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E-grāmata: Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics

4.11/5 (17 ratings by Goodreads)
(University of California, Berkeley, USA)
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Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and EnergeticsPalielināt
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This pioneering graduate textbook provides readers with the concepts and practical tools required to understand the maximum entropy principle, and apply it to an understanding of ecological patterns. Rather than building and combining mechanistic models of ecosystems, the approach is grounded in information theory and the logic of inference. Paralleling the derivation of thermodynamics from the maximum entropy principle, the state variable theory of ecology developed in this book predicts realistic forms for all metrics of ecology that describe patterns in the distribution, abundance, and energetics of species over multiple spatial scales, a wide range of habitats, and diverse taxonomic groups.

The first part of the book is foundational, discussing the nature of theory, the relationship of ecology to other sciences, and the concept of the logic of inference. Subsequent sections present the fundamentals of macroecology and of maximum information entropy, starting from first principles. The core of the book integrates these fundamental principles, leading to the derivation and testing of the predictions of the maximum entropy theory of ecology (METE). A final section broadens the book's perspective by showing how METE can help clarify several major issues in conservation biology, placing it in context with other theories and highlighting avenues for future research.

Recenzijas

All those new to research, regardless of discipline, would do well to read at least the first part of this book and there is plenty other material to help understand 'how to do research'. There is plenty for physicists to enjoy and think about at a casual level without the need to commit to finding out a huge amount about ecology. It was a pleasure to review this book. * Colin Axon, Physics Energy Group Newsletter *

Preface vi
Glossary of symbols xiv
Acronyms and abbreviations xv
Part I Foundations
1 The nature of theory
3(16)
1.1 What is a theory?
3(3)
1.1.1 Falsifiability
3(1)
1.1.2 Comprehensiveness
4(1)
1.1.3 Parsimony
4(1)
1.1.4 Easier said than done
4(2)
1.2 Ecology and physics
6(2)
1.3 Types of theories
8(8)
1.3.1 The role of mechanism in science
9(2)
1.3.2 Mechanistic theories and models in ecology
11(1)
1.3.3 Statistical theory and models in ecology
12(1)
1.3.4 Neutral theories of ecology
12(1)
1.3.5 Theories based on an optimization principle
13(1)
1.3.6 State variable theories
14(1)
1.3.7 Scaling theories
14(2)
1.4 Why keep theory simple?
16(1)
1.5 Exercises
17(2)
2 The logic of inference
19(8)
2.1 Expanding prior knowledge
20(1)
2.2 Sought knowledge can often be cast in the form of unknown probability distributions
21(1)
2.3 Prior knowledge often constrains the sought-after distributions
22(1)
2.4 We always seek the least-biased distribution
22(1)
2.5 Exercises
23(4)
Part II Macroecology
3 Scaling metrics and macroecology
27(60)
3.1 Thinking like a macroecologist
27(2)
3.1.1 Questioning like a macroecologist
27(1)
3.1.2 Censusing like a macroecologist
28(1)
3.2 Metrics for the macroecologist
29(3)
3.2.1 Units of analysis
30(2)
3.3 The meaning of the metrics
32(20)
3.3.1 Species-level spatial abundance distribution
33(2)
3.3.2 Range---area relationship
35(2)
3.3.3 Species-level commonality
37(2)
3.3.4 Intra-specific energy distribution
39(1)
3.3.5 Dispersal distributions
40(1)
3.3.6 The species---abundance distribution (SAD)
41(1)
3.3.7 Species---area relationship (SAR)
41(5)
3.3.8 The endemics---area relationship (EAR)
46(1)
3.3.9 Community commonality
46(1)
3.3.10 Community energy distribution
47(1)
3.3.11 Energy--- and mass---abundance relationships
47(4)
3.3.12 Link distribution in a species network
51(1)
3.3.13 Two other metrics: The inter-specific dispersal---abundance relationship and the metabolic scaling rule
52(1)
3.4 Graphs and patterns
52(26)
3.4.1 Species-level spatial-abundance distributions: Π(n|A, n0, A0)
61(2)
3.4.2 Range---area relationship: B(A|n0, A0)
63(1)
3.4.2.1 A note on the nomenclature of curvature
63(1)
3.4.3 Species-level commonality: C(A, D|n0, A0)
63(2)
3.4.4 Intra-specific distribution of metabolic rates: Θ(ε|n0)
65(1)
3.4.5 Intra-specific distribution of dispersal distances: Δ(D)
65(1)
3.4.6 The species---abundance distribution: Φ(n|S0, N0, A0)
66(2)
3.4.7 The species---area relationship: S (A|N0, S0, A0)
68(5)
3.4.8 The endemics---area relationship: E(A|N0, S0, A0)
73(1)
3.4.9 Community-level commonality: X(A, D|N0, S0, A0)
73(2)
3.4.10 Energy and mass distributions and energy--- and mass---abundance relationships
75(1)
3.4.11 A(l|S0, L0)
76(1)
3.4.12 ε(m)
77(1)
3.5 Why do we care about the metrics?
78(5)
3.5.1 Estimating biodiversity in large areas
78(1)
3.5.2 Estimating extinction
79(3)
3.5.3 Estimating abundance from sparse data
82(1)
3.6 Exercises
83(4)
4 Overview of macroecological models and theories
87(30)
4.1 Purely statistical models
87(10)
4.1.1 The Coleman model: Distinguishable individuals
87(2)
4.1.2 Models of Indistinguishable Individuals
89(3)
4.1.2.1 Generalized Laplace model
92(1)
4.1.2.2 HEAP
93(2)
4.1.3 The negative binomial distribution
95(2)
4.1.4 The Poisson cluster model
97(1)
4.2 Power-law models
97(5)
4.2.1 Commonality under self-similarity
100(2)
4.3 Other theories of the SAD and/or the SAR
102(7)
4.3.1 Preston's theory
102(2)
4.3.2 Hubbell's neutral theory of ecology
104(1)
4.3.3 Niche-based models
105(3)
4.3.4 Island biogeographic theory
108(1)
4.4 Energy and mass distributions
109(1)
4.5 Mass---abundance and energy---abundance relationships
110(1)
4.6 Food web models
110(1)
4.7 A note on confidence intervals for testing model goodness
111(1)
4.8 Exercises
112(5)
Part III The maximum entropy principle
5 Entropy, information, and the concept of maximum entropy
117(13)
5.1 Thermodynamic entropy
117(4)
5.2 Information theory and information entropy
121(2)
5.3 MaxEnt
123(7)
6 MaxEnt at work
130(11)
6.1 What if MaxEnt doesn't work?
130(1)
6.2 Some examples of constraints and distributions
131(2)
6.3 Uses of MaxEnt
133(3)
6.3.1 Image resolution
133(1)
6.3.2 Climate envelopes
133(1)
6.3.3 Economics
134(1)
6.3.4 Food webs and other networks
135(1)
6.3.5 Classical and non-equilibrium thermodynamics and mechanics
135(1)
6.3.6 Macroecology
136(1)
6.4 Exercises
136(5)
Part IV Macroecology and MaxEnt
7 The maximum entropy theory of ecology (METE)
141(36)
7.1 The entities and the state variables
141(1)
7.2 The structure of METE
142(4)
7.2.1 Abundance and energy distributions
142(4)
7.2.2 Species-level spatial distributions across multiple scales
146(1)
7.3 Solutions: R(n, ε) and the metrics derived from it
146(11)
7.3.1 Rank distributions for Ψ(ε), Θ(ε), and Φ(n)
152(1)
7.3.2 Implications: extreme values of n and ε
153(2)
7.3.3 Predicted forms of other energy and mass metrics
155(2)
7.4 Solutions: Π(n) and the metrics derived from it
157(5)
7.5 The predicted species---area relationship
162(5)
7.5.1 Predicting the SAR: Method 1
163(3)
7.5.2 The special case of S(A) for 1 --- A/A0 << 1
166(1)
7.6 The endemics---area relationship
167(1)
7.7 The predicted collector's curve
168(1)
7.8 When should energy-equivalence and the Damuth relationship hold?
169(4)
7.9 Miscellaneous predictions
173(1)
7.10 Summary of predictions
174(1)
7.11 Exercises
175(2)
8 Testing METE
177(24)
8.1 A general perspective on theory evaluation
177(1)
8.2 Datasets
178(2)
8.2.1 Some warnings regarding censusing procedures
180(1)
8.3 The species-level spatial abundance distribution
180(6)
8.3.1 A note on use of an alternative entropy measure
186(1)
8.4 The community-level species---abundance distribution
186(4)
8.5 The species---area and endemics---area relationships
190(3)
8.6 The distribution of metabolic rates
193(3)
8.7 Patterns in the failures of METE
196(1)
8.8 Exercises
197(4)
Part V A wider perspective
9 Applications to conservation
201(7)
9.1 Scaling up species' richness
201(1)
9.2 Inferring abundance from presence---absence data
202(1)
9.3 Estimating extinction under habitat loss
202(1)
9.4 Inferring associations between habitat characteristics and species occurrence
203(3)
9.5 Exercises
206(2)
10 Connections to other theories
208(5)
10.1 METE and the Hubbell neutral theory
208(1)
10.2 METE and metabolic scaling theories
209(1)
10.3 METE and food web theory
210(1)
10.4 Other applications of MaxEnt in macroecology
210(2)
10.5 Exercise
212(1)
11 Future directions
213(16)
11.1 Incorporating spatial correlations into METE
213(5)
11.1.1 Method 1: Correlations from consistency constraints
213(3)
11.1.2 Method 2: A Bayesian approach to correlations
216(2)
11.2 Understanding the structure of food webs
218(1)
11.3 Toward a dynamic METE
218(7)
11.4 Exercises
225(4)
Epilogue: Is a comprehensive unified theory of ecology possible? What might it look like?
229(15)
Appendix A Access to plant census data from a serpentine grassland
232(1)
Appendix B A fractal model
233(7)
Appendix C Predicting the SAR: An alternative approach
240(4)
References 244(9)
Index 253
John Harte is a Professor of Ecosystem Sciences at the University of California, Berkeley. Following undergraduate studies at Harvard and a doctoral degree in Physics from the University of Wisconsin, he was an NSF Postdoctoral Fellow at CERN, Geneva and an Assistant Professor of Physics at Yale. His research interests include climate-ecosystem interactions, theoretical ecology, and environmental policy. He is the recipient of a Pew Scholars Prize in Conservation and the Environment, a Guggenheim Fellowship, the 2001 Leo Szilard prize from the American Physical Society, the 2004 UC Berkeley Graduate Mentorship Award, a Miller Professorship, and is a co-recipient of the 2006 George Polk award in journalism. He is an elected Fellow of the California Academy of Sciences and the American Physical Society. He has also served on six National Academy of Sciences Committees and has authored over 190 scientific publications, including seven books.
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