Materials and Methods
Additional data and methodology are described in SI Appendix .
Data Compilation. We extracted Neotropical clades that
included at least 80% of the species distributed in the Neotropics as
defined in (Antonelli et al. 2018c). From large-scale
phylogenies, we relied on the time-calibrated phylogenies of frogs and
toads (Hutter et al. 2017), salamanders (Pyron et al. ,
2013; Pyron, 2014), Squamata (lizards and snakes) (Pyron & Burbrink
2014), birds (Jetz et al. 2012) (including only species for which
genetic data was available), mammals (Bininda-Emonds et al. ,
2007; Kuhn et al. , 2011), and plants (Zanne et al. 2014).
In addition, phylogenies of particular lineages not represented in the
global trees (or with improved taxon sampling) were obtained from
published studies (SI Appendix , Tables S1-5) or reconstructedde novo (for caviomorph rodents, including 199 species, and
phyllostomid bats, 170 species; see SI Appendix ). However,
whenever possible, we preferred to extract phylogenies from a single
dated tree rather than performing a meta-analysis of individual trees
coming from different sources (e.g., ref. (Hoorn et al.2010; Jansson et al. 2013)), such that divergence times would be
comparable. In addition, this procedure allows reducing the bias of
focusing on particular taxonomic levels (i.e., individual studies
often focus on genera) and thus comparing lineages of similar ages
(Wiens 2017). When the most inclusive Neotropical clade was over the
family rank, or for extremely species-rich groups, like the family
Solanaceae with more than 2,700 species, we split the phylogenies into
smaller clades (e.g., tribes) to take into account the
heterogeneity of evolutionary histories likely characterized by
different morphologies and key adaptations.
Diversification Analyses. We designed and applied a
series of 10 birth-death diversification models estimating speciation(λ) and extinction (μ) rates for each of the 150
phylogenies with the R-package RPANDA 1.3 (Morlon et al. 2016).
We first fitted a constant-rate birth–death model (null model) and a
set of three models in which speciation and/or extinction vary according
to time (Morlon et al. 2011): λ(t) and μ(t) . For
time-dependent models, we measured rate variation for speciation and
extinction with α and β , respectively: α andβ > 0 reflect decreasing speciation and extinction
toward the present, respectively, while α and β< 0 indicate the opposite, increasing speciation and
extinction toward the present.
We further investigated the effect of environmental changes, here
approximated by mean global temperatures and paleo-elevations of the
Andes (the main factors hypothesized behind the origin of Neotropical
diversity). Temperature variations during the Cenozoic were obtained
from global compilations of deep-sea oxygen isotope
(δ18O) from ref. (Prokoph et al. 2008; Zachoset al. 2008), but also on other different paleotemperature
curves, to assess the impact of paleotemperature uncertainty on our
results (SI Appendix ). For Andean paleo-elevations we retrieved a
generalized model of the paleoelevation history of the tropical Andes,
compiled from several references (ref. (Lagomarsino et al. 2016)
and references therein)). We examined whether speciation and/or
extinction correlate with one of these variables using
paleoenvironment-dependent diversification models (Condamine et
al. 2013), which extends time-dependent models to account for potential
dependencies between diversification rates and measured environmental
variables. The elevation of the Andes caused dramatic modifications in
Neotropical landscapes and could have impacted indirectly the
diversification of non-Andean groups. Therefore, we decided to apply
uplift models to all clades in our study, independently on whether their
distribution is centered in the Andes or not. We fitted three models in
which speciation and/or extinction vary continuously with temperature
changes (λ(T) and μ(T)) , and three others with the
elevation of the Andes (λ(A) and μ(A)) . In this case,λ0 (μ0) is the expected
speciation (extinction) rate under a temperature of 0°C (or a
paleoelevation of 0 m for the uplift models). For the groups in which
diversification was temperature- or uplift-dependent, we analyzed
whether the speciation (α) and extinction (β) dependency
was positive or negative. For temperature models, α (β )
> 0 reflect increasing speciation (extinction) with
increasing temperatures, and conversely. For the uplift models, α(β ) > 0 reflect increasing speciation (extinction)
with increasing Andean elevations, and conversely. We accounted for
missing species for each clade in the form of sampling fraction(ρ) (Morlon et al. 2011) and assessed the strength of
support of the models by computing Akaike information criterion (AICc),
∆AICc, and Akaike weights (AICω) to select the best-fit model.
Finally, we tested the effect of clade age, size and sampling fraction
on the preferred model using Kruskal-Wallis rank sum test and performed
multiple pairwise-comparison between groups with corrections for
multiple testing using Wilcoxon rank sum test. We also evaluate if the
estimated diversification trends (i.e. , increasing, constant,
decreasing) differ between lineages characterized by different
geographic distributions, altitudinal ranges and habitat preferences
(see SI Appendix ).
Our study focuses on the effects of environmental factors over
diversity, and in this sense we did not directly assess the effect of
biotic factors. Only few models of that type exist, and none explicitly
incorporate species interactions within clades. The most relevant of
such models are diversity-dependent models where the number of extant
species in a clade affects diversification rates (Etienne et al.2012). We did not fit these models for three additional reasons:(i) it is not straightforward to compare diversity-dependent
models with other diversification models on the basis of AICc (Etienneet al. 2016); (ii) the sampling scheme currently
implemented in diversity-dependent models assumes that exactly nspecies are sampled (n -sampling), while other models assume that
each species is sampled with a fixed probability (ρ-sampling), and
likelihoods associated to these two sampling schemes are not directly
comparable (Stadler 2009; Lambert 2017); and (iii) previous
simulations have shown that these models tend to over-fit the data when
speciation decreases toward the present regardless the cause of decline
(Etienne et al. 2016; Condamine 2018). This latter artifact might
affect more than 78% of clades in our study, since we found most
phylogenies are characterized by constant or decreasing rates through
time (see Results ). Nonetheless, we acknowledge that the role of
biotic interactions in explaining Neotropical diversity remains to be
tested.
Acknowledgments. We thank all researchers who shared
their published data through databases or to us directly (Drs. Arevalo,
Martins, Fortes Santos, Simon, Lohmann, Mendoza, Swenson, Erkens, van
der Meijden, and Freitas), Dr. Sanmartín and Dr. Manzano for invaluable
comments on the manuscript and analyses. This work was funded by an
“Investissements d’Avenir ” grant managed by the Agence
Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01) and by the ANR
GAARAnti project (ANR-17-CE31-0009). A.A. is supported by the Knut and
Alice Wallenberg Foundation, the Swedish Research Council, the Swedish
Foundation for Strategic Research, and the Royal Botanic Gardens, Kew.