Wildland fire deficit and surplus in the western United States, 1984–2012

Wildland fire is an important disturbance agent in the western US and globally. However, the natural role of fire has been disrupted in many regions due to the influence of human activities, which have the potential to either exclude or promote fire, resulting in a ‘‘fire deficit’’ or ‘‘fire surplus’’, respectively. In this study, we developed a model of expected area burned for the western US as a function of climate from 1984 to 2012.We then quantified departures from expected area burned to identify geographic regions with fire deficit or surplus. We developed our model of area burned as a function of several climatic variables from reference areas with low human influence; the relationship between climate and fire is strong in these areas. We then quantified the degree of fire deficit or surplus for all areas of the western US as the difference between expected (as predicted with the model) and observed area burned from 1984 to 2012. Results indicate that many forested areas in the western US experienced a fire deficit from 1984 to 2012, likely due to fire exclusion by human activities. We also found that large expanses of non-forested regions experienced a fire surplus, presumably due to introduced annual grasses and the prevalence of anthropogenic ignitions. The heterogeneity in patterns of fire deficit and surplus among ecoregions emphasizes fundamentally different ecosystem sensitivities to human influences and suggests that largescale adaptation and mitigation strategies will be necessary in order to restore and maintain resilient, healthy, and naturally functioning ecosystems

Wildland fire deficit and surplus in the western United States, 1984–2012

Wildland fire is an important disturbance agent in the western US and globally. However, the natural role of fire has been disrupted in many regions due to the influence of human activities, which have the potential to either exclude or promote fire, resulting in a ‘‘fire deficit’’ or ‘‘fire surplus’’, respectively. In this study, we developed a model of expected area burned for the western US as a function of climate from 1984 to 2012.We then quantified departures from expected area burned to identify geographic regions with fire deficit or surplus. We developed our model of area burned as a function of several climatic variables from reference areas with low human influence; the relationship between climate and fire is strong in these areas. We then quantified the degree of fire deficit or surplus for all areas of the western US as the difference between expected (as predicted with the model) and observed area burned from 1984 to 2012. Results indicate that many forested areas in the western US experienced a fire deficit from 1984 to 2012, likely due to fire exclusion by human activities. We also found that large expanses of non-forested regions experienced a fire surplus, presumably due to introduced annual grasses and the prevalence of anthropogenic ignitions. The heterogeneity in patterns of fire deficit and surplus among ecoregions emphasizes fundamentally different ecosystem sensitivities to human influences and suggests that largescale adaptation and mitigation strategies will be necessary in order to restore and maintain resilient, healthy, and naturally functioning ecosystems

Wildland fire deficit and surplus in the western United States, 1984-2012

Wildland fire is an important disturbance agent in the western US and globally. However, the natural role of fire has been disrupted in many regions due to the influence of human activities, which have the potential to either exclude or promote fire, resulting in a fire deficit or fire surplus, respectively. In this study, we developed a model of expected area burned for the western US as a function of climate from 1984 to 2012. We then quantified departures from expected area burned to identify geographic regions with fire deficit or surplus. We developed our model of area burned as a function of several climatic variables from reference areas with low human influence; the relationship between climate and fire is strong in these areas. We then quantified the degree of fire deficit or surplus for all areas of the western US as the difference between expected (as predicted with the model) and observed area burned from 1984 to 2012. Results indicate that many forested areas in the western US experienced a fire deficit from 1984 to 2012, likely due to fire exclusion by human activities. We also found that large expanses of non-forested regions experienced a fire surplus, presumably due to introduced annual grasses and the prevalence of anthropogenic ignitions. The heterogeneity in patterns of fire deficit and surplus among ecoregions emphasizes fundamentally different ecosystem sensitivities to human influences and suggests that large scale adaptation and mitigation strategies will be necessary in order to restore and maintain resilient, healthy, and naturally functioning ecosystems

Contributions of Fire Refugia to Resilient Ponderosa Pine and Dry Mixed‐Conifer Forest Landscapes

Altered fire regimes can drive major and enduring compositional shifts or losses of forest ecosystems. In western North America, ponderosa pine and dry mixed‐conifer forest types appear increasingly vulnerable to uncharacteristically extensive, high‐severity wildfire. However, unburned or only lightly impacted forest stands that persist within burn mosaics—termed fire refugia—may serve as tree seed sources and promote landscape recovery. We sampled tree regeneration along gradients of fire refugia proximity and density at 686 sites within the perimeters of 12 large wildfires that occurred between 2000 and 2005 in the interior western United States. We used generalized linear mixed‐effects models to elucidate statistical relationships between tree regeneration and refugia pattern, including a new metric that incorporates patch proximity and proportional abundance. These relationships were then used to develop a spatially explicit landscape simulation model. We found that regeneration by ponderosa pine and obligate‐seeding mixed‐conifer tree species assemblages was strongly and positively predicted by refugia proximity and density. Simulation models revealed that for any given proportion of the landscape occupied by refugia, small patches produced greater landscape recovery than large patches. These results highlight the disproportionate importance of small, isolated islands of surviving trees, which may not be detectable with coarse‐scale satellite imagery. Findings also illustrate the interplay between patch‐scale resistance and landscape‐scale resilience: Disturbance‐resistant settings (fire refugia) can entrain resilience (forest regeneration) across the burn matrix. Implications and applications for land managers and conservation practitioners include strategies for the promotion and maintenance of fire refugia as components of resilient forest landscapes

Soil moisture monitoring in a temperate peatland using multi-sensor remote sensing and linear mixed effects

The purpose of this research was to use empirical models to monitor temporal dynamics of soil moisture in a peatland using remotely sensed imagery, and to determine the predictive accuracy of the approach on dates outside the time series through statistically independent validation. A time series of seven Moderate Resolution Imaging Spectroradiometer (MODIS) and Synthetic Aperture Radar (SAR) images were collected along w

Lynx: A Programmatic SAT Solver for the RNA-folding Problem

15th International Conference, Trento, Italy, June 17-20, 2012. ProceedingsThis paper introduces Lynx, an incremental programmatic SAT solver that allows non-expert users to introduce domain-specific code into modern conflict-driven clause-learning (CDCL) SAT solvers, thus enabling users to guide the behavior of the solver. The key idea of Lynx is a callback interface that enables non-expert users to specialize the SAT solver to a class of Boolean instances. The user writes specialized code for a class of Boolean formulas, which is periodically called by Lynx’s search routine in its inner loop through the callback interface. The user-provided code is allowed to examine partial solutions generated by the solver during its search, and to respond by adding CNF clauses back to the solver dynamically and incrementally. Thus, the user-provided code can specialize and influence the solver’s search in a highly targeted fashion. While the power of incremental SAT solvers has been amply demonstrated in the SAT literature and in the context of DPLL(T), it has not been previously made available as a programmatic API that is easy to use for non-expert users. Lynx’s callback interface is a simple yet very effective strategy that addresses this need. We demonstrate the benefits of Lynx through a case-study from computational biology, namely, the RNA secondary structure prediction problem. The constraints that make up this problem fall into two categories: structural constraints, which describe properties of the biological structure of the solution, and energetic constraints, which encode quantitative requirements that the solution must satisfy. We show that by introducing structural constraints on-demand through user provided code we can achieve, in comparison with standard SAT approaches, upto 30x reduction in memory usage and upto 100x reduction in time

Design principles for riboswitch function

Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems. RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship. One prominent example of RNA-based regulatory components is riboswitches, genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms. While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration, no quantitative frameworks exist to investigate or guide riboswitch tuning. Here, we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance. Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms. We also found that practical system restrictions, such as an upper limit on ligand concentration, can significantly alter the requirements for riboswitch performance, necessitating alternative tuning strategies. Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions. From our results, we developed a set of general design principles for synthetic riboswitches. Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands

Prediction of RNA secondary structure by maximizing pseudo-expected accuracy

<p>Abstract</p> <p>Background</p> <p>Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.</p> <p>Results</p> <p>Instead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the <it>pseudo</it>-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the γ-centroid estimator.</p> <p>Conclusions</p> <p>This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.</p

Identification of DNA-Damage DNA-Binding Protein 1 as a Conditional Essential Factor for Cytomegalovirus Replication in Interferon-γ-Stimulated Cells

The mouse cytomegaloviral (MCMV) protein pM27 represents an indispensable factor for viral fitness in vivo selectively, antagonizing signal transducer and activator of transcription 2 (STAT2)-mediated interferon signal transduction. We wished to explore by which molecular mechanism pM27 accomplishes this effect. We demonstrate that pM27 is essential and sufficient to curtail the protein half-life of STAT2 molecules. Pharmacologic inhibition of the proteasome restored STAT2 amounts, leading to poly-ubiquitin-conjugated STAT2 forms. PM27 was found in complexes with an essential host ubiquitin ligase complex adaptor protein, DNA-damage DNA-binding protein (DDB) 1. Truncation mutants of pM27 showed a strict correlation between DDB1 interaction and their ability to degrade STAT2. SiRNA-mediated knock-down of DDB1 restored STAT2 in the presence of pM27 and strongly impaired viral replication in interferon conditioned cells, thus phenocopying the growth attenuation of M27-deficient virus. In a constructive process, pM27 recruits DDB1 to exploit ubiquitin ligase complexes catalyzing the obstruction of the STAT2-dependent antiviral state of cells to permit viral replication