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Threat sensitivity, an individual difference construct reflecting variation in responsiveness to threats of various types, predicts physiological reactivity to aversive stimuli and shares heritable variance with anxiety disorders in adults. However, no research has been conducted yet with youth to examine the heritability of threat sensitivity or evaluate the role of genetic versus environmental influences in its relations with mental health problems. The current study addressed this gap by evaluating the psychometric properties of a measure of this construct, the 20-item Trait Fear scale (TF-20), and examining its phenotypic and genotypic correlations with different forms of psychopathology in a sample of 346 twin pairs (121 monozygotic), aged 9–14 years. Analyses revealed high internal consistency and test-retest reliability for the TF-20. Evidence was also found for its convergent and discriminant validity in terms of phenotypic and genotypic correlations with measures of fear-related psychopathology. By contrast, the TF-20’s associations with depressive conditions were largely attributable to environmental influences. Extending prior work with adults, current study findings provide support for threat sensitivity as a genetically-influenced liability for phobic fear disorders in youth.
Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. These observations consist of flights over the Western North Atlantic Ocean, providing a large repository of data including aerosol, meteorological, and microphysical conditions in and out of clouds. We investigate this dataset using principal component analysis (PCA), a linear dimensionality reduction technique, and an autoencoder, a deep learning non-linear dimensionality reduction technique. We find that we can reduce the dimensionality of the parameter space by more than a factor of 2 and verify that the deep learning method outperforms a PCA baseline by two orders of magnitude. Analysis in the low dimensional space of both these techniques reveals two consistent physically interpretable regimes—a low pollution regime and an in-cloud regime. Through this work, we show that unsupervised machine learning techniques can learn useful information from in-situ atmospheric observations and provide interpretable results of low-dimensional variability.
The first demonstration of laser action in ruby was made in 1960 by T. H. Maiman of Hughes Research Laboratories, USA. Many laboratories worldwide began the search for lasers using different materials, operating at different wavelengths. In the UK, academia, industry and the central laboratories took up the challenge from the earliest days to develop these systems for a broad range of applications. This historical review looks at the contribution the UK has made to the advancement of the technology, the development of systems and components and their exploitation over the last 60 years.
Ecosystem modeling, a pillar of the systems ecology paradigm (SEP), addresses questions such as, how much carbon and nitrogen are cycled within ecological sites, landscapes, or indeed the earth system? Or how are human activities modifying these flows? Modeling, when coupled with field and laboratory studies, represents the essence of the SEP in that they embody accumulated knowledge and generate hypotheses to test understanding of ecosystem processes and behavior. Initially, ecosystem models were primarily used to improve our understanding about how biophysical aspects of ecosystems operate. However, current ecosystem models are widely used to make accurate predictions about how large-scale phenomena such as climate change and management practices impact ecosystem dynamics and assess potential effects of these changes on economic activity and policy making. In sum, ecosystem models embedded in the SEP remain our best mechanism to integrate diverse types of knowledge regarding how the earth system functions and to make quantitative predictions that can be confronted with observations of reality. Modeling efforts discussed are the Century ecosystem model, DayCent ecosystem model, Grassland Ecosystem Model ELM, food web models, Savanna model, agent-based and coupled systems modeling, and Bayesian modeling.
The systems ecology paradigm (SEP) is presented as the right science and analytical approach at the right time for resolving many of the Earth’s natural resource, environmental, and societal challenges. SEP embodies two major parts. One, the systems ecology approach, which is the holistic, systems thinking perspective and methodology developed for the rigorous study of ecosystems, including humans. Two, the use of ecosystem science, the vast body of scientific knowledge, much of which has been assembled using the ecosystem and systems ecology approaches. The fundamental philosophy, evolution, and application of the SEP are defined in this chapter. The organizing principles of the SEP include: many problems are complex and complicated and may have multiple causes; precise definitions of problems and their spatial, temporal, and organizational hierarchical scales are critical; collaborative decision making including scientists, technical and administrative staff members, and essential stakeholders is essential; transparent, honest, and effective communication is required; globalization of collaboration within interdisciplinary networks has been a hallmark of the paradigm; and integration of simulation modeling, field and laboratory studies has proven indispensable for many scientific breakthroughs. A call for integration of transdisciplinary science, policy making, and management is presented.
Ecosystem science and the systems ecology paradigm co-evolved starting in the late 1960s within the milieu of substantial research funding from the US National Science Foundation-supported US International Biological Program (IBP). Nationally, educational programs focusing on ecosystem structure and functioning, and mathematical modeling, were slow to develop except at Colorado State University (CSU). There, leaders in the Natural Resource Ecology Laboratory (NREL) and the Department of Range Science (DRS) established internationally recognized interdisciplinary programs and outreach in basic and applied ecosystem science and systems ecology. Operating from the sound research base within a major Land Grant University (CSU), the NREL, with IBP funding, supported many graduate students housed in the academic DRS. As the systems ecology approach expanded, other ecosystem-focused research programs developed, and graduate students entered other academic departments. Outgrowths from the early diffused educational training were innovative cross-departmental and cross-college programs addressing the systems ecology paradigm. Recently, a new Department of Ecosystem Science and Sustainability was established housing both graduate and undergraduate programs. As formal academic training developed on-campus, environmental literacy efforts were developed, including: training programs for K-12 students and teachers; online distance education programs; Citizen Science training; and numerous institutes, short courses, and workshops.
The Systems Ecology Paradigm (SEP) incorporates humans as integral parts of ecosystems and emphasizes issues that have significant societal relevance such as grazing land, forestland, and agricultural ecosystem management, biodiversity and global change impacts. Accomplishing this societally relevant research requires cutting-edge basic and applied research. This book focuses on environmental and natural resource challenges confronting local to global societies for which the SEP methodology must be utilized for resolution. Key elements of SEP are a holistic perspective of ecological/social systems, systems thinking, and the ecosystem approach applied to real world, complex environmental and natural resource problems. The SEP and ecosystem approaches force scientific emphasis to be placed on collaborations with social scientists and behavioral, learning, and marketing professionals. The SEP has given environmental scientists, decision makers, citizen stakeholders, and land and water managers a powerful set of tools to analyse, integrate knowledge, and propose adoption of solutions to important local to global problems.
The rocky shores of the north-east Atlantic have been long studied. Our focus is from Gibraltar to Norway plus the Azores and Iceland. Phylogeographic processes shape biogeographic patterns of biodiversity. Long-term and broadscale studies have shown the responses of biota to past climate fluctuations and more recent anthropogenic climate change. Inter- and intra-specific species interactions along sharp local environmental gradients shape distributions and community structure and hence ecosystem functioning. Shifts in domination by fucoids in shelter to barnacles/mussels in exposure are mediated by grazing by patellid limpets. Further south fucoids become increasingly rare, with species disappearing or restricted to estuarine refuges, caused by greater desiccation and grazing pressure. Mesoscale processes influence bottom-up nutrient forcing and larval supply, hence affecting species abundance and distribution, and can be proximate factors setting range edges (e.g., the English Channel, the Iberian Peninsula). Impacts of invasive non-native species are reviewed. Knowledge gaps such as the work on rockpools and host–parasite dynamics are also outlined.