Tree dieback, woody plant diversity, and ecosystem driven by topography in semi-arid mountain forests: Implication for ecosystem management.

Mountain landscapes are highly heterogeneous due to topography, notably positions along slope and slope shapes, which control ecosystem mechanisms. We hypothesized that tree dieback is controlled by topography, selecting productive and less diverse communities in lower slopes, and stress-resistant and more diverse communities on upper slopes. Understanding how this heterogeneity drives vegetation patterns should provide benchmarks for ecosystem management of mountain forest dominated by Quercus brantii. Woody communities were sampled along convex vs concave topography (i.e., ridge vs talweg), and with measurements of tree dieback severity, environmental variables (litter depth, soil quality, rock outcrop), stand structure (canopy cover, mistletoe infestation, tree diameter and height, diameter and height differentiations, oaks' number from sprout-clumps or seed-origin), and biodiversity. Slope position was the most significant driver that affected all variables, excepted evenness. Dieback severity was higher on slope shoulders and summits, and lower in lower slopes where trees were the most productive: taller, larger, more homogeneous, and mostly seed-origin. Catena shape affected the diversity and dieback severity, both higher in talwegs, but had no effect on environmental variables and little on stand structure. Outputs indicate that the higher diversity of woody plants is on upper slopes supporting stress-resistant community associated with more severe dieback and mistletoe infection probably because frugivore birds attracted by the shrubs' fruits. Semi-arid forest management must consider the shaped-slope ecosystem heterogeneity by preserving ridges that are more susceptible to tree dieback, and naturally support biodiversity. Restoration measures on lower fertile slopes could be carried out by oak planting or seedlings under the cover of shrubs to counter dieback effects and environmental stresses. In addition, forestry measures can be taken in lower positions for the conversion of coppice to high oak forest to potentially consider a moderate forestry.

Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms.

Assessing the role of machine learning (ML) models concerning environmental predictors on spatial variation of soil organic carbon stocks (SOCS) in arid rangelands is very necessary. This study was conducted to explore the variability of surface SOCS in rangeland in the west of Iran using ML approaches. A number of 33 environmental predictors derived from Sentinel-2B and DEM were used. The optimal soil sampling (n = 80) position was determined by Latin hypercube sampling (cLHS) method. Robust and popular random Forest (RF), cubist (CB) along with random forest-ordinary kriging (RF-OK), and cubist-ordinary kriging (CB-OK) hybrid ML models were applied to the prediction of SOCS. Ten-fold CV was implemented for modeling performance and uncertainty map. According to data analysis, the maximum, minimum, and average values of SOCS are 44.50, 10.50, and 20.50 (ton. ha-1) at the surface depth (0-30 cm), respectively. In general, normalized and standardized height covariates had a higher effect related to other predictors. On the other hand, two remote sensing (RS) indices, including salinity ratio (salinity) and GNDVI index, had a better impact on SOCS variability. The external validation of model performance indicated that RF-OK with (R2 = 0.75, RMSE = 6.33 ton. ha-1) with the high and low uncertainty range (3.33-9.50 ton. ha-1) was the outperformed ML model in compare with other models as RF (R2 = 0.65, RMSE = 7.38 ton. ha-1), CB-OK (R2 = 0.56, RMSE = 9.22 ton. ha-1), and CB (R2 = 0.33, RMSE = 10.42 ton. ha-1). In general, the hybrid models improved the accuracy of RF and CB with increased 0.11 until 0.23 of R2, and 1.05 to 1.2 (ton. ha-1) decreased RMSE of model's prediction. Hence, we conclude that the topographic attributes (especially normalized and standardized height) were the most critical factors in controlling surface SOCS in arid rangelands when combining with robust RF ML model, and optimized soil sampling methods like RF-cLHS can prepare acceptable soil properties maps.