Abrupt and persistent reconfiguration of ecosystem’s structure and function has been observed on a wide variety of ecosystems worldwide. While scientist believe that such phenomena could become more common and severe in the near future, little is known about the patterns of regime shifts’ causes and consequences for human well-being. This thesis aims to assess global patterns of regime shifts in social-ecological systems. A framework for comparing regime shifts has been developed as well as a public forum for discussing knowledge about regime shifts, namely the regime shift database. The most common drivers and expected impacts on ecosystem services have been identified by studying the qualitative topology of causal networks as well as the statistical properties that explain their emergent patters. Given that long time series data for ecosystems monitoring is rather sparse, and experimenting with ecosystems at the scales required to understand their feedback dynamics is rarely an option; we also proposed an indirect computationally based method for monitoring changes in ecosystem services. I hope the results here presented offer useful guidance for managers and policy makers on how to prioritize drivers or impacts of regime shifts: one take home message is that well-understood variables are not necessary the ones where most managerial efforts need to be taken. I also hope the scientific community rigorously criticize our results, but also acknowledge that when doing theoretical or empirical work, our methods tend to ignore the multi-causal nature of regime shifts. By bringing back multi-causality to the scientific debate, I hope our results offer new avenues for hypothesis exploration and theory development on the human endeavour of understanding Nature. / Transiciones críticas o cambios de régimen en ecosistemas se definen como reconfiguraciones abruptas de su estructura y función. Estos cambios, en ocasiones inesperados, se han documentado en una gran variedad de ecosistemas en todo el planeta. Algunos científicos proponen que en el futuro cercano dichos fenómenos pueden volverse más frecuentes y severos. Sin embargo, sabemos muy poco sobre las causas y consecuencias potenciales para el bienestar humano. El objetivo de esta tesis es evaluar patrones globales de cambios de régimen en sistemas socio-ecológicos. Un marco conceptual para comparar cambios de régimen y un foro público de discusión sobre el estado del arte en su conocimiento fue desarrollado en la base de datos virtual www.regimeshifts.org. Las causas más comunes y los impactos en servicios ecosistémicos más esperados han sido identificados estudiando las propiedades topológicas de redes causales, así como las propiedades estadísticas que explican sus propiedades emergentes. Dado que experimentar con ecosistemas a la escala adecuada para capturar sus mecanismos causales generalmente no es una opción, y dado que la disponibilidad de datos de largo plazo necesarios para monitorear cambios de régimen son la excepción y no la regla, proponemos un método indirecto computacional para monitorear cambios en servicios ecosistémicos. Espero que los resultados sean de utilidad para actores encargados del diseño de políticas o del manejo de ecosistemas, especialmente espero que ofrezcan una guía sobre cómo priorizar causas y consecuencias de estos cambios de régimen: una lección clave es que las variables que mejor entendemos o las que más monitoreamos no son necesariamente aquellas en las que debemos enfocar las estrategias de manejo. También espero que la comunidad científica critique con rigor nuestros resultados, pero a su vez reconozca que tanto el trabajo empírico y teórico como los métodos que comúnmente se utilizan para estudiar cambios de régimen tienden a ignorar su naturaleza multi-causal. Al enfatizar la diversidad de sus causas, espero que los resultados ofrezcan nuevas posibilidades para la exploración de hipótesis y el desarrollo de teorías para entender mejor la Naturaleza. / Abrupt och ihållande omkonfigurering av ekosystems struktur och funktion har observerats i en mängd olika ekosystem världen över. Forskning visar på att dessa fenomen antas bli vanligare och allvarligare inom vår närmsta framtid. Kunskapen kring dessa s.k. regimskiften är dock bristfällig, framförallt kring dess konsekvenser för mänskligt välbefinnande. Denna avhandling syftar till att bedöma globala mönster av regimskiften. Ett ramverk för att jämföra regimskiften, samt ett offentligt forum, “the regime shifts database”, för att främja diskussion och sprida kunskap om regimskiften, har utvecklats. De mest förekommande drivkrafter och effekter på ekosystemtjänster har identifierats genom att studera kvalitativa topologiska och kausala nätverk, samt de statistiska egenskaperna som förklarar deras framväxande mönster. Då långvariga tidsserier av ekosystemövervakning är få, och då de experiment som krävs för att förstå regimskiftens återkopplingsdynamik sällan är möjliga, föreslås också en indirekt beräkningsmetod för övervakning av förändringar i ekosystemtjänster. Resultaten från denna avhandling ämnar ger värdefull vägledning för beslutsfattare om prioriteringsordningen mellan olika typer av drivkrafter och effekter av regimskiften. En viktig slutsats är att gedigen kunskap om en viss variabel inte nödvändigtvis ger området där insatser bör tillsättas. Vidare, genom att föra tillbaka multi-kausalitet till den vetenskapliga debatten, erbjuder avhandlingen nya vägar för hypotesprövning och teoriutveckling inom vår gemensamma strävan att förstå Naturen. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 4: Manuscript.</p>

There is a continuous demand for new and improved methods of understanding our climate system. The work in this thesis focuses on the study of delayed feedback and critical transitions. There is much room to develop upon these concepts in their application to the climate system. We explore the two concepts independently, but also note that the two are not mutually exclusive. The thesis begins with a review of delay differential equation (DDE) theory and the use of delay models in climate, followed by a review of the literature on critical transitions and examples of critical transitions in climate. We introduce various methods of deriving delay models from more complex systems. Our main results center around the Saltzman and Maasch (1988) model for the Pleistocene climate (`Carbon cycle instability as a cause of the late Pleistocene ice age oscillations: modelling the asymmetric response.' Global biogeochemical cycles, 2(2):177-185, 1988). We observe that the model contains a chain of first-order reactions. Feedback chains of this type limits to a discrete delay for long chains. We can then approximate the chain by a delay, resulting in scalar DDE for ice mass. Through bifurcation analysis under varying the delay, we discover a previously unexplored bistable region and consider solutions in this parameter region when subjected to periodic and astronomical forcing. The astronomical forcing is highly quasiperiodic, containing many overlapping frequencies from variations in the Earth's orbit. We find that under the astronomical forcing, the model exhibits a transition in time that resembles what is seen in paleoclimate records, known as the Mid-Pleistocene Transition. This transition is a distinct feature of the quasiperiodic forcing, as confi rmed by the change in sign of the leading nite-time Lyapunov exponent. Additional results involve a box model of the Atlantic meridional overturning circulation under a future climate scenario and time-dependent freshwater forcing. We find that the model exhibits multiple types of critical transitions, as well as recovery from potential critical transitions. We conclude with an outlook on how the work presented in this thesis can be utilised for further studies of the climate system and beyond.

<p dir="ltr">Increased frequency and intensity of disasters necessitate the dynamic post-disaster recovery process. Developing human mobility patterns, household return decision-making models, and agent-based simulations in disaster management has opened a new door towards more intricate and enduring recovery frameworks. Despite these opportunities, the importance of a unified framework is underestimated to identify the underlying mechanisms hindering the post-disaster recovery process. My research has been geared towards forging advancements in civil and disaster management, focusing on two main areas: (1) modeling the post-disaster recovery process and (2) identifying critical transitions within the recovery process.</p><p dir="ltr">My dissertation explores the collective and individual dynamics of post-disaster recovery across different spatial and temporal scales. I have identified the best recovery strategies for various contexts by constructing data-driven socio-physical multi-agent systems. Employing various advanced computational methodologies, including machine learning, system dynamics, causal discovery, econometrics, and network analysis, has been instrumental. I start with aggregated level analysis for post-disaster recovery. Initially, I examined the system dynamics model for the post-discovery recovery process in socio-physical systems, using normalized visit density of points of interest and power outage information. Through counterfactual analyses of budget allocation strategies, I discovered their significant impact on recovery trajectories, noting that specific budget allocations substantially enhance recovery patterns. I also revealed the urban-rural dissimilarity by the data-driven causal discovery approach. I utilized county-level normalized visit density of points of interest and nighttime light data to identify the relationship between counties. I found that urban and rural areas have similar but different recovery patterns across different types of points of interest.</p><p dir="ltr">Moving from aggregated to disaggregated level analysis on post-disaster recovery, I investigated household-level decision-making regarding disaster-induced evacuation and return behaviors. The model yielded insights into the varying influences of certain variables across urban and rural contexts. Subsequently, I developed a unified framework integrating aggregated and disaggregated level analyses through multilayer multi-agent systems to model significant shifts in the post-disaster recovery process. I evaluated various scenarios to pinpoint conditions for boosting recovery and assessing the effects of different intervention strategies on these transitions. Lastly, a comparison between mathematical models and graph convolutional networks was conducted to better understand the conditions leading to critical transitions in the recovery process. The insights and methodologies presented in this dissertation contribute to the broader understanding of the disaster recovery process in complex urban systems, advocating for a shift towards a unified framework over individual models. By harnessing big data and complex systems modeling, I can achieve a detailed quantitative analysis of the disaster recovery process, including critical transition conditions of the post-disaster recovery. This approach facilitates the evaluation of such recovery policies through inter-regional comparisons and the testing of various policy interventions in counterfactual scenarios.</p>