Case studies: contrasting latitudinal gradients in trees and
reef
fish
We used our approach to analyze two empirical datasets documenting
latitudinal diversity gradients (LDG) in reef fish and trees. The trend
of increasing diversity from poles to equator is one of most prominent
global biodiversity patterns that occurs in many taxa and at different
spatial scales (Fine, 2015; Hillebrand, 2004; Willig et al., 2003). All
components, particularly N and the SAD, likely vary along the gradient,
though how they combine to form the LDG at a given scale, and whether
this varies among taxa, is less well known.
For example, N is expected to vary with energy- or resource availability
and, accordingly, the more-individual hypothesis (MIH) is one of the
classic explanations for the LDG (Brown, 2014; Srivastava & Lawton,
1998; Wright, 1983). Historically, the MIH has referred to a collection
of different mechanisms by which higher total abundance translates to
higher species diversity, including effects on extinction and speciation
rates (Evans et al., 2005; Scheiner & Willig, 2005; Storch et al.,
2018). However, here we use the term more narrowly to only mean passive
sampling effects (Coleman et al., 1982), which is the process by which
larger communities (e.g. in the tropics) randomly sample a larger
portion of a species pool than small ones (e.g. in temperate regions)
(Wright, 1983). Abundance-related processes that influence extinction
(e.g. demographic stochasticity) and diversification rates over the
longer term likely alter the SAD and regional species pool, and
therefore would be captured by SAD-effects in our framework. Indeed,
there are a large number of ecological and evolutionary mechanisms that
shape and maintain latitudinal gradients in regional SADs. These include
differences in time for speciation, environmental stability, species
interactions, and niche-processes (Fine, 2015). While the LDG is
generally strongest at larger spatial grains (Hillebrand, 2004), it is
largely unknown how such species pool gradients combine with gradients
of total abundance to determine local-scale diversity gradients.
Here, we applied the analytical framework to analyze latitudinal
gradients of two publicly available datasets with standardized community
surveys: (1) forest trees from the Gentry plot dataset (Gentry, 1988,
Phillips & Miller, 2002), and (2) reef fish from the Reef Life Survey
(Edgar et al., 2020; Edgar & Stuart-Smith, 2014). Importantly, both
datasets use a fixed sampling effort in terms of plot/transect size for
their respective sites. Therefore, latitudinal variation in sample
diversity reflects changes in the regional species pool (SAD) as well as
natural variation in the observed number of individuals (i.e.
more-individuals effect).
Because our main focus was to illustrate the analytical framework,
rather than an exhaustive analysis of these datasets, we reduced both
datasets into one latitudinal “slice” to minimize other well-known
confounds, such as biogeographic factors, that influence the magnitude
of the gradient. For trees, we focused on the plots located in the
Americas, so as to avoid the potential influence of continent on tree
diversity (Qian & Ricklefs, 2000). And for the reef fish, we only
included surveys from the Indo-Pacific area where diversity is highest,
and biogeographic effects (e.g., distance from diversity center) were
minimized (Blowes et al., 2017). For both data sets, we excluded sites
with fewer than 20 individuals (we also used different cutoff-levels to
test the robustness of our results). Supplementary Figure S3 shows the
geographical location of samples included in our analyses.
After selecting the sites, we dissected the observed diversity of each
sample into the SAD-component and the N-component, assuming a reference
sample size of n=20. To do this, we calculated the observed richness and
the rarefied richness (Sn) for n=20, and derived the
corresponding ENS values using Eqn 3 (i.e. EN and
En, respectively). En represents the
SAD-component. The difference between EN (total
diversity) and En (SAD-component) is the diversity
component that results from the changes in N or the more-individuals
effect (N-component). We then modeled the two components along the
latitudinal gradient using simple linear models with absolute latitude
as the independent variable, and the SAD and N components as dependent
variables. We used the regression coefficients (or slopes) as the effect
sizes for the respective components. Since our partitioning framework is
additive and models are linear, the effect sizes (i.e. slopes) of the
two components add up to the effect size (i.e. slope) of the total
diversity gradient.
Both trees and reef fish showed similar slopes along their respective
latitudinal gradient for the overall richness gradient, but they
differed in how the underlying component contributions changed along the
gradient (Fig 3). The trees had a relatively large SAD-effect; that is,
even when the number of individuals was standardized, the diversity
gradient remained quite strong. This suggests that the diversity
gradient is mostly underlain by changes in the species pool and
associated patterns of commonness and rarity (i.e., the SAD).
Nonetheless, the N-effect also contributed to the total diversity
gradient, as total tree abundance tended to increase as absolute
latitude decreased. In contrast to the trees, the reef fish diversity
gradient was strongly dominated by the N-effect. For a standardized
number of individuals, the fish diversity gradient was relatively weak
(see SAD-component). This reflects that species rich reef fish
communities are often dominated by a few species, the number of which
does not vary strongly along the gradient. For a constant sample size,
the many rare species in diverse fish communities have little weight in
the diversity estimate. That is, they mostly affect the diversity for
communities with more individuals, and are captured more-individual
effect.
The contrasting results between fishes and trees could reflect
biological differences of the two groups. Fish move in a
three-dimensional space, which allows for much stronger gradients in
total abundance. In forests, on the other hand, stem density is likely
more strongly limited by available space. This suggests that for
forests, community assembly processes change more strongly along the
gradient, leading to communities with high relative evenness in the
tropics (Ulrich et al., 2016). This is reflected in the strong
SAD-effect. Conversely, the schooling nature of some tropical fishes
allows for the dominance of a few species. Additionally, the number of
dominant fish species does not vary strongly along the gradient, whereas
the number of rare species (which are affected by sampling effects)
does. Hence, we find the large N-effect in fishes.