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Coarse woody debris (CWD), represented by logs and snags >10 cm in diameter and >1 m in length, was sampled at eight sites in Russian boreal forests to determine the specific density of decay classes and decomposition rates. Tree species sampled included Abies siberica Ledeb., Betula pendula Roth., Betula costata Trautv., Larix siberica Ledeb., Larix dahurica Turcz., Picea abies (L.) Karst., Picea obovata Ledeb., Picea ajanensis Fisch., Pinus koraiensis Sieb. et Zucc., Pinus siberica Ledeb., Pinus sylvestris L., and Populus tremula L. The mean densities for decay clas ses 1 through 5 ranged from 0.516 to 0.084 g·cm–3, respectively. Annual decomposition rates varied among the species, and for logs, decomposition rates ranged from 4.2 to 7.8% for B. pendula, 2.6 to 4.9% for Picea spp., 2.7 to 4.4% for Pinus sylvestris, 1.5 to 3.1% for Larix spp., and 1.5 to 1.9% for Pinus koraiensis and Pinus siberica. Logs decomposed faster than snags. Among the sites examined, temperature and precipitation did not correlate with decomposition rates, which is consistent with other studies in the boreal region. Globally, a positive correlation between decomposition and mean annual temperatures was found, with decay-resistant trees less responsive than those with low decay resistance.

A chronosequence of three species of logs (Pinus sylvestris L., Picea abies (L.) Karst, and Betula pendula Roth.) from northwestern Russia was resampled to develop a new method to estimate rates of biomass, volume, and density loss. We call this resampling of a chronosequence the decomposition-vector method, and it represents a hybrid between the chronosequence and time-series approaches. The decomposition-vector method with a 3-year resampling interval gave decomposition rates statistically similar to those of the one-time chronosequence method. This indicated that, for most cases, a negative exponential pattern of biomass, volume, and density loss occurred. In the case of biomass loss of P. sylvestris, however, polynomial regression indicated decomposition rates were initially low, then increased, and then decreased as biomass was lost. This strongly suggests three distinct phases: the first when decomposers colonized the woody detritus, a second period of rapid exponential mass loss, and a third period of slow decomposition. The consequences for this complex pattern of decomposition were explored at the ecosystem level using a simple model. We found that a single rate constant can be used if inputs vary within a factor of 10, but that this approach is problematical if inputs are more variable. Résumé : Une chronoséquence des billes appartenant à trois espèces (Pinus sylvestris L., Picea abies (L.) Karst et Betula pendula Roth.) et provenant du nord-ouest de la Russie a été rééchantillonnée dans le but de développer une nouvelle méthode pour estimer les taux de perte de biomasse, de volume et de densité. Nous appelons ce rééchantillonnage d'une chronoséquence la méthode de décomposition en vecteur, laquelle constitue une forme hybride entre les approches par chronoséquence et série temporelle. Avec un rééchantillonnage à intervalle de 3 ans, la méthode de décomposition en vecteur a produit des taux de décomposition statistiquement semblables à ceux obtenus par la méthode de la chronoséquence avec un seul échantillonnage. Cela montre que, dans la plupart des cas, la perte de biomasse, de volume et de densité a suivi une courbe exponentielle négative. Cependant, dans le cas de la perte de biomasse chez le P. sylvestris, une régression polynomiale montre que le taux de décomposition était initialement faible puis a augmenté et finalement diminué avec la perte de biomasse. Cela suggère fortement qu'il existe trois phases distinctes : la première, lorsque les décomposeurs colonisent les débris ligneux, une deuxième période avec une perte de masse exponentielle rapide et une troisième période de décomposition lente. Les conséquences de ce patron complexe de décomposition ont été explorées à l'échelle de l'écosystème à l'aide d'un modèle simple. Nous avons constaté qu'on peut utiliser une constante de décomposition unique si le taux de décomposition varie par un facteur de 10 ou moins, mais que cette approche est problématique si le taux de décomposition est plus variable. [Traduit par la Rédaction] Harmon et al. 84

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The three objectives of this paper were: to summarize net primary production (NPP) and carbon allocation patterns for boreal forests, to examine relationships between climatic and biological variables and NPP, and to examine carbon allocation coefficients for all boreal forests or types of boreal forests that can be used to estimate NPP from easily measured components of NPP. Twenty‐four Class I stands (complete NPP budgets) and 45 Class II boreal forest stands (aboveground NPP [NPPA] and budget only) were identified. The geographic distribution of the Class I stands was not uniform; 46% of the stands were from two studies in North America, and only one stand was from the important larch forests of Eurasia. Total (above‐ and belowground) net primary production (NPPT) ranged from 52 to 868 g C·m−2·yr−1 and averaged 424 g C·m−2·yr−1. NPPA was consistently larger for deciduous than for evergreen boreal forests in each of the major boreal regions, especially for boreal forests in Alaska. Belowground net primary production:total net primary production (NPPB : NPPT) ratios were consistently larger for evergreen (0.36) than deciduous (0.19) boreal forests. NPP of different‐aged stands in age sequence varied from 44% to 77%, a magnitude equal to or greater than that of climatic factors or vegetation type. NPP and NPPA were positively correlated (r2 = 0.66–0.68) to mean annual aboveground increment for Class I stands, and this empirical relationship explained 81% of the observed variation of NPPA for Class II stands. These robust relationships provide an approach for increasing the number and spatial coverage of boreal forest NPP data needed to evaluate NPP estimates from ecosystem models. Notable deficiencies of boreal forest NPP data were ground layer vegetation and belowground NPP data, NPP data for boreal forest age sequences, and NPP data for boreal larch ecosystems in Eurasia.