Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Abstract

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.

The growth characteristics of Juniperus excelsa M. Bieb. in the greenhouse conditions of the Southern coast of the Crimea

“…The intensity of growth is a function of the response to the impact of the studied environmental factors. This "optimum zone" includes points with a growth intensity value of more than 90% of d max, which allowed us to determine the optimal and limiting conditions for maxima and the boundaries of the growth optima regions and the numerical coefficients of nonlinear regression equations of these dependencies [16][17]. Fig.…”

One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

“…For sites not covered by LiDAR data, but where high spatial resolution is required, the free Global DEM at 30 m spatial resolution generated by stereoscopy of ASTER satellite data is a suitable alternative to improve the spatial scale of OCC maps [65,95,96]. Similarly, DEMs generated at high spatial resolution (12 m) from ALOS PALSAR data were used successfully to map habitat suitability of plant species of the genus Juniperus in Iran [97].…”

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

“…Techniques such as K-nearest-neighbor (Peterson 2009), artificial neural network (ANN, Sarle 1994), support vector machines (SVM, Wang 2005), neurofuzzy (Nauck et al 1997), decision tree classifiers (Safavian and Landgrebe 1991), and random forests (Liaw and Wiener 2002) are most commonly employed in microbiology and environmental disciplines (e.g. Cai et al 2019;Clercq et al 2019;Dong and Chen 2019;Qdais et al 2010;Rahimian Boogar et al 2019;Thompson et al 2019;Wang et al 2020b). Still, we lack rigorous machine learning investigations in which sampling and experimental designs move beyond feature identification to translating selected features into meaningful ecological outcomes that acknowledge the environmental complexities of soils.…”