mice benefitted from diverse bacterial communities inoculated in their guts, by
registering higher regulatory T cells, modulation of adiposity, and higher cecal
metabolite concentrations. However, the effect of diversity was strongly modulated
by the presence of a few taxa like Collinsella aerofaciens, Subdoligranulum variable, and several Bacteroides strains. Instead of supporting the complementarity
model we described above, these findings support the selection model (Grime 1998)
where, as diversity increases, the chances of adding species that are particularly
efficient at performing a function increases too (Fig 17.2a).
In other systems, a single keystone species maintain diversity and supports the
entire function. For example, Niu and collaborators (Niu et al. 2017) inoculated
maize roots with communities assembled from 7 strains of bacteria and found that
Enterobacter cloacae is a keystone species that maintains microbial diversity and
protects the plant against the pathogenic fungi Fusarium verticilloides. In the
absence of E. cloacae, the community is dominated by Curtobacterium pusillum
and the resulting low diversity facilitates growth of F. verticilloides. Here, while the
function of pathogen defense correlates with diversity, a specific species maintains
community function and diversity corresponding to the keystone species BEF model
(Scherer-Lorenzen 2005).
The truth behind diversity–function relationships lies somewhere between observational studies and manipulative experiments. Observational studies provide a
glimpse into natural host–microbe interactions and manipulative experiments generate extended gradients in diversity and composition to directly test their role in
function. However, both approaches have limitations. On one hand, observational
studies are unable to distinguish between effects from competing drivers of diversity
and function such as diet, physiological, or morphological changes over time (Faith
et al. 2014). On the other hand, manipulative experiments have been criticized for
being artificial, and not representing natural host–microbe, or microbe–microbe
relationships (Scherer-Lorenzen 2005). Only by combining observational studies
of natural gradients in diversity and controlled manipulative experiments can we
fully understand how microbial communities assemble and provide function within
hosts.
17.3.2 Broad and Specific Functions
Traditionally, the study of biodiversity–function relationships in ecology focuses on
broad functions like primary productivity, or biomass accumulation (Hooper and
Dukes 2004; Cardinale et al. 2012; Leibold et al. 2017). The majority of evidence
supports positive biodiversity–function relationships (Balvanera et al. 2006; Tilman
et al. 2014). Many of these studies focus on plant communities with a variety of life
histories, where adding species increases resource use efficiency, leading to higher
biomass and productivity. While primary productivity and biomass accumulation
may be important in the context of host-associated microbial communities, these
describe only a few of the functions that have effects upon host fitness.
308
C. Cuellar-Gempeler
registering higher regulatory T cells, modulation of adiposity, and higher cecal
metabolite concentrations. However, the effect of diversity was strongly modulated
by the presence of a few taxa like Collinsella aerofaciens, Subdoligranulum variable, and several Bacteroides strains. Instead of supporting the complementarity
model we described above, these findings support the selection model (Grime 1998)
where, as diversity increases, the chances of adding species that are particularly
efficient at performing a function increases too (Fig 17.2a).
In other systems, a single keystone species maintain diversity and supports the
entire function. For example, Niu and collaborators (Niu et al. 2017) inoculated
maize roots with communities assembled from 7 strains of bacteria and found that
Enterobacter cloacae is a keystone species that maintains microbial diversity and
protects the plant against the pathogenic fungi Fusarium verticilloides. In the
absence of E. cloacae, the community is dominated by Curtobacterium pusillum
and the resulting low diversity facilitates growth of F. verticilloides. Here, while the
function of pathogen defense correlates with diversity, a specific species maintains
community function and diversity corresponding to the keystone species BEF model
(Scherer-Lorenzen 2005).
The truth behind diversity–function relationships lies somewhere between observational studies and manipulative experiments. Observational studies provide a
glimpse into natural host–microbe interactions and manipulative experiments generate extended gradients in diversity and composition to directly test their role in
function. However, both approaches have limitations. On one hand, observational
studies are unable to distinguish between effects from competing drivers of diversity
and function such as diet, physiological, or morphological changes over time (Faith
et al. 2014). On the other hand, manipulative experiments have been criticized for
being artificial, and not representing natural host–microbe, or microbe–microbe
relationships (Scherer-Lorenzen 2005). Only by combining observational studies
of natural gradients in diversity and controlled manipulative experiments can we
fully understand how microbial communities assemble and provide function within
hosts.
17.3.2 Broad and Specific Functions
Traditionally, the study of biodiversity–function relationships in ecology focuses on
broad functions like primary productivity, or biomass accumulation (Hooper and
Dukes 2004; Cardinale et al. 2012; Leibold et al. 2017). The majority of evidence
supports positive biodiversity–function relationships (Balvanera et al. 2006; Tilman
et al. 2014). Many of these studies focus on plant communities with a variety of life
histories, where adding species increases resource use efficiency, leading to higher
biomass and productivity. While primary productivity and biomass accumulation
may be important in the context of host-associated microbial communities, these
describe only a few of the functions that have effects upon host fitness.
308
C. Cuellar-Gempeler