Stream nutrient uptake, forest succession, and biogeochemical theory

Abstract

Theories of forest succession predict a close relationship between net biomass increment and catchment nutrient retention. Retention, therefore, is expected to be greatest during aggrading phases of forest succession. In general, studies of this type have compared watershed retention efficiency by monitoring stream nutrient export at the base of the catchment. As such, streams are viewed only as transport systems. Contrary to this view, the nutrient spiraling concept emphasizes transformation and retention of nutrients within stream ecosystems. In this paper, we address how biogeochemical theory developed for forests may apply to lotic ecosystems in the context of catchment-level succession. Using measures of nutrient spiraling to document uptake, we focus on later seral stages by comparing streams draining second-growth (i.e., 75-100-yr stands) and old-growth (i.e., >400 yr) forests of the southern Appalachian Mountains, USA. Standing stocks of large woody debris (LWD) in old-growth streams were orders of magnitude greater than in second-growth streams where logging practices removed LWD from stream channels. Debris dams were also more frequent in old-growth streams. Solute injections were used to quantify retention of dissolved inorganic phosphate (PO4-P), the limiting nutrient in Appalachian streams. Uptake velocities in old-growth streams were significantly greater than in second-growth streams and were closely related to debris dam frequency, LWD volume, and the proportion of fine-grained (<2 mm) sediments present in the stream bed. These data suggest that streams of old-growth forests have greater demand for PO4-P compared to streams draining aggrading second-growth catchments. Finally, we present a schematic model of forest succession, aquatic-terrestrial interaction, and biogeochernical functioning in stream ecosystems emphasizing that the successional time course of retention in lotic ecosystems may be very different than that predicted for forests.