Evaluation of stand structural complexity at different developmental stages in Larix gmelinii secondary forests of the Greater Khingan Mountains, China.

Based on data from 79 plots of Larix gmelinii secondary forests in the Greater Khingan Mountains, we classified the developmental stages of the stands by using affinity propagation clustering algorithm with thirteen eva-luation indicators from three aspects including tree species diversity, non-spatial structure, and spatial structure characteristics. We then compared the consistency of the full indicator set (13 indicators) and the minimum indicator set (five indicators) in evaluating stand structural complexity across different developmental stages by radar chart analysis. The main aim was to provide a theoretical basis for full-cycle multi-functional management of L. gmelinii secondary forests in the Greater Khingan Mountains. The results showed that the key indicators constraining stand structural complexity were mean diameter at breast height (Dg), dominant tree height (Ht), density degree (C), stand density (N), and stand volume (V), totaling five indicators. The affinity propagation clustering algorithm divided all plots into three developmental stages (Stage 1, Stage 2, and Stage 3). Among these, Dg and C showed a significant increasing trend with developmental stage, while N and Ht exhibited a significant decreasing trend. There was a significant difference of V between Stage 2 and Stage 3. The evaluation results based on the full indicator set showed that the stand structural complexity indices for the three developmental stages were 0.18, 0.22, and 0.31, respectively, while those based on the minimum indicator set were 0.10, 0.15, and 0.26. The indices obtained from the two methods were positively correlated (r>0.55). Using the minimum indicator set as the stan-dard, only one indicator performed well in Stage 1 (N) and Stage 2 (Ht), while three indicators (Dg, Ht, and V) reached the standard in Stage 3. Therefore, the structural complexity of L. gmelinii secondary forests gradually increased across developmental stages. The evaluation method based on the minimum indicator set showed high reliability and could provide targeted measures for forest management according to the key factors constraining structural development at each stage.