Abstract
BackgroundSuitable 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.ResultsThere 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.ConclusionsConsidering 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.
Highlights
Predicting the distribution of plant species in a specific region has become an increasingly important issue in ecology, phytogeography, and conservation biology (Mi et al 2017)
This study aimed to (1) assess the capability of the species distribution models (SDMs) algorithms, i.e., backpropagation artificial neural network (BP-artificial neural networks (ANN)), maximum entropy (MaxEnt), and generalized linear model (GLM) as well as the ensemble technique in predicting habitat suitability of A. scoparia; (2) identify the most suitable habitats for A. scoparia and its ecological needs for restoration purposes; and (3) evaluate the suitable habitat distribution of A. scoparia in different landform classes based on landscape metrics analysis
Prediction performance of the models Prediction performance of the DOMAIN model showed that this model was highly capable of identifying suitable and unsuitable areas (AUC=0.94)
Summary
Predicting the distribution of plant species in a specific region has become an increasingly important issue in ecology, phytogeography, and conservation biology (Mi et al 2017). One of the ecological theories supporting species distribution modeling is the niche theory. Plant response to variation of environmental factors is recognized as one of the important aspects of species ecological niche. It is important to determine the ecological range of a species or its response model to environmental variables. Plant response could play a significant role in management and restoration of a species in natural habitats (Coudun and Gégout 2006). 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
