An autoparasitoid wasp, inferior at resource exploitation, outcompetes primary parasitoids by using competitor females to produce males

1. Autoparasitoids are intraguild consumers that attack and kill heterospecific and conspecific parasitoids as well as immature stages of hemipteran hosts, such as aphids, whiteflies and soft scales. Field experiments assessing the importance of interspecific competition between autoparasitoids and primary parasitoids, as well as its impact on herbivore suppression, are scarcely found in the ecological literature. 2. Using field data from 40 olive orchards, this study examined the mechanisms that regulate: (i) the interspecific competition between primary parasitoids of the genus Metaphycus and the autoparasitoid Coccophagus lycimnia; and (ii) the density of their shared herbivore host, the soft scale Saissetia oleae. 3. Metaphycus parasitoids used smaller hosts than C. lycimnia, yet did not outcompete C. lycimnia. On the other hand, C. lycimnia preferred to use Metaphycus females as secondary hosts for producing males rather than their own females. This preference might explain why the autoparasitoid negatively affected the density of the primary parasitoids. 4. Parasitism by the autoparasitoid C. lycimnia at the beginning of the season was the sole variable positively related to host mortality throughout the season, showing its greater effect on herbivore suppression. 5. In this study, an autoparasitoid, inferior at resource exploitation, was shown to outcompete a primary parasitoid without disrupting herbivore suppression.info:eu-repo/semantics/publishedVersio

Some Objects Are More Equal Than Others: Measuring and Predicting Importance

We observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. We argue that a goal of visual recognition is, therefore, not only to detect and classify objects but also to associate with each a level of priority which we call 'importance'. We propose a definition of importance and show how this may be estimated reliably from data harvested from human observers. We conclude by showing that a first-order estimate of importance may be computed from a number of simple image region measurements and does not require access to image meaning

Integrated step selection analysis: Bridging the gap between resource selection and animal movement

A resource selection function is a model of the likelihood that an available spatial unit will be used by an animal, given its resource value. But how do we appropriately define availability? Step selection analysis deals with this problem at the scale of the observed positional data, by matching each 'used step' (connecting two consecutive observed positions of the animal) with a set of 'available steps' randomly sampled from a distribution of observed steps or their characteristics. Here we present a simple extension to this approach, termed integrated step selection analysis (iSSA), which relaxes the implicit assumption that observed movement attributes (i.e. velocities and their temporal autocorrelations) are independent of resource selection. Instead, iSSA relies on simultaneously estimating movement and resource selection parameters, thus allowing simple likelihood-based inference of resource selection within a mechanistic movement model. We provide theoretical underpinning of iSSA, as well as practical guidelines to its implementation. Using computer simulations, we evaluate the inferential and predictive capacity of iSSA compared to currently used methods. Our work demonstrates the utility of iSSA as a general, flexible and user-friendly approach for both evaluating a variety of ecological hypotheses, and predicting future ecological patterns

A fault identification and classification scheme for an automobile door assembly process

A process fault identification and classification scheme for an automobile door assembly process is presented based on multivariate in-line dimensional measurements and principal component factor analysis. First, the door assembly process and the dimensional measurement system are briefly introduced. Second, the technique of principal component factor analysis is presented for process fault identification. Process faults are summarized based on off-line identified case studies. Finally a machine classification scheme based on principal components and principal factors is presented and evaluated, using the pattern knowledge obtained off-line. This scheme is shown to be effective in classifying process faults using production data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45569/1/10696_2005_Article_BF01324797.pd

Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi

Wetensch. publicatieFaculteit der Wiskunde en Natuurwetenschappe

Effect of human activity on habitat selection in the endangered Barbary macaque

The exponential growth of human population and infrastructure is significantly reducing the amount of ecological resources available for wild animals. We analyzed the effect of human activity on Barbary macaques (Macaca sylvanus), an endangered species restricted to the fragmented forests of Morocco and Algeria, using location data from five social groups inhabiting Ifrane National Park, Morocco. We used a resource selection function to explore the effects of anthropogenic disturbance on macaque habitat selection, using nine natural, social, and anthropogenic disturbance variables as predictors. Forest cover, home range overlap, herding route proximity, and road proximity were all significant predictors of habitat use. Macaques avoided areas used by local shepherds, to reduce the risk of attack by shepherds’ dogs, but approached roads to increase the chances of provisioning by tourists. However, herding route and road use varied seasonally in line with levels of human use, suggesting that macaques may be navigating their environment strategically (in space and time) to balance food acquisition and risk avoidance. The results of this study highlight the importance of assessing human impact on habitat selection in both space and time. Our data on seasonal variations in macaques’ use of roads can help prevent road injuries, a major source of mortality for provisioned macaques, by focusing management efforts by national park workers in time and space. Furthermore, understanding when and where macaques seek provisioning from tourists can help combat provisioning, which negatively impacts macaque health, behavior, and susceptibility to poaching

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown