Traditional sampling protocols designed to capture stand scale responses lack the fine resolution needed to observe ecological attributes exhibiting spatial heterogeneity at a sub-stand scale. With this hypothesis in mind, AFERP is incorporating new sampling approaches to adequately observe sub-stand scale forest attributes (e.g., tree regeneration, deadwood, and salamander habitat). Presently, a spatially-explicit sampling design is being used to capture and analyze the pattern of an important ecological process: forest regeneration.
Forest regeneration in the Acadian forest can be a highly complex process, even among forest ecosystems. Much of the complexity arises from the spatial heterogeneity of forest composition and environmental drivers (e.g. soil moisture and light) and disturbance history. AFERP has hypothesized that one complication to understanding forest regeneration stems from the absence of factoring explicit spatial pattern of important process components into predictive models (e.g. depth to water table, light availability, and seed source). In order to test this hypothesis, the spatial pattern of overstory trees, tree seedlings and saplings, and understory light is being captured using unequally spaced plots (4 m2) arrayed along a network of random transects. The design is an adaptation of the repeating pattern cyclical sampling (RPCS) following Scheller and Mladenoff (2002) (Figure). The RPCS design enables the estimation of parameters necessary for geostatistical and autoregressive modeling procedures. Transect data will be joined with spatially-explicit data on depth to water table to generate spatial models of forest regeneration.