PS 73-147
Sampling intensity and uncertainty in litterfall mass and nutrient flux in northern hardwoods
Leaf litterfall constitutes one of the largest annual nutrient fluxes in temperate forests. The calculation of litterfall nutrient fluxes requires estimates of litterfall mass and litterfall chemistry. Both of these properties vary across time and space. Understanding the relative contribution of these sources of variation can help to optimize sampling schemes. We compared the coefficient of variation of litterfall mass and chemistry (N, P, Ca, Mg and K) at different spatial scales and across multiple years from 23 hardwood stands in the White Mountains of New Hampshire. Species studied were American beech (Fagus grandifolia Ehrh.), yellow birch (Betula alleghaniensis Britt.), white birch (Betula papyrifera Marshall.), sugar maple (Acer saccharam Marsh.), red maple (Acer rubrum L.) and pin cherry (Prunus pensylvanica L.f.). We used linear regression to examine the effects of stand characteristics (slope, elevation, age, diversity) on spatial variation of litterfall mass. Finally, we used a bootstrapping approach to demonstrate how different spatial and temporal sampling schemes can affect the confidence in estimates of litterfall mass and concentration.
Results/Conclusions
The spatial variation of litter mass was significantly larger (P=0.001) within plots (mean CV=17%) than stands (12%) treating the 23 stands as replicates. Stands with steeper slopes and higher elevations had higher spatial variation of total litter mass (P=0.04). The spatial variation of nutrient concentrations varied more across stands than within stands for all elements (P<0.001). Phosphorus was the most spatially variable across stands (P=0.08), and it was also the most variable across species (P=0.06). Our bootstrapping analysis revealed a greater reduction in standard error by increasing sampling years than by increasing the number of plots sampled. Understanding the variability of litter mass and concentrations at relevant spatial scales can help to allocate sampling efforts to best reduce uncertainty in estimates of litterfall nutrient fluxes.