Tree diversity effects on forest productivity: disentangling the effects of tree species addition vs. substitution

Mixture effect on stand productivity is usually apprehended through a substitutive approach, whereby productivity in mixed stands is compared to productivity in monocultures, at equivalent stand density. This approach has proved that in many cases mixed stands perform better than monospecific forests, however, we do not yet have a solid theory about species behaviour in the mixture or even guidelines for combining species. The addition of a second tree species to an existing mono-specific stand has received much less consideration. Yet, this approach has the potential to separate the facilitation effect from the complementarity effect. We compared the effect of tree species substitution vs. addition on the productivity of maritime pine and silver birch in a young tree diversity experiment implemented in 2008 in SW France. Substituting pines with birches to create two-species mixtures resulted in an increase of tree productivity at stand level beyond what was expected from monocultures (i.e., overyielding). In contrast, creating mixture through the addition of birches to pine stands had no effect on the maritime pine stand productivity (transgressive mixture effect not significant). This absence of effect is produced by two distinct density-dependence responses at an individual level. Our results allow clarifying the cases in which a mixed stand can be considered as an alternative to a monoculture of a productive species. In particular, the addition of a pioneer and soil low-demanding species during young developmental stages is a possibility to diversify the stand and potentially to increase ecosystem services without altering the productivity of the target species.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

The mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.