bluuebunny/crossref_metadata_2025 huggingface dataset:
Paleolimnology: The Past Meets the Future
Authors: Cohen Andrew S.
Published: November 2020

Exciting days lie ahead for paleolimnology. As we embark on a new millennium, the opportunities and challenges in this field are extremely bright. As an epilogue to this book, it seems appropriate to conclude with a few of the developments that seem to me particularly promising for the near future. 1. Increasing application of paleolimnological data to address problems in global climate change. Paleolimnologists need to make governments and societies aware of the importance of high-resolution paleorecords from lakes for providing information about baseline variability of the biosphere, consequences and histories of past climate change events, and past responses of our precious aquatic resources to such changes. Paleolimnology should and will increasingly play a role in providing decision-makers with critical information about earth system history as they formulate policies to cope with these changes. Few, if any, paleoenvironmental records provide earth history records in environments as intimately associated with human activity as lake deposits. Lakes and wetlands are increasingly recognized as potentially important components of the global carbon cycle, especially as environments for sequestering large volumes of carbon, and future research will undoubtedly quantify the magnitude and dynamics of this role. Paleolimnologists will need to work even more closely with climate modelers, hydrologists, and atmospheric scientists in years to come, to insure that the paleorecords we study will help resolve important questions about the earth’s climate system. 2. Advances in geobiology. The rapid developments of new and automated tools in molecular biology and organic geochemistry for analyzing small sample volumes and extracting compound-specific isotopic information from organic compounds have important implications for paleolimnology. In years to come we will increasingly rely on organic geochemistry and microbial geobiology to help decipher the organic record of algal primary producers, decomposers, and other elements of the microbial food web. These are components of a lake’s ecosystem that ecologists recognize as immensely important in biogeochemical cycles and as being on the front line of lake responses to changes in climate and watershed processes, but which have heretofore been largely intractable to any detailed interpretation by paleolimnologists.
Overview
Authors: Brock Fred V. , Richardson Scott J.
Published: November 2020

Measurements are required to obtain quantitative information about the atmosphere. Elements of a good measurement system, one that produces high-quality information, are briefly described in the following sections. All of these items are, or should be, of concern to everyone who uses data. None may be safely delegated, in their entirety, to those who have little or no interest in the ultimate use of the data. An instrument is a device containing at least a sensor, a signal conditioning device, and a data display. In addition, the instrument may contain an analog-to-digital converter, data transmission and data storage devices, a microprocessor, and a data display. The sensor is one of the essential elements because it interacts with the variable to be measured (the measurand), and generates an output signal proportional to that variable. At the other end of this chain, a data display is also essential, for the instrument must deliver data to the user. To understand a sensor, one must explore the physics of the sensor and of sensor interaction with the measurand. There is a wide variety of sensors available for measuring pressure, temperature, humidity, and so on, and this text discusses each individually. Therefore, each chapter must deal with many different physical principles. Sensor performance can be described by reference to a standardized set of performance definitions. These characteristics are used by manufacturers to describe instruments and as purchase specifications by buyers. Static characteristics are those obtained when the sensor input and output are static (i.e., not changing in time). Static sensitivity is an example of a static characteristic and is particularly useful in sensor analysis. When raw sensor output is plotted as a function of the input, the slope of this curve is called the static sensitivity. Relating static sensitivity to fundamental physical parameters is a systematic way of revealing sensor physics and leads to an understanding of the sensor and of how to improve the design. Dynamic characteristics are a way of defining a sensor response to a changing input. The most widely known dynamic performance parameter is the time constant, discussed in chap. 6.
Modeling Spatial Phenomena
Authors: Krzanowski Roman M. , Raper Jonathan
Published: November 2020

In part II we describe some possible methods of modeling spatial phenomena with spatial evolutionary algorithms. We will explain what spatial evolutionary models and spatial evolutionary algorithms are and how they can be designed. We will also provide a general framework for spatial evolutionary modeling. We believe that this framework can be used to create evolutionary models (and algorithms) of spatial phenomena that will reach well beyond the model discussed in the book. Wherever possible we will give examples to illustrate the concepts, terms, and procedures we discuss. In fact, by the end of part II we will have built, using presented principles, a complete spatial evolutionary model—a spatial evolutionary model of a wireless communication system. We shall begin our discussion with an explanation of the distinction between spatial evolutionary models and evolutionary models of spatial phenomena. As we shall see, the difference between these two terms, while subtle, is very important for the understanding of spatial modeling in general and evolutionary spatial modeling in particular. . . . "Spatial Evolutionary Models" Versus "Evolutionary Models of Spatial Phenomena" . . . The differences between the terms spatial evolutionary models and evolutionary models of spatial phenomena extend well beyond their lexical dissimilarities and touch upon very basic issues of evolutionary and spatial modeling. The term spatial evolutionary model, as used here, refers to an evolutionary model that constitutes a separate, distinct class of computer evolutionary models. In contrast, the term evolutionary models of spatial phenomena denotes applications of existing evolutionary methods (or mere extensions of established evolutionary methodologies) to problems defined in space. Our view of the science of spatial modelling is driven by the choice of which definition, along with its consequences, that we accept. If we accept that spatial evolutionary models constitute a separate and distinct class of evolutionary models, then we will also have to accept the proposition that they possess unique rules governing their behavior, a unique genome design to represent a model-specific data structure, and a set of unique operators that cannot be readily applied to nonspatial problems. Moreover, it will follow that these evolutionary models also possess problem-specific language, that is language specific to the domain of spatial evolutionary models.
Ecology of the Shortgrass Steppe
Authors: Lauenroth W. K. , Burke I. C.
Published: November 2020

Ecology of the Shortgrass Steppe: A Long-Term Perspective summarizes and synthesizes more than sixty years of research that has been conducted throughout the shortgrass region in North America. The shortgrass steppe was an important focus of the International Biological Program's Grassland Biome project, which ran from the late 1960s until the mid-1970s. The work conducted by the Grassland Biome project was preceded by almost forty years of research by U.S. Department of Agriculture researchers-primarily from the Agricultural Research Service-and was followed by the Shortgrass Steppe Long-Term Ecological Research project. This volume is an enormously rich source of data and insight into the structure and function of a semiarid grassland.
Climate of the Shortgrass Steppe
Authors: Pielke Roger A. , Doesken Nolan J.
Published: November 2020

The climate of a region involves the short- and long-term interaction among the atmospheric, hydrologic, ecologic, oceanographic, and cryospheric components of the earth’s environmental system (Hayden, 1998; Pielke, 1998, 20 01a,b). These interactions occur across a ll spatial and temporal scales, from turbulence generated by diurnal cycles at a landscape scale, to globalscale circulation. The establishment of particular ecosystem types is associated with a nonlinear feedback between the atmosphere and the underlying vegetation (Pielke a nd Vidale, 1995). Wang a nd E ltahir (20 0 0) and Claussen (1998) have demonstrated that vegetation patterning cannot be accurately simulated in a model unless vegetation–atmosphere feedbacks are included. In this chapter we summarize the climate system of the shortgrass steppe. This is a region of large seasonal contrasts, and of interannual and longer term variability. It is also a region that has undergone major human impacts during the past 150 years. We present both average conditions and examples of extreme events in the shortgrass steppe to illustrate the variable climate of this interesting ecosystem. Geographic factors play a large role in determining the climatic characteristics of the shortgrass steppe (Lauenroth and Burke, 1995; Lauenroth and Milchunas, 1992; Lauenroth et al., 1999). Key factors for this region include its mid-latitude position, its relatively high elevations, its interior continental location, and its proximity to the Rocky Mountains, a substantial north–south-oriented mountain barrier immediately to the west. Air masses affecting the region consist of continental polar air from the north, humid continental air masses from the east, humid subtropical air masses from the southeast and south, and Paci8 c maritime air masses from the west. The latter can be signi8 cantly modi8 ed as they cross a series of mountain ranges and interior dry regions before reaching the shortgrass steppe region. Each of these geographic and atmospheric features contributes to the climate of the region. Latitude determines day length and sun angle, and, hence, solar insolation. This, in turn, greatly affects air temperature. Upper level westerly winds increase over the mid-latitudes in the fall and winter in response to strengthening north–south temperature gradients in the atmosphere. Paci8 c air masses are carried eastward over the Rocky Mountains, depositing considerable cool-season precipitation in the mountains, but rarely on the shortgrass steppe.
Trophic Structure and Nutrient Dynamics of the Belowground Food Web within the Rhizosphere of the Shortgrass Steppe
Authors: Moore John C. , Sipes Jill
Published: November 2020

Belowground organisms are key components of the trophic structure and they mediate the dynamics of nutrients of all terrestrial ecosystems. The interactions among assemblages of belowground microorganisms and their consumers mediate the cycling of plant-limiting nutrients, influence aboveground plant productivity, affect the course of plant community development, and affect the dynamic stability of aboveground communities following natural and anthropogenic disturbances (Clarholm, 1985; Ingham et al., 1985; Laakso and Setälä, 1999; Naeem et al., 1994; Tilman et al., 1996; Wall and Moore, 1999). The influence of belowground organisms on the aboveground plant community is heightened in systems such as the shortgrass steppe (Blair et al., 2000), given the relatively high percentage of plant production that is diverted belowground through plant roots. Many of the human-induced changes that the shortgrass steppe has been subjected to during the past 150 years fall outside the scope of the natural variations in climate and grazing. This conflict between the natural history of the shortgrass steppe and the more recent human legacy forms the backdrop of this chapter. First we present a detailed description of the belowground food web for the native shortgrass steppe and present its structure in terms of the patterns of trophic interactions, the distribution of biomass, the flow of energy, and the strengths of interactions. Second, we explore how three disturbances—managed grazing, agricultural practices, and climate change (altered precipitation and temperature, and elevated CO2)—have altered the structure of the belowground community. We conclude with a synthesis of the common patterns that we observed in the grassland’s response to these disturbances, and speculate on their consequences. Aboveground plant parts provide from 20% to 40% cover with exposed soil between them (Lauenroth and Milchunas, 1991). Much of the aboveground production remains in place as standing dead, rather than falling to the soil surface as litter. The ratio of shoot production to root production is roughly 1:1, contrasting sharply with forests, where far more production is allocated aboveground (Jackson et al., 1996; Milchunas and Lauenroth, 1993, 2000). Hence, in the shortgrass steppe, plant roots provide the major input of carbon to soil. As such, plant roots are the focal point of biological activity in soils (Coleman et al., 1983).
Industrial Revolution
Authors: Lienhard John H.
Published: November 2020

The Industrial Revolution is an easily misunderstood event. In many people’s minds the phrase suggests mass production, assembly lines, and the heavy industry of the late nineteenth century, but these things all came much later. When Arnold Toynbee coined the term Industrial Revolution, he applied it to the technology-driven change of British life as it occurred from 1760 to 1840, opening a very large umbrella. Yet even that umbrella still did not cover the first mass production and assembly lines, nor did it encompass our images of modern heavy industry. Toynbee’s dating of the Industrial Revolution starts when its causes were just taking form, and ends when England had become a mature industrial power. He took in the whole saga of the revolution, but within that saga we can identify the Revolution as a much more specific moment in British history. It is the point at which technology suddenly joined hands with radical social and economic changes. In the 1780s Watt’s advanced steam engines, Hargreaves’ spinning jenny, Cort’s improvement of wrought-iron production, and Wilkinson’s cylinder-boring mill all came into being. At the same time, economic theoreticians David Hume and Adam Smith were setting forth a new economic and social system. This convergence of inventions was part and parcel of the other great revolutions of the late eighteenth century—the American Revolution, the French Revolution, and a spate of lesser European revolutions. We have to understand it in the context of those political and social upheavals. In England, social revolution grew out of eighteenth-century Protestant reform. The Wesleyan movement and the various dissident Protestant groups counted the makers of the Industrial Revolution among their members. The mid-eighteenth century was marked by worldwide discontent with authoritarianism and with the tyranny of the mercantile economic system. The French kings loved elaborate clocks and clockwork toys—devices that were completely preprogrammed. By the late seventeenth century, they had joined with the other western European nations in a clockwork economic system as well. The mercantile economic equation specified trade balances, such that raw material flowed in, manufactured goods flowed out, and gold flowed in.
Heroic Materialism
Authors: Lienhard John H.
Published: November 2020

When Kenneth Clark wrote the concluding section of his magnificent thirteen-part television series, Civilisation, he gave it the title “Heroic Materialism.” The series had been based on Clark’s definition of civilization. Each part displayed an epoch of Western history marked by particular creative energy. Clark finished by showing how, in the early days of the nineteenth century, engineers began building a new world of cast iron—a man-made material world of heroic proportions. Clark’s title was a wry and masterful bit of misdirection, just as the great works he described have also misdirected our attention. If he had used the term heroic materialism to describe medieval cathedrals instead of the great engineering works of the nineteenth and twentieth centuries, we might have balked. When we look at a Gothic cathedral we see not cold, material stone, but a flight of the human spirit. We see mind rising over matter. However, our first reaction is to accept Clark’s seeming claim that nineteenth-century iron was merely materialistic. It is a characterization that makes sense to us. But once he has shown the stereotypical view of things, Clark begins steering us in unexpected directions. He shows how all that heroic iron triggered a new spirit of social reform. By the end of the nineteenth century, Victorian iron had even played a role in bringing art back from heroic themes to the humanizing influence of the Impressionist artists. That should be no surprise since, as we have already stressed, machines relate directly and intimately to essential human needs, and they have always been an equalizing force in society. By the 1930s the Swiss-French architect Le Corbusier was proclaiming machinery and craftsmanship to be the one truth in a world full of lies: “Machines are truly humane, but we do not know machines.” He cried, “The world lacks harmonisers to make palpable the humane beauty of modern times.” In this chapter we look at the machine as a heroic figure, and what we see is a progression from megalomania to humanization—from the obsessiveness of Napoleon Bonaparte to the world-unifying effort to go into space.
Organizing Information : The Language Of Science
Authors: Headrick Daniel R.
Published: November 2020

In June 1735, The Twenty-Eight-Year-Old Carl Von Linné, Known To US as Linnaeus (1707–1778), arrived in the Netherlands to obtain a doctorate. He headed for Harderwijk, a little university town known for its instant degrees. After a few formalities, he presented his thesis, which he had brought with him from Sweden. Six days after arriving, he was awarded a doctor of medicine degree. Though Linnaeus was undoubtedly eager to get his degree, the real purpose of his trip was to meet other botanists. Before arriving, he had already lectured at the University of Uppsala in Sweden and had traveled to Lapland—then as remote and exotic as Siberia or North America—to seek plants unknown to botanists. He chose Holland because it was the home of the great naturalist Hermann Boerhaave(1668-1738), superintendent of the botanical garden at Leiden. With colonies in Brazil, the Caribbean, South Africa, and the East Indies, Holland was the European center for botanical studies. Linnaeus did not arrive empty-handed; he carried a short manuscript entitled Systema naturae (The system of nature), containing his ideas on the reformation of botany. Boerhaave was so impressed that he urged Linnaeus to join an expedition to southern Africa and the Americas, promising him a professorship at Leiden on his return. Linnaeus declined the offer but accepted another that was even better. George Clifford, a wealthy merchant, had filled his estate with the most extensive collection of plants in Holland and even a zoo. He invited young Linnaeus to become his personal physician and superintendent of his garden, with a large salary, a huge budget, and luxurious living accommodations. In the three years he spent in Holland, Linnaeus not only reorganized Clifford’s garden but also published fourteen works in quick succession. The first were Fundamenta botanica and Bibliotheca botanica, dealing with the history of botany up to that time. Systema naturae, also published in 1735, divided nature into three kingdoms—animal, vegetable, and mineral—and presented a method of classifying the plant kingdom by class, order, genus, and species.
Self-Organized Construction in Sparse Random Arrays of Conway’s Game of Life
Authors: Gotts Nicholas M.
Published: November 2020

The construction problems and techniques described in this chapter arose out of a single problem: . . . What happens in very low density infinite random arrays of Conway’s Game of Life? . . . However, the work reported has wider implications, briefly discussed in the final section. Conway’s Game of Life (henceforth GoL) is a deterministic cellular automaton (CA), which is binary (a cell has two possible states: 0 and 1) and runs on an infinite two-dimensional grid of cells. A deterministic CA cell’s state at time step t is determined, according to a transition rule, by those of a set of in-neighbors at step t — 1, and its own state at step t — 1 can affect the state of i out-neighbors at t. In GoL, in-neighbors and out-neighbors coincide, and include the cell itself. The neighborhood is a 3 x 3 square of cells. GoL’s transition rule specifies that a cell is in state 1 at step t if and only if either of the following held at t - 1. 1. The cell and either two or three other cells in its neighborhood were in state 1. 2. The cell was in state 0, and exactly three other cells in its neighborhood were in state 1. By a random array, I mean one in which the initial probability p of each cell being in state 1 is the same for all cells, and the initial state is determined independently for each cell. Of course, we cannot actually construct such an array, but we can reason about it. Toward the end of the chapter, large finite random arrays will be considered, but it is simpler to start with the infinite case. In fact, none of the reasoning used in the infinite case depends upon the distribution of state 1 cells being strictly random, provided the frequency of all finite arrangements of cell-states is as expected in a random array with the same density of state 1 cells. A sparse random array is one in which p is very low (a more precise definition is given below).
Seasonal Climate Predictions: Farmers, Planners, and Policymakers
Authors: Rosenzweig Cynthia , Hillel Daniel
Published: November 2020

Researchers from the physical, biological, and social sciences, in communication with decision-makers, are working to improve and apply seasonal climate forecasts relevant to risk management in climate-sensitive systems. Noteworthy is the mission of the International Research Institute for Climate and Society (IRI), which focuses on integrating the roles of science and society to forecast climate phenomena in general, and the El Niño–Southern Oscillation (ENSO) in particular. The National Oceanic and Atmospheric Administration created IRI in 1996 at Columbia University in New York to engage in climate research and modeling on the seasonalto- interannual time scale and to provide the results of the research to people affected by climate in various regions of the world. Agrawala et al. (2001) characterize the IRI as a “boundary” institution, straddling two major divides: one between fundamental research and societal applications, and the other between developed and developing countries. The motivations for its creation included fostering a multidisciplinary approach to applications, building on current programs and policies, and redressing inequity in large-scale climate research. Farmers and other agricultural decision-makers are a major group of potential users of seasonal climate forecasts. Water-resource managers are another such group. Interdisciplinary efforts have deepened the realization that improved climate information systems are embedded in social, economic, and political contexts and that understanding these contexts is required in order to improve the use of forecasts. A key aspect of the context of climate forecasting is the interrelationship of climate, climate forecasts, and risk. A growing body of research pertains to how agricultural decision-makers relate to risk and how responding to climate forecasts may help them manage it. This research is in the process of being consolidated into a framework by which forecasts can be made, disseminated, and utilized effectively by a range of decision-makers. Questions relevant to the use of climate predictions include: How can agricultural practitioners at different levels of social organization use climate forecasts to improve their planning and management decisions? How are climate risks perceived and acted on? What are the potential economic benefits? What policies can facilitate the use of climate-forecast information?
Rhodes
Authors: Fant Clyde E. , Reddish Mitchell G.
Published: November 2020

Nearly two million visitors a year come to the historic island of Rhodes to enjoy its sun, beaches, and famous medieval city. Rhodes is the largest island of the Dodecanese, or Twelve Islands, although there are actually two hundred small islands that compose the group. Historically it was the home of the world-renowned Colossus of Rhodes, one of the seven wonders of the ancient world. It is also mentioned in the Bible as one of the ports visited by the boat carrying the Apostle Paul to Jerusalem on his return from his third, and last, missionary journey. The island of Rhodes lies much closer to Turkey than to Greece, but it can be easily reached by frequent flights from Athens or by ferry from Piraeus (14 hours), the port of Athens; from Kusadasi through Samos (6 hours); or from Bodrum, Marmaris, or Fethiye (between 1½ and 2 hours). Flights are also available from Thessaloniki and Crete, and in summer from Santorini and Mykonos as well. Because of its favorable location close to the shoreline of Asia Minor and between Greece and Israel, Rhodes was favored for development in antiquity. Both its eastern and western ports were frequented by traders and merchants, and numerous ancient writers mention it as a place of both economic and cultural achievement. In the 4th century B.C.E. Rhodes even surpassed Athens as a center for trade and commerce. The island also became renowned for its school of rhetoric, founded in 324 B.C.E., at which such distinguished Romans as Cicero, Julius Caesar, Pompey, and Tiberius studied. Famous citizens of Rhodes included the poet Apollonios and the sculptors Pythocretes (who created the famed Nike of Samothrace, which was dedicated by the citizens of Rhodes to commemorate their victory over Antiochus III in 190 B.C.E.) and Chares of Lindos (sculptor of the Colossus of Rhodes). The world-famous Laocoön, a sculpture that depicts the priest of Apollo and his children in the grip of two great snakes, was produced by three sculptors from Rhodes, Agesander, Athinodoros, and Polydoros.
Assos
Authors: Fant Clyde E. , Reddish Mitchell G.
Published: November 2020

With its acropolis perched on a steep hill overlooking the Aegean Sea, the city of Assos provides a spectacular view for the visitor. From the acropolis one can look down on the ruins of the ancient buildings on the slope and also see the remains of the city’s harbor in the Aegean. On a clear day the island of Lesbos is visible approximately 7 miles south across the Bay of Edremit. From this island came the first settlers of ancient Assos. The site of ancient Assos is located in the southern part of the Troad area of Turkey, on the modern highway that runs along the Aegean coast and connects the towns of Geyikli and Ayvacïk. Assos was in the ancient region of Mysia. Today the village of Behramkale occupies the site of ancient Assos. During the 7th century B.C.E. Aeolian Greeks from the town of Methymna (modern Molivos) on the island of Lesbos crossed the Edremit Bay and founded the city of Assos. During the first half of the 6th century, King Croesus of Lydia (whose capital was at Sardis) captured and controlled Assos. Lydian domination ended in 546 B.C.E., when Cyrus of Persia defeated Croesus and brought this area of Asia Minor under Persian control. During the following century Assos gained its freedom when a coalition of Greek city-states defeated the Persians. Assos then became a part of the Delian League under the leadership of Athens. One of the rulers of Assos in the 4th century was Hermias, who had been a student, along with Aristotle, of the philosopher Plato. At the invitation of Hermias, Aristotle went to Assos and lived there from 348 to 345 B.C.E., marrying Hermias’ niece. The Persians recaptured the city and killed Hermias, but their control ended with the conquests of Alexander the Great. After Alexander’s untimely death the Seleucids ruled Assos. In 241 B.C.E. the city became a part of the Pergamene kingdom and remained under the control of the kings of Pergamum until the death of Attalus III in 133 B.C.E., at which time the city passed into Roman hands.
Attalia
Authors: Fant Clyde E. , Reddish Mitchell G.
Published: November 2020

Antalya, the modern name for ancient Attalia, is a delightful city perched on the Mediterranean coast of southern Turkey. The eleventh largest city in modern Turkey, Antalya is a thriving tourist center. Although many visitors to the city use it as a base for visiting beaches along Turkey’s Mediterranean coast or archaeological sites in nearby locations, Antalya has plenty of charm and interest of its own. Attalia was a city in the region known as Pamphylia, an area bounded by the Taurus Mountains on the north and the Mediterranean Sea on the south. Situated on what is now called the Gulf of Antalya, the city served as the major port in Pamphylia during Hellenistic, Roman, and Byzantine times. Attalus II, who was king of Pergamum from 159 to 138 B.C.E., founded the city and named it after himself. When Attalus III (r. 138–133 B.C.E.) bequeathed the Pergamum kingdom to Rome in his will, Attalia was one of the areas excluded and thus became a free city for a while. In 77 B.C.E. Attalia was annexed by the Romans. During the 2nd century C.E. Emperor Hadrian conferred the status of colony on the city and visited Attalia in 130 C.E. The Hadrian Gate was built to commemorate this visit. Dedicated to the emperor, the triple-arched gate was made of marble and contained a dedicatory inscription in bronze letters. During the Byzantine era the city was known as Adalia and continued to serve as an important port city. Used by the Crusaders as a harbor on their way to the Holy Land, the city was conquered by the Seljuk Turks in 1207. The Seljuks left their mark upon the city by means of several buildings, some of which still decorate the city’s landscape. Around the end of the 14th century, the Ottomans gained control of the city. During this period Antalya continued to flourish and serve as an important harbor on the Mediterranean. When the Allies dismantled the Ottoman Empire at the end of World War I, Antalya was given to Italy, only to be retaken by the Turkish army in 1921.
Miletus
Authors: Fant Clyde E. , Reddish Mitchell G.
Published: November 2020

Originally famed for its philosophers of nature, Miletus became one of the great cities of commerce of the ancient world. Its four harbors and strategic location on the west coast of Asia Minor gave the city unique advantages as a vital port in both peace and war. Yet these factors also were the cause of repeated periods of invasion and destruction. Eventually Miletus ceased to be a major player in world affairs, not because of the fortunes of war, but because of the slower but deadlier effects of the gentle Meander River, which silted its harbors and created malaria-ridden marshes. Miletus is easily reached from Izmir by taking E87 south to Selçuk, then proceeding on highway 525 through Söke to Akköy, then north through Balat to the site of Miletus. Today it is difficult to imagine that Miletus once was situated on a narrow peninsula and boasted of four harbors, three on the west and one on the east. Due to the continual silting effects of the Meander River, the ruins of Miletus now are situated in a broad plain some 5 miles from the sea. The island of Lade, where the Persian armada burned and destroyed the Ionian fleet in 494 B.C.E., was once to the west of the coast of Miletus. Now it is merely a hill 4 miles west of Miletus. A Mycenaean colony that had cultural contacts with Crete and Greece existed in this location from 1400 B.C.E. Greeks settled in the area by at least the 10th century B.C.E. The city prospered and grew wealthy from its colonies on the Mediterranean, the Black Sea, and even in Egypt. It was one of the first cities in the ancient world to mint coins. Soon Miletus became the most important of the twelve cities of the region of Ionia. The city came under Persian control in 546 B.C.E. and later opposed them in the Battle of Lade, but the result was the loss of their fleet and the complete destruction of their city in 494 B.C.E. Herodotus, in fact, said that Miletus was reduced to slavery. Subsequently, Ephesus surpassed Miletus as the first city of the region.
The Paradox Introduced: Concepts and Cases
Authors: van Eeten Michel J. G. , Roe Emery
Published: November 2020

To recapitulate, the hard paradox is this: how do you improve ecological functions and related human services at the same time, if not everywhere then at least over the ecosystem and landscape as a whole? How do decision makers meet the twofold recoupling goal: (1) where they are managing for reliable ecosystem services, they would also be improving the associated ecosystem functions, and/or (2) where they are managing for improved ecosystem functions, they would also be better ensuring the reliability of the ecosystem services associated with those functions. In short, how do decision makers recouple ecosystem functions and services that over time have been decoupled to their detriment? A set of terms have just been introduced that require explanation. The terms “recoupling,” “decoupling,” and, by implication, “coupling” are central to the arguments of our book and are formalized more fully in later chapters. (The controversial terms, “functions” and “services,” are discussed in the next section.) Basically, the literature uses the former terms to refer to biophysical connections, organizational connections, or both. An example of the first is Ausubel (1996, pp. 1, 7, 8), who notes that agricultural modernization has meant “food decoupled from acreage” through the production of more crops on less land. Advances in science and technology “increasingly decouple our goods and services from the demands on planetary resources.” Ausubel adds that we can expect “further decoupling [of] food from land. For more green occupations, today’s farmers might become tomorrow’s park rangers and ecosystem guardians. In any case, the rising yields, spatial contraction of agriculture, and sparing of land are a powerful antidote to the current losses of biodiversity and related environmental ills.” Opschoor (1995) speaks of a similar technological phenomenon, “delinking,” where rising incomes are decoupled over time from intensive material use. Also, the third Dutch national environmental policy plan seeks as one of its goals the decoupling of economic growth from environmental pollution (Ministry of Housing, Spatial Planning and Environment 1998). These uses of “decoupling” all refer to the relation between services and environmental degradation. We, on the other hand, are talking about the relation between services and environmental assets, that is, ecosystem functions.
Species Diversity, Environmental Heterogeneity, and Species Interactions
Authors: Mitchell William A. , Kotler Burt P.
Published: November 2020

Despite their apparent simplicity, arid environments can be quite heterogeneous. From small-scale variation in substrate and slope to large-scale geographic variation in solar input and productivity, drylands and deserts provide organisms with a tremendous range of ecological challenges (Schmidt-Nielsen 1964, Huggett 1995). Any single species is unable to meet all of these challenges equally well. A species will do better in some environments than others because evolution in heterogeneous environments is constrained by fitness tradeoffs. Such tradeoffs prevent the evolution of a versatile species, competitively superior to all other species across the entire spectrum of heterogeneity (Rosenzweig 1987). Although fitness tradeoffs may hinder species’ evolution in heterogeneous environments, they are a blessing for biodiversity. The source of biodiversity that we address in this chapter is the interplay of heterogeneity, tradeoffs, and density dependence. While we focus on species interactions at the local scale, our presentation includes a model that predicts changes in local diversity as a function of climate. The model’s predictions are based on changes in the nature of competition wrought by changes in productivity levels and climatic regimes. Cast in terms of evolutionary stable strategies (ESSs), the predictions refer to evolutionary as well as ecological patterns. A mechanism of coexistence consists of an axis of environmental heterogeneity together with an axis that indicates a tradeoff in the abilities of species to exploit different parts of the axis. In the absence of some kind of heterogeneity, there is only one environmental type, and whatever species is best adapted to it will competitively exclude others. In the absence of a tradeoff, one species could evolve competitive superiority over the full range of heterogeneity, again resulting in a monomorphic community. Consider some examples of mechanisms of species’ coexistence from dryland communities (Kotler and Brown 1988, Brown et al. 1994). For many taxa, spatial heterogeneity in predation risk is a consequence of the pattern of bushy and open areas common in drylands. In certain rodent communities, some species are able to exploit the relatively riskier open microhabitats by virtue of antipredator morphologies (Kotler 1984).
Unified Framework I: Interspecific Interactions and Species Diversity in Drylands
Authors: Polis Gary A. , Holt Robert D.
Published: November 2020

The goal of this chapter is to delineate how abiotic conditions, regional processes, and species interactions influence species diversity at local scales in drylands. There is a very rich literature that bears on this topic, but here we focus on mechanisms that promote or constrain local diversity and ask how these factors apply to deserts. We ask, “What is different about deserts, relative to other habitats, in their patterns of diversity, temporal variability in productivity, and spatial heterogeneity?” We assess how such differences might modify extant theory, and sketch relevant examples. Compared with other biomes, productivity, population densities, and community biomass are much lower in deserts, and temporal heterogeneity is typically higher. Do these differences imply distinct ecological processes and patterns in deserts? Or, do processes operate in deserts in similar ways as in tropical forests or grasslands? For example, it is often assumed that abiotic factors are more important in deserts. If so, how do abiotic factors modify biotic interactions? How do we integrate physical and biotic interactions? More generally, we ask what should be the main goals and approaches of a research program to understand the role of species interactions in determining community structure in drylands, as modified by abiotic factors and regional processes. . . . What Is Different About Drylands? . . . Deserts are traditionally perceived as relatively simple ecosystems harboring low species diversity. Yet increasing evidence suggests that desert communities can be highly diverse and complex. To our knowledge the only systematic analysis of the relative diversity in desert versus nondesert communities was compiled by Polis (1991a). These data suggest that patterns differ widely among taxonomic groups. In some cases, deserts support high diversity, comparable to or even higher than nonarid areas (see Polis 1991b). For example, while avian (Wiens 1991) and anuran (Woodward and Mitchell 1991) diversities are low compared with other biomes, desert annual plants show extremely high species diversity (Inouye 1991). Ants, succulent plants, lizards, scorpions, and tenebrionid beetles also have relatively high diversity in deserts (Polis 1991a–c, Wiens 1991). But, while very high diversity may occur, local diversity varies greatly in space and time (e.g., ants and annual plants: Danin 1977, Inouye 1991, MacKay 1991).
Unified Framework III: Human Interactions with Biodiversity
Authors: Sher Anna A. , Kahn Bruce M.
Published: November 2020

Considering humans as components of ecosystems is not new; geographers have been doing it in human ecology departments for decades (see Field and Burch 1988). There have also been many volumes dedicated to the subject (McDonnel and Pickett 1993, Schnaiberg and Gould 1994, Catton 1982, Wilson 1988). Recently there has been a development in the field of ecology to consider humans as a part of ecosystems, rather than simply agents of destruction, including the full complexity of human interactions (including social, cultural, and economic) with the environment (chapter 17 this volume, Folke et al. 1996, Turner and Carpenter 1999, Pickett et al. 1999, Haeuber and Ringold 1998). The goal of this chapter is to provide a framework for the types of interactions between humans and biodiversity. We use biodiversity as an umbrella term encompassing genetic, species, and landscape diversities (chapter 1 this volume). In particular, we emphasize human–biodiversity interactions in the context of arid and semiarid ecosystems. In part I, we analyze the various types of human–biodiversity interactions. In part II we suggest a framework for the study of these interactions. Not only do humans have the power to affect biodiversity, but biodiversity impacts humans as well. The nature of this reciprocal relationship can be positive and/or negative. Our reference to positive and negative impacts on biodiversity will usually be in the mathematical sense, that is, an increase or decrease in species and landscape diversity. However, we must be careful not to put a value judgment on such numbers. Increases in species or habitat diversity are not necessarily desirable for all ecosystems or management goals. All the elements of biodiversity are not equal in terms of ecological and economical value. For example, restoration efforts for a few endemic species may be detrimental to other, nonendemic species, resulting in less species diversity. This may especially be true when the diversity of weedy species that have taken over a disturbed area is threatened by restoring historical conditions. In this case, a lower level of biodiversity that includes native species may be more desirable than a higher level of nonendemic species diversity.
Spin–Orbit Configuration Interaction Methods
Authors: Dyall Kenneth G. , Faegri Knut
Published: November 2020

In this chapter, we address the following problem: Assume that we have derived a method that gives a good approximation to the treatment of relativistic effects in molecular systems, but that leaves out any explicit references to the spin—that is, a spin-free relativistic approximation. How can we go about getting a reliable estimate of the spin–orbit interaction in the system?There may be several reasons why we would want to take such an approach. One of them is that nonrelativistic calculations are much simpler to handle with respect to symmetry and logistics, and so if we could do the heavy computational work within the least demanding framework, it might entail considerable savings. Another reason might be that we are interested in light systems where the spin-free relativistic effects are small, but where the symmetry breaking induced by spin–orbit interaction may be of crucial importance for near-degenerate states and surface crossings. To solve this problem, we have to answer two questions, “What?” and “How?” The first one is concerned with finding an operator Ĥso that describes the spin–orbit interaction that has been left out of our zeroth-order Hamiltonian, Ĥ0, making the total Hamiltonian . . . Ĥ = Ĥ0 + Ĥso The form of this operator may be a matter of choice, but we would preferably like to restore the terms dropped in developing Ĥ0 from the fully relativistic Hamiltonian. This in turn leads to different Ĥso operators depending on the approach taken towards Ĥ0. The other question is concerned with how to treat these Ĥso operators in a computationally efficient manner in a configuration interaction calculation. Although the operators may differ, the problems inherent in their application are common for many of the choices of zeroth-order method. A further challenge is that spin–orbit energies are of comparable magnitude to the correlation energies for heavy elements. Thus, the two should be treated on a reasonably equal footing.
Population Cycles Inferences from Experimental, Modeling, and Time Series Approaches
Authors: Lambin Xavier , Krebs Charles J.
Published: November 2020

Some of the most interesting debates in population ecology have taken place within the context of population cycles. Their study has been a fertile ground for the development of ideas on how population models should be formulated and confronted with data. It is the setting in which the use of field experiments became established in ecology (e.g., Krebs and DeLong 1965), and also the context of many methodological and conceptual developments in the fields of population demography (Leslie and Ranson 1940), pest management (Berryman 1982), and community dynamics (Sinclair et al. 2000). Yet, as with many other issues in population dynamics, identifying without ambiguity the causes of population cycles in general, and for any organism in particular, continues to prove an extraordinarily difficult task. The major purpose of this book is to review recent research developments on the role of food web architecture, and more specifically on the effects of food, predators, and pathogens in population cycles. Its stated aim is to present evidence that population cycles could be caused by food web architecture in some natural systems. Whereas in chapter 1 Alan Berryman promotes a research program centered on the analysis of time series data for formulating, selecting, and even testing hypotheses on population cycles, the case studies encompass a much broader diversity of research approaches. The authors and coworkers of the seven case studies have combined time series analysis, model building, natural history observation, and experiments in different proportions to reach the conclusion that trophic interactions play an important role in generating cyclic dynamics. This diversity of approaches reflects, in part, a taxonomic divide between vertebrates and invertebrates, experiments being more common with the former, but also profound differences in research traditions. Indeed, the investment required to estimate population size and quantify the causes of mortality of moths and beetles is substantially less than that required for estimating the abundance of voles, hares, and grouse and their predators. From these practical constraints, divergent research traditions have evolved.