Key message: Patterns of stem radial variations showed thatCedrus libaniA. Rich. was less limited by summer drought than co-occurringJuniperus excelsaM. Bieb.Cedrus libanirecovered faster from tree water deficit and showed significantly higher radial growth rates and annual stem increments thanJ. excelsa. However, the ability ofJ. excelsato grow more hours per year may indicate a potential benefit in more extreme conditions. Context: Knowledge about species-specific drought responses is needed to manage productive forests in drought prone areas. Under water shortage, trees commonly show stem shrinkage, which is assumed to inhibit growth. Aims: We investigated whether the two co-existing conifers Juniperus excelsa M. Bieb. and Cedrus libani A. Rich. (growing at the Taurus Mountains, SW-Turkey) show differences in water relations and stem growth in order to evaluate their respective drought tolerance. Methods: Stem radius changes were hourly monitored over 2 years using high-resolution point dendrometers. Radial stem growth, tree water deficit-induced stem shrinkage, and maximum daily shrinkage were extracted from stem radius change measurements, investigated for their patterns, and related to environmental conditions. Results: Cedrus libani recovered from tree water deficit under higher temperature and vapor pressure deficit than J. excelsa. The number of hours during which stem growth occurred was higher for J. excelsa; however, growth rates and annual increments were significantly lower than in C. libani. Both species showed highest maximum daily shrinkage during the driest months indicating the ability to maintain gas exchange all year round. Conclusion: Juniperus excelsa showed a more conservative growth strategy while C. libani was less limited by summer drought and showed more annual stem increment under the conditions investigated.

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications. Since fire damage varies according to vegetation and fire characteristics, regional assessment of fire severity is crucial. The objectives of this study were: (1) to test the performance of different satellite imagery and spectral indices, and two field—measured severity indices, CBI (Composite Burn Index) and GeoCBI (Geometrically structured Composite Burn Index) to assess fire severity; (2) to calculate classification thresholds for spectral indices that performed best in the study areas; and (3) to generate fire severity maps that could be used to determine the ecological impact of forest fires. Five large fires in Pinus brutia (Turkish pine) and Pinus nigra subsp. pallasiana var. pallasiana (Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites. The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI. While Sentinel-2 and Landsat-8/OLI images performed similarly well, MODIS performed poorly. Fire severity classification thresholds were determined for Sentinel-2 based RdNBR, dNBR, dSAVI, dNDVI, and dNDMI and Landsat-8/OLI based dNBR, dNDVI, and dSAVI. Among several spectral indices, the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR (72.1%) and Landsat-8/OLI based dNBR (69.2%). The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

Vegetation is the only component that can be directly managed to reduce the negative consequences of wildland fires. Flammability indexes provide information about plant flammability characteristics and are commonly used in wildland fuel management. However, previous flammability indexes were usually based on only two or three flammability components. We propose an advanced approach for leaf flammability index estimation that integrates all four flammability components (ignitability, combustibility, sustainability and consumability) using cluster and linear discriminant analyses. We measured time to ignition, ignition temperature, maximum flame height, combustion time, mass loss percentage and fuel moisture content of 15 plant species most affected by forest fires in Turkey (every two weeks, from May to September). Species were divided into different flammability classes and ranked according to their flammability index (FI) values. Classifications and FI rankings showed seasonal variations for some of the species. During May and June, fuel moisture contents explained ~50–60% of variation in leaf flammability but this relationship steadily decreased towards September (13%) when weather conditions became drier. The proposed approach for FI estimation offers a more detailed flammability assessment and comparison between plants. It can be used to identify less flammable plants for fuel breaks and landscaping in the wildland–urban interface.

The risk of forest fires is a major problem in Türkiye's Mediterranean region and has a significant impact on ecosystems and atmospheric conditions. Throughout the previous century, a significant portion of Türkiye's Mediterranean Region has been destroyed by forest fires. This study aims to determine the meteorological covariates, such as relative humidity, maximum temperature, and wind speed, that affect forest fires. We classified forest fires into two groups. The first group (F1) refers to small forest fires, with burned forest areas of less than 10 hectares. The second group (F2), representing rare events, corresponds to burned areas of more than 10 hectares. The data is composed of binary values (F1=0 and F2=1) taken between the years 2015-2019 from different locations in the Mediterranean Region of Türkiye. For binary data modeling, the ordinary logistic regression (LR) has been frequently used. However, such a method tends to give biased results when using rare event data. Therefore, we employed three different modeling techniques specifically designed for rare event data. According to the results obtained from the best model, Firth's Logistic Regression (FLR), wind speed, and maximum temperature are found to be statistically significant variables in the occurrence of forest fires greater than 10 hectares.