Population dynamics play a key role in climate change. Elucidating the mechanisms involved in the accumulation and depletion of deadwood and seedlings can enhance our understanding of fundamental processes and ecological characteristics. This greater understanding will facilitate better predictions of future changes related to alternative management and climate scenarios. A conceptual framework for deadwood or seedling dynamics has been generally accepted but has not been fully tested with empirical data. This study used a long-term monitoring data set (i.e., Jiri Mountain and Seorak Mountain) containing measurements of live and dead trees and seedlings. Through multi-model inference, this data represented numerous subalpine forests and could help assess relationships between environmental factors affecting subalpine coniferous species (i.e., Abies koreana and Abies nephrolepis), with particular focus on the various components of subalpine environments. Our study analyzed relationships using generalized linear regression and AICc based multi-model inference. Deadwood models constructed with combinations of 8 variables were demonstrated. Environmental gradients emerged as the most influential factors determining the deadwood and seedling density of these diverse region types. Physical environmental gradients were strongly influential factors representing the deadwood density. Seedling models were constructed with combinations of 10 variables. Abies koreana seedling density was positively related to rock exposure (p < 0.001), species richness (p < 0.05), and herbaceous cover (p < 0.001). Abies nephrolepis seedling density was positively related to rock exposure (p < 0.001) and negatively related to species richness (p < 0.05). This study found a positive association between the Abies koreana seedling density in subalpine forests and biotic environmental factors and Abies nephrolepis seedling density in subalpine forests and certain physical environmental factors. Therefore, environmental gradients in relation to rocks and grass apply, even in these habitats. These findings are relevant to a wide range of applications, from ecosystem multi-models to the development of resource management plans under alternative future climate changes.