ecological patterns and processes. We provide examples of these rescaling processes and describe ecological consequences across terrestrial, freshwater and marine ecosystems. Rescaling depends in part on characteristics including connectivity, stability and heterogeneity. Our STAR hypothesis challenges traditional assumptions about how the spatial and temporal scales of processes and interactions operate in different types of ecosystems and provides a lens through which to understand macrosystem‐scale environmental change.     

A phylogenetic and trait‐based analysis of community assembly in a subtropical forest in central China

Despite several decades of study in community ecology, the relative importance of the ecological processes that determine species co‐occurrence across spatial scales remains uncertain. Some of this uncertainty may be reduced by studying the scale dependency of community assembly in the light of environmental variation. Phylogenetic information and functional trait information are often used to provide potentially valuable insights into the drivers of community assembly. Here, we combined phylogenetic and trait‐based tests to gain insights into community processes at four spatial scales in a large stem‐mapped subtropical forest dynamics plot in central China. We found that all of the six leaf economic traits measured in this study had weak, but significant, phylogenetic signal. Nonrandom phylogenetic and trait‐based patterns associated with topographic variables indicate that deterministic processes tend to dominate community assembly in this plot. Specifically, we found that, on average, co‐occurring species were more phylogenetically and functionally similar than expected throughout the plot at most spatial scales and assemblages of less similar than expected species could only be found on finer spatial scales. In sum, our results suggest that the trait‐based effects on community assembly change with spatial scale in a predictable manner and the association of these patterns with topographic variables, indicates the importance of deterministic processes in community assembly relatively to random processes.     

An ecological framework of neophobia: from cells to organisms to populations

Neophobia is the fear of novel stimuli or situations. This phenotype has recently received much ecological attention, primarily in the context of decision making. Here, we explore neophobia across biological levels of organisation, first describing types of neophobia among animals and the underlying causes of neophobia, highlighting high levels of risk and uncertainty as key drivers. We place neophobia in the framework of Error Management Theory and Signal Detection Theory, showing how increases in overall risk and uncertainty can lead to costly non‐responses towards novel threats unless individuals lower their response threshold and become neophobic. We then discuss how neophobic behaviour translates into population and evolutionary consequences before introducing neophobia‐like processes at the cellular level, where some phenomena such as allergy and autoimmunity can parallel neophobic behaviour. Finally, we discuss neophobia attenuation, considering how a sudden change in the environment from dangerous to safe can lead to problematic over‐responses (i.e. the ‘maladaptive defensive carry‐over’ hypothesis), and discuss treatment methods for such over‐responses. We anticipate that bridging the concept of neophobia with a process‐centered perspective can facilitate a transfer of insight across organisational levels.     

Toward spatio‐temporal delineation of positive interactions in ecology

Given unprecedented rates of biodiversity loss, there is an urgency to better understand the ecological consequences of interactions among organisms that may lost or altered. Positive interactions among organisms of the same or different species that directly or indirectly improve performance of at least one participant can structure populations and communities and control ecosystem process. However, we are still in need of synthetic approaches to better understand how positive interactions scale spatio‐temporally across a range of taxa and ecosystems. Here, we synthesize two complementary approaches to more rigorously describe positive interactions and their consequences among organisms, across taxa, and over spatio‐temporal scales. In the first approach, which we call the mechanistic approach, we make a distinction between two principal mechanisms of facilitation—habitat modification and resource modification. Considering the differences in these two mechanisms is critical because it delineates the potential spatio‐temporal bounds over which a positive interaction can occur. We offer guidance on improved sampling regimes for quantification of these mechanistic interactions and their consequences. Second, we present a trait‐based approach in which traits of facilitators or traits of beneficiaries can modulate their magnitude of effect or how they respond to either of the positive interaction mechanisms, respectively. Therefore, both approaches can be integrated together by quantifying the degree to which a focal facilitator's or beneficiary's traits explain the magnitude of a positive effect in space and time. Furthermore, we demonstrate how field measurements and analytical techniques can be used to collect and analyze data to test the predictions presented herein. We conclude by discussing how these approaches can be applied to contemporary challenges in ecology, such as conservation and restoration and suggest avenues for future research.     

Vegetation demographics in Earth System Models: A review of progress and priorities

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real‐world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first‐generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter‐disciplinary communication.     

Seeing through the static: the temporal dimension of plant–animal mutualistic interactions

Most studies of plant–animal mutualistic networks have come from a temporally static perspective. This approach has revealed general patterns in network structure, but limits our ability to understand the ecological and evolutionary processes that shape these networks and to predict the consequences of natural and human‐driven disturbance on species interactions. We review the growing literature on temporal dynamics of plant–animal mutualistic networks including pollination, seed dispersal and ant defence mutualisms. We then discuss potential mechanisms underlying such variation in interactions, ranging from behavioural and physiological processes at the finest temporal scales to ecological and evolutionary processes at the broadest. We find that at the finest temporal scales (days, weeks, months) mutualistic interactions are highly dynamic, with considerable variation in network structure. At intermediate scales (years, decades), networks still exhibit high levels of temporal variation, but such variation appears to influence network properties only weakly. At the broadest temporal scales (many decades, centuries and beyond), continued shifts in interactions appear to reshape network structure, leading to dramatic community changes, including loss of species and function. Our review highlights the importance of considering the temporal dimension for understanding the ecology and evolution of complex webs of mutualistic interactions.     

Scaling up and down: movement ecology for microorganisms

Movement is critical for the fitness of organisms, both large and small. It dictates how individuals acquire resources, evade predators, exchange genetic material, and respond to stressful environments. Movement also influences ecological and evolutionary dynamics at higher organizational levels, such as populations and communities. However, the links between individual motility and the processes that generate and maintain microbial diversity are poorly understood. Movement ecology is a framework linking the physiological and behavioral properties of individuals to movement patterns across scales of space, time, and biological organization. By synthesizing insights from cell biology, ecology, and evolution, we expand theory from movement ecology to predict the causes and consequences of microbial movements.     

The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity

Climate alteration is one of the most cited ecological consequences of urbanization. However, the magnitude of this impact is likely to vary with spatial scale. We investigated how this alteration affects the biological fitness of insects, which are especially sensitive to ambient conditions and well‐suited organisms to study urbanization‐related changes in phenotypic traits. We monitored temperature and relative air humidity in wooded sites characterized by different levels of urbanization in the surroundings. Using a split‐brood design experiment, we investigated the effect of urbanization at the local (i.e., 200 × 200 m) and landscape (i.e., 3 × 3 km) scale on two key traits of biological fitness in two closely related butterfly species that differ in thermal sensitivity. In line with the Urban Heat Island concept, urbanization led to a 1°C increase in daytime temperature and an 8% decrease in daytime relative humidity at the local scale. The thermophilous species Lasiommata megera responded at the local scale: larval survival increased twofold in urban compared to rural sites. Urbanized sites tended to produce bigger adults, although this was the case for males only. In the woodland species Pararge aegeria, which has recently expanded its ecological niche, we did not observe such a response, neither at the local, nor at the landscape scale. These results demonstrate interspecific differences in urbanization‐related phenotypic plasticity and larval survival. We discuss larval pre‐adaptations in species of different ecological profiles to urban conditions. Our results also highlight the significance of considering fine‐grained spatial scales in urban ecology.     

Overview: Spatial information and indicators for sustainable management of natural resources

Natural resource management (NRM) is becoming increasingly important at all scales, local, regional, national and global, because of an increasing human population and increasing per capita use of resources and space. Conflicts are intensifying between different interest groups. Production and conservation aspects are particularly debated because conservation often conflicts with economic and social sustainability. There is public demand for objective decision based NRM but limitations are all pervasive due to the spatial and temporal complexity and interdisciplinary nature.This special issue explores the use of spatial data and models to overcome some limitations of NRM decision making. The papers in this issue show modern approaches of natural resources management with a particular focus on spatial data collection, analysis and the development of spatial indicators. This issue presents a balanced mix of review and research papers that give examples of how to find or improve the spatial information base for evidence-based decision making.This overview makes the argument that understanding complex spatial pattern and processes, and the development of spatial indicators, is an essential aspect of evidence-based NRM. If spatial and temporal patterns are complex, ecological evidence from field data or experiments may have limited value for NRM and observational study designs become more appropriate for understanding complex spatial pattern and processes. Data quality should be documented as a combination of accuracy and spatio-temporal representativeness in order to be useful in the NRM decision process.     

The problem and promise of scale in multilayer animal social networks

Scale remains a foundational concept in ecology.Spatial scale,for instance,has become a central consideration in the way we understand landscape ecology and animal space use.Meanwhile,scale-dependent social processes can range from fine scale interactions to co-occurrence and overlapping home ranges.Furthermore,sociality can vary within and across seasons.Multilayer networks promise the explicit integration of the social,spatial,and temporal contexts.Given the complex interplay of sociality and animal space use in heterogeneous landscapes,there remains an important gap in our understanding of the influence of scale on animal social networks.Using an empirical case study,we discuss ways of considering social,spatial,and temporal scale in the context of multilayer caribou social networks.Effective integration of social and spatial processes,including biologically meaningful scales,within the context of animal social networks is an emerging area of research.We incorporate perspectives that link the social environment to spatial processes across scales in a multilayer context.     

Using value of information to prioritize research needs for migratory bird management under climate change: a case study using federal land acquisition in the United States

In response to global habitat loss, many governmental and non‐governmental organizations have implemented land acquisition programs to protect critical habitats permanently for priority species. The ability of these protected areas to meet future management objectives may be compromised if the effects of climate change are not considered in acquisition decisions. Unfortunately, the effects of climate change on ecological systems are complex and plagued by uncertainty, making it difficult for organizations to prioritize research needs to improve decision‐making. Herein, we demonstrate the use of qualitative value of information analysis to identify and prioritize which sources of uncertainty should be reduced to improve land acquisition decisions to protect migratory birds in the face of climate change. The qualitative value of information analysis process involves four steps: (i) articulating alternative hypotheses; (ii) determining the magnitude of uncertainty regarding each hypothesis; (iii) evaluating the relevance of each hypothesis to acquisition decision‐making; and (iv) assessing the feasibility of reducing the uncertainty surrounding each hypothesis through research and monitoring. We demonstrate this approach using the objectives of 3 U.S. federal land acquisition programs that focus on migratory bird management. We used a comprehensive literature review, expert elicitation, and professional judgement to evaluate 11 hypotheses about the effect of climate change on migratory birds. Based on our results, we provide a list of priorities for future research and monitoring to reduce uncertainty and improve land acquisition decisions for the programs considered in our case study. Reducing uncertainty about how climate change will influence the spatial distribution of priority species and biotic homogenization were identified as the highest priorities for future research due to both the value of this information for improving land acquisition decisions and the feasibility of reducing uncertainty through research and monitoring. Research on how changes in precipitation patterns and winter severity will influence migratory bird abundance is also expected to benefit land acquisition decisions. By contrast, hypotheses about phenology and migration distance were identified as low priorities for research. By providing a rigorous and transparent approach to prioritizing research, we demonstrate that qualitative value of information is a valuable tool for prioritizing research and improving management decisions in other complex, high‐uncertainty cases where traditional quantitative value of information analysis is not possible. Given the inherent complexity of ecological systems under climate change, and the difficulty of identifying management‐relevant research priorities, we expect this approach to have wide applications within the field of natural resource management.     

生态功能区划理论研究进展

作为生态系统管理的重要手段,开展科学合理的生态功能区划,已成为世界各国走向可持续发展所面临的关键挑战之一。生态功能区划针对一定区域内自然地理环境分异性、生态系统多样性、以及经济与社会发展不均衡性的现状,结合自然资源保护和可持续开发利用的思想,整合与分异生态系统服务功能对区域人类活动影响的生态敏感性,将区域空间划分为不同生态功能区的研究过程。生态功能区划反映了基于景观特征的主要生态模式,强调了不同时空尺度的景观异质性。通过梳理生态功能区划的概念与内涵、形成与发展及其理论基础,提出了生态功能区划是以恢复区域持续性、完整性的生态系统健康为目标,基于区域的自然地理背景,界定生态功能分区及其子系统的边界,结合区域水陆生态系统、社会经济与土地利用的现状评价与问题诊断,识别生态系统空间格局的分布特征、生态过程的关键因子以及动态演替的驱动因子,明确影响生态系统服务功能的景观格局与结构、景观过程与功能以及景观动态变化,构建生态功能区划的指标体系与技术体系,实现生态功能多级区划,并为决策者更为全面和综合地开展生态系统管理提供科学依据。     

How to make more out of community data? A conceptual framework and its implementation as models and software

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non‐manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data‐driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species‐to‐species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R‐ and Matlab‐packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time‐series data. We illustrate the use of this framework through a series of diverse ecological examples.     

Value of long‐term ecological studies

Long‐term ecological studies are critical for providing key insights in ecology, environmental change, natural resource management and biodiversity conservation. In this paper, we briefly discuss five key values of such studies. These are: (1) quantifying ecological responses to drivers of ecosystem change; (2) understanding complex ecosystem processes that occur over prolonged periods; (3) providing core ecological data that may be used to develop theoretical ecological models and to parameterize and validate simulation models; (4) acting as platforms for collaborative studies, thus promoting multidisciplinary research; and (5) providing data and understanding at scales relevant to management, and hence critically supporting evidence‐based policy, decision making and the management of ecosystems. We suggest that the ecological research community needs to put higher priority on communicating the benefits of long‐term ecological studies to resource managers, policy makers and the general public. Long‐term research will be especially important for tackling large‐scale emerging problems confronting humanity such as resource management for a rapidly increasing human population, mass species extinction, and climate change detection, mitigation and adaptation. While some ecologically relevant, long‐term data sets are now becoming more generally available, these are exceptions. This deficiency occurs because ecological studies can be difficult to maintain for long periods as they exceed the length of government administrations and funding cycles. We argue that the ecological research community will need to coordinate ongoing efforts in an open and collaborative way, to ensure that discoverable long‐term ecological studies do not become a long‐term deficiency. It is important to maintain publishing outlets for empirical field‐based ecology, while simultaneously developing new systems of recognition that reward ecologists for the use and collaborative sharing of their long‐term data sets. Funding schemes must be re‐crafted to emphasize collaborative partnerships between field‐based ecologists, theoreticians and modellers, and to provide financial support that is committed over commensurate time frames.     

Testing metapopulation dynamics using genetic, demographic and ecological data

The metapopulation concept is a cornerstone in the recent history of ecology and evolution. However, determining whether a natural system fits a metapopulation model is a complex issue. Extinction-colonization dynamics are indeed often difficult to quantify because species detectability is not always 100%, resulting in an imperfect record of extinctions. Here, we explore whether combining population genetics with demographic and ecological surveys can yield more realistic estimates of metapopulation dynamics. We apply this approach to the freshwater snail Drepanotrema depressissimum in a fragmented landscape of tropical ponds. In addition to studying correlations between genetic diversity and demographical or ecological characteristics, we undertake, for the first time, a detailed search for genetic signatures of extinction-recolonization events using temporal changes in allele frequencies within sites. Surprisingly, genetic data indicate that extinction is much rarer than suggested by demographic surveys. Consequently, this system is better described as a set of populations with different sizes and immigration rates than as a true metapopulation. We identify several cases of apparent extinction owing to nondetection of low-density populations, and of aestivating individuals in desiccated ponds. More generally, we observed a frequent mismatch between genetic and demographical/ecological information at small spatial and temporal scales. We discuss the causes of these discrepancies and show how these two types of data provide complementary information on population dynamics and history, especially when temporal genetic samples are available.