Mountains, Climate and Biodiversity E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1704

Veröffentlichungsjahr: 2018

Beschreibung

Mountains, Climate and Biodiversity: A comprehensive and up-to-date synthesis for students and researchers

Mountains are topographically complex formations that play a fundamental role in regional and continental-scale climates. They are also cradles to all major river systems and home to unique, and often highly biodiverse and threatened, ecosystems. But how do all these processes tie together to form the patterns of diversity we see today?

Written by leading researchers in the fields of geology, biology, climate, and geography, this book explores the relationship between mountain building and climate change, and how these processes shape biodiversity through time and space.

• In the first two sections, you will learn about the processes, theory, and methods connecting mountain building and biodiversity

• In the third section, you will read compelling examples from around the world exploring the links between mountains, climate and biodiversity

• Throughout the 31 peer-reviewed chapters, a non-technical style and synthetic illustrations make this book accessible to a wide audience

• A comprehensive glossary summarises the main concepts and terminology

Readership: Mountains, Climate and Biodiversity is intended for students and researchers in geosciences, biology and geography. It is specifically compiled for those who are interested in historical biogeography, biodiversity and conservation.

Leseprobe

Table of Contents

Cover

Title Page

List of contributors

Acknowledgments

Foreword

The organization of life on land: biomes

Mountains as cradles of biodiversity

Our influence on the future

Biography

Biography of Editors

Glossary

About the Companion Website

1 Mountains, Climate and Biodiversity: an Introduction

1.1 Introduction

1.2 What are Mountains?

1.3 The Physiography of Mountains and Patterns of Biodiversity

1.4 Plate Tectonics, Mountain Building and the Biological (R)evolution

1.5 Mountains, Climate and Biodiversity: A Short Overview

1.6 Outlook

Acknowledgments

References

Part I: Mountains, Relief and Climate

2 Simple Concepts Underlying the Structure, Support and Growth of Mountain Ranges, High Plateaus and Other High Terrain

2.1 Introduction

2.2 Support of High Terrain: Isostasy

2.3 Plate Tectonics and High Terrain

2.4 The Growth of Mountain Ranges and High Plateaus

2.5 Destruction of Mountain Ranges and Other High Terrain

2.6 Conclusion

Acknowledgments

References

3 An Overview of Dynamic Topography: The Influence of Mantle Circulation on Surface Topography and Landscape

3.1 Introduction

3.2 What is Dynamic Topography?

3.3 Residual Topography

3.4 Modeling of Mantle Flow

3.5 Interaction of Dynamic Topography with the Landscape

3.6 Conclusion

Acknowledgments

References

4 Mountain Relief, Climate and Surface Processes

4.1 Introduction

4.2 Relationships Between Climate, Erosion and Relief: Models and Concepts

4.3 Measuring (Changes in) Erosion Rates in Mountain Belts

4.4 Reconstructing Relief Change in Mountain Belts

4.5 Discussion: Is There a Climatic Control on Mountain‐Belt Erosion and Relief?

4.6 Conclusion

References

5 Dating mountain Building: Exhumation and Surface Uplift

5.1 Introduction

5.2 Mountain Building

5.3 Studying Long‐Term Exhumation with Low‐Temperature Thermochronology

5.4 Studying Short‐Term Erosion from Terrestrial Cosmogenic Nuclide Analysis

5.5 Numeric Modeling of Thermal Histories and Exhumation

5.6 Case Study: Merida Andes of Venezuela

5.7 Case Study: East African Rift System

5.8 Conclusion

References

6 Stable Isotope Paleoaltimetry: Paleotopography as a Key Element in the Evolution of Landscapes and Life

6.1 Introduction

6.2 Oxygen and Hydrogen Isotopes in Precipitation

6.3 Paleoaltimetry: Determining Surface Uplift

6.4 Modeling Approaches to Determining Stable Isotopes in Precipitation Patterns

6.5 Examples of Stable Isotope Paleoaltimetry

6.6 Conclusion

References

7 Phytopaleoaltimetry: Using Plant Fossils to Measure Past Land Surface Elevation

7.1 Introduction

7.2 Plants and Climate

7.3 Lapse Rates and Enthalpy

7.4 Conclusion

References

8 Cenozoic Mountain Building and Climate Evolution

8.1 Introduction

8.2 Mountain and Climate Interactions

8.3 Paleoaltimetry Approaches

8.4 Surface Uplift and Climate Change

8.5 Conclusion

References

9 Paleoclimate

9.1 Earth’s Climate System: Lessons from the Past

9.2 Early Earth’s climates

9.3 Hothouse climates of the Mesozoic and Paleogene

9.4 The Greenhouse–Icehouse Transition of the Cenozoic

9.5 Quaternary Ice Age Cycles and Rapid Climate Change

9.6 The Holocene

9.7 Conclusion

References

Part II: When Biology Meets Mountain Building

10 Mountain Geodiversity: Characteristics, Values and Climate Change

10.1 Introduction

10.2 Geodiversity and the Definition of Mountains

10.3 Mountain Geodiversity at a Global Scale

10.4 Mountain Geodiversity at Regional to Local Scales

10.5 Values of Mountain Geodiversity

10.6 Mountain Geodiversity and Climate Change

10.7 Conclusion

Acknowledgments

References

11 Geodiversity Mapping in Alpine Areas

11.1 Geodiversity Mapping

11.2 Geological and Geomorphological Overview of Vorarlberg

11.3 Index‐Based Geodiversity Mapping of Vorarlberg

11.4 Fine‐Scale Geodiversity: The Au West Case Study

11.5 Conclusion

Acknowledgments

References

12 Historical Connectivity and Mountain Biodiversity

12.1 Introduction

12.2 The Flickering Connectivity System

12.3 Components of the FCS

12.4 Perspectives on Paleogeographic Reconstructions and Historical Connectivity

12.5 Conclusion

Acknowledgments

References

13 The Environmental Heterogeneity of Mountains at a Fine Scale in a Changing World

13.1 The Mosaic of Environmental Heterogeneity at a Fine Scale

13.2 Drivers of Isolation at a Fine Scale

13.3 Adaptation and Diversification at a Fine Scale

13.4 Heterogeneous Microhabitats as a Field Laboratory to Study Reactions to Climate Change

13.5 Conclusion

Acknowledgments

References

14 Mountains, Climate and Mammals

14.1 Introduction

14.2 Mammal Diversity Across Continents

14.3 Topographic Diversity Gradients at the Regional Scale

14.4 Topographic Diversity Gradients in Deep Time

14.5 Mammals that Drive the Topographic Diversity Gradient

14.6 Biogeographic Processes in Topographically Complex Regions

14.7 Effects of Modern Climate Change on Montane Diversity

14.8 Conclusion

Acknowledgments

References

15 Inferring Macroevolutionary Dynamics in Mountain Systems from Fossils

15.1 Introduction

15.2 Geological and Evolutionary Dynamics

15.3 Case Study: Rodent Diversification in North America

15.4 PyRate Analytical Framework

15.5 Preservation Rates and Model Selection

15.6 Rodent Diversification in Active Montane Regions and Quiescent Plains

15.7 Conclusion

References

16 The Interplay between Geological History and Ecology in Mountains

16.1 Introduction

16.2 Overview of Mountain Formation and Resulting Geologic and Climatic Complexity

16.3 Geologic and Climatic Factors Influencing Montane Diversity

16.4 Case Study: The Northern Andes

16.5 Conclusion

References

17 Mountains and the Diversity of Birds

17.1 Introduction

17.2 Methods

17.3 The Avifauna of Montane Environments

17.4 The Effect of Latitude

17.5 The Role of Niche Conservatism

17.6 How did Species Diversity Build Up in Tropical Mountain Regions?

17.7 The Next Challenge: Does Geology also Play a Role?

Acknowledgments

References

18 Teasing Apart Mountain Uplift, Climate Change and Biotic Drivers of Species Diversification

18.1 Seeking the Causes of Species Diversification and Extinction

18.2 Defining the Abiotic and Biotic Drivers of Diversification: A Real Dichotomy?

18.3 Phylogenetic Approaches to Study Diversification

18.4 A Unified Framework to Tease Apart the Drivers of Diversification

18.5 Case Study: The Andean Radiation of Hummingbirds

18.6 Limitations and Perspectives

18.7 Conclusion

Acknowledgments

References

19 Upland and Lowland Fishes: A Test of the River Capture Hypothesis

19.1 Introduction

19.2 Methods: Developing a River Capture Curve

19.3 Results

19.4 Discussion

19.5 Conclusion

Acknowledgments

References

20 Different Ways of Defining Diversity, and How to Apply Them in Montane Systems

20.1 Introduction

20.2 Quantifying Diversity

20.3 Documenting Diversity Patterns

20.4 Final Notes Related to Diversity in Montane Systems

References

21 A Modeling Framework to Estimate and Project Species Distributions in Space and Time

21.1 Species Niches and Their Reciprocal Spatial Distributions

21.2 Species Presence Data

21.3 Abiotic Spatial Data

21.4 Species Distribution Models

21.5 Projecting SDMs in Time and Space

21.6 Conclusion

Acknowledgements

References

Part III: Mountains and Biota of the World

22 Evolution of the Isthmus of Panama: Biological, Paleoceanographic and Paleoclimatological Implications

22.1 Introduction

22.2 A brief History of the Isthmus Landscape Construction

22.3 Thermohaline Circulation

22.4 Northern Hemisphere Glaciation

22.5 The Caribbean Sea

22.6 The Great American Biotic Interchange

22.7 Unresolved Questions

Acknowledgments

References

23 The Tepuis of the Guiana Highlands

23.1 Introduction

23.2 Geology

23.3 Hydrology

23.4 Climate

23.5 Guiana Orography

23.6 Phytogeographical Provinces in the Guiana Shield

23.7 Animal Life in the Pantepui Region

23.8 Evolution of the Pantepui Biota

23.9 Conclusion

Acknowledgments

References

24 Ice‐Bound Antarctica: Biotic Consequences of the Shift from a Temperate to a Polar Climate

24.1 Introduction

24.2 Early Geological History of Antarctica

24.3 Antarctica and Gondwana: the Break‐up of a Supercontinent

24.4 Volcanism

24.5 How Antarctica Became An Ice‐bound Continent

24.6 Antarctica’s Fossil Biota

24.7 Antarctica’s Contemporary Biota

24.8 The Role of Volcanism and Montane Ecosystems in Supporting Antarctica’s Unique Biota

24.9 Conclusion

Acknowledgments

References

25 The Biogeography, Origin and Characteristics of the Vascular Plant Flora and Vegetation of the New Zealand Mountains

25.1 New Zealand Mountain Environments

25.2 Origin of the New Zealand Mountain Landscape

25.3 Vegetation of the New Zealand Mountains

25.4 Alpine Plant Traits

25.5 Alpine Radiations and Endemism

25.6 Biogeographic Relationships of the Alpine Flora

25.7 Origins of the Vascular Montane and Alpine Flora

25.8 Conclusion

Acknowledgments

References

26 The East African Rift System: Tectonics, Climate and Biodiversity

26.1 The East African Rift System

26.2 Continental Rift Zones

26.3 Tectonic development of the East African Rift System

26.4 Global Climate Change and East Africa

26.5 Biodiversity in the East African Rift Lakes

26.6 The Advent of Hominins

26.7 Conclusion

Acknowledgments

References

27 The Alps: A Geological, Climatic and Human Perspective on Vegetation History and Modern Plant Diversity

27.1 Introduction

27.2 Present Flora and Vegetation Patterns in the Physiographic, Climatic and Geological Context of the Alps

27.3 Vegetation History of the Alps Since the Late Eocene

27.4 Climate and Paleoaltitude Reconstructions of the Alps Since the Late Eocene

27.5 How Do Regional Geological Evolution, Global Climatic Changes and Human Pressure Affect Alpine Plant Diversity and Vegetation?

27.6 Conclusion

Acknowledgments

References

28 Cenozoic Evolution of Geobiodiversity in the Tibeto‐Himalayan Region

28.1 Introduction: Tropical and Subtropical Mountains and Biodiversity

28.2 Evolution of Geodiversity in the Tibeto‐Himalayan Region

28.3 Evolution of Present‐Day Biodiversity in the THR

28.4 The Mountain‐Geobiodiversity Hypothesis

28.5 Conclusion

Acknowledgments

References

29 Neogene Paleoenvironmental Changes and their Role in Plant Diversity in Yunnan, South‐Western China

29.1 Geomorphology, Tectonic History and Modern Plant Diversity of Yunnan

29.2 Neogene Climates of Yunnan

29.3 Plant Diversity Changes in Response to Monsoon Intensification in Yunnan

29.4 Conclusion

Acknowledgments

References

30 Influence of Mountain Formation on Floral Diversification in Japan, Based on Macrofossil Evidence

30.1 Introduction

30.2 Distribution and Characteristics of Mountain Vegetation and Flora in Japan

30.3 Mountains in the Development of Geomorphology in Japan

30.4 Development of Mountain Flora Since the Paleogene

30.5 Discussion

30.6 Conclusion

References

31 The Complex History of Mountain Building and the Establishment of Mountain Biota in Southeast Asia and Eastern Indonesia

31.1 Introduction

31.2 Present Montane Vegetation of Southeast Asia

31.3 Late Quaternary Vegetation Dynamics

31.4 The Mountain Ranges of Southeast Asia and New Guinea, and Their Uplift History

31.5 Dispersal and Evolution

31.6 Conclusion

Acknowledgments

References

Index

End User License Agreement

List of Tables

Chapter 05

Table 5.1 Average closure temperatures with respect to cooling rate (°C).

Table 5.2 Partial annealing and retention zones for a 20 My hold time.

Chapter 07

Table 7.1 Comparisons of terrestrial lapse rates based on mean annual temperature (MAT), warmest month mean temperature (WMMT), coldest month mean temperature (CMMT) and mean annual range of temperature (MART) for continental versus island settings. While lapse rates are usually negative (temperature cools with increasing altitude), note the positive continental MART lapse rate and the differences between the climate variables.

Source:

Data from Meyer (1992).

Chapter 10

Table 10.1 Definition of mountain classes based on elevation and local elevation range (>300 m/5 km).

Table 10.2 Classification of mountain origins by plate tectonic setting.

Table 10.3 Sediment cascade in mountain systems.

Table 10.4 Ecosystem services provided by mountain geodiversity.

Chapter 11

Table 11.1 Metadata for the original input data sets used to calculate the geodiversity index (GI) for the Vorarlberg case study.

Table 11.2 Classification and areal coverage of the geodiversity classes in Vorarlberg.

Table 11.3 (a) The morphogenetic classification scheme used to generate the digital geomorphological map of Au West in Figure 11.3 (subset of the Seijmonsbergen et al. 2014 scheme). The standard weighting and ranking criteria (including the numerical values) used in the assessment of geoconservation potential are also shown for the morphogenetic types (see Box 11.1 for a detailed explanation). Columns include the following data for each process group: GIS code of types of geomorphosites, explanation of the GIS codes (landform and deposit types) and standard numerical values of weighting and ranking criteria used in GIS. (b) Classes of geoconservation potential. The numerical values are the summed scores of the weighting and ranking criteria for the morphogenetic types described in (a).

Table 11.4 Cross‐tabulation (%) showing the occurrence of morphogenetic units in the seven main biotope units in the Au West area. For the locations of the biotope units, refer to Figure 11.3. The numerical coding of the biotope units refers to the classification presented in the municipality reports of Mellau and Au.

Chapter 13

Table 13.1 Standardized selection gradients (

β

) across microhabitats differing in snowmelt timing (ridges and snowbeds) in

Salix herbacea

in the Swiss Alps. Linear mixed models were run separately for the two relative fitness proxies – proportion of flowering stems (

h

2

 = 0.049) and change in stem number (

h

2

 = 

0.071

) – and included the traits leaf size (

h

2

 = 0.386), interval between snowmelt and leaf expansion (

h

2

 = 0.178), thermal duration until leaf expansion (

h

2

 = 0.469) and flowering (

h

2

 = 0.399), as well as their interactions with microhabitat type (MH, microhabitat: positive interaction with ridges if

β

 > 0 and positive interaction with snowbeds if

β

 < 0), with the plot nested within the transect as a random effect. Estimates of narrow‐sense heritability (

h

2

) are based on the multivariate animal model with a marker‐based relatedness matrix (Lynch & Ritland 1999). Significant values (P‐value < 0.5) are in bold, based on the F statistic (F) and its P‐value (p). Significant

h

2

values are also in bold.

Chapter 15

Table 15.1 List of the main parameters inferred within the PyRate analytical framework.

Table 15.2 Results of model testing. The statistical fit of six birth–death models for each region was evaluated by estimating the respective marginal likelihoods. Log Bayes factors were calculated between the best fitting model (BD6 in all regions) and all other models. An alternative way to look at the statistical support obtained by each model is to compute its relative probability, reported in the last column.

Chapter 16

Table 16.1 Geological, climatic and biodiversity characteristics of different regions in the Colombian Andes of South America. Local relief is based on Figure 16.3 and is calculated with a 3 km moving window using SRTM‐90 m topographic data. Precipitation is from radar‐based precipitation measurements, based on the Tropical Rainfall Measuring Mission (TRMM) data (see Figure 16.3). Species richness is calculated by combing species distribution models from 5400 plant and animal species.

Chapter 18

Table 18.1 Summary of diversification analyses on the Andean hummingbird clade. (a) Eleven models were fitted using the analytical framework presented. The models are categorized according to the three model types tested: null models, models testing the Red Queen and models testing the Court Jester. For each model, we reported values corresponding to the fit of maximum likelihood approaches. (b) Model comparison between the best models of each series. DDL, speciation declines linearly with diversity and no extinction; DDL + E, speciation declines linearly with diversity and extinction is non‐zero but constant; DDL + EL, speciation declines linearly and extinction increases linearly with diversity; NP, number of parameters; logL, log likelihood; AICc, corrected Akaike Information Criterion;

λ

, speciation rate;

α

, rate of variation of the speciation according to the paleoenvironmental variable;

μ

, extinction rate;

β

, rate of variation of the extinction according to the paleoenvironmental variable; K, estimated carrying capacity for the diversity‐dependence models; and

r

, ratio of linear dependencies in speciation and extinction rates. The units of

λ

and

μ

are events/lineage/My;

α

and

β

are the rate of change over time (no unit).

Chapter 19

Table 19.1 Some well‐known groups of montane fishes.

Table 19.2 Literature references for data used in river capture curve (RCC) analyses. Capture age and area estimates from geology references. Species estimates as fraction of native total in modern basin from fish species references. Additional fish biogeographic studies from biogeography references. Data arranged alphabetically by continent and capture event.

Table 19.3 Mega‐river capture events used to generate river capture curves (RCCs). Geographic locations illustrated in Figure 19.5. Encroaching basin listed first for each capture event. Estimated fish species richness of paleobasins estimated from capture area as percentage of modern basin area. Events classified as lowland or upland with reference to the 250 m elevation contour, as active or stable and as tributary or distributary, based on conditions of modern landscapes. References in Table 19.2. L, Lower; M, Middle; U, Upper; E, East; W. West.

Chapter 21

Table 21.1 SDM algorithms. The most widely used are indicated with bold text.

Chapter 23

Table 23.1 High mountains and tepuis of Pantepui in the Venezuelan Guiana.

Chapter 25

Table 25.1 Global distribution patterns of New Zealand alpine genera.

Chapter 27

Table 27.1 Structure of the vegetation belts of the Alps in relation to the mean minimum temperatures of the coldest month of the year (

m

), and their correspondence with two concepts of the Mediterranean vegetation belts.

Table 27.2 Shifts in altitude of vegetation belts in relation to different terrestrial lapse rates and to latitudinal gradients estimated for different epochs (Fauquette et al. 2015). The minimum (0.36 °C/100 m) and maximum (0.8 °C/100 m) lapse rates are those reconstructed by Meyer (1992) for many areas of the world. The lines in bold corresponds to the estimates of terrestrial lapse rate for each studied period based on the standard modern relationship.

Chapter 28

Table 28.1 Species richness (number of species) of plants, mammals and birds in forest and high‐alpine ecosystems along the southern fringe and the alpine central region of the Qinghai–Tibetan Plateau. Species numbers have ranges where more than one ecoregion is considered. The ecoregions included here are (with ecoregion numbers according to Wikramanayake at el. (2002) following in parentheses): Himalayan subtropical broadleaf and pine forests (ecoregions 25 and 31), Eastern and Western Himalayan broadleaf forests (ecoregions 26 and 27), Eastern and Western Himalayan sub‐alpine conifer forests (ecoregions 28 and 29), Northwestern, Western and Eastern Himalayan alpine shrub and meadows (ecoregions 37, 38 and 39) and Central Tibetan Plateau alpine steppe (ecoregion 41). N.A., not applicable, as Wikramanayake at el. (2002) did not define a separate ecoregion for central Himalayan forests.

Table 28.2 Divergence time estimates for key events during the evolutionary history of terrestrial vertebrates and plants in the Tibeto‐Himalayan region (THR).

List of Illustrations

Chapter 01

Figure 1.1 The center section of Humboldt’s classical tableau, illustrating a cross‐section of the Chimborazo volcano in Ecuador, the highest mountain peak as measured from the center of the globe. This detailed drawing depicts one of the earliest studies of how the mountain biota is structured along an elevation gradient. Humboldt recognized the existence of distinct vegetation zones at different elevations with largely unique sets of species, constrained by climatic and physiological adaptations. This pioneering work is often considered a landmark in biogeography.

Figure 1.2 A selection of the most prominent mountain systems on Earth, as well as several other major geologic features and systems discussed throughout this book (image courtesy Suzette Flantua). Americas: 1, Aleutian Arc; 2, Cascades; 3, Rocky Mountains (Rockies); 4, Basin and Range Province; 5, Great Plains; 6, Appalachians; 7, Sierra Madre; 8, Panama Isthmus; 9, Northern Andes; 10, Guiana Highlands; 11, Central Andes; 12, Bolivian Altiplano; 13, Southern Andes; 14, Brazilian Highlands. Europe: 15, Scandinavian Mountains; 16, Jura Mountains; 17, Alps; 18, Pyrenees; 19, Apennines; 20, Carpathians. Africa‐Arabia: 21, Atlas Mountains; 22, Ahaggar (Hoggar) Mountains; 23, Yemen Highlands; 24, Ethiopian Highlands; 25, East African Rift System (EARS); 26, Rwenzori Mountains; 27, Drakensberg. Asia: 28, Ural Mountains; 29, Caucasus Mountains; 30, Zagros Mountains; 31, Tien Shan; 32, Hindu Kush; 33, Kunlun Shan; 34, Tibetan Plateau; 35, Himalaya; 36, Deccan Plateau; 37, Western and Eastern Ghats; 38, Altai Mountains; 39, Hengduan Mountains; 40, Japanese Alps. Oceania: 41, New Guinea Highlands; 42, Eastern Highlands (Australia); 43, Southern Alps. Antarctica: 44, Transantarctic Mountains. See also Plate 2 in color plate section.

Figure 1.3 Mountain areas based on ruggedness, as defined by Körner et al. (2011) (maximal elevational distance between nine grid points of 30″ in 2.5′ pixel; for a 2.5′ pixel to be defined as “rugged” (i.e., mountainous), the difference between the lowest and highest of the nine points must exceed 200 m). Numbers indicate topographic profiles of selected mountain ranges around the world. The characteristic topography of a mountain directly relates to the potential impact and frequency of connectivity breaks caused by Pleistocene glacial cycles, and thus the expression of the flickering connectivity system. Bars below profiles indicate a 100 km distance proportional to the profile shown.

Figure 1.4 (a) Global plant diversity measured by estimated number of vascular plant species per 10,000 km

2

. (b) Estimated patterns of species diversity for terrestrial mammals. This map was constructed using estimated natural species ranges without human influences, and plotted using an equal‐area Behrmann projection with colors proportional to the number of expected species. Several mountain regions have noticeably higher diversity than surrounding areas (e.g., the Rocky Mountains and Sierra Nevada in North America, the Andes in South America and, in particular, the East African Rift System).

Figure 1.5 Schematic representation of the complex interaction between four of Earth’s “spheres”: the geosphere, biosphere, atmosphere and hydrosphere. Each sphere is surrounded by a selection of words indicating the terminology and processes associated with it. The Earth’s layers are indicated in the cut‐out, but are not drawn to scale. The lithosphere comprises the crust and the uppermost solid mantle.

Chapter 02

Figure 2.1 Vertically exaggerated cross‐section through the upper mantle, showing crust and mantle, which differ from each other in chemical and mineralogical composition. The asthenosphere is a weak layer that underlies the stronger mantle lithosphere. The lithosphere includes the coldest uppermost part of the mantle and the crust. Note that the thicknesses of both crust and mantle lithospheres vary from place to place, but in general, where the crust is thick, the surface stands high to form a mountain belt or high plateau. The gradation in tone through the lithosphere is meant to show how strength decreases downward through it to the asthenosphere.

Figure 2.2 Block diagram illustrating the essentials of plate tectonics – specifically, the relative motion of plates of lithosphere ~100 km thick with respect to one another. Separate plates of lithosphere form and move apart at spreading centers or mid‐ocean ridges, and slide past one another at transform faults. At subduction zones, one plate plunges beneath another. This diagram was constructed to show a simplified view of the South Pacific and its margins. Two plates form at the East Pacific Rise: one is subducted beneath South America, and the other beneath the Tongan Islands. In this simplification, a plate on which the Tongan Islands lie plunges beneath Vanuatu (previously the New Hebrides Islands), which is shown here to lie on the Pacific Plate; actually, Vanuatu lies on a separate, small plate, which we ignore here. The Tonga and Vanuatu subduction zones are connected by a transform fault.

Figure 2.3 Map of the world, showing places mentioned in the text. See also Plate 2 in color plate section.

Figure 2.4 Simple schematic representations of isostasy. (a) In Airy isostasy, crust has thickened, and the mass above sea level is compensated by light crust that reaches into the mantle. (b) For Pratt isostasy, density varies laterally, and as shown here, lateral variation occurs largely in the mantle, because under high terrain, the mantle is hot. (c) Plots of temperature versus depth for two parts of (b): the dashed line shows the higher temperatures beneath the hot part of (b) and the solid line shows the cold regions flanking it. At any depth below the depth of compensation, the mass per unit area – and hence pressure – is treated as constant.

Figure 2.5 Simple schematics illustrating crustal thickening in a state of Airy isostasy. (a) Crust is compressed horizontally by an amount

D

, and thickens over a region of width

W

. The relationships of the height of the range,

h

, the thickness of excess crust in the crustal root, Δ

H

, and the amount of thickening, Δ

H

 + 

h

, are given by Equations 2.2.1 and 2.2.2 (see Box 2.2). The crustal budget requires that

DH

 = 

W

(Δ

H

 + 

h

). (b) Once a mountain belt has been built, in order to accommodate continued convergence by an amount shown here as Δ

D

, the belt grows wider. The crustal budget requires that Δ

DH

 = Δ

W

(Δ

H

 + 

h

).

Figure 2.6 Simple cross‐sections across the Himalaya and the Peruvian Andes. (a) The Himalaya have been built by slices of India’s crust being scraped off its northern edge as the subcontinent moves northward and thrusts beneath southern Tibet. The question mark covers a region where ignorance would risk making any image misleading. (b) The Peruvian Andes have been built by the westward thrusting of the Brazilian Shield beneath the Andes and by the intrusion of granite formed both by melting of mantle beneath the regions as oceanic lithosphere has been thrust beneath the western edge of the belt and by melting Brazilian crust thrust beneath the belt.

Figure 2.7 Simple, vertically exaggerated cross‐section through a mantle plume of rising hot material that spreads out at the base of the crust; high topography, extensive basaltic cover or “flood basalt” overlies the “plume head,” and in some cases – as shown here – a rift valley forms. The Ethiopian Plateau, crossed by the northern branch of the eastern rift of the East African Rift System (Figure 2.3), serves as an example.

Figure 2.8 Sequence of cartoons showing the growth and collapse of a mountain range built by horizontal compression and thickening of lithosphere. (a) A region of lithosphere, including both the crust and its mantle part, is put under horizontal compression, where two adjacent plates of lithosphere are pushed towards one another. (b) The crust has thickened in response to the horizontal compression, and the surface above the thickened crust has risen. To a first approximation, this compression and thickening of crust accounts for the mean elevations of high terrain in both narrow mountain belts and some high plateaus. The mantle lithosphere has thickened, for the horizontal compression of the crust requires a comparable amount of horizontal compression of the mantle lithosphere. The thickened mantle lithosphere acts as a heavy load on the crust above it, and in principle the surface above it should not stand as high as it would in the absence of lateral variations in density in the mantle, much as a ship floats lower in the water when filled with cargo. Airy isostasy is therefore imperfect in the sense that the surface is lower than that expected simply from the thickening of the crust. (c) Following removal of a heavy load of thick, cold, dense mantle lithosphere – indicated by the light shading where thickened mantle lithosphere is shown in (b) – the surface should have risen and should stand higher than it would for strict Airy isostasy. The thin layer of dark shading at the top of the thick crust shows the modest gain in elevation, and the thin layer of light shading at the bottom shows, too, that the base of the crust should have risen by the same amount. Thus, both Pratt and Airy isostasy contribute to the elevation of the high terrain. (d) As a consequence of this excess elevation, the high terrain spreads apart, somewhat like ripe Camembert cheese removed from its box. The horizontal forces that thickened the crust no longer suffice to support the crust that has been elevated higher than it would be in strict Airy isostatic equilibrium. If the lateral forces imposed by converging plates are insufficient, the high terrain spreads apart, the mantle lithosphere thins yet more and volcanism becomes likely.

Figure 2.9 Simple cartoon illustrating an example of removal of mantle lithosphere. Horizontal compression of the lithosphere, indicated by arrows on the flanks of the diagram, induces thickening of the crust and mantle lithosphere. The mantle lithosphere, denser than the underlying asthenosphere, is gravitationally unstable, and blobs (or drips or sheets) of thickened mantle lithosphere sink into the asthenosphere. For the parameters used for this calculation, the sinking blobs developed not beneath the locus of maximum shortening, but on the flanks.

Chapter 03

Figure 3.1 Variation of Earth’s topography. Histograms of surface elevations using data from ETOPO1 (Amante & Eakins 2009). The distribution is dominated by the ocean–continent dichotomy. High topography (>1 km) – the focus of this book – covers less than 15% of Earth’s surface.

Figure 3.2 Illustration of dynamic topography. Mass anomalies induce convective flow in the mantle (arrows) due to density differences. This flow exerts forces on the base of tectonic plates, causing deflection of the surface topography.

Figure 3.3 Map showing one example of a global residual topography calculation. The crustal and lithospheric isostatic component of topography has been subtracted from the present‐day observed topography. This calculation was based on a model of lithospheric structure from Naliboff et al. (2012) and has been smoothed (averaged) over 10 × 10° bins to focus on longer‐wavelength features. Prominent residual highs (light grays) are seen over the Western Pacific, the East African Rift, Antarctica and the North Atlantic. Meanwhile, residual lows (dark grays) are visible over Europe, the East Pacific Rise and the Australian–Antarctic Discordance (south of Australia). Thin white lines on the map delineate land surfaces, and black dashed lines show plate boundaries. See also Plate 7 in color plate section.

Figure 3.4 Tectonic setting of Peruvian flat‐slab subduction and western Amazonia. The total area thought to be covered by the Solimões Formation is outlined by the thick dashed white line, based on the maps of Hoorn (1994). Isopachs of the Solimões Formation thickness in the Acre and Solimões Basins are available; these are shown by thin white solid lines, after Latrubesse et al. (2010). Volcano locations (white triangles) are from Siebert & Simkin (2002). The gray arrow represents the absolute rate of motion of the Nazca Plate, from HS3‐NUVEL1A (Gripp & Gordon 2002). The dotted line, a–a′, represents the location of the cross‐section in Figure 3.5. Gray shading in the background represents topography/bathymetry, and the thin black lines are rivers.

Figure 3.5 Profiles illustrating the different contributions from dynamic topography and flexure to the total topography across the Amazonian foreland. The thick profiles depict the impact of relative dynamic topography, given a transition from “normal” subduction (thick gray line) to flat‐slab subduction (thick black line) (see the location of profile a–a′ in Figure 3.4). This results in relative dynamic uplift beneath the Peruvian Andes but relative dynamic subsidence beneath the western Amazon Basin (distal foreland). The Sub‐Acre/Solimões Basin (gray shaded region) is outside the influence of flexure driven by the mountain loading (thin gray lines), but does coincide with relative dynamic subsidence from the arrival of the flat slab. Two flexure profiles are shown for lithospheres of two different strengths or effective elastic thicknesses (Te). The depth of the basin (dotted black line) was determined from an isopach map (Latrubesse et al. 2010). Present‐day topography (dot‐dash line) was taken from ETOPO1 (Amante & Eakins 2009).

Figure 3.6 Schematic illustrations demonstrating how a change in subduction style could have driven the topographic and sedimentary evolution of western Amazonia since the Miocene. (a,c) Maps of the predicted topography from flexural and dynamic calculations both before (a) and on the arrival (c) of the flat slab. The scale bar for both maps is plotted below (a). White arrows represent drainage directions. The thin dotted lines in (a) and (c) represent the approximate locations of the cartoon cross‐sections shown in (b) and (d), respectively. In (b) and (d), gray dotted regions indicate sedimentary infill, with region 1 indicating the oldest deposits and region 3 the youngest. The letters I and P mark the estimated locations of the Iquitos and Purus Arches, respectively. (e) Similar cross‐section, extending from the trench to the Atlantic Ocean, showing the present‐day configuration of drainage and the locations of the sedimentary deposits that preserve the record of landscape evolution hypothesized in (b) and (d).

Chapter 04

Figure 4.1 Cenozoic evolution of climate and erosional/weathering fluxes to the global ocean. (a) Deep‐sea oxygen‐isotope record. Dots are individual data, gray line is running average, bottom scale shows average seawater temperature calculated for an ice‐free ocean. (b) Estimates of atmospheric CO

2

partial pressure (open diamonds with error bars; scale on bottom) reconstructed from different marine and terrestrial proxies, with associated uncertainties. (c,d) Seawater Sr‐ (shaded circles) and Li‐isotope (open squares) records, tracking the continental weathering flux to the global ocean. Li'isotope data are reported as δ

7

Li (scale on bottom) with respect to NIST standard and include 2σ error bars. (e) Histogram of sediment mass deposited in the global ocean over 5 My intervals. Major tectonic and climatic events during the Cenozoic are indicated. K‐T, Cretaceous–Tertiary; PETM, Paleocene–Eocene Thermal Maximum.

Figure 4.2 Uplift of mountain summits by the isostatic response to erosion, following the model of Molnar & England (1990). Note that summit uplift can only take place if relief increases, because erosion is concentrated in the valleys and is associated with a decrease in the average elevation of the region.

Figure 4.3 Evolution of a doubly vergent critically tapered wedge in response to changes in tectonics or climate. (a) Conceptual model of an orogenic wedge developing in response to tectonic accretion with a tectonic influx,

F

T

.

The wedge is characterized by its width,

W

, height (relief),

R

, and rock‐uplift rate,

U.

Erosion of the wedge feeds an erosional outflux,

F

E

. (b) Responses of the wedge width,

W

, erosional flux,

F

E

, and uplift rate,

U

, to step changes in tectonic influx (convergence rate) or climatically modulated erosional efficiency (normalized to a value of 1 before the change): left panels show response to a twofold increase (dashed gray lines) or decrease (continuous black lines) in tectonic influx; right panels to a twofold increase (continuous black lines) or decrease (dashed gray lines) in erosional efficiency. Results are predicted by an analytical model of a doubly vergent critical wedge coupled to a stream‐power erosion law.

Figure 4.4 Schematic showing the different couplings and feedback loops inferred to control Earth’s tectonic–erosion–climate system. The arrows and numbered circles indicate the different proposed couplings; bold numbers are couplings discussed in this chapter. (1) Tectonics controls topography, because crustal thickening due to tectonic accretion leads to surface uplift and an increase in relief. (2) Topography feeds back into tectonic deformation, through its influence on the crustal stress field. (3) Erosion rates are strongly dependent on relief. (4) Surface processes also modify topographic relief. (5) Topography directly controls climate, through its effect on atmospheric and oceanic circulation patterns. (6) Erosionally controlled continental silicate weathering and organic carbon burial act as sinks for atmospheric CO

2

, thereby modulating climate. (7) Climate may influence erosion rates through the amount and temporal distribution of precipitation, and through the influence of glaciations. (8) A direct link between erosion and tectonics occurs through the thermal effect of exhumation and associated rock advection. (9) Tectonics may also directly affect erosion through rock fracturing. (10) Deep‐Earth processes may affect climate directly through volcanic and metamorphic outgassing of CO

2

. Closed circuits of arrows constitute potential feedback loops.

Figure 4.5 (a) Plot of erosion rate as a function of mean local relief (calculated in a 10 km‐radius window) from sediment‐flux data from rivers draining mostly tectonically inactive areas (open circles) and from diverse data (mostly thermochronology, but also cosmogenic data and landslide mapping) from tectonically active areas, such as the South Island of New Zealand, Taiwan and various parts of the Himalaya, including the very rapidly eroding syntaxes (black squares with error bars). The combined data clearly show the nonlinear relationship between relief and erosion rate, with mean local relief saturating at ~1500 m. (b) Magnified view of river sediment‐flux data, showing the linear relationship for erosion rates <1 mm/y (corresponding to gray shaded box in (a)).

Figure 4.6 Experimental landscape response to changes in tectonic or climatic forcing. (a,b) Oblique views of an experimental landscape in a variable‐rainfall experiment (experimental landscapes are 20 cm in length): (a) for a high rainfall rate of ~180 mm/h; (b) for a low rainfall rate of ~90 mm/h. (c,d) Evolution of the topography (mean elevation), rainfall, uplift and denudation rates for (c) an experiment with a constant uplift rate and a decrease in rainfall rate from ~180 to ~90 mm/h at 300 min and (d) an experiment with constant rainfall rate and an increase in uplift rate from 1.0 to 1.5 cm/h at 350 min. Both models start at topographic and erosional steady state in response to initial conditions, and are run until steady state is reattained in response to modified conditions. Times corresponding to (a) and (b) are indicated on (c).

Chapter 05

Figure 5.1 (a) Schematic presentation of the concept of surface uplift and rock uplift with respect to the centre of the Earth, and exhumation with respect to the Earth’s surface. (b) Schematic concept showing the relationship between tectonics, surface processes and cooling of rocks during exhumation in a convergent orogenic setting. AFT and ZFT, apatite and zircon fission‐track analysis; AHe and ZHe, apatite and zircon (U‐Th)/He analysis. Isotherms (indicated by black dashed lines) relate to the average closure temperatures of the different dating techniques, depending on the cooling rate (here about 15 °C/My). See also Plate 8 in color plate section.

Figure 5.2 Time–temperature history models are powerful tools for interpreting thermochronological data. The examples given here are for the East African Rift. (a)

HeFTy

model showing good (dark gray) and acceptable (light gray) model solutions for the time–temperature history based on apatite fission‐track data. The thermal history shows relatively fast cooling between 60 and 50 Ma, followed by slow cooling between 50 and 10 Ma and renewed fast cooling between 10 Ma and the present. (b)

QTQt

model showing t–T paths for a set of three samples, including the 95% confidence interval error envelope. The model shows rapid cooling between 60 and 50 Ma, slight reheating between 50 and 10 Ma and fast cooling from 10 Ma to the present day.

Figure 5.3 Three‐dimensional time–temperature history modeling is useful for modeling the thermal and tectonic evolution of mountain belts. Shown here are

PeCUBE

models for (a) the topographic evolution (layers indicate isotherms, with the temperature values (°C) noted alongside) and (b) predicted zircon He ages at the surface of both sides of the Boconó fault. Inclusion of the fault in the middle of the study area permits the analysis of the exhumation of two tectonic blocks independently. See also Plate 9 in color plate section.

Figure 5.4 (a) Overview map of the Northern Andes in Colombia and Venezuela. ELB, El Carmen block; SNB, Sierra Nevada block. (b) Correlation of long‐term erosion rates derived from apatite fission‐track data from river sediments in the Merida Andes against relief, precipitation and seismic strain rate determined from seismic energy release.

Figure 5.5 (a) Map of the East African Rift System (EARS), showing the rift shoulders and rift basin and the different lithospheric plates controlling the geodynamic evolution. The dashed gray box denotes the area shown in (b). (b) Schematic cross‐section of the EARS. Swath profile from the Shuttle Radar Topography Mission (SRTM), 90 m, 100× vertical exaggeration.

Chapter 06

Figure 6.1 Compilation of river‐based δ

18

O‐elevation relationships across: (a) a high plateau, Central Himalaya/Tibet; and (b) a low plateau, the Turkish–Anatolian Plateau. δ

18

O values decrease systematically with elevation along the windward side of the mountain ranges (Himalayan and Tauride transects). In contrast, aridity in the lee of the mountain ranges (“plateau interior”) results in higher δ

18

O values, due to evaporation and water recycling. Circles represent data recovered from stream water and diamonds show averages from long‐term station data. The solid line indicates smoothed regional topography. SMOW refers to isotope ratios normalized to standard mean ocean water.

Figure 6.2 Schematic and simplified change in δ

18

O values of precipitation following Rayleigh fractionation and cooling of air masses. Water vapor is assumed to start with δ

18

O = −11‰ at 25 °C, and cools during ascent to −25 °C. At 0 °C, oxygen isotope fractionation changes from vapor‐water to snow‐water fractionation. The elevation curve (dashed line) assumes a temperature lapse rate of 5 °C/km. In this example, atmospheric moisture content at 20 °C and 1000 m elevation (white circles) is still relatively high, and corresponds to higher rainfall δ

18

O values when compared to rainfall at 10 °C and 3000 m elevation (white squares).

Figure 6.3 Change in δ

18

O of river water as a function of net elevation change. Compilation is restricted to moderate elevation, mid‐latitude settings and averages −2.8‰/km (solid black line; linear fit), with uncertainty estimates (dotted) after Poage & Chamberlain (2001). An alternative approach models δ

18

O values of precipitation normalized (to sea level) as a function of elevation (curved gray line; modified after Rowley & Garzione 2007). The solid gray curve represents mean result based on Monte Carlo simulations of air parcels with starting temperature, T, and relative humidity, RH, corresponding to 22 °C and 80%, respectively. Dashed gray curves show the impact of temperature varying by ±4 °C.

Figure 6.4 Practical approaches in stable isotope paleoaltimetry. (a) δ

18

O values of precipitation decrease systematically during orographic rainout and associated cooling and condensation of water vapor as a function of elevation, z. Increasing δ

18

O values in the lee of the mountain range result from evaporative

18

O enrichment in precipitation. Over geologic time, δ

18

O values of advected water vapor may change due to changing climate conditions (white arrows). (b) Reconstructing differences in the oxygen isotopic composition of precipitation, Δ(δ

18

O), between low‐elevation sites (e.g., samples from foreland basin, squares) and sites at unknown high elevation (intramontane basins or faults/detachments, circles) eliminates some of the uncertainties associated with single‐site stable isotope paleoaltimetry. ET, evapotranspiration; E, evaporation.

Chapter 07

Figure 7.1 Altitude‐related vegetation zones as recorded by C.H. Merriam on San Francisco Peak, AZ, USA. This zonation is specific to the southwestern USA, and elsewhere the component taxa differ. Note the aspect‐related asymmetry.

Figure 7.2 Summary of the leaf characters used in a CLAMP analysis. For the scoring protocols, see the CLAMP Web site: http://clamp.ibcas.ac.cn. See also Plate 13 in color plate section.

Figure 7.3 Comparison of isopleths representing enthalpy with those of terrestrial lapse rates across North America. (a) Isopleths representing the modern enthalpy field across North America (expressed as energy (kJ) per kilogram of air). (b) Lapse rates, expressed in °C/km. These are far more spatially variable than the enthalpy field, which is more zonal, varying mostly in relation to latitude and invariant with longitude.

Figure 7.4 Graph showing the relationship between the relative positions of modern vegetation sites (open gray circles) along the CLAMP‐derived enthalpy vector (vector score) (Box 7.1) and the observed enthalpy (measured as energy in kilojoules per kilogram of air) at those locations. The vector scores for four sea‐level fossil sites of different ages (early Eocene to middle Miocene) from northern India (solid vertical gray lines) intersect at the filled circles with the enthalpy regression curve derived from the modern calibration data giving the predicted paleoenthalpy values for those sites (horizontal dotted lines). Note that all northern India sea‐level sites have similar paleoenthalpies irrespective of age. In contrast, the 15 Ma Namling site from southern Tibet yields a much lower enthalpy value, meaning it must have been much higher. The figure shows the raw (uncorrected for paleoposition) enthalpy difference, which gives an elevation for Namling of 5888 m. Statistical uncertainties (±1 SD) in positioning the fossil sites are shown. The ages of the fossil sites are from Spicer et al. (2003) (Namling), Shukla et al. (2014) (Gurha), Khan et al. (2014) (Darjeeling) and Ojha et al. (2009) (Kameng River).

Chapter 08

Figure 8.1 Schematic of South American climatology with (a) low, (b) medium and (c) high surface uplift. Thin vectors represent lower‐level winds. Diagonal hatching indicates regions with mean annual precipitation greater than 150 cm. Dark shading indicates cooler temperatures and light shading indicates warmer temperatures. See also Plate 14 in color plate section.

Figure 8.2 Schematic of North American climatology with (a) low, (b) medium and (c) high surface uplift. Thin vectors represent lower‐level winds. Diagonal hatching indicates regions with mean annual precipitation greater than 150 cm. Dark shading indicates cooler temperatures and light shading indicates warmer temperatures. See also Plate 15 in color plate section.

Chapter 09

Figure 9.1 Model results from the coupled ocean–atmosphere weather model ECBilt (Bosmans 2014), simulating the impact of precession minima and maxima on precipitation. Shown are the differences in average precipitation (mm/day) and surface winds (m/s) for precession minima minus precession maxima for average northern hemisphere (a) summer and (b) winter periods. The dark‐gray colors on the northern hemisphere continents in their summer and the hatched dark‐gray colors on the southern hemisphere continents in their summer exemplify the intensified monsoon activity during precession minima and precession maxima, respectively. Note the large longitudinal variation in precipitation and winds within a single precession phase, highlighting the impossibility of interpreting astronomical forcing at Earth’s surface directly from solar insolation changes at the top of the atmosphere.

Figure 9.2 Cenozoic paleoclimate trends, rhythms and aberrations on various time scales in oxygen isotope profiles from different records, from (a) the recent past to (d) much of the Cenozoic. Generally, heavier δ18O ratio indicate glaciated and/or cooler stages. Panels (d) and (c) denote an updated version of the deep‐sea benthic foraminiferal calcite stable oxygen isotope compilation by James C. Zachos (University of California, Santa Cruz, USA). Abbreviations denote specific events: PETM, Paleocene–Eocene Thermal Maximum; EECO, Early Eocene Climate Optimum; MECO, Middle Eocene Climate Optimum; Oi‐1, Oligocene Isotope stage 1; Mi‐1 and ‐3b, Miocene Isotope stages 1 and 3b; MCO, Miocene Climate Optimum. (d) Million‐year scale trends. (c) Glacial–interglacial cyclicity of benthic oxygen isotopes over the last million years, fluctuating at ten‐ to hundred‐thousand‐year intervals. Along this record, marine oxygen isotope stage numbering is given with even and odd numbers for glacial and interglacial stages, respectively. (b) Stable oxygen isotope record of ice in the Northern Greenland Icecore Project (NGRIP) core from Greenland, denoting the variability that occurred between the last and the current interglacials. Note the fluctuations at thousand‐year time scales, such as the labelled Heinrich events (shown as H1–H6 and shaded in gray) and shorter Dansgaard–Oeschger cycles (labelled D and consecutively numbered). Stage 1 and 5 refer to MIS 1 and 5. YD, Younger Dryas cold stage. (a) A calcite stable oxygen isotope record from a stalagmite of the Dongge Cave in central China, denoting the climate variability that occurs at that spot over the course of a seemingly stable interglacial MIS 1 up to the recent past. Note the variability at hundred‐year time scales.

Chapter 10

Figure 10.1 Geodiversity of mountain landscapes. (a) Sagarmatha National Park, Nepalese Himalaya: very high mountains of the Alpine–Himalayan belt characterized by high relief, active glacial and slope processes and shrinking valley glaciers with extensive rock debris cover. (b) The Cairngorm Mountains, Scotland: low mountains of the Caledonide belt, formed in a dissected Silurian granite intrusion with glacial landforms incised into a series of paleosurfaces. (c) The Blue Mountains, New South Wales, Australia: a dissected plateau on a passive margin uplifted during the Jurassic. (d) Allardyce Range, South Georgia: heavily glacierized mountains at sea level, comprising folded lower Cretaceous volcaniclastic sandstones and mudstones. (e) Zagros Mountains, Iran: differential erosion of folded Carboniferous–Miocene sedimentary rocks, forming a landscape of linear ridges and valleys and revealing a salt dome in the center of the image. (f) Cerro Fitz Roy massif, southern Andes: granitic buttresses and towers formed in a Neogene igneous intrusion. (g) Tadrart Acacus, Libya: a monocline in Paleozoic sedimentary rocks uplifted and dissected during the Cenozoic. (h) Cotopaxi, Ecuador: an active, glacier‐capped stratovolcano with flanks scarred by tracks of lahars and meltwater floods. (i) Mount Kenya: an extinct Plio–Pleistocene stratovolcano heavily dissected by glacial erosion and with small remnant glaciers. See also Plate 17 in color plate section.

Figure 10.2 Geodiversity of mountain landforms and geomorphological processes. (a) Schematic representation of the altitudinal zonation of geomorphological processes and depositional environments, central Karakoram. (b) Recession of cirque glacier from Little Ice Age moraines, Kebnekaise, Sweden. (c) Moraines formed by glaciers during the Younger Dryas, Scottish Highlands. (d) Landslide on Mount Dixon, Southern Alps, New Zealand, January 21, 2013. (e) Rock weathering, talus formation and debris flows, Tatra Mountains, Poland. (f) Periglacial blockfield and small plateau icecaps, Lyngsalpene, northern Norway. (g) Glacial trough and alluvial fans, Southern Alps, New Zealand. (h) End moraine breach and glacial lake outburst flood (GLOF) deposits following the failure of the moraine dam at Tam Pokhari (Sabai Tsho) glacial lake, Hinku Valley, Nepal, September 3, 1998. (i) Rock glacier, Wrangell Mountains, Alaska. See also Plate 18 in color plate section.

Figure 10.3 Breiðamerkurjökull, Vatnajökull National Park, Iceland. Many glaciers worldwide are popular (geo)tourist destinations where visitors can both enjoy the spectacular scenery and appreciate the effects of climate change, which are clearly demonstrated in glacier recession. See also Plate 19 in color plate section.

Chapter 11

Figure 11.1 Outline of Vorarlberg, showing its main tectonic zones (in different shades of gray). The names of cities and villages are underlined. The lowest area is at Lake Constance, the highest mountains are in and around the Montafon region. The case study area is located west of the village of Au in the Bregenzerwald region.

Figure 11.2 Geodiversity index (GI) map of Vorarlberg. Grid size in all maps is 1 × 1 km. Geodiversity for each cell follows the five‐class scheme presented in the figure, ranging from very low to very high for each index, with the exception of the map for Ddi, where each cell is marked solely on presence (dark gray) or absence (white) of drainage. Tdi, tectonic diversity index; Gdi, geological diversity index; Ddi, drainage diversity index; Edi, elevation diversity index; Sdi, slope diversity index; Sri, solar radiation diversity index; GI, geodiversity index. See also Plate 22 in color plate section.

Figure 11.3 (a) Digital geomorphological map of the Au West area, displayed as a semi‐transparent overlay on a DEM‐derived hillshade map. The location of the biotope units is indicated. (b) Potential geoconservation map of the Au West area, shown on a background of 25 m‐contour lines. Refer to Section 11.4.3 for further explanation. See also Plate 23 in color plate section.

Figure 11.4 (a) View to the south of part of the Obere Alpe glacial niche. Rockfall and debris‐flow deposits cover the lower slopes of the headwall, which is part of the Gungern‐Klippern mountain range. The hummocky topography around and to the left of Obere Alpe (center‐right) is formed by an intricate pattern of glacially eroded bedrock and ablation tills. (b) View to the south‐west of the glacial landscape in the central part of the study area. The east‐sloping surface to the left of Ghf. Edelweiss is underlain by subglacial till (exposed in the flank of the Leuebach incision in the lower‐central part of the photo). The steep Korbschrofen cliff (left) produces abundant scree. The central‐right cliff, with an apron of talus, is the headwall of the Obere Alpe glacial niche. See also Plate 24 in color plate section.

Chapter 12

Figure 12.1 Conceptual framework of the flickering connectivity system (FCS). (a) The background drivers of speciation are the large Pleistocene climate fluctuations and highly complex montane topography. The δ

18

O curve is based on composite stable oxygen isotope ratios from benthic foraminifera and is an indicator of global ice volume and temperature (Lisiecki & Raymo 2005). (b) Altitudinal migrations of hypothetical high‐mountain biota, shown in a simple two‐phase setting reflecting warmer and cooler conditions. (c) Schematic representation of the intrinsic processes of the FCS as a result of changes in connectivity: fragmentation (Fr), colonization (Co), intermixing (In) and hybridization (Hy). (d) The “mountain fingerprint” is defined by the interaction between climate and topography. It is a unique mountain identifier in which the processes of (a) occur in a spatially and temporally complex way, and therefore causes different timings and patterns of species diversification when comparing between mountains. See also Plate 25 in color plate section.

Figure 12.2 Mountain areas based on ruggedness, as defined by Körner et al. (2011) (maximal elevational distance between nine grid points of 30″ in 2.5′ pixel; for a 2.5′ pixel to be defined as “rugged” (i.e., mountainous), the difference between the lowest and highest of the nine points must exceed 200 m). Numbers indicate topographic profiles of selected mountain ranges around the world. The characteristic topography of a mountain directly relates to the potential impact and frequency of connectivity breaks caused by Pleistocene glacial cycles, and thus the expression of the flickering connectivity system. Bars below profiles indicate a 100 km distance proportional to the profile shown.

Figure 12.3 (a–m) Spatial reconstructions of tropical alpine systems (páramo and glaciers; black) in the northern Andes during the last 280 ky, showing the upper forest line (UFL) moving between elevations of 2100 and 3200 m. Each map represents a simplified reconstruction of the distribution of the alpine Andean ecosystem (the páramo) using a digital elevation model. (n) Estimated elevations of the UFL are inferred from the Fúquene‐9C pollen record (Bogotá‐Angel et al. 2011; Groot et al. 2011). Letters correspond to the maps. Low UFL reflects cooler periods, such as the Last Glacial Maximum (LGM), while a higher UFL reflects warmer periods (interglacial conditions, such as the present). Different regions experience alpine system connectivity and fragmentation at different moments in time. Some páramo areas persist continuously (resistant sky islands), while others appear and disappear (occasional sky islands). See also Plate 26 in color plate section.

Figure 12.4 Spatial representation of the four intrinsic processes of the FCS in the Eastern Cordillera of the Colombian Andes. The potential distribution of páramo is shown during (a) cooler and (b) warmer conditions. The figure shows how different processes can occur at different locations throughout a mountain system, and as a result cause a spatially complex biogeographic pattern. The many possible intermediate configurations are shown in Figure 12.3. (c) Cool climate corridor of alpine species through a mid‐elevation or lowland canyon. Glaciers are seen on the mountain tops. The arrow indicates the direction of connectivity. (d) Cross‐mountain corridor between populations on either side of a mountain. Alpine species are restricted here to high elevation, and connectivity is reduced. See also Plate 27 in color plate section.

Chapter 13

Figure 13.1 Mosaic of snowbeds and exposed ridges in the spring (May 2011) on Wannengrat, Switzerland. See also Plate 28 in color plate section.

Figure 13.2 Day of snowmelt predicts when flowering starts for 274 female

Salix herbacea

patches growing on ridges (○) and snowbeds (●) and for 85 male

S. herbacea

patches growing on ridges (Δ) and snowbeds (▲) surveyed in (a) 2011 and (b) 2012. Dashed lines are regression lines (R

2

 = 0.827, P‐value < 0.001).

Figure 13.3 Estimates of the number of migrants per generation (N

e

m) between microhabitats differing in snowmelt timing (ridges and snowbeds) in

Salix herbacea

from three mountains in the Swiss Alps.

Figure 13.4 Connections between approaches to studying the genetics of ecologically relevant variation. Numbers indicate the sections in this chapter dealing with the specific concepts.

Figure 13.5 Between‐microhabitat genomic divergence in

Salix herbacea

. Sliding‐window analysis for the average between‐microhabitat fixation index (F

ST

). The window size is 1 × 106 basepairs (bps) and the step size is 200 kilobases (kb). Results of all windowed analyses are plotted against window midpoints in millions of bps (Mb). Black and gray colors highlight different chromosomes, identified by roman numerals. The lower and upper gray dashed horizontal lines indicate the genome‐wide average and the threshold for the identification of outliers, respectively.

Figure 13.6 Scenarios where (a) plasticity, (b) local adaptation and (c) plasticity with a genetic basis explain trait variation across different microhabitats (snowbed, S, and ridge, R). Distinct lines (dashed: S; continuous: R) denote different genotypes reciprocally transplanted to each microhabitat.

Chapter 14

Figure 14.1 Conceptual diagram linking the tectonic and climate processes shaping regional landscapes, environmental conditions and species richness over geologic time scales. Within topographically and climatically complex regions, biogeographic processes include dispersal, geographic‐range shifts and fragmentation of species ranges. These processes can promote origination and extinction, and geographic differences in origination and extinction rates in tectonically active versus passive regions can strengthen or weaken the topographic diversity gradient.

Figure 14.2 New World gradients of mammal richness, elevation and climatic variables. (a) Species richness of continental mammals, compiled at a spatial resolution of 10 km

2

, in North and South America, based on species ranges from NatureServe. (b) Mean elevation of grid cells at 1 km

2

resolution. Note that the species richness of mammals follows the topographic gradient as well as the latitudinal gradient. (c) Annual range of temperature (shading), a measure of climatic seasonality, shows a strong latitudinal gradient. Contours of mean annual precipitation follow the orientation of the major mountain ranges in North and South America. The contour interval is 500 mm for precipitation. Climatic variables and elevation are both significant predictors of mammal diversity.

Figure 14.3 Richness of extant mammals, compiled for 10 km

2

grid cells. Overprinting the latitudinal diversity gradient is a strong topographic diversity gradient on all continents, excluding Antarctica. Mammalian species richness is elevated in the montane west of North America, along the Andes of South America, in the Alps of Europe, within the topographically complex East African Rift, over mountainous regions in India and south‐eastern Asia and along the eastern coastal ranges of Australia.

Figure 14.4 Transects illustrating topographic diversity gradients in relation to elevation and precipitation variables across three US states. Species richness is based on the distribution of species geographic ranges in 1° bins of longitude. Elevation is shown at a resolution of 1 km (lateral distance). Note that the vertical axis for mean annual precipitation changes from (b) and (d) to (f). (a) Transect across Colorado at 40° N. Species richness is low in eastern Colorado, where relief is low, then rises along the foothills and the front range of the Rocky Mountains around 105° W, dips slightly in the high mountains and rises again in western Colorado. (b) Variation in mean annual precipitation (including snow) follows elevation closely, with the highest values in the high mountains. Seasonality of precipitation is almost the mirror image of precipitation, with low values in the mountains and high values over the plains of eastern Colorado. (c) Transect across Kansas at 39° N. Species richness fluctuates between 55 and 70 species per bin. Elevation increases from about 300 m in eastern Kansas to about 1200 m at the western border. (d) Mean annual precipitation steadily rises from west to east, the inverse of the trend in elevation; seasonality of precipitation varies little over eastern Kansas and increases slightly in the western half of the state. Gradients in species richness, elevation and precipitation variables provide a contrast with those from topographically complex regions in Colorado and Oregon. (e) Transect across Oregon at 45° N. Species richness ranges from 75 to 95 species per bin, with the highest values over the Cascade Mountains in western Oregon. The elevation profile captures the complex topography of the northern Basin and Range in the east and the Cascade Mountains, Willamette Valley and Pacific Coast Range in the west. (f) Mean annual precipitation shows strong rain shadows from the Pacific coast and the Cascade Mountains in the west, with about 500 mm per year persisting over much of eastern Oregon. Seasonality of precipitation is higher in the west and declines across the eastern part of the state.

Figure 14.5 Transect across Ecuador at the equator. (a) Species richness for 1° bins of longitude, based on the distribution of geographic ranges that occur between 0 and 2° S. From east to west, species richness rises from ~160 species in the Ecuadorian Amazon Basin to ~225 along the eastern Andes, then declines towards the western Andes and the coast. Elevation, shown at a resolution of 1 km (lateral distance), is low east and west of the Andes and rises to nearly 5000 m in the eastern Andes. (b) The distribution of annual precipitation reflects moist air from Atlantic and Pacific sources, with peaks on the eastern and western flanks of the Andes. Mean annual precipitation is high throughout eastern Ecuador but declines steeply from the western Andes towards the Pacific coast. Seasonality of precipitation is highest in western Ecuador and declines across the mountains into the Amazon Basin. Note that the scale for all four variables differs from those in Figure 14.4.

Figure 14.6 Elevational ranges for mammals in Ecuador, based on species from the transect in Figure 14.5. The range of each species is plotted as the lowest and highest reported elevational limits (Tirira 2007). Species are clustered geographically to illustrate the different geographic components of regional diversity. High regional richness is the consequence of species that uniquely occur at high elevations in combination with species that occur at lower elevations on either side of the Andes, as well as wide‐ranging species.

Figure 14.7 Fossil record of rodent richness from the tectonically active region of the western USA and the tectonically stable (passive) Great Plains. (a) Map of fossil localities from the western region (shaded circles) and the Great Plains (open circles) from 25 to 5 Ma. (b) Rodent richness at the species level, compiled for 1 million‐year intervals, from the western region (shaded circles) and the Great Plains (open circles). Rodent richness was much greater in the active region between 17 and 13 Ma, during an interval of global warming and widespread tectonic activity across western North America.

Chapter 15

Figure 15.1 Relationships between mountains, climate and biodiversity. (a) Uplift of a mountain range through time. (b) Associated changes in elevation, habitat heterogeneity, climate and species diversity. (c) Rates of change corresponding to the different processes involved, namely the rate of tectonic activity/uplift, the rate of climatic change and the rates of speciation (gray line) and local extinction (dashed line). The gradual change of the relief increases topographic complexity, creates novel habitats and affects regional climatic conditions. Such changes, even if moderate, will likely affect the rates of speciation (afforded by adaptation to the novel conditions and divergence) and local extinction (if lineages fail to adapt). Continued tectonic change may result in a state shift (“tipping point”), causing profound and rapid climatic changes. A corresponding peak in species extinction is followed by an increase in immigration and in situ speciation, for example by pre‐adapted lineages from other regions. As clades diversify, they fill ecological niche space, and the rate of speciation slows again. See also Plate 30 in color plate section.

Figure 15.2 Example showing the estimation of speciation and extinction times (

s

,

e

) for a species, while modeling the preservation process. Circles indicate dated fossil occurrences; curves represent the resulting posterior distributions of

s

and

e

.