Diagnostic Yield of Decubitus CT Myelography for Detection of CSF-Venous Fistulas.

BACKGROUND AND PURPOSE: Various imaging techniques have been described to detect CSF-Venous Fistulas (CVFs) in the setting of Spontaneous Intracranial Hypotension (SIH), including decubitus CT myelography (dCTM). The expected diagnostic yield of dCTM for CVF detection is not fully established. The purpose of this study was to assess the yield of dCTM among consecutive patients presenting for evaluation of possible SIH, and to examine what impact brain MRI findings of SIH had on diagnostic yield. MATERIALS AND METHODS: Single-center, retrospective cohort of consecutive patients presenting over a one-year period who underwent CTM and had no CSF identified in the epidural space. Patients with epidural CSF leaks were included in a secondary cohort. Subjects were grouped according to positioning for the myelogram, either decubitus or prone, and the presence of imaging findings of SIH on pre-procedure brain MRI. Diagnostic yields for each subgroup were calculated, and the yield of dCTM was compared to prone CTM. RESULTS: The study cohort included 302 subjects, including 247 patients with no epidural fluid. Diagnostic yield of dCTM for CVF detection among subjects with positive brain MRI and no epidural fluid was 73%. No CVFs were identified among subjects with negative brain imaging. Among subjects with epidural leak, brain MRI was negative for signs of SIH in 22%. Prone CTM identified a CVF less commonly than dCTM (43% vs. 73%, p=0.19), although the difference was not statistically significant in this small subgroup. CONCLUSIONS: We found a diagnostic yield of dCTM to be similar to the yield previously reported for digital subtraction myelography among patients with positive brain imaging. No CVFs were identified in patients with negative brain imaging; epidural CSF leaks accounted for all cases of patients who had SIH with negative brain imaging. This study provides useful data for counseling patients and helps establish a general benchmark for dCTM yield for CVF detection.ABBREVIATIONS: SIH = spontaneous intracranial hypotension; CVF = CSF-Venous Fistula; CTM = CT Myelography; dCTM = decubitus CT myelography; EBP = epidural blood patch.

Finite mixtures of mean-parameterized Conway-Maxwell-Poisson models.

For modeling count data, the Conway-Maxwell-Poisson (CMP) distribution is a popular generalization of the Poisson distribution due to its ability to characterize data over- or under-dispersion. While the classic parameterization of the CMP has been well-studied, its main drawback is that it is does not directly model the mean of the counts. This is mitigated by using a mean-parameterized version of the CMP distribution. In this work, we are concerned with the setting where count data may be comprised of subpopulations, each possibly having varying degrees of data dispersion. Thus, we propose a finite mixture of mean-parameterized CMP distributions. An EM algorithm is constructed to perform maximum likelihood estimation of the model, while bootstrapping is employed to obtain estimated standard errors. A simulation study is used to demonstrate the flexibility of the proposed mixture model relative to mixtures of Poissons and mixtures of negative binomials. An analysis of dog mortality data is presented. Supplementary Information: The online version contains supplementary material available at 10.1007/s00362-023-01452-x.

Effectiveness of forest density reduction treatments for increasing drought resistance of ponderosa pine growth.

As the climate changes, it is increasingly important to understand how forests will respond to drought and how forest management can influence those outcomes. In many forests that have become unnaturally dense, "restoration treatments," which decrease stand density using fire and/or mechanical thinning, are generally associated with reduced mortality during drought. However, the effects of such treatments on tree growth during drought are less clear. Previous studies have yielded apparently contradictory results, which may stem from differences in underlying aridity or drought intensity across studies. To address this uncertainty, we studied the growth of ponderosa pine (Pinus ponderosa) in paired treated and untreated areas before and during the extreme California drought of 2012-2016. Our study spanned gradients in climate and tree size and found that density reduction treatments could completely ameliorate drought-driven declines in growth under some contexts, specifically in more mesic areas and in medium-sized trees (i.e., normal annual precipitation > ca. 1100 mm and tree diameter at breast height < ca. 65 cm). Treatments were much less effective in ameliorating drought-associated growth declines in the most water-limited sites and largest trees, consistent with underlying ecophysiology. In medium-sized trees and wetter sites, growth of trees in untreated stands decreased by more than 15% during drought, while treatment-associated increases in growth of 25% or more persisted during the drought. Trees that ultimately died due to drought showed greater growth reductions during drought relative to trees that survived. Our results suggest that density reduction treatments can increase tree resistance to water stress, and they highlight an important pathway for treatments to influence carbon sequestration and other ecosystem services beyond mitigating tree mortality.

Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States.

Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration.

Rectangular multivariate normal prediction regions for setting reference regions in laboratory medicine.

Reference intervals are among the most widely used medical decision-making tools and are invaluable in the interpretation of laboratory results of patients. Moreover, when multiple biochemical analytes are measured on each patient, a multivariate reference region (MRR) is needed. Such regions are more desirable than separate univariate reference intervals since the latter disregard the cross-correlations among variables. Traditionally, assuming multivariate normality, MRRs have been constructed as ellipsoidal regions, which cannot detect componentwise extreme values. Consequently, MRRs are rarely used in actual practice. In order to address the above drawback of ellipsoidal reference regions, we propose a procedure to construct rectangular MRRs under multivariate normality. The rectangular MRR is computed using a prediction region criterion. However, since the population correlations are unknown, a parametric bootstrap approach is employed for computing the required prediction factor. Also addressed in this study is the computation of mixed reference intervals, which include both two-sided and one-sided prediction limits, simultaneously. Numerical results show that the parametric bootstrap procedure is quite accurate, with estimated coverage probabilities very close to the nominal level. Moreover, the expected volumes of the proposed rectangular regions are substantially smaller than the expected volumes obtained from Bonferroni simultaneous prediction intervals. We also explore the computation of covariate-dependent MRRs in a multivariate regression setting. Finally, we discuss real-life applications of the proposed methods, including the computation of reference ranges for the assessment of kidney function and for components of the insulin-like growth factor system in adults.

A mixture model with Poisson and zero-truncated Poisson components to analyze road traffic accidents in Turkey.

The analysis of traffic accident data is crucial to address numerous concerns, such as understanding contributing factors in an accident's chain-of-events, identifying hotspots, and informing policy decisions about road safety management. The majority of statistical models employed for analyzing traffic accident data are logically count regression models (commonly Poisson regression) since a count - like the number of accidents - is used as the response. However, features of the observed data frequently do not make the Poisson distribution a tenable assumption. For example, observed data rarely demonstrate an equal mean and variance and often times possess excess zeros. Sometimes, data may have heterogeneous structure consisting of a mixture of populations, rather than a single population. In such data analyses, mixtures-of-Poisson-regression models can be used. In this study, the number of injuries resulting from casualties of traffic accidents registered by the General Directorate of Security (Turkey, 2005-2014) are modeled using a novel mixture distribution with two components: a Poisson and zero-truncated-Poisson distribution. Such a model differs from existing mixture models in literature where the components are either all Poisson distributions or all zero-truncated Poisson distributions. The proposed model is compared with the Poisson regression model via simulation and in the analysis of the traffic data.

Effects of postfire climate and seed availability on postfire conifer regeneration.

Large, severe fires are becoming more frequent in many forest types across the western United States and have resulted in tree mortality across tens of thousands of hectares. Conifer regeneration in these areas is limited because seeds must travel long distances to reach the interior of large burned patches and establishment is jeopardized by increasingly hot and dry conditions. To better inform postfire management in low elevation forests of California, USA, we collected 5-yr postfire recovery data from 1,234 study plots in 19 wildfires that burned from 2004-2012 and 18 yrs of seed production data from 216 seed fall traps (1999-2017). We used these data in conjunction with spatially extensive climate, topography, forest composition, and burn severity surfaces to construct taxon-specific, spatially explicit models of conifer regeneration that incorporate climate conditions and seed availability during postfire recovery windows. We found that after accounting for other predictors both postfire and historical precipitation were strong predictors of regeneration, suggesting that both direct effects of postfire moisture conditions and biological inertia from historical climate may play a role in regeneration. Alternatively, postfire regeneration may simply be driven by postfire climate and apparent relationships with historical climate could be spurious. The estimated sensitivity of regeneration to postfire seed availability was strongest in firs and all conifers combined and weaker in pines. Seed production exhibited high temporal variability with seed production varying by over two orders of magnitude among years. Our models indicate that during droughts postfire conifer regeneration declines most substantially in low-to-moderate elevation forests. These findings enhance our mechanistic understanding of forecasted and historically documented shifts in the distribution of trees.

Nonparametric hyperrectangular tolerance and prediction regions for setting multivariate reference regions in laboratory medicine.

Reference regions are widely used in clinical chemistry and laboratory medicine to interpret the results of biochemical or physiological tests of patients. There are well-established methods in the literature for reference limits for univariate measurements; however, limited methods are available for the construction of multivariate reference regions, since traditional multivariate statistical regions (e.g. confidence, prediction, and tolerance regions) are not constructed based on a hyperrectangular geometry. The present work addresses this problem by developing multivariate hyperrectangular nonparametric tolerance regions for setting the reference regions. The approach utilizes statistical data depth to determine which points to trim and then the extremes of the trimmed dataset are used as the faces of the hyperrectangular region. Also presented is a strategy for determining the number of points to trim based on previously established asymptotic results. An extensive coverage study shows the favorable performance of the proposed procedure for moderate to large sample sizes. The procedure is applied to obtain reference regions for addressing two important clinical problems: (1) assessing kidney function in adolescents and (2) characterizing insulin-like growth factor concentrations in the serum of adults.

Improving coverage probabilities for parametric tolerance intervals via bootstrap calibration.

Statistical tolerance intervals are commonly employed in biomedical and pharmaceutical research, such as in lifetime analysis, the assessment of biosimilarity of branded and generic versions of biopharmaceutical drugs, and in quality control of drug products to ensure that a specified proportion of the products are covered within established acceptance limits. Exact two-sided parametric tolerance intervals are only available for the normal distribution, while exact one-sided parametric tolerance limits are available for a limited number of distributions. Approximations to two-sided parametric tolerance intervals often use the Bonferroni correction on the one-sided tolerance interval calculation; however, this often incurs a higher coverage probability than the nominal level. Recently, the usage of a bootstrap calibration has been demonstrated as a way to improve coverage probabilities of tolerance intervals for very specific and complex distributional settings. We present a focused treatment on using a single-layer bootstrap calibration to improve the coverage probabilities of two-sided parametric tolerance intervals. Simulation results clearly demonstrate the improved coverage probabilities towards the nominal level over the uncalibrated setting. Applications to medical data for various parametric distributions also highlight the utility of constructing these calibrated tolerance intervals.

Forest recovery following extreme drought in California, USA: natural patterns and effects of pre-drought management.

Rising temperatures and more frequent and severe droughts are driving increases in tree mortality in forests around the globe. However, in many cases, the likely trajectories of forest recovery following drought-related mortality are poorly understood. In many fire-suppressed western U.S. forests, management is applied to reverse densification and restore natural forest structure and species composition, but it is unclear how such management affects post-mortality recovery. We addressed these uncertainties by examining forest stands that experienced mortality during the severe drought of 2012-2016 in California, USA. We surveyed post-drought vegetation along a gradient of overstory mortality severity in paired treated (mechanically thinned or prescribed-burned) and untreated areas in the Sierra Nevada. Treatment substantially reduced tree density, particularly in smaller tree size classes, and these effects persisted through severe drought-related overstory mortality. However, even in treated areas with severe mortality (>67% basal area mortality), the combined density of residual (surviving) trees (mean 44 trees/ha) and saplings (mean 189 saplings/ha) frequently (86% of plots) fell within or exceeded the natural range of variation (NRV) of tree density, suggesting little need for reforestation intervention to increase density. Residual tree densities in untreated high-mortality plots were significantly higher (mean 192 trees/ha and 506 saplings/ha), and 96% of these plots met or exceeded the NRV. Treatment disproportionately removed shade-tolerant conifer species, while mortality in the drought event was concentrated in pines (Pinus ponderosa and P. lambertiana); as a consequence, the residual trees, saplings, and seedlings in treated areas, particularly those that had experienced moderate or high drought-related mortality, were more heavily dominated by broadleaf ("hardwood") trees (particularly Quercus kelloggii and Q. chrysolepis). In contrast, residual trees and regeneration in untreated stands were heavily dominated by shade-tolerant conifer species (Abies concolor and Calocedrus decurrens), suggesting a need for future treatment. Because increased dominance of hardwoods brings benefits for plant and animal diversity and stand resilience, the ecological advantages of mechanical thinning and prescribed fire treatments may, depending on the management perspective, extend even to stands that ultimately experience high drought-related mortality following treatment.

Decreased snowpack and warmer temperatures reduce the negative effects of interspecific competitors on regenerating conifers.

The persistence and distribution of species under changing climates can be affected by both direct effects of the environment and indirect effects via biotic interactions. However, the relative importance of direct and indirect climate effects on recruitment stages is poorly understood. We conducted a manipulative experiment to test the multiway interaction of direct and competition-mediated effects of climate change on vegetation dynamics. Following stand-replacing fire in California mixed-conifer forest, we seeded two conifer species, Pinus ponderosa and Abies concolor, in two consecutive years, one relatively normal and the other with an unusually wet and snowy winter followed by a hot summer. We additionally manipulated snow amount and competitive environment for both years. We found the effects of the snowpack treatment were contingent upon other abiotic factors (year of seeding) and biotic factors (shrub competition). Under ambient snowpack, shrubs reduced recruitment of P. ponderosa seedlings, but this negative effect disappeared with reduced snowpack. Additionally, the effects of shrubs on seedlings differed between cohorts and by life stage. In a warmer future, decreased snowpack may increase seedling emergence, but hotter and drier summers will decrease seedling survival; the effects of shrubs on conifers may become less negative as temperatures increase.

Influence of Adrenal Venous Sampling on Management in Patients with Primary Aldosteronism Independent of Lateralization on Cross-Sectional Imaging.

BACKGROUND: Patients with primary aldosteronism undergo imaging of the adrenal glands after confirmation of the disease. Adrenal venous sampling (AVS) is a useful adjunct to imaging, and advocates believe that AVS should be performed before surgical management. Others argue that patients with unilateral lesions on imaging do not require AVS. Although AVS accuracy has been established, few studies have evaluated how AVS alters management. Our study aimed to determine how AVS affected management of these patients. STUDY DESIGN: Patient data were collected retrospectively from the electronic medical records at a single institution. Patients aged 18 years or older who underwent AVS with successful adrenal vein cannulation from 2007 to 2016 were included. The laterality of AVS was compared with laterality of preprocedural imaging for each patient. The management plan before AVS was determined by laterality on preprocedural imaging. The primary outcomes were management of primary aldosteronism, change in management compared with the plan before AVS, and antihypertensive medication use after therapy. RESULTS: Seventy-four patients had successful adrenal venous cannulation. Thirty-three (44.6%) patients had AVS lateralization that was concordant with preprocedural imaging. Forty-one (55.4%) patients had AVS lateralization that was non-concordant with preprocedural imaging. There was a change in management in 29 (39.2%) patients. CONCLUSIONS: Adrenal venous sampling can delineate the source of aldosterone hypersecretion, and often this is not concordant with cross-sectional imaging. We found that many patients avoided a potentially non-curative operation due to AVS. Adrenal venous sampling frequently alters the management of aldosteronomas and should be highly considered in patients before surgical intervention.

Forest structure and climate mediate drought-induced tree mortality in forests of the Sierra Nevada, USA.

Extreme drought stress and associated bark beetle population growth contributed to an extensive tree mortality event in California, USA, resulting in more than 129 million trees dying between 2012 and 2016. Although drought is an important driver of this mortality event, past and ongoing fire suppression and the consequent densification of forests may have contributed. In some areas, land management agencies have worked to reduce stand density through mechanical treatments and prescribed fire to restore forests to less dense, more open conditions that are presumably more resilient to disturbance and changing climate. Here, we evaluate if stand structural conditions associated with treated (e.g., thinned and prescribed burned) forests in the Sierra Nevada of California conferred more resistance to the bark beetle epidemic and drought event of 2012-2016. We found that, compared to untreated units, treated units had lower stand densities, larger average tree diameters, and greater dominance of pines (Pinus), the historically dominant trees. For all tree species studied, mortality was substantially greater in climatically drier areas (i.e., lower elevations and latitudes). Both pine species studied (ponderosa pine [Pinus ponderosa] and sugar pine [Pinus lambertiana]) had greater mortality in areas where their diameters were larger, suggesting a size preference for their insect mortality agents. For ponderosa pine, the tree species experiencing greatest mortality, individual-tree mortality probability (for a given tree diameter) was significantly lower in treated stands. Ponderosa pine mortality was also positively related to density of medium- to large-sized conspecific trees, especially in areas with lower precipitation, suggesting that abundance of nearby host trees for insect mortality agents was an important determinant of pine mortality. Mortality of incense cedar (Calocedrus decurrens) and white fir (Abies concolor) was positively associated with basal area, suggesting sensitivity to competition during drought, but overall mortality was lower, likely because the most prevalent and effective mortality agents (the bark beetles Dendroctonus brevicomis and D. ponderosae) are associated specifically with pine species within our study region. Our findings suggest that forest thinning treatments are effective in reducing drought-related tree mortality in forests, and they underscore the important interaction between water and forest density in mediating bark beetle-caused mortality.

Post-fire forest regeneration shows limited climate tracking and potential for drought-induced type conversion.

Disturbance such as wildfire may create opportunities for plant communities to reorganize in response to climate change. The interaction between climate change and disturbance may be particularly important in forests, where many of the foundational plant species (trees) are long-lived and where poor initial tree establishment can result in conversion to shrub- or graminoid-dominated systems. The response of post-disturbance vegetation establishment to post-disturbance weather conditions, particularly to extreme weather, could therefore provide useful information about how forest communities will respond to climate change. We examined the effect of post-fire weather conditions on post-fire tree, shrub, and graminoid recruitment in fire-adapted forests in northern California, USA, by surveying regenerating vegetation in severely burned areas 4-5 yr after 14 different wildfires that burned between 2004 and 2012. This time period (2004-2016) encompassed a wide range of post-fire weather conditions, including a period of extreme drought. For the most common tree species, we observed little evidence of disturbance-mediated community reorganization or range shifts but instead either (1) low sensitivity of recruitment to post-fire weather or (2) weak but widespread decreases in recruitment under unusually dry post-fire conditions, depending on the species. The occurrence of a single strong drought year following fire was more important than a series of moderately dry years in explaining tree recruitment declines. Overall, however, post-fire tree recruitment patterns were explained more strongly by long-term climate and topography and local adult tree species abundance than by post-fire weather conditions. This observation suggests that surviving adult trees can contribute to a "biological inertia" that restricts the extent to which tree community composition will track changes in climate through post-disturbance recruitment. In contrast to our observations in trees, we observed substantial increases in shrub and graminoid establishment under post-fire drought, suggesting that shifts in dominance between functional groups may become more likely in a future with more frequent and intense drought.

Polymodal TRPV1 and TRPV4 Sensors Colocalize but Do Not Functionally Interact in a Subpopulation of Mouse Retinal Ganglion Cells.

Retinal ganglion cells (RGCs) are projection neurons that transmit the visual signal from the retina to the brain. Their excitability and survival can be strongly influenced by mechanical stressors, temperature, lipid metabolites, and inflammatory mediators but the transduction mechanisms for these non-synaptic sensory inputs are not well characterized. Here, we investigate the distribution, functional expression, and localization of two polymodal transducers of mechanical, lipid, and inflammatory signals, TRPV1 and TRPV4 cation channels, in mouse RGCs. The most abundant vanilloid mRNA species was Trpv4, followed by Trpv2 and residual expression of Trpv3 and Trpv1. Immunohistochemical and functional analyses showed that TRPV1 and TRPV4 channels are expressed as separate molecular entities, with TRPV1-only (∼10%), TRPV4-only (∼40%), and TRPV1 + TRPV4 (∼10%) expressing RGC subpopulations. The TRPV1 + TRPV4 cohort included SMI-32-immunopositive alpha RGCs, suggesting potential roles for polymodal signal transduction in modulation of fast visual signaling. Arguing against obligatory heteromerization, optical imaging showed that activation and desensitization of TRPV1 and TRPV4 responses evoked by capsaicin and GSK1016790A are independent of each other. Overall, these data predict that RGC subpopulations will be differentially sensitive to mechanical and inflammatory stressors.

Increased white matter metabolic rates in autism spectrum disorder and schizophrenia.

Both autism spectrum disorder (ASD) and schizophrenia are often characterized as disorders of white matter integrity. Multimodal investigations have reported elevated metabolic rates, cerebral perfusion and basal activity in various white matter regions in schizophrenia, but none of these functions has previously been studied in ASD. We used 18fluorodeoxyglucose positron emission tomography to compare white matter metabolic rates in subjects with ASD (n = 25) to those with schizophrenia (n = 41) and healthy controls (n = 55) across a wide range of stereotaxically placed regions-of-interest. Both subjects with ASD and schizophrenia showed increased metabolic rates across the white matter regions assessed, including internal capsule, corpus callosum, and white matter in the frontal and temporal lobes. These increases were more pronounced, more widespread and more asymmetrical in subjects with ASD than in those with schizophrenia. The highest metabolic increases in both disorders were seen in the prefrontal white matter and anterior limb of the internal capsule. Compared to normal controls, differences in gray matter metabolism were less prominent and differences in adjacent white matter metabolism were more prominent in subjects with ASD than in those with schizophrenia. Autism spectrum disorder and schizophrenia are associated with heightened metabolic activity throughout the white matter. Unlike in the gray matter, the vector of white matter metabolic abnormalities appears to be similar in ASD and schizophrenia, may reflect inefficient functional connectivity with compensatory hypermetabolism, and may be a common feature of neurodevelopmental disorders.

Long-term climate and competition explain forest mortality patterns under extreme drought.

Rising temperatures are amplifying drought-induced stress and mortality in forests globally. It remains uncertain, however, whether tree mortality across drought-stricken landscapes will be concentrated in particular climatic and competitive environments. We investigated the effects of long-term average climate [i.e. 35-year mean annual climatic water deficit (CWD)] and competition (i.e. tree basal area) on tree mortality patterns, using extensive aerial mortality surveys conducted throughout the forests of California during a 4-year statewide extreme drought lasting from 2012 to 2015. During this period, tree mortality increased by an order of magnitude, typically from tens to hundreds of dead trees per km2 , rising dramatically during the fourth year of drought. Mortality rates increased independently with average CWD and with basal area, and they increased disproportionately in areas that were both dry and dense. These results can assist forest managers and policy-makers in identifying the most drought-vulnerable forests across broad geographic areas.

Western Juniper Management: Assessing Strategies for Improving Greater Sage-grouse Habitat and Rangeland Productivity.

Western juniper (Juniperus occidentalis subsp. occidentalis) range expansion into sagebrush steppe ecosystems has affected both native wildlife and economic livelihoods across western North America. The potential listing of the greater sage-grouse (Centrocercus urophasianus) under the U.S. Endangered Species Act has spurred a decade of juniper removal efforts, yet limited research has evaluated program effectiveness. We used a multi-objective spatially explicit model to identify optimal juniper removal sites in Northeastern California across weighted goals for ecological (sage-grouse habitat) and economic (cattle forage production) benefits. We also extended the analysis through alternative case scenarios that tested the effects of coordination among federal agencies, budgetary constraints, and the use of fire as a juniper treatment method. We found that sage-grouse conservation and forage production goals are somewhat complementary, but the extent of complementary benefits strongly depends on spatial factors and management approaches. Certain management actions substantially increase achievable benefits, including agency coordination and the use of prescribed burns to remove juniper. Critically, our results indicate that juniper management strategies designed to increase cattle forage do not necessarily achieve measurable sage-grouse benefits, underscoring the need for program evaluation and monitoring.

Plant communities in harsh sites are less invaded: a summary of observations and proposed explanations.

Plant communities in abiotically stressful, or 'harsh', habitats have been reported to be less invaded by non-native species than those in more moderate habitats. Here, we synthesize descriptive and experimental evidence for low levels of invasion in habitats characterized by a variety of environmental stressors: low nitrogen; low phosphorus; saline, sodic or alkaline soils; serpentine soils; low soil moisture; shallow/rocky soils; temporary inundation; high shade; high elevation; and high latitude. We then discuss major categories of hypotheses to explain this pattern: the propagule limitation mechanism suggests invasion of harsh sites is limited by relatively low arrival rates of propagules compared with more moderate habitats, while invasion resistance mechanisms suggest that harsh habitats are inherently less invasible due to stressful abiotic conditions and/or increased effects of biotic resistance from resident organisms. Both propagule limitation and invasion resistance may simultaneously contribute to low invadedness of harsh sites, but the management implications of these mechanisms differ. If propagule limitation is more important, managers should focus on reducing the likelihood of propagule introductions. If invasion resistance mechanisms are in play, managers should focus on restoring or maintaining harsh conditions at a site to reduce invasibility.

Choosing a coverage probability for forecasting the incidence of cancer.

Loddon Mallee Integrated Cancer Service plays a key role in planning the delivery of cancer services in the Loddon Mallee Region of Victoria, Australia. Such planning relies on the accuracy of forecasting the incidence of cancer. Perhaps more importantly is the need to reflect the uncertainty of these forecasts, which is usually carried out through prediction intervals. Standard confidence levels (e.g., 90% or 95%) are typically employed when forecasting the incidence of cancer, but decision-theoretic approaches are available to help choose an optimal coverage probability by minimizing the combined risk of the interval width and noncoverage of the interval. We proceed with the decision-theoretic framework and discuss some general strategies for defining candidate loss functions for forecasting the incidence of cancer, such as the data we analyze for the Loddon Mallee Region.