Interactions in a tritrophic acarine predator– prey metapopulation system: prey location and distance moved by Phytoseiulus persimilis (Acari: Phytoseiidae)

The between-plant movement of the predatory mite Phytoseiulus persimilis was studied in a greenhouse. The aims were to determine the distance moved by P. persimilis and the response of the predator to the location of a plant infested with two-spotted spider mite, Tetranychus urticae. In addition, we tested whether the predator exhibits random movement between plants or whether its dispersal is oriented. We found that a high proportion of the predators released on a central plant were able to reach plants at the periphery provided the plants were connected to the central plant with 'bridges'. The results further showed that P. persimilis does not disperse randomly to the surrounding plants. The distribution of immigrants was influenced by the position of an infested plant in the neighbourhood, but light/shadow effects in the greenhouse may also influence the choice of direction. The likely implications of the findings for biological control are discussed. © Rapid Science Ltd. 1998     

Thermodynamics of a designed protein catenane

Topological linking of proteins is a new approach for stabilizing and controlling the oligomerization state of proteins that fold in an interwined manner. The recent design of a backbone cyclized protein catenane based on the p53tet domain suggested that topological cross-linking provided increased stability against thermal and chemical denaturation. However, the tetrameric structure complicated detailed biophysical analysis of this protein. Here, we describe the design, synthesis and thermodynamic characterization of a protein catenane based on a dimeric mutant of the p53tet domain (M340E/L344K). The formation of the catenane proceeded efficiently, and the overall structure and oligomerization of the domain was not affected by the formation of the topological link. Unfolding and refolding of the catenane was consistent with a two-state process. The topological link stabilized the dimer against thermal and chemical denaturation considerably, raising the apparent melting temperature by 59 degrees C and the midpoint of denaturation by 4.5M GuHCl at a concentration of 50 microM. The formation of the topological link increased the resistance of the dimer to proteolysis. However, the m value decreased by 1.7kcalmol(-1)M(-1), suggesting a decrease in accessible surface area in the unfolded state. This implies that the stabilization from the topological link is largely due to a destabilization of the unfolded state, similar to other cross-links in proteins. Topological linking therefore provides a powerful and orthogonal tool for the stabilization of peptide and protein oligomers.     

A diagnostic for Cox regression with discrete failure-time models

Changes in maximum likelihood parameter estimates due to deletion of individual observations are useful statistics, both for regression diagnostics and for computing robust estimates of covariance. For many likelihoods, including those in the exponential family, these delete-one statistics can be approximated analytically from a one-step Newton-Raphson iteration on the full maximum likelihood solution. But for general conditional likelihoods and the related Cox partial likelihood, the one-step method does not reduce to an analytic solution. For these likelihoods, an alternative analytic approximation that relies on an appropriately augmented design matrix has been proposed. In this paper, we extend the augmentation approach to explicitly deal with discrete failure-time models. In these models, an individual subject may contribute information at several time points, thereby appearing in multiple risk sets before eventually experiencing a failure or being censored. Our extension also allows the covariates to be time dependent. The new augmentation requires no additional computational resources while improving results.     

AN “ENVIRO‐INFORMATIC” ASSESSMENT OF SAGINAW BAY (LAKE HURON,USA) PHYTOPLANKTON: DATA‐DRIVEN CHARACTERIZATION AND MODELING OF MICROCYSTIS (CYANOPHYTA)1

Phytoplankton and Microcystis aeruginosa (Kütz.) Kütz. biovolumes were characterized and modeled, respectively, with regard to hydrological and meteorological variables during zebra mussel invasion in Saginaw Bay (1990–1996). Total phytoplankton and Microcystis biomass within the inner bay were one and one‐half and six times greater, respectively, than those of the outer bay. Following mussel invasion, mean total biomass in the inner bay decreased 84% but then returned to its approximate initial value. Microcystis was not present in the bay during 1990 and 1991 and thereafter occurred at/in 52% of sample sites/dates with the greatest biomass occurring in 1994–1996 and within months having water temperatures >19°C. With an overall relative biomass of 0.03 ± 0.01 (mean + SE), Microcystis had, at best, a marginal impact upon holistic compositional dynamics. Dynamics of the centric diatom Cyclotella ocellata Pant. and large pennate diatoms dominated compositional dissimilarities both inter‐ and intra‐annually. The environmental variables that corresponded with phytoplankton distributions were similar for the inner and outer bays, and together identified physical forcing and biotic utilization of nutrients as determinants of system‐level biomass patterns. Nonparametric models explained 70%–85% of the variability in Microcystis biovolumes and identified maximal biomass to occur at total phosphorus (TP) concentrations ranging from 40 to 45 μg · L^?1. From isometric projections depicting modeled Microcystis/environmental interactions, a TP concentration of <30 μg · L^?1 was identified as a desirable contemporary “target” for management efforts to ameliorate bloom potentials throughout mussel‐impacted bay waters.     

The evolution of virulence in primate malaria parasites based on Bayesian reconstructions of ancestral states

Plasmodium parasites, the causative agents of malaria, are generally considered as harmful parasites, but many of them cause mild symptoms. Little is known about the evolutionary history and phylogenetic constraints that generate this interspecific variation in virulence due to uncertainties about the phylogenetic associations of parasites. Here, to account for such phylogenetic uncertainty, phylogenetic methods based on Bayesian statistics were followed in combination with sequence data from five genes to estimate the ancestral state of virulence in primate Plasmodium parasites. When recent parasites were categorised according to the damage caused to the host, Bayesian estimates of ancestral states indicated that the acquisition of a harmful host exploitation strategy is more likely to be a recent evolutionary event than a result of an ancient change in a character state altering virulence. On the contrary, there was more evidence for moderate host exploitation having a deep origin along the phylogenetic tree. Moreover, the evolution of host severity is determined by the phylogenetic relationships of parasites, as severity gains did not appear randomly on the evolutionary tree. Such phylogenetic constraints can be mediated by the acquisition of virulence genes. As the impact of a parasite on a host is the result of both the parasite’s investment in reproduction and host sensitivity, virulence was also estimated by calculating peak parasitemia after eliminating host effects. A directional random-walk evolutionary model showed that the ancestral primate malarias reproduced at very low parasitemia in their hosts. Consequently, the extreme variation in the outcome of malaria infection in different host species can be better understood in light of the phylogeny of parasites.     

Dynamics of Fst for the island model

F(st) is a measure of genetic differentiation in a subdivided population. Sewall Wright observed that F(st)=1/1+2Nm in a haploid diallelic infinite island model, where N is the effective population size of each deme and m is the migration rate. In demonstrating this result, Wright relied on the infinite size of the population. Natural populations are not infinite and therefore they change over time due to genetic drift. In a finite population, F(st) becomes a random variable that evolves over time. In this work we ask, given an initial population state, what are the dynamics of the mean and variance of F(st) under the finite island model? In application both of these quantities are critical in the evaluation of F(st) data. We show that after a time of order N generations the mean of F(st) is slightly biased below 1/1+2Nm. Further we show that the variance of F(st) is of order 1/d where d is the number of demes in the population. We introduce several new mathematical techniques to analyze coalescent genealogies in a dynamic setting.     

Measurement error in racial and ethnic statistics

In the United States, the racial and ethnic statistics published by the National Center for Health Statistics (NCHS) assume that each member of the U.S. population has a race and ethnicity and that if a member is black or white with respect to his risk of one disease, he is the same race with respect to his risk of another. Such an assumption is mistaken. Race and ethnicity are taken by the NCHS to be an intrinsic property of members of a population, when they should be taken to depend on interest. The actual or underlying race or ethnicity of members of a population depends on the risk whose variation within the population we wish to describe or explain.

Microsatellite null alleles and estimation of population differentiation

Microsatellite null alleles are commonly encountered in population genetics studies, yet little is known about their impact on the estimation of population differentiation. Computer simulations based on the coalescent were used to investigate the evolutionary dynamics of null alleles, their impact on F(ST) and genetic distances, and the efficiency of estimators of null allele frequency. Further, we explored how the existing method for correcting genotype data for null alleles performed in estimating F(ST) and genetic distances, and we compared this method with a new method proposed here (for F(ST) only). Null alleles were likely to be encountered in populations with a large effective size, with an unusually high mutation rate in the flanking regions, and that have diverged from the population from which the cloned allele state was drawn and the primers designed. When populations were significantly differentiated, F(ST) and genetic distances were overestimated in the presence of null alleles. Frequency of null alleles was estimated precisely with the algorithm presented in Dempster et al. (1977). The conventional method for correcting genotype data for null alleles did not provide an accurate estimate of F(ST) and genetic distances. However, the use of the genetic distance of Cavalli-Sforza and Edwards (1967) corrected by the conventional method gave better estimates than those obtained without correction. F(ST) estimation from corrected genotype frequencies performed well when restricted to visible allele sizes. Both the proposed method and the traditional correction method have been implemented in a program that is available free of charge at http://www.montpellier.inra.fr/URLB/. We used 2 published microsatellite data sets based on original and redesigned pairs of primers to empirically confirm our simulation results.     

ANNA: A new prediction method for bioassessment programs

1. Cluster analysis of reference sites with similar biota is the initial step in creating River Invertebrate Prediction and Classification System (RIVPACS) and similar river bioassessment models such as Australian River Assessment System (AUSRIVAS). This paper describes and tests an alternative prediction method, Assessment by Nearest Neighbour Analysis (ANNA), based on the same philosophy as RIVPACS and AUSRIVAS but without the grouping step that some people view as artificial. 2. The steps in creating ANNA models are: (i) weighting the predictor variables using a multivariate approach analogous to principal axis correlations, (ii) calculating the weighted Euclidian distance from a test site to the reference sites based on the environmental predictors, (iii) predicting the faunal composition based on the nearest reference sites and (iv) calculating an observed/expected (O/E) analogous to RIVPACS/AUSRIVAS. 3. The paper compares AUSRIVAS and ANNA models on 17 datasets representing a variety of habitats and seasons. First, it examines each model's regressions for Observed versus Expected number of taxa, including the r², intercept and slope. Second, the two models’ assessments of 79 test sites in New Zealand are compared. Third, the models are compared on test and presumed reference sites along a known trace metal gradient. Fourth, ANNA models are evaluated for western Australia, a geographically distinct region of Australia. The comparisons demonstrate that ANNA and AUSRIVAS are generally equivalent in performance, although ANNA turns out to be potentially more robust for the O versus E regressions and is potentially more accurate on the trace metal gradient sites. 4. The ANNA method is recommended for use in bioassessment of rivers, at least for corroborating the results of the well established AUSRIVAS‐ and RIVPACS‐type models, if not to replace them.     

Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics

Aim The study and prediction of species–environment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process‐based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process‐based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process‐based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.