The conversion of natural forested lands to managed forests has reduced the number of older, structurally diverse forests worldwide. In the conifer forests of the Pacific Northwest (USA), the long-term impacts of timber harvesting are not fully understood. We used a chronosequence of forests in southwestern Oregon that ranged from 25 to 445 years of age to compare changes in plant communities in logged stands with those in stands in late-succession conditions. The chronosequence consisted of 13 50m² permanent plots with similar elevation, aspect, slope, and forest type that were previously sampled in 2003. In 2021, we resurveyed the herbaceous understory in each plot to evaluate if the relationship between stand attributes and understory vegetation had changed over the 18-year period. Our results support the non-linear relationship between stand age and richness also observed in 2003, such that richness was highest in the youngest stands, reached a low point in mid-aged stands, and then reached high levels of richness in the oldest stands. However, changes in structural conditions and the response of understory vegetation varied among stands. We found that canopy cover increased (24%) over the 18-year period, with most of the increase occurring as young, recently clearcut sites entered a phase of canopy closure. These same stands lost an average of 11 understory species between 2003 and 2021, and the community composition of these younger stands became more similar to closed canopy stands. We observed a decline in evenness and diversity in the mid-age and older stands due to an increase in dominance of shade-tolerant plants. Overall, these results demonstrate a legacy effect of the historic phase of clearcut logging in the Pacific Northwest in which a large portion of forests on the landscape have now entered a period of high canopy cover and low richness and diversity.

The boreal forest, resilient to many natural and anthropogenic disturbances, faces significant changes due to forest fragmentation resulting from oil and gas operations. This fragmentation, particularly prominent in Alberta's northeast region, alters forest dynamics and affects wildlife habitat. In this study, we investigated changes in tree structure and composition associated with oil sands footprints on adjacent forest edges.

Specifically, we used a combination of field surveys and Light Detection and Ranging (LiDAR) data to test the influence of disturbance type (gap size), orientation, and distance from forest edges on forest structure and composition. Our study encompasses two disturbance types that vary in gap size: legacy seismic lines and abandoned well pads. For field sites, we examined tree species distribution within different size classes along 100 m-long belt transects perpendicular to forest edges. Paired with these field methods, we used LiDAR to analyze variations in structural patterns for different height strata of forests adjacent to these disturbances. Finally, we assessed whether the abundance of large trees, snags and downed woody debris changes with type and distance from edges. Snags and woody debris are crucial habitats for boreal fauna.

Our findings aim to inform whether and how oil sands disturbances affect boreal forest ecosystems, particularly at forest edges. Determining these effects and the scale of their responses will help guide management practices, restoration, and conservation efforts in the increasingly fragmented oil sands region.

Land use/land cover is one of the major common global environmental challenges. Land use explains the interactions between humans and the environment, the results of anthropogenic activities, and the changes resulting from the activities. These changes contribute greatly to Earth–atmosphere interactions, biodiversity loss, and forest degradation. The detection of changes in land use/land cover gives important information about the trends in these changes. Their analysis will help to make informed and necessary decisions for climate regulation policy in Ibadan. This study analyzed the land use and land cover dynamics of the 11 local governments in the city of Ibadan using Landsat images taken from USGS Earth Explorer. The images were classified by Maximum Likelihood classification into four LULC classes: bare land, built-up areas, vegetation, and water bodies. The results showed that, in the city of Ibadan, there was a 29.93% decrease in land covered with vegetation between 2002 and 2022; 20.4% of vegetation areas was converted into built-up areas; and about 8.2% of vegetation areas was converted to bare land . The share of built-up areas increased from 10.32% of the total area to 36.12% in 2022. The NDVI result (0.59 in 2022, 0.56 in 2014, and 0.39 in 2022) showed a decrease in green areas due to the increase in built-up areas. This study revealed that urbanization processes are mainly responsible for land use/land cover change in Ibadan. In conclusion, this study advanced our knowledge of land use/land cover in Ibadan by providing information that is useful for policymakers and will help guide meaningful actions toward climate regulation.

Tropical dry deciduous forests play a vital role in maintaining biodiversity and ecosystem services, yet they are increasingly threatened by climate change. This study aims to assess the significance of Pakistan's tropical dry deciduous forests in the context of ecological variables and climate change impacts. A comprehensive phytosociological survey was conducted to understand species composition, vegetation patterns, and environmental drivers influencing these forests. The study area, located in the lesser Himalayan mountains of Pakistan, revealed the presence of 140 woody plant species belonging to 52 families. Through various multivariate analyses, nine distinct plant communities were identified, with the Dodonaea viscosa-Acacia modesta-Dalbergia sissoo community being the most dominant. Cluster analysis grouped these communities into five clusters, highlighting the spatial distribution patterns across the study area. Detrended Correspondence Analysis (DCA) and Canonical Correspondence Analysis (CCA) were employed to assess the relative significance of environmental variables in shaping species composition and distribution. Altitude, precipitation, and temperature emerged as primary factors influencing the distribution and composition of tropical dry deciduous forests along the Himalayan foothills. Higher altitude forests, characterised by maximum rainfall and lower temperatures, exhibited rich vegetation diversity, whereas lower altitude forests experienced higher temperatures and lower precipitation levels. Notable discrepancies were observed between protected and unprotected forest areas, emphasising the importance of immediate management interventions and in-situ conservation strategies. Based on the findings, recommendations include adopting mitigation and adaptation approaches to combat the increasing temperature and low precipitation in lower altitude areas. Mitigation strategies such as afforestation and renewable energy promotion, coupled with adaptation measures like forest restoration and community-based conservation, are essential for ensuring the resilience and sustainability of Pakistan's tropical dry deciduous forests in the face of climate change.

Qiongzhuea tumidinoda(a type of dwarf bamboo) stands out as an endemic bamboo species of significant conservation importance in southwest China, particularly in the upper reaches of the Yangtze River. It holds a pivotal role in poverty alleviation through the commercialization of its wood and bamboo shoots. However, the suitable area of this species is undergoing rapid changes due to climate change, resulting in species redistribution and potential losses for bamboo farmers. We utilized 209 presence records and 11 selected environmental variables to predict the potential suitable habitats for Qiongzhuea tumidinoda using Maxent and ArcGIS. Our findings revealed a southeastward shift and an elevation increase in the potential suitable habitats of Qiongzhuea tumidinoda. The area of potential suitable habitats in the study region exhibited fluctuating growth, expanding from 3063.42 km2 to 7054.38 km2. Mean monthly potential evapotranspiration (Pet) emerged as a critical determinant in shaping the distribution of potential suitable habitats for Qiongzhuea tumidinoda. Our study sheds light on the response of Qiongzhuea tumidinoda to climate change, offering valuable insights into the development and management of plantation industries associated with this species. In future, considering the high growth rate and possible larger suitable habitats, Qiongzhuea tumidinoda could be cultivated as a major crop to absorb carbon from the atmosphere, thus mitigating climate change.

Wood–plastic composites (WPCs) are interesting materials as the bio-based content is determined by the inclusion of wood particles regenerated from residual wood sources of biomass products. At present, the aim is to increase the wood content in WPCs above 60 %, while it is currently limited to around 40 %. The rationale behind this is based on the increase in performance of the WPC, the relatively cheap price of wood and the aim to augment the biobased content. Most studies are presently carried out with a maximum of 50 % wood particles (preferably ranging around 30 – 40 %), while there are only very few sources where the wood concentration was increased to 70 %, but the formulations were not yet optimized and there were problems in interface compatibility, leading to weak mechanical properties. Problems in augmentation of the wood content have to be controlled, e.g., aggregation, dimensional stability and water absorption. Alternative approaches for the treatment of the wood chips before (or during) compounding with the polymer matrix should be developed. As water resistance is mainly related to control of the surface properties of the hydroscopic wood particles, possible solutions should consider better protection of the individual wood particles' surface against water ingress, better development of the wood–polymer interface and the hindrance of the formation of a continuous network with contacting wood particles. Therefore, this presentation gives an overview of the technological feasibility of various processing routes together with economic feasibility potential based on various sources from the literature, including the effects of compatibilizers and additives, spray-coating of wood particles, chemical pretreatment, physical modifications and thermal treatment of the wood fillers.

Ungulates shape forest ecosystems through the selective browsing of different plant species, which serves a regulatory role in the interspecific competition between plants. Therefore, accurately predicting the extent of browsing on different plant species may promote forest management policies which advocate biodiversity alongside economic interests. This study applied a machine learning (ML) approach to create an accurate and robust system for predicting selective browsing behavior.

Using data from seven forested areas in Hungary, three ML models were constructed to predict the extent of browsing on different plant species based on the available plant supply within the habitat: a Random Forest, Gradient Boosting, and a Zero-Inflated Beta Regressor. The models were evaluated in terms of mean absolute prediction error and the ability to correctly predict larger browsing extents. The latter is a challenging task due to the sparse and mostly small-valued nature of the data. Additionally, in light of the effort required to collect browsing data, the models’ robustness against smaller sample sizes was tested. The Gradient Boosting Regressor performed best in every aspect, achieving the highest accuracy even with lower sample sizes. A two-way ANOVA test confirmed that both the choice of ML model and the sample size has a significant effect on the prediction performance. The Gradient Boosting Regressor was integrated into a Jupyter Notebook pipeline designed for easy re-use on unseen forest data.

The results suggest that ML offers a viable approach to predict selective browsing in forest habitats, and such models can be integrated into simple applications for forest management and research applications. Future research could focus on testing the approach in a wider variety of habitats and on increasing the ease-of-use of the technique for users less familiar with ML.

Climate-smart forestry (CSF) has recently emerged as a ‘hot topic’ in the U.S. forestry community. Forest development, the health benefits of forests, and other forest products and services are long-term goals. Finally, CSFs enable forests and communities to adapt and mitigate the impacts of climate change. Forests are considered an important part of the carbon cycle and play a key role in controlling, mitigating, and adapting to climate change. Poor forest management accounts for 17% of global carbon emissions. However, forests are likely to contribute approximately 10% of estimated global emissions between 2024 and 2050. Additionally, forest products continue to store carbon even after deforestation, and their shelf life varies depending on how long they have been used. CSF's mission is to reduce greenhouse gas emissions and improve forest management to achieve efficient and sustainable forest products and services. Afforestation and reforestation (AR) on marginal lands is a natural solution to climate change. There is a gap in understanding the potential of augmented reality for conservation and commercial use to influence forest management and the use of trees for climate change mitigation. Here, we summarize these differences to assess 100-year economic and conservation realities (known and new) across different cropping and thinning systems using quality of life and various survival measures. The new AR industry can reduce greenhouse gas emissions from wood products (CLT) and biochar (3.73 × 3.69 GtCO2e) over 100 years compared to the anti-AR industry (3.35 × 3.69 GtCO2-eq.) and augmented reality. This resulted in a reduction of 4.15 Gt of CO2 emissions, especially in the study area. Forests, which store more carbon than produce CO2 (3.17 to 3.51 GtCO2e), are cold and dry and have variable soil binding sites and CLT odors. In the short term (approximately 50 years), eliminating AR would lead to greater reductions in greenhouse gas emissions.

Timber volume estimation is fundamental for sustainable forest management. This study focused on estimating the form factor of Shorea robusta in various physiographic regions of Nepal, with a specific aim to develop a diameter-height model for the Terai region. Data collection involved the destructive sampling of 109 randomly selected S. robusta trees, with recorded information including diameter at breast height (DBH), tree height, and crown parameters. After destructive felling, overbark and underbark stem diameters, along with section lengths, were measured at intervals of 1 m up to the tree tip.

The analysis revealed average overbark form factors of 0.42, 0.41, and 0.4, and underbark form factors of 0.41, 0.4, and 0.38 in the Terai, Siwalik, and Middle Mountain regions, respectively. Form factor significantly varied with diameter class in Terai (P = 0.02 < 0.05) and (P = 0.04 < 0.05), Siwalik (P = 0.05 ≤ 0.05) and (P = 0.05 ≤ 0.05), and the Middle Mountain region (P = 0.006 < 0.05) and (P = 0.01 < 0.05) for overbark and underbark, respectively. Correlation analysis indicated a significant relationship, with DBH increasing with height (0.86, 0.80, and 0.83) while decreasing with both DBH (-0.47, -0.42, -0.64) and form factor (-0.41, -0.34, -0.40), respectively, in all regions. The Pearson correlation test further confirmed these relationships at the 5% level of significance.

For estimating tree height in the Terai region, a power form of the model H = 1.3 + 4.194 * DBH^0.464 demonstrated the best fit, with an adjusted R-squared of 0.79, RMSE of 3.47, AIC of 267.22, and a significant p-value (P < 0.0001). This model contributes valuable insights for height estimation in Shorea robusta stands in the Terai region.

This research provides species-specific form factors that help improve the quantification of volume and other forest products, contributing to sustainable forest management in Nepal.

Our study established transcriptomic and soil metagenomic resources for Quercus agrifolia (leaf). Q. agrifolia is a hardwood that may live for centuries.

Samples were collected from three fire history areas in Gold Creek Preserve, Angeles National Forest. Low, medium, and high intensity burn areas are referred to as the Blue, Green, and Red Trails. In our previous work, we showed taxonomic differences in fungi and bacteria in the three different fire areas. The current study sought associations between plant gene expression and microbial functions.

During May 2022, snap frozen plant tissue samples for RNA sequencing and soil samples for metagenome sequencing were collected. The bioinformatics for plant RNAseq was QC in SOAPnuke and MultiQC, Trinity de novo assembly, BUSCO evaluation, salmon quantification, and annotation with Trinotate. Metabarcoding analysis used DNA Subway Purple Line. Soil metagenomic analysis was carried out in Nephele BioBakery, MicrobiomeDB, and STAMP.

For oaks, the transcriptome was 96.17% complete according to the BUSCO assessment. Three trees were sampled from each of 3 burn areas which allowed for contrasts in DEseq2 based on the tree location. There was evidence of differential gene expression related to the isoprenoid pathway, freezing resistance, drought response, and pathogen defense. However, there was some evidence of heterogeneity of gene expression within the clusters, for the top 25 differentially expressed genes.

In WGS results, there was not strong evidence of functional differences associating the soil microbiome of Q. agrifolia with plant secondary metabolite production. However, there were taxonomic & alpha diversity differences associated with different areas of the preserve, each having different fire histories. Further studies should focus on microbes associated with plant tissues and their potential association with plant secondary metabolite production. For example, further experiments should use woody tissues.