Abstract In plasma membranes, the generation of reactive oxygen species (ROS) is primarily mediated by the activation of NADPH oxidases (NOXs). This study investigated the role of NOX-derived ROS in the germination of Melanoxylon brauna under optimal (25 °C) and heat stress (40 °C) conditions. Seeds were treated with diphenyleneiodonium (DPI), a NOX inhibitor, and evaluated after 48 and 96 h for germination rate, germination speed index, internal anatomy, hydrogen peroxide (H 2 O 2 ) content, and the activity of amylases and antioxidant enzymes. Internal anatomical changes were examined by light microscopy, whereas H 2 O 2 levels and enzyme activities were determined spectrophotometrically. Under optimal conditions, DPI treatment reduced germination rate and speed by 27% and 35.4%, respectively, and hindered seed coat loosening. In addition, DPI lowered H 2 O 2 content and suppressed both amylase and antioxidant enzyme activities. Under heat stress, seeds failed to germinate regardless of treatment; however, ROS inhibition further decreased H 2 O 2 levels and reduced the activities of superoxide dismutase and ascorbate peroxidase. Collectively, these results indicate that in M. brauna , heat stress is the dominant factor impairing germination, and inhibition of NOX-derived ROS does not mitigate—and may even aggravate—the deleterious effects of elevated stress.

Abstract Regeneration failure is a pressing issue endangering the health of many forests in North America. Invasive plants and Odocoileus virginianus (white-tailed deer) contribute to regeneration failure by impacting tree seedling survival and growth. This study investigated the individual and interactive effects of deer and woody invasive plants on seedlings in an early successional forest. In a stand of Juniperus virginiana near Oxford, OH, we initiated a factorial experiment with each combination of deer access/exclosure and invasive woody plants removed/not removed. Deer were excluded with 2.13 m tall fences using four trees as corner posts. We planted native tree seedlings and monitored natural regeneration (tree seedlings 0.3–2 m) in each plot. Survival was low for planted seedlings and growth differed across species but, in general, change in height was significantly impacted by deer and the interaction: excluding deer resulted in less height loss and this effect was greater where invasives were present. Change in height of natural regeneration (mostly Fraxinus americana ) was significantly affected by deer; seedlings grew taller in deer exclosures. The total number of seedlings recruiting per plot did not differ among treatments. However, the number of recruits excluding F. americana seedlings showed a marginally significant interaction: number of recruits was greatest where deer and invasives were removed. Overall, deer had a greater impact than invasives on natural regeneration and planted seedlings. These small exclosures required minimal cost, installation time, and maintenance. These findings lead us to recommend this method to land managers.

Abstract Natural range contraction and millennia of anthropogenic disturbance have led to a steady decline in stands of Caledonian Pine Forest (CPF) across the Scottish Highlands. Much of the land is now dominated by short-stature shrubs, with fragmented areas of native forest and commercial plantation. Several surmountable barriers exist to large-scale reforestation of the CPF, including tensions with existing economies, land ownership patterns, and the uncertainties posed by a changing climate. To address these challenges and support decision-making, we developed a data-driven approach to identify optimal sites for native forest restoration across the CPF. We trained, validated, and deployed five machine learning classification models – multilayer perceptron, naïve Bayes, random forest, support vector machine and XGBoost – to predict which sites across the CPF ecoregion were most suitable for one of three broad native forest community types: Scots pine, oak woodland and birch woodland. In the least restrictive reforestation scenario, we identified a total of 844,339 hectares of potential reforestation area while the most restrictive reforestation scenario identified 210,703, hectares. Birch, Scots pine and then Oak ranked most to least for predicted sites. Among the models, XGBoost demonstrated the highest predictive power using area under the receiver operating characteristic (AUC = 0.974, Accuracy = 0.918) while Naïve Bayes performed the least effectively (AUC = 0.794, Accuracy = 0.655). Our findings provide a spatially explicit foundation for prioritising reforestation efforts, enabling stakeholders to maximise ecological gains while navigating competing land use pressures.