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Introduction: Mounting evidence suggests that geographic ranges of tree species worldwide are shifting under global environmental changes. Little is known, however, about if and how these species' range shifts may trigger the range shifts of various types of forests. Markowitz's portfolio theory of investment and its broad application in ecology suggest that the range shift of a forest type could differ substantially from the range shifts of its constituent tree species. Methods: Here, we tested this hypothesis by comparing the range shifts of forest types and the mean of their constituent species between 1970-1999 and 20002019 across Alaska, Canada, and the contiguous United States using continent- wide forest inventory data. We first identified forest types in each period using autoencoder neural networks and K-means cluster analysis. For each of the 43 forest types that were identified in both periods, we systematically compared historical range shifts of the forest type and the mean of its constituent tree species based on the geographic centroids of interpolated distribution maps. Results: We found that forest types shifted at 86.5 km-decade-1 on average, more than three times as fast as the average of constituent tree species (28.8 km-decade-1). We showed that a predominantly positive covariance of the species range and the change of species relative abundance triggers this marked difference. Discussion: Our findings provide an important scientific basis for adaptive forest management and conservation, which primarily depend on individual species assessment, in mitigating the impacts of rapid forest transformation under climate change. [ABSTRACT FROM AUTHOR]

A new multiscale Standardized Precipitation Evapotranspiration Index (SPEI) dataset is provided for a reference period (1960–1999) and two future time horizons (2040–2079) and (2060–2099). The historical forcing is based on combined climate observations and reanalysis (WATer and global CHange Forcing Dataset), and the future projections are fed by the Fast Track experiment of the Inter-Sectoral Impact Model Intercomparison Project under representative concentration pathways (RCPs) 4.5 and 8.5 and by an additional Earth system model (CMCC-CESM) forced by RCP 8.5. To calculate the potential evapotranspiration (PET) input to the SPEI, the Hargreaves–Samani and Thornthwaite equations were adopted. This ensemble considers uncertainty due to different climate models, development pathways, and input formulations. The SPEI is provided for accumulation periods of potential moisture deficit from 1 to 18 months starting in each month of the year, with a focus on the within-period variability, excluding long-term warming effects on PET. In addition to supporting drought analyses, this dataset is also useful for assessing wetter-than-normal conditions spanning one or more months. The SPEI was calculated using the SPEIbase package. Dataset: https://doi.org/10.25424/cmcc-mfd5-t060 (accessed on 3 February 2023). Dataset License: The dataset is made available under license CC-BY. [ABSTRACT FROM AUTHOR]

In March 2020, the first known cases of COVID‐19 occurred in Europe. Subsequently, the pandemic developed a seasonal pattern. The incidence of COVID‐19 comprises spatial heterogeneity and seasonal variations, with lower and/or shorter peaks resulting in lower total incidence and higher and/or longer peaks resulting higher total incidence. The reason behind this phenomena is still unclear. Unraveling factors that explain why certain places have higher versus lower total COVID‐19 incidence can help health decision makers understand and plan for future waves of the pandemic. We test whether differences in the total incidence of COVID‐19 within five European countries (Norway, Sweden, Germany, Italy, and Spain), correlate with two environmental factors: the Köppen‐Geiger climate zones and the Continentality Index, while statistically controlling for crowding. Our results show that during the first 16 months of the pandemic (March 2020 to July 2021), climate zones with larger annual differences in temperature and annually distributed precipitation show a higher total incidence than climate zones with smaller differences in temperature and dry seasons. This coincides with lower continentality values. Total incidence increases with continentality, up to a Continentality Index value of 19, where a peak is reached in the semicontinental zone. Low continentality (high oceanic influence) appears to be a strong suppressing factor for COVID‐19 spread. The incidence in our study area is lowest at open low continentality west coast areas. Plain Language Summary: In March 2020, the first known cases of COVID‐19 occurred in Europe. Over the next 16 months (March 2020 to July 2021) a pattern emerged where some areas had higher versus lower COVID‐19 spread. We studied whether this pattern could be explained by climatological factors in five European countries (Norway, Sweden, Germany, Italy, and Spain). Our results show that areas with larger annual temperature ranges and year‐round rain had higher COVID‐19 spread than areas with smaller annual temperature ranges and dry seasons. We also examined continentality, which measures the influence of the ocean on climate. In Europe, where predominant winds come from the west, we find the highest oceanic influence at open west coasts; in our study area these are represented by north‐west Spain, northernmost Germany, and south‐west Norway. In these areas, COVID‐19 spread was lowest. With decreasing oceanic influence, COVID‐19 spread was higher. For the five countries we studied, we found highest COVID‐19 spread in south‐east Norway, the entire south of Sweden, the south‐eastern part of Germany, northern Italy, and central Spain. Healthcare decision makers in areas that have wide ranges of temperature and rain throughout the year or that have little oceanic influence should expect a higher COVID‐19 spread. Key Points: We study the reason for within country differences of the COVID‐19 pandemic for Norway, Sweden, Germany, Italy, and SpainCOVID‐19 incidence is lower in climate zones with smaller annual temperature ranges and a clear division between wet and dry seasonsIncidence patterns can be explained by continentality; oceanic impact limits COVID‐19 at open west coasts in the European west wind zone [ABSTRACT FROM AUTHOR]

Multi-purpose crops as maize, rice, soybean, and wheat are key in the debate concerning food, land, water and energy security and sustainability. While strong evidence exists on the effects of climate variability on the production of these crops, so far multifaceted attributes of droughts—magnitude, frequency, duration, and timing—have been tackled mainly separately, for a limited part of the cropping season, or over small regions. Here, a more comprehensive assessment is provided on how droughts with their complex patterns—given by their compound attributes—are consistently related to negative impacts on crop yield on a global scale. Magnitude and frequency of both climate and yield variability are jointly analysed from 1981 to 2016 considering multiscale droughts, i.e., dry conditions occurring with different durations and timings along the whole farming season, through two analogous and standardized indicators enabling comparison among crops, countries, and years. Mainly winter wheat and then spring wheat, soybean and the main maize's season reveal high susceptibility of yield under more complex drought patterns than previously assessed. The second maize's season and rice present less marked and more uncertain results, respectively. Overall, southern and eastern Europe, the Americas and sub-Saharan Africa presents multi-crop susceptibility, with eastern Europe, Middle East and Central Asia appearing critical regions for the most vulnerable crop, which is wheat. Finally, yield losses for wheat and soybean clearly worsen when moving from moderate to extreme multiscale droughts. [ABSTRACT FROM AUTHOR]

Forest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models ( SDMs) to five Global Circulation Models ( GCMs) driven by two Representative Concentration Pathways ( RCPs), to produce maps of future climate-driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the 'likelihood' adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration. [ABSTRACT FROM AUTHOR]

A relatively small number of broad-scale patterns describe the distribution of biodiversity across the earth. All of them explore biodiversity focusing on a mono or bi-dimensional space. Conversely, the volume of the forests is rarely considered. In the present work, we tested a global correlation between vascular plant species richness (S) and average forest canopy height ( H), the latter regarded as a proxy of volume, using the NASA product of Global Forest Canopy Height map and the global map of plant species diversity. We found a significant correlation between H and S both at global and macro-climate scales, with strongest confidence in the tropics. Hence, two different regression models were compared and discussed to provide a possible pattern of the H-S relation. We suggested that the volume of forest ecosystems should be considered in ecological studies as well as in planning and managing natural sites, although in this first attempt, we cannot definitively prove our hypothesis. Again, high-resolution spatial data could be highly important to confirm the H-S relation, even at different scales. [ABSTRACT FROM AUTHOR]

The Mediterranean basin is one of the most varied areas worldwide in terms of biodiversity and species richness due to its climatic and geomorphological features, and it is characterized by multi-faceted habitats where forests play a crucial role. Nowadays, the geographic distribution of forest species is well known and multiple geographic datasets are available with different spatial details. However, protection and conservation strategies need more specific information to identify areas with high conservation priority or more vulnerable to the ongoing environmental change ("hot spots"). To this purpose, tree species distribution data were investigated through hot spot analysis using Geographic Information Systems. The analysis was carried out on presence data of ten relevant forest tree species/classes across Mediterranean Europe. By combining spatial analysis and spatial statistics, we identified high and very high hot spot areas for the selected species/classes, which were validated by assessing their biological significance. Given the sub-continental extent of the study, a multiple scale approach was applied ranging from regional, subregional to local scale, coherently with the potential multi-level and multi-sector users of similar data and tools. Our results confirm the feasibility of the approach used to increase the quality and quantity of information achievable from available forest distribution datasets. The hot spot maps obtained are a useful support for further spatial evaluations, and may help environmental decision makers to identify priority areas for forest protection and conservation. [ABSTRACT FROM AUTHOR]

Degradation, a reduction of the ecosystem's capacity to supply goods and services, is widespread in tropical forests and mainly caused by human disturbance. To maintain the full range of forest ecosystem services and support the development of effective conservation policies, we must understand the overall impact of degradation on different forest resources. This research investigates the response to disturbance of forest structure using several indicators: soil carbon content, arboreal richness and biodiversity, functional composition (guild and wood density), and productivity. We drew upon large field and remote sensing datasets from different forest types in Ghana, characterized by varied protection status, to investigate impacts of selective logging, and of illegal land use and resources extraction, which are the main disturbance causes in West Africa. Results indicate that functional composition and the overall number of species are less affected by degradation, while forest structure, soil carbon content and species abundance are seriously impacted, with resources distribution reflecting the protection level of the areas. Remote sensing analysis showed an increase in productivity in the last three decades, with higher resiliency to change in drier forest types, and stronger productivity correlation with solar radiation in the short dry season. The study region is affected by growing anthropogenic pressure on natural resources and by an increased climate variability: possible interactions of disturbance with climate are also discussed, together with the urgency to reduce degradation in order to preserve the full range of ecosystem functions. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the clinical efficacy and safety of HYAFF 11-based autologous dermal and epidermal grafts in the management of diabetic foot ulcers.Research Design and Methods: A total of 79 patients with diabetic dorsal (n = 37) or plantar (n = 42) ulcers were randomized to either the control group with nonadherent paraffin gauze (n = 36) or the treatment group with autologous tissue-engineered grafts (n = 43). Weekly assessment, aggressive debridement, wound infection control, and adequate pressure relief (fiberglass off-loading cast for plantar ulcers) were provided in both groups. Complete wound healing was assessed within 11 weeks. Safety was monitored by adverse events.Results: Complete ulcer healing was achieved in 65.3% of the treatment group and 49.6% of the control group (P = 0.191). The Kaplan-Meier mean time to closure was 57 and 77 days, respectively, for the treatment versus control groups. Plantar foot ulcer healing was 55% and 50% in the treatment and control groups, respectively. Dorsal foot ulcer healing was significantly different, with 67% in the treatment group and 31% in the control group (P = 0.049). The mean healing time in the dorsal treatment group was 63 days, and the odds ratio for dorsal ulcer healing compared with the control group was 4.44 (P = 0.037). Adverse events were equally distributed between the two groups, and none were related to the treatments.Conclusions: The autologous tissue-engineered treatment exhibited improved healing in dorsal ulcers when compared with the current standard dressing. For plantar ulcers, the off-loading cast was presumably paramount and masked or nullified the effects of the autologous wound treatment. This treatment, however, may be useful in patients for whom the total off-loading cast is not recommended and only a less effective off-loading device can be applied. [ABSTRACT FROM AUTHOR]

This study presents a new global gridded dataset of bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions. The dataset, called CMCC-BioClimInd, provides a set of 35 bioclimatic indices, expressed as mean values over each time interval, derived from post-processing both climate reanalysis for historical period (1960–1999) and an ensemble of 11 bias corrected CMIP5 simulations under two greenhouse gas concentration scenarios for future climate projections along two periods (2040–2079 and 2060–2099). This new dataset complements the availability of spatialized bioclimatic information, crucial aspect in many ecological and environmental wide scale applications and for several disciplines, including forestry, biodiversity conservation, plant and landscape ecology. The data of individual indicators are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Measurement(s) bioclimatic indicators Technology Type(s) Geographic Information System • computational modeling technique Factor Type(s) temporal interval • geographic location Sample Characteristic - Environment climate system Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12927443 [ABSTRACT FROM AUTHOR]