Does the crested porcupine select coppice forest? Habitat preference and activity patterns of a large rodent in the Lago di Vico Natural Reserve

The crested porcupine Hystrix cristata L. is a large rodent, which mainly occurs in agro-forestry ecosystems in Italy. In this study, we modelled the occupancy of this species in forest ecosystems, to identify environmental characteristics affecting its presence. The study was conducted at Lago di Vico Natural Reserve (Latium, Central Italy) in 2018–2019. The sampling design included a 1 km2 grid, where 263 detections were recorded at 39 out of 57 camera-trap points. Dendroauxometric data were collected at each site as covariates in the statistical models. According to our best occupancy model, the crested porcupine mostly occurs in habitats not totally covered by forests, but composed by mixed landscape patches both for the land use (crops, woods) and for the coverage (forested areas, open areas, bushes). We also analysed activity rhythms of the crested porcupine across seasons and in relation to the moon phases. The analysis of 543 videos showed that crested porcupine is strictly nocturnal throughout the year and avoided bright nights, despite the local absence of potential predators.

Modelling Stand Variables of Beech Coppice Forest Using Spectral Sentinel-2A Data and the Machine Learning Approach

Background and Purpose: Coppice forests have a particular socio-economic and ecological role in forestry and environmental management. Their production sustainability and spatial stability become imperative for forestry sector as well as for local and global communities. Recently, integrated forest inventory and remotely sensed data analysed with non-parametrical statistical methods have enabled more detailed insight into forest structural characteristics. The aim of this research was to estimate forest attributes of beech coppice forest stands in the Sarajevo Canton through the integration of inventory and Sentinel S2A satellite data using machine learning methods. Materials and Methods: Basal area, mean stand diameter, growing stock and total volume data were determined from the forest inventory designed for represented stands of coppice forests. Spectral data were collected from bands of Sentinel S2A satellite image, vegetation indices (difference, normalized difference and ratio vegetation index) and biophysical variables (fraction of absorbed photosynthetically active radiation, leaf area index, fraction of vegetation cover, chlorophyll content in the leaf and canopy water content). Machine learning rule-based M5 model tree (M5P) and random forest (RF) methods were used for forest attribute estimation. Predictor subset selection was based on wrapping assuming M5P and RF learning schemes. Models were developed on training data subsets (402 sample plots) and evaluations were performed on validation data subsets (207 sample plots). Performance of the models was evaluated by the percentage of the root mean squared error over the mean value (rRMSE) and the square of the correlation coefficient between the observed and estimated stand variables. Results and Conclusions: Predictor subset selection resulted in a varied number of predictors for forest attributes and methods with their larger contribution in RF (between 8 and 11). Spectral biophysical variables dominated in subsets. The RF resulted in smaller errors for training sets for all attributes than M5P, while both methods delivered very high errors for validation sets (rRMSE above 50%). The lowest rRMSE of 50% was obtained for stand basal area. The observed variability explained by the M5P and RF models in training subsets was about 30% and 95% respectively, but those values were lower in test subsets (below 12%) but still significant. Differences of the sample and modelled forest attribute means were not significant, while modelled variability for all forest attributes was significantly lower (p<0.01). It seems that additional information is needed to increase prediction accuracy, so stand information (management classes, site class, soil type, canopy closure and others), new sampling strategy and new spectral products could be integrated and examined in further more complex modelling of forest attributes.

Soil properties and carbon sequestration in Persian oak (Quercus brantii var. persica) forests, Iran

One of the important issues both in the political discussion about climate change and in forest ecosystem research is carbon sequestration. In this paper, we estimated soil carbon sequestration (SCS) in two Persian oak forest stands of different origin (seed and coppice). Soil samples were taken at two soil depths (0–15 and 15–30 cm) and locations (under the tree crown and open area) in each oak stand. Results showed that surface layers (0–15 cm) had the highest soil carbon sequestration ranging from 41.2 t·ha–1 to 47.9 t·ha–1 for both oak forests. The total SCS was higher (between 79.5 and 89.07 t·ha–1) in open areas of the two forest stands than under the crowns of oak trees. Finally, the amount of total SCS in seed originated forest (SOF) (86.52 t·ha–1) was significantly greater (P < 0.05) than in coppice forest (CF) (77.70 t·ha–1). The results indicate that a relatively large proportion of C loss in CF is due to overgrazing, forest degradation and conversion to coppice forests in the study area.