Estimation of ancestral ranges
We used a Microsoft Access database to compile locality data from 4050
Preponini specimens deposited at five butterfly collections: the McGuire
Center for Lepidoptera and Biodiversity, Florida Museum of Natural
History, University of Florida (Gainesville, FL, USA), National Museum
of Natural History, Smithsonian Institution (Washington D.C., USA),
American Museum of Natural History (New York, NY, USA), Instituto
Alexander von Humboldt (Villa de Leyva, Colombia), and the Instituto de
Ciencias Naturales - Universidad Nacional de Colombia Sede Bogotá
(Bogotá, Colombia). We georeferenced localities using Google Earth,
literature and published/unpublished gazetteers, and cleaned the
database to remove erroneous and imprecise localities. Our final
database contained 1121 locality records for the 31 taxa in
consideration.
We used the R package BioGeoBEARS 1.1.1
(Matzke, 2018) to estimate the
ancestral range of Preponini under the Dispersal-Extinction Cladogenesis
(DEC) model (Ree and Smith, 2008)
and a maximum likelihood implementation of the Dispersal-Vicariance
analysis (DIVALIKE) model (Ronquist,
1997). We did not include models with the parameter J since they seem
less relevant to continental settings and have been shown to be
difficult to compare statistically to other non-nested models
(Ree & Sanmartín, 2018). We tested
eight different hypotheses to evaluate the influence of different
biogeographic events and distance among areas in the evolutionary
history of Preponini (see Table 2 for details).
Briefly, the hypotheses account for the influence of the closing of the
Panama isthmus, the formation of the Andes and distance among eight
geographic areas in the Neotropics. We coded the areas as Central
America, Caribbean, Chocó and Caribbean lowlands, Western Andes, Eastern
Andes, Amazon (including Chaco and Cerrado), Guianas, and Atlantic
Forest, are based on NatureServe’s classification of the Neotropics into
’Ecological Systems’ (Josse, 2003).
Since one of our objectives was to test for the influence of major
biogeographical events, we designated four time slices representing
different paleo-geological stages. The four time slices used in our
analyses were: (1) 32-23; (2) 23-10 Ma , (3) 10-7 Ma, and (4) 7
Ma-present which represent the gradual formation of the connection
between South and Central America and Andean Uplift
(Bacon et al., 2015; Condamine et
al., 2012; Montes et al., 2015). We allowed the probability of movement
across areas to change in time, accounting for geographic position,
distance and for barriers to dispersal, and penalized accordingly (Table
S4 – S11). We evaluated the effect of distance by penalizing dispersal
probabilities with a factor proportional to the distance among the
centroids of the areas (Table S5, S8, S9, S11).
Both DEC and DIVA are biased towards estimating widespread ranges in
deep nodes (Buerki et al., 2011;
Clark et al., 2008; Matzke, 2014; Ree and Smith, 2008). In an attempt to
avoid this potential bias, the maximum number of areas any ancestor may
occupy was set to six, since this is the maximum number of areas
observed to be currently occupied by any single Preponini species
(Ronquist and Sanmartin, 2011; e.g.Archaeoprepona demophoon ). We reduced the number of potential
ranges in the reconstruction by including only geographic ranges with
adjacent areas. This resulted in 113 potential ranges from the 248
possible combinations. To identify the hypotheses with strongest
support, we compared AIC among the eight hypotheses and two possible
models.
Results