Distribution Of Trees Research Articles

Effectiveness of land use and disturbance measures, compared to climate, soil, and topography, for modeling relative abundances of tree species in the eastern United States

Environmental factors control species distributions and abundances, but effectiveness of land use and disturbance variables for modeling species generally is unknown compared to climate, soil, and topography variables. Therefore, I used predictor variables from categories of 1) land use and disturbance, 2) climate, and 3) soil, topography, and wind speed to model the relative abundances (i.e., percentage of all trees) of 65 common tree species in the eastern United States, with a contrast to presence-absence models of species distributions. First, I modeled variables within each category to identify the five most important variables. Then, I combined variables from each category to isolate most important variables, based on five model combinations of input variables from each category, ranging from one (i.e., three total) to five (i.e., 15 total) variables. From the five models of combined categories for each tree species, I identified the model with the greatest R2 value. Overall, climate variables were most important for tree species models with one and two input variables from each category, but land use and disturbance variables were most important for models with three to five input variables from each category. Although a range of R2 values occurred by species and number of input model variables, 32 species had best models with greatest R2 values of 0.50 to 0.81. For all best species models, the most important variables were temperature of the warmest quarter, historical fire return interval for all fires, agricultural area during years 1850 to 1997, and precipitation of the driest month. Current land cover classes, which are accessible and the most commonly modeled land use variables, were not important for modeling tree species abundances or distributions. Climate variables were most important for modeling species distributions. Results support the concept that while climate sets soft boundaries on distributions, relative abundances within distributions are affected by other filters. Future modeling may establish other important land use and disturbance variables, or refinements within the important variables of historical fire return interval and agricultural area over time, advancing integration of both land use and climate variables into studies.

A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method

Network layer multicast is a powerful method for transmitting data from sources to multiple group members. When joining a multicast group, a group member first sends a request to a designated router (DR). Then, the DR selects a node in the existing multicast tree (known as a multicast joining node, or MJN) to establish a multicast distribution path from the MJN to itself. The MJN selection method runs on the DR and has a significant impact on the distribution of the multicast tree, that directly affects the load distribution in the network. However, the current MJN selection method cannot effectively detect the load status of the downlink multicast path in the case of asymmetric routing, leading to network congestion and limiting the number of multicast groups that the network can accommodate (multicast capacity). To solve this problem, we propose an MJN selection method based on the reverse shortest path tree (RSPT). RSPT can effectively detect the load status of downlink multicast paths in case of routing asymmetry. Based on the detection results of RSPT, DR can select the MJN with the lowest path load to join the multicast tree. Our experimental results indicate that compared to existing multicast methods, our method has a lower cost and delay, and can effectively balance the network load in the case of asymmetric routing, increasing multicast capacity by more than two times.

Socioeconomics explain tree diversity, abundance, and composition in the compact city of Barcelona, Spain

Trees are frequently unevenly distributed in urban areas, and their diversity, abundance, and composition vary spatially. Some studies have shown that the unequal distribution of urban trees can be associated with demographic and socioeconomic factors. However, most studies have been conducted in diffuse urban systems using remotely-sensed tree cover within single or aggregated land-use types. We examined if the relationship between tree diversity, abundance, composition, and socioeconomic factors varied across land-use types (parks, streets, zonal areas, and total) in the compact city of Barcelona, Spain. We calculated tree species richness and abundance across 73 neighborhoods using 229,962 geo-referenced records of individual trees sampled in 2021. Life expectancy positively explained tree richness and abundance across land-use types, reinforcing that access to urban green infrastructure promotes longer life spans. In contrast, income did not positively explain tree richness or abundance, indicating that tree distribution does not directly result in biodiversity and green space inequalities for disadvantaged groups in terms of income. Total tree composition across neighborhoods responded to socioeconomic gradients, evidencing that neighborhood appearances are socially stratified. Street tree abundance increased in densely populated neighborhoods, possibly because intense street tree plantings in these neighborhoods compensate for the lack of other green infrastructure. Urban tree planting, particularly in streets, can be an effective strategy to increase green spaces in compact cities to overcome the disparities resulting from the unequal distribution of urban vegetation. Long-term data on socioeconomic factors and urban tree distribution are necessary to determine how social stratification affects urban diversity.

Environmental drivers of the current and future distribution of high-yielding lacquer trees (Toxicodendron vernicifluum (stokes) F. A. Barkley)

Abstract Lacquer trees (Toxicodendron vernicifluum (Stokes) F.A. Barkley) are an important natural resource with significant economic and cultural value in East Asia. The main product, raw lacquer, is in high demand due to its commercially valuable characteristics. This study analyzed environmental drivers of the current and future distribution of lacquer trees in high-yielding locations using a machine-learning algorithm and Maxent models. Results identified suitable value ranges for four key environmental variables and their relative contribution to environmentally suitable areas (shown as percentages in parentheses): solar radiation, 12 000–13 000 kJ m−2 day−1 (43.1 per cent); min temperature of the coldest month, −3.5 to 3°C (18.7 per cent); annual precipitation, 900–1400 mm (13.9 per cent) and water vapour pressure, 1.2–1.6 KPa (5.1 per cent). Overall, projected climate change until 2100 will reduce the extent of environments suitable for high-yielding lacquer trees in China and the Republic of Korea, although these areas will expand in Japan. In addition to the three East Asian countries in which lacquer trees and production are currently important, the study identified environmentally suitable areas for growing lacquer trees in other countries worldwide under future climate conditions. The study’s methodology, which divided high-yielding records from other occurrence records and modeled them separately, was applicable in analyzing environmental drivers and modelling suitable areas for lacquer trees. This approach may also be beneficial to study the distributions of other plants, especially economically important crops and trees. In future studies, additional data sets capturing anthropogenic drivers and information on single tree level could further improve models exploring the productivity and sustainability of lacquer trees under future climates.

Effects of vegetation structure and environmental characteristics on pollinator diversity in urban green spaces

With accelerating urbanization, insect pollinators in urban ecosystems face challenges such as reduced pollen sources, habitat fragmentation, and damage to the nesting environment. Urban green spaces (UGS) are essential for the stability of pollinator communities. However, little is known about the relationship between vertical layer heterogeneity and horizontal layer complexity of vegetation structure in UGS and pollinator communities. The present study aimed to assess how vegetation structure and environmental characteristics shape the insect pollinator community in UGS. To this end, this study was conducted with seven typical vegetation types which were selected according to the biotope mapping classification system (BMCS) in the ring parks around Hefei City, in Anhui province, China. A total of 11,401 pollinators belonging to 6 orders and 34 families were identified during the eight-month survey. Among the seven habitats under the BMCS, mainly successional short-cut shrub and partly open green space, trees two- or multi-layered broad-leaved mixed forest and partly closed green space, and mainly successional tall grass and partly open green space were identified as high-quality insect pollinator habitats. According to the results of the generalized linear regression, the explanatory power of the four best-fitting generalised linear models is relatively high (over 77%). In four optimal models, the effect of vegetation structure on pollinator community was greater than that of environmental characteristics. The redundancy discriminant analysis showed that the flowering abundance of nectar plants, herb richness, and shrub coverage rate were the three most important factors influencing insect pollinator communities, with a cumulative explanatory power of up to 78.8%. Pollinator abundance was positively influenced by spontaneous herbs and low-intensity management. However, high-intensity management, low diversity of plants, low nectar plant richness, ignoring seasonal nectar plant configuration, and dense tree distribution could limit pollinator reproduction and population growth. These results reflect the status of insect pollinator community in UGS in Hefei city and present a possible direction for improving urban green habitats and plant configurations.

Big-sized trees and higher species diversity improve water holding capacities of forests in northeast China

High water-holding forests are essential for adapting to drought climates under global warming, and a central issue is which type of forests could conserve more water in the ecosystem. This paper explores how forest structure, plant diversity, and soil physics impact forest water-holding capacities. We investigated 720 sampling plots by measuring water-holding capacities from 1440 soil and litter samples, 8400 leaves, and 1680 branches and surveying 18,054 trees in total (28 species). Water-holding capacities were measured as four soil indices (Maxwc, maximum water-holding capacity; Fcwc, field water-holding capacity; Cpwc, soil capillary water-holding capacity; Ncpwc, non-capillary water-holding capacity), two litter metrics (Maxwcl, maximum water-holding capacity of litters; Ewcl, effective water-holding capacity of litters), and canopy interception (C, the sum of estimated water interception of all branches and leaves of all tree species in the plot). We found that water-holding capacity in the big-sized tree plots was 4–25 % higher in the litters, 54–64 % in the canopy, and 6–37 % in the soils than in the small-sized plots. The higher species richness increased all soil water-holding capacities compared to the lowest richness plot. Higher Simpson and Shannon-Wiener plots had 10–27 % higher Ewcl and C than the lowest plots. Bulk density had the strongest negative relations with Maxwc, Cpwc, and Fcwc, whereas field soil water content positively affected them. Soil physics, forest structure, and plant diversity explained 90.5 %, 5.9 %, and 0.2 % of the water-holding variation, respectively. Tree sizes increased C, Ncpwc, Ewcl directly (p < 0.05), and richness increased Ewcl directly (p < 0.05). However, the direct effects from the uniform angle index (tree distribution evenness) were balanced by their indirect effect from soil physics. Our findings highlighted that the mixed forests with big-sized trees and rich species could effectively improve the water-holding capacities of the ecosystem.

Climate change impacts the distribution of Quercus section Cyclobalanopsis (Fagaceae), a keystone lineage in East Asian evergreen broadleaved forests

East Asian evergreen broadleaved forests (EBFLs) harbor high species richness, but these ecosystems are severely impacted by global climate change and deforestation. Conserving and managing EBLFs requires understanding dominant tree distribution dynamics. In this study, we used 29 species in Quercus section Cyclobalanopsis—a keystone lineage in East Asian EBLFs—as proxies to predict EBLF distribution dynamics using species distribution models (SDMs). We examined climatic niche overlap, similarity, and equivalency among seven biogeographical regions’ species using ‘ecospat’. We also estimated the effectiveness of protected areas in the predicted range to elucidate priority conservation regions. Our results showed that the climatic niches of most geographical groups differ. The western species under the Indian summer monsoon regime were mainly impacted by temperature factors, whereas precipitation impacted the eastern species under the East Asian summer monsoon regime. Our simulation predicted a northward range expansion of section Cyclobalanopsis between 2081 and 2100, except for the ranges of the three Himalayan species analyzed, which might shrink significantly. The greatest shift of highly suitable areas was predicted for the species in the South Pacific, with a centroid shift of over 300 km. Remarkably, only 7.56% of suitable habitat is currently inside protected areas, and the percentage is predicted to continue declining in the future. To better conserve Asian EBLFs, establishing nature reserves in their northern distribution ranges, and transplanting the populations with predicted decreasing numbers and degraded habitats to their future highly suitable areas, should be high-priority objectives.

Spatial distribution of invasive large fever berry trees (Croton megalobotrys) in Sengwa Wildlife Research Area: Gokwe, Zimbabwe

BackgroundThe invasion of rangelands by invasive plant species is a major threat to biodiversity in most parts of Zimbabwe posing not only an ecological challenge but a growing management and eradication challenge as well. However, there is sparse information relating to the spatial distribution of these species and the conditions promoting their growth and development particularly in Sothern Africa. The study assessed the spatial distribution of the invasive large fever berry tree and associated soil type in Sengwa Wildlife Research Area. A mixed methods research design triangulating qualitative and quantitative methods was utilized. All known sites occupied by large fever berry trees were obtained from the Sengwa Wildlife Research Institute’s records on invasive plant species. Ground truthing was done for all sites and global positioning system coordinates of occupied areas were collected. Mapping of invaded areas by the large fever berry tree was done using Quantum GIS software. Coordinates were imported to show points with the large fever berry tree. Altitude of invaded areas and soil samples were also collected for soil analysis and a soil texture triangle was used to come up with the soil type associated with the growth and spread of the large fever berry trees.ResultsThe results show that the large fever berry tree occupied areas along major rivers and streams on loam soils. An area of 16.5km2 which is 4.4% of the Sengwa Wildlife Research Area is invaded by the large fever berry tree. Results further indicated that sandy- loam soils were associated with the growth and development of the large fever berry tree in Sengwa Wildlife Research Area. Veld fires were also identified as a factor influencing the spread of the large fever berry tree species in the Sengwa Wildlife Research Area.ConclusionIn conclusion, a holistic framework was developed to curb the invasion of the large fever berry tree in Sengwa Wildlife Research Area. It is recommended that further studies be conducted outside the protected area to establish soil characteristics and invasion rates in order to fully understand drivers of its invasion.

Towards network-assisted publish–subscribe over wide area networks

The Internet is poised to support new classes of industrial applications driven by large numbers of sensors and actuators generating and consuming sensor data streams. While the publish–subscribe (PubSub) architecture is well-suited for such large-scale data dissemination, existing over-the-top PubSub systems are not only vulnerable to network-induced performance degradation, but also unable to orchestrate the underlying network for better usage. This paper proposes a network-assisted PubSub architecture called SNAPS which enables seamless control of distributed PubSub brokers at the edge, as well as the networks that interconnect them. SNAPS leverages SRv6-based network programmability to build and maintain network-efficient, shareable data distribution trees across one or more administrative domains. The architecture is independent of broker implementation, making it suitable for “bringing your own brokers” at available edge locations. We describe how the networks interconnecting the brokers can be programmed for efficient, shareable data distribution via SRv6. Through simulation and prototype evaluation, we show the efficacy of SNAPS in providing better service for PubSub data delivery and better usage of the network resources.

Scale‐dependent diversity–biomass relationships can be driven by tree mycorrhizal association and soil fertility

AbstractDiversity–biomass relationships (DBRs) often vary with spatial scale in terrestrial ecosystems, but the mechanisms driving these scale‐dependent patterns remain unclear, especially for highly heterogeneous forest ecosystems. This study explores how mutualistic associations between trees and different mycorrhizal fungi, i.e., arbuscular mycorrhizal (AM) vs. ectomycorrhizal (EM) association, modulate scale‐dependent DBRs. We hypothesized that in soil‐heterogeneous forests with a mixture of AM and EM tree species, (i) AM and EM tree species would respond in contrasting ways (i.e., positively vs. negatively, respectively) to increasing soil fertility, (ii) AM tree dominance would contribute to higher tree diversity and EM tree dominance to greater standing biomass, and that as a result (iii) mycorrhizal associations would exert an overall negative effect on DBRs across spatial scales. To empirically test these hypotheses, we collected detailed tree distribution and soil information (e.g., nitrogen, phosphorus, organic matter, pH) from seven temperate and subtropical AM–EM mixed forest megaplots (16–50 ha). Using a spatial codispersion null model and structural equation modeling, we identified the relationships among AM or EM tree dominance, soil fertility, tree species diversity, and biomass and, thus, DBRs across 0.01‐ to 1‐ha scales. We found the first evidence overall supporting the three aforementioned hypotheses in these AM–EM mixed forests: (i) In most forests, with increasing soil fertility, tree communities changed from EM‐dominated to AM‐dominated; (ii) increasing AM tree dominance had an overall positive effect on tree diversity and a negative effect on biomass, even after controlling for soil fertility and number of trees. Together, (iii) the changes in mycorrhizal dominance along soil fertility gradients weakened the positive DBR observed at 0.01‐ to 0.04‐ha scales in nearly all forests and drove negative DBRs at 0.25‐ to 1‐ha scales in four out of seven forests. Hence, this study highlights a soil‐related mycorrhizal dominance mechanism that could partly explain why, in many natural forests, biodiversity–ecosystem functioning (BEF) relationships shift from positive to negative with increasing spatial scale.

Forgotten forest relics: Apple trees (Malus spp.) in eastern U.S. forests

One potential legacy of the widespread deforestation of the 1700-1800s and subsequent agricultural abandonment and reforestation across the eastern United States (U.S.) during the 1900s is the establishment of introduced apple trees (Malus domestica) exclusive of naturally occurring native crab apple species. As these apple trees might provide ecological services and a genetic reserve of long-forgotten apple cultivars, a region-wide forest inventory was used to estimate the current extent, condition, and changes in the population of apple trees across eastern forests of the U.S. It was found that hundreds of millions of apple trees currently exist in the forests of the northern U.S. with indications of population senescence as the abundance of seedlings and saplings decline while the abundance of the largest-sized trees and standing dead trees increase. The distribution of forest apple trees is only aligned with native crab apple distributions in limited areas, suggesting distinct feral apple populations derived from agricultural abandonment decades ago. As these extant apple trees are likely relics of former orchards and/or their naturally dispersed progeny, these populations may serve as an in-situ gene bank representing hundreds, if not thousands, of apple cultivars forgotten long ago but adapted to local conditions and perhaps of importance to ensuring the future resilience of domestic apple production in the context of global change.

How future climate and tree distribution changes shape the biodiversity of macrofungi across Europe

AbstractAimClimate change is affecting biodiversity at an accelerating rate. Despite the importance of fungi in ecosystems in general, and in the global carbon and nitrogen cycle in particular, there is little research on the response of fungi to climate change compared with plants and animals. Earlier studies show that climatic factors and tree species are key determinants of macrofungal diversity and distribution at large spatial scales. However, our knowledge of how climate change will affect macrofungal diversity and distribution in the future remains poorly understood.LocationEurope.MethodsUsing openly available occurrence data of 1845 macrofungal species from eight European countries (i.e. Norway, Sweden, Finland, Denmark, Netherlands, Germany, France and Spain), we built ensemble species distribution models to predict macrofungal response to climate change alone and combined climate and tree distribution change under the IPCC special report on 2080 emissions scenarios (SRES A2 and B2).ResultsConsidering climate change alone, we predict that about 77% (74.1%–80.7%) of the modelled species will expand their distribution range, and around 57% (56.1%–58.4%) of the modelled area will have an increase in macrofungal species richness. However, when considering the combined climate and tree species distribution change, only 50% (50%–50.9%) of the species are predicted to expand their distribution range and 49% (47.4%–51.1%) of the modelled area will experience an increase in macrofungal species richness.Main ConclusionsOverall, our models projected that large areas would exhibit increased macrofungal species richness under future climate change. However, tree species distribution might play a restrictive role in the future distributional shifts of macrofungi. In addition, macrofungal responses appear heterogeneous, varying among species and regions. Our findings highlight the importance of including tree species in the projection of climate change impacts on the macrofungal diversity and distribution on a continental scale.

Sub-continental-scale carbon stocks of individual trees in African drylands

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1–14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15–18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year−1 rainfall zone by coupling field data19, machine learning20–22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha−1 and 63 kg C tree−1 in the arid zone to 3.7 Mg C ha−1 and 98 kg tree−1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24–29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Soil drainage-class influences on the distribution of witness trees (1664–1700) in Wicomico County, Maryland, USA

Given the long-term human habitation and prolific land use change trajectories in the eastern United States, few remnants of the pre-European settlement Mid-Atlantic forest remain on the Atlantic Coastal Plain. This study utilized metes and bounds witness tree locations and descriptions produced during colonial land subdivision to understand the relationships between tree types and biogeographic environments for an area on Maryland's Eastern Shore. Using correspondence analysis, we reveal habitat partitioning among witness tree species based on soil drainage characteristics, that enable the visualization of a pre-European settlement reconstructed forest. Our research finds that oak ( Quercus), most often white oak ( Quercus alba), was the dominant genus for most of the study area underlain by well-drained soils. To a much smaller extent, gums were also associated with these well-drained soils. Areas of poor drainage most often contained pine ( Pinus) and an assemblage of different oaks ( Quercus spp.). Hickory ( Carya), also noted in the witness tree record in small numbers, was found across all soil drainages.

Drivers of compositional turnover of narrow-ranged versus widespread naturalised woody plants in South Africa

IntroductionAlien trees and shrubs have become increasingly common invaders globally and have caused major negative impacts to ecosystems and society. Non-native woody plant species make up the majority of legislated invasive alien taxa in South Africa and contribute substantially to recorded negative impacts. It is of management interest to elucidate the macroecological processes that mediate the assembly of alien taxa, as this is expected to be associated with anthropogenic factors (e.g., human activity, introduction events, pathways of propagule dispersal mediated by humans) and bioclimatic factors (such as diurnal temperature range and precipitation gradients). These analyses require large species-occurrence datasets with comprehensive sampling across broad environmental conditions. Efforts of citizen scientists produce large numbers of occurrence records in a consistent manner which may be utilised for scientific investigations.MethodsResearch Grade occurrence data on naturalised plants of South Africa were extracted from the citizen scientist platform iNaturalist. Sampling bias was mitigated using statistical modelling of background points estimated from a Target Group of species which identifies well sampled communities. The drivers of assembly for alien plants at different range sizes were identified using multi-site generalised dissimilarity modelling (MS-GDM) of zeta diversity. The predicted compositional similarity between all cells was computed based on the subset of identified well sampled communities and using generalised dissimilarity modelling (GDM). From this, alien bioregions were identified using a k-means cluster analysis.Results and DiscussionBioclimatic factors significantly influenced community turnover in inland areas with large diurnal temperature ranges, and in areas with high precipitation. Communities separated by large geographical distances had significantly different compositions, indicating little contribution of long-range propagule movement by humans, and the presence of localised introduction hubs within the country which harbour unique species compositions. Analyses also showed a significant contribution of road density to turnover, which may be moderated by the habitat service provided by road verges. The same is true for natural dispersalviarivers in arid areas. The distribution of naturalised tree and shrub species is geographically clustered and forms six alien bioregions that are distinct from the South African biomes defined by native species distributionanalysis.

Carbon stocks of tree plantations in a Western Ghats landscape, India: influencing factors and management implications.

Biomass and carbon stock assessments in data-deficient plantations and identifying the factors influencing tree growth, distribution, and carbon stocks are extremely important for implementing sound silvicultural management and monitoring practices to achieve REDD+ goals. We conducted carbon stock assessments in five major plantation types in a regional landscape in the central Western Ghats, India, by establishing fifty 0.1-ha plots across the landscape. We quantified the overall carbon stocks by summing the carbon pools across mature trees, deadwood, and soil (0-15cm) components. Allometric equations were compared to address the uncertainty in the tree biomass carbon. The tree biomass carbon and soil organic carbon varied significantly across the plantation types (F = 55.23, p < 0.00). The present study yielded the highest carbon stocks in Pinus plantation (201.91 ± 9.52Mgha-1) and the least in Eucalyptus (122.63 ± 9.73Mgha-1). The correlation analysis displayed a strong influence of mean annual precipitation and edaphic factors on soil organic carbon, while basal area and elevation were good predictors of tree biomass carbon. The principal component analysis revealed an association of predictor variables in the distribution of plantation types. We found a strong association between mean annual precipitation on Pinus plantation and mean annual temperature on Eucalyptus and Acacia plantations. On the other hand, teak pure plantation was associated with structural and topographic variables, while edaphic factors mainly influenced the distribution of teak mixed plantations. The findings of the present study conclude substantial carbon storage ability of the plantations in the studied landscape which can play a significant role in mitigating the effects of climate change and reaching carbon neutrality.

TreeSearch: Morphological Phylogenetic Analysis in R

TreeSearch is an R package for phylogenetic analysis, optimized for discrete character data. Tree search may be conducted using equal or implied step weights with an explicit (albeit inexact) allowance for inapplicable character entries, avoiding some of the pitfalls inherent in standard parsimony methods. Profile parsimony and user-specified optimality criteria are supported. A graphical interface, which requires no familiarity with R, is designed to help a user to improve the quality of datasets through critical review of underpinning character codings; and to obtain additional information from results by identifying and summarizing clusters of similar trees, mapping the distribution of trees, and removing 'rogue' taxa that obscure underlying relationships. Taken together, the package aims to support methodological rigour at each step of data collection, analysis, and the exploration of phylogenetic results.

The First Report on Nutrient Distribution and Resorption in Mature Alternate Bearer Seedless Barberry Trees: Storage Pools of Nitrogen, Phosphorus, Potassium and Carbohydrates

The First Report on Nutrient Distribution and Resorption in Mature Alternate Bearer Seedless Barberry Trees: Storage Pools of Nitrogen, Phosphorus, Potassium and Carbohydrates

Multi-Feature-Based Identification of Subtropical Evergreen Tree Species Using Gaofen-2 Imagery and Algorithm Comparison

The species and distribution of trees in a forest are critical to the understanding of forest ecosystem processes and the development of forest management strategies. Subtropical forest landscapes feature a complex canopy structure and high stand density. Studies on the effects of classification algorithms on the remote sensing-based identification of tree species are few. GF-2 is the first satellite in China with sub-meter accuracy which has the high resolution and short replay cycle. Here, we considered three representative tree types (Masson pine, Chinese fir, and broadleaved evergreen trees) in the southern subtropical evergreen broadleaved forest region of China as research objects. We quantitatively compared the effects of five machine learning algorithms, including the backpropagation neural network, k-nearest neighbour, polytomous logistic regression, random forest (RF) and support vector machine (SVM), and four features (vegetation index, band reflectance, textural features, and topographic factors) on tree species identification using Gaofen-2 panchromatic and multispectral remote sensing images and field survey data. All five classification algorithms could effectively identify major tree species in subtropical forest areas (overall accuracy [OA] &gt; 87.40%, kappa coefficient &gt; 81.08%). The SVM model exhibited the best identification ability (OA = 90.27%, kappa coefficient = 85.37%), followed by RF (OA = 88.90%, Kappa coefficient = 83.30%). The combination of band reflectance, vegetation index, and the topographic factor performed exhibited the best, followed by the combination of band reflectance, vegetation index, textural feature, and topographic factor. In addition, we find that the classifier constructed by a single feature is not as effective as the combination of multiple feature factors. The addition of topographic factors can significantly improve the ability of tree species identification. According to the results of the five classifiers, the separability of the three tree species was good. The producer’s accuracy and user’s accuracy of Masson pine were more than 90%, and the evergreen broad-leaved tree and Chinese fir were more than 80%. The commission errors and omission errors of the three tree species were evergreen broadleaved tree &gt; Chinese fir &gt; Masson pine. The variable importance assessment results showed that the normalized difference greenness index, altitude, and the modified soil-adjusted vegetation index were the key variables. The results of this study used GF-2 to accurately identify the main tree species of subtropical evergreen forests in China, which can help forest managers to regularly monitor tree species composition and provide theoretical support for forest managers to formulate policies, monitor sustainable plans for wood mining, and forest conservation and management measures.

Geology and vegetation control landsliding on forest-managed slopes in scarplands

Abstract. Landslides are important agents of sediment transport, cause hazards and are key agents for the evolution of scarplands. Scarplands are characterized by high-strength layers overlying low-inclined landslide-susceptible layers that precondition and prepare landsliding on geological timescales. These landslides can be reactivated, and their role in past hillslope evolution affected geomorphometry and material properties that set the framework for present-day shallow landslide activity. To manage present-day landslide hazards in scarplands, a combined assessment of deep-seated and shallow landsliding is required to quantify the interaction between geological conditions and vegetation that controls landslide activity. For this purpose, we investigated three hillslopes affected by landsliding in the Franconian scarplands. We used geomorphic mapping to identify landforms indicating landslide activity, electrical resistivity to identify shear plane location and a mechanical stability model to assess the stability of deep-seated landslides. Furthermore, we mapped tree distribution and quantified root area ratio and root tensile strength to assess the influence of vegetation on shallow landsliding. Our results show that deep-seated landslides incorporate rotational and translational movement and suggest that sliding occurs along a geologic boundary between permeable Rhätolias sandstone and impermeable Feuerletten clays. Despite low hillslope angles, landslides could be reactivated when high pore pressures develop along low-permeability layers. In contrast, shallow landsliding is controlled by vegetation. Our results show that rooted area is more important than species-dependent root tensile strength and basal root cohesion is limited to the upper 0.5 m of the surface due to geologically controlled unfavourable soil conditions. Due to low slope inclination, root cohesion can stabilize landslide toes or slopes undercut by forest roads, independent of potential soil cohesion, when tree density is sufficient dense to provide lateral root cohesion. In summary, geology preconditions and prepares deep-seated landslides in scarplands, which sets the framework of vegetation-controlled shallow landslide activity.