Abstract
The site index (SI) is a quantitative indicator of productive capacity at the plot level, whereas site quality represents its spatial evaluation. As a typical arid inland river basin, the Manas River Basin in Xinjiang requires reliable forest site quality assessment to support sustainable forest management under heterogeneous environmental conditions across this highly arid basin. This study focused on
Spruce
,
Poplar
,
Elaeagnus angustifolia
,
Populus Euphrates Oliv
, and
Mixed Wood
. Using the SI tables developed by our research team as reference data, environmental variables were extracted, and a site quality assessment model was established with a back-propagation (BP) neural network. By fitting the nonlinear relationships between environmental variables and SI, the model used plot-scale simulated SI values to predict site quality throughout the watershed and classified site quality levels through a dynamic classification method. The results showed that: (1) Pre was the core factor controlling site quality, whereas DEM exerted an indirect regulatory influence by affecting hydrothermal conditions; the BP neural network model showed satisfactory fitting performance for all studied tree species. (2) Site quality for each tree species displayed a distinct vertical zonation pattern:
Spruce
was concentrated in the southern mountainous region and had relatively high site quality;
poplar
occurred primarily in the central oasis plain, where site quality was moderate and uniform;
Mixed Wood
was distributed across the transition from the piedmont zone to the oasis, presenting the broadest distribution range and the strongest spatial heterogeneity;
Elaeagnus angustifolia
and
Populus Euphrates Oliv
were concentrated along the oasis margins and downstream desert belt, respectively, and both showed very low site quality. (3) Overall site quality across the basin was relatively low and exhibited a pronounced declining gradient from south to north. The "fair," "poor," and "very poor" classes together represented 82.6% of the study area, whereas the "excellent" class accounts for < 1%. From a methodological perspective, this study confirms the feasibility of combining SI tables with BP neural networks for continuous prediction of forest site quality, thereby reducing the spatial discontinuity associated with conventional plot-based surveys. Scientifically, a clear gradient from "high site quality–narrow ecological amplitude" to "low site quality–strong drought tolerance” was identified, providing the first spatially explicit assessment of species-specific habitat suitability within an arid inland river basin. Practically, the hierarchical management strategy developed from this assessment offer useful spatial information that, with field validation, can inform sustainable forest management in other arid regions along the northern foothills of the Tianshan Mountains. The findings provide a scientific basis for sustainable forest management in the Manas River Basin and in comparable arid regions located along the northern foothills of the Tianshan Mountains. However, given the moderate predictive performance for some species, these spatial results should be interpreted as indicative patterns requiring field validation.