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Genetic monitoring estimates temporal changes in population parameters from molecular marker information. Most populations are complex in structure and change through time by expanding or contracting their geographic range, becoming fragmented or coalescing, or increasing or decreasing density. Traditional approaches to genetic monitoring rely on quantifying temporal shifts of specific population metrics—heterozygosity, numbers of alleles, effective population size—or measures of geographic differentiation such as FST. However, the accuracy and precision of the results can be heavily influenced by the type of genetic marker used and how closely they adhere to analytical assumptions. Care must be taken to ensure that inferences reflect actual population processes rather than changing molecular techniques or incorrect assumptions of an underlying model of population structure. In many species of conservation concern, true population structure is unknown, or structure might shift over time. In these cases, metrics based on inappropriate assumptions of population structure may not provide quality information regarding the monitored population. Thus, we need an inference model that decouples the complex elements that define population structure from estimation of population parameters of interest and reveals, rather than assumes, fine details of population structure. Encompassing a broad range of possible population structures would enable comparable inferences across biological systems, even in the face of range expansion or contraction, fragmentation, or changes in density. Currently, the best candidate is the spatial Λ‐Fleming‐Viot (SLFV) model, a spatially explicit individually based coalescent model that allows independent inference of two of the most important elements of population structure: local population density and local dispersal. We support increased use of the SLFV model for genetic monitoring by highlighting its benefits over traditional approaches. We also discuss necessary future directions for model development to support large genomic datasets informing real‐world management and conservation issues.

Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

Long-distance seed dispersal influences many key aspects of the biology of plants, including spread of invasive species, metapopulation dynamics, and diversity and dynamics in plant communities. However, because long-distance seed dispersal is inherently hard to measure, there are few data sets that characterize the tails of seed dispersal curves. This paper is structured around two lines of argument. First, we argue that long-distance seed dispersal is of critical importance and, hence, that we must collect better data from the tails of seed dispersal curves. To make the case for the importance of long-distance seed dispersal, we review existing data and models of long-distance seed dispersal, focusing on situations in which seeds that travel long distances have a critical impact (colonization of islands, Holocene migrations, response to global change, metapopulation biology). Second, we argue that genetic methods provide a broadly applicable way to monitor long-distance seed dispersal; to place this argument in context, we review genetic estimates of plant migration rates. At present, several promising genetic approaches for estimating long-distance seed dispersal are under active development, including assignment methods, likelihood methods, genealogical methods, and genealogical/demographic methods. We close the paper by discussing important but as yet largely unexplored areas for future research.

In their classic study, Alston and Turner (American Journal of Botany, vol. 50, 159-173, 1963) documented extensive hybridization among four morphologically distinct Baptisia species native to East Texas. While Alston and Turner found putative F1 hybrids in great numbers, they found no evidence of backcrossing. In this study prezygotic and postzygotic reproductive barriers between two of these species, B. leucophaea and B. sphaerocarpa, were investigated and found to be quite weak. Flowering times overlap and bumble bees were observed visiting both species and intermediate hybrids. While pollinator constancy in flights between B. leucophaea and B. sphaerocarpa was moderately strong, significant levels of constancy were not observed in flights involving hybrids and either parental species. Thus, backcrossing was not impeded by pollinator behavior. Further, hybrid pollen was highly stainable (93.5%) and able to effectively set seed in crossing experiments with both parental species. Pollinator behavior was compared in experimental populations with and without hybrid ramets and found to differ between these two treatments. Hybrids were found to facilitate pollinator movement between species. In total, these results suggest that reproductive isolation is not responsible for the rarity of backcrossing in naturally hybridizing B. leucophaea and B. sphaerocarpa populations.

Plant mating systems often involve a mixture of self fertilizations and outcross fertilizations. The degree of selfing has a large impact on the genetic composition of natural populations and on the evolution of the mating system itself in response to such factors as inbreeding depression. This paper describes a means of estimating the long-term rate of self-fertilization from samples of alleles taken from individuals in a population. Use is made of the genealogy of pairs of alleles at a locus within individuals and pairs between individuals. The degree of selfing is closely related to the extent to which the number of nucleotide sites differing within an individual is reduced relative to the number differing between individuals. Importantly, the estimate of long-term selfing is largely independent of population size and is not affected by historical fluctuations in population size; instead it responds directly to the mating system itself. The approach outlined here is most appropriate to evolutionary problems in which the long-term nature of the mating system is of interest, such as to determine the relationship between prior inbreeding and inbreeding depression.

We obtained a microsatellite-enriched genomic library isolated from the tissue of a single columbine (Aquilegia sp.) plant taken from a southwestern USA natural population. The primers developed for these microsatellite loci performed consistently in polymerase chain reactions and yielded multiallelic genotypes with relatively high observed heterozygosities. We describe polymerase chain reaction primers and conditions to amplify 16 unique, codominant di-, tri- and tetra-nucleotide microsatellite DNA loci so that other population biology researchers using columbine natural populations as a model system may benefit.

One of the major problems faced by conservation biologists is the allocation of scarce resources to an overwhelmingly large number of species in need of preservation efforts. Both demographic and genetic information have been brought to bear on this problem; however, the role of information obtained from genetic markers has largely been limited to the characterization of gene frequencies and patterns of diversity. While the genetic consequences of rarity may be a contributing factor to endangerment, it is widely recognized that demographic factors often may be more important. Because patterns of genetic marker variation are influenced by the same demographic factors of interest to the conservation biologist, it is possible to extract useful demographic information from genetic marker data. Such an approach may be productive for determining plant mating systems, inbreeding depression, effective population size, and metapopulation structure. In many cases, however, data consisting only of marker frequencies are inadequate for these purposes. Development of genealogical based analytical methods coupled with studies of DNA sequence variation within and among populations is likely to yield the most information on demographic processes from genetic marker data. Indeed, in some cases it may be the only means of obtaining information on the long-term demographic properties that may be most useful for determining the future prospects of a species of interest.

Although a major component of fitness, male reproductive success is generally extremely difficult to estimate. As a result, genetic methods and maximum likelihood models have been developed to estimate male parentage, but all are limited in practice by the degree of genetic variation observable. Scoring individuals phenotypically at a large number of random loci exhibiting dominance (e.g. RAPD markers) may provide a means of detecting sufficient genetic variation. Dominance, however, represents a loss of information and therefore greater variation in the estimate of paternity. A mixture model describing mating in a population is presented to quantify the trade-off between marker types when estimates of male fertility are sought. A sample size 1.5-2.0 times greater is required for dominant markers under some conditions to obtain the same confidence in fertility estimates as for codominant markers, although with large sample sizes the fertility estimates are similar for either marker type. Since the number of dominant DN A markers is not limited in the same manner as is the number of codominant protein markers, one's confidence in the estimates can be increased above that possible from proteins by surveying additional loci. However, for a fixed sample size a trade-off exists between the number of progeny assayed per female and the number of loci surveyed. In many cases more progeny per female provide better estimates of fertility than more loci.

Although numerous exceptions are well known, organelle DNA is often exclusively or predominantly maternally inherited. In such cases, maternal inheritance is often documented by the study of progeny derived from crosses between parents with distinguishable organelle genotypes. These studies generally detect a single type of progeny, those containing organelles derived from only one of the parents; no progeny containing organelles derived either from the opposite parent or from both parents are found. However, in most cases the number of progeny examined is quite small. I present a simple binomial model of organelle inheritance to determine the power of such experiments to distinguish between the hypothesis of strict maternal inheritance and a more complex hypothesis involving the presence of both organelle types within a progeny array. Extremely large sample sizes of progeny are required to distinguish these two hypotheses with reasonable confidence when organelle transmission is not strictly maternal. As a result, studies involving simple progeny testing may be misleading. Larger samples, more complex breeding designs, or more sensitive molecular methods are required to document adequately strict maternal inheritance of organelles.

In contrast to animal mitochondrial DNA, intraspecific variation in chloroplast DNA is thought to be very rare. This presumption has prevented the application to plant population biology of the diversity of molecular genetic techniques now well established for animal mitochondrial DNA. In Trifolium pratense, however, extensive intrapopulational variation does exist. In two paper I report a characterization of unprecedented restriction fragment profile variation within single populations. Populations typically contain a common genotype and many rare ones; often the rare genotypes differ from population to population. While both nucleon and nucleotide diversity, as well as estimates of Wright's F ST, indicate a large within-population component and relatively little diversity among populations, the distribution of plastid genotype frequencies in each population is not homogeneous. Estimates of migration rate based on chloroplast DNA genotypes suggest a moderate number of migrants per generation. The unusually high level of genetic variation in Trifolium chloroplast DNA provides the first opportunity to use the plastid genome of plants to study population differentiation. Furthermore, it suggests that the plastid genome may not be as invariant as previously believed, but may instead exhibit high levels of genetic diversity at the population level.

The nature of heterotachy at the center of recent controversy over the relative performance of tree-building methods is different from the form of heterotachy that has been inferred in empirical studies. The latter have suggested that proportions of variable sites (p v a r ) vary among orthologues and among paralogues. However, the strength of this inference, describing what may be one of the most important evolutionary properties of sequence data, has remained weak. Consequently, other models of sequence evolution have been proposed to explain some long-branch attraction (LBA) problems that could be attributed to differences in p v a r . For an empirical case with plastid and eubacterial RNA polymerase sequences, we confirm using capture-recapture estimates and simulations that p v a r can differ among orthologues in anciently diverged evolutionary lineages. We find that parsimony and a least squares distance method that implements an overly simple model of sequence evolution are susceptible to LBA induced by this form of heterotachy. Although homogeneous maximum likelihood inference was found to be robust to model misspecification in our specific example, we caution against assuming that it will always be so.

Relatedness between individuals is central to many studies in genetics and population biology. A variety of estimators have been developed to enable molecular marker data to quantify relatedness. Despite this, no effort has been given to characterize the traditional maximum-likelihood estimator in relation to the remainder. This article quantifies its statistical performance under a range of biologically relevant sampling conditions. Under the same range of conditions, the statistical performance of five other commonly used estimators of relatedness is quantified. Comparison among these estimators indicates that the traditional maximum-likelihood estimator exhibits a lower standard error under essentially all conditions. Only for very large amounts of genetic information do most of the other estimators approach the likelihood estimator. However, the likelihood estimator is more biased than any of the others, especially when the amount of genetic information is low or the actual relationship being estimated is near the boundary of the parameter space. Even under these conditions, the amount of bias can be greatly reduced, potentially to biologically irrelevant levels, with suitable genetic sampling. Additionally, the likelihood estimator generally exhibits the lowest root mean-square error, an indication that the bias in fact is quite small. Alternative estimators restricted to yield only biologically interpretable estimates exhibit lower standard errors and greater bias than do unrestricted ones, but generally do not improve over the maximum-likelihood estimator and in some cases exhibit even greater bias. Although some nonlikelihood estimators exhibit better performance with respect to specific metrics under some conditions, none approach the high level of performance exhibited by the likelihood estimator across all conditions and all metrics of performance.

Recent advances in the application of the polymerase chain reaction make it possible to score individuals at a large number of loci. The RAPD (random amplified polymorphic DNA) method is one such technique that has attracted widespread interest. The analysis of population structure with RAPD data is hampered by the lack of complete genotypic information resulting from dominance, since this enhances the sampling variance associated with single loci as well as induces bias in parameter estimation. We present estimators for several population-genetic parameters (gene and genotype frequencies, within- and between-population heterozygosities, degree of inbreeding and population subdivision, and degree of individual relatedness) along with expressions for their sampling variances. Although completely unbiased estimators do not appear to be possible with RAPDs, several steps are suggested that will insure that the bias in parameter estimates is negligible. To achieve the same degree of statistical power, on the order of 2 to 10 times more individuals need to be sampled per locus when dominant markers are relied upon, as compared to codominant (RFLP, isozyme) markers. Moreover, to avoid bias in parameter estimation, the marker alleles for most of these loci should be in relatively low frequency. Due to the need for pruning loci with low-frequency null alleles, more loci also need to be sampled with RAPDs than with more conventional markers, and some problems of bias cannot be completely eliminated.

Background: The fat body is the main organ of intermediary metabolism in insects and the principal source of hemolymph proteins. As part of our ongoing efforts to understand mosquito fat body physiology and to identify novel targets for insect control, we have conducted a transcriptome analysis of the fat body of Aedes aegypti before and in response to blood feeding. Results: We created two fat body non-normalized EST libraries, one from mosquito fat bodies non-blood fed (NBF) and another from mosquitoes 24 hrs post-blood meal (PBM). 454 pyrosequencing of the non-normalized libraries resulted in 204,578 useable reads from the NBF sample and 323,474 useable reads from the PBM sample. Alignment of reads to the existing reference Ae. aegypti transcript libraries for analysis of differential expression between NBF and PBM samples revealed 116,912 and 115,051 matches, respectively. De novo assembly of the reads from the NBF sample resulted in 15,456 contigs, and assembly of the reads from the PBM sample resulted in 15,010 contigs. Collectively, 123 novel transcripts were identified within these contigs. Prominently expressed transcripts in the NBF fat body library were represented by transcripts encoding ribosomal proteins. Thirty-five point four percent of all reads in the PBM library were represented by transcripts that encode yolk proteins. The most highly expressed were transcripts encoding members of the cathepsin b, vitellogenin, vitellogenic carboxypeptidase, and vitelline membrane protein families. Conclusion: The two fat body transcriptomes were considerably different from each other in terms of transcript expression in terms of abundances of transcripts and genes expressed. They reflect the physiological shift of the pre-feeding fat body from a resting state to vitellogenic gene expression after feeding.

The degree to which conspecific populations are interconnected via ongoing gene flow remains an important focus of evolutionary biology. One major difficulty in distinguishing ongoing gene flow from historical subdivision is that either process can generate similar estimates of apparent gene flow. Thus, gene flow estimates themselves are insufficient to distinguish between these alternatives. However, genetic data coupled with additional information about demography and distribution do allow a distinction to be made. Here we address the specific question, does gene flow link populations of Aquilegia? In a survey of a 525 B.P. chloroplast DNA fragment sampled from 251 individual plants from 18 populations of three taxa, five haplotypes were identified. No significant relationship between geographic distance and apparent gene flow between population pairs existed. Further, the estimated level of gene flow was entirely compatible with a historical subdivision of Aquilegia populations during the late Pleistocene or early Holocene. Therefore, these patterns of variation are due not to ongoing gene flow, but rather to historical association among populations. Thus Aquilegia populations may be considered as distiict evolutionary entities with regard to seed- mediated processes. As a result, comparative analysis of ecological traits undergoing potentially rapid evolution (e.g., life histories, mating systems, inbreeding depression) should be possible in these taxa.

Theoretical studies of character displacement lead to the view that evolutionary divergence depends primarily on incomplete utilization of available resources. Those models which incorporate constraints preventing complete utilization of resources, even in the absence of competitors, all predict character displacement. Those models which allow greater flexibility of resource use within a species predict correspondingly less divergence. Indeed, Matessi and Jayakar (1980, 1981) based their conditions for occurrence of character displacement on underutilization of resources. I extend a model used by Slatkin (1980, 1983) and Taper and Case (1985) which allows each species to fully utilize its resources in the absence of competitors. I concentrate on the biologically reasonable case in which the species, though similar, differ in their ecological characteristics. As a result of this greater biological realism, I arrive at a different conclusion regarding the conditions which lead to character displacement. The presence of a variety of biological differences between species-including as a subset those which result from resource underutilization-leads to divergence with respect to a quantitatively inherited character, due to interspecific competitive interactions. The resulting displacement can be large and depends little on the parameters chosen. The only exception, involving a character with very low heritability, occurs when the non-interactive phenotypic differences are much greater than those associated with studies of character displacement in natural populations. Thus, under conditions comparable to those encountered in the field, involving similar yet not identical species, evolutionary divergence is a consequence of interspecific competition.

The plastid genome from subclover, Trifolium subterraneum, is unusual in a variety of respects, compared with other land-plant chloroplast DNAs. Gene mapping of subclover chloroplast DNA reveals major structural reorganization of the genome. Ten clusters of genes are rearranged in both order and orientation. Eight large inversions are sufficient to explain this reorganization; however, the actual evolutionary changes may have been more complex. For example, a fine-scale analysis of a set of ribosomal protein genes reveals the occurrence of insertions, deletions, and transpositions. Associated with this unusually unstable genome are two structural features potentially involved in the rearrangements. A dispersed family of repeats, with each element about 1 kb in length, is present in at least six copies. A survey of a wide taxonomic range of species indicates that these elements are unique to the chloroplast DNAs of subclover and two closely related species. Several of the repeated elements are associated with genomic rearrangements, and one repeat is inserted within a normally highly conserved series of genes. This set of dispersed repeats may be the first family of transposable elements found in any organelle genome. In addition, the subclover genome is much larger than those in other closely related legumes, even when one takes into account the presence of the repeated elements. Some of the extra DNA has no sequence similarity to other chloroplast genomes and may represent insertion of DNA from another genome. These unusual features are not found in the structurally stable chloroplast genomes of other vascular plants and may, therefore, be implicated in the rapid and major reorganization of the chloroplast DNA in subclover.