Introduction
Mixed species plantations with high value species attract increased
interest as they may provide a broader supply of ecological and
socio-economic benefits (Felton et al., 2016; Gamfeldt et al., 2013;
Heinrichs et al., 2019; Isbell et al., 2011). Mixed species stands have
particularly been reported to provide higher biomass production
(Erskine, Lamb, & Bristow, 2006) and to be more productive, stable and
climate resistant than the average monocultures of the same species
(Bauhus et al., 2017; H. Pretzsch et al., 2015; H Pretzsch et al.,
2019).
As compared to monoculture, higher biomass production in mixed species
stands can be attributed to several factors, including greater species
diversity, greater stand structuring and canopy packing (H Pretzsch et
al., 2019), facilitation and better resource utilization (Jactel et al.,
2018; Hans Pretzsch, Forrester, & Bauhus, 2017). For instance, species
rich stands may allow coexistence of functionally different species
(species of different functional trait or attributes) that therefore
efficiently access and utilize limiting resources, thereby enhancing
biomass production through efficient resource-use. It is further
possible that in mixed species stands, certain species (e.g.
nitrogen-fixing species) improve growing conditions for others, thereby
enhancing overall production through facilitation (Erskine et al.,
2006). This is consistent with the niche complementarity and
facilitation mechanisms (Loreau & Hector, 2001), and have important
implications for silviculture, as well managed mixed, more diverse and
uneven-aged plantations would have higher net primary production than
monoculture stand (Kelty, 1992, 2006). On the other hand, occurrence of
dominant and highly productive tree species (with dominant traits) can
positively influence biomass production, i.e. one or two species in
mixed species plantation can largely explain increase in biomass
production if they are dominant. This lends support to the
sampling/selection effect hypothesis which posits that biomass
production is enhanced through functional traits of the dominant
species. Although the selection effects may seem more evident, because
few larger trees often contain large portion of the stand aboveground
biomass (Bastin et al., 2015; Fotis et al., 2018; Lin et al., 2016; S.
Mensah, Veldtman, Du Toit, Kakaï, & Seifert, 2016; Sylvanus Mensah,
Veldtman, & Seifert, 2017), studies have also lent support to both
mechanisms, which are demonstrated to be non-mutually exclusive
(Cavanaugh et al., 2014; Hooper, Chapin III, & Ewel, 2005; S. Mensah,
du Toit, & Seifert, 2018; S. Mensah, Veldtman, Assogbadjo, Glèlè Kakaï,
& Seifert, 2016; Ruiz-Benito et al., 2014; Ruiz-Jaen & Potvin, 2010;
Wu et al., 2015), but can have different relative importance in
different contexts (Fargione et al., 2007; Potvin & Gotelli, 2008), as
a result of differences in functional traits among species, resource
allocation and resource use efficiency (Huston, 1997; Tilman, Lheman, &
Thomson, 1997). For instance in a recent study, we showed that both
mechanisms operate through competitive exclusion imposed by dominant
species (selection effects) and complementary use of resources by weak
competitors (S. Mensah, du Toit, et al., 2018). Therefore both
mechanisms may also prevail for mixed species stands, and understanding
their relative contribution may inform about appropriate silvicultural
options for their management.
Decades of research have helped establish positive relationships between
species richness and ecosystem biomass or carbon storage, as the most
dominant pattern. Across scales and biomes, several studies have
reported positive effects of species diversity on stand biomass
(Barrufol et al., 2013; Cheng, Zhang, Zhao, & von Gadow, 2018; Huang,
Su, Li, Liu, & Lang, 2019; Liang et al., 2016; Liu et al., 2018; S.
Mensah, Veldtman, Du Toit, et al., 2016; Paquette & Messier, 2011;
Ruiz-Benito et al., 2014; Vilà et al., 2007), although neutral and
negative patterns also exist (An-ning, Tian Zhen, & Jian Ping, 2008;
Ruiz-Jaen & Potvin, 2011; Szwagrzyk & Gazda, 2007). While the positive
relationship between species richness and biomass production can be used
as a persuasive argument for the conservation of biodiversity and
encourage more diverse plantations (Erskine et al., 2006), many previous
studies have focused on species richness or related taxonomic indexes,
which do not fully capture certain functional differences or
similarities between species (Cardinale et al., 2006), nor are they
sufficient to reflect the complexity of the stand community (Morin,
Fahse, Scherer-Lorenzen, & Bugmann, 2011). Much research is still
needed across scales of the analysis (global, national or subnational),
and in relation to the measure of biodiversity.
Apart from species richness or related taxonomic indices such as Shannon
index, Pielou evenness and Simpson index, functional trait diversity
(richness, evenness, dispersion and divergence), functional trait
dominance or identity (community-weighted mean of a given functional
trait) or structural diversity have been reported to predict stand
aboveground biomass or productivity (Y. Li et al., 2019; Lin et al.,
2016; S. Mensah, du Toit, et al., 2018; S. Mensah, Veldtman, Assogbadjo,
et al., 2016; S. Mensah, Veldtman, Du Toit, et al., 2016; Prado-junior
et al., 2016; Thom & Keeton, 2019; Wen et al., 2019; Zhang & Chen,
2015). Because species may differ in functional traits that drive
differences in resource capture, rates of photosynthesis and biomass
allocation (Falster, Duursma, & FitzJohn, 2018; Poorter et al., 2012),
functional trait diversity, dominance or identity would better capture
the degree of functional redundancy and niche overlap (Lasky et al.,
2014; Prado-junior et al., 2016; Ruiz-Benito et al., 2014). Further,
structural diversity (tree size variation and inequality) reflects how
different species occupy different vertical and horizontal layers, and
therefore may indicate the degree of complementarity (e.g. light-adapted
and shade-tolerant species), competition and resource utilisation.
Nevertheless, some recent studies showed controversy in the relationship
between these structural and functional diversity/dominance metrics
(Finegan et al., 2015; Lin et al., 2016; Prado-junior et al., 2016; Xu
et al., 2019). For instance, Lin et al. (2016), after accounting for
topographic variables and tree stem density, found that functional
dominance was the main driving factor for forest aboveground carbon,
while functional diversity had negligible effects. The authors argued
that this could have been due to the fact functional traits that relate
strongly to plant complementary resource use were not included in the
analysis. However, even after using five functional traits (maximum tree
height; leaf carbon content; leaf nitrogen content; leaf area; and
specific leaf area), Xu et al. (2019) reported that neither the
functional nor the phylogenetic diversity showed a significant advantage
in predicting aboveground biomass and biomass production when compared
with species richness in old-growth temperate forests. Further, Fotis et
al. (2018) recently reported limited effect of functional diversity on
aboveground biomass in mixed mesophytic temperate forests of the eastern
USA, whereas Szwagrzyk and Gazda (2007) found that negative effects of
functional diversity on aboveground biomass in Central Europe.
Consequently, the relative importance of functional dominance (selection
effects), functional diversity and structural diversity (niche
complementarity) for stand biomass is still controversial, and requires
further investigation, especially in mixed species stands that harbor
species occupying different positions across the vertical layer,
possibly favoring a better use of resources (e.g., light) and reduced
competition.
Combining information on taxonomic, functional, and structural diversity
would provide additional insights into our understanding of mechanisms
behind diversity-biomass relationships in mixed species stands. However,
it is unclear how each particular metric would predict AGB, and whether
some have significant advantage in mediating AGB response. Therefore, in
this study, we used taxonomy-, structure-, and functional trait-based
diversity to examine the relationships between AGB and multiple
diversity metrics. First we sought to determine the most important
taxonomic diversity measure among species richness, Shannon diversity,
Pielou evenness and Simpson index, for predicting AGB. Second we tested
for clear effects of multiple trait-functional diversity, single
trait-functional diversity, functional dominance, and structural
diversity on AGB. Finally, we retained the most important structure-,
and functional trait-based diversity metrics, and tested for their
mediation role in predicting AGB response to species richness.