Introduction
To disentangle the mechanisms producing the biological diversity seen in
nature, ecologists increasingly seek to integrate ecology and evolution
(Wiens and Donoghue 2004; Jetz et al. 2012; McGill et al. 2019). Mapping
traits onto phylogenies is essential for such integration, as mapping
traits reveals the rates and tempo of evolution of behavioral,
morphological and ecological characteristics (Bollback 2006). Knowledge
of trait evolution has often been applied to evaluate the evolutionary
mechanisms producing, for example, the appearance of ecological
innovations and the bursts behind evolutionary radiations (Cantalapiedra
et al. 2014; Joy et al. 2016; Maestri et al. 2017). Nevertheless,
despite extensive study on rates of trait evolution over time and across
clades (Gingerich 2009; Joy et al. 2016 and references therein),
understanding how these rates vary over space is equally challenging,
and still little understood.
In terms of species diversification, rates are heterogeneous over space.
Between-biome comparisons suggest that some biomes are more
speciation-prone than others (Goldberg et al. 2005; Davies and Buckley
2011; Antonelli et al. 2018). For example, Amazonian tropical forests
were inferred to be the main source of Neotropical biodiversity due to
high speciation and low extinction rates, yielding species accumulation
within tropical forests (Davies and Buckley 2011; Antonelli et al.
2018). Marine tropical biomes appear to be sources of temperate-region
bivalves owing to the dispersal of taxa that evolved in tropical regions
(Goldberg et al. 2005). Montane portions of the Andes and also of the
Atlantic Rainforest were shown to be centers of early rodent
diversification and diversity accumulation into the Neotropics (Leite et
al. 2014; Maestri et al. 2019). Although these findings implicate
cradles and museums of biodiversity, we still need to know the
situations where diversification results in trait evolution (e.g.,
Oliveira et al. 2016), as well the roles of historical and ecological
factors in producing spatial heterogeneity in trait evolution.
Here, we tested whether within-biome heterogeneity in species trait
evolution would be related to the distance from spatially and temporally
unstable ecotones. If this relationship holds, then assemblage position
relative to ecoregion boundary, or its interaction with habitat type,
should be the main predictor of evolutionary speed relative to other
ecological and historical variables like habitat type, neighborhood
characteristics, and location (either in the Andes or Atlantic
Rainforest). Ecotone is a concept used at several spatial scales to
characterize the boundary between habitat patches; the environmental
contrast between adjacent patches can produce boundaries that organisms
may perceive (Cadenasso et al. 2003). We hypothesize that assemblages
located at ecoregion ecotones have species with more changes from the
ancestral character state (higher transition rates), have maintained the
ancestral character state for shorter time (lower stasis time) and have
more recent transitions to the current character state (lower last
transition times) than assemblages from ecoregion cores.
Region cores are more homogeneous environmentally and in terms of
selection pressures exerted on organisms, since environmental changes
are buffered before they reach cores (Mayle et al. 2004; Mayle and Power
2008; Donoghue and Edwards 2014). Populations inhabiting region cores
should be large and stable in size over time, as well as occur under
environmental conditions similar to the conditions found in the
ancestral range (Wiens and Graham 2005; Pearman et al. 2008; Davies and
Buckley 2011). In contrast, region ecotones are more heterogeneous, both
environmentally and in relation to selection pressures exerted on
organisms, because environmental challenges are first noticed in
ecotones, which leads to changes in vegetation development and in the
location, quality and type of habitats and limiting resources on which
individuals depend (De Vivo and Carmignotto 2004; Eckert et al. 2008;
Sexton et al. 2009; Donoghue and Edwards 2014). Populations inhabiting
ecotones should be smaller, be under stronger extinction pressure, and
have less stable population size than core populations (Karanth et al.
2006). They also should show shifts from ancestral characters as these
shifts may be needed to persist in ecotones (Pearman et al. 2008; Sexton
et al. 2009; Benton 2010; Donoghue and Edwards 2014).
Spatial heterogeneity in the rates of species diversification and trait
evolution is well known (Benton 2010; Jetz et al. 2012; Oliveira et al.
2016). But while there are a few metrics to evaluate spatial
heterogeneity in rates of diversification (including the tip-based
metrics reviewed in Title and Rabosky 2019), there is no consensus or
metric on how to evaluate spatial heterogeneity in rates of trait
evolution. In order to evaluate our hypothesis, we propose three
tip-based metrics for calculating trait transition rates, stasis time
and last transition time (TR, ST and LT, respectively). All three
metrics aim to calculate species-specific direction and time of
character-state transitions from the phylogeny tips to the root.
Transition rates indicate how many times the ancestral character has
changed over time. Stasis time indicates the maximum branch length (time
interval) over which the current tip-character was maintained across the
whole phylogeny. Finally, last transition time is the sum of branch
lengths from the tip to the prior/previous node with a reconstructed
character equal to current tip-character (Fig. 1). To calculate the
three tip-based metrics, we mapped and estimated ancestral states using
stochastic mapping of discrete traits via Bayesian inference (Bollback
2006), which allows calculating the time at which a trait changed along
phylogeny branches and not just at the nodes. Tip-based metrics such as
TR, ST and LT can be later summarized across assemblages, allowing
assessments of the effect of spatial, environmental, and historical
factors on trait evolution rates. Here, we averaged tip-based metrics
across all species occurring in a given assemblage to obtain
assemblage-level TR, ST, and LT –hereafter aTR, aST, and aLT– to then
test whether evolution has been faster at ecotones.
The test involved a thorough
consideration of phylogenetic uncertainty from character reconstruction
to hypothesis testing (Fig. 1).
We tested our hypothesis of faster trait evolution at ecotones by
integrating data on distribution, diet, and phylogeny of sigmodontine
rodents. Sigmodontinae is a subfamily within the family Cricetidae
(Musser and Carleton 2005) that arrived in South America before the
final closure of Panama Isthmus (~10 Ma; Leite et al.
2014; Steppan and Schenk 2017). They are a useful group for testing our
hypothesis because the species are sensitive to habitat stability at
fine scales due to their small body-size, short generation time, small
geographic range, and narrow microhabitat requirements for feeding,
reproducing, and avoiding predation (Patton et al. 2015). One notable
aspect about sigmondontine rodents is the uncertain phylogenetic
relationships among species, genera and tribes. Different phylogenies
show equally plausible but differing topologies (e.g., Weksler 2006;
Machado et al. 2013; Leite et al. 2014; Steppan and Schenk 2017),
suggesting that reliance on only one phylogeny may be insufficient to
understand the evolution of the group (Rangel et al. 2015; Upham et al.
2019). Another remarkable aspect about them is that, since their
colonization of South America, they experienced a rapid evolutionary
radiation that has allowed sigmodontine species to spread into many
types of habitats (Patton et al. 2015), without radical changes from
their ancestral morphology (Maestri et al. 2017). However, sigmodontine
rodents do show an impressive variation in diet (Paglia et al. 2012;
Missagia et al. 2019). Many species are highly specialized to consume a
few items from specific habitats, such as herbs and seeds from open
habitats or leaves and fruits from forested habitats (Paglia et al.
2012; Pardiñas et al. 2020). Thus, diet should evolve as a response to
the spatial heterogeneity and temporal instability of ecotones.