Impacts de l’intensification agricole et de la structure du paysage sur les relations tri – trophiques entre un oiseau hôte, des mouches ectoparasites et leur parasitoïdes.

L’intensification des pratiques agricoles a été identifiée comme cause majeure du déclin de la biodiversité. Plusieurs études ont documenté l’impact de la fragmentation du paysage naturel et de l’agriculture intensive sur la diversité des espèces, mais très peu ont quantifié le lien entre la structure du paysage et les interactions trophiques, ainsi que les mécanismes d’adaptation des organismes. J’ai étudié un modèle biologique à trois niveaux trophiques composé d’un oiseau hôte, l’hirondelle bicolore Tachycineta bicolor, de mouches ectoparasites du genre Protocalliphora et de guêpes parasitoïdes du genre Nasonia, au travers d’un gradient d’intensification agricole dans le sud du Québec. Le premier objectif était de déterminer l’abondance des espèces de mouches ectoparasites et de leurs guêpes parasitoïdes qui colonisent les nids d’hirondelles dans la zone d’étude. La prévalence de nids infectés par Protocalliphora spp. était de 70,8% en 2008 et 34,6% en 2009. Le pourcentage de nids comprenant des pupes de Protocalliphora parasitées par Nasonia spp. était de 85,3% en 2008 et 67,2% en 2009. Trois espèces de Protocalliphora ont été observées (P. sialia, P. bennetti et P. metallica) ainsi que deux espèces de Nasonia (N. vitripennis et N. giraulti). Il s’agit d’une première mention de P. bennetti et de N. giraulti dans la province de Québec. Mon deuxième objectif était d’évaluer l’impact de l’intensification agricole et de la structure du paysage sur les relations tri-trophiques entre les organismes à l’étude. Les résultats révèlent que les réponses à la structure du paysage de l’hirondelle, de l’ectoparasite et de l’hyperparasite dépendantent de l’échelle spatiale. L’échelle spatiale fonctionnelle à laquelle les espèces répondent le plus varie selon le paramètre du paysage modélisé. Les analyses démontrent que l’intensification des pratiques agricoles entraîne une diminution des populations d’oiseaux, d’ectoparasites et d’hyperparasites. De plus, les populations de Protocalliphora et de Nasonia sont menacées en paysage intensif puisque la dégradation du paysage associée à l’intensification des pratiques agricoles agit directement sur leurs populations et indirectement sur les populations de leurs hôtes. Mon troisième objectif était de caractériser les mécanismes comportementaux permettant aux guêpes de composer avec la variabilité de la structure du paysage et de la qualité des hôtes. Nos résultats révèlent que les femelles Nasonia ajustent la taille de leur ponte en fonction de la taille de la pupe hôte et de l’incidence d’hyperparasitisme. Le seul facteur ayant une influence déterminante sur le ratio sexuel est la proportion de paysage dédié à l’agriculture intensive. Aucune relation n’a été observée entre la structure du paysage et la taille des filles et des fils produits par les femelles Nasonia fondatrices. Ce phénomène est attribué aux comportements d’ajustement de la taille de la ponte et du ratio sexuel. En ajustant ces derniers, minimisant ainsi la compétition entre les membres de leur progéniture, les femelles fondatrices sont capables de maximiser la relation entre la disponibilité des ressources et la valeur sélective de leur progéniture. En conclusion, ce travail souligne l’importance de considérer le contexte spatial des interactions trophiques, puisqu’elles influencent la biodiversité locale et le fonctionnement de l’écosystème.

An illustration of variable precision rough set theory : The gender classification of the European barn swallow (Hirundo rustica)

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.

How is a fruit tree like you? Using artistic metaphors to explore and develop emotional competence in children

Counselling children often requires the use of supplementary strategies in order to interest and engage the child in the therapeutic process. One such strategy is the Metaphorical Fruit Tree (MFT); an art metaphor suited to exploring and developing self-concept. Quantitative and qualitative data was used to explore the relationships between children’s ability to use metaphor, age, gender, and level of emotional competence (N = 58). Quantitative and qualitative analyses revealed a significant negative relationship between self-reported emotional competence and ability to use the MFT. It is proposed that children rely on different processes to understand self and as children’s ability to cognitively report on their emotional capabilities via the Emotional Competence Questionnaire (ECQ) increases, their ability to report creatively on those capabilities via the MFT is undermined. It is suggested that the MFT may be used, via creative processes and as an alternative to cognitive processes, to increase understanding and awareness of intrapersonal and interpersonal concepts of self in the child during counselling.

Document clustering with K-tree

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.

Random Indexing K-tree

Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering.

Thai word segmentation with hidden Markov Model and decision tree

The Thai written language is one of the languages that does not have word boundaries. In order to discover the meaning of the document, all texts must be separated into syllables, words, sentences, and paragraphs. This paper develops a novel method to segment the Thai text by combining a non-dictionary based technique with a dictionary-based technique. This method first applies the Thai language grammar rules to the text for identifying syllables. The hidden Markov model is then used for merging possible syllables into words. The identified words are verified with a lexical dictionary and a decision tree is employed to discover the words unidentified by the lexical dictionary. Documents used in the litigation process of Thai court proceedings have been used in experiments. The results which are segmented words, obtained by the proposed method outperform the results obtained by other existing methods.

Tree of knowledge memorial

This article discusses the design of a memorial space of the Tree of Knowledge, located in Oak Street, Barcaldine, Queensland designed by Brian Hooper Architect and M3 Architecture. Features of the design include the dead tree trunk underneath pieces of timber hanging down to show the original size of the tree in the 1890s and the root ball visible under a glass floor.

Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.