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.     

From forest fires to fisheries management: Anthropology,conservation biology,and historical ecology

Human‐environmental relationships have long been of interest to a variety of scientists, including ecologists, biologists, anthropologists, and many others. 1 , 2 In anthropology, this interest was especially prevalent among cultural ecologists of the 1970s and earlier, who tended to explain culture as the result of techno‐environmental constraints. 3 More recently researchers have used historical ecology, an approach that focuses on the long‐term dialectical relationship between humans and their environments, as well as long‐term prehuman ecological datasets. 4 - 7 An important contribution of anthropology to historical ecology is that anthropological datasets dealing with ethnohistory, traditional ecological knowledge, and human skeletal analysis, as well as archeological datasets on faunal and floral remains, artifacts, geochemistry, and stratigraphic analysis, provide a deep time perspective (across decades, centuries, and millennia) on the evolution of ecosystems and the place of people in those larger systems. Historical ecological data also have an applied component that can provide important information on the relative abundances of flora and fauna, changes in biogeography, alternations in food webs, landscape evolution, and much more.     

Quantifying ecological memory in plant and ecosystem processes

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18–28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time‐series data and facilitating new hypothesis generation and additional research.     

景观生态学三维格局研究进展

非生物与生物的密切交互造就了多种多样的三维景观格局,景观生态学作为研究格局和生态学过程的学科,其在三维空间的探索随着激光雷达等三维数据提取技术的完善取得了快速进步.三维数据的引入使得研究更贴近真实地表景观,研究结果与相关生态学指标具有更明显的同向趋势.这改善了传统研究缺乏生态学意义的问题.但由于研究背景不同,研究方法和研究结论仍缺乏普适性.另一方面,研究结果对尺度变化的敏感度也因三维数据量的扩大而增加,如何选择并处理适宜尺度的数据使结论更加科学,是未来需要注意并探讨的问题.未来,随着数据获取更加便捷,研究将向长时间多尺度多源数据集成化发展,除动态监测及预测外,结合景观规划设计的可持续规划和生态恢复应用也是重要的研究方向之一.     

Circumventing structural uncertainty: A Bayesian perspective on nonlinear forecasting for ecology

As a consequence of the complexity of ecosystems and context-dependence of species interactions, structural uncertainty is pervasive in ecological modeling. This is particularly problematic when ecological models are used to make conservation and management plans whose outcomes may depend strongly on model formulation. Nonlinear time series approaches allow us to circumvent this issue by using the observed dynamics of the system to guide policy development. However, these methods typically require long time series from stationary systems, which are rarely available in ecological settings. Here we present a Bayesian approach to nonlinear forecasting based on Gaussian processes that readily integrates information from several short time series and allows for nonstationary dynamics. We demonstrate the utility of our modeling methods on simulated from a wide range of ecological scenarios. We expect that these models will extend the range of ecological systems to which nonlinear forecasting methods can be usefully applied.     

Maternal effects and the outcome of interspecific competition

1. Maternal environmental effects create lagged population responses to past environments. Although they are ubiquitous and vary in expression across taxa, it remains unclear if and how their presence alters competitive interactions in ecological communities.
2. Here, we use a discrete‐time competition model to simulate how maternal effects alter competitive dynamics in fluctuating and constant environments. Further, we explore how omitting maternal effects alter estimates of known model parameters from observational time series data.
3. Our simulations demonstrate that (i) maternal effects change competitive outcomes, regardless of whether competitors otherwise interact neutrally or exhibit non‐neutral competitive differences, (ii) the consequences of maternal effects for competitive outcomes are mediated by the temporal structure of environmental variation, (iii) even in constant conditions, competitive outcomes are influenced by species'' maternal effects strategies, and (iv) in observational time series data, omitting maternal effects reduces variation explained by models and biases parameter estimates, including competition coefficients.
4. Our findings demonstrate that the ecological consequences of maternal effects hinge on the competitive environment. Evolutionary biologists have long recognized that maternal effects can be an important but often overlooked strategy buffering populations from environmental change. We suggest that maternal effects are similarly critical to ecology and call for research into maternal effects as drivers of dynamics in populations and communities.

     

Combining contemporary ecology and palaeolimnology to understand shallow lake ecosystem change

1. Palaeolimnology and contemporary ecology are complementary disciplines but are rarely combined. By reviewing the literature and using a case study, we show how linking the timescales of these approaches affords a powerful means of understanding ecological change in shallow lakes. 2. Recently, palaeolimnology has largely been pre‐occupied with developing transfer functions which use surface sediment‐lake environment datasets to reconstruct a single environmental variable. Such models ignore complex controls over biological structure and can be prone to considerable error in prediction. Furthermore, by reducing species assemblage data to a series of numbers, transfer functions neglect valuable ecological information on species’ seasonality, habitat structure and food web interactions. These elements can be readily extracted from palaeolimnological data with the interpretive assistance of contemporary experiments and surveys. For example, for one shallow lake, we show how it is possible to infer long‐term seasonality change from plant macrofossil and fossil diatom data with the assistance of seasonal datasets on macrophyte and algal dynamics. 3. On the other hand, theories on shallow lake functioning have generally been developed from short‐term (<1–15 years) studies as opposed to palaeo‐data that cover the actual timescales (decades–centuries) of shallow lake response to stressors such as eutrophication and climate change. Palaeolimnological techniques can track long‐term dynamics in lakes whilst smoothing out short‐term variability and thus provide a unique and important means of not only developing ecological theories, but of testing them. 4. By combining contemporary ecology and palaeolimnology, it should be possible to gain a fuller understanding of changing ecological patterns and processes in shallow lakes on multiple timescales.     

微生物生态学理论框架

微生物是生态系统的重要组成部分,直接或间接地参与所有的生态过程。微生物生态学是基于微生物群体的科学,利用微生物群体DNA/RNA等标志物,重点研究微生物群落构建、组成演变、多样性及其与环境的关系,在生态学理论的指导和反复模型拟合下由统计分析得出具有普遍意义的结论。其研究范围从基因尺度到全球尺度。分子生物学技术的发展,使人们可以直接从基因水平上考查其多样性,从而使得对微生物空间分布格局及其成因的深入研究成为可能。进而可以从方法学探讨微生物生物多样性、分布格局、影响机制及其对全球变化的响应等。在微生物生态学研究中,群落构建与演化、分布特征(含植物-微生物相互关系)、执行群体功能的机理(生物地球化学循环等)、对环境变化的响应与反馈机理是今后需要关注的重点领域。概述了微生物生态学的概念,并初步提出其理论框架,在对比宏观生态学基础理论和模型的基础上,分析微生物多样性的研究内容、研究方法和群落构建的理论机制,展望了今后研究的重点领域。     

N:P化学计量学在生态学研究中的应用

化学计量学很早就被应用于生态学研究中,但长期以来几乎被生态学家所忽视.近年来,由于认识到化学计量学研究可以把生态实体的各个层次在元素水平上统一起来,因此元素化学计量学成为近年来新兴的一个生态学研究领域.氮磷作为植物生长的必需矿质营养元素和生态系统常见的限制性元素,在植物体内存在功能上的联系,二者之间具有重要的相互作用.近年来由于人类活动的强烈影响,这两种元素的循环在速度和规模上都发生了前所未有的改变,导致一系列环境问题的出现,因此N:P化学计量学研究就显得极为重要.本文论述了N:P化学计量学在物种、群落、生态系统等各层次的应用现状,同时从分子生物学角度分析了应用N:P化学计量学的可行性,并指出了N:P化学计量学研究的应用前景和存在的缺陷.     

A plea for more ecology in phytoplankton ecology

When looking for a pattern of phytoplankton behaviour across trophic gradients, we need to cross the boundaries between different disciplinary areas, from autoecology to systems ecology, because eutrophication is a complex process which involves different time scales and different levels of community structure. Thus, we submit our observations to the muddled conceptual world of assemblage ecology. These inaccuracies arise, for example, from both species and community arguments; eutrophication as a fertilization or a metabolic phenomenon; and the notions frequently interwoven of pattern, process and rules. We suggest that it is advantageous to tackle this issue from the perspective of general ecology, rather than from a specifically planktonic orientation. In this way, useful general ecological tools, for example, time series and assembly-rule studies, can be used. Time-series study allows the dynamics of any variable to be described or to show that long term variable fluctuations may sometimes be unregulated, in response to some exogenous factor. Rules of assembly help us to resolve which traits are selectively involved during the eutrophication process. In this context, we advocate (1) the use of traits instead of morphospecies in phytoplankton studies, (2) looking for the dynamic patterns of phytoplankton with eutrophication, (3) the use of time series techniques to study phytoplankton trajectories, (4) the use of assembly rules to discern patterns in the formation of multispecies assemblages, (5) the consideration of the pelagic food-web in studies of phytoplankton dynamics and, as an overall suggestion, to borrow knowledge and inspiration from general ecology.     

Ecological implications of behavioural syndromes

Interspecific trait variation has long served as a conceptual foundation for our understanding of ecological patterns and dynamics. In particular, ecologists recognise the important role that animal behaviour plays in shaping ecological processes. An emerging area of interest in animal behaviour, the study of behavioural syndromes (animal personalities) considers how limited behavioural plasticity, as well as behavioural correlations affects an individual's fitness in diverse ecological contexts. In this article we explore how insights from the concept and study of behavioural syndromes provide fresh understanding of major issues in population ecology. We identify several general mechanisms for how population ecology phenomena can be influenced by a species or population's average behavioural type, by within-species variation in behavioural type, or by behavioural correlations across time or across ecological contexts. We note, in particular, the importance of behavioural type-dependent dispersal in spatial ecology. We then review recent literature and provide new syntheses for how these general mechanisms produce novel insights on five major issues in population ecology: (1) limits to species' distribution and abundance; (2) species interactions; (3) population dynamics; (4) relative responses to human-induced rapid environmental change; and (5) ecological invasions.     

Developing flow–ecology relationships: Implications of nonlinear biological responses for water management

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Science, Art, or Application—the "Karma" of Restoration Ecology

The present state of restoration ecology is far away from Bradshaw’s “acid test for ecology.” The conclusions drawn from the series of papers in this issue and from the Jena workshop suggest some directions in which the field may progress. More attention must be paid to the degraded state, which should be evaluated by its specific features and carefully analyzed before any restoration plan is laid down. Restoration goals have to be realistic, which includes the appreciation of globally changing conditions, resulting in a paradigm‐shift toward “forward‐restoration.” Basically, the transition from the degraded state conditions to the target state is a kind of succession that is manipulated by the application of goal‐orientated and system‐specific disturbances. Whenever possible, restorationists should step back and make use of naturally occurring succession, which requires a sophisticated restoration strategy, involving flexible management responses, multiple alternative target states, robust measurements for the restoration progress, and careful long‐term monitoring. The unique feature of restoration ecology is the involvement of socioeconomic decisions, and conceptual frameworks for ecological restoration have to implement the specific links to natural succession. To bridge the gap between ecological theory and on the ground restoration, it is essential that restoration practice is translated into the vocabulary and thinking of basic ecology. If all these aspects are integrated, ecological restoration as an application—and restoration ecology as an applied science—may develop into an acid test for our understanding of interactions between people and their environment, rather than for pure ecology.     

Inference for nonlinear epidemiological models using genealogies and time series

Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.     

From interesting details to dynamical relevance: toward more effective use of empirical insights in theory construction

A perennial challenge in ecology is to develop dynamical systems models that appropriately abstract and characterize the dynamics of natural systems. Deriving an appropriate model of system dynamics can be a long and iterative process whose outcome depends critically on the quality of empirical data describing the long‐term behavior of a natural system. Most ecological time series are insufficient to offer insight into the way organizational hierarchies and spatial scales are causally linked to natural system dynamics. Moreover, the classic tradition of hypothesis testing in ecology is not likely to lead to those key insights. This because empirical research is geared almost exclusively toward testing model predictions based on underlying causal relationships assumed by theorists. So, empirical research relies heavily on theory for guidance on what is or is not dynamically relevant. I argue here that it is entirely possible to reduce much of this guesswork involved with deciding on causal structure by giving empirical research a new role in theory development. In this role, natural history and field observations are used to develop stochastic, individual‐based and spatially explicit computational models or IBMs that can explore the range of contingency and complexity inherent in real‐world systems.
IBMs can be used to run simulations allowing deductions to be made about the causal linkages between organizational hierarchies, spatial scales, and dynamics. These deductions can be tested under field conditions using experiments that manipulate the putative causal structure and evaluate the dynamical consequences. The emerging insights from this stage can then be used to inspire an analytical construct that embodies the dynamically relevant scales and mechanisms. In essence, computational modeling serves as an intermediate step in theory development in that a wide range of possibly important biological details are considered and then reduced to a subset that is dynamically relevant.     

Niche divergence and lineage diversification among closely related Sistrurus rattlesnakes

Comparing niche divergence among closely related taxa can yield important insights into the ecological distinctiveness of genetically similar forms, and identify the processes that are responsible for diversification in such organisms. Here, we apply newly developed techniques for analysing niche divergence to assess how ecologically distinct a group of closely related rattlesnakes (Sistrurus sp.) are and to explore the role that niche divergence may have played in their diversification. We find that all taxa even the most recently evolved subspecies (approximately 100,000 years old) are now ecologically distinct, implying a role for ecology in the diversification process. Statistical analysis based on comparisons with null models show that niche divergence between forms is more common than niche conservation. Finally, there is nonlinear relationship between phylogenetic and niche divergence in this group whereby niche divergence develops more rapidly between recently diverged subspecies than more distantly related forms. Overall, our results argue that ecology may play an important role in the diversification process in these snakes.     

Deriving land surface phenology indicators from CO2 eddy covariance measurements

Recent progress of CO₂ eddy covariance (EC) technique and accumulation of measurements offer an unprecedented perspective to study the land surface phenology (LSP) in a more objective way than previously possible by allowing the actual photosynthesis measurement – gross primary productivity (GPP). Because of the spatial, temporal, and ecological complexity of processes controlling GPP time series, the extraction of important LSP dates from GPP has been elusive. Here, we present objective measures of several LSP metrics from GPP time series data. A case study based on long term GPP measurements over a mature boreal deciduous forest is provided together with LSP estimates from remote sensing data. Results show that most LSP metrics are interrelated within each season (spring and autumn) both from GPP and remote sensing based estimates. We provide simple mathematical derivatives of GPP time series to objectively estimate key LSP metrics such as: the start, end and length of growing season; end of greenup; start of browndown; length of canopy closure; start, end and length of peak; and peak of season. These key LSP metrics indicate the collective ecological responses to environmental changes over space and time.     

Riverine landscapes: taking landscape ecology into the water

1. Landscape ecology deals with the influence of spatial pattern on ecological processes. It considers the ecological consequences of where things are located in space, where they are relative to other things, and how these relationships and their consequences are contingent on the characteristics of the surrounding landscape mosaic at multiple scales in time and space. Traditionally, landscape ecologists have focused their attention on terrestrial ecosystems, and rivers and streams have been considered either as elements of landscape mosaics or as units that are linked to the terrestrial landscape by flows across boundaries or ecotones. Less often, the heterogeneity that exists within a river or stream has been viewed as a `riverscape' in its own right.
2. Landscape ecology can be unified about six central themes: (1) patches differ in quality (2) patch boundaries affect flows, (3) patch context matters, (4) connectivity is critical, (5) organisms are important, and (6) the importance of scale. Although riverine systems differ from terrestrial systems by virtue of the strong physical force of hydrology and the inherent connectivity provided by water flow, all of these themes apply equally to aquatic and terrestrial ecosystems, and to the linkages between the two.
3. Landscape ecology therefore has important insights to offer to the study of riverine ecosystems, but these systems may also provide excellent opportunities for developing and testing landscape ecological theory. The principles and approaches of landscape ecology should be extended to include freshwater systems; it is time to take the `land' out of landscape ecology.