Network structure in dry forest versus rainforest in relation to
null models
Contrary to initial predictions that mutualistic networks in the
rainforests would become more modular and less nested during El Niño
than in a normal year, while networks in the dry forests would become
less modular and more nested, we observed similar changes to network
structure in response to El Niño for both forests. In both forests,
aspects of the observed changes in network structure are likely to have
contrasting consequences for network resilience. For example, nested
mutualistic networks are thought to contribute to an increase in the
maximum amount of biodiversity supported in the environment (Bastolla et
al. 2009). A decrease in nestedness, as observed in both forest types,
may thus be related to an increase in effective competition (Bastolla et
al. 2009) driving niche separation. This is important as lower
nestedness was found across most of the networks in the present study
and nestedness helps to buffer against secondary extinctions and
temporal fluctuations (Tylianakis, Laliberté, Nielsen, & Bascompte,
2010). Similarly, the decrease in connectance is worrying as this
network metric is thought to contribute to ecosystem functional
stability during fluctuating environmental conditions (Tylianakis et al.
2010).
Most observations of robustness to species extinctions also suggest a
decrease in the stability of the communities and resilience of
biological interactions, likely as a result of decreases in connectance
and nestedness in the networks (Thébault & Faontaine, 2010). These
effects are particularly important as connectance and nestedness are
thought to show little temporal variation within and between years
(Dupont, Padrón, Olesen, & Petanidou, 2009; Vázquez, Blüthgen, Cagnolo,
& Chacoff, 2009). In habitats such as forest and savannah, recovery to
the conditions before disturbances such as floods and droughts is slow
(Maron, McAlpine, Watson, Maxwell, & Barnard, 2015). Thus a significant
deviation in network structure in normal years following these extreme
climatic events would be expected. Overall, this effect might reduce the
biodiversity supported in these ecosystems, especially when taking into
consideration the expected increase in the frequency of strong El Niño
events and the worldwide trend for wet areas to become wetter and dry
areas to become drier (Chou et al. 2013; Cai et al. 2014).
We observed higher values of modularity than those expected under null
models for both forests, also suggesting that the current interacting
species are showing higher differentiation in their niche use.
Modularity was not only significantly higher than expected by chance,
but values for both forests were also higher than the calculated ones
using a similar algorithm for previously observed mutualistic networks
of phyllostomid bats in other regions of South America during normal
conditions (Mello et al. 2011). Following a similar trend, the increase
in compartmentalization of both habitats might reduce the number of
coexisting species as fully connected networks promote a reduction in
the effective interspecific competition (Bastolla et al. 2009). On the
other hand, compartmentalization has been linked to greater stability,
slower spread of disturbance, and smaller likelihood of trophic cascades
in networks (Tylianakis et al. 2010).
It is interesting to note that the similar increases in
compartmentalization and modularity alongside a decrease in nestedness
might have arisen due to the same causes in each forest. Changes in
rainfall have an impact in different groups of herbivorous mammal
populations through alterations in the amount and quality of food
resources (Mandujano, 2006; White, 2008). Severe droughts in some
Pacific areas provoked by El Niño were responsible for increased
production of flowers and fruit of the entire plant community (Wright &
Calderon, 2006), meanwhile in rainforests flowering was triggered by
heavy rain (Wright, 1991). In Central American tropical forests, the
fall of leaves after droughts that occurred during El Niño events tended
to be associated with subsequent increases in seed production (Detto,
Wright, Calderón, & Muller-Landau, 2018). These events, when both
droughts and floods were associated with increased productivity of
fruits and flowers could likely be the explanation to pattern that we
have witnessed where the dry forests and rainforests showed similar
changes in network structure. On the other hand, the drought that
occurred in the dry forests of ACG promoted by the strong El Niño of
2015 caused a reduction in seed production that remained even after the
return to normal levels of the rainfall (O’Brien et al. 2018). Thus,
this effect was probably the main responsible for the changes in our
observed networks for ACG, with the reduction in fruit availability
leading to a higher resource specialization, which promoted an increase
in modularity but a decrease in nestedness. Despite the contrasting
causes, similar responses to opposite water stress in two very
dissimilar species communities suggests a generalized response to stress
that may become more prevalent as extreme weather cycles increase in
frequency (also see Butt et al. 2015).
One of the limitations of our comparisons is that we do not have data
collected for a normal year during both seasons from any of the forest
types. Thus, it is hard to fully understand how our results are limited
to the data and null models that we have used for the comparisons, or if
they also reflect a real comparison with values gathered from a normal
year for both forests. Another limitation is that for all almost all
networks, except for two, we have lower values of sampling completeness
than the rule of thumb proposed by Macgregor et al. (2017) (90%),
though not by much. In addition, most of the rarefaction curves built to
estimate the number plant species present in the diet of each bat
species did not reach an asymptote. However, we focused our study mostly
on network metrics that do not show a strong bias by network size which
should minimize the impact of these issues. Finally, it is hard to
assess the influence of markers choice that we used for plant
identification on the values of network metrics. Multiple genetic
markers have been proposed in various combinations to identify different
plant species (matK , trnH-psbA , rbcL , ITS2), but
still not sufficient to discriminate closely related species in some
taxonomic groups, especially those with recent and intense species
radiation (Hollingsworth et al. 2011). For example, the fig tree
(Ficus ), one of the common genera consumed by bats, is extremly
specious and demonstrated poor resolution on species level usingrbcL and ITS (Ronsted, Weiblen, Clement, Zerega, & Savolainen,
2008). As such, some of our identificaitons should be treated
provisionally. However, our analysis of data limited only to genera (see
supplement) suggests our observations are robust to these effects. One
major benefit of our molecular approach is the inclusion of plant
species which might otherwise be missed when their seeds are not
consumed. Our ability to identify plants from consumed pollen or fruit
pulp provides a more complete perspective than many previous analytical
approaches.