Rising forest exposure and fire severity from climate warming amplify tree cover losses from wildfire in California

Warmer temperatures and severe drought are driving increases in wildfire activity in the western United States, threatening forest ecosystems. However, identifying the influence of fire severity on tree cover loss (TCL) is challenging using commonly used categorical metrics. In this study, we quantify regional trends in wildfire-driven TCL as the product of annual burned area, average forest exposure (pre-fire tree cover), and average fire severity (relative loss of tree cover). We quantified these trends with Landsat-based 30 m resolution fire and tree cover datasets for California wildfires from 1986-2021. Rates of TCL rose faster than trends in burned area, with the magnitude of tree cover area loss per unit of area burned increasing by 70% from 0.20 ± 0.05 during 1986-1996 to 0.34 ± 0.10 during 2011-2021. Forest exposure (pre-fire tree cover) within fires increased by 41% from a decadal mean of 23.4% ± 5.5% (1986-1996) to 33.1% ± 7.8% (2011-2021). Increasing forest exposure is associated with a recent expansion of fires in dense northern forests. Concurrently, fire severity (relative TCL) rose by 30% from a decadal mean of 50.4% ± 7.2% during 1986-1996 to 65.6% ± 6.5% during 2011-2021. We developed and applied a simple conceptual framework to quantify the combined effect of wildfires affecting denser forests and burning more severely. The combined effect of these two processes contributed to nearly half (47%) of the TCL since 1986, highlighting that recent changes in burned areas alone cannot explain observed tree cover trends. Linear regression analysis revealed that warmer summers and drier winters were significant drivers of increasing forest exposure, fire severity, and burned area (R2 from 0.54 to 0.80, p ⩽ 0.001), particularly in the northern forests. Climate extremes had a disproportionate impact on dense forests that were once more resistant to wildfire but now face risks from a shifting wildfire regime

Recent fire history enhances semi-arid conifer forest drought resistance

Climate change is amplifying both wildfire burned area and severity, as well as incidents of drought-induced tree mortality (dieback). Direct effects from climate change amplify wildfires and episodes of drought-induced dieback have well-known impacts on forest’s ability to regulate climate, provide water, and store carbon. Less understood are how past disturbances produce interaction effects that can change subsequent disturbance occurrence and intensity, with implications for management decisions that can promote forest resistance and resilience. We constructed two parallel forest chrono-sequences by combining a geospatial database of historical fire with satellite and airborne observations of forests in the Sierra Nevada of California to assess the impact of fire history on vegetation recovery, water use (evapotranspiration), and drought-induced forest dieback. We used these data sets to assess two research questions: (1.) Does fire history amplify or reduce drought-dieback intensity? (2.) What mechanisms explain how fire-induced changes to forest structure and ET alter subsequent forest dieback intensity? We show that recent fire history decreased drought-induced forest dieback intensity, compared to unburned controls. These fire-affected forests were characterized by reduced tree cover and decreased evapotranspiration, which combined to increase drought resistance more than would be expected by either effect individually. Two decades post-fire, evapotranspiration returned to pre-fire conditions. Tree and shrub cover started to approach pre-fire conditions, except for high severity fires where decreased tree cover and increased shrub cover persisted. Field based research on fuels treatments suggests that fire history may also increase longer term forest resilience. In fire-prone conifer forests, interaction effects from recent low and moderate severity fires will increase drought resistance and perhaps longer-term forest stability

Comprehensive Sex Steroid Profiling in Multiple Tissues Reveals Novel Insights in Sex Steroid Distribution in Male Mice

A comprehensive atlas of sex steroid distribution in multiple tissues is currently lacking, and how circulating and tissue sex steroid levels correlate remains unknown. Here, we adapted and validated a gas chromatography tandem mass spectrometry method for simultaneous measurement of testosterone (T), dihydrotestosterone (DHT), androstenedione, progesterone (Prog), estradiol, and estrone in mouse tissues. We then mapped the sex steroid pattern in 10 different endocrine, reproductive, and major body compartment tissues and serum of gonadal intact and orchiectomized (ORX) male mice. In gonadal intact males, high levels of DHT were observed in reproductive tissues, but also in white adipose tissue (WAT). A major part of the total body reservoir of androgens (T and DHT) and Prog was found in WAT. Serum levels of androgens and Prog were strongly correlated with corresponding levels in the brain while only modestly correlated with corresponding levels in WAT. After orchiectomy, the levels of the active androgens T and DHT decreased markedly while Prog levels in male reproductive tissues increased slightly. In ORX mice, Prog was by far the most abundant sex steroid, and, again, WAT constituted the major reservoir of Prog in the body. In conclusion, we present a comprehensive atlas of tissue and serum concentrations of sex hormones in male mice, revealing novel insights in sex steroid distribution. Brain sex steroid levels are well reflected by serum levels and WAT constitutes a large reservoir of sex steroids in male mice. In addition, Prog is the most abundant sex hormone in ORX mice

Further evidence for a variable fine-structure constant from Keck/HIRES QSO absorption spectra

[Abridged] We previously presented evidence for a varying fine-structure constant, alpha, in two independent samples of Keck/HIRES QSO spectra. Here we present a detailed many-multiplet analysis of a third Keck/HIRES sample containing 78 absorption systems. We also re-analyse the previous samples, providing a total of 128 absorption systems over the redshift range 0.2<z_abs<3.7. All three samples separately yield consistent, significant values of da/a. The analyses of low- and high-z systems rely on different ions/transitions with very different dependencies on alpha, yet they also give consistent results. We identify additional random errors in 22 high-z systems characterized by transitions with a large dynamic range in apparent optical depth. Increasing the statistical errors on da/a for these systems gives our fiducial result, a weighted mean da/a=(-0.543+/-0.116)x10^-5, representing 4.7-sigma evidence for a smaller weighted mean alpha in the absorption clouds. Assuming that da/a=0 at z_abs=0, the data marginally prefer a linear increase in alpha with time: dota/a=(6.40+/-1.35)x10^-16 yr^-1. The two-point correlation function for alpha is consistent with zero over 0.2-13 Gpc comoving scales and the angular distribution of da/a shows no significant dipolar anisotropy. We therefore have no evidence for spatial variations in da/a. We extend our previous searches for possible systematic errors, identifying atmospheric dispersion and isotopic structure effects as potentially the most significant. However, overall, known systematic errors do not explain the results. Future many-multiplet analyses of QSO spectra from different telescopes and spectrographs will provide a now crucial check on our Keck/HIRES results.Comment: 31 pages, 25 figures (29 EPS files), 8 tables. Accepted by MNRAS. Colour versions of Figs. 6, 8 & 10 and text version of Table 3 available at http://www.ast.cam.ac.uk/~mim/pub.htm

Network Compression as a Quality Measure for Protein Interaction Networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients