Light and nitrogen interact to influence regeneration in old-growth Nothofagus-dominated forests in south-central Chile

- Regeneration responses to light and nitrogen (N) were modeled in Nothofagus stands.
- Light and N affected growth differentially based on species shade tolerance.
- Post-disturbance community organization is impacted by light and N.

Light is one of the most important factors governing development of understory vegetation in forest ecosystems, including tree establishment and growth. Information about interactive effects of light with other resources such as water and nutrients is relatively scarce. How varying resources affect tree growth in forest understories is still unclear and current knowledge is largely confined to the northern hemisphere. A field experiment in which high-grading of an old-growth forest in the Andes of south-central Chile was followed by ground disturbance provided data about tree basal diameter growth responses under a wide range of light conditions, total nitrogen (tN) concentration in leaves, and water potential. We used a model selection approach to determine whether light is co-limiting with tN and water potential (or their interactions) for three species typically found in these forests and are known to vary in resource-use strategies. Species differed in growth response to light and tN, but not to water potential. For instance, radial growth of the tree species (Nothofagus dombeyi) with greatest light demand was strongly related to tN at high-light conditions only. The mid-shade tolerant species (Nothofagus alpina) had better basal diameter growth with high tN at high- and low-light environments. Contrary to expectations, radial growth of the late-successional shrub species (Drimys andina) was positively affected by light and tN in low-light environments only. Our results suggest that the species differences in regards to the impact of tN concentration along a light gradient are important factors that could influence plant community development. Restoration and management treatments can be more efficiently targeted if they are based on information about species sensitivities to interacting resource levels.