Dr. Alba Márquez graduated with a thesis entitled “Artificial Intelligence and Modeling Frontiers: Shaping Social-Ecological Systems Towards Climate Change” at Universitat de Lleida. 

Dr. Márquez has carried out her research with FPI grant under María de Maeztu 2018-2021. Dr Márquez´s thesis has been supervised by BC3 researchers Ferdinando Villa and Stefano Balbi, and was mentored by Lluís Coll from the Univarsitat de Lleida.

Abstract: 

Climate change is a multifaceted challenge that impacts agricultural, forestry, and urban settings in deep ways throughout the world. This thesis addresses the pressing need to understand and mitigate risks caused by climate change from an overall perspective of integrating the ecological and social dimensions of the impacts. The central thesis question investigates the role that integrated socio-ecological modeling can play in enhancing resilience and adaptive capacity within these systems against climate change challenges. Current research typically analyses the impacts of climate change within isolated frameworks, focusing on either ecological or social impacts.

The interrelationship between these systems is ignored in this conceptualization and fails to capture the overall risk and opportunities for adaptation. This thesis identifies a critical gap in the integrated assessment of climate risks and responses across different socio-ecological contexts by sharing solutions with scientists and land managers. This research seeks to address this gap by generating resilient and effective strategies that enhance the long-term sustainability of these systems. Empowered by AI-driven and machine reasoning techniques and in line with open and collaborative science, the methodology of this thesis is both innovative and interdisciplinary. It encompasses the use of spatially explicit models adapted to the context, by remotely sensed data, use of geographic information systems, and advanced machine learning algorithms. This methodological framework allows for a detailed analysis of the complex interactions between climate and environmental variables of socio-ecological systems. The results from this research highlight the efficiency of integrated modeling in understanding, forecasting, and mitigating potential impacts of climate change. In agricultural systems, the models project changes in livestock dynamics, nitrogen leaching, and pasture health, leading to integral sustainable management practices. In forestry, fire risk models show an increase in accuracy in predicting the probability of wildfires and better inform effective management and prevention practices. Urban analysis reveals clues to the cooling effects of green spaces, informing urban planning initiatives toward boosting city resilience to rising temperatures. The findings of this work identify the crucial role that strategic integrated modeling plays in understanding and acting upon the complex climate change challenges of socio-ecological systems. The implications of this research are significant, and call for policy support in terms of innovative technologies that can be developed and implemented collaboratively. Such policies will ensure that socio-ecological systems are fit not just for current climate risks but are also able to adapt to changing climatic conditions. Moreover, it calls for an effort to promote open science, as well as continued cross-cutting collaboration, to guarantee the sustainable development and resilience of social-ecological systems at multiple scales

Thesis Committee: 

Adrian Regos 

Victor Resco 

Anna Sperotto