Movement patterns of Rhyssomatus lineaticollis Say (Coleoptera: Curculionidae) within and among Asclepias syriaca (Asclepiadaceae) patches in a fragmented landscape

Abstract.  1. Dispersal capabilities of organisms are critical in determining the landscape population structure of species as well as their likelihood of survival in fragmented landscapes. Using mark–recapture techniques on the monophagous weevil Rhyssomatus lineaticollis Say (Curculionidae), within- and between-patch dispersal capabilities, landscape level population structure, and the role of beetle density and host patch characteristics in setting distances, amounts, and timing of dispersal were studied.
2. The data indicate that R. lineaticollis is sedentary, with 50% of recaptured beetles moving < 1 m and the maximum distance moved < 1 km. Within- and between-patch movement of beetles was unrelated to host plant patch characteristics and beetle densities.
3. Despite limited dispersal, R. lineaticollis probably functions as a patchy population in east-central Iowa, U.S.A. because dispersals between patches are common and because all host patches surveyed contained this herbivore, indicating a lack of suitable vacant patches, a prerequisite for metapopulation structure.
4. Between-patch distances are well within the dispersal capabilities of R. lineaticollis , although this may be the result of an increase in the density of patches of its host, Asclepias syriaca , in the landscape over the last 150 years as a result of human disturbance and this species' weedy habit.
5. Metapopulation structure in monophagous prairie herbivores may be most likely in species whose non-weedy host plants form highly predictable resources in space and time, but which are now widely scattered in habitat fragments.     

Bayesian covariance selection in generalized linear mixed models

The generalized linear mixed model (GLMM), which extends the generalized linear model (GLM) to incorporate random effects characterizing heterogeneity among subjects, is widely used in analyzing correlated and longitudinal data. Although there is often interest in identifying the subset of predictors that have random effects, random effects selection can be challenging, particularly when outcome distributions are nonnormal. This article proposes a fully Bayesian approach to the problem of simultaneous selection of fixed and random effects in GLMMs. Integrating out the random effects induces a covariance structure on the multivariate outcome data, and an important problem that we also consider is that of covariance selection. Our approach relies on variable selection-type mixture priors for the components in a special Cholesky decomposition of the random effects covariance. A stochastic search MCMC algorithm is developed, which relies on Gibbs sampling, with Taylor series expansions used to approximate intractable integrals. Simulated data examples are presented for different exponential family distributions, and the approach is applied to discrete survival data from a time-to-pregnancy study.     

Effects of landscape composition on farmland and red-listed birds in boreal agricultural-forest mosaics

Farmland birds have declined in large areas of western and northern Europe. This decline has been connected with changes in the agricultural landscape. We studied the effects of landscape composition on birds in a boreal agricultural-forest mosaic in SW Finland. This study was carried out with a grid-based approach: bird pairs were counted in 105 grid squares of 25 ha within an area of 26.25 km². The total density of farmland birds and density of red-listed species were related to the land cover variables using generalized linear modelling (GLM). Farmland birds consist of a variable group of species either breeding or feeding in agricultural land. The model explained a moderate proportion (49%) of the variation in the total density of farmland birds in the landscape. In a regression analysis cover of non-arable agricultural land (semi-natural grasslands, built-up areas) explained a much higher proportion (r²=0.49) of the variation in farmland bird density than that of arable land (cultivated fields and set-aside fields, r²=0.04). Semi-natural grasslands, which have drastically declined throughout NW Europe, and built-up areas (mainly farmyards) had the most significant positive effects on the density of red-listed species. The results emphasize the significance of semi-natural grasslands for the declining red-listed farmland bird species. Birds are usually not restricted to certain patches of habitat but use several patches in their home range. Thus, when studying bird-landscape relations for land use planning, we also recommend grid-based approaches covering the whole landscape variation.     

Factors influencing reproductive success in the clonal moss, Hylocomium splendens

Female reproductive success in the unisexual perennial clonal moss Hylocomium splendens was examined by recording, if the segment was reproductive [produced sporophyte(s)] or not, together with several distance-to-male and male density variables, and segment size. This was done for every female segment in a population over a 5 year study period. A high fraction of the population could be sexed because we monitored the population in situ for 5 years, and thereafter harvested the population for electrophoretic analysis from which the clonal identity and expressed sex could be deduced. Fertilization distances in H. splendens were short, indicated by the fact that as many as 85% of the female segments with sporophytes were situated within a distance of 5.0 cm from the nearest male. The longest distance measured between a sporophytic female and the closest male was 11.6 cm. However, analysed within a generalized linear modelling (GLM) framework, the year was the best single predictor for the presence of H. splendens sporophyte although female-segment size and distance to the closest situated male were also strongly significant. The two latter factors explained larger fractions of variation in sporophyte presence in a GLM model with three predictors than in single-predictor models. This is because (i) the large variation in sporophyte production among years partly obscures the strong general increase in sporophyte production with increasing female-segment size and vitality, and (ii) the between-year variation and the size obscure the effect of the distance to the most proximate male. To our knowledge, this study is the first to incorporate into one model the relative importance of several factors for bryophyte reproductive success. Our results demonstrate the value of multiple-predictor approaches in studies of reproductive success.     

Factor analytic models of clustered multivariate data with informative censoring

This article describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censoring process, and we account for dependency between these latent variables through a hierarchical model. A linear model is used to relate covariates and latent variables to the primary outcomes for each subunit. A generalized linear model accounts for covariate and latent variable effects on the probability of censoring for subunits within each cluster. The model accounts for correlation within clusters and within subunits through a flexible factor analytic framework that allows multiple latent variables and covariate effects on the latent variables. The structure of the model facilitates implementation of Markov chain Monte Carlo methods for posterior estimation. Data from a spermatotoxicity study are analyzed to illustrate the proposed approach.     

The effect of resource aggregation at different scales: optimal foraging behavior of Cotesia rubecula

Resources can be aggregated both within and between patches. In this article, we examine how aggregation at these different scales influences the behavior and performance of foragers. We developed an optimal foraging model of the foraging behavior of the parasitoid wasp Cotesia rubecula parasitizing the larvae of the cabbage butterfly Pieris rapae. The optimal behavior was found using stochastic dynamic programming. The most interesting and novel result is that the effect of resource aggregation within and between patches depends on the degree of aggregation both within and between patches as well as on the local host density in the occupied patch, but lifetime reproductive success depends only on aggregation within patches. Our findings have profound implications for the way in which we measure heterogeneity at different scales and model the response of organisms to spatial heterogeneity.     

Evolution of acuteness in pathogen metapopulations: conflicts between “classical” and invasion-persistence trade-offs

Classical life-history theory predicts that acute, immunizing pathogens should maximize between-host transmission. When such pathogens induce violent epidemic outbreaks, however, a pathogen’s short-term advantage at invasion may come at the expense of its ability to persist in the population over the long term. Here, we seek to understand how the classical and invasion-persistence trade-offs interact to shape pathogen life-history evolution as a function of the size and structure of the host population. We develop an individual-based infection model at three distinct levels of organization: within an individual host, among hosts within a local population, and among local populations within a metapopulation. We find a continuum of evolutionarily stable pathogen strategies. At one end of the spectrum—in large well-mixed populations—pathogens evolve to greater acuteness to maximize between-host transmission: the classical trade-off theory applies in this regime. At the other end of the spectrum—when the host population is broken into many small patches—selection favors less acute pathogens, which persist longer within a patch and thereby achieve enhanced between-patch transmission: the invasion-persistence trade-off dominates in this regime. Between these extremes, we explore the effects of the size and structure of the host population in determining pathogen strategy. In general, pathogen strategies respond to evolutionary pressures arising at both scales.     

Determinants of species richness patterns in the Netherlands across multiple taxonomic groups

We examined the species richness patterns of five different species groups (mosses, reptiles and amphibians, grasshoppers and crickets, dragonflies, and hoverflies) in the Netherlands (41,500 km²) using sampling units of 5 × 5 km. We compared the spatial patterns of species richness of the five groups using Spearman’s rank correlation and used a stepwise multiple regression generalized linear modelling (GLM) approach to assess their relation with a set of 36 environmental variables, selected because they can be related to the several hypotheses on biodiversity patterns. Species richness patterns of the five groups were to a certain extent congruent. Our data suggest that environmental heterogeneity (in particular habitat heterogeneity) is one of the major determinants of variation in species richness within these five groups. We found that for taxonomic groups comprising a low number of species, our regression model explained more of the variability in species richness than for taxonomic groups with a large number of species.     

Aggregation of parasitism risk in an aphid-parasitoid system: Effects of plant patch size and aphid density

Few studies have linked density dependence of parasitism and the tritrophic environment within which a parasitoid forages. In the non-crop plant-aphid, Centaurea nigra–Uroleucon jaceae system, mixed patterns of density-dependent parasitism by the parasitoids Aphidius funebris and Trioxys centaureae were observed in a survey of a natural population. Breakdown of density-dependent parasitism revealed that density dependence was inverse in smaller colonies but direct in larger colonies (>20 aphids), suggesting there is a threshold effect in parasitoid response to aphid density. The CV² of searching parasitoids was estimated from parasitism data using a hierarchical generalized linear model, and CV²>1 for A. funebris between plant patches, while for T. centaureae CV²>1 within plant patches. In both cases, density independent heterogeneity was more important than density-dependent heterogeneity in parasitism. Parasitism by T. centaureae increased with increasing plant patch size. Manipulation of aphid colony size and plant patch size revealed that parasitism by A. funebris was directly density dependent at the range of colony sizes tested (50–200 initial aphids), and had a strong positive relationship with plant patch size. The effects of plant patch size detected for both species indicate that the tritrophic environment provides a source of host density independent heterogeneity in parasitism, and can modify density-dependent responses.     

Spatial variation in coevolutionary dynamics

Summary Computer simulations of coevolutionary dynamics between two hosts and two parasites show that extensive spatial variation in polymorphism can be maintained among environmentally identical patches. Spatial variation can be maintained under frequent migration when the dynamics within patches are locally unstable, and the cycles in host and parasite abundances remain out of phase among patches. Additionally, spatial variation can be maintained when host-parasite interactions cause local extinctions, and migration subsequently allows for recolonization. The temporal dynamics that cause spatial variation are difficult to study directly because of the long time scale over which they occur. The simulations suggest that sampling over space at one or a few points in time may provide much information about temporal dynamics.     

Maximum likelihood estimation of generalized linear models for adaptive designs: Applications and asymptotics

Due to increasing discoveries of biomarkers and observed diversity among patients, there is growing interest in personalized medicine for the purpose of increasing the well‐being of patients (ethics) and extending human life. In fact, these biomarkers and observed heterogeneity among patients are useful covariates that can be used to achieve the ethical goals of clinical trials and improving the efficiency of statistical inference. Covariate‐adjusted response‐adaptive (CARA) design was developed to use information in such covariates in randomization to maximize the well‐being of participating patients as well as increase the efficiency of statistical inference at the end of a clinical trial. In this paper, we establish conditions for consistency and asymptotic normality of maximum likelihood (ML) estimators of generalized linear models (GLM) for a general class of adaptive designs. We prove that the ML estimators are consistent and asymptotically follow a multivariate Gaussian distribution. The efficiency of the estimators and the performance of response‐adaptive (RA), CARA, and completely randomized (CR) designs are examined based on the well‐being of patients under a logit model with categorical covariates. Results from our simulation studies and application to data from a clinical trial on stroke prevention in atrial fibrillation (SPAF) show that RA designs lead to ethically desirable outcomes as well as higher statistical efficiency compared to CARA designs if there is no treatment by covariate interaction in an ideal model. CARA designs were however more ethical than RA designs when there was significant interaction.     

Insect density–plant density relationships: a modified view of insect responses to resource concentrations

Habitat area is an important predictor of spatial variation in animal densities. However, the area often correlates with the quantity of resources within habitats, complicating our understanding of the factors shaping animal distributions. We addressed this problem by investigating densities of insect herbivores in habitat patches with a constant area but varying numbers of plants. Using a mathematical model, predictions of scale-dependent immigration and emigration rates for insects into patches with different densities of host plants were derived. Moreover, a field experiment was conducted where the scaling properties of odour-mediated attraction in relation to the number of odour sources were estimated, in order to derive a prediction of immigration rates of olfactory searchers. The theoretical model predicted that we should expect immigration rates of contact and visual searchers to be determined by patch area, with a steep scaling coefficient, μ = ?1. The field experiment suggested that olfactory searchers should show a less steep scaling coefficient, with μ ≈ ?0.5. A parameter estimation and analysis of published data revealed a correspondence between observations and predictions, and density-variation among groups could largely be explained by search behaviour. Aphids showed scaling coefficients corresponding to the prediction for contact/visual searchers, whereas moths, flies and beetles corresponded to the prediction for olfactory searchers. As density responses varied considerably among groups, and variation could be explained by a certain trait, we conclude that a general theory of insect responses to habitat heterogeneity should be based on shared traits, rather than a general prediction for all species.     

Semiparametric models for missing covariate and response data in regression models

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods.     

Search mechanism of a stream grazer in patchy environments: the role of food abundance

Summary The search behavior of the grazing stream insect Baetis tricaudatus (Ephemeroptera: Baetidae) was examined in field and laboratory experiments. Regardless of food abundance in experimental habitats, nymphs spent significantly more time in food patches than predicted if they had moved randomly with respect to patches. A significant reduction in movement rate within patches relative to movement rate between patches largely accounted for these results. The movement pattern within patches was highly systematic and in agreement with predictions of optimal foraging theory since food was uniformly distributed within patches. Between-patch search movements were affected by food abundance in the most recently grazed patch. Search intensity after departure from a patch was positively related to food abundance in the patch while movement rate after patch departure was inversely related to patch food level. These effects produced between-patch movement patterns that were suboptimal in the experimental habitats because they resulted in revisitation of previously depleted patches. However, differences between experimental and natural habitats in the spatial occurrence of patch types suggest that Baetis between-patch search behavior may be adaptive in natural habitats.     

Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales

Abstract. Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alternative methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive models and classification tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better accuracy than GLM with quadratic and polynomial terms. This is probably because distributions at finer scales represent a linear sub‐sample of entire realized niches of species. In contrast to GLM, the performance of GAM was constant across scales being more data‐oriented. The predictive accuracy of GAM was always at least equal to other techniques, suggesting that this modelling approach is more robust to variations of scale because it can deal with any response shape.     

Demography when history matters: construction and analysis of second-order matrix population models

History matters when individual prior conditions contain important information about the fate of individuals. We present a general framework for demographic models which incorporates the effects of history on population dynamics. The framework incorporates prior condition into the i-state variable and includes an algorithm for constructing the population projection matrix from information on current state dynamics as a function of prior condition. Three biologically motivated classes of prior condition are included: prior stages, linear functions of current and prior stages, and equivalence classes of prior stages. Taking advantage of the matrix formulation of the model, we show how to calculate sensitivity and elasticity of any demographic outcome. Prior condition effects are a source of inter-individual variation in vital rates, i.e., individual heterogeneity. As an example, we construct and analyze a second-order model of Lathyrus vernus, a long-lived herb. We present population growth rate, the stable population distribution, the reproductive value vector, and the elasticity of λ to changes in the second-order transition rates. We quantify the contribution of prior conditions to the total heterogeneity in the stable population of Lathyrus using the entropy of the stable distribution.     

Evolutionary branching of dispersal strategies in structured metapopulations

 Dispersal polymorphism and evolutionary branching of dispersal strategies has been found in several metapopulation models. The mechanism behind those findings has been temporal variation caused by cyclic or chaotic local dynamics, or temporally and spatially varying carrying capacities. We present a new mechanism: spatial heterogeneity in the sense of different patch types with sufficient proportions, and temporal variation caused by catastrophes. The model where this occurs is a generalization of the model by Gyllenberg and Metz (2001). Their model is a size-structured metapopulation model with infinitely many identical patches. We present a generalized version of their metapopulation model allowing for different types of patches. In structured population models, defining and computing fitness in polymorphic situations is, in general, difficult. We present an efficient method, which can be applied also to other structured population or metapopulation models. Received: 6 March 2001 / Revised version: 12 February 2002 / Published online: 17 July 2002     

Simulation of movement and egglaying inDanaus plexippus (Lepidoptera: Nymphalidae)

A model simulating the movement and oviposition of monarch butterflies over a female's life time is presented. The model's predictions compare favourably with observed data and suggest that females who lay eggs in an optimal fashion should have low directionalities in areas with high host plant density (patches and single plants) and high directionalities in areas with low host plant density. The model also provides one means of combining individual animal processes and spatial heterogeneity into population dynamics.     

The generalized linear model for spatial data: assessing the effects of environmental covariates on population density in the field

With interest in spatial ecology growing, correlational field studies are likely to become increasingly important. Unfortunately, ecological field data often do not follow the assumptions of classical statistics, so techniques like the popular and powerful multiple linear regression and its variants are often unreliable, and results can be misleading. The generalized linear model (GLM) is a flexible extension of linear regression that has proved especially useful for discrete data. In this paper, the technique is adapted to accommodate spatially correlated, discrete data. Specifically, to demonstrate the approach, Japanese beetle grub [Popillia japonica Newman (Coleoptera, Scarabaeidae)] population density in the field is modeled as a function of soil organic matter content. The response variable (grub counts in small soil samples) was a spatially autocorrelated, discrete random variable. Three classes of GLMs of the association between soil organic matter content and grub density were compared: (i) regression (assuming normally distributed response variable), (ii) GLM assuming negative binomial counts, and (iii) GLM based on the assumption that the counts conformed to Taylor's power law (TPL). Because the grubs were distributed in patches rather than at random, models that explicitly accounted for the spatial autocorrelation of grub counts were constructed, and compared with models that assumed independent observations. The fitted values for the discrete GLMs [viz., (ii) and (iii)] differed noticeably from the fitted values from multiple regression; but fitted values among the negative binomial and TPL GLMs were virtually identical, regardless of whether the spatial covariance was incorporated into a model, whether a spherical or exponential variogram model was used, or whether variance function parameters were estimated over a large or small scale. However, P‐values for the overall significance of the models depended heavily on whether the GLM assumed a discrete or continuous response variable, and whether or not spatial autocorrelation in the response variable was accounted for. On average, P‐values were 45‐fold higher in the spatial GLMs than in the non‐spatial and 23‐fold higher in the discrete GLMs than in the continuous.