Methods
What to measure? Diet-breadth dependent performance trade-offs
could result from any number of mechanistic interactions between a
herbivorous insect and a host plant. On a particular host plant, in
comparison to a specialist, a generalist might have (1) a reduced
ability to initiate feeding, (2) a lower feeding rate, (3) less
efficient utilization of host nutrients, (4) greater susceptibility to
host defenses, or (5) more exposure to natural enemies. No matter the
mechanism, any trade-offs that drive the evolution of specialization
would need to ultimately limit survival or fecundity. If specialization
is an adaptive response to trade-offs between performance on alternative
hosts, specialists should have higher survival or fecundity than
generalists on shared resources. In the studied tropical forest plots,
we were not able to measure survival or fecundity directly, but we were
able to measure the abundance and patch occupancy of each diaspidid
species on each host-plant species. As mentioned earlier, because after
the crawler stage each diaspidid is stuck for life on one host, an
observation of a second-instar or adult individual on a host is evidence
of successful development on the host (Hill and Holmes 2009). Moreover,
the relative abundance of diaspidid species on each host-plant species
is an especially synthetic proxy for fitness – integrating across
host-dependent differences in diaspidid fecundity and survival.
Sampling. We surveyed diaspidids at two tropical rainforest
sites: (1) San Lorenzo National Park, Panama and (2) Lambir Hills
National Park, Malaysia (on the island of Borneo). At each site, we used
a crane to reach the forest canopy. We were not able to search each tree
in each plot, so we used preexisting databases of the trees at each site
to divide identified individual trees into sampling groups of one
randomly-selected individual per tree species. Only trees over 10 cm
diameter at breast height were considered. We did not sample any tree
individual more than once, so tree species with only one individual were
present only in the first round of sampling, those with two individuals
were present in the first two rounds, and so on. This protocol allowed
us to sample across the full diversity of host taxa while also getting
multiple samples from common host species.
At each focal tree, 20 person-minutes were allocated to visual searching
of accessible foliage. Any leaves and twigs that we saw were infested by
diaspidids, we cut from the tree and collected. From each tree we also
haphazardly took one 20 cm twig sample and one 20 cm2bark sample. Removed plant material was stored in plastic bags and
transferred to the lab for processing under magnification; live
diaspidids were cut from the surrounding plant material and preserved in
95% ethanol. Specimens were subsequently sorted to life stage and
second-instars and adult females were regarded as evidence of successful
establishment.
Phylogenetics. DNA was extracted from all second-instar and adult
females and using Qiagen DNeasy Blood & Tissue kits (Qiagen, Valencia,
CA) following the procedure outlined in Normark et al. (2014). We
amplified three loci that have previously been used for diaspidid
phylogenetics: elongation factor 1-α (EF1α), part of the large ribosomal
subunit rDNA gene (28S), and a part of the mitochondrial genome spanning
cytochrome c oxidase I and II (COI-II). PCR primers and protocols
followed Andersen et al. (2010) and Gwiazdowski et al. (2011). PCR
products were visualized using 1.5% agarose gels with SYBRsafe
(Invitrogen, Carlsbad, CA, USA) and successful reactions were purified
with Exo SAP-IT enzymatic digestion (Affymetrix, Cleveland, OH, USA).
Sanger sequencing of the PCR products was completed by Macrogen
(Cambridge, MA, USA) or Eton Biosciences (San Diego, CA, USA). Genbank
Accessions are provided in S1.
Phylogenetic relationships among all sampled individuals were estimated
from the DNA sequence data. Sequences from each genetic locus were
aligned using PASTA (Mirarab et al. 2014), and alignments were trimmed
to include only sites with non-gap sequence for at least 80% of
specimens (Capella-Gutiérrez et al. 2009). Genealogies were inferred
using the GTR+CAT model in RAxML (Stamatakis 2014). The three
single-locus alignments were then combined as one supermatrix, from
which we also inferred a phylogeny with RAxML. For use in comparative
analyses, we made a version of the phylogeny with just one tip per
species, and scaled branch lengths to time using an auto-correlated
model of among-lineage rate variation, fit with penalized likelihood as
implemented in treePL (Smith and O’Meara 2012), and constraining the
armored scale root to be 50-75 million years old (Vea and Grimaldi
2016).
Species Delimitation and Identification. We delimited putative
species with a version of the genealogical concordance method (as in
Gwiazdowski et al. 2011). All clades shared by at least two gene trees,
and not contradicted by the third gene tree, were considered
evolutionarily independent lineages. Species were defined provisionally
as the most inclusive independent lineages containing at least three
terminal branches and no more exclusive independent lineages. This
method precludes delimitation of species represented by fewer than three
specimens. To work around this problem, we calculated the minimum
divergence between provisional species clades, and used that value as a
maximum threshold for within-species divergence. Any specimens separated
by more than this distance from all other specimens were also considered
distinct species. We also examined specimens and identified them
according to standard morphological criteria to the extent that this was
possible. Because second instars and adults were both included in this
study, whereas standard keys and descriptions are based on adults only,
direct morphological comparisons and identifications were not always
possible. The analyses below were repeated for DNA-based and
morphology-based species delimitations. We retained all specimens in
both analyses, whether or not they were morphologically identifiable;
for a few specimens that were not morphologically identifiable, in the
morphology-based analysis we defaulted to the DNA-based species.
Statistical Analysis. We characterized host-use specialization by
diaspidid species in two ways, each applied at three levels of host
plant taxonomy (species, genus, and family). First, we quantified diet
specificity; we asked whether diaspidids used less diverse hosts than
expected by chance. Concretely, for each diaspidid species, we
quantified host-taxon diversity using Simpson’s Reciprocal Diversity
Index (RDI), which is essentially evenness-corrected host-taxon
richness. We compared empirical RDIs to those expected under a null
model of random host use. We simulated 1000 null data sets by randomly
permuting the associations between diaspidid species and individual host
trees; then for each permutation, we again calculated the mean RDI for
the hosts of each diaspidid. With this approach, a diaspdid species is a
specialist if its host RDI is lower than expected under the null model.
In a second view of host-use specialization, we calculated the
phylogenetic conservatism of host use across diaspidid species. In other
words, we asked if evolutionary history constrains host use. We used the
R package (R Core Team 2017) MCMCglmm (Hadfield and Nakagawa 2010) to
measure the phylogenetic signal of host use by estimating the proportion
of variance in the binary use-or-non-use of each host taxon that could
be explained by the diaspidid phylogeny. Empirical values for
phylogenetic signal were then compared to those calculated under a null
model. Null data sets were produced by randomly swapping associations
between diaspidid species and host taxa until the associations were
thoroughly shuffled (the number of random swaps was 10 times the overall
number of associations). This preserved the empirical distribution of
diet breadths while randomizing specific associations. P- values
for the empirical phylogenetic signal values were calculated using aZ -test against each parameter’s null data set values (which were
approximately normally distributed). We corrected for multiple
comparisons by assigning statistical significance according to a false
discovery rate (FDR; Benjamini & Hochberg 1995) of 0.05. The FDR
procedure was conducted separately for each host-taxon level because
these analyses were not independent, and must be interpreted as
alternative configurations of the same data.
We investigated the strength of performance trade-offs by calculating
for each host tree taxon the correlation between diaspidid diet breadth
(count of host taxa) and mean abundance. If performance trade-offs are
strong, on any given host taxon, we expect generalist diaspidids to be
less abundant than specialists. We also investigated the relationship
between diet breadth and the proportion of host trees of a taxon
colonized at each site, as patch occupancy may be a better indicator of
fitness than local abundance in a metapopulation of discrete colonies
(Gyllenberg and Metz 2001). Using R, we fit generalized linear models.
For local abundance, the response variable was the number of diaspidid
individuals identified per host tree, assuming a Poisson distribution.
For metapopulation colonization-rate, the response variable was the
probability that an individual tree within each host taxon would be
colonized by a diaspidid species, assuming a binomial distribution and
excluding host taxa with fewer than 3 trees surveyed. Both models only
incorporated data for host-taxon-by-diaspidid associations with at least
one record. To assess statistical significance, we compared empirical
coefficients to those estimated from 1000 null data sets, produced by
randomly permuting the empirical data.
The scripts used for the analysis will be made available at Dryad.