Global pattern of forest disturbances and its shift under climate change

Climatic differences among habitats shape the balance between maximum lifespan and life expectancy in Japanese tree species

Old trees, often living for hundreds or even thousands of years, play vital roles in maintaining biodiversity and ecosystem services. However, their extraordinary longevity occurs under rare circumstances, as most individuals succumb to mortality. From an optimal resource allocation perspective, species adopting a life-history strategy with a high potential maximum lifespan (PML) are expected to also have a higher life expectancy (LE). Here we developed a framework to assess the longevity of 1-cm-diameter trees and calculated the PML and LE for 53 major tree species in Japan. The results revealed that the PML (mean 378 years) was 4.7 times higher than the LE (mean 81 years). Both indices showed a positive correlation, with a regression slope of 0.34; however, the explanatory power of the model was low (R2 = 0.22). This can be attributed to the fact that LE exhibits a stronger negative response to climate-related habitat severity compared with that of PML. Our findings suggest two key points: (1) trees may adopt a hierarchical ordering of demographic parameters that prioritize long-term survival duration over average survival rate, and (2) considering this balance, which varies among species, could enhance the cost-effectiveness of ecosystem restoration.

Phenotypic plasticity does not prevent impairment of aboveground biomass production due to increased light and water deficit in Dimorphandra exaltata, an endangered species

Phenotypic plasticity may allow plant species to cope with environmental variability that influences plant growth and may limit the distribution of a species. The present study investigated the morphophysiology and phenotypic plasticity responses due to light and water variability of young Dimorphandra exaltata plants, an endemic threatened tree from the Atlantic Forest. After emergence, plants were grown in two light conditions: shading (70%) and full sun. At 160 days old, we measured chlorophyll a fluorescence, chlorophyll indices, and biomass allocation. Afterward, the plants were subdivided into two water regimes: irrigation vs suspension of irrigation. At 310 days old, morphophysiological measurements and stem water potential were taken. D. exaltata plants showed higher specific leaf area (SLA, 160 days old) and chlorophyll b (310 days old) under shading. Over time, plants under shading showed a decrease in SLA. Also, there was a decrease in the leaf area ratio in both light treatments and an increase in the phenotypic plasticity index. Even showing morphological adjustments to light and water deficit, the higher biomass allocation to roots at the expense of the aboveground part could impair the growth of young plants in understory areas. The phenotypic plasticity presented by D. exaltata does not guarantee that the species can withstand severe disturbance while maintaining normal development. Therefore, it is important to understand the effects of ecosystem fragmentation and water variation and their impacts on the maintenance of species in their areas of occurrence, especially endangered species such as D. exaltata.

Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation

Forest fires (FF) in tropical seasonal forests impact ecosystem. Addressing FF in tropical ecosystems has become a priority to mitigate impacts on biodiversity loss and climate change. The escalating frequency and intensity of FF globally have become a mounting concern. Understanding their tendencies, patterns, and vulnerabilities is imperative for conserving ecosystems and facilitating the development of effective prevention and management strategies. This study investigates the trends, patterns, and spatiotemporal distribution of FF for the period of 2001-2022, and delineates the forest fire susceptibility zones in Odisha State, India. The study utilized: (a) MODIS imagery to examine active fire point data; (b) Kernel density tools; (c) FF risk prediction using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF); (d) Receiver Operating Characteristic and Area Under the Curve, along with various evaluation metrics; and (e) a total of 19 factors, including three topographical, seven climatic, four biophysical, and five anthropogenic, to create a map indicating areas vulnerable to FF. The validation results revealed that the RF model achieved a precision exceeding 94 % on the validation datasets, while the SVM model reached 89 %. The estimated forest fire susceptibility zones using RF and SVM techniques indicated that 20.14 % and 16.72 % of the area, respectively, fall under the "Very High Forest Fire" susceptibility class. Trend analysis reveals a general upward trend in forest fire occurrences (R2 = 0.59), with a notable increase after 2015, peaking in 2021. Notably, Angul district was identified as the most affected area, documenting the highest number of forest fire incidents over the past 22 years. Additionally, forest fire mitigation plans have been developed by drawing insights from forest fire management strategies implemented in various countries worldwide. Overall, this analysis provides valuable insights for policymakers and forest management authorities to develop effective strategies for forest fire prevention and mitigation.

Rising Temperatures, Falling Leaves: Predicting the Fate of Cyprus's Endemic Oak under Climate and Land Use Change

Endemic island species face heightened extinction risk from climate-driven shifts, yet standard models often underestimate threat levels for those like Quercus alnifolia, an iconic Cypriot oak with pre-adaptations to aridity. Through species distribution modelling, we investigated the potential shifts in its distribution under future climate and land-use change scenarios. Our approach uniquely combines dispersal constraints, detailed soil characteristics, hydrological factors, and anticipated soil erosion data, offering a comprehensive assessment of environmental suitability. We quantified the species' sensitivity, exposure, and vulnerability to projected changes, conducting a preliminary IUCN extinction risk assessment according to Criteria A and B. Our projections uniformly predict range reductions, with a median decrease of 67.8% by the 2070s under the most extreme scenarios. Additionally, our research indicates Quercus alnifolia's resilience to diverse erosion conditions and preference for relatively dry climates within a specific annual temperature range. The preliminary IUCN risk assessment designates Quercus alnifolia as Critically Endangered in the future, highlighting the need for focused conservation efforts. Climate and land-use changes are critical threats to the species' survival, emphasising the importance of comprehensive modelling techniques and the urgent requirement for dedicated conservation measures to safeguard this iconic species.