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  • 1
    Online Resource
    Online Resource
    Newark :John Wiley & Sons, Incorporated,
    Keywords: Biodiversity. ; Food chains (Ecology). ; Ecology--Mathematical models. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (398 pages)
    Edition: 1st ed.
    ISBN: 9781118502150
    DDC: 577.16
    Language: English
    Note: Food Webs and Biodiversity: Foundations, Models, Data -- Contents -- Acknowledgments -- List of Symbols -- Part I: Preliminaries -- 1 Introduction -- 2 Models and Theories -- 2.1 The usefulness of models -- 2.2 What models should model -- 2.3 The possibility of ecological theory -- 2.4 Theory-driven ecological research -- 3 Some Basic Concepts -- 3.1 Basic concepts of food-web studies -- 3.2 Physical quantities and dimensions -- Part II: Elements of Food-Web Models -- 4 Energy and Biomass Budgets -- 4.1 Currencies of accounting -- 4.2 Rates and efficiencies -- 4.3 Energy budgets in food webs -- 5 Allometric Scaling Relationships Between Body Size and Physiological Rates -- 5.1 Scales and scaling -- 5.2 Allometric scaling -- 6 Population Dynamics -- 6.1 Basic considerations -- 6.1.1 Exponential population growth -- 6.1.2 Five complications -- 6.1.3 Environmental variability -- 6.2 Structured populations and density-dependence -- 6.2.1 The dilemma between species and stages -- 6.2.2 Explicitly stage-structured population dynamics -- 6.2.3 Communities of structured populations -- 6.3 The Quasi-Neutral Approximation -- 6.3.1 The emergence of food webs -- 6.3.2 Rana catesbeiana and its resources -- 6.3.3 Numerical test of the approximation -- 6.4 Reproductive value -- 6.4.1 The concept of reproductive value -- 6.4.2 The role of reproductive value in the QNA -- 6.4.3 Body mass as a proxy for reproductive value -- 7 From Trophic Interactions to Trophic Link Strengths -- 7.1 Functional and numerical responses -- 7.2 Three models for functional responses -- 7.2.1 Linear response -- 7.2.2 Type II response -- 7.2.3 Type II response with prey switching -- 7.2.4 Strengths and weaknesses of these models -- 7.3 Food webs as networks of trophic link strengths -- 7.3.1 The ontology of trophic link strengths -- 7.3.2 Variability of trophic link strengths. , 8 Tropic Niche Space and Trophic Traits -- 8.1 Topology and dimensionality of trophic niche space -- 8.1.1 Formal setting -- 8.1.2 Definition of trophic niche-space dimensionality -- 8.2 Examples and ecological interpretations -- 8.2.1 A minimal example -- 8.2.2 Is the definition of dimensionality reasonable? -- 8.2.3 Dependencies between vulnerability and foraging traits of a species -- 8.2.4 The range of phenotypes considered affects niche-space dimensionality -- 8.3 Determination of trophic niche-space dimensionality -- 8.3.1 Typical empirical data -- 8.3.2 Direct estimation of dimensionality -- 8.3.3 Iterative estimation of dimensionality -- 8.4 Identification of trophic traits -- 8.4.1 Formal setting -- 8.4.2 Dimensional reduction -- 8.5 The geometry of trophic niche space -- 8.5.1 Abstract trophic traits -- 8.5.2 Indeterminacy in abstract trophic traits -- 8.5.3 The D-dimensional niche space as a pseudo-Euclidean space -- 8.5.4 Linear transformations of abstract trophic traits -- 8.5.5 Non-linear transformations of abstract trophic traits -- 8.5.6 Standardization and interpretation of abstract trophic traits -- 8.5.7 A hypothesis and a convention -- 8.5.8 Getting oriented in trophic niche space -- 8.6 Conclusions -- 9 Community Turnover and Evolution -- 9.1 The spatial scale of interest -- 9.2 How communities evolve -- 9.3 The mutation-for-dispersion trick -- 9.4 Mutation-for-dispersion in a neutral food-web model -- 10 The Population-Dynamical Matching Model -- Part III: Mechanisms and Processes -- 11 Basic Characterizations of Link-Strength Distributions -- 11.1 Modelling the distribution of logarithmic link strengths -- 11.1.1 General normally distributed trophic traits -- 11.1.2 Isotropically distributed trophic traits -- 11.2 High-dimensional trophic niche spaces. , 11.2.1 Understanding link stengths in high-dimensional trophic niche spaces -- 11.2.2 Log-normal probability distributions -- 11.2.3 The limit of log-normally distributed trophic link strength -- 11.2.4 Correlations between trophic link strengths -- 11.2.5 The distribution of the strengths of observable links -- 11.2.6 The probability of observing links (connectance) -- 11.2.7 Estimation of link-strength spread and Pareto exponent -- 11.2.8 Empirical examples -- 12 Diet Partitioning -- 12.1 The diet partitioning function -- 12.1.1 Relation to the probability distribution of diet proportions -- 12.1.2 Another probabilistic interpretation of the DPF -- 12.1.3 The normalization property of the DPF -- 12.1.4 Empirical determination of the DPF -- 12.2 Modelling the DPF -- 12.2.1 Formal setting -- 12.2.2 Diet ratios -- 12.2.3 The DPF for high-dimensional trophic niche spaces -- 12.2.4 Gini-Simpson dietary diversity -- 12.2.5 Dependence of the DPF on niche-space dimensionality -- 12.3 Comparison with data -- 12.4 Conclusions -- 13 Multivariate Link-Strength Distributions and Phylogenetic Patterns -- 13.1 Modelling phylogenetic structure in trophic traits -- 13.1.1 Phylogenetic correlations among logarithmic link strengths -- 13.1.2 Phylogenetic correlations among link strengths -- 13.1.3 Phylogenetic patterns in binary food webs -- 13.2 The matching model -- 13.2.1 A simple model for phylogenetic structure in food webs -- 13.2.2 Definition of the matching model -- 13.2.3 Sampling steady-state matching model food webs -- 13.2.4 Alternatives to the matching model -- 13.3 Characteristics of phylogenetically structured food webs -- 13.3.1 Graphical representation of food-web topologies -- 13.3.2 Standard parameter values -- 13.3.3 Intervality -- 13.3.4 Intervality and trophic niche-space dimensionality -- 13.3.5 Degree distributions. , 13.3.6 Other phylogenetic patterns -- 13.3.7 Is phylogeny just a nuisance? -- 14 A Framework Theory for Community Assembly -- 14.1 Ecological communities as dynamical systems -- 14.2 Existence, positivity, stability, and permanence -- 14.3 Generic bifurcations in community dynamics and their ecological phenomenology -- 14.3.1 General concepts -- 14.3.2 Saddle-node bifurcations -- 14.3.3 Hopf bifurcations -- 14.3.4 Transcritical bifurcations -- 14.3.5 Bifurcations of complicated attractors -- 14.4 Comparison with observations -- 14.4.1 Extirpations and invasions proceed slowly -- 14.4.2 The logistic equation works quite well -- 14.4.3 IUCN Red-List criteria highlight specific extinction scenarios -- 14.4.4 Conclusion -- 14.5 Invasion fitness and harvesting resistance -- 14.5.1 Invasion fitness -- 14.5.2 Harvesting resistance: definition -- 14.5.3 Harvesting resistance: interpretation -- 14.5.4 Harvesting resistance: computation -- 14.5.5 Interpretation of h → 0 -- 14.6 Community assembly and stochastic species packing -- 14.6.1 Community saturation and species packing -- 14.6.2 Invasion probability -- 14.6.3 The steady-state distribution of harvesting resistance -- 14.6.4 The scenario of stochastic species packing -- 14.6.5 A numerical example -- 14.6.6 Biodiversity and ecosystem functioning -- 15 Competition in Food Webs -- 15.1 Basic concepts -- 15.1.1 Modes of competition -- 15.1.2 Interactions in communities -- 15.2 Competition in two-level food webs -- 15.2.1 The Lotka-Volterra two-level food-web model -- 15.2.2 Computation of the equilibrium point -- 15.2.3 Direct competition among producers -- 15.2.4 Resource-mediated competition in two-level food webs -- 15.2.5 Consumer-mediated competition in two-level food webs -- 15.3 Competition in arbitrary food webs -- 15.3.1 The general Lotka-Volterra food-web model. , 15.3.2 The competition matrix for general food webs -- 15.3.3 The L-R-P formalism -- 15.3.4 Ecological interpretations of the matrices L, R, and P -- 15.3.5 Formal computation of the equilibrium point -- 15.3.6 Consumer-mediated competition in general food webs -- 15.3.7 Consumer-mediated competitive exclusion -- 15.3.8 Conclusions -- 16 Mean-Field Theory of Resource-Mediated Competition -- 16.1 Transition to scaled variables -- 16.1.1 The competitive overlap matrix -- 16.1.2 Free abundances -- 16.2 The extended mean-field theory of competitive exclusion -- 16.2.1 Assumptions -- 16.2.2 Separation of means and residuals -- 16.2.3 Mean-field theory for the mean scaled abundance -- 16.2.4 Mean-field theory for the variance of scaled abundance -- 16.2.5 The coefficient of variation of scaled abundance -- 16.2.6 Related theories -- 17 Resource-Mediated Competition and Assembly -- 17.1 Preparation -- 17.1.1 Scaled vs. unscaled variables and parameters -- 17.1.2 Mean-field vs framework theory -- 17.2 Stochastic species packing under asymmetric competition -- 17.2.1 Species richness and distribution of invasion fitness (Part I) -- 17.2.2 Community response to invasion -- 17.2.3 Sensitivity of residents to invaders -- 17.2.4 Species richness and distribution of invasion fitness (Part II) -- 17.2.5 Random walks of abundances driven by invasions -- 17.2.6 Further discussion of the scenario -- 17.3 Stochastic species packing with competition symmetry -- 17.3.1 Community assembly with perfectly symmetric competition -- 17.3.2 Community assembly under nearly perfectly symmetric competition -- 17.3.3 Outline of mechanism limiting competition avoidance -- 17.3.4 The distribution of invasion fitness -- 17.3.5 Competition between residents and invaders -- 17.3.6 Balance of scaled biomass during assembly -- 17.3.7 Competition avoidance. , 17.3.8 Numerical test of the theory.
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  • 2
    Publication Date: 2017-01-31
    Description: Highlights: • A simple quantitative method for choosing ecological indicators and target ranges is proposed. • Sustainable use of ecosystems requires freedom of usage choice for each generation. • Sustainability so limits any state indicator to the range from which timely recovery is feasible. • Relevant state indicators are those that anthropogenic pressure might drive out of this range. • The method extends to pressure- and auxiliary indicators, and suites of indicators. Abstract: Wide-ranging, indicator-based assessments of large, complex ecosystems are playing an increasing role in guiding environmental policy and management. An example is the EU’s Marine Strategy Framework Directive, which requires Member States to take measures to reach “good environmental status” (GES) in European marine waters. However, formulation of indicator targets consistent with the Directive’s high-level policy goal of sustainable use has proven challenging. We develop a specific, quantitative interpretation of the concepts of GES and sustainable use in terms of indicators and associated targets, by sharply distinguishing between current uses to satisfy current societal needs and preferences, and unknown future uses. We argue that consistent targets to safeguard future uses derive from a requirement that any environmental state indicator should recover within a defined time (e.g. 30 years) to its pressure-free range of variation when all pressures are hypothetically removed. Within these constraints, specific targets for current uses should be set. Routes to implementation of this proposal for indicators of fish-community size structure, population size of selected species, eutrophication, impacts of non-indigenous species, and genetic diversity are discussed. Important policy implications are that (a) indicator target ranges, which may be wider than natural ranges, systematically and rationally derive from our proposal; (b) because relevant state indicators tend to respond slowly, corresponding pressures should also be monitored and assessed; (c) support of current uses and safeguarding of future uses are distinct management goals, they require different types of targets, decision processes, and management philosophies.
    Type: Article , PeerReviewed
    Format: text
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