Aspects Of Population Biology Research Articles

Species distribution models rarely predict the biology of real populations

Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence (~53% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best‐case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand‐ins for the population parameters we seek to know.

Contributions of domesticated plant studies to our understanding of plant evolution.

Plant evolutionary theory has been greatly enriched by studies on crop species. Over the last century, important information has been generated on many aspects of population biology, speciation and polyploid genetics. Searches for quantitative trait loci (QTL) in crop species have uncovered numerous blocks of genes that have dramatic effects on adaptation, particularly during the domestication process. Many of these QTL have epistatic and pleiotropic effects making rapid evolutionary change possible. Most of the pioneering work on the molecular basis of self-incompatibility has been conducted on crop species, along with the sequencing of the phytopathogenic resistance genes (R genes) responsible for the 'gene-to-gene' relations of coevolution observed in host-pathogen relationships. Some of the better examples of co-adaptation and early acting inbreeding depression have also been elucidated in crops. Crop-wild progenitor interactions have provided rich opportunities to study the evolution of novel adaptations subsequent to hybridization. Most crop/wild F1 hybrids have reduced fitness, but in some instances the crop relatives have acquired genes that make them more efficient weeds through crop mimicry. Studies on autopolyploid alfalfa and potato have uncovered the means by which polyploid gametes are formed and have led to hypotheses about how multiallelic interactions are associated with fitness and self-fertility. Research on the cole crops and wheat has discovered that newly formed polyploids can undergo dramatic genome rearrangements that could lead to rapid evolutionary change. Many more important evolutionary discoveries are on the horizon, now that the whole genome sequence is available of the two major subspecies of rice Oryza sativa ssp. japonica and O. sativa ssp. indica. The rice sequence data can be used to study the origin of genes and gene families, track rates of sequence divergence over time, and provide hints about how genes evolve and generate products with novel biological properties. The rice sequence data has already been mined to show that transposable elements often carry fragments of cellular genes. This type of genome shuffling could play a role in creating novel, reorganized genes with new adaptive properties.

Genetic structure in a solitary rodent (Ctenomys talarum): implications for kinship and dispersal

The genetic structure of a population provides critical insights into patterns of kinship and dispersal. Although genetic evidence of kin structure has been obtained for multiple species of social vertebrates, this aspect of population biology has received considerably less attention among solitary taxa in which spatial and social relationships are unlikely to be influenced by kin selection. Nevertheless, significant kin structure may occur in solitary species, particularly if ecological or life history traits limit individual vagility. To explore relationships between genetic structure, kinship, and dispersal in a solitary vertebrate, we compared patterns of genetic variation in two demographically distinct populations of the talar tuco-tuco (Ctenomys talarum), a solitary species of subterranean rodent from Buenos Aires Province, Argentina. Based on previous field studies of C. talarum at Mar de Cobo (MC) and Necochea (NC), we predicted that natal dispersal in these populations is male biased, with dispersal distances for males and females being greater at NC. Analyses of 12 microsatellite loci revealed that in both populations, kin structure was more apparent among females than among males. Between populations, kinship and genetic substructure were more pronounced at MC. Thus, our findings were consistent with predicted patterns of dispersal for these animals. Collectively, these results indicate that populations of this solitary species are characterized by significant kin structure, suggesting that, even in the absence of sociality and kin selection, the spatial distributions and movements of individuals may significantly impact patterns of genetic diversity among conspecifics.

An Instructional Approach to Modeling in Microevolution

Microevolution has been described as the evolutionary change that occurs in local breeding populations (Dobzhansky 1951). This, of course, is the general theme of basic population genetics, a significant element of many biology courses, from introductory biology and genetics to advanced courses in evolution and population biology. As Wallace (1981) so clearly articulates, the beauty of population genetics lies in its abstractions, or models, rather than with a detailed listing or description of the actual events. This is not meant to denigrate the importance of empirical studies, but rather to stress the role that models serve in the theoretical framework of the subject, on which the empirical studies depend. Historically, models and model building have been closely associated with the development of various aspects of population biology. The classic works of Fisher (1930), Wright (1931) and Haldane (1932) in population genetics are only a few of many examples. Modeling not only continues to play a major role in population genetics, but functions in a similar way in other areas of population biology, as exemplified by the work of Maynard-Smith (1974) in population ecology. Because modeling and the use of models are an important aspect of population biology, a part of our instruction to students should include the importance, utility and development of models. As with a number of other biologists (Spain 1982; Hedrick 1984 & Price 1985), one of my goals as an instructor is to assist students in developing an appreciation for models and how they can enhance our understanding of biological processes, in particular microevolutionary processes. With this in mind, I have developed a course in population genetics and evolution which incorporates instruction in modeling and the development of microcomputer models, along with the traditional declarative aspects of the material in micro and macroevolution. This paper describes this approach as well as some of the ways the models can be used to enhance understanding of the processes being studied. The Instructional Plan At the beginning of the course, the foundations of modeling and simulation are first presented in a lecture-demonstration fashion. Students are encouraged to apply these principles to the construction of simple mathematical expressions of events such as exponential and logistic growth. Throughout the semester, one period per week is set aside to individually examine existing microcomputer models, develop skill in BASIC programming, or, as a group project, develop a specific model in microevolution. Because all biology majors at Ithaca College are required to complete a year of college level mathematics, and because the majority of students have also had an introductory course in programming, the initial introduction to modeling and programming goes very quickly. For example, by the fourth week of the semester the students usually are capable of writing a program that will simulate a simple model of complete selection against a recessive gene. Table 1 is a listing of a typical program; it shows the modest level of programming skill needed for this simulation. To assist the students in their programming, I have assembled a simplified manual on Applesoft BASIC for student use. I also provide an initialized programming diskette containing a general purpose graphic utility file constructed for use in this class. This graphics file, which we use in all our microcomputer models, allows students to display the numeric data from the simulation models in the form of line graphs. Following the initial introduction to programming and modeling, the students, working as a team with me serving as a resource person, approach the major class project of the semester-the modeling of one of the phenomena of microevolution. This includes such topics as genetic drift, a general selection model and migration. Instead of constructing a programming flow chart, our usual format is first to outline what the model must do biologically. This includes all the possible events we can easily follow. However, I learned early in the development of this inSteven Thompson is an associate professor of biology at Ithaca College, Ithaca, NY 14850. He received a B.S. in general studies (science) from Portland State College and an M.S. and Ph.D in zoology (genetics) from Oregon State University. He has published articles on genetics and on the application of computers in biology education and is the author of a commercially published statistics software package. At Ithaca College, he teaches genetics and introductory biology and is the biology coordinator of the Science Education Program.