Factors Affecting Current and Future Treeline Locations and Dynamics in the Peruvian Andes

The elevational distributions of tropical treelines are thought to be determined by temperature, and are predicted to shift upslope in response to global warming. In contrast to this hypothesis, global-scale studies have shown that only half of all studied treelines are shifting upslope. Understanding how treelines will respond to climate change has important implications for global biodiversity, especially in the tropics, because tropical treelines generally represent the upper-elevation distribution limit of the hyper-diverse cloudforest ecosystem. In Chapter 1, I introduce the idea that grasslands found above tropical treelines may represent a potential grass ceiling which forest species cannot cross or invade. I use an extensive literature review to outline potential mechanisms which may be acting to stabilize treeline and prevent forest expansion into high-elevation grasslands. In Chapters 2-4, I begin to explore these potential mechanisms through the use of observational and experimental methods. In Chapter 2, I show that there are significant numbers of seedlings occurring just outside of the treeline in the open grasslands and that seed rain is unlikely to limit seedling recruitment above treeline. I also show that microclimates outside of the closed-canopy cloudforest are highly variable and that mean temperatures are likely a poor explanation of tropical treeline elevations. In Chapter 3, I show that juvenile trees maintain freezing resistances similar to adults, but nighttime radiative cooling near the ground in the open grassland results in lower cold temperatures relative to the free atmosphere, exposing seedlings of some species growing above treeline to lethal frost events. In Chapter 4, I use a large-scale seedling transplant experiment to test the effects of mean temperature, absolute low temperature and shade on transplanted seedling survival. I find that increasing mean temperature negatively affects seedling survival of two treeline species while benefiting another. In addition, low temperature extremes and the presence of shade also appear to be important factors affecting seedling survival above tropical treelines. This work demonstrates that mean temperature is a poor predictor of tropical treelines and that temperature extremes, especially low temperatures, and non-climatic variables should be included in predictions of current and future tropical treeline dynamics.

Factors affecting current and future treeline locations and dynamics in the Peruvian Andes

The elevational distributions of tropical treelines are thought to be determined by temperature, and are predicted to shift upslope in response to global warming. In contrast to this hypothesis, global-scale studies have shown that only half of all studied treelines are shifting upslope. Understanding how treelines will respond to climate change has important implications for global biodiversity, especially in the tropics, because tropical treelines generally represent the upper-elevation distribution limit of the hyper-diverse cloudforest ecosystem. In Chapter 1, I introduce the idea that grasslands found above tropical treelines may represent a potential grass ceiling which forest species cannot cross or invade. I use an extensive literature review to outline potential mechanisms which may be acting to stabilize treeline and prevent forest expansion into high-elevation grasslands. In Chapters 2-4, I begin to explore these potential mechanisms through the use of observational and experimental methods. In Chapter 2, I show that there are significant numbers of seedlings occurring just outside of the treeline in the open grasslands and that seed rain is unlikely to limit seedling recruitment above treeline. I also show that microclimates outside of the closed-canopy cloudforest are highly variable and that mean temperatures are likely a poor explanation of tropical treeline elevations. In Chapter 3, I show that juvenile trees maintain freezing resistances similar to adults, but nighttime radiative cooling near the ground in the open grassland results in lower cold temperatures relative to the free atmosphere, exposing seedlings of some species growing above treeline to lethal frost events. In Chapter 4, I use a large-scale seedling transplant experiment to test the effects of mean temperature, absolute low temperature and shade on transplanted seedling survival. I find that increasing mean temperature negatively affects seedling survival of two treeline species while benefiting another. In addition, low temperature extremes and the presence of shade also appear to be important factors affecting seedling survival above tropical treelines. This work demonstrates that mean temperature is a poor predictor of tropical treelines and that temperature extremes, especially low temperatures, and non-climatic variables should be included in predictions of current and future tropical treeline dynamics.^

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.