Using life-history traits to explain bird population responses to changing weather variability

Bird population dynamics are expected to change in response to increased weather variability, an expression of climate change. The extent to which species are sensitive to effects of weather on survival and reproduction depends on their life-history traits. We investigated how breeding bird species can be grouped, based on their life-history traits and according to weather-correlated population dynamics. We developed and applied the linear trait–environment method (LTE), which is a modified version of the fourth-corner method. Despite our focus on single traits, 2 strategies—combinations of several traits—stand out. As expected, breeding populations of waterfowl species are negatively impacted by severe winters directly preceding territory monitoring, probably because of increased adult mortality. Waterfowl species combine several traits: they often breed at ground or water level, feed on plant material, are precocial and are generally short-distance or partial migrants. Furthermore, we found a decline in population growth rates of insectivorous long-distance migrants due to mild winters and warm springs in the year before territory monitoring, which may be caused by reduced reproduction due to trophic mismatches. We identify species that are expected to show the most significant responses to changing weather variability, assuming that our conclusions are based on causal relationships and that the way species, weather variables and habitat interact will not alter. Species expected to respond positively can again be roughly categorized as waterfowl species, while insectivorous long-distance migrants are mostly expected to respond negatively. As species traits play an important role in constructing functional groups that are relevant to the provisioning of ecosystem services, our study enables the incorporation of ecosystem vulnerability to climate change into such functional approache

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrate

How do Customers Alter Their Basket Composition When They Perceive the Retail Store to Be Crowded? An Empirical Study

Using data from a large-scale field study, we show that (perceptions of) crowding change(s) the composition of a consumer's shopping basket. Specifically, as shoppers experience more crowding, their shopping basket contains (a) relatively more affect-rich (“hedonic”) products, and (b) relatively more national brands. We offer a plausible dual-process explanation for this phenomenon: Crowding induced distraction limits cognitive capacity, increasing the relative impact of affective responses in purchase decisions. As we are the first to show that level of crowding relates to what shoppers buy (at both product and brand level), the implications of these effects for retailers are discussed

A Bayesian palaeoenvironmental transfer function model for acidified lakes

A Bayesian approach to palaeoecological environmental reconstruction deriving from the unimodal responses generally exhibited by organisms to an environmental gradient is described. The approach uses Bayesian model selection to calculate a collection of probability-weighted, species-specific response curves (SRCs) for each taxon within a training set, with an explicit treatment for zero abundances. These SRCs are used to reconstruct the environmental variable from sub-fossilised assemblages. The approach enables a substantial increase in computational efficiency (several orders of magnitude) over existing Bayesian methodologies. The model is developed from the Surface Water Acidification Programme (SWAP) training set and is demonstrated to exhibit comparable predictive power to existing Weighted Averaging and Maximum Likelihood methodologies, though with improvements in bias; the additional explanatory power of the Bayesian approach lies in an explicit calculation of uncertainty for each individual reconstruction. The model is applied to reconstruct the Holocene acidification history of the Round Loch of Glenhead, including a reconstruction of recent recovery derived from sediment trap data.The Bayesian reconstructions display similar trends to conventional (Weighted Averaging Partial Least Squares) reconstructions but provide a better reconstruction of extreme pH and are more sensitive to small changes in diatom assemblages. The validity of the posteriors as an apparently meaningful representation of assemblage-specific uncertainty and the high computational efficiency of the approach open up the possibility of highly constrained multiproxy reconstructions

Predicting changes in ecosystem functioning from shifts in plant traits

Contains fulltext : 135500.pdf (publisher's version ) (Open Access

Relationships between quality of life and family function in caregiver

<p>Abstract</p> <p>Background</p> <p>There are caregivers who see their quality of life (QoL) impaired due to the demands of their caregiving tasks, while others manage to adapt and overcome the crises successfully. The influence of the family function in the main caregiver's situation has not been the subject of much evaluation. The aim of this study is to analyse the relationship between the functionality of the family and the QoL of caregivers of dependent relatives.</p> <p>Methods</p> <p>We conducted a cross-sectional study including 153 caregivers. Setting: Two health centers in the city of Salamanca(Spain). Caregiver variables analysed: demographic characteristics, care recipient features; family functionality (Family APGAR-Q) and QoL (Ruiz-Baca-Q) perceived by the caregiver. Five multiple regressions are performed considering global QoL and each of the four QoL dimensions as dependent variables. The Canonical Correspondence Analysis (CCA) was used to study the influence of the family function questionnaire on QoL.</p> <p>Results</p> <p>Family function is the only one of the variables evaluated that presented an association both with global QoL and with each of the four individual dimensions (p < 0.05). Using the CCA, we found that the physical and mental well-being dimensions are the ones which present a closer relationship with family functionality, while social support is the quality dimension that is least influenced by the Family APGAR-Q.</p> <p>Conclusion</p> <p>We find an association between family functionality and the caregiver's QoL. This relation holds for both the global measure of QoL and each of its four individual dimensions.</p

Bayesian Methods for Analysis and Adaptive Scheduling of Exoplanet Observations

We describe work in progress by a collaboration of astronomers and statisticians developing a suite of Bayesian data analysis tools for extrasolar planet (exoplanet) detection, planetary orbit estimation, and adaptive scheduling of observations. Our work addresses analysis of stellar reflex motion data, where a planet is detected by observing the "wobble" of its host star as it responds to the gravitational tug of the orbiting planet. Newtonian mechanics specifies an analytical model for the resulting time series, but it is strongly nonlinear, yielding complex, multimodal likelihood functions; it is even more complex when multiple planets are present. The parameter spaces range in size from few-dimensional to dozens of dimensions, depending on the number of planets in the system, and the type of motion measured (line-of-sight velocity, or position on the sky). Since orbits are periodic, Bayesian generalizations of periodogram methods facilitate the analysis. This relies on the model being linearly separable, enabling partial analytical marginalization, reducing the dimension of the parameter space. Subsequent analysis uses adaptive Markov chain Monte Carlo methods and adaptive importance sampling to perform the integrals required for both inference (planet detection and orbit measurement), and information-maximizing sequential design (for adaptive scheduling of observations). We present an overview of our current techniques and highlight directions being explored by ongoing research.Comment: 29 pages, 11 figures. An abridged version is accepted for publication in Statistical Methodology for a special issue on astrostatistics, with selected (refereed) papers presented at the Astronomical Data Analysis Conference (ADA VI) held in Monastir, Tunisia, in May 2010. Update corrects equation (3

Globally sparse PLS regression

Volume 56 ; Print ISBN : 978-1-4614-8282-6Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and re- sponse variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a new criterion for sparse PLS by adding a structured sparsity constraint to the global SIMPLS optimization. The constraint is a sparsity-inducing norm, which is useful for selecting the important variables shared among all the components. The optimization is solved by an augmented Lagrangian method to obtain the PLS components and to perform variable selection simultaneously. We propose a novel greedy algorithm to overcome the computation difficulties. Experiments demonstrate that our approach to PLS regression attains better performance with fewer selected predictor