Drought then wildfire reveals a compound disturbance in a resprouting forest

Assessing wildfire impact on Trigonella elliptica habitat using random forest modeling

Wildfires have a significant impact on ecosystems worldwide, especially on the degradation of arid and semi-arid rangelands. This research focuses on assessing the effects of wildfires on the habitat of Trigonella elliptica, a valuable herb species found in the central rangelands of Iran. To achieve this, the Random Forest (RF) algorithm has been deployed to predict T. elliptica habitat and fire hazard using socio-environmental variables in Yazd province, Iran. 225 fire points and 103 habitat locations were used for model training and testing. The IncNodePurity index and Probability Curves (PC) have been utilized to determine the influence of socio-environmental variables. The combination of the prediction maps of the habitat and wildfires pointed out the possible damage due to fire. The high performance of the RF model is confirmed by the area under the curve (AUC) and the true skill statistic (TSS) values (0.90 and 0.81 for the habitat; 0.92 and 0.82 for the wildfire). The importance assessment of variables revealed that elevation, slope, and precipitation are the most influential variables in the distribution of T. elliptica, while distance to roads, population density, and wind speed are the key factors affecting wildfire occurrence. In the final map, a comparison of different regions of T. elliptica habitat under fire hazard with fire-free habitats using Kruskal-Wallis and Dunn tests indicated that the fire hazard in the T. elliptica habitat is a serious concern. Since the areas with the highest fire hazard and the highest presence of T. elliptica cover approximately 2311.38 km2, neglecting these regions could lead to the gradual reduction of T. elliptica, and create conditions for secondary succession dominated by less valuable annual species. The findings of this study underscore the importance of implementing fire management strategies, protection projects, and continuous monitoring to ensure the safety and conservation of the T. elliptica habitat.

Drought then wildfire reveals a compound disturbance in a resprouting forest

The frequency and intensity of forest disturbances, such as drought and fire, are increasing globally, with an increased likelihood of multiple disturbance events occurring in short succession. Disturbances layered over one another may influence the likelihood or intensity of subsequent events (a linked disturbance) or impact response and recovery trajectories (a compound disturbance), with substantial implications for ecological spatiotemporal vulnerability. This study evaluates evidence for disturbance interactions of drought followed by wildfire in a resprouting eucalypt-dominated forest (the Northern Jarrah Forest) in southwestern Australia. Sites were stratified by drought (high, low), from previous modeling and ground validation, and fire severity (high, moderate, unburnt), via remote sensing using the relative difference normalized burn ratio (RdNBR). Evidence of a linked disturbance was assessed via fine fuel consumption and fire severity. Compound disturbance effects were quantified at stand scale (canopy height, quadratic mean diameter, stem density) and stem scale (mortality). There was no evidence of prior drought influencing fine fuel consumption or fire severity and, hence, no evidence of a linked disturbance. However, compound disturbance effects were evident; stands previously affected by drought experienced smaller shifts in canopy height, quadratic mean diameter, and stem density than stands without prior drought impact. At the stem scale, size and fire severity were the strongest determinants of stem survival. Proportional resprouting height was greater in high drought sites than in low drought sites (p < 0.01), meaning, structurally, the low drought stands decreased in height more than the high drought stands. Thus, a legacy of the drought was evident after the wildfire. Although these resprouting eucalypt forests have been regarded as particularly resilient, this study illustrates how multiple disturbances can overwhelm the larger tree component and promote an abundance of smaller stems. We suggest that this is early evidence of a structural destabilization of these forests under a more fire-prone, hotter, and drier future climate.

Assessment of the effects of the biotic and abiotic harmful factors on the amount of industrial wood production with deep learning

The protection and sustainability of forest assets is possible with planned production of forest products to lead to minimum loss. One of the products obtained from forests is the industrial wood, which is the most important raw material for many sectors. Thus, changes in industrial wood production amounts directly affect these sectors. For this reason, it is important to detect and examine the factors affecting industrial wood production amounts for optimum production and use of this raw material. This study aims to investigate and assess the effects of two biotic and two abiotic harmful factors on the amount of industrial wood production by building a deep learning estimation model. These factors are forest fires, insect outbreaks, diseases, and severe weather events. The study shows that the most harmful factor decreasing the industrial wood production level is diseases. The second effective factor, however, appears to be severe weather events. The third and the fourth factors were determined to be insect outbreaks and burned forest areas, respectively.