Modelling coral reef ecosystems with limited observational data

Ecosystem models represent potentially powerful tools for coral reef ecosystem managers. They can provide insight into ecosystem dynamics not achievable through alternative means allowing coral reef managers to assess the potential outcome of any given management decision. One of the main limitations in the applicability of ecosystem models is that they often require detailed empirical data and this can restrict their applicability to ecosystems that are either currently well studied or have the resources available to collect the required data. This study describes the development of a coral reef ecosystem model that can be calibrated to an ecosystem with limited empirical data. Based on the assumption that coral reef ecological structure is generic across all tropical coral reefs and that the magnitude of the interactions between ecological components is reef specific, the dynamics of the ecosystem can be replicated based on limited empirical data. The model successfully replicated the dynamics of three individual reef systems including an inshore and oceanic reef within the Great Barrier Reef and a Caribbean reef system. It highlighted the importance of understanding the specific dynamics of a given reef and that a positive management intervention in one system may result in a negative outcome for another. The model was also used to assess the importance of various interactions within coral reef ecosystems. It identified the interactions between hard corals and other non-algal benthic components as being an important (but currently understudied) facet of coral reef ecology. The development of this modelling approach provides access to ecosystem modelling tools for coral reef managers previously excluded due to a lack of resources or technical expertise.     

Rethinking Community-Based Conservation

Abstract:  Community-based conservation (CBC) is based on the idea that if conservation and development could be simultaneously achieved, then the interests of both could be served. It has been controversial because community development objectives are not necessarily consistent with conservation objectives in a given case. I examined CBC from two angles. First, CBC can be seen in the context of paradigm shifts in ecology and applied ecology. I identified three conceptual shifts—toward a systems view, toward the inclusion of humans in the ecosystem, and toward participatory approaches to ecosystem management—that are interrelated and pertain to an understanding of ecosystems as complex adaptive systems in which humans are an integral part. Second, I investigated the feasibility of CBC, as informed by a number of emerging interdisciplinary fields that have been pursuing various aspects of coupled systems of humans and nature. These fields—common property, traditional ecological knowledge, environmental ethics, political ecology, and environmental history—provide insights for CBC. They may contribute to the development of an interdisciplinary conservation science with a more sophisticated understanding of social-ecological interactions. The lessons from these fields include the importance of cross-scale conservation, adaptive comanagement, the question of incentives and multiple stakeholders, the use of traditional ecological knowledge, and development of a cross-cultural conservation ethic.     

Progress in global ecological modelling

The development of ecological modelling on global level since the middle of the 19th century is first reviewed, including application of statistical analysis, introduction of logistic curve, earth surface modeling, systems ecology, computer-oriented mathematical models and spatially explicit models. Finally, we discuss problems existing in ecological modelling on global level.     

Ecological Consequences of Recent Climate Change

Abstract: Global climate change is frequently considered a major conservation threat. The Earth's climate has already warmed by 0.5° C over the past century, and recent studies show that it is possible to detect the effects of a changing climate on ecological systems. This suggests that global change may be a current and future conservation threat. Changes in recent decades are apparent at all levels of ecological organization: population and life-history changes, shifts in geographic range, changes in species composition of communities, and changes in the structure and functioning of ecosystems. These ecological effects can be linked to recent population declines and to both local and global extinctions of species. Although it is impossible to prove that climate change is the cause of these ecological effects, these findings have important implications for conservation biology. It is no longer safe to assume that all of a species' historic range remains suitable. In drawing attention to the importance of climate change as a current threat to species, these studies emphasize the need for current conservation efforts to consider climate change in both in situ conservation and reintroduction efforts. Additional threats will emerge as climate continues to change, especially as climate interacts with other stressors such as habitat fragmentation. These studies can contribute to preparations for future challenges by providing valuable input to models and direct examples of how species respond to climate change.     

Advancing the Integration of History and Ecology for Conservation

Abstract: The important role of humans in the development of current ecosystems was recognized decades ago; however, the integration of history and ecology in order to inform conservation has been difficult. We identified four issues that hinder historical ecological research and considered possible solutions. First, differences in concepts and methods between the fields of ecology and history are thought to be large. However, most differences stem from miscommunication between ecologists and historians and are less substantial than is usually assumed. Cooperation can be achieved by focusing on the features ecology and history have in common and through understanding and acceptance of differing points of view. Second, historical ecological research is often hampered by differences in spatial and temporal scales between ecology and history. We argue that historical ecological research can only be conducted at extents for which sources in both disciplines have comparable resolutions. Researchers must begin by clearly defining the relevant scales for the given purpose. Third, periods for which quantitative historical sources are not easily accessible (before AD 1800) have been neglected in historical ecological research. Because data from periods before 1800 are as relevant to the current state of ecosystems as more recent data, we suggest that historical ecologists actively seek out data from before 1800 and apply analytic methods commonly used in ecology to these data. Fourth, humans are not usually considered an intrinsic ecological factor in current ecological research. In our view, human societies should be acknowledged as integral parts of ecosystems and societal processes should be recognized as driving forces of ecosystem change.     

A review of recent developments in lake modelling

This paper reviews the lake models published the last five years, mainly in Ecological Modelling. The review shows that structurally dynamic modelling and coupling between hydrodynamic and ecological models are applied increasingly. A number of processes that have not been included in lake models before have been proposed. It has been shown that these additional processes in specific case studies are significant, for instance the competition between phytoplankton and macrophytes or cyanobacteria growth and growth of mussels. It is recommended to study these models for the development of models for case studies where these processes are relevant.     

Ecological relevance of performance criteria for species distribution models

Species distribution models have often been developed based on ecological data. To develop reliable data-driven models, however, a sound model training and evaluation procedures are needed. A crucial step in these procedures is the assessment of the model performance, with as key component the applied performance criterion. Therefore, we reviewed seven performance criteria commonly applied in presence-absence modelling (the correctly classified instances, Kappa, sensitivity, specificity, the normalised mutual information statistic, the true skill statistic and the odds ratio) and analysed their application in both the model training and evaluation process. Although estimates of predictive performance have been used widely to assess final model quality, a systematic overview was missing because most analyses of performance criteria have been empirical and only focused on specific aspects of the performance criteria. This paper provides such an overview showing that different performance criteria evaluate a model differently and that this difference may be explained by the dependency of these criteria on the prevalence of the validation set. We showed theoretically that these prevalence effects only occur if the data are inseparable by an n-dimensional hyperplane, n being the number of input variables. Given this inseparability, different performance criteria focus on different aspects of model performance during model training, such as sensitivity, specificity or predictive accuracy. These findings have important consequences for ecological modelling because ecological data are mostly inseparable due to data noise and the complexity of the studied system. Consequently, it should be very clear which aspect of the model performance is evaluated, and models should be evaluated consistently, that is, independent of, or taking into account, species prevalence. The practical implications of these findings are clear. They provide further insight into the evaluation of ecological presence/absence models and attempt to assist modellers in their choice of suitable performance criteria.     

Ecosystems stochasticity model

The chaotic dynamics observed in a number of natural and artificial ecosystems is assumed to be caused by an intrinsic stochasticity of the modelling dynamical systems involved. The paper considers the transition to stochasticity and its peculiarities in the dynamical system simulating most typical ecological interactions.     

The Robert H. MacArthur Award Lecture. Timescales, dynamics, and ecological understanding

Explicit consideration of timescales and dynamics is required for an understanding of fundamental issues in ecology. Endogenous dynamics can lead to transient states where asymptotic behavior is very different from dynamics on short timescales. The causes of these kinds of transients can be placed in one of three classes: linear systems with different timescales embedded or exhibiting reactive behavior, the potentially long times to reach synchrony across space for oscillating systems, and the complex dynamics of systems with strong density-dependent (nonlinear) interactions. It is also important to include the potentially disparate timescales inherent in ecological systems when determining the endogenous dynamics. I argue that the dynamics of ecological systems can best be understood as the response, which may include transient dynamics, to exogenous influences leading to the observed dynamics on ecologically relevant timescales. This view of ecosystem behavior as responses of ecological systems governed by endogenous dynamics to exogenous influences provides a synthetic way to unify different approaches to population dynamics, to understand mechanisms that determine the distribution and abundance of species, and to manage ecosystems on appropriate timescales. There are implications for theoretical approaches, empirical approaches, and the statistical approaches that bridge theory and observation.     

Species distribution models and ecological theory: A critical assessment and some possible new approaches

Given the importance of knowledge of species distribution for conservation and climate change management, continuous and progressive evaluation of the statistical models predicting species distributions is necessary. Current models are evaluated in terms of ecological theory used, the data model accepted and the statistical methods applied. Focus is restricted to Generalised Linear Models (GLM) and Generalised Additive Models (GAM). Certain currently unused regression methods are reviewed for their possible application to species modelling.A review of recent papers suggests that ecological theory is rarely explicitly considered. Current theory and results support species responses to environmental variables to be unimodal and often skewed though process-based theory is often lacking. Many studies fail to test for unimodal or skewed responses and straight-line relationships are often fitted without justification.Data resolution (size of sampling unit) determines the nature of the environmental niche models that can be fitted. A synthesis of differing ecophysiological ideas and the use of biophysical processes models could improve the selection of predictor variables. A better conceptual framework is needed for selecting variables.Comparison of statistical methods is difficult. Predictive success is insufficient and a test of ecological realism is also needed. Evaluation of methods needs artificial data, as there is no knowledge about the true relationships between variables for field data. However, use of artificial data is limited by lack of comprehensive theory.Three potentially new methods are reviewed. Quantile regression (QR) has potential and a strong theoretical justification in Liebig's law of the minimum. Structural equation modelling (SEM) has an appealing conceptual framework for testing causality but has problems with curvilinear relationships. Geographically weighted regression (GWR) intended to examine spatial non-stationarity of ecological processes requires further evaluation before being used.Synthesis and applications: explicit theory needs to be incorporated into species response models used in conservation. For example, testing for unimodal skewed responses should be a routine procedure. Clear statements of the ecological theory used, the nature of the data model and sufficient details of the statistical method are needed for current models to be evaluated. New statistical methods need to be evaluated for compatibility with ecological theory before use in applied ecology. Some recent work with artificial data suggests the combination of ecological knowledge and statistical skill is more important than the precise statistical method used. The potential exists for a synthesis of current species modelling approaches based on their differing ecological insights not their methodology.     

The concepts of emergent and collective properties in individual-based models—Summary and outlook of the Bornhöved case studies

Ecology requires the conceptual and technical ability to analyse complex and dynamic systems consisting of a high and variable number of components and relations. These components are part of a variable interaction structure in a spatially heterogeneous context. The components of ecological interaction networks can give rise to self-organised, and scale-dependent interaction patterns and processes, which are the underlying causes of the overall ecological systems states.The individual-based modelling approach provides a widely applicable simulation framework based on a ‘hierarchy theory’ view of ecological systems.Here, we summarise and generalise the theoretical implications of the modelling studies presented in this volume in the field of terrestrial and aquatic, animal and plant ecology. The case studies cover a representative profile of processes related to ecological applications, such as food web interactions, population dynamics, dispersal, energy physiology, nutrient allocation and mutual impact of morphological and physiological development. The generic approach applied in this context allows a hierarchical representation of ecological systems and their components. Model results are obtained as self-organised structural relation networks and as aggregated quantitative states. In order to address different model characteristics we distinguish collective and emergent properties. Collective properties are those that are attributed equally to different organisation levels of the system. Emergent properties result from the activities of lower level entities on a higher organisation level, while not being present on the lower level. They can be subdivided into aggregational and connective properties. Emergent properties that are aggregational are those which emerge as a result of an aggregation procedure by an observer on the higher level which does not make sense or is not applicable on lower levels. Emergent properties that are connective, however, are based on an interaction network of lower level entities, which brings about the specific system characteristic.This classification of model results will allow to generalise the achievements and potential of the individual-based modelling approach in ecology.     

Challenges of simulating complex environmental systems at the landscape scale: A controversial dialogue between two cups of espresso

With the advancement of computational systems and the development of model integration concepts, complexity of environmental model systems increased. In contrast to that, theory and knowledge about > environmental systems as well as the capability for environmental systems analyses remained, to a large extent, unchanged. As a consequence, model conceptualization, data gathering, and validation, have faced new challenges that hardly can be tackled by modellers alone. In this discourse-like review, we argue that modelling with reliable simulations of human-environmental interactions necessitate linking modelling and simulation research much stronger to science fields such as landscape ecology, community ecology, eco-hydrology, etc. It thus becomes more and more important to identify the adequate degree of complexity in environmental models (which is not only a technical or methodological question), to ensure data availability, and to test model performance. Even equally important, providing problem specific answers to environmental problems using simulation tools requires addressing end-user and stakeholder requirements during early stages of problem development. In doing so, we avoid modelling and simulation as an end of its own.     

Control system approaches to ecological systems analysis: Invariants and frequency response

The steady-state assumption is a mainstay for the analysis of ecological systems with more than three or four states. However, it is well accepted in ecology that inputs to large systems come in pulses assumed to have a reasonably constant magnitude and frequency. Steady pulse inputs and the use of electro-chemical–mechanical control systems methodology enables limited short term dynamic responses of ecological systems of a scale often occurring in systems of potential engineering importance to be analyzed. This paper explores and presents a survey of multi-input–multi-output (MIMO) control systems analysis of ecosystem network models to better understand pulse frequency issues and further develop experimentally verifiable approaches to testing the MIMO concept. The analysis process is demonstrated using two network model exemplars. Two aspects of MIMO analyses appear relevant to understanding ecological systems: (1) Eigenvalue invariant analyses and singular value decomposition (SVD) analyses enable assessment of stability and relative strength of states. Eigenvalues reflect time constants and provide a check on experimentally determined system matrices. (2) Analysis of SVD versus frequency for each output indicates maximum pulse frequencies that allow system components to benefit from pulsing. As a group, MIMO analyses complement other analytical methods and provide a theoretical systems focus convenient for analyzing ecosystems from an engineering perspective.     

Ecoregions and ecosystem management in China

Ecological regionalization is a base for rational management and sustainable utilization of ecosystems and natural resources. It can provide a scientific basis for constructing healthy ecological environments and making policies of environmental management. In this paper, based on synthetic analysis of the characteristics of the ecology and environments of China, the principles of ecological regionalization are discussed, and indices and nomenclature of ecological regionalization are proposed. The ecoregions on a national scale are divided. The results show that there are 3 domains, 13 ecoregions and 57 ecodistricts. The present scheme can be used as a framework for ecosystem assessment and management. Based on the ecoregions, measurements of management for forest, grassland, agriculture and wetland ecosystems are recommended. The experience and information can be used within and beyond the national scale for land-use planning, biodiversity conservation and ecosystem restoration.     

Ecological state equation

The direct equivalence between ecology and thermodynamics has not been attained despite accepted thermodynamic features of the ecosystem. This article explores the homeomorphism between ecology and statistical mechanics by analysis of ruderal vegetation. In conventional thermostatistical algorithm, the pro-kinetic effect of temperature on molecules was replaced by the anti-kinetic effect of species diversity on biological individuals. The existence of an ecological equivalent of the thermodynamic Boltzmann constant was empirically verified. From the relationship of this constant with biocenological variables, we derived a probable ecological equation of state under stationary and quasi-stationary conditions. This equation of state is homeomorphic with regard to the ideal gas state equation, and it is useful to infer the value of some biocenological parameters whose direct measurement is difficult, as biomass, energy and dispersal. According to these results, ecosystem assessment from conventional thermostatistics is plausible and empirically verifiable. This approach offers useful analytical tools for the conservation and restoration of ecosystems.     

The Impending Peak and Decline of Petroleum Production: an Underestimated Challenge for Conservation of Ecological Integrity

Abstract: In the last few decades petroleum has been consumed at a much faster pace than new reserves have been discovered. The point at which global oil extraction will attain a peak (“peak oil”) and begin a period of unavoidable decline is approaching. This eventuality will drive fundamental changes in the quantity and nature of energy flows through the human economic system, which probably will be accompanied by economic turmoil, political conflicts, and a high level of social tension. Besides being a geological and economic issue, peak oil is also a fundamental concern as it pertains to ecological systems and conservation because economics is a subsystem of the global ecosystem and changes in human energy‐related behaviors can lead to a broad range of effects on natural ecosystems, ranging from overuse to abandonment. As it becomes more difficult to meet energy demands, environmental considerations may be easily superseded. Given the vital importance of ecosystems and ecosystem services in a postpetroleum era, it is crucially important to wisely manage our ecosystems during the transition period to an economy based on little or no use of fossil fuels. Good policies can be formulated through awareness and understanding gained from scenario‐based assessments. Presently, most widely used global scenarios of environmental change do not incorporate resource limitation, including those of the Millennium Ecosystem Assessment and the Intergovernmental Panel on Climate Change. Considering the potential magnitude of the effects of peak oil on society and nature, the development of resource‐constrained scenarios should be addressed immediately. Ecologists and conservation biologists are in an important position to analyze the situation and provide guidance, yet the topic is noticeably absent from ecological discussions. We urge politicians, corporate chief executives, thought leaders, and citizens to consider this problem seriously because it is likely to develop into one of the key environmental issues of the 21st century.     

The Ethics of Ecological Field Experimentation

Ecological field research has generated a wealth of valuable information, much of which informs conservation efforts. Such research raises ethical issues when we propose to modify or endanger the individual organism, population, species, or ecosystem we study. Yet the scientific literature contains little explicit consideration of the impact of ecological study itself on organisms or ecosystems. We propose that the lack of public ethical discourse among field scientists is due to (1) trepidation about initiating a controversy that could endanger future research efforts; (2) an assumption that the relative benefits of our research outweigh potential short-term costs to the study object (that is, increased knowledge of the study object may inform its conservation); (3) difficulties in perceiving the potential negative impact of our work; (4) tacit assumptions about certain experiments that are simply "wrong" to do. Ethical considerations have already arisen with the collection of organisms, the effects of scientific observation on survivorship and behavior, and experimental manipulations of whole ecosystems. The literature on environmental ethics and animal experimentation, two sources that bear tangentially on the issues of ecological ethics, may afford diverse and occasionally conflicting value systems on which to build an ethic of ecological experimentation. Although methods specifically devised to minimize the impact on study subjects have been published, we find few existing formal mechanisms of external regulation or self-regulation of research activities in the field. We suggest that ethical considerations, conservation and restoration measures that are compatible with sound scientific protocols, may be profitably incorporated into the design of field experiments. We encourage a dialogue between scientists and philosophers on this issue.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

Single-species versus multiple-species models: the economic implications

Ecologists frequently note the importance of modelling entire ecosystems rather than individual species, but most bioeconomic models in the current literature focus on a single species. While the mathematical difficulty of modelling multiple species may be substantial, it is important to recognise the implications of the single-species assumption to a model’s results. In this paper, the authors address the economic significance of this assumption through the development of an analytical multiple-species model and demonstrate the importance of ecological interrelationships and economic values to the survival of endangered species.