Data Analysis
We developed two types of occupancy models, single-species single-season
and co-occurrence models. The first ones estimate the probability of
occupancy for each site while considering the probability of detection
and assessing the effect of covariates on both probabilities (Mackenzie
et al., 2006). For each species we developed detection histories for
each site, considering 15 consecutive camera trap days as a sampling
occasion, thus having four sampling occasions for MML and three for OL.
For ungulates we included six additional sampling occasions
corresponding to the transects.
For each species, we first modelled detection probability using
covariates within the cell and the type of sampling, and selected the
best model using AIC and AICw (Burnham & Anderson, 2002). We then
evaluated separately the influence of each individual covariable on the
probability of occupancy and then generated additive models combining
the variables with the best response and with AIC < 2. For
those variables in the best models we estimated 80% confidence
intervals to assess whether β coefficients, which describe the effect,
overlapped with zero (Burnham & Anderson, 2002). All models were
adjusted using the package “Unmarked” from R (Fiske & Chandler,
2011). In this way, we assessed the most important variables affecting
occupancy in each landscape, both for shared species and for species
unique to each landscape. For the agouties and anteaters, we compared
species from the same genus between landscapes, and we did the same for
the two species of curassow, which have similar habits and are both
subject to hunting by humans. To evaluate the interacting effects of
landscape and body size in occupancy levels we conducted a tow-way
ANOVA, splitting the species in two groups, larger or smaller than 30kg.
To determine the effects of one species on the probability of detection
or occupancy of another species, we used co-occurrence occupancy models
(Mackenzie et al., 2004). For each landscape we developed models for
interactions between: Panthera onca (only MML), Puma
concolor , Cuniculus paca, Dasyprocta punctata ( MML) and D.
fuliginosa (OL). We used the parameterization\(\frac{\psi^{\text{Ba}}}{r^{\text{Ba}}}\) in the package RPresence for
R (MacKenzie & Hines, 2020) and estimated the following probabilities:\(\psi^{A}\) probability of occupancy of the dominant species (A),\(\psi^{\text{BA}}\) probability of occupancy of the subordinate species
when the dominant species is present, and \(\psi^{\text{Ba}}\) the
probability of occupancy of the subordinate species when the dominant
species is absent. We developed a set of models a priori , which
assumed that the presence of the dominant species influenced the
presence of the subordinate species
(\(\psi^{\text{BA}}\ \neq\ \psi^{\text{Ba}}\)), and constrained models
where the occupancy of the subordinate was independent of the presence
of the dominant species (\(\psi^{\text{BA}}\ =\ \psi^{\text{Ba}}\)).
We included those variables most important for each species derived from
the single-species models and we run two models for each pair of
species, switching the dominant species, as we assumed the effects were
not symmetrical (\(\psi^{\text{BA}}\ \neq\ \psi^{\text{AB}}\)).
For the probabilities of detection, we estimated the following
parameters: \(P^{A}\) probability of detecting the dominant species
given the absence of the subordinate, \(P^{B}\) probability of detecting
the subordinate given the absence of the dominant, \(r^{A}\) probability
of detecting the dominant given that both are present, \(r^{\text{BA}}\)probability of detecting the subordinate given that both are present and
the dominant is detected, \(r^{\text{Ba}}\) probability of detecting the
subordinate given that both are present and the dominant is not
detected. We built a set of models a priori , assuming that
detection probabilities of each species were independent of the presence
of the other species (\(p^{A}=r^{A}\) and\(p^{B}=r^{\text{BA}}=r^{\text{Ba}}\)), others in which only the
subordinate species was influenced by the presence of the dominant one
(\(p^{A}=r^{A}\) and \(p^{B}\neq r^{\text{BA}}=r^{\text{Ba}}\)) and
models where each species was influenced by the presence and detection
of the other species (\(p^{A}\neq r^{A}\) and\(p^{B}\neq r^{\text{BA}}\neq r^{\text{Ba}}\)).