A Likelihood Ratio Test for the Nonparametric Behrens‐Fisher Problem

The nonparametric Behrens‐Fisher hypothesis is the most appropriate null hypothesis for the two‐sample comparison when one does not wish to make restrictive assumptions about possible distributions. In this paper, a numerical approach is described by which the likelihood ratio test can be calculated for the nonparametric Behrens‐Fisher problem. The approach taken here effectively reduces the number of parameters in the score equations to one by using a recursive formula for the remaining parameters. The resulting single dimensional problem can be solved numerically. The power of the likelihood ratio test is compared by simulation to that of a generalized Wilcoxon test of Brunner and Munzel. The tests have similar power for all alternatives considered when a simulated null distribution is used to generate cutoff values for the tests. The methods are illustrated on data on shoulder pain from a clinical trial.     

Backward simulation of ancestors of sampled individuals

If the population is large and the sampling mechanism is random, the coalescent is commonly used to model the haplotypes in the sample. Ordered genotypes can then be formed by random matching of the derived haplotypes. However, this approach is not realistic when (1) there is departure from random mating (e.g., dominant individuals in breeding populations or monogamy in humans), or (2) the population is small and/or the individuals in the sample are ascertained by applying some particular non-random sampling scheme, as is usually the case when considering the statistical modeling and analysis of pedigree data. For such situations, we present here a data generation method where an ancestral graph with non-overlapping generations is first generated backwards in time, using ideas from coalescent theory. Alleles are randomly assigned to the founders, and subsequently the gene flow over the entire genome is simulated forwards in time by dropping alleles down the graph according to recombination model without interference. The parameters controlling the mating behavior of generated individuals in the graph (degree of monogamy) can be tuned in order to match a particular demographic situation, without restriction to simple random mating.The performance of the approach is illustrated with a simulation example. The software (written in C-language) is freely available for research purposes at http://www.rni.helsinki.fi/∼dag/.     

Stratification based on reproductive state reveals contrasting patterns of age-related variation in demographic parameters in the kittiwake

Heterogeneity in individual quality can be a major obstacle when interpreting age‐specific variation in life‐history traits. Heterogeneity is likely to lead to within‐generation selection, and patterns observed at the population level may result from the combination of hidden patterns specific to subpopulations. Population‐level patterns are not relevant to hypotheses concerning the evolution of age‐specific reproductive strategies if they differ from patterns at the individual level. We addressed the influence of age and a variable used as a surrogate of quality (yearly reproductive state) on survival and breeding probability in the kittiwake. We found evidence of an effect of age and quality on both demographic parameters. Patterns observed in breeders are consistent with the selection hypothesis, which predicts age‐related increases in survival and traits positively correlated with survival. Our results also reveal unexpected age effects specific to subgroups: the influence of age on survival and future breeding probability is not the same in nonbreeders and breeders. These patterns are observed in higher‐quality breeding habitats, where the influence of extrinsic factors on breeding state is the weakest. Moreover, there is slight evidence of an influence of sex on breeding probability (not on survival), but the same overall pattern is observed in both sexes. Our results support the hypothesis that age‐related variation in demographic parameters observed at the population level is partly shaped by heterogeneity among individuals. They also suggest processes specific to subpopulations. Recent theoretical developments lay emphasis on integration of sources of heterogeneity in optimization models to account for apparently “sub‐optimal” empirical patterns. Incorporation of sources of heterogeneity is also the key to investigation of age‐related reproductive strategies in heterogeneous populations. Thwarting “heterogeneity's ruses” has become a major challenge: for detecting and understanding natural processes, and a constructive confrontation between empirical and theoretical studies.     

An under-appreciated difficulty: sampling of plant populations for analysis using molecular markers

Results of studies using molecular markers for determining demographic and genetical population parameters especially in plants or sessile animals under field conditions are strongly dependent on the sampling strategy adopted. There are two critical decisions to make when determining this strategy: (i) what is the unit to be sampled?, (ii) how should units to be sampled in the field be selected? For the first decision, there are two conceptually different approaches: sampling ramets of clonal plants as units (to get information about within-genet parameters, such as genet sizes or numbers) and sampling genets of clonal or non-clonal plants as units (to get information of the genetic structure of the population). For the second decision, it is critically important to make the goal of the study explicit. We argue that in this case fully random sampling is needed only when an estimate of the true value of the population parameter is needed; if a comparison between populations is the goal, however, other sampling schemes may be adopted. The efficiency of different types of sampling strategies to recover relative values in a spatially extended population is studied by means of a spatially explicit simulation model. The results show that a regular pattern of sampling is best for obtaining information on genet sizes or inbreeding coefficients; in contrast, random or hierarchical sampling strategies are better for obtaining information on parameters that are based on comparison of pairs of individuals, such as distribution of genet sizes or autocorrelation in genetic structure. A set of recommendations is provided for designing a good sampling strategy.     

Differences among Yanomama Indian villages: Do the patterns of allele frequencies,anthropometrics and map locations correspond?

In order to determine the degree of correspondence between sets of multivariate observations based on different kinds of traits, two new methods, derived from fundamentally different notions of “correspondence,” are adopted here and compared. Using networks or trees to represent contemporary relationships, the first method tests the similarity of the cluster or hierarchic structures implicit in two sets of data. The second approach tests the departure from perfect geometric congruence or superimposability. Computer simulation was used to generate the distributions needed for significance tests under the null hypothesis. By the first technique, we find significant correspondence among the cluster structures for geographic, allele frequency, and anthropometric data on 19 Yanomama Indian villages. The results are similar and more precise for a subset consisting of seven villages. Some of these results differ from the conclusions which would be reached with the conventional correlations based upon entries in distance tables. The direct test of congruence, used only for the data on the subset of seven villages, gives results which differ substantially from those based on cluster-structure. There are, however, similarities between the measure of congruence and the simple correlations based on entries in the distance tables. The significant correspondences observed call for some explanation. Cultural and demographic features determine the particular non-random allocation of individuals to village fragments when a village splits. These social phenomena are invoked in tentative explanation of the agreement among historical, biological, and geographic relationships of villages.     

Genomic inference accurately predicts the timing and severity of a recent bottleneck in a nonmodel insect population

The analysis of molecular data from natural populations has allowed researchers to answer diverse ecological questions that were previously intractable. In particular, ecologists are often interested in the demographic history of populations, information that is rarely available from historical records. Methods have been developed to infer demographic parameters from genomic data, but it is not well understood how inferred parameters compare to true population history or depend on aspects of experimental design. Here, we present and evaluate a method of SNP discovery using RNA sequencing and demographic inference using the program δaδi, which uses a diffusion approximation to the allele frequency spectrum to fit demographic models. We test these methods in a population of the checkerspot butterfly Euphydryas gillettii. This population was intentionally introduced to Gothic, Colorado in 1977 and has as experienced extreme fluctuations including bottlenecks of fewer than 25 adults, as documented by nearly annual field surveys. Using RNA sequencing of eight individuals from Colorado and eight individuals from a native population in Wyoming, we generate the first genomic resources for this system. While demographic inference is commonly used to examine ancient demography, our study demonstrates that our inexpensive, all‐in‐one approach to marker discovery and genotyping provides sufficient data to accurately infer the timing of a recent bottleneck. This demographic scenario is relevant for many species of conservation concern, few of which have sequenced genomes. Our results are remarkably insensitive to sample size or number of genomic markers, which has important implications for applying this method to other nonmodel systems.     

Indirect genetic effects and kin recognition: estimating IGEs when interactions differ between kin and strangers

Social interactions among individuals are widespread, both in natural and domestic populations. As a result, trait values of individuals may be affected by genes in other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. The traditional IGE model assumes that an individual interacts equally with all its partners, whether kin or strangers. There is abundant evidence, however, that individuals behave differently towards kin as compared with strangers, which agrees with predictions from kin-selection theory. With a mix of kin and strangers, therefore, IGEs estimated from a traditional model may be incorrect, and selection based on those estimates will be suboptimal. Here we investigate whether genetic parameters for IGEs are statistically identifiable in group-structured populations when IGEs differ between kin and strangers, and develop models to estimate such parameters. First, we extend the definition of total breeding value and total heritable variance to cases where IGEs depend on relatedness. Next, we show that the full set of genetic parameters is not identifiable when IGEs differ between kin and strangers. Subsequently, we present a reduced model that yields estimates of the total heritable effects on kin, on non-kin and on all social partners of an individual, as well as the total heritable variance for response to selection. Finally we discuss the consequences of analysing data in which IGEs depend on relatedness using a traditional IGE model, and investigate group structures that may allow estimation of the full set of genetic parameters when IGEs depend on kin.     

MetaPopGen: an r package to simulate population genetics in large size metapopulations

Population genetics simulation models are useful tools to study the effects of demography and environmental factors on genetic variation and genetic differentiation. They allow for studying species and populations with complex life histories, spatial distribution and many other complicating factors that make analytical treatment impracticable. Most simulation models are individual‐based: this poses a limitation to simulation of very large populations because of the limits in computer memory and long computation times. To overcome these limitations, we propose an intermediate approach that allows modelling of very complex demographic scenarios, which would be intractable with analytical models, and removes the limitations imposed by large population size, which affect individual‐based simulation models. We implement this approach in a software package for the r environment, MetaPopGen. The innovative concept of this approach with respect to the other population genetic simulators is that it focuses on genotype numbers rather than on individuals. Genotype numbers are iterated through time by using random number generators for appropriate probabilistic distributions to reproduce the stochasticity inherent to Mendelian segregation, survival, dispersal and reproduction. Features included in the model are age structure, monoecious and dioecious (or separate sexes) life cycles, mutation, dispersal and selection. The model simulates only one locus at a time. All demographic parameters can be genotype‐, sex‐, age‐, deme‐ and time‐dependent. MetaPopGen is therefore indicated to study large populations and very complex demographic scenarios. We illustrate the capabilities of MetaPopGen by applying it to the case of a marine fish metapopulation in the Mediterranean Sea.     

DNA fingerprinting data and the analysis of population genetic structure by comparing band-sharing patterns

Genetic isolation among populations can be effectively investigated by multilocus DNA fingerprinting. If populations have diverged, it is expected that the mean proportion of bands shared by individuals from the same population, Bw, exceeds the corresponding mean, Bb, calculated from pairs of individuals from distinct populations. A problem arises in deciding whether any difference between Bw and Bb is statistically significant. In fact, any two band-sharing data (bij), contributing to Bw or Bb, are not independent if they share a common individual (like bij and bjl). This prevents a correct application of parametric tests, such as the Student's t-test. Recently, a modification of this test has been proposed that should avoid the independence problem. Using a large number of samples of fingerprints, simulated from an appropriate 'genetic' model, under a wide range of conditions, we compared the performances of the Student's t-test, the modified t-test and five new permutation tests, where individuals, rather than bij values, are permuted. We found that: (i) the Student's t-test can be very permissive, rejecting too often the null hypothesis when true, but is correct or conservative in certain cases; (ii) the modified t-test is extremely conservative when the null hypothesis is true and very inefficient otherwise; (iii) all five permutation tests are strictly correct, provided that individuals are ordered randomly on gels; and (iv) in this case, the permutation tests are equally efficient, and are not inferior to the Student's t-test when the latter is approximately correct and provides a fair benchmark.     

Estimating dispersal distributions at multiple scales: within‐colony and among‐colony dispersal rates,distances and directions in European Shags Phalacrocorax aristotelis

Knowledge of the rate, distance and direction of dispersal within and among breeding areas is required to understand and predict demographic and genetic connectivity and resulting population and evolutionary dynamics. However dispersal rates, and the full distributions of dispersal distances and directions, are rarely comprehensively estimated across all spatial scales relevant to wild populations. We used re‐sightings of European Shags Phalacrocorax aristotelis colour‐ringed as chicks on the Isle of May (IoM), UK, to quantify rates, distances and directions of dispersal from natal to subsequent breeding sites both within IoM (within‐colony dispersal) and across 27 other breeding colonies covering 1045 km of coastline (among‐colony dispersal). Additionally, we used non‐breeding season surveys covering 895 km of coastline to estimate breeding season detection probability and hence potential bias in estimated dispersal parameters. Within IoM, 99.6% of individuals dispersed between their natal and observed breeding nest‐site. The distribution of within‐colony dispersal distances was right‐skewed; mean distance was shorter than expected given random settlement within IoM, yet some individuals dispersed long distances within the colony. The distribution of within‐colony dispersal directions was non‐uniform but did not differ from expectation given the spatial arrangement of nest‐sites. However, 10% of all 460 colour‐ringed adults that were located breeding had dispersed to a different colony. The maximum observed dispersal distance (170 km) was much smaller than the maximum distance surveyed (690 km). The distribution of among‐colony dispersal distances was again right‐skewed. Among‐colony dispersal was directional, and differed from random expectation and from the distribution of within‐colony dispersal directions. Non‐breeding season surveys suggested that the probability of detecting a colour‐ringed adult at its breeding location was high in the northeastern UK (98%). Estimated dispersal rates and distributions were therefore robust to incomplete detection. Overall, these data demonstrate skewed and directionally divergent dispersal distributions across small (within‐colony) and large (among‐colony) scales, indicating that dispersal could create genetic and demographic connectivity within the study area.     

A model‐based approach to characterize individual inbreeding at both global and local genomic scales

Inbreeding results from the mating of related individuals and may be associated with reduced fitness because it brings together deleterious variants in one individual. In general, inbreeding is estimated with respect to an arbitrary base population consisting of ancestors that are assumed unrelated. We herein propose a model‐based approach to estimate and characterize individual inbreeding at both global and local genomic scales by assuming the individual genome is a mosaic of homozygous‐by‐descent (HBD) and non‐HBD segments. The HBD segments may originate from ancestors tracing back to different periods in the past defining distinct age‐related classes. The lengths of the HBD segments are exponentially distributed with class‐specific parameters reflecting that inbreeding of older origin generates on average shorter stretches of observed homozygous markers. The model is implemented in a hidden Markov model framework that uses marker allele frequencies, genetic distances, genotyping error rates and the sequences of observed genotypes. Note that genotyping errors, low‐fold sequencing or genotype‐by‐sequencing data are easily accommodated under this framework. Based on simulations under the inference model, we show that the genomewide inbreeding coefficients and the parameters of the model are accurately estimated. In addition, when several inbreeding classes are simulated, the model captures them if their ages are sufficiently different. Complementary analyses, either on data sets simulated under more realistic models or on human, dog and sheep real data, illustrate the range of applications of the approach and how it can reveal recent demographic histories among populations (e.g., very recent bottlenecks or founder effects). The method also allows to clearly identify individuals resulting from extreme consanguineous matings.     

Comparison of parameters from rhythmometric models with multiple components on hybrid data

Population multiple components is a statistical tool useful for the analysis of time-dependent hybrid data. With a small number of parameters, it is possible to model and to predict the periodic behavior of a population. In this article, we propose two methods to compare among populations rhythmometric parameters obtained by multiple component analysis. The first is a parametric method based in the usual statistical techniques for comparison of mean vectors in multivariate normal populations. The method, through MANOVA analysis, allows comparison of the MESOR and amplitude-acrophase pair of each component among two or more populations. The second is a nonparametric method, based in bootstrap techniques, to compare parameters from two populations. This test allows one to compare the MESOR, the amplitude, and the acrophase of each fitted component, as well as the global amplitude, orthophase, and bathyphase estimated when all fitted components are harmonics of a fundamental period. The idea is to calculate a confidence interval for the difference of the parameters of interest. If this interval does not contain zero, it can be concluded that the parameters from the two models are different with high probability. An estimation of p-value for the corresponding test can also be calculated. Both methods are illustrated with an example, based on clinical data. The nonparametric test can also be applied to paired data, a special situation of great interest in practice. By the use of similar bootstrap techniques, we illustrate how to construct confidence intervals for any rhythmometric parameter estimated from population multiple components models, including the orthophase, bathyphase, and global amplitude. These tests for comparison of parameters among populations are a needed tool when modeling the nonsinusoidal rhythmic behavior of hybrid data by population multiple component analysis.     

Demographic parameters of sexes in an elusive insect: implications for monitoring methods

Estimating demographic parameters in rare species is challenging because of the low number of individuals and their cryptic behaviour. One way to address this challenge is to gather data from several regions or years through mark-release-recapture (MRR) and radio-tracking monitoring. However, the comparison of demographic estimates obtained using these methods has rarely been investigated. Using 5 years of intensive MRR and radio-tracking surveys of an elusive and endangered saproxylic insect, the hermit beetle (Osmoderma eremita), in two regions of France, we aimed to estimate population size at the adult stage for each sex separately and to assess differences in demographic parameter estimates between survey methods. We found that males were approximately three times more likely to be recaptured than females. Taking this into account, we determined that the sex ratio was male-biased in almost all populations, except in Malus trees, where it was female-biased. Temporal fluctuations of sex ratios were also detected in one region. The radio-tracking transmitter (450 mg) allowed only the largest individuals (>2 g) to be targeted. However, we found that, among non-equipped individuals, the larger males survived better than the smaller males. We also confirmed that transmitter-equipped individuals survived approximately 25 % better than non-equipped individuals. Extrapolating the estimates from radio-tracking surveys to the population scale may result in overly optimistic population projections. Our results revealed large temporal and spatial variations in population size and sex ratios. This knowledge is crucial for predicting the persistence of small populations in fragmented landscapes. This study also questioned the representativeness of radio-tracking surveys for insect species in estimating demographic parameters at the population scale.     

Stage,age and individual stochasticity in demography

Demography is the study of the population consequences of the fates of individuals. Individuals are differentiated on the basis of age or, in general, life cycle stages. The movement of an individual through its life cycle is a random process, and although the eventual destination (death) is certain, the pathways taken to that destination are stochastic and will differ even between identical individuals; this is individual stochasticity. A stage‐classified demographic model contains implicit age‐specific information, which can be analyzed using Markov chain methods. The living stages in the life cycles are transient states in an absorbing Markov chain; death is an absorbing state. This paper presents Markov chain methods for computing the mean and variance of the lifetime number of visits to any transient state, the mean and variance of longevity, the net reproductive rate R₀, and the cohort generation time. It presents the matrix calculus methods needed to calculate the sensitivity and elasticity of all these indices to any life history parameters. These sensitivities have many uses, including calculation of selection gradients. It is shown that the use of R₀ as a measure of fitness or an invasion exponent gives erroneous results except when R₀=λ=1. The Markov chain approach is then generalized to variable environments (deterministic environmental sequences, periodic environments, iid random environments, Markovian environments). Variable environments are analyzed using the vec‐permutation method to create a model that classifies individuals jointly by the stage and environmental condition. Throughout, examples are presented using the North Atlantic right whale (Eubaleana glacialis) and an endangered prairie plant (Lomatium bradshawii) in a stochastic fire environment.     

Comparison of Parameters from Rhythmometric Models with Multiple Components on Hybrid Data

Population multiple components is a statistical tool useful for the analysis of time-dependent hybrid data. With a small number of parameters, it is possible to model and to predict the periodic behavior of a population. In this article, we propose two methods to compare among populations rhythmometric parameters obtained by multiple component analysis. The first is a parametric method based in the usual statistical techniques for comparison of mean vectors in multivariate normal populations. The method, through MANOVA analysis, allows comparison of the MESOR and amplitude-acrophase pair of each component among two or more populations. The second is a nonparametric method, based in bootstrap techniques, to compare parameters from two populations. This test allows one to compare the MESOR, the amplitude, and the acrophase of each fitted component, as well as the global amplitude, orthophase, and bathyphase estimated when all fitted components are harmonics of a fundamental period. The idea is to calculate a confidence interval for the difference of the parameters of interest. If this interval does not contain zero, it can be concluded that the parameters from the two models are different with high probability. An estimation of p-value for the corresponding test can also be calculated. Both methods are illustrated with an example, based on clinical data. The nonparametric test can also be applied to paired data, a special situation of great interest in practice. By the use of similar bootstrap techniques, we illustrate how to construct confidence intervals for any rhythmometric parameter estimated from population multiple components models, including the orthophase, bathyphase, and global amplitude. These tests for comparison of parameters among populations are a needed tool when modeling the nonsinusoidal rhythmic behavior of hybrid data by population multiple component analysis.     

Correlated responses to clonal selection in populations of Daphnia pulicaria: mechanisms of genetic correlation and the creative power of sex

Genetic correlations among traits alter evolutionary trajectories due to indirect selection. Pleiotropy, chance linkage, and selection can all lead to genetic correlations, but have different consequences for phenotypic evolution. We sought to assess the mechanisms contributing to correlations with size at maturity in the cyclic parthenogen Daphnia pulicaria. We selected on size in each of four populations that differ in the frequency of sex, and evaluated correlated responses in a life table. Size at advanced adulthood, reproductive output, and adult growth rate clearly showed greater responses in high‐sex populations, with a similar pattern in neonate size and r. This pattern is expected only when trait correlations are favored by selection and the frequency of sex favors the creation and demographic expansion of highly fit clones. Juvenile growth and age at maturity did not diverge consistently. The inter‐clutch interval appeared to respond more strongly in low‐sex populations, but this was not statistically significant. Our data support the hypothesis that correlated selection is the strongest driver of genetic correlations, and suggest that in organisms with both sexual and asexual reproduction, adaptation can be enhanced by recombination.     

Life history evolution and demographic stochasticity

Summary Can demographic stochasticity bias the evolution of life history traits? Under a neutral version of the Cole-Charnov-Schaffer model, variance in offspring number for both annuals and perennials depends on the precise values of fitness components. Either annuals or perennials may have the larger variance (for equal ), depending on the importance of random survivalversus fixed reproduction. By extension, the variance in offspring number should generally depend on whether is mainly composed of highly variable elements or elements with limited variation. Thus, data about the variability of demographic parameters may be as important as data about their mean values.This result concerns only one source of demographic stochasticity, the probabilistic nature of demographic processes like survival. The other source of demographic stochasticity is the fact that populations are composed of whole numbers of individuals (integer arithmetic). Integer arithmetic without probabilistic demography (or environmental variation) can make it difficult for rare invaders to persist in populations even when selection would favour the invaders in a deterministic model. Integer arithmetic can also cause population coexistence when the equivalent deterministic model leads to exclusion. This effect disappears when demography is probabilistic, and probably also when there is environmental variation. Thus probabilistic demography and environmental variation may make some population patterns more, rather than less, understandable.     

Demographic consequences of nestbox use for Red‐footed Falcons Falco vespertinus in Central Asia

Nestbox programmes are frequently implemented for the conservation of cavity‐nesting birds, but their effectiveness is rarely evaluated in comparison with birds not using nestboxes. In the European Palaearctic, Red‐footed Falcon Falco vespertinus populations are both of high conservation concern and are strongly associated with nestbox programmes in heavily managed landscapes. We used a 21‐year monitoring dataset collected on 753 nesting attempts by Red‐footed Falcons in unmanaged natural or semi‐natural habitats to provide basic information on this poorly known species; to evaluate long‐term demographic trends within this population; and to evaluate response of demographic parameters of Red‐footed Falcons to environmental factors including use of nestboxes. We observed significant differences among years in laying date, offspring loss and numbers of fledglings produced, but not in egg production. Of these four parameters, offspring loss and, to a lesser extent, number of fledglings exhibited directional trends over time. Variation in laying date and in numbers of eggs were not well explained by any one model of environmental factors, but instead by combinations of models, each with informative terms for nest type. Nevertheless, laying in nestboxes occurred 2.10 ± 0.70 days earlier than in natural nests. In contrast, variation in both offspring loss and numbers of fledglings produced were fairly well explained by a single model including terms for nest type, nest location and an interaction between the two parameters (65 and 81% model weights, respectively), with highest offspring loss in nestboxes on forest edges. Because, for other species, earlier laying dates are associated with more fit individuals, this interaction highlighted a possible ecological trap, whereby birds using nestboxes on forest edges lay eggs earlier but suffer greater offspring loss and produce lower numbers of fledglings than do those in other nesting settings. If nestboxes increase offspring loss for Red‐footed Falcons in heavily managed landscapes where populations are at greater risk, or for the many other species of rare or endangered birds supported by nestbox programmes, these processes could have important demographic and conservation consequences.     

Effect of Ascaridia compar infection on rock partridge population dynamics: empirical and theoretical investigations

Helminth parasites have the potential to significantly affect the dynamics of their hosts. As a consequence, they can dramatically threaten the persistence of endangered species, such as rock partridge Alectoris graeca saxatilis, found in the Province of Trento (northern Italy). The aim of this work was to understand the effect of helminth parasites on rock partridge fitness, and the subsequent potential effects on host population dynamics. In particular, we investigated the hypothesis that infections from Ascaridia compar induce rock partridge population cycles observed in Trentino. In order to support this hypothesis, we compared the predictions obtained from a host–parasite interaction model including variable parasite aggregation with multi‐annual empirical data of A. compar infection in natural host populations. We estimated host demographic parameters using rock partridge census data from Trentino, and the parasitological parameters from a series of experimental infections in a captive rock partridge population. The host–parasite model predicted higher A. compar abundance in rock partridge populations exhibiting cyclic dynamics compared to non‐cyclic ones. In addition, for cyclic host populations, the model predicted an increase in mean parasite burden with the length of cycle period. Model predictions were well‐supported by field data: significant differences in parasite infection between cyclic and non‐cyclic populations and among cyclic populations with different oscillation periods were observed. On the basis of these results, we conclude that helminth parasites can not be ruled out as drivers of rock partridge population dynamics in Trentino and must be considered when planning conservation strategies of this threatened species.     

Neutral processes forming large clones during colonization of new areas

In species reproducing both sexually and asexually clones are often more common in recently established populations. Earlier studies have suggested that this pattern arises due to natural selection favouring generally or locally successful genotypes in new environments. Alternatively, as we show here, this pattern may result from neutral processes during species’ range expansions. We model a dioecious species expanding into a new area in which all individuals are capable of both sexual and asexual reproduction, and all individuals have equal survival rates and dispersal distances. Even under conditions that favour sexual recruitment in the long run, colonization starts with an asexual wave. After colonization is completed, a sexual wave erodes clonal dominance. If individuals reproduce more than one season, and with only local dispersal, a few large clones typically dominate for thousands of reproductive seasons. Adding occasional long‐distance dispersal, more dominant clones emerge, but they persist for a shorter period of time. The general mechanism involved is simple: edge effects at the expansion front favour asexual (uniparental) recruitment where potential mates are rare. Specifically, our model shows that neutral processes (with respect to genotype fitness) during the population expansion, such as random dispersal and demographic stochasticity, produce genotype patterns that differ from the patterns arising in a selection model. The comparison with empirical data from a post‐glacially established seaweed species (Fucus radicans) shows that in this case, a neutral mechanism is strongly supported.