Data from: Long-term capture data uncover shifts in the daily activity patterns of Amazonian birds

Although the duration of biological rhythms varies from milliseconds to
decades, daily cycles are especially widespread for individual organisms.
These circadian rhythms are synchronized by light-dark cycles, which
govern predictable changes in other microclimate variables. Despite the
fixed, regular nature of these abiotic cycles at low latitudes, little is
known about how different animals structure activity along these
gradients. We used 25 years of capture-recapture data and ~25,000 unique
captures to characterize ‘activity’ (time of capture) for 65 species of
Amazonian birds in the lowest forest stratum (0–2.5 m). To quantify
vertical niche breadths, we also measured foraging heights for these same
species in the field. Activity peaked within the first two hours after
sunrise for most species (71%), while others peaked 1–3 hours later (29%).
This difference was associated with an apparent preference for specific
vertical strata, as terrestrial and near-ground birds peaked ~1 hour after
sunrise, while birds from higher strata delayed their first bouts of
activity in the lower understory (by an average of 40 to 100 minutes).
Peak capture times also correlated with long-term abundance trends, as
only species with the earliest capture times showed strong declines,
suggesting that associations with mid- or late-morning microclimates may
contribute to resiliency in birds typical of higher strata. Because
arboreal species largely avoid the lower understory in the early morning,
when terrestrial birds are active, we infer that they are poorly adapted
to low-light environments. However, with their broad vertical niches and
reliance on brighter microhabitats, we suggest that an arboreal lifestyle
diminishes specialization and may increase the capacity to cope with a
wider range of daily microclimate conditions. We predict that subtle, but
important, shifts in daily activity patterns would materialize if other
biodiversity-monitoring programs could analyze similar temporal datasets.