Classification de pollens par réseau neuronal : application en reconstructions paléo-environnementales de populations marginales

La hausse actuelle du climat pousse les espèces d’arbres tempérés à migrer vers le nord. En vue de comprendre comment certaines espèces réagiront face à cette migration, nous pouvons porter notre regard vers les populations marginales. Les études paléoécologiques de ces populations – situées au-delà de l’aire de répartition continue de l’espèce – peuvent nous informer quant aux conditions écologiques nécessaires à leur migration. Ce mémoire analyse un peuplement d’érables à sucre (Acer saccharum Marsh.) situé à la limite nordique de la répartition de l’espèce, dans la forêt tempérée mixte québécoise. L’objectif de la recherche est d’identifier quand et sous quelles conditions écologiques A. saccharum s’est établi en situation marginale. À ces fins, cette étude propose l’analyse des fossiles extraits des sédiments lacustres d’un lac situé à proximité de l’érablière. Un modèle d’apprentissage-machine est entraîné à l’aide d’images de pollens et permet la classification des pollens extraits des sédiments lacustres – le premier de la sorte. Notre méthode proposée emploi un protocole d’extraction fossile accéléré et des réseaux de neurone convolutifs permettant de classifier les pollens des espèces les plus retrouvées dans les sédiments quaternaires du nord-est de l’Amérique. Bien qu’encore incapable de classifier précisément toutes les espèces présentes dans une telle séquence fossile, notre modèle est une preuve de concept envers l’automatisation de la paléo-palynologie. Les résultats produits par le modèle combinés à l’analyse des charbons fossiles permettent la reconstruction de la végétation et des feux des 10,000 dernières années. L’établissement régional d’A. saccharum est daté à 4,800 cal. BP, durant une période de refroidissement climatique et de feux fréquents mais de faible sévérité. Sa présence locale est prudemment établie à 1,200 cal. BP. Les résultats de ce mémoire soulignent le potentiel de la paléo-palynologie automatique ainsi que la complexité de l’écologie d’A. saccharum.The current global climate warming is pushing temperate tree species to migrate northwards. To understand how certain species will undergo this migration, we can look at marginal populations. The paleoecological studies of such populations, located beyond the species’ core distribution range, can inform us of the conditions needed for a successful migration. This research thesis analyses a sugar maple (Acer saccharum Marsh.) stand located at the northern boundary of the species’ limit, in Québec’s mixed-temperate forest. The objective of this research is to identify when and under which ecological conditions did A. saccharum establish itself as the stand’s dominant species. To that end, this study analyses the fossil record found in a neighbouring lake’s organic sediments. A machine learning-powered model is trained using pollen images to classify the lacustrine sediment’s pollen record. The first of its kind, our proposed method employs an accelerated fossil pollen extraction protocol and convolutional neural networks and can classify the species most commonly found in Northeastern American Quaternary fossil records. Although not yet capable of accurately classifying a complete fossil pollen sequence, our model serves as a proof of concept towards automation in paleo-palynology. Using results generated by our model combined with the analysis of the fossil charcoal record, the past 10,000 years of vegetation and fire history is reconstructed. The regional establishment of A. saccharum is conservatively dated at 4,800 cal. BP, during a period of climate cooling and frequent, although non-severe, forest fires. Its local presence can only be attested to since 1,200 cal. BP. This thesis’ results highlight both the potential of automated paleo-palynology and the complexity of A. saccharum’s ecological requirements

Reconocimiento y clasificación automatizada de especies de polen alergénicas

El presente trabajo de tesis doctoral está centrado en la detección de granos de polen en imágenes palinológicas tomadas de muestras estándar utilizando técnicas de aprendizaje profundo. La localización y clasificación de granos de polen es una tarea manual, muy laboriosa, que llevan a cabo palinólogos experimentados para estimar las concentraciones de los tipos de polen atmosférico presentes en distintas áreas geográficas. Este proceso se realiza a partir de muestras obtenidas en captadores de partículas aerobiológicas que, tras un procesamiento, deben visualizarse con un microscopio óptico. La estimación de los distintos tipos de polen resulta de gran utilidad en varios campos de la ciencia como en alergología, agricultura, ciencias forenses o paleopalinología. Desde el año 2012 el campo de la inteligencia artificial ha experimentado un desarrollo muy importante en detección de objetos en imágenes, gracias al exitoso desarrollo de técnicas basadas en redes neuronales convolucionales. Parte del éxito logrado se ha debido a la aparición en el mercado de unidades de procesamiento gráfico con grandes capacidades de cálculo paralelo, pero también resultó importante la recopilación de grandes conjuntos de imágenes clasificadas. Esta tesis doctoral tiene por objetivo principal evaluar la idoneidad de un método basado en redes neuronales convolucionales, que permita realizar la localización y detección de granos de varios tipos de polen de forma robusta. Consideramos éste un primer paso para desarrollar un sistema que pudiese servir de ayuda en un laboratorio de palinología.This doctoral thesis is focused on the detection of pollen grains in palynological images from standard samples, using deep learning techniques. The localization and classification of pollen grains is a very laborious manual task, which is carried out by experienced palynologists to estimate the concentrations of the different types of atmospheric pollen present in given geographic areas. This process is performed on the basis of samples obtained in aerobiological particle collectors, which after processing, must be visualized with an optical microscope. The estimation of the different pollen types is very useful in several areas of science such as allergology, agriculture, forensic science or paleopalinology. Since 2012, the field of artificial intelligence has achieved a very important progress in image object detection, thanks to the successful development of techniques based on convolutional neural networks. Part of the success achieved has been due to the appearance on the market of graphics processing units with large parallel computing capabilities, but the collection of large sets of classified images was also important. The main objective of this doctoral thesis is to evaluate the suitability of a method based on convolutional neural networks for the localization and detection of grains of pollen of various types in a robust way. This method is considered a first step in the development of a system that could be helpful in a palinology laboratory

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done