Using hybrid modelling to predict basal area and evaluate effects of climate change on growth of Norway spruce and Scots nine stands

When modelling forest growth, capturing the effects of climate change is needed for reliable longterm predictions and management choices. This remains a challenge because commonly used mensurational forest growth and yield models, relying on inventory data, cannot account for climate change effects. We developed hybrid physiological/mensurational basal area growth and yield models, which combine physiological response to climatic conditions and empirical relations. We included climate and site effects by replacing time with light sums of photosynthetically active radiation and modifying the latter with monthly soil water, vapour pressure deficit, temperature, and frost days. When parameterised with permanent sample plot data for Scots pine and Norway spruce across Sweden, the hybrid models could reproduce observations well, although with no increase in precision compared with time-based mensurational models. When considering different climate scenarios, a significant impact on productivity from climate change emerged. For example, a 2 degrees C warming enhanced Scots pine production by up to 14% in regions where temperatures were originally cooler and soil water deficit was low (i.e. northwest Sweden), but depressed it, up to 9%, elsewhere. Hence, climate-sensitive models that take local variations into account are necessary for accurate predictions and sustainable forest management

Comparing basal area growth models for Norway spruce and Scots pine dominated stands

Models that predict forest development are essential for sustainable forest management. Constructing growth models via regression analysis or fitting a family of sigmoid equations to construct compatible growth and yield models are two ways these models can be developed. In this study, four species-specific models were developed and compared. A compatible growth and yield stand basal area model and a five-year stand basal area growth model were developed for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). The models were developed using data from permanent inventory plots from the Swedish national forest inventory and long-term experiments. The species-specific models were compared, using independent data from long-term experiments, with a stand basal area growth model currently used in the Swedish forest planning system Heureka (Elfving model). All new models had a good, relatively unbiased fit. There were no apparent differences between the models in their ability to predict basal area development, except for the slightly worse predictions for the Norway spruce growth model. The lack of difference in the model comparison showed that despite the simplicity of the compatible growth and yield models, these models could be recommended, especially when data availability is limited. Also, despite using more and newer data for model development in this study, the currently used Elfving model was equally good at predicting basal area. The lack of model difference indicate that future studies should instead focus on model development for heterogeneous forests which are common but lack in growth and yield modelling research

Influence of climate and forest management on damage risk by the pine weevil Hylobius abietis in northern Sweden

The pine weevil L. is an economically important pest insect that kills high proportions of conifer seedlings in reforestation areas. It is present in conifer forests all over Europe but weevil abundance and risk for damage varies considerably between areas. This study aimed to obtain a useful model for predicting damage risks by analyzing survey data from 292 regular forest plantations in northern Sweden. A model of pine weevil attack was constructed using various site characteristics, including both climatic factors and factors related to forest management activities. The optimal model was rather imprecise but showed that the risk of pine weevil attack can be predicted approximatively with three principal variables: 1) the proportion of seedlings expected to be planted in mineral soil rather than soil covered with duff and debris, 2) age of clear-cut at the time of planting, and 3) calculated temperature sum at the location. The model was constructed using long-run average temperature sums for epoch 2010, and so effects of climate change can be inferred from the model by adjustment to future epochs. Increased damage risks with a warmer climate are strongly indicated by the model. Effects of a warmer climate on the geographical distribution and abundance of the pine weevil are also discussed. The new tool to better estimate the risk of damage should provide a basis for foresters in their choice of countermeasures against pine weevil damage in northern Europe.Hylobius abieti

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Turning a "useless" ligand into a "useful" ligand:a magneto-structural study of an unusual family of Cu(II) wheels derived from functionalised phenolic oximes

While the phenolic oximes (R-saoH(2)) are well known for producing monometallic complexes of the type [MII(R-saoH)(2)] with Cu-II ions in near quantitative yield, their derivatisation opens the door to much more varied and interesting coordination chemistry. Here we show that combining the complimentary diethanolamine and phenolic oxime moieties into one organic framework (H4L1 and H4L2) allows for the preparation and isolation of an unusual family of [Cu-II](n) wheels, including saddle-shaped, single-stranded [Cu-8(II)] wheels of general formula [Cu-8(HL1)(4)(X)(4)] n[Y] (when n = 0, X = Cl-, NO3-, AcO-, N-3(-); when n = 2+X = (OAc)(2)/(2,2'-bpy)(2) and Y = [BF4](2)) and [Cu-8(HL2)(4)(X)(4)](X = Cl-, Br-), a rectangular [Cu-6(HL1)(4)] wheel, and a heterometallic [Cu4Na2(HL1)2(H2L1)(2)] hexagon. Magnetic studies show very strong antiferromagnetic exchange between neighbouring metal ions, leading to diamagnetic ground states in all cases. DFT studies reveal that the magnitude of the exchange constants are correlated to the Cu-N-O-Cu dihedral angles, which in turn are correlated to the planarity/puckering of the [Cu-II](n) rings

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570