The Impact of Hurricane Disturbances on a Tropical Forest: Implementing a Palm Plant Functional Type and Hurricane Disturbance Module in ED2-HuDi V1.0

Hurricanes commonly disturb and damage tropical forests. It is predicted that changes in climate will result in changes in hurricane frequency and intensity. Modeling is needed to investigate the potential response of forests to future disturbances. Unfortunately, existing models of forests dynamics are not presently able to account for hurricane disturbances. We implement the Hurricane Disturbance in the Ecosystem Demography model (ED2) (ED2-HuDi). The hurricane disturbance includes hurricane-induced immediate mortality and subsequent recovery modules. The parameterizations are based on observations at the Bisley Experimental Watersheds (BEW) in the Luquillo Experimental Forest in Puerto Rico. We add one new plant functional type (PFT) to the model—Palm, as palms cannot be categorized into one of the current existing PFTs and are known to be an abundant component of tropical forests worldwide. The model is calibrated with observations at BEW using the generalized likelihood uncertainty estimates (GLUE) approach. The optimal simulation obtained from GLUE has a mean relative error of −21 %, −12 %, and −15 % for stem density, basal area, and aboveground biomass, respectively. The optimal simulation also agrees well with the observation in terms of PFT composition (+1%, −8 %, −2 %, and +9 % differences in the percentages of Early, Mid, Late, and Palm PFTs, respectively) and size structure of the forest (+0.8 % differences in the percentage of large stems). Lastly, using the optimal parameter set, we study the impact of forest initial condition on the recovery of the forest from a single hurricane disturbance. The results indicate that, compared to a no-hurricane scenario, a single hurricane disturbance has little impact on forest structure (+1 % change in the percentage of large stems) and composition (< 1 % change in the percentage of each of the four PFTs) but leads to 5 % higher aboveground biomass after 80 years of succession. The assumption of a less severe hurricane disturbance leads to a 4 % increase in aboveground biomass.

Regeneration Status and Role of Traditional Ecological Knowledge for Cloud Forest Ecosystem Restoration in Ecuador

The importance of forests for biodiversity conservation has been well recognized by the global community; as a result, conservation efforts have increased over the past two decades. In Ecuador, the lack of integrated information for defining and assessing the status of local ecosystems is a major challenge for designing conservation and restoration plans. Thus, the objectives of this study were (1) to examine the regeneration status of cloud forest remnants, some of which had experienced past human disturbance events, (2) to explore a local rural community’s traditional ecological knowledge (TEK) relevant for restoration and (3) to investigate the integration between TEK and ecological science-based approaches. A survey of regeneration status was conducted in four remnants of cloud forests (n = 16) in Cosanga, Napo Province, in the Andes of northeastern Ecuador. The species of young trees (0.5–5 m height) were identified over 0.16 ha. In-depth interviews of individuals from local communities (n = 48) were conducted to identify socio-ecologically important native species. The results showed significant differences (p < 0.001) in species richness and the stem density of seedlings and saplings in gaps. The stem density of Chusquea sp., a bamboo species, explained 63% of the variation in species richness and 48% of the variation in the abundance of seedlings and saplings between plots. Informants cited 32 socio-ecologically important species, of which 26 species were cited as sources of food and habitats for wildlife. The ranking of species based on a relative importance index and a cultural value index—taking into account both the spread of knowledge among local informants and the multiplicity of uses—revealed that Hyeromina duquei, Citharexylum montanum, Eugenia crassimarginata and Sapium contortum were traditionally the most valuable species for both humans and wildlife. Informants also recommended 27 species for future planting, of which 19 species were amongst the rarest species in the regeneration survey. In conclusion, the results demonstrate a synergy between TEK and ecological science-based approaches (regeneration survey) to natural ecosystem research. Thus, traditional ecological knowledge can provide insights into ecosystem–plant–animal interaction, and to identify native species useful for both humans and wildlife for forest restoration projects to reconnect isolated cloud forest fragments.

Forest Vegetation and Dynamics Studies in India

Forests across the globe have been exploited for resouces, and over the years the demand has increased, and forests are rather exploited instead of sustainable use. Focussed research on vegetation and forerst dynamics is necessary to preserve biodiversity and functioning of forests for sustanence of human life on Earth.This article emphasis that the India has a long history of traditional knowledge on forest and plants, and explorations from 17th century on forests and provided subsequent scientific approach on classification of forests. This also explains the developments of quantitative approach on the understanding of vegetation and forest diversity. Four case studies viz., Mudumalai, Sholayar, Uppangala, Kakachi permanent plots in the forests of Western Ghats has been explained in detail about their sampling methods with a note on the results of forest monitoring. In the case of deciduous forests, the population of plant species showed considerable fluctuations but basal area has been steadily increasing over time, and this is reflecting carbon sequestration. In Sholayar, a total of 25390 individuals of 106 woody species was recorded for < 1 cm diameter at breast height in the first census of the 10 ha plot in the tropical evergreen forest. In Uppangala, 1) a 27- year long investigation revealed that residual impact of logging in the evergreen forests and such forests would take more time to resemble unlogged forests in terms of composition and structure; 2) across a similar temporal scale, the unlogged plots trees < 30 cm gbh showed a more or less similar trend in mortality (an average of 0.8% year-1) and recruitment (1%). The Kakachi plot study revealed that 1) endemic species showed least change in stem density and basal area whereas widely distributed species showed greater change in both; 2) The overall recruitment of trees was 0.86 % per year and mortality 0.56% per year resulting in an annual turnover of 0.71% ; 3) majority of the gap species had high levels of recruitment and mortality resulting in a high turnover.Such studies can be used as early warning system to understand how the response of individual plants, species and forests with the climatic variability. In conclusion, the necessity of implementation of national level projects, the way forward of two such studies: 1) impact of climate change on Indian forests through Indian Council of Forestry Research and Education (ICFRE) colloborations and 2) Indian long term ecological observatorion, including the sampling protocols of such studies. This will be the first of its kind in India to address climate change issues at national and international level and helps to trace footprints of climate change impacts through vegetation and also reveals to what extent our forests are resilient to changes in the climate.

Productivity indicators and correlations between them in spring barley cultivars of Uman National University of Horticulture

The study of crop formation processes by constituent elements revealed the dependence of the parameters of the number of grains in the ear and the weight of barley grain at the same level of grain yield on the number of productive stems per unit area. It is established that the increase in the number of productive shoots per 1 m2 is accompanied by a significant decrease in the productivity potential of the ear in terms of the number of grains and the parameters of the mass of one grain. The correlation dependence of these yield elements on the stem density of barley is strong. In the studies of 2018–2021, the number of productive stems, plant height, productivity of one ear and weight of 1000 grains of spring barley varieties Daniele, Gezine, Beatrix, Soldo, 5/18, Fabiola, Sangria, Utah, 9/19, Mompi 19, Lyuba and Champush. Correlation holidays between these indicators were also established. The number of productive stems of spring barley varieties on average for 2018–2021 amounted to 0.87–1.39 pieces/1 plant. The average biotype ranged from 0.89 in 2018 to 1.47 in 2021. There was a medium and strong variation in the number of productive stems depending on genotypes. In most collection specimens, the rate varied greatly depending on growing conditions. Collectible samples 5/18 is a semi-dwarf, other biotypes are dwarfs. The highest plants were in 2020. The plant height of most cultivars varied slightly over the years of research. The average productivity of one ear of the analyzed collection samples was 0.63–1.17 g. The lowest indicators were noted in 2018, the highest – in 2020. The average weight of 1000 grains was 45.2–53.5 g with the highest indicator in 2021 g. The correlation between plant height and ear weight per ear was positive medium and close; between the number of productive stems and plant height – positive average, weak and negative average; between the number of productive stems and the mass of grain from the ear – a weak positive and negative correlation; between the number of productive stems and the mass of 1000 grains – the average positive and weak negative; between the height of the plants and the mass of 1000 grains and between the mass of grains from the ear and the mass of 1000 grains – from the average positive to the average negative.

Assessment of woody plant species composition in secondary deciduous forests of Odisha, India

Plant species composition according to their functional types, distribution pattern are crucial for biodiversity conservation in tropical deciduous forest. The study assessed the woody plant species diversity, stand structure and population density in the secondary deciduous forest of Chandaka wildlife sanctuary, Odisha, India. A total of 70 species belonging to 63 genera and families were documented in this study.The stem density was found to be 1080 stems/ha with reverse J-shaped population structure indicating good regeneration potential of the forests. Shannon diversity Index varied from 0 to 2.31 whereas Simpson's index varied from 0 to 0.85. The correlation study between Importance Value Index and basal area were significant (p=40.63). The present study would be helpful for conservation and management of biodiversity in secondary dry deciduous forests of Chandaka Wildlife Sanctury in particular and tropical dry forests in general.

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Exploring the Recruitment Dynamics of Sugar Maple and Yellow Birch Saplings into Merchantable Stems Following Harvesting in the Acadian Forest Region of New Brunswick, Canada

The recruitment of saplings in forest stands into merchantable stems is a very complex process, thus making it challenging to understand and predict. The recruitment dynamics in the Acadian Forest Region of New Brunswick are not well known or documented. Our objective was to draw an inference from existing large scale routine forest inventories as to the different dynamics behind the recruitment from the sapling layer into the commercial tree size layer in terms of density and occurrence of sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis Britt.) following harvesting, by looking at many factors on a wide range of spatial and temporal scales using models. Results suggest that the variation in density and probability of occurrence is best explained by the intensity of silvicultural treatment, by the merchantable stem density in each plot, and by the proportion of merchantable basal area of each group of species. The number of recruits of sugar maple and yellow birch stems tend be higher when time since last treatment increases, when mid to low levels of silvicultural treatment intensity were implemented, and within plots having intermediate levels of merchantable stem density. Lastly, our modeling efforts suggest that the probability of occurrence and density of recruitment of both species tend to increase while its share of merchantable basal area increases.

Unveiling effects of growth conditions on crown architecture and growth potential of Scots pine trees

Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Thus, the aim was to study the crown structure of individual Scots pine trees in forest stands with varying stem densities due to past forest management practices. Furthermore, we wanted to understand how crown and stem attributes as well as tree growth affects stem area at the height of maximum crown diameter (SAHMC), which could be used as a proxy for tree growth potential. We used terrestrial laser scanning (TLS) to generate attributes characterizing crown size and shape. The results showed that increasing stem density decreased Scots pine crown size. TLS provided more detailed attributes for crown characterization compared to traditional field measurements. Furthermore, decreasing stem density increased SAHMC and strong relationships (Spearman correlations >0.5) were found between SAHMC and crown and stem size as well as stem growth. Thus, this study provided quantitative and more comprehensive characterization of Scots pine crowns and their growth potential.

Fuelwood Harvest and No Harvest Effects on Forest Composition, Structure, and Diversity of Arasbaran Forests—A Case Study

The impact of fuelwood harvesting on forest structure and composition is not clear, especially on the understudied and scarce Arasbaran forests in Iran. This research compared woody species density, species diversity, forest composition, and regeneration status in areas of continuous and ceased fuelwood harvesting in Arasbaran forests. We expected fuelwood harvesting to decrease stem density, species diversity, tree size (diameter at the breast height (DBH) and height), and shift composition away from preferred fuelwood species. We measured woody species size and frequency and identified species in three fuelwood harvest and three no harvest sites, with six sample plots (100 m × 50 m) per site. Results tended to show differences in composition, diversity, woody species height, and density. Carpinus orientalis, a preferred fuelwood species, tended to be more dominant in no harvest (importance values index (IVI) = 173.4) than harvest areas (IVI = 4.4). The diversity or richness of woody species tended to be higher in harvest (20 ± 1 species per ha) than in no harvest (14 ± 2 species per ha) areas, and other measures of diversity supported this trend as well. Harvest areas tended to also be characterized by shorter tree height and lower density of trees, a higher density of regeneration, and fewer small pole-sized trees than no harvest areas. Ongoing fuelwood harvests may further shift composition and structure away from no harvest area, compromising future fuelwood availability, but further detailed research is needed. Close to nature practices may be useful in sustaining fuelwood harvest areas and diversifying areas where fuelwood harvesting has ceased.

Investigating the Effects of k and Area Size on Variance Estimation of Multiple Pixel Areas Using a k-NN Technique for Forest Parameters

Combining auxiliary variables and field inventory data of forest parameters using the model-based approach is frequently used to produce synthetic estimates for small areas. These small areas arise when it may not be financially feasible to take ground measurements or when such areas are inaccessible. Until recently, these estimates have been calculated without providing a measure of the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m2 ha−1), stem density (n/ha−1), and volume (m3 ha−1), and subsequently estimates the variance of multiple pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce (Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured, both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R2 values for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22% (0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands was used to validate the modelled estimates. A comparison of measured values versus modelled estimates was carried out for a range of area sizes with results showing that estimated values in areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased, the estimated values became increasingly analogous to the measured values for all parameters. The results of the variance estimation highlighted: (i) a greater value of k was needed for small areas compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest managers to better understand how aspects of this variance estimation technique affect the accuracy of the uncertainty associated with parameter estimates. Utilising this information can provide forest managers with inventories of greater accuracy, therefore ensuring a more informed management decision. These results also add further weight to the applicability of the k-NN variance estimation technique in a range of forests landscapes.