Scale dependence of the diversity effect.
Previous studies have showed that the relationship between diversity and
productivity (biomass) changed with plot size (Chisholm et al.2013; Thompson et al. 2018; Luo et al. 2019). We also
found that the explanatory power of diversity increased from the 400 to
1200 m2 grain size for biomass and productivity (Fig.
1). It is generally hypothesized that the biodiversity effect will first
increase with larger plot size, and then show a decrease. At smaller
plot sizes, environmental heterogeneity increases with increased grain
size and thus biodiversity also accumulates, which lead to higher
biodiversity effect. However, when diversity gradually saturates with
further increase in grain size, the effect of diversity on ecosystem
functions will weaken (Chisholm et al . 2013, Thompson et
al. 2018). As for the grain size with the strongest diversity effect on
forest biomass and productivity, (Chisholm et al. 2013) found
that the optimal grain size was 0.04 ha, and that biodiversity effect
may become null or even negative for plot size > 0.1 ha.
However, later studies suggest that the optimal grain size is around 0.1
ha (Thompson et al. 2018). Our results suggest that the optimal
grain size may be larger than 1200 m2 for forest
biomass and productivity. A recent study also found that the diversity
effect is the strongest at a grain size of 0.25 ha in a temperate forest
(Luo et al. 2019). These results are clearly different from previous
idea that biodiversity effect should be the strongest at the smallest
grain size, where community processes (e.g. complimentary and sampling
effect) play a dominant role (Chisholm et al. 2013).
Compared with forest biomass and productivity per se , the change
of diversity effect on their stability with grain size has much less
been reported with field data. However, Wang et al. (2014) proposed a
theoretical model and predicted that ecosystem stability itselfshould increase from local to regional scales. As for
diversity-stability relationship, their model predicts distinctive
pattern between the local and regional scales: 1) at the local community
scale, the diversity-stability relationship do not change with grain
size; 2) while at the regional (metacommunity) scale, ecosystem
stability should be higher with both increasing diversity and grain
size. Interesting, our results obtained at the community scale also
showed that the effect of diversity on biomass and productivity
stability did not showed clear trend across grain sizes (Fig. 1), and
thus provide support to their first prediction. Meanwhile, their second
prediction is also consistent with a large-scale study, which found that
the effect of biodiversity on productivity stability was stronger at a
larger grid size of ~ 55 km (0.5 °) than the
~ 5 km grids (Mazzochini et al. 2019). Thus,
biodiversity is crucial for ecosystem stability from local community to
large scales.
In previous studies that examined the scale dependence of diversity
effect, results based on subplots that differed greatly in sample size
were generally compared directly (e.g. Chisholm et al. 2013, Thompson et
al. 2018, Luo et al. 2019). Our analysis seems to be the first one that
test the potential influence of sample size on the scale-dependence of
biodiversity effects. Our test showed that while the results based on
different subplot numbers (Fig. S3) showed some similarity with that
based on random sampling of subplots (Fig. 1), there were also notable
differences. For instances, for biomass and productivity, Fig. S3 can
not provide a clear picture of increasing diversity effect with larger
plot size, as revealed by Fig. 1. However, this difference seems not to
be mainly caused by difference in sample size. Instead, it seems to be
caused by the fact that: when the diversity effect is weak, diversity
indices have higher probability to be excluded from the models, which
result in the occasionally high explanatory power of diversity in Fig.
S3A and S3B (this is also evident when comparing the effect of stand
factors on ecosystem stability between Fig. 1 and Fig. S3). Thus, we
suggest the random sampling method may provide a more robust way to
examine the relative effect of diversity vs. other factors, and their
scale dependence. It remains unclear whether the different optimal grain
size found by previous studies (e.g. Chisholm et al. 2013, Thompson et
al. 2018) from ours results were caused by this statistic issue. It is a
pity that the maximum plot size in our dataset is not large enough. We
suggest authors with larger plots to test our method, for a better
understanding of the scale dependence of biodiversity effect.