Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR's footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran's I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran's I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

Pine trees structure plant biodiversity patterns in savannas.

Overstory trees serve multiple functions in grassy savannas. Past research has shown that understory species can vary along gradients of canopy cover and basal area in savannas. This variation is frequently associated with light availability but could also be related to other mechanisms, such as heterogeneity in soil and litter depth and fire intensity. Several savanna studies have found differences in understory plant functional groups within the local environment near trees versus away from them in canopy openings. Although small-scale variation is known to be high in southeastern U.S. pine savannas, patterns in understory species diversity have not been examined at the scale of individual overstory pine trees in this system. We conducted an observational study of the relationship between understory plant communities and proximity to individual pine trees in xeric and mesic pine savannas in frequently burned sites (1-3 year intervals). We recorded the plant community composition in plots adjacent to tree boles (basal) or outside crown driplines (open). Within each environment, raw species richness was significantly greater in open locations, where light transmittance was greater. In contrast, rarified species richness did not differ. Multivariate analyses showed that community composition differed significantly between basal and open plots. One native, woody species in each environment, Serenoa repens (W. Bartram) Small in mesic and Diospyros virginiana L. in xeric, was more abundant in basal plots. In mesic environments, eight species had greater occurrence in open plots. In xeric environments, four understory forbs were more abundant in open plots. Our results support previous research indicating that individual pine trees are associated with significant variation in understory vegetation in pine savannas.

Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes.

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769 kg ha-1) and water productivity (1,050 to 1,445 kg m-3) with minimal application depths (< 150 mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.

Introduction: Unmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice. Methods: To investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT). Results: The results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R 2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R 2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%-13.5%), demonstrating excellent practical applicability. Discussion: Model evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

Environmental Injustice, Tree Canopy Cover, and Academic Proficiency at Utah Public Primary Schools.

Background: Tree canopy cover has mental and physical health benefits for children, yet distributional environmental injustices in tree canopy cover near schools are rarely investigated. Some evidence suggests that tree canopy coverage positively influences aggregated school-level children's academic proficiency metrics. There is a lack of research if canopy cover moderates the negative effect of particulate matter on academic proficiency. Methods: We linked data on schools from the National Center for Education Statistics, Utah's Student Assessment of Growth and Excellence, the National Land Cover Database, and the U.S. Environmental Protection Agency. We used generalized estimating equations, which account for non-normally distributed data and clustering, to analyze data from the 2015 to 2016 school year. Results: Greater school social disadvantage (higher percentages of students qualifying for free/reduced price meals and higher percentages of racial/ethnic minority students) was significantly associated with reductions in tree canopy cover within 1000 m and 500 m of the school. Greater tree canopy coverage at 1000 m and 500 m was significantly associated with a lower percentage of students testing below proficient on year-end math and language arts exams. Additional days of peak fine particulate matter were associated with higher percentages of students testing below proficient in math. Discussion: Socially disadvantaged primary schools in Utah were surrounded by less canopy cover. There was a protective effect of tree canopy cover on academic proficiency in math and language arts. Conclusion: Findings suggest targeting carefully designed tree planting efforts at socially disadvantaged schools and testing interventions involving tree planting and changes in academic proficiency.

Assessment of vegetation change using NDVI, LST, and carbon analyses in Çankiri Karatekin University, Turkey.

Due to a rising population and urbanization, the green areas have been decreasing in cities, with a negative impact on air pollution, human health, and ecosystem. As part of the urban environment, university campuses contribute to urban ecosystem with their vegetation. This study aims to (1) assess the change of vegetative land cover of the Çankiri Karatekin University in Turkey and (2) evaluate its benefits to the ecosystem in terms of carbon sequestration, storage, and improvement of air quality by means of a simulation. In the study, the density and vegetation change were assessed with NDVI and LST analyses in ArcGIS; carbon emissions and air pollution benefits were estimated in i-Tree Canopy tool. The study showed that the healthy vegetation consisting of trees/shrubs and grass/herbaceous, which was 32.2% (28 ha) in 2000, increased to 85% (74 ha) in 2020 NDVI maps, and the surface temperature also increased between 2000 and 2020 in LST maps.The rise in vegetation as grass/herbaceous areas instead of trees/shrubs and the use of impervious buildings/roads on the land surface increased the land surface temperature. As a result of the analyses in the i-Tree-Canopy tool, it was estimated that the trees/shrubs and grass/herbaceous vegetation canopy covering 31.42% of the study area removed a total of 512,845.65 g of pollutant gas and particles from the air, 20.79 tonnes of carbon sequestered annually, and 522.01 tonnes of carbon stored by vegetative land cover. In the simulation, where 32.62% soil/bare ground areas were converted to trees/shrubs in order to improve vegetation cover in the area, it was determined that it contributed 5 times more to the ecosystem service value for removing pollutants from the air, carbon storage, and improving the ecosystem. It was revealed that the vegetative land cover formed by tree/shrub species should be increased in the campus in the future. The study method model serves as a tool for planning and designing eco-friendly urban environment.

Impact of a nuclear power station effluent on marine forests: A case study in SE Brazil and insights for global warming scenarios.

One of the main disturbances caused by coastal nuclear power plants is the discharge of thermal effluents capable of affecting a number of marine systems, including macroalgal forests that support key ecosystem services such as carbon uptake, fisheries increment and coastal protection. This study aimed at describing the long-term trend (1992-2022) in the abundance of Sargassum forests from sites located inside and outside areas affected by the thermal effluent discharged by the Brazilian Nuclear Power Station (BNPS) and at evaluating the relationship between Sargassum cover and seawater temperature. This information is interesting to provide insights on whether and how Sargassum populations would likely be affected by increasing temperature due to climate change. We detected a long-term decline in Sargassum cover inside, but not outside the area affected by the BNPS thermal plume. Mean summer surface seawater temperature above 30 °C was identified as an important factor driving the decline of Sargassum abundance. This study highlights the impact caused by decades of discharge of the BNPS thermal effluent on Sargassum forests, which leads to predict the likely disappearance of marine forests under a climate change scenario in other sites situated in warm temperate regions.

Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia.

Continuous mapping of the height and canopy cover of forests is vital for measuring forest biomass, monitoring forest degradation and restoration. In this regard, the contribution of Light Detection and Ranging (LiDAR) sensors, which were developed to obtain detailed data on forest composition across large geographical areas, is immense. Accordingly, this study aims to predict forest canopy cover and height in tropical forest areas utilizing Global Ecosystem Dynamics Investigation (GEDI) LIDAR, multisensor images, and random forest regression. To achieve this, we gathered predictor variables from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Sentinel-2 multispectral datasets, and Sentinel-1 synthetic aperture radar (SAR) backscatters. The model's accuracy was evaluated based on a validation dataset of GEDI Level 2A and Level 2B. The random forest method was used the combination of data layers from Sentinel-1, Sentinel-2, and topographic measurements to model forest canopy cover and height. The produced canopy height and cover maps had a resolution of 30 m with R2 = 0.86 and an RMSE of 3.65 m for forest canopy height and R2 = 0.87 and an RMSE of 0.15 for canopy cover for the year 2022. These results suggest that combining multiple variables and data sources improves canopy cover and height prediction accuracy compared to relying on a single data source. The output of this study could be helpful in creating forest management plans that support sustainable utilization of the forest resources.

Irrigation scheduling for potatoes (Solanum tuberosum L.) under drip irrigation in an arid region using AquaCrop model.

Potato is one of the key food crops and China is the largest potato producer in the world. However water scarcity is the major constraint to increase the productivity of potato in the arid regions such as Ningxia in northwest China where this crop is extensively cultivated. The overall objective of this study was to optimize the irrigation for potato cultivated under the drip irrigation. To do this, the AquaCrop model was calibrated and validated using the data obtained from two years of field experiment. Then, the calibrated crop model was used to simulate growth and tuber yield of potato in response to 30 different irrigation schemes under two different irrigation scenarios. The crop model evaluation parameters namely, the root mean square error (RMSE), the index of agreement (d), the normalized root mean square error (NRMSE) and the coefficient of determination (R2) showed that the AquaCrop model could simulate the growth and yield of potato under the drip irrigation with different irrigation treatments with reasonable accuracy. Furthermore, yield of potato has increased with increasing amount of total irrigation under drip irrigation; however, yield begins to decline when the amount of total irrigation exceeds 2500 m3 ha-1. The study also found that the optimum irrigation schedule for potato was 20 mm of irrigation quota at 7 days of irrigation cycle (i.e., 1800 m3 ha-1 or 180 mm of total irrigation). The above irrigation scheduling has achieved 46.77 t ha-1 of tuber yield with 15.74 kg m-3 of water use efficiency. These findings may be evaluated in potato cultivation across different climate and soil conditions for wide applicability at different arid regions of the world.

A simplified approach to satellite-based monitoring system of sugarcane plantation to manage yield decline at Wonji-Shoa Sugar Estate, central Ethiopia.

Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant (p < 0.001), with 84%-95% accuracy in all the indexes. The majority of LCVIs showed significant (p < 0.05) relationships with yield components and FGCC. The time-series NDVI also demonstrated a significant relationship with cane yield (R2 = 0.73-0.85) at the age of 10 months and above. The accuracy level of LCVIs varies with varieties and crop types, but the Normalized Difference Phenology Index (NDPI), Soil Adjusted Vegetation Index (SAVI), and NDVI were identified as the most consistent and effective LCVIs for sugarcane monitoring. Therefore, the accuracy of LCVIs was dependable and can be used effectively in monitoring sugarcane plantations to tackle the problem of continuous decline in the yield of the crop.

A national assessment of urban forest carbon storage and sequestration in Canada.

During a time of rapid urban growth and development, it is becoming ever more important to monitor the carbon fluxes of our cities. Unlike Canada's commercially managed forests that have a long history of inventory and modelling tools, there is both a lack of coordinated data and considerable uncertainty on assessment procedures for urban forest carbon. Nonetheless, independent studies have been carried out across Canada. To improve upon Canada's federal government reporting on carbon storage and sequestration by urban forests, this study builds on existing data to develop an updated assessment of carbon storage and sequestration for Canada's urban forests. Using canopy cover estimates derived from ortho-imagery and satellite imagery ranging from 2008 to 2012 and field-based urban forest inventory and assessment data from 16 Canadian cities and one US city, this study found that Canadian urban forests store approximately 27,297.8 kt C (- 37%, + 45%) in above and belowground biomass and sequester approximately 1497.7 kt C year-1 (- 26%, + 28%). In comparison with the previous national assessment of urban forest carbon, this study suggested that in urban areas carbon storage has been overestimated and carbon sequestration has been underestimated. Maximizing urban forest carbon sinks will contribute to Canada's mitigation efforts and, while being a smaller carbon sink compared to commercial forests, will also provide important ecosystem services and co-benefits to approximately 83% of Canadian people.

ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform.

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams' segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale.

Nitrous oxide and methane emissions from coffee agroforestry systems with different intensities of canopy closure.

Agroforestry-based coffee production systems (AFs) contribute to climate change mitigation through carbon sequestration. However, it is unclear whether AFs produce lower nitrous oxide (N2O) and methane (CH4) emissions than the open-shade coffee production system. In addition, little to no evidence is available to explain the relationship between canopy cover levels and greenhouse gas (GHG) emissions in AFs. The aim of this study was to investigate N2O, CH4 and yield-scaled emissions in AFs with differing shade-tree canopy levels. Three canopy cover levels were identified: (i) dense shade (80 % canopy closure), (ii) medium shade (49 % canopy closure), and (iii) open-shade (full sun) production. To determine the effect of canopy cover on GHG emissions under varying soil fertility management practices, three soil fertilization strategies were included: (i) mineral fertilizer, (ii) compost, and (iii) control (i.e., without soil amendment). The results showed that N2O emissions were two-to-three times greater when there was dense canopy cover than from open-shade production. The effect of canopy cover on N2O emission was more pronounced under the mineral fertilizer treatment. CH4 emissions were 44-64 % greater under the open-shade production system than under AFs. The yield-scaled global warming potential of 1 kg of fresh coffee cherries was 0.72 kg CO2eq for open-shade production, 0.58 kg CO2eq for medium canopy cover and 0.52 kg CO2eq for dense canopy cover. This study provides the first evidence of the importance of considering canopy cover intensity when determining the spatial-temporal variations in GHG emissions from agroforestry systems.

Tree canopy cover affects basal resources and nutrient profiles of Aedes and Culex larvae in cemetery vases in New Orleans, Louisiana, United States.

Cemetery vases are important habitat for vector mosquito production, yet there is limited understanding on their food web dynamics and how they vary across environmental gradients. Tree cover is one factor that varies widely across cemeteries, and influence food webs by means of detrital inputs, temperature mediation, and light availability. Such information can be important for determining mosquito adult body size, fecundity, and competition outcomes, all of which may influence mosquito population and disease risk. This study evaluates the relationship between tree canopy cover and indicators of basal resources for Aede aegypti (L.), Aedes albopictuss (Skuse), and Culex quinquefasciatus (Say) larvae, such as stable isotopes (δ13C and δ15N) and nutrient stoichiometry in cemeteries of New Orleans, Louisiana (USA). Stable isotope values suggest that larvae feed directly on the Particulate Organic Matter (POM) suspended in the vase's water, and that POM composition influence the nutrient profiles of mosquito larvae. The POM of open canopy vases had higher δ13C values, than that of closed canopy vases indicating differences in relative proportion of basal carbon sources, with open canopy POM having a lower proportion of allochthonous carbon, and a higher proportion of authoctonous carbon. Accordingly, mosquito larvae collected from open canopy vases had higher δ13C values, and higher C:N than larvae from closed canopy vases. The results of this study show a shift in food web dynamics driven by canopy cover in cemetery vases that directly influence the nutrient profiles of mosquito larvae. The implications for mosquito ecology, and vector management are discussed.

[Bryophyte diversity and microhabitat characteristics of bryophyte-dominated biological soil crusts development in water-wind erosion crisscross region of the northern Loess Plateau, China]. / é»„åœŸé«˜åŽŸåŒ—éƒ¨æ°´èš€é£Žèš€äº¤é”™åŒºè‹”è—“å¤šæ ·æ€§åŠè‹”è—“ç»“çš®å‘è‚²çš„å¾®ç”Ÿå¢ƒç‰¹å¾.

Microhabitat factors play an important role in regulating bryophyte species distribution and the development of bryophyte-dominated biological soil crusts (hereafter bryophyte crusts). We investigated the distribution and development of bryophytes in eight microhabitats in the water-wind erosion crisscross region of the Loess Pla-teau. We used the line intercept transects to explore and quantify the influencing pathways of microhabitat factors on bryophyte diversity and analyzed the influencing pathways of plant cover, slope aspect, and slope gradient by using structural equation model to quantify influencing coefficients. Our results showed that: 1) The Patrick, Shannon, Pielou, and Simpson indcies of bryophytes under plant canopy were 63.4%, 66.6%, 91.0%, and 68.3% lower than that without plant canopy, respectively, while the thickness, biomass, and chlorophyll content of bryophyte crusts were 0.5, 0.2, and 1.3 times higher than that without plant canopy, respectively. 2) The Patrick, Shannon, Pielou, and Simpson indexes of bryophytes on the north slope were 0.6, 0.9, 5.6, and 0.9 times higher than those on the south slope, while the thickness, biomass, and chlorophyll content of bryophyte crusts were 0.3, 0.3, and 0.6 times higher than those on the south slope, respectively. 3) As the slope increasing from 14° to 34°, the Patrick, Shannon, Pielou, and Simpson indexes of bryophyte were decreased by 59.8%, 84.1%, 57.3% and 68.0%, and the thickness, biomass, and chlorophyll content of bryophyte crusts were decreased by 15.2%, 25.0%, and 16.5%, respectively. 4) The importance of the three microhabitat factors on bryophyte diversity and the development of bryophyte crusts followed an order of plant canopy cover > slope aspect > slope gradient. The primary influencing pathway varied among the microhabitat factors. In conclusion, plant cover, slope aspect, and slope gradient significantly affected the distribution of bryophytes species and developmental level of bryophyte crusts through direct and indirect pathways. Therefore, full consideration should be given to microhabitat conditions when using bryophyte crusts to control desertification.

The association between tree canopy cover over streets and elderly pedestrian falls: A health disparity study in urban areas.

Older pedestrians are vulnerable to outdoor falling while walking on streets/sidewalks, but few studies have examined the role of the street environment and tree canopy cover over streets in relation to pedestrian falls among the elderly. We used spatial analysis to examine the association between tree canopy cover over streets and pedestrian falls reported to Emergency Medical Service (EMS) providers from March 2013 to February 2020 among adults aged 65 and older living in urban areas of Marin County, CA. Tree canopy cover over streets was measured using 1-m resolution of tree canopy within street polygons. After controlling for socioeconomic status and built environments, we found an inverse association between tree canopy cover over streets and elderly pedestrian fall rates at the census block level. Specifically, with a 10-percentage point increase in tree canopy cover over streets of a block, we expected to see about an 11.2% decrease in the elderly pedestrian fall rate. We found that the inverse relationship between tree canopy cover over streets and elderly pedestrian falls was only significant during the leaf-on season in the spring and summer. Finally, sub-analysis found that the relationship between tree canopy cover over streets and elderly pedestrian falls was stronger in low-income areas, compared to high-income areas. Planting street trees is a potential evidence-based intervention to prevent pedestrian falls. However, special attention must go beyond the quantity of tree canopy cover over streets to consider biophysical factors and social conditions.

Rethinking forest monitoring for more meaningful global forest landscape change assessments.

Forest ecosystems play an indispensable role in addressing various pressing sustainability and social-ecological challenges such as climate change, biodiversity loss, and ecosystem services deterioration, hence the monitoring of the world's forests is crucial. As part of the global forest assessment workflow, a forest is generally classified and mapped based on land use and/or using a tree canopy cover threshold. In this paper, we examine the limitations of this approach and argue that the use of a land use-based forest definition and tree canopy cover thresholds can overlook forest degradation and enhancement, disguise the actual status of forest landscapes, and misinform management planning. These limitations can delay the development and implementation of forest restoration and conservation measures. To help overcome these issues, we propose some enhancements to the global forest assessment workflow, including the sharing of spatial data and inclusion of tree canopy cover estimates in assessment reports. Such enhancements are needed to achieve more meaningful forest monitoring and reporting in the context of global environmental initiatives, such as those related to climate change mitigation and adaptation, forest restoration, biodiversity conservation, and ecosystem services monitoring.

Fungal Community Development in Decomposing Fine Deadwood Is Largely Affected by Microclimate.

Fine woody debris (FWD) represents the majority of the deadwood stock in managed forests and serves as an important biodiversity hotspot and refuge for many organisms, including deadwood fungi. Wood decomposition in forests, representing an important input of nutrients into forest soils, is mainly driven by fungal communities that undergo continuous changes during deadwood decomposition. However, while the assembly processes of fungal communities in long-lasting coarse woody debris have been repeatedly explored, similar information for the more ephemeral habitat of fine deadwood is missing. Here, we followed the fate of FWD of Fagus sylvatica and Abies alba in a Central European forest to describe the assembly and diversity patterns of fungal communities over 6 years. Importantly, the effect of microclimate on deadwood properties and fungal communities was addressed by comparing FWD decomposition in closed forests and under open canopies because the large surface-to-volume ratio of FWD makes it highly sensitive to temperature and moisture fluctuations. Indeed, fungal biomass increases and pH decreases were significantly higher in FWD under closed canopy in the initial stages of decomposition indicating higher fungal activity and hence decay processes. The assembly patterns of the fungal community were strongly affected by both tree species and microclimatic conditions. The communities in the open/closed canopies and in each tree species were different throughout the whole succession with only limited convergence in time in terms of both species and ecological guild composition. Decomposition under the open canopy was characterized by high sample-to-sample variability, showing the diversification of fungal resources. Tree species-specific fungi were detected among the abundant species mostly during the initial decomposition, whereas fungi associated with certain canopy cover treatments were present evenly during decomposition. The species diversity of forest stands and the variability in microclimatic conditions both promote the diversity of fine woody debris fungi in a forest.

Herbivory on the pedunculate oak along an urbanization gradient in Europe: Effects of impervious surface, local tree cover, and insect feeding guild.

Urbanization is an important driver of the diversity and abundance of tree-associated insect herbivores, but its consequences for insect herbivory are poorly understood. A likely source of variability among studies is the insufficient consideration of intra-urban variability in forest cover. With the help of citizen scientists, we investigated the independent and interactive effects of local canopy cover and percentage of impervious surface on insect herbivory in the pedunculate oak (Quercus robur L.) throughout most of its geographic range in Europe. We found that the damage caused by chewing insect herbivores as well as the incidence of leaf-mining and gall-inducing herbivores consistently decreased with increasing impervious surface around focal oaks. Herbivory by chewing herbivores increased with increasing forest cover, regardless of impervious surface. In contrast, an increase in local canopy cover buffered the negative effect of impervious surface on leaf miners and strengthened its effect on gall inducers. These results show that-just like in non-urban areas-plant-herbivore interactions in cities are structured by a complex set of interacting factors. This highlights that local habitat characteristics within cities have the potential to attenuate or modify the effect of impervious surfaces on biotic interactions.

The efficiency of the Standardized Evapotranspiration Deficit Index (SEDI) in assessing the impact of drought on vegetation cover.

It is important to choose an indicator that can optimally demonstrate the effects of drought intensity on soil moisture access, evapotranspiration and the changes in vegetation cover at a regional scale. Therefore, herein, SEDI was developed by the fit of the experimental distribution of Gringorten on evapotranspiration deficit based on TerraClimate data at the time scales of 1, 3, 6 and 12 months, and its relationship with Standardized Precipitation-Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSMI), Normalized Ecosystem Drought Index (NEDI) and Normalized Difference Vegetation Index (NDVI) were investigated. The results indicated that SEDI has the highest significant correlation (above 95%) with NEDI and SPEI, especially for the 1-month time scale. This index also revealed the lowest correlation (less than 25%) with SPI on short-term time scales. The relationship between SEDI and SSMI indicated the high sensitivity of SEDI to the cumulative reduction of low amounts of soil moisture. According to the findings, the 6-month SEDI with NDVI showed the highest correlation with a 1-month delay (r = 0.64) and the best fit between them occurred in wet months. However, in the dry months, the relationship between SEDI with NDVI was affected via water availability stresses, grazing intensity and pest infestation. Finally, the use of SEDI at a regional scale, especially in arid and semi-arid regions like Lorestan, could be recognized as an important index in depicting the effects of drought on vegetation cover, due to the use of the actual evapotranspiration factor.