Integrating modelling of biodiversity composition and ecosystem function

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative‐mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management. Synthesis There is increasing attention from researchers and policy makers around the world on both assessing and projecting the state of the planet's biodiversity, its ecosystems and the essential services they provide to society. However, existing modelling approaches largely ignore the interactions between biodiversity composition and ecosystem function. We highlight the key challenges and potential solutions to developing integrated models of composition and function. Such models will require a new effort and focus from ecologists, yet the benefits are likely to be substantial, including better informing the management of natural systems at regional, national and international scales.     

The rise of research on futures in ecology: rebalancing scenarios and predictions

Concern about the ecological consequences of global change has increasingly stimulated ecologists to examine the futures of ecological systems. Studying futures is not only a crucial element of the interaction between science, management and decision making , but also a critical research challenge per se , especially because futures cannot be observed or experimented on. In addition, researchers can encounter methodological and theoretical difficulties, which make interpretations and predictions problematic. In the literature which deals with futures of ecological systems two main lines of research can be distinguished: a predictive approach, which dominates the literature, can be contrasted with a rarer number of studies that elaborate potential scenarios for ecological systems. Scenario approaches currently concern mainly contacts with stakeholders or decision makers, or the use of climate scenarios to derive projections about ecological futures. We argue that a new direction for ecological futures research could be explored by using ecological scenarios in combination with predictive models to further fundamental ecological research, in addition to enhancing its applied value.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

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.     

The risk of establishment of aquatic invasive species: joining invasibility and propagule pressure

Invasive species are increasingly becoming a policy priority. This has spurred researchers and managers to try to estimate the risk of invasion. Conceptually, invasions are dependent both on the receiving environment (invasibility) and on the ability to reach these new areas (propagule pressure). However, analyses of risk typically examine only one or the other. Here, we develop and apply a joint model of invasion risk that simultaneously incorporates invasibility and propagule pressure. We present arguments that the behaviour of these two elements of risk differs substantially--propagule pressure is a function of time, whereas invasibility is not--and therefore have different management implications. Further, we use the well-studied zebra mussel (Dreissena polymorpha) to contrast predictions made using the joint model to those made by separate invasibility and propagule pressure models. We show that predictions of invasion progress as well as of the long-term invasion pattern are strongly affected by using a joint model.     

湖泊生态服务受益者分析及生态生产函数构建

生态系统服务管理作为生态系统管理的优化方式,是生态学研究的前沿方向。湖泊生态系统服务管理是指综合利用生态学、经济学、社会学和管理学等学科知识,对影响湖泊生态系统结构、过程、功能的关键因子进行调控,提高湖泊生态系统服务供给水平和供给能力的过程。近年来国内外学者针对湖泊生态系统服务内涵、分类、经济价值评估等方面开展了大量研究,极大地促进了湖泊生态系统服务从认知走向管理实践。然而,现有研究在开展湖泊生态系统服务价值评估时多忽略生态系统服务受益者和生态系统特征对生态系统服务的边际影响分析,无法揭示生态系统服务空间流动和转移特征及生态系统服务时空权衡关系,制约了生态系统服务研究与管理决策和政策设计结合。在综述湖泊生态系统服务定量评估方法的基础上,认为通过生态系统服务受益者分析确定湖泊生态系统最终服务,并通过构建生态生产函数确定湖泊生态系统服务权衡关系及湖泊生态系统特征对生态系统最终服务的边际影响,是生态系统服务走向管理实践和政策设计的科学依据,可以确保生态、社会、经济可持续发展。     

The big ecological questions inhibiting effective environmental management in Australia

The need to improve environmental management in Australia is urgent because human health, well‐being and social stability all depend ultimately on maintenance of life‐supporting ecological processes. Ecological science can inform this effort, but when issues are socially and economically complex the inclination is to wait for science to provide answers before acting. Increasingly, managers and policy‐makers will be called on to use the present state of scientific knowledge to supply reasonable inferences for action based on imperfect knowledge. Hence, one challenge is to use existing ecological knowledge more effectively; a second is to tackle the critical unanswered ecological questions. This paper identifies areas of environmental management that are profoundly hindered by an inability of science to answer basic questions, in contrast to those areas where knowledge is not the major barrier to policy development and management. Of the 22 big questions identified herein, more than half are directly related to climate change. Several of the questions concern our limited understanding of the dynamics of marine systems. There is enough information already available to develop effective policy and management to address several significant ecological issues. We urge ecologists to make better use of existing knowledge in dialogue with policy‐makers and land managers. Because the challenges are enormous, ecologists will increasingly be engaging a wide range of other disciplines to help identify pathways towards a sustainable future.     

Spatial Uncertainty and Ecological Models

Applied ecological models that are used to understand and manage natural systems often rely on spatial data as input. Spatial uncertainty in these data can propagate into model predictions. Uncertainty analysis, sensitivity analysis, error analysis, error budget analysis, spatial decision analysis, and hypothesis testing using neutral models are all techniques designed to explore the relationship between variation in model inputs and variation in model predictions. Although similar methods can be used to answer them, these approaches address different questions. These approaches differ in (a) whether the focus is forward or backward (forward to evaluate the magnitude of variation in model predictions propagated or backward to rank input parameters by their influence); (b) whether the question involves model robustness to large variations in spatial pattern or to small deviations from a reference map; and (c) whether processes that generate input uncertainty (for example, cartographic error) are of interest. In this commentary, we propose a taxonomy of approaches, all of which clarify the relationship between spatial uncertainty and the predictions of ecological models. We describe existing techniques and indicate a few areas where research is needed.     

Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW

Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.     

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.     

Updated assessment of potential biopesticide options for managing fall armyworm (Spodoptera frugiperda) in Africa

The fall armyworm (FAW, Spodoptera frugiperda) has recently spread to many countries in Africa, the Near East, Asia and the Pacific. In sub-Saharan Africa (SSA), more than 300 million people depend on FAW’s preferred host plant, maize, as a staple crop. Hence, the spread of FAW in SSA has the potential to negatively affect livelihoods and food security. Many farmers have responded to FAW by increasing their use of synthetic pesticides, but these are not always used safely or effectively. More information on sustainable alternatives to high-risk synthetic pesticides is needed to inform decisions by farmers and policy makers. In a previous paper, the authors responded to this information need by identifying fifty biopesticides which had been registered for FAW management in one or more of 30 countries in FAWs native region and Africa. For each biopesticide identified, detailed profiles with information on their efficacy against FAW; associated human health and environmental hazards; their agronomic sustainability; and whether or not they are practical for use by smallholder farmers were developed Research for development (R4D) efforts is ongoing in Africa and Asia for development and use of biopesticides for FAW management. Hence, in this study the authors assessed the current state of knowledge and documented how information gaps have been filled (or not) since the previous paper was published. The authors found that for many biopesticides there is a growing body of information on their efficacy in the field in Africa and increased availability of commercialized products. They also note remaining information gaps, particularly the compatibility of the biopesticides with other recommended management practices, and cost-benefit analyses, important for developing and implementing sustainable IPM. An updated list of priority biopesticides for research, development and promotion is provided.     

Critical slowing down as an indicator of transitions in two-species models

Transitions in ecological systems often occur without apparent warning, and may represent shifts between alternative persistent states. Decreasing ecological resilience (the size of the basin of attraction around a stable state) can signal an impending transition, but this effect is difficult to measure in practice. Recent research has suggested that a decreasing rate of recovery from small perturbations (critical slowing down) is a good indicator of ecological resilience. Here we use analytical techniques to draw general conclusions about the conditions under which critical slowing down provides an early indicator of transitions in two-species predator-prey and competition models. The models exhibit three types of transition: the predator-prey model has a Hopf bifurcation and a transcritical bifurcation, and the competition model has two saddle-node bifurcations (in which case the system exhibits hysteresis) or two transcritical bifurcations, depending on the parameterisation. We find that critical slowing down is an earlier indicator of the Hopf bifurcation in predator-prey models in which prey are regulated by predation rather than by intrinsic density-dependent effects and an earlier indicator of transitions in competition models in which the dynamics of the rare species operate on slower timescales than the dynamics of the common species. These results lead directly to predictions for more complex multi-species systems, which can be tested using simulation models or real ecosystems.     

An engineering approach to modelling, decision support and control for sustainable systems

Engineering research and development contributes to the advance of sustainable agriculture both through innovative methods to manage and control processes, and through quantitative understanding of the operation of practical agricultural systems using decision models. This paper describes how an engineering approach, drawing on mathematical models of systems and processes, contributes new methods that support decision making at all levels from strategy and planning to tactics and real-time control. The ability to describe the system or process by a simple and robust mathematical model is critical, and the outputs range from guidance to policy makers on strategic decisions relating to land use, through intelligent decision support to farmers and on to real-time engineering control of specific processes. Precision in decision making leads to decreased use of inputs, less environmental emissions and enhanced profitability-all essential to sustainable systems.     

How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration–exploitation trade‐offs

Understanding how humans and other animals behave in response to changes in their environments is vital for predicting population dynamics and the trajectory of coupled social‐ecological systems. Here, we present a novel framework for identifying emergent social behaviours in foragers (including humans engaged in fishing or hunting) in predator–prey contexts based on the exploration difficulty and exploitation potential of a renewable natural resource. A qualitative framework is introduced that predicts when foragers should behave territorially, search collectively, act independently or switch among these states. To validate it, we derived quantitative predictions from two models of different structure: a generic mathematical model, and a lattice‐based evolutionary model emphasising exploitation and exclusion costs. These models independently identified that the exploration difficulty and exploitation potential of the natural resource controls the social behaviour of resource exploiters. Our theoretical predictions were finally compared to a diverse set of empirical cases focusing on fisheries and aquatic organisms across a range of taxa, substantiating the framework's predictions. Understanding social behaviour for given social‐ecological characteristics has important implications, particularly for the design of governance structures and regulations to move exploited systems, such as fisheries, towards sustainability. Our framework provides concrete steps in this direction.     

Combining α - and β -diversity models to fill gaps in our knowledge of biodiversity

For many taxonomic groups, sparse information on the spatial distribution of biodiversity limits our capacity to answer a variety of theoretical and applied ecological questions. Modelling community-level attributes (α- and β-diversity) over space can help overcome this shortfall in our knowledge, yet individually, predictions of α- or β-diversity have their limitations. In this study, we present a novel approach to combining models of α- and β-diversity, with sparse survey data, to predict the community composition for all sites in a region. We applied our new approach to predict land snail community composition across New Zealand. As we demonstrate, these predictions of metacommunity composition have diverse potential applications, including predicting γ-diversity for any set of sites, identifying target areas for conservation reserves, locating priority areas for future ecological surveys, generating realistic compositional data for metacommunity models and simultaneously predicting the distribution of all species in a taxon consistent with known community diversity patterns.     

The principle of complementarity in the design of reserve networks to conserve biodiversity: A preliminary history

Explicit, quantitative procedures for identifying biodiversity priority areas are replacing the often ad hoc procedures used in the past to design networks of reserves to conserve biodiversity. This change facilitates more informed choices by policy makers, and thereby makes possible greater satisfaction of conservation goals with increased efficiency. A key feature of these procedures is the use of the principle of complementarity, which ensures that areas chosen for inclusion in a reserve network complement those already selected. This paper sketches the historical development of the principle of complementarity and its applications in practical policy decisions. In the first section a brief account is given of the circumstances out of which concerns for more explicit systematic methods for the assessment of the conservation value of different areas arose. The second section details the emergence of the principle of complementarity in four independent contexts. The third section consists of case studies of the use of the principle of complementarity to make practical policy decisions in Australasia, Africa, and America. In the last section, an assessment is made of the extent to which the principle of complementarity transformed the practice of conservation biology by introducing new standards of rigor and explicitness.     

Quantifying the Adaptive Cycle

The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994–2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.     

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.