Predation pressure
To quantify bird predator presence, we used observation data from
checklists submitted to eBird for species that are known to prey on
lizards. All potential lizard predators included species that
opportunistically feed on anoles, and were determined by considering the
diet of the bird species listed by the Cornell Lab of Ornithology
(https://birdsoftheworld.org/bow/home), or National Audubon
Society (https://www.audubon.org/bird-guide). Miguel Landestoy also
provided insight into the importance of certain Dominican Republic
species as predator of anoles, based on personal observation.
We looked at all eBird checklists for Jamaica and the Dominican Republic
from August 2010 – August 2020 and filtered to include both stationary
and travelling checklists (omitting incidental observations), with a
maximum of 5 hours duration, 10 observers, and 10 km distance. All data
were accessed in September of 2020. These checklists were treated as
presence-absence data (i.e., we assumed that all species not listed were
not observed).
We then used a general additive model using a binomial error
distribution to model the probability of each bird species being present
throughout Jamaica and the Dominican Republic, granting us the ability
to predict presence-probability in the vicinity of each lizard plot
location. Specifically, modeled bird presence was based on mean annual
temperature (bio1), temperature seasonality (bio4), annual precipitation
(bio12) and precipitation seasonality (bio15) obtained from WorldClim
(Fick and Hijmans 2017), and also considered checklist duration,
location, number of observers, distance travelled, day of year the
checklist was recorded on, and time of day at which observations began.
All continuous variables were implemented with thin-plate splines,
allowing the model to flexibly estimate the degree of curvature in the
relationship between the variable and bird occurrence. In addition, we
incorporated a two-dimensional thin plate spline based on the latitude
and longitude of the checklist to both account for spatial
autocorrelation into predictions, and to incorporate any unmeasured
environmental factors that correlate with space. Predictions of
probability of occurrence were then made for each species at the plot
locations where lizards were surveyed, at a resolution of approximately
1 km2 grid cells. To obtain standardized estimates of
predation pressure we considered a hypothetical eBird transect conducted
by a single observer, lasting for 60 minutes, and spanning 1 km. Because
bird occurrence varies between time of day and day of year, we made an
ensemble of predictions for each plot location evaluating hypothetical
transects every hour on the hour between 4 am and 3 pm, for all days of
the year. To summarize per species predation pressure at each plot
location we considered the maximum probability of presence across all
hours and across the entire year, under the theory that this value best
represents true occurrence probability (whereas lower values may be
primarily influenced by detection probability from non-ideal times of
day for sighting the species in question). In addition to using maximum
predator occurrence we also considered mean occurrence, but results were
qualitatively similar, and so we present only the maximum.
We used three complimentary sets of bird species to analyze predation
pressure. First, we considered all potential lizard predators, second
all cuckoo species, and third all pigeon species. The two taxonomically
restricted sets were chosen to represent specialized lizard predators,
and definitive non-predators respectively. Cuckoos in particular are
important predators of anoles, and both islands have a specialized
species of lizard-cuckoo. This led us to hypothesize that they might
represent a group for which predation signals on community structure may
be especially clear. In contrast pigeons are not predators (or
competitors) with anoles, and thus serve as a neutral “control” group,
for which we would not expect to observe any signal from either top-down
or bottom-up forces. Analysis of pigeons thus allows us to more
accurately interpret whether any observed effects are due to predator
species per se , or just correlation with general bird presence.
To obtain total predation pressure we summed the individual presence
values for all bird species in each category (i.e. , all predatory
birds, all cuckoos, and all pigeons [non-predatory control]). These
probabilities of predatory bird presence provided us with quantifiable
estimates of predation at a comparable spatial and temporal scale as the
lizard community data.
To evaluate whether greater predator occurrence is associated with
increased or decreased lizard community metrics we ran a linear
mixed-effects model for anole species richness and abundance, the latter
of which was square-root transformed to fulfill model assumptions of
residual normality. Predictor variables included our metric of predator
presence, habitat type (i.e., forest or human-modified), and
island identity, with site (where 3 to 6 plots were grouped) as a random
effect. We included two interaction terms. One was a nuisance term to
account for potential interactions between habitat type and island,
thereby allowing the effect of deforestation on anole community
structure to differ between Hispaniola and Jamaica. The second was a
hypothesis-driven interaction effect examining differences in the way
predation pressure affected anole communities on the two islands. We
also included mean annual temperature and mean annual precipitation to
control for climate. We conducted backward model selection, dropping
each term until only significant ones remained. All modeling was done in
R 1.4.1103.