Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

The influence of diet on the distribution of nitrogen isotopes in animals was investigated by analyzing animals grown in the laboratory on diets of constant nitrogen isotopic composition. 
The isotopic composition of the nitrogen in an animal reflects the nitrogen isotopic composition of its diet. The δ^(15)N values of the whole bodies of animals are usually more positive than those of their diets. Different individuals of a species raised on the same diet can have significantly different δ^(15)N values. The variability of the relationship between the δ^(15)N values of animals and their diets is greater for different species raised on the same diet than for the same species raised on different diets. Different tissues of mice are also enriched in ^(15)N relative to the diet, with the difference between the δ^(15)N values of a tissue and the diet depending on both the kind of tissue and the diet involved. The δ^(15)N values of collagen and chitin, biochemical components that are often preserved in fossil animal remains, are also related to the δ^(15)N value of the diet. 
The dependence of the δ^(15)N values of whole animals and their tissues and biochemical components on the δ^(15)N value of diet indicates that the isotopic composition of animal nitrogen can be used to obtain information about an animal's diet if its potential food sources had different δ^(15)N values. The nitrogen isotopic method of dietary analysis probably can be used to estimate the relative use of legumes vs non-legumes or of aquatic vs terrestrial organisms as food sources for extant and fossil animals. However, the method probably will not be applicable in those modern ecosystems in which the use of chemical fertilizers has influenced the distribution of nitrogen isotopes in food sources. 
The isotopic method of dietary analysis was used to reconstruct changes in the diet of the human population that occupied the Tehuacan Valley of Mexico over a 7000 yr span. Variations in the δ^(15)C and δ^(15)N values of bone collagen suggest that C_4 and/or CAM plants (presumably mostly corn) and legumes (presumably mostly beans) were introduced into the diet much earlier than suggested by conventional archaeological analysis.

Influence of Diet On the Distribtion of Nitrogen Isotopes in Animals

A DNA test to sex most birds.

Movement of individual organisms is fundamental to life, quilting our planet in a rich tapestry of phenomena with diverse implications for ecosystems and humans. Movement research is both plentiful and insightful, and recent methodological advances facilitate obtaining a detailed view of individual movement. Yet, we lack a general unifying paradigm, derived from first principles, which can place movement studies within a common context and advance the development of a mature scientific discipline. This introductory article to the Movement Ecology Special Feature proposes a paradigm that integrates conceptual, theoretical, methodological, and empirical frameworks for studying movement of all organisms, from microbes to trees to elephants. We introduce a conceptual framework depicting the interplay among four basic mechanistic components of organismal movement: the internal state (why move?), motion (how to move?), and navigation (when and where to move?) capacities of the individual and the external factors affecting movement. We demonstrate how the proposed framework aids the study of various taxa and movement types; promotes the formulation of hypotheses about movement; and complements existing biomechanical, cognitive, random, and optimality paradigms of movement. The proposed framework integrates eclectic research on movement into a structured paradigm and aims at providing a basis for hypothesis generation and a vehicle facilitating the understanding of the causes, mechanisms, and spatiotemporal patterns of movement and their role in various ecological and evolutionary processes. "Now we must consider in general the common reason for moving with any movement whatever." (Aristotle, De Motu Animalium, 4th century B.C.).

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A movement ecology paradigm for unifying organismal movement research

The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Long distance migration has evolved in many organisms moving through different media and using various modes of locomotion and transport. Migration continues to evolve or become suppressed as shown by ongoing dynamic and rapid changes of migration patterns. This great evolutionary flexibility may seem surprising for such a complex attribute as migration. Even if migration in most cases has evolved basically as a strategy to maximise fitness in a seasonal environment, its occurrence and extent depend on a multitude of factors. We give a brief overview of different factors (e.g. physical, geographical, historical, ecological) likely to facilitate and/or constrain the evolution of long distance migration and discuss how they are likely to affect migration. The basic driving forces for migration are ecological and biogeographic factors like seasonality, spatiotemporal distributions of resources, habitats, predation and competition. The benefit of increased resource availability will be balanced by costs associated with the migratory process in terms of time (incl. losses of prior occupancy advantages), energy and mortality (incl. increased exposure to parasites). Furthermore, migration requires genetic instructions (allowing substantial room for learning in some of the traits) about timing, duration and distance of migration as well as about behavioural and physiological adaptations (fuelling, organ flexibility, locomotion, use of environmental transport etc) and control of orientation and navigation. To what degree these costs and requirements put constraints on migration often depends on body size according to different scaling relationships. From this expos it is clear that research on migration warrants a multitude of techniques and approaches for a complete as possible understanding of a very complex evolutionary syndrome. In addition, we also present examples of migratory distances in a variety of taxons. In recent years new techniques, especially satellite radio telemetry, provide new information of unprecedented accuracy about journeys of individual animals, allowing re-evaluation of migration, locomotion and navigation theories. (Less)

/pdf/long-distance-migration-evolution-and-determinants-2eco5tfv6q.pdf

Long-distance migration: evolution and determinants

The implications of location accuracy for the interpretation of satellite-tracking data

Optimal migration theory predicts that birds minimizing the overall time of migration should adjust stopover duration with respect to the rate of fuel accumulation. Recent theoretical developments also take into account the wind situation and predict that there is a time window (a set of days) during which birds should depart when assisted by winds but will not do so if there are head winds. There is also a final day when birds will depart irrespective of wind conditions. Hence, the wind model of optimal migration theory predicts that birds should be sensitive to winds and that there should be a correlation between departures and winds blowing towards the intended migration direction. We tested this assumption by tracking the departures of radio-tagged passerines during autumn migration in southern Sweden. Our birds were moderately to very fat when released and therefore energetically ready for departure. There was a significant correlation between direction of departure and wind direction. We also found that during days when birds departed there was a significantly larger tail wind component than during days when birds were present but did not depart. Our results show that passerines do take the current wind situation into account when departing on migratory flights. We also briefly discuss possible clues that birds use when estimating wind direction and strength. The inclusion of wind is an important amendment to optimal migration theory of birds and should be explored further.

Wind selectivity of migratory flight departures in birds

Across a range of organisms, related species or even populations of the same species exhibit strikingly different scales and patterns of movement. A significant proportion of the phenotypic variance in migratory traits is genetic, but the genes involved in shaping these phenotypes are still unknown. Although recent achievements in genomics will evolve migratory genetics research from a phenotypic to a molecular approach, fully sequenced and annotated genomes of migratory species are still lacking. Consequently, many of the genes involved in migration are unavailable as candidates. Migration is central to the life-history adaptations of many animals. Here, we review current understanding of the genetic architecture of migratory traits and discuss the significant implications this will have for other areas of biology, including population responses to climate change, speciation and conservation management.

/pdf/the-genetics-of-migration-on-the-move-384srkr84n.pdf

The genetics of migration on the move.

Migratory animals show a suite of adaptations to cope with their journeys. These include not only morphological features for efficient locomotion and storage of energy but also behavioral adjustments to exploit winds and currents or to avoid drift caused by moving fluids. Migration strategies across locomotory modes can be analyzed in the context of optimality models, using some general principles concerning migration range and selection criteria. Comparisons of model predictions with natural behavior help researchers understand the selection pressures that underlie migration strategies. We give examples of typical migration speeds and distances for animals using different locomotion models. Successful migration also requires accurate orientation and/or navigation between distant areas for reproduction and survival. Animals can use a suite of different compasses, which may be cross-calibrated or integrated for direction finding, depending on the geographical and ecological situation, and may be used with an endogenous clock for time compensation.

https://www.jstor.org/stable/10.1641/B570207

Susanne Åkesson

Papers

Long-distance migration: evolution and determinants

The implications of location accuracy for the interpretation of satellite-tracking data

Wind selectivity of migratory flight departures in birds

The genetics of migration on the move.

How Migrants Get There: Migratory Performance and Orientation