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Approximately 15.5% of global forest is controlled by ~1 billion local people and the area under community control is increasing. However, there is limited empirical evidence as to whether community control is effective in providing critical global ecosystem services, such as biodiversity conservation and carbon storage. We assess the effectiveness of one example of community-controlled forest, Nepal's Community Forestry Program (CFP), at providing biodiversity conservation and carbon storage. Using data from 620 randomly selected CFP and non-CFP forest plots, we apply a robust matching method based on covariates to estimate whether CFPs are associated with greater biodiversity conservation or carbon storage. Our results reveal a significant positive effect of CFP on biodiversity, which is robust against the influence of unobserved covariates. Our results also suggest a significant negative effect of the CFP on aboveground tree and sapling carbon (AGC) at the national scale (-15.11 Mg C ha-1). However, the CFP has a mixed effect on carbon across geographic and topographic regions and in forests with different canopy covers. Though there were no significant effects of the CFP on AGC at lower altitudes, in the Terai or hill regions, and under closed canopies, there were positive effects in open canopies (25.84 Mg C ha-1) at lower slopes (25.51 Mg C ha-1) and negative effects at higher altitudes (-22.81 Mg C ha-1) and higher slopes (-17.72 Mg C ha-1). Our sensitivity analysis revealed that the positive effects are robust to unobserved covariates, which is not true for the negative results. In aggregate, our results demonstrate that CFP can be an effective forest management strategy to contribute to global ecosystem services such as biodiversity, and to a lesser extent carbon.

Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders. •We quantitatively summarize the literature on emotion regulation (ER) using ALE.•Using MACM and quantitative functional inference we develop a neural model of ER.•DLPFC is related to higher order “cold” regulatory processes.•VLPFC evaluates salience and indicates need to regulate.•STG, angular gyrus and SMA are associated to execution of regulation.

Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden 1 . Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks (s.d., 4.5 weeks) before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% (s.d., 3%), which is superior to the state-of-the-art method 2 . cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia. Expression signatures from cell-free RNA of pregnant women can be used to reveal normal biology of pregnancy and predict development of pre-eclampsia.

Both fatty bone marrow (FBM) and somatic mutations in hematopoietic stem cells (HSCs), also termed clonal hematopoiesis (CH) accumulate with human aging. However it remains unclear whether FBM can modify the evolution of CH. To address this question, we herein present the interaction between CH and FBM in two preclinical male mouse models: after sub-lethal irradiation or after castration. An adipogenesis inhibitor (PPARγ inhibitor) is used in both models as a control. A significant increase in self-renewal can be detected in both human and rodent DNMT3A Mut -HSCs when exposed to FBM. DNMT3A Mut -HSCs derived from older mice interacting with FBM have even higher self-renewal in comparison to DNMT3A Mut -HSCs derived from younger mice. Single cell RNA-sequencing on rodent HSCs after exposing them to FBM reveal a 6-10 fold increase in DNMT3A Mut -HSCs and an activated inflammatory signaling. Cytokine analysis of BM fluid and BM derived adipocytes grown in vitro demonstrates an increased IL-6 levels under FBM conditions. Anti-IL-6 neutralizing antibodies significantly reduce the selective advantage of DNMT3A Mut -HSCs exposed to FBM. Overall, paracrine FBM inflammatory signals promote DNMT3A -driven clonal hematopoiesis, which can be inhibited by blocking the IL-6 pathway. Age related accumulation of adipocytes in the bone marrow could alter normal and leukemic haematopoiesis. Here, in fatty bone marrow (FBM) preclinical models, the authors show that inflammatory cytokines increased in the FBM, such as IL-6, promote DNMT3a driven clonal hematopoiesis.

Climate change will accelerate forest mortality due to insects, disease, and wildfire. As a result, substantial resources will be necessary where and when forest managers seek to maintain multiple management objectives. Because of the increasing managerial requirements to offset climate change and related disturbances, the uncertainty about future forest conditions is magnified relative to climate change alone. We provide an analytical approach that quantifies the key drivers of forest change—climate, disturbance, and forest management—using scenarios paired with simulation modeling to forecast and quantify uncertainties in the Lake Tahoe Basin of California and Nevada (USA), a montane seasonally dry conifer forest. We partitioned uncertainty among climate change (including associated changes to wildfire and insect outbreaks), forest management (including thinning, prescribed fire, and fire suppression), and other sources using a fully factorial experimental design and analysis of variance. We focused on three metrics that are important for forest management objectives for the area: forest carbon storage, area burned at high severity, and total area burned by wildfire. Management explained a substantial amount of variance in the short term for area burned at high severity and longer term carbon storage, while climate explained the most variance in total area burned. Our results suggest that simulated extensive management activities will not meet all the desired management objectives. Both the extent and intensity of forest management will need to increase significantly to keep pace with predicted climate and wildfire conditions.

The impacts of climatic changes on forests may appear gradually on time scales of years to centuries due to the long generation times of trees. Consequently, current forest extent may not reflect current climatic patterns. In contrast with these lagged responses, abrupt transitions in forests under climate change may occur in environments where alternative vegetation states are influenced by disturbances, such as fire. The Klamath forest landscape (northern California and southwest Oregon, USA) is currently dominated by high biomass, biodiverse temperate coniferous forests, but climate change could disrupt the mechanisms promoting forest stability (e.g. growth, regeneration and fire tolerance). Using a landscape simulation model, we estimate that about one-third of the Klamath forest landscape (500,000 ha) could transition from conifer-dominated forest to shrub/hardwood chaparral, triggered by increased fire activity coupled with lower post-fire conifer establishment. Such shifts were widespread under the warmer climate change scenarios (RCP 8.5) but were surprisingly prevalent under the climate of 1949–2010, reflecting the joint influences of recent warming trends and the legacy of fire suppression that may have enhanced conifer dominance. Our results demonstrate that major forest ecosystem shifts should be expected when climate change disrupts key stabilizing feedbacks that maintain the dominance of long-lived, slowly regenerating trees.

Forest managers working in dry forest ecosystems must contend with the costs and benefits of fire, and they are seeking forest management strategies that enhance the resilience of forests and landscapes to future disturbances in a changing climate. An interdisciplinary science team worked with resource managers and stakeholders to assess future forest ecosystem dynamics, given potential climatic changes and management strategies, across a 23,000-ha landscape in the Lake Tahoe basin of California and Nevada in support of the Lake Tahoe West Restoration Partnership. We projected forest growth and fire dynamics using a landscape change model, upon which the science team layered additional modeling to evaluate changes in wildlife habitat, water, and economics. Managers and stakeholders used the findings of this integrated modeling effort to inform the design of a landscape restoration strategy that balanced risks and benefits based on a robust scientific foundation. The results, published in this Special Feature, suggest that a continuation of status quo management would be less effective at protecting and improving desired outcomes than more active and extensive management approaches. In addition, the types of management activity also affected ecosystem outcomes. Results from across the studies in this special feature suggest that thinning and prescribed fire were complementary, although they resulted in somewhat different effects, and that low-severity use of fire had the greatest array and magnitude of ecosystem benefits. A notable exception was carbon storage, which declined with more active management and prescribed fire in particular. We highlight key findings from this Special Feature and summarize key challenges and some lessons learned in our experience of co-producing science. In short, science-management partnerships require cooperation, patience, and skill, but they are effective in increasing the capacity of land managers to navigate in an environment of rapid change and increasing uncertainty.

Because of past land use changes and changing climate, forests are moving outside of their historical range of variation. As fires become more severe, forest managers are searching for strategies that can restore forest health and reduce fire risk. However, management activities are only one part of a suite of disturbance vectors that shape forest conditions. To account for the range of disturbance intensities and disturbance types (wildfire, bark beetles, and management), we developed a disturbance return interval (DRI) that represents the average return period for any disturbance, human or natural. We applied the DRI to examine forest change in the Lake Tahoe Basin of California and Nevada. We specifically investigated the consequences of DRI on the proportion of high-severity fire and the net sequestration of carbon. In order to test the management component of the DRI, we developed management scenarios with forest managers and stakeholders in the region; these scenarios were integrated into a mechanistic forest landscape model that also accounted for climate change, as well as natural disturbances of wildfire and insect outbreaks. Our results suggest increasing the frequency of disturbances (a lower DRI) would reduce the percentage of high-severity fire on landscape but not the total amount of wildfire in general. However, a higher DRI reduced carbon storage and sequestration, particularly in management strategies that emphasized prescribed fire over hand or mechanical fuel treatments.

Smoke from wildfires has become a growing public health issue around the world but especially in western North America and California. At the same time, managers and scientists recommend thinning and intentional use of wildland fires to restore forest health and reduce smoke from poorly controlled wildfires. Because of the changing climate and management paradigms, the evaluation of smoke impacts needs to shift evaluations from the scale of individual fire events to long-term fire regimes and regional impacts under different management strategies. To confront this challenge, we integrated three widely used modeling tools to analyze smoke impacts across different management scenarios within a future of changing climate. We applied this multi-stage framework to a case study analysis in the Lake Tahoe basin, in which managers proposed scenarios that involved varying levels of hand- and mechanical-thinning treatments and prescribed fires. We began by using the LANDIS-II model to project daily emissions of fine particulate matter from wildland fires under various climate and management scenarios over a century. We also modeled dispersion and health impacts based upon individual wildfire events selected to be representative of different management scenarios. For those events, we modeled smoke conveyance to downwind communities from representative future fires using the BlueSky smoke dispersion model. Lastly, we estimated human health impacts resulting from the modeled smoke using the U.S. Environmental Protection Agency's BenMAP model. Our results suggest that emissions from wildfires will substantially increase in future decades; however, increased levels of forest thinning could substantially reduce those emissions and harmful health impacts from large wildfires. We also found that increased use of prescribed burning could reduce the health impacts associated with large wildfires but would also increase the frequency of low levels of emissions. Furthermore, the modeling results suggested that individual prescribed fires could have substantial health impacts if dispersion conditions are unfavorable. Our results suggest that increased management is likely to yield important benefits given expected increases in wildfire activity associated with climate change. However, there remain many challenges to projecting the effects of alternative management regimes, especially ones that involve substantial increases in intentional burning.

Wildfires play an important ecological role in fire-adapted landscapes throughout California. However, there is a growing awareness that large wildfires in increasingly populated areas incur costs that may not be acceptable to society. Various forest management strategies have been proposed that seek to reduce the prevalence and severity of wildfires in areas where these costs are high. In this study we estimate the financial costs of various hypothetical forest management scenarios in the Lake Tahoe West landscape of Northern California. The objective of the study was to quantify trade-offs and cost constraints that would affect the feasibility of each scenario. The scenarios ranged from minimal forest management to several options for more intensive fuels management that relied to varying degrees on thinning and prescribed burning. We assessed stand-level costs associated with thinning, prescribed burn management, and timber and biomass transport, as well as revenues from timber and energy chips sold. Using modeled fire occurrence and severity metrics, we also used historical wildfire data to estimate plausible fire suppression costs. Our findings suggest that increased forest management, through the use of either hand/mechanical treatments or prescribed fire, can reduce fire suppression costs relative to recent practices by more than US$400,000 per year. These more intensive management scenarios differ in their cost-effectiveness. Scenarios that increase the use of prescribed fire appear to be the more cost-effective management interventions available with annual costs roughly half as much as a scenario focused on increased hand and mechanical thinning. The results are useful for understanding the financial implications of modifying forest management practices designed to lower the private and social costs of wildfire in the region.