Spatio-temporal log-Gaussian Cox processes for modelling wildfire occurrence: the case of Catalonia, 1994–2008

Wildfires have become one of the principal environmental problems in the Mediterranean basin. While fire plays an important role in most terrestrial plant ecosystems, the potential hazard that it represents for human lives and property has led to the application of fire exclusion policies that, in the long term, have caused severe damage, mainly due to the increase of fuel loadings in forested areas, in some forest systems. The lack of an easy solution to forest fire management highlights the importance of preventive tasks. The observed spatio-temporal pattern of wildfire occurrences may be idealized as a realization of some stochastic process. In particular, we may use a space–time point pattern approach for the analysis and inference process. We studied wildfires in Catalonia, a region in the north-east of the Iberian Peninsula, and we analyzed the spatio-temporal patterns produced by those wildfire incidences by considering the influence of covariates on trends in the intensity of wildfire locations. A total of 3,166 wildfires from 1994–2008 have been recorded. We specified spatio-temporal log-Gaussian Cox process models. Models were estimated using Bayesian inference for Gaussian Markov Random Field through the integrated nested Laplace approximation algorithm. The results of our analysis have provided statistical evidence that areas closer to humans have more human induced wildfires, areas farther have more naturally occurring wildfires. We believe the methods presented in this paper may contribute to the prevention and management of those wildfires which are not random in space or time.     

Mapping Change in Human Pressure Globally on Land and within Protected Areas

It is widely accepted that the main driver of the observed decline in biological diversity is increasing human pressure on Earth's ecosystems. However, the spatial patterns of change in human pressure and their relation to conservation efforts are less well known. We developed a spatially and temporally explicit map of global change in human pressure over 2 decades between 1990 and 2010 at a resolution of 10 km². We evaluated 22 spatial data sets representing different components of human pressure and used them to compile a temporal human pressure index (THPI) based on 3 data sets: human population density, land transformation, and electrical power infrastructure. We investigated how the THPI within protected areas was correlated to International Union for Conservation of Nature (IUCN) management categories and the human development index (HDI) and how the THPI was correlated to cumulative pressure on the basis of the original human footprint index. Since the early 1990s, human pressure increased 64% of the terrestrial areas; the largest increases were in Southeast Asia. Protected areas also exhibited overall increases in human pressure, the degree of which varied with location and IUCN management category. Only wilderness areas and natural monuments (management categories Ib and III) exhibited decreases in pressure. Protected areas not assigned any category exhibited the greatest increases. High HDI values correlated with greater reductions in pressure across protected areas, while increasing age of the protected area correlated with increases in pressure. Our analysis is an initial step toward mapping changes in human pressure on the natural world over time. That only 3 data sets could be included in our spatio‐temporal global pressure map highlights the challenge to measuring pressure changes over time. Mapeo del Cambio en la Presión Humana Global en Tierra y Dentro de Áreas Protegidas     

Uranium enrichment by the separation-nozzle process

The Science of Nature - The development of reliable manufacturing methods for commercial separation elements, the successful operation of separative stages, and extensive tests performed on plant...     

An interdisciplinary review of current and future approaches to improving human–predator relations

In a world of shrinking habitats and increasing competition for natural resources, potentially dangerous predators bring the challenges of coexisting with wildlife sharply into focus. Through interdisciplinary collaboration among authors trained in the humanities, social sciences, and natural sciences, we reviewed current approaches to mitigating adverse human–predator encounters and devised a vision for future approaches to understanding and mitigating such encounters. Limitations to current approaches to mitigation include too much focus on negative impacts; oversimplified equating of levels of damage with levels of conflict; and unsuccessful technical fixes resulting from failure to engage locals, address hidden costs, or understand cultural (nonscientific) explanations of the causality of attacks. An emerging interdisciplinary literature suggests that to better frame and successfully mitigate negative human–predator relations conservation professionals need to consider dispensing with conflict as the dominant framework for thinking about human–predator encounters; work out what conflicts are really about (they may be human–human conflicts); unravel the historical contexts of particular conflicts; and explore different cultural ways of thinking about animals. The idea of cosmopolitan natures may help conservation professionals think more clearly about human–predator relations in both local and global context. These new perspectives for future research practice include a recommendation for focused interdisciplinary research and the use of new approaches, including human‐animal geography, multispecies ethnography, and approaches from the environmental humanities notably environmental history. Managers should think carefully about how they engage with local cultural beliefs about wildlife, work with all parties to agree on what constitutes good evidence, develop processes and methods to mitigate conflicts, and decide how to monitor and evaluate these. Demand for immediate solutions that benefit both conservation and development favors dispute resolution and technical fixes, which obscures important underlying drivers of conflicts. If these drivers are not considered, well‐intentioned efforts focused on human–wildlife conflicts will fail.     

Threats to intact tropical peatlands and opportunities for their conservation

Large, intact areas of tropical peatland are highly threatened at a global scale by the expansion of commercial agriculture and other forms of economic development. Conserving peatlands on a landscape scale, with their hydrology intact, is of international conservation importance to preserve their distinctive biodiversity and ecosystem services and maintain their resilience to future environmental change. We explored threats to and opportunities for conserving remaining intact tropical peatlands; thus, we excluded peatlands of Indonesia and Malaysia, where extensive deforestation, drainage, and conversion to plantations means conservation in this region can protect only small fragments of the original ecosystem. We focused on a case study, the Pastaza‐Marañón Foreland Basin (PMFB) in Peru, which is among the largest known intact tropical peatland landscapes in the world and is representative of peatland vulnerability. Maintenance of the hydrological conditions critical for carbon storage and ecosystem function of peatlands is, in the PMFB, primarily threatened by expansion of commercial agriculture linked to new transport infrastructure that is facilitating access to remote areas. There remain opportunities in the PMFB and elsewhere to develop alternative, more sustainable land‐use practices. Although some of the peatlands in the PMFB fall within existing legally protected areas, this protection does not include the most carbon‐dense (domed pole forest) areas. New carbon‐based conservation instruments (e.g., REDD+, Green Climate Fund), developing markets for sustainable peatland products, transferring land title to local communities, and expanding protected areas offer pathways to increased protection for intact tropical peatlands in Amazonia and elsewhere, such as those in New Guinea and Central Africa which remain, for the moment, broadly beyond the frontier of commercial development.     

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease‐driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host–pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious‐disease systems.