Figure legends
Fig. 1: Analytical scheme used to test whether evolution is faster at
ecotones, which involved 1) calculating tip-based ancestral trait state
and its change over time and 2) spatializing changes from the ancestral
trait state using assemblage-level metrics (aTR, aST, aLT), and 3)
propagating uncertainty across the previous steps (gray arrow in the
background). To calculate tip-based metrics at the species level, we
mapped and estimated ancestral states using stochastic mapping of
discrete traits via Bayesian inference, which allows calculating the
time at which a trait changed along phylogeny nodes. The tip trait state
is taken into account when calculating TR (as seen for Sp. 1). Note that
transitions not fixed at the nodes are not considered when calculating
TR (e.g., the brief transitions between n1 to n2 from
plant→ insect to insect→plant), although such brief transitions do
reduce ST and LT. Also note that ST is the maximum time length between
two nodes, and LT is the sum of branch lengths with reconstructed traits
equal to the tip trait. Values of tip-based metrics are equal for sister
species (Sp. 6 and 5, Sp. 4 and 3) because trait change occurred exactly
in the same nodes.
Fig. 2: Density plots of the intercept (expected mean) of assemblage
transition rates aTR, and regression coefficient (deviation from the
mean) of the most important variables. In each plot, the intercept is
represented by the gray line and the regression coefficient is
represented by the black line. Estimates were extracted from Linear
Mixed Models that consider ecoregion-scale variables as fixed effects,
ecoregion ID as random effect, and exponential correlation structure
with nugget effect to accommodate spatial autocorrelation. Intercept and
regression coefficients were extracted from each one of the 2,000
models. Boxplot in the upper margin shows average and
1st and 3rd quartiles of the
distribution of aTR.
Fig. 3: Density plots of the intercept (expected mean) of assemblage
stasis time aST (millions of years), and regression coefficient
(deviation from the mean) of the most important variables. In each plot,
the intercept is represented by the gray line and the regression
coefficient is represented by the black line. Estimates were extracted
from Linear Mixed Models that consider ecoregion-scale variables as
fixed effects, ecoregion ID as random effect, and exponential
correlation structure with nugget effect to accommodate spatial
autocorrelation. Intercept and regression coefficients were extracted
from each one of the 2,000 models. Boxplot in the upper margin shows
average and 1st and 3rd quartiles of
the distribution of aST.
Fig. 4: Density plots of the intercept (expected mean) of assemblage
last transition time aLT (millions of years), and regression coefficient
(deviation from the mean) of the most important variables. In each plot,
the intercept is represented by the gray line and the regression
coefficient is represented by the black line. Estimates were extracted
from Linear Mixed Models that consider ecoregion-scale variables as
fixed effects, ecoregion ID as random effect, and exponential
correlation structure with nugget effect to accommodate spatial
autocorrelation. Intercept and regression coefficients were extracted
from each one of the 2,000 models. Boxplot in the upper margin shows
average and 1st and 3rd quartiles of
the distribution of aLT.
Fig. 5: Mapped assemblage-level transition rates (aTR), stasis time
(aST), and last transition time (aLT) of sigmodontine rodent assemblages
at points in ecoregion cores and ecotones. Tip-based metrics in the left
maps (A,C,E) were obtained by averaging metrics across 10,000 estimates
(100 phylogenies, 100 simulations per phylogeny). Phylogenetic
uncertainty on estimates of the tip-based metrics, represented in the
right maps (B,D,F), were calculated through the standard deviation of
the metrics across 10,000 estimates.
Data accessibility statement
All data we used are already available in online repositories. Range
maps are available in the Dryad Digital Repository
(http://dx.doi.org/10.5061/dryad.8vt6s95). Phylogenies were
published in 2019 by N. Upham and collaborators in PLOS Biology
(https://doi.org/10.1371/journal.pbio.3000494). A shapefile with
ecoregions is available at
https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world.
A shapefile with Central Andes boundaries was published by
Löwemberg-Neto in 2015, and it is available at
http://dx.doi.org/10.11646/zootaxa.3985.4.9. A shapefile with
Atlantic Rainforest boundaries was published by Muylaert and
collaborators in 2018, and it is available at
https://github.com/LEEClab/ATLANTIC-limits. Mammal diet data were
published by Wilman and collaborators in 2014 and are available at
https://doi.org/10.1890/13-1917.1. Finally, the R codes used to
calculate the three new tip-based metrics will be available on the
GitHub page of the first author.
Competing interest statement
We declare that there are no competing interests in relation to this
study.
Author contributions
ALL, RM, SMH, and LDSD conceived the ideas and designed the methodology.
RM and BDP provided occurrence and phylogenetic data. ALL and VJD wrote
the R functions. ALL, RM, VJD, BDP, SMH and LDSD contributed to data
analysis and wrote the manuscript. All authors contributed critically to
the drafts and gave final approval for publication.
Acknowledgements
ALL received a PhD fellowship from the Brazilian Federal Agency for
Support and Assessment of Post-Graduate Education (CAPES). LD and SMH
received funding from the National Council for Scientific and
Technological Development (CNPq; proc. 307527/2018-2 and 304820/2014-8,
respectively). We thank Gabriel Nakamura and Arthur Rodrigues (UFRGS)
for their suggestions during study development and data analysis. We
thank Vinicius G. Bastazini (UFRGS), Maria João Pereira (UFRGS), Augusto
Ferrari (FURG), Adriano S. Melo (UFRGS), Fernanda T. Brum (UFPR), Marcus
V. Cianciaruso (UFG) and Ricardo Dobrovolski (UFBA) for discussions and
suggestions in previous versions of the manuscript.