Theoretical and data-driven models in Ecology

Life on Earth has evolved over billions of years, resulting in a rich diversity of species and ecosystems that provide essential services for human survival and well-being. However, this biodiversity is rapidly declining due to human activities such as habitat destruction, climate change, invasive species and emerging diseases. These interconnected drivers are causing widespread loss of species and degradation of ecosystems, threatening global ecological stability and human prosperity. Addressing this crisis requires an interdisciplinary approach to understand and mitigate its impacts, ensuring the preservation of biodiversity and the sustainability of human societies. In this thesis we develop theoretical and data-driven methods to address pressing issues in Ecology and Conservation Biology through the lens of Complex Systems and interdisciplinary research. We address a range of contemporary challenges related to biodiversity loss driven by climate change and emerging diseases. These challenges include the spread of diseases, ocean acidification, and the decline of critical ecosystems such as coral reefs and seagrass meadows. We rely on a combination of theoretical models, computational simulations, and advanced data analysis techniques to gain a deeper understanding of these complex ecological phenomena. In the first two parts of this thesis we develop mathematical models of disease spread to fill knowledge gaps in the transmission dynamics of marine and vector-borne plant diseases. We focus on two case studies: the Mass Mortality Event (MME) of Pinna nobilis and the vector-borne plant diseases caused by the bacterium Xylella fastidiosa. We investigate the role of key factors such as temperature or pathogen mobility in the transmission of the MME and the impact of the non-periodic seasonal abundance of insect vectors on the spread of plant diseases. These models provide insights into the mechanisms driving the dynamics of these diseases and the potential for their control and management. In the third part, we apply this knowledge to develop a novel theoretical framework to predict the potential distribution of vector-borne plant diseases based on environmental and climatic factors. We demonstrate the utility of this model by predicting the risk of Pierce’s disease of grapevines, caused by Xylella fastidiosa, under current and future climate scenarios. Our methodology represents a significant advancement in the field of disease biogeography, providing a way to integrate the inherent complex ecological interactions of diseases to predict their potential establishment based on environmental conditions. Finally, in the fourth part of this thesis, we develop and apply data-driven methods to monitor and assess the health of coastal marine ecosystems. We present an innovative framework to reconstruct missing data from ocean pH time-series using deep learning techniques, which enhance our ability to monitor ocean acidification accurately. Additionally, we employ machine learning and satellite imagery to map and evaluate the condition of seagrass meadows, offering a scalable and cost-effective approach to ecosystem monitoring. Moreover, we conduct a global analysis of the spatial properties of coral reefs using remote sensing data, uncovering universal patterns in reef size distribution and geometry. These insights are crucial for developing targeted conservation strategies to protect these vulnerable ecosystems. This thesis underscores the importance of interdisciplinary research, integrating ecological theory, complex systems’ science, and artificial intelligence to tackle ecological challenges. The findings contribute to the development of effective conservation strategies, aiming to mitigate the impacts of climate change and emergent diseases on biodiversity. Ultimately, this work supports efforts to preserve the integrity of ecosystems and ensure the sustainability of human societies in the face of ongoing environmental changes.