Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. A number of hot issues can be investigated in desert ecosystems , including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species distribution models (SDMs) can be used to understand these relationships. We used the data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light detection and ranging) data of one square kilometers in the Inner Mongolia region of China to construct the SDMs. We evaluated the performance of the SDMs constructed with both the variants of the parametric and nonpara-metric algorithms (bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, generalized linear model (GLM), generalized additive model (GAM), random forest (RF), and support vector machine (SVM)). The area under the receiver operating characteristic curve was used to evaluate the algorithms. The SDMs constructed with RF appeared to be the best based on the area under the curve (0.7733). We also generated Nitraria tangu-torum Bobr. distribution maps with the constructed SDMs and the suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to the southern part with a slope of 0-20 degree at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on desert species distribution on a small scale. The presented SDMs will have important applications for predicting species distribution and will be useful for preparing conservation and management plans for desert ecosystems on a small scale.
An accurate estimate of the site index is essential for informing decision-making in forestry. In this study, we developed site index (SI) models using stem analysis data to estimate the site index and the dominant height growth for Larix gmelinii var. principis-rupprechtii in northern China. The data included 5122 height–age pairs from 75 dominant trees in 29 temporary sample plots (TSPs). Nine commonly used growth functions were parameterized using the modeling method, which accounts for heterogeneous variance and autocorrelation in the time-series data and introduces sample plot-level random effects in the model. The results show that the Duplat and Tran-Ha I model with random effects described the largest proportion of the dominant height variation. This model accurately evaluated the site quality and predicted the dominant tree height growth in natural Larix forests in the Guandi Mountain region. As an important supplement in improving methods for site quality evaluation, the model may serve as a fundamental tool in the scientific management of larch forests. The research results can inform an accurate evaluation of the site quality and predict the growth of the dominant height in a larch forest in the Guandi Mountain forest area as well as provide a theoretical basis for forest site quality evaluation at similar sites.
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