Participatory Ecological Monitoring of the Alaotra Wetlands in Madagascar

Participatory ecological monitoring is a realistic and effective approach in wetlands such as Alaotra, Madagascar, where important biodiversity is found in an area with high human population density. Since 2001, Durrell Wildlife Conservation Trust, government technical services, regional non-governmental organisations and local communities have collected data on key species, such as waterbirds, a locally endemic lemur and useful natural resources. The monitoring was linked with environmental quizzes and an inter-village competition, which helped raise interest in the monitoring and publicise results. The monitoring has assisted wetland management by guiding amendments to and increasing respect for the regional fishing convention, raising awareness, catalysing marsh management transfer to communities and stimulating collaboration and good governance. The sustainability of the monitoring scheme and the usefulness of the data for detecting trends and guiding local managements are discussed.     

Perspective: Reproductive isolation caused by natural selection against immigrants from divergent habitats

The classification of reproductive isolating barriers laid out by Dobzhansky and Mayr has motivated and structured decades of research on speciation. We argue, however, that this classification is incomplete and that the unique contributions of a major source of reproductive isolation have often been overlooked. Here, we describe reproductive barriers that derive from the reduced survival of immigrants upon reaching foreign habitats that are ecologically divergent from their native habitat. This selection against immigrants reduces encounters and thus mating opportunities between individuals from divergently adapted populations. It also reduces the likelihood that successfully mated immigrant females will survive long enough to produce their hybrid offspring. Thus, natural selection against immigrants results in distinctive elements of premating and postmating reproductive isolation that we hereby dub "immigrant inviability." We quantify the contributions of immigrant inviability to total reproductive isolation by examining study systems where multiple components of reproductive isolation have been measured and demonstrate that these contributions are frequently greater than those of traditionally recognized reproductive barriers. The relevance of immigrant inviability is further illustrated by a consideration of population-genetic theory, a review of selection against immigrant alleles in hybrid zone studies, and an examination of its participation in feedback loops that influence the evolution of additional reproductive barriers. Because some degree of immigrant inviability will commonly exist between populations that exhibit adaptive ecological divergence, we emphasize that these barriers play critical roles in ecological modes of speciation. We hope that the formal recognition of immigrant inviability and our demonstration of its evolutionary importance will stimulate more explicit empirical studies of its contributions to speciation.     

Effects of variance-function misspecification in analysis of longitudinal data

The approach of generalized estimating equations (GEE) is based on the framework of generalized linear models but allows for specification of a working matrix for modeling within-subject correlations. The variance is often assumed to be a known function of the mean. This article investigates the impacts of misspecifying the variance function on estimators of the mean parameters for quantitative responses. Our numerical studies indicate that (1) correct specification of the variance function can improve the estimation efficiency even if the correlation structure is misspecified; (2) misspecification of the variance function impacts much more on estimators for within-cluster covariates than for cluster-level covariates; and (3) if the variance function is misspecified, correct choice of the correlation structure may not necessarily improve estimation efficiency. We illustrate impacts of different variance functions using a real data set from cow growth.     

Robust estimating functions and bias correction for longitudinal data analysis

Robust methods are useful in making reliable statistical inferences when there are small deviations from the model assumptions. The widely used method of the generalized estimating equations can be "robustified" by replacing the standardized residuals with the M-residuals. If the Pearson residuals are assumed to be unbiased from zero, parameter estimators from the robust approach are asymptotically biased when error distributions are not symmetric. We propose a distribution-free method for correcting this bias. Our extensive numerical studies show that the proposed method can reduce the bias substantially. Examples are given for illustration.     

Conditional likelihood score functions for mixed models in linkage analysis

In this paper, we develop a general strategy for linkage analysis, applicable for arbitrary pedigree structures and genetic models with one major gene, polygenes and shared environmental effects. Extending work of Whittemore (1996), McPeek (1999) and Hossjer (2003d), the efficient score statistic is computed from a conditional likelihood of marker data given phenotypes. The resulting semiparametric linkage analysis is very similar to nonparametric linkage based on affected individuals. The efficient score S depends not only on identical-by-descent sharing and phenotypes, but also on a few parameters chosen by the user. We focus on (1) weak penetrance models, where the major gene has a small effect and (2) rare disease models, where the major gene has a possibly strong effect but the disease causing allele is rare. We illustrate our results for a large class of genetic models with a multivariate Gaussian liability. This class incorporates one major gene, polygenes and shared environmental effects in the liability, and allows e.g. binary, Gaussian, Poisson distributed and life-length phenotypes. A detailed simulation study is conducted for Gaussian phenotypes. The performance of the two optimal score functions S(wpairs) and S(normdom) are investigated. The conclusion is that (i) inclusion of polygenic effects into the score function increases overall performance for a wide range of genetic models and (ii) score functions based on the rare disease assumption are slightly more powerful.     

On criteria for evaluating models of absolute risk

Absolute risk is the probability that an individual who is free of a given disease at an initial age, a, will develop that disease in the subsequent interval (a, t]. Absolute risk is reduced by mortality from competing risks. Models of absolute risk that depend on covariates have been used to design intervention studies, to counsel patients regarding their risks of disease and to inform clinical decisions, such as whether or not to take tamoxifen to prevent breast cancer. Several general criteria have been used to evaluate models of absolute risk, including how well the model predicts the observed numbers of events in subsets of the population ("calibration"), and "discriminatory power," measured by the concordance statistic. In this paper we review some general criteria and develop specific loss function-based criteria for two applications, namely whether or not to screen a population to select subjects for further evaluation or treatment and whether or not to use a preventive intervention that has both beneficial and adverse effects. We find that high discriminatory power is much more crucial in the screening application than in the preventive intervention application. These examples indicate that the usefulness of a general criterion such as concordance depends on the application, and that using specific loss functions can lead to more appropriate assessments.     

Plant species richness and spatial organization at different small scales in western Mediterranean landscapes

The relationship between plant species richness and the space organization of the community at different small scales was studied. The study was based on 51 sites distributed along a belt from Central Spain to Portugal. Each site was analyzed with a transect cutting across the boundary between two neighboring patches of shrubland and grassland. Local spatial organization of vegetation was analyzed at different levels of detail and each transect was divided into successively smaller portions. The first division coincides with a physiognomic perception of the site in two patches (shrubland and grassland). The average spatial niche width of the species was used to calculate the spatial organization of the vegetation of each division in each site. The correlation between species richness and spatial organization depended on the block size under consideration. A physiognomic criterion, sectorizing the sites into patches of shrubland and grassland, determines noteworthy floristic changes but does not enable us to express satisfactorily the variability in plant richness. In order to account for this variation, other factors must be taken into account which act at a more detailed small-scale and which determine the internal variability of these patches. In the case studied, the species richness of the sites increases along with an increase in the percentage of species whose occupation of the space is relatively restricted within the site. Many of these species are, however, frequent within the whole of the territory studied. The results highlight the importance of the level of local scale at which the factors influencing occupation of the space, and consequentially, plant richness, preferentially act. This circumstance ought to be taken into consideration in strategies for the conservation of biological diversity, and based on the delimitation of protected spaces with criteria frequently linked to the physiognomy of the vegetation.Nomenclature: Follows T.G. Tutin et al. 1964-1980. Flora Europaea. Cambridge University Press, Cambridge     

Effect of indinavir used alone or in double or triple combination with AZT and ddC on human immune functions

Indinavir (IDV) is a potent and selective human immunodeficiency virus type 1 (HIV-1) protease inhibitor (PI) widely used in antiretroviral therapy, but its effects on the immune system are relatively unknown. In this study we have investigated the in vitro effect of IDV on normal human peripheral blood mononuclear cells (PBMC). We used the drug alone or in double and triple combination with AZT and ddC to assess whether IDV interferes with the previously observed immunomodulatory effects induced by AZT and ddC. We found that proliferative response, induction of immunoglobulins (Ig) production and cytokine production was not modulated by IDV. More importantly, IDV used in double or triple combination with AZT and ddC, does not further strenghten the inhibition of proliferative response induced by AZT and is able to abrogate the inhibitory effect induced by ddC on proliferative response. Similarly, IDV/AZT, IDV/ddC and IDV/AZT/ddC combinations does not strenghten the modulation of TNF-alpha, IFN-gamma and IL-4 induced by AZT, ddC and AZT/ddC. On the other hand, IDV neutralizes the up-regulating effects of AZT on IL-2 production while the up-regulating effects of ddC on IL-2 production is not affected. These data suggest that IDV used in combination with AZT and ddC did not add any further immunotoxicity.     

Correlates of cell-mediated immunity in nestling house sparrows

Cell-mediated immunity is an important vertebrate defense against pathogens, but components of this response may vary in quality. Such variation could arise through the effects of ecology on optimal immunocompetence. We used injections of phytohaemagglutinin (PHA) to measure the factors influencing T-cell proliferation in nestling house sparrows (Passer domesticus). Bivariate analyses revealed positive associations with nestling mass and size, but no effect of ectoparasites. The response to PHA was, however, strongly affected by brood identity. A mixed model with brood identity as a random factor and nestling mass, size, number of ectoparasites, parental feeding rate, clutch size, brood size at hatching, and date uncovered significant positive correlations between PHA response and both nestling mass and the brood size at hatching. Because many of these variables are related hierarchically, we used path analysis to explore the relationships in more detail. We found that a nestling immune response was affected by several indirect paths. Brood size at hatch had both positive and negative paths, and date in the season had several indirect negative effects through its effect on brood size and nestling mass. The approach used and the results obtained offer some new ideas for incorporating immune responses into life history theory.