Drivers of understory species richness in reconstructed boreal ecosystems: a structural equation modeling analysis

Understory vegetation accounts for the most diverse part of the plant community in boreal forests and plays a critical role in stand dynamics and ecosystem functions. However, the ecological processes that drive understory species diversity are poorly understood and largely unexplored for reconstructed boreal ecosystems. The current study explored the relationships between understory species richness and biotic and abiotic factors in sites reclaimed after oil sands mining in northern Alberta, Canada, three and six growing seasons post-reclamation. Reclaimed sites with two main surface soils, forest floor mineral soil mix (FFMM) and peat mineral soil mix (PMM), were used along with post-fire benchmarks. A number of soil physicochemical (including nutrients) and vegetation properties were measured and considered in the a-priori hypothesis framework. Structural equation models (SEM) were used to evaluate the multivariate relationships. In general, the FFMM sites had greater species richness than the PMM sites, even six growing seasons after reclamation. A maximum 254% increase in graminoid and shrub cover was observed on FFMM between year 3 and 6 post-reclamation, whereas a maximum 137% increase in forb and bryophyte cover was recorded on PMM. The post-fire sites showed a significant increase (70%) only in shrub cover. Major driving factors of understory species richness varied among soil types. The SEM revealed a strong interdependency between species richness and soil and vegetation factors on FFMM with a positive control from soil N on species richness. In contrast, on PMM soil nutrients had a negative effect on species richness. Temporal changes in the drivers of species richness were mostly observed on FFMM through a negative vegetation control on species richness. The models and significant causal paths can be used in monitoring changes in understory species relationships in reclaimed sites and in identifying future research priorities in similar systems.