Other affiliations: Norwegian University of Science and Technology, University of Chicago, University of Wisconsin-Madison ...read more
Bio: Russell Lande is an academic researcher from Imperial College London. The author has contributed to research in topics: Population & Genetic model. The author has an hindex of 86, co-authored 179 publications receiving 49795 citations. Previous affiliations of Russell Lande include Norwegian University of Science and Technology & University of Chicago.
Papers published on a yearly basis
TL;DR: Measures of directional and stabilizing selection on each of a set of phenotypically correlated characters are derived, retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection.
Abstract: Natural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution. In contrast, evolutionary response to selection, the genetic change that occurs from one generation to the next, does depend on genetic variation. Animal and plant breeders routinely distinguish phenotypic selection from evolutionary response to selection (Mayo, 1980; Falconer, 1981). Upon making this critical distinction, emphasized by Haldane (1954), precise methods can be formulated for the measurement of phenotypic natural selection. Correlations between characters seriously complicate the measurement of phenotypic selection, because selection on a particular trait produces not only a direct effect on the distribution of that trait in a population, but also produces indirect effects on the distribution of correlated characters. The problem of character correlations has been largely ignored in current methods for measuring natural selection on quantitative traits. Selection has usually been treated as if it acted only on single characters (e.g., Haldane, 1954; Van Valen, 1965a; O'Donald, 1968, 1970; reviewed by Johnson, 1976 Ch. 7). This is obviously a tremendous oversimplification, since natural selection acts on many characters simultaneously and phenotypic correlations between traits are ubiquitous. In an important but neglected paper, Pearson (1903) showed that multivariate statistics could be used to disentangle the direct and indirect effects of selection to determine which traits in a correlated ensemble are the focus of direct selection. Here we extend and generalize Pearson's major results. The purpose of this paper is to derive measures of directional and stabilizing (or disruptive) selection on each of a set of phenotypically correlated characters. The analysis is retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection. Nevertheless, the measures we propose have a close connection with equations for evolutionary change. Many other commonly used measures of the intensity of selection (such as selective mortality, change in mean fitness, variance in fitness, or estimates of particular forms of fitness functions) have little predictive value in relation to evolutionary change in quantitative traits. To demonstrate the utility of our approach, we analyze selection on four morphological characters in a population of pentatomid bugs during a brief period of high mortality. We also summarize a multivariate selection analysis on nine morphological characters of house sparrows caught in a severe winter storm, using the classic data of Bumpus (1899). Direct observations and measurements of natural selection serve to clarify one of the major factors of evolution. Critiques of the "adaptationist program" (Lewontin, 1978; Gould and Lewontin, 1979) stress that adaptation and selection are often invoked without strong supporting evidence. We suggest quantitative measurements of selection as the best alternative to the fabrication of adaptive scenarios. Our optimism that measurement can replace rhetorical claims for adaptation and selection is founded in the growing success of field workers in their efforts to measure major components of fitness in natural populations (e.g., Thornhill, 1976; Howard, 1979; Downhower and Brown, 1980; Boag and Grant, 1981; Clutton-Brock et
TL;DR: Methods of multivariate analysis, functional analysis and optimality criteria popular among evolutionists, do not account for dynamical constraints imposed by the pattern of genetic variation within populations.
Abstract: Darwin (1859, pp. 11-14, 143-150) stressed the evolutionary importance of covariation between characters in populations and its "most imperfectly understood" connection with correlated responses to artificial and natural selection. After the turn of the century the discoveries of pervasive pleiotropy and linkage of Mendelian factors revealed the underlying genetic mechanisms. Existing theory on the dynamics of correlated characters has been developed in the limited framework of practical plant and animal breeding. Methods of multivariate analysis, functional analysis and optimality criteria popular among evolutionists, do not account for dynamical constraints imposed by the pattern of genetic variation within populations. Consideration of phenotypic variation often does not suggest any clear mechanism connecting growth patterns or adult variation to interspecific evolution. When there is individual variation in development, no necessary correspondence exists between ontogenetic and adult variation in a population (Cock, 1966, pp. 148-15 1). It is also common for the pattern of adult variation within a species to differ from that at higher taxonomic levels (Simpson, 1953, pp. 25-29). An example which will be investigated here is the brain weight:body weight relationship. At various taxonomic levels, brain and body weights tend to follow the allometric equation
TL;DR: The practical need in biological conservation for understanding the interaction of demographic and genetic factors in extinction may provide a focus for fundamental advances at the interface of ecology and evolution.
Abstract: Predicting the extinction of single populations or species requires ecological and evolutionary information. Primary demographic factors affecting population dynamics include social structure, life history variation caused by environmental fluctuation, dispersal in spatially heterogeneous environments, and local extinction and colonization. In small populations, inbreeding can greatly reduce the average individual fitness, and loss of genetic variability from random genetic drift can diminish future adaptability to a changing environment. Theory and empirical examples suggest that demography is usually of more immediate importance than population genetics in determining the minimum viable sizes of wild populations. The practical need in biological conservation for understanding the interaction of demographic and genetic factors in extinction may provide a focus for fundamental advances at the interface of ecology and evolution.
TL;DR: The models elucidate genetic mechanisms that can initiate or contribute to rapid speciation by sexual isolation and divergence of secondary sexual characters in polygamous species.
Abstract: The joint evolution of female mating preferences and secondary sexual characters of males is modeled for polygamous species in which males provide only genetic material to the next generation and females have many potential mates to choose among. Despite stabilizing natural selection on males, various types of mating preferences may create a runaway process in which the outcome of phenotypic evolution depends critically on the genetic variation parameters and initial conditions of a population. Even in the absence of genetic instability, rapid evolution can result from an interaction of natural and sexual selection with random genetic drift along lines of equilibria. The models elucidate genetic mechanisms that can initiate or contribute to rapid speciation by sexual isolation and divergence of secondary sexual characters.
TL;DR: These models utilize the statistical relationship which exists between genotype‐environment interaction and genetic correlation to describe evolution of the mean phenotype under soft and hard selection in coarse‐grained environments.
Abstract: Studies of spatial variation in the environment have primarily focused on how genetic variation can be maintained. Many one-locus genetic models have addressed this issue, but, for several reasons, these models are not directly applicable to quantitative (polygenic) traits. One reason is that for continuously varying characters, the evolution of the mean phenotype expressed in different environments (the norm of reaction) is also of interest. Our quantitative genetic models describe the evolution of phenotypic response to the environment, also known as phenotypic plasticity (Gause, 1947), and illustrate how the norm of reaction (Schmalhausen, 1949) can be shaped by selection. These models utilize the statistical relationship which exists between genotype-environment interaction and genetic correlation to describe evolution of the mean phenotype under soft and hard selection in coarse-grained environments. Just as genetic correlations among characters within a single environment can constrain the response to simultaneous selection, so can a genetic correlation between states of a character which are expressed in two environments. Unless the genetic correlation across environments is ± 1, polygenic variation is exhausted, or there is a cost to plasticity, panmictic populations under a bivariate fitness function will eventually attain the optimum mean phenotype for a given character in each environment. However, very high positive or negative correlations can substantially slow the rate of evolution and may produce temporary maladaptation in one environment before the optimum joint phenotype is finally attained. Evolutionary trajectories under hard and soft selection can differ: in hard selection, the environments with the highest initial mean fitness contribute most individuals to the mating pool. In both hard and soft selection, evolution toward the optimum in a rare environment is much slower than it is in a common one. A subdivided population model reveals that migration restriction can facilitate local adaptation. However, unless there is no migration or one of the special cases discussed for panmictic populations holds, no geographical variation in the norm of reaction will be maintained at equilibrium. Implications of these results for the interpretation of spatial patterns of phenotypic variation in natural populations are discussed.
28 Jul 2005
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to
TL;DR: The development of a xenograft assay that identified human brain tumour initiating cells that initiate tumours in vivo gives strong support for the CSC hypothesis as the basis for many solid tumours, and establishes a previously unidentified cellular target for more effective cancer therapies.
Abstract: The cancer stem cell (CSC) hypothesis suggests that neoplastic clones are maintained exclusively by a rare fraction of cells with stem cell properties. Although the existence of CSCs in human leukaemia is established, little evidence exists for CSCs in solid tumours, except for breast cancer. Recently, we prospectively isolated a CD133+ cell subpopulation from human brain tumours that exhibited stem cell properties in vitro. However, the true measures of CSCs are their capacity for self renewal and exact recapitulation of the original tumour. Here we report the development of a xenograft assay that identified human brain tumour initiating cells that initiate tumours in vivo. Only the CD133+ brain tumour fraction contains cells that are capable of tumour initiation in NOD-SCID (non-obese diabetic, severe combined immunodeficient) mouse brains. Injection of as few as 100 CD133+ cells produced a tumour that could be serially transplanted and was a phenocopy of the patient's original tumour, whereas injection of 10(5) CD133- cells engrafted but did not cause a tumour. Thus, the identification of brain tumour initiating cells provides insights into human brain tumour pathogenesis, giving strong support for the CSC hypothesis as the basis for many solid tumours, and establishes a previously unidentified cellular target for more effective cancer therapies.
TL;DR: In this article, the authors suggest that the term "fragmentation" should be reserved for the breaking apart of habitat, independent of habitat loss, and that fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative.
Abstract: ■ Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in different ways and, as a consequence, drawing different conclusions regarding both the magnitude and direction of its effects. Habitat fragmentation is usually defined as a landscape-scale process involving both habitat loss and the breaking apart of habitat. Results of empirical studies of habitat fragmentation are often difficult to interpret because (a) many researchers measure fragmentation at the patch scale, not the landscape scale and (b) most researchers measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss. Empirical studies to date suggest that habitat loss has large, consistently negative effects on biodiversity. Habitat fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative. Therefore, to correctly interpret the influence of habitat fragmentation on biodiversity, the effects of these two components of fragmentation must be measured independently. More studies of the independent effects of habitat loss and fragmentation per se are needed to determine the factors that lead to positive versus negative effects of fragmentation per se. I suggest that the term “fragmentation” should be reserved for the breaking apart of habitat, independent of habitat loss.