Pan-genome and transcriptome analyses provide insights into genomic variation and differential gene expression profiles related to disease resistance and fatty acid biosynthesis in eastern black walnut (Juglans nigra)

Walnut(Juglans)species are used as nut crops worldwide.Eastern black walnut(EBW,Juglans nigra),a diploid,horticultural important woody species is native to much of eastern North America.Although it is highly valued for its wood and nut,there are few resources for understanding EBW genetics.Here,we present a high-quality genome assembly of J.nigra based on Illumina,Pacbio,and HiC technologies.The genome size was 540.8 Mb,with a scaffold N50 size of 35.1 Mb,and 99.0%of the assembly was anchored to 16 chromosomes.Using this genome as a reference,the resequencing of 74 accessions revealed the effective population size of J.nigra declined during the glacial maximum.A single whole-genome duplication event was identified in the J.nigra genome.Large syntenic blocks among J.nigra,Juglans regia,and Juglans microcarpa predominated,but inversions of more than 600 kb were identified.By comparing the EBW genome with those of J.regia and J.microcarpa,we detected InDel sizes of 34.9 Mb in J.regia and 18.3 Mb in J.microcarpa,respectively.Transcriptomic analysis of differentially expressed genes identified five presumed NBS-LRR(NUCLEOTIDE BINDING SITELEUCINE-RICH REPEAT)genes were upregulated during the development of walnut husks and shells compared to developing embryos.We also identified candidate genes with essential roles in seed oil synthesis,including FAD(FATTY ACID DESATURASE)and OLE(OLEOSIN).Our work advances the understanding of fatty acid bioaccumulation and disease resistance in nut crops,and also provides an essential resource for conducting genomics-enabled breeding in walnut.

Urban Three-dimensional Expansion and Its Driving Forces——A Case Study of Shanghai,China

Urban expansion is a phenomenon of urban space increase,and an important measuring index of the process of urbanization.Taking Shanghai as an example,the changes of urban average height and built-up area were studied to represent city's vertical and horizontal increases respectively,and statistical methods were used to analyze the driving forces of urban expansion.The research drew following conclusions:1) The urban expansion process of Shanghai from 1985 to 2006 had a clear periodic feature,and could be divided into three stages:vertical expansion in dominance,coordinated vertical and horizontal expansion,and horizontal expansion in dominance.2) The average height and quantity of buildings in core city were significantly bigger than those in suburbs,but the changing speed of the latter was faster.And 3) urbanization process was the major driving force for the city's horizontal expansion,while industrial structure improvement was the key driving factor for the vertical expansion.Those two driving forces were simultaneously affected by city's political factors.

Drone remote sensing for forestry research and practices

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing ini- tiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, mea- suring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it isimportant to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

Development of cork oak (Quercus suber L.) seedlings in response to tree shelters and mulching in northwestern Tunisia

The need for reforestation in cork oak （Quercus suber L.） areas is challenged by difficulties. Principal among these is herbivory of young plants, vegetative competition, and slow growth rates of cork oak seedlings. We evaluated the early development of cork oak seedlings treated using tree shelters and mulching in northwestern Tunisia. We tested three tree shelter treatments （non-vented, vented, and control） to shield seedlings from animal damage and five mulch types to control competing vegetation （Italian Stone Pine, Lentisk, combination of Italian Stone Pine and Lentisk （organic mulches）, gravel （inorganic mulch） and no mulch）. At the end of the two-year experiment, sheltered seedlings were 89-99% taller than unshelteredseedlings and had higher numbers and lengths of shoot growth flushes. In contrast, both stem diameter growth and dry weight biomass （from samples extracted after two years） were significantly reduced inside tree shelters. Root-to-shoot ratio was not significantly different in sheltered vs. unsheltered seedlings, suggesting that tree shelters do not adversely affect this parameter. Mulching alone did not favour growth, but could be beneficial when combined with tree shelters. The combination of vented tree shelters and gravel mulch was the most effective treatment for promoting diameter, height and stem volume growth.

Changes in soil properties under Eucalyptus relative to Pinus massoniana and natural broadleaved forests in South China

The ecological effects of eucalypt plantations（EPs） have garnered increasing attention.To understand their effect on soil quality at a landscape scale,and to determine whether soil quality parameters differ due to different stand types,we evaluated soil characteristics in twenty-one groups of EPs,Pinus massoniana Lamb.plantations（PMPs） and natural broadleaved forests（NBFs）across Guangdong Province,China.Both the physical characteristics of soil hydrology and the properties of soil nutrients in A and B horizons were determined.Results showed that,compared to NBFs,EPs and PMPs produced a shallower litter layer,reduced canopy density,higher soil bulk density,significantly lower total porosity,non-capillary porosity,total water volume,and hygroscopic water in the A horizon（P〈0.05）.Moreover,total N,available K,and soil organic carbon（SOC） in EPs and PMPs were significantly lower than in NBFs.EPs and PMPs did not differ significantly in N,P or K content,but PMPs had significantly lower SOC and boron in the A horizon than EPs.Low p H and poor capacity to buffer acidification generally occurred in all cover types.Both EPs and PMPs showed a decline in soil properties relative to NBFs,but EPs and PMPs exhibited no significant difference.These results indicate that actions are needed to ameliorate the potential negative effects on soil quality in forestry plantations.

Performance of statistical and machine learning-based methods for predicting biogeographical patterns of fungal productivity in forest ecosystems

Background:The prediction of biogeographical patterns from a large number of driving factors with complex interactions,correlations and non-linear dependences require advanced analytical methods and modeling tools.This study compares different statistical and machine learning-based models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modeling approaches to provide accurate and ecologically-consistent predictions.Methods:We evaluated and compared the performance of two statistical modeling techniques,namely,generalized linear mixed models and geographically weighted regression,and four techniques based on different machine learning algorithms,namely,random forest,extreme gradient boosting,support vector machine and artificial neural network to predict fungal productivity.Model evaluation was conducted using a systematic methodology combining random,spatial and environmental blocking together with the assessment of the ecological consistency of spatially-explicit model predictions according to scientific knowledge.Results:Fungal productivity predictions were sensitive to the modeling approach and the number of predictors used.Moreover,the importance assigned to different predictors varied between machine learning modeling approaches.Decision tree-based models increased prediction accuracy by more than 10%compared to other machine learning approaches,and by more than 20%compared to statistical models,and resulted in higher ecological consistence of the predicted biogeographical patterns of fungal productivity.Conclusions:Decision tree-based models were the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modeling data.In this study,we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas.This allows for reducing the dimensions of the ecosystem space described by the predictors of the models,resulting in higher similarity between the modeling data and the environmental conditions over the whole study area.When dealing with spatial-temporal data in the analysis of biogeographical patterns,environmental blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales.

Polymorphism and heredity of cpDNA and mtDNA in the Section Leuce of Populus

The chloroplast DNA （cpDNA） and mitochondrial DNA （mtDNA） of 16 Populus species （Section Leuce） and their F1 generation were detected using PCR-RFLP technique. The results show that cpDNA in the F1 generation of 22 hybrid combinations was inherited maternally, which supported the conclusions of the study of plasmid cytology. The mtDNA fragments amplified by PCR were consistent with the restriction maps in all hybrid combinations and no polymorphism was detected, indicating that the Section Leuce is highly conserved in mitochondrial gene sequences. These results provided direct evidence of maternal chloroplast inheritance in Populus tomentosa, P. bolleana, P. davidiana, P. adenopoda, P. tomentosa × P. bolleana, P. alba × P. glandulosa and P. alba × P. tomentosa.

The de novo genome assembly of Tapiscia sinensis and the transcriptomic and developmental bases of androdioecy

Tapiscia sinensis(Tapisciaceae)possesses an unusual androdioecious breeding system that has attracted considerable interest from evolutionary biologists.Key aspects of T.sinensis biology,including its biogeography,genomics,and sexlinked genes,are unknown.Here,we report the first de novo assembly of the genome of T.sinensis.The genome size was 410 Mb,with 22,251 predicted genes.Based on whole-genome resequencing of 55 trees from 10 locations,an analysis of population genetic structure indicated that T.sinensis has fragmented into five lineages,with low intrapopulation genetic diversity and little gene flow among populations.By comparing whole-genome scans of male versus hermaphroditic pools,we identified 303 candidate sex-linked genes,79 of which(25.9%)were located on scaffold 25.A 24-kb region was absent in hermaphroditic individuals,and five genes in that region,TsF-box4,TsF-box10,TsF-box13,TsSUT1,and TsSUT4,showed expression differences between mature male and hermaphroditic flowers.The results of this study shed light on the breeding system evolution and conservation genetics of the Tapisciaceae.

Measuring tree stem diameters and straightness with depth-image computer vision

Current techniques of forest inventory rely on manual measurements and are slow and labor intensive.Recent developments in computer vision and depth sensing can produce accurate measurement data at significantly reduced time and labor costs.We developed the ForSense system to measure the diameters of trees at various points along the stem as well as stem straightness.Time use,mean absolute error(MAE),and root mean squared error(RMSE)metrics were used to compare the system against manual methods,and to compare the system against itself(reproducibility).Depth-derived diameter measurements of the stems at the heights of 0.3,1.4,and 2.7 m achieved RMSE of 1.7,1.5,and 2.7 cm,respectively.The ForSense system produced straightness measurement data that was highly correlated with straightness ratings by trained foresters.The ForSense system was also consistent,achieving sub-centimeter diameter difference with subsequent measures and less than 4%difference in straightness value between runs.This method of forest inventory,which is based on depth-image computer vision,is time efficient compared to manual methods and less computationally and technologically intensive compared to Structure-from-Motion(SFM)photogrammetry and ground-based LiDAR or terrestrial laser scanning(TLS).

Interannual dynamics of stemwood nonstructural carbohydrates in temperate forest trees surrounding drought

Interactions between water and carbon dynamics underlie drought-related tree mortality.While whole-tree water relations have been shown to play a key role in the response to and recovery from drought,the role of nonstructural carbohydrates(NSC) and how their storage and allocation changes surrounding drought events deserves further attention and is critical for understanding tree survival.Here,we quantified in situ NSC responses of temperate forest trees to the 2016 drought in the northeastern United States.Sugar and starch concentrations were measured in the stemwood of five tree species from 2014 to 2019,which allowed us to monitor NSCs in relation to climatic conditions before,during,and after the natural drought.We found that immediately following the drought,measured stemwood NSC concentrations decreased.However,NSC concentrations rebounded quickly within three years.Notably,trees allocated proportionally more to starch than to sugars following the 2016 drought.In winter 2017,starch comprised 45% of total stemwood stores,whereas starch made up only 1-2% in other years.Further,we modeled and assessed the climatic drivers of total NSC concentrations in the stem.Variation in total NSC concentrations was significantly predicted by the previous year’s temperature,precipitation,and standardized precipitation-evapotranspiration index(SPEI),with stemwood concentrations decreasing following hotter,drier periods and increasing following cooler,wetter periods.Overall,our work provides insight into the climatic drivers of NSC storage and highlights the important role that a tree’s carbon economy may play in its response and recovery to environmental stress.

A commentary review on the use of normalized diff erence vegetation index(NDVI)in the era of popular remote sensing

The Normalized Diff erence Vegetation Index(NDVI),one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery,is now the most popular index used for vegetation assessment.This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band.Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite,aerial,and increasingly—Unmanned Aerial Systems(UAS).While studies have found that the NDVI is effective for expressing vegetation status andquantified vegetation attributes,its widespread use and popularity,especially in UAS applications,carry inherent risks of misuse with end users who received little to no remote sensing education.This article summarizes the progress of NDVI acquisition,highlights the areas of NDVI application,and addresses the critical problems and considerations in using NDVI.Detailed discussion mainly covers three aspects:atmospheric eff ect,saturation phenomenon,and sensor factors.The use of NDVI can be highly eff ective as long as its limitations and capabilities are understood.This consideration is particularly important to the UAS user community.

The progress of operational forest fire monitoring with infrared remote sensing

Forest wildfires pose significant and growing threats to human safety, wildlife habitat, regional economies and global climate change. It is crucial that forest fires be subject to timely and accurate monitoring by forest fire managers and other stake-holders. Measurement by spaceborne equipment has become a practical and appealing method to monitor the occurrence and development of forest wildfires. Here we present an overview of the principles and case studies of forest fire monitoring（FFM） with satelliteand drone-mounted infrared remote sensing（IRRS）. This review includes four types of FFM-relevant IRRS algorithms： bi-spectral methods, fixed threshold methods, spatial contextual methods, and multi-temporal methods. The spatial contextual methods are presented in detail since they can be applied easily with commonly available satellite IRRS data, including MODIS, VIIRS, and Landsat 8 OLI. This review also evaluates typical cases of FFM using NOAAAVHRR, EOS-MODIS, S-NPP VIIRS, Landsat 8 OLI,MSG-SEVIRI, and drone infrared data. To better implement IRRS applications in FFM, it is important to develop accurate forest masks, carry out systematic comparative studies of various forest fire detection systems（known as forest fire products）, and improve methods for assessing the accuracy of forest fire detection. Medium-resolution IRRS data are effective for landscape-scale FFM, and the VIIRS 375 m contextual algorithm and RST-FIRES algorithm are helpful for closely tracking forest fires（including small and shortlived fires） and forest-fire early warning.

Practice and Research Progress on Ecosystem Conservation in Transboundary Areas

Many ecosystems extend across national or political boundaries. The consistent and effective protection of these ecosystems in transboundary areas(ETAs) is an important global research focus. Previous research on the protection of such areas can be categorized into seven themes: 1) ecological conservation of a single ETA; 2) investigation of the effects of a single conservation measure on a specific ETA; 3) determination of species-level effects due to ETA conservation; 4) comparison of the same protection measures between different ETAs; 5) introduction of a single conservation measure to a specific ETA; 6) understanding the relationship between conservation and sustainable development; and 7) generalization across multiple ETA conservation cases. The protection of ETAs involves various considerations, including funding support, demand and will for collaboration, community and public participation, historical and cultural factors, political backgrounds, uniqueness of biological resources, formulation of laws and regulations, founding of specialized administrative departments, non-governmental organizations, and fairness. Here, we briefly explain the research themes and considerations related to ETA conservation. The most important finding is that most major research themes do not focus on the challenges of ETA conservation. We use two nature reserves located between China and North Korea as examples to identify specific ways to improve ETA conservation on Changbai Mountains. The efficiency of ETA administration still remains low. The study of ETA conservation should focus on concrete regional information and aim to improve existing measures through the accumulation of experience.

Correction to:Commentary review on the use of normalized difference vegetation index(NDVI)in the era of popular remote sensing

The article“A commentary review on the use of normalized difference vegetation index(NDVI)in the era of popular remote sensing”,written by Sha Huang·Lina Tang·Joseph P.Hupy·Yang Wang and Guofan Shao,was originally published electronically on the publisher’s internet portal on 31st May 2020 without open access.With the author(s)’decision to opt for Open Choice the copyright of the article changed on 25th July 2020 to©The Author(s)2020 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License(https://creativecommons.org/licenses/by/4.0/),which permits use,sharing,adaptation,distribution and reproduction in any medium or format,as long as you give appropriate credit to the original author(s)and the source,provide a link to the Creative Commons licence,and indicate if changes were made.

Estimating the opportunity costs of avoiding oil palm-based deforestation in Indonesia:Implications for REDD

Reducing emissions due to deforestation is considered a low-cost option for mitigating climate change.However,the recent literature suggests higher opportunity costs because of specific deforestation drivers,which render reducing emissions from deforestation and forest degradation(REDD+)for mitigating climate change an uncertain,less attractive,and controversial option.Indonesia is one of the largest greenhouse gas emitters.Since 1989,53.80%of its oil palm expansion has come from forestlands,which has generated a significant amount of carbon emissions.This study uses official data and results from the relevant literature to estimate the costs of oil palm-based deforestation under various scenario assumptions,including different output prices,yields,time horizons,and discount rates.We also calculate the additional cost to preserve a 1-ha forest.We demonstrate that the average opportunity cost from avoiding oil palm-based deforestation is 24.42 USD/tCO_(2)eq in Indonesia,approximately 1.3 times the 2011 EUA carbon price.Additional sums of around 5,466.90–11,042.96 USD/ha should be provided to landowners for the deforestation avoidance caused by oil palm expansion.Special attention should be given to the extensive oil palm expansion in Indonesia and the resulting high opportunity costs for achieving the REDD+​target.

Gene flow among wild and cultivated common walnut(Juglans regia)trees in the Qinling Mountains revealed by microsatellite markers

Patterns of gene flow and gene introgres sion can be used to assess the risk of genetic pollution of wild forest trees from widespread cultivated trees.A comprehensive understanding of the genetic relationships and levels of gene flow among wild and cultivated common walnut(Juglans regia)has become an urgent issue.Using twelve microsatellite markers,we investigated the genetic diversity and gene flow between cultivated and wild trees of J.regia in the Qinling Mountains,China.A high level of genetic variation was detected in both cultivated and wild trees.The mean number of alleles per locus was 17.5.Observed heterozygosity(HO)and expected heterozygosity(HE)were 0.777 and 0.800,respectively.Pollination of mother trees was not by nearest neighbors,and a paternity of 60.7%of offspring evaluated could not be assigned to a local,sampled tree.Pollen flow from cultivated trees to wild trees was infrequent(5.4%),and selfing rates ranged from zero to 25.0%.Male parents were located from 0 to 1005 m from their female partners,with an average pollination distance of 285.1 m.These results are discussed in light of the cultivated species’diversity,outlining the frequent spontaneous genetic contributions from the wild to the cultivated compartment.In addition,the pollen flow parameters provide useful information about the dynamics of pollen movement within J.regia populations.

The Role of Clouds in Global Radiation Changes Measured in Israel during the Last Sixty Years

An analysis of global radiation measurements and fractional cloud cover observations made in the Israel Meteorological Service’s network of climate stations demonstrated a significant decrease in the transmittance of solar radiation through the atmosphere during the last 60 years. The major cause was the reduced transparency of clouds. Under completely overcast skies with complete cloud cover transmission in the industrialized central coastal region decreased from 0.41 in the mid-20th century to 0.21 in the first decade of the 21st century. Under cloudless skies the reduction in the transmission of global radiation was less, from 0.79 to 0.71, and not statistically significant. Similar but somewhat smaller changes were observed in the less industrialized central hill region. Multi-linear analysis showed that since 1970, 61% of the measured decline in global radiation was attributable to changes in fractional cloud cover but only 2% to the marked increase in local fuel combustion;there was no statistically significant interaction between the two parameters.

Soil moisture, field-scale toposequential position, and slope effects on yields in irrigated rice (<i>Oryza sativa</i>L.) fields in Honduras

Rice (Oryza sativa L.) is an important cash crop in Honduras. The availability of inexpensive irrigation in the study area (Flores, La Villa de San Antonio, Comayagua) encourages rice farmers to neglect prescribed methods of soil and water conservation, such land leveling, puddling, and soil bunds. This study looked at the effect of failure to mitigate water loss on sloping fields. Soil moisture (Volumetric Water Content) was measured using a soil moisture probe after the termination of the first irrigation within the tillering/vegetative, panicle emergence/flowering, post-flowering/pre-maturation and maturation stages. Yield data were obtained by harvesting on 1 m2 plots in each soil moisture testing site. Data analyses looked at the relationship between yield and slope, soil moisture, farmers, and toposequential position along transects. Toposequential position influenced yields more than slope and soil moisture was not a significant predictor of yields. Irrigation politics, high water inputs, and land tenure were proposed as the major reasons for this result.

Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening

Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

河岸植被缓冲带与河岸带管理

河岸带是水陆交错带的一种景观表现形式 ,即岸边陆地上同河水发生作用的植被区域 ,是介于河溪和高地植被之间的生态过渡带 .目前 ,河岸带的保护和管理日益为人们所关注 ,并成为自然资源经营及管理中不可缺少的部分 .本文对国外河岸带管理有关的研究和实践进行了总结 ,对河岸带管理的目标、作用、一般途径、面临的问题以及将来发展趋势进行了讨论 ,并详细介绍了USDA FS的河岸植被缓冲带系统 .文章最后指出 ,有必要在国内尽快开展河岸带管理的研究和实践 .