Landscape structure and suitable habitat analysis for effective restoration planning in semi-arid mountain forests

Abstract

Background Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects. This study aimed to model the habitat suitability of wild almond (Amygdalus scoparia Spach) using three individual species distribution models (SDMs), i.e., backpropagation artificial neural network (BP-ANN), maximum entropy (MaxEnt), generalized linear model (GLM), as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province, southern Iran. Results There was no clear difference in the prediction performance of the models. The BP-ANN had the highest accuracy (AUC = 0.935 and k= 0.757) in modeling habitat suitability of A. scoparia, followed by the ensemble technique, GLM, and MaxEnt models with the AUC values of 0.890, 0.887, and 0.777, respectively. The highest discrimination capacity was associated to the BP-ANN model, and the highest reliability was related to the ensemble technique. Moreover, evaluation of variable importance showed that the occurrence of A. scoparia was strongly dependent on climatic variables, particularly isothermality (Bio 3), temperature seasonality (Bio 4), and precipitation of driest quarter (Bio 17). Analysis of the distribution of species habitat in different landform classes revealed that the canyon, mountain top, upland drainage, and hills in valley classes had the highest suitability for the species establishment. Conclusions Considering the importance of landform in the establishment of plant habitats, the combination of the outputs of the SDMs, landform, and the use of landscape metrics could provide both a clear view of habitat conditions and the possibility of analyzing habitat patches and their relationships that can be very useful in managing the remaining forests in semi-arid regions. The canyon, mountain top, and upland drainage classes were found to be the most important landforms to provide the highest suitable environmental conditions for the establishment of A. scoparia. Therefore, such landforms should be given priority in restoration projects of forest in the study area.

Modeling the effects of climate change on the potential distribution of the rangeland species Gymnocarpus decander Forssk (case study: Arid region of southeastern Iran)

“…Therefore, identifying and determining the role of climatic factors on the distribution of species, especially rangeland species that have a less tolerance range than tree and forest species, are vital in comprehensive rangeland management and conservation of rangeland ecosystems. Sahragard et al (2021) studied three models of the artificial neural network, maximum entropy, and GLM to identify the habitat suitability of almond species in Fars province. Their results also showed the good performance of all three models on independent bioclimatic variables.…”