Study Site.

We studied Tropical Screech-Owls at Estação Ecológica de Itirapina (EEI), a reserve located between Itirapina and Brotas municipalities in the central state of São Paulo, southeastern Brazil (22°15′S, 47°49′W). The EEI covers approximately 2300 ha, including elevations ranging from 705 to 750 m (SEMA 2000). Average annual rainfall is 1459 mm, with a dry season occurring from April to September (23–75 mm per month) and a rainy season from October to March (120–275 mm per month; Silva 2005).

The EEI is one of the last major preserved natural savanna (cerrado) patches in the state of São Paulo, and in terms of vegetation density, its physiognomies range from open grasslands to closed woodland savannas (Coutinho 1978, Fig. 1) as well as gallery forests, wetlands, and small patches of invasive pine (Pinus elliottii; Zanchetta and Diniz 2006).

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Owl Capture and Radio-tagging.

Between October 2009 and February 2011, we trapped Tropical Screech-Owls using mist nets, with broadcasts of conspecific vocalizations to attract owls (Whalen and Watts 1999). For each captured owl, we collected a blood sample for sex determination (Cerit and Avanus 2007), banded the owl with one aluminum band (IBAMA 1994), weighed it with a Pesola scale, and attached a 5-g radio transmitter (model TXB-120, TELENAX, Playa del Carmen, Quintana Roo, Mexico) in a backpack configuration with waxed cotton lines (Smith and Gilbert 1981). After handling, we released the owls at the capture sites.

Telemetry Data Collection.

Between October 2009 and January 2010 and, subsequently, between December 2010 and June 2011, we recorded the locations of the owls in nocturnal and diurnal periods via both triangulation and direct observation (White and Garrot 1990, Millspaugh and Marzluff 2001). We recorded all owl locations using a radio receiver (model LA12-Q, AVM Instrument Company, Colfax, California, U.S.A.), a three-element yagi antenna, a GPS device, headphones, and a compass to triangulate locations. We obtained all locations in a UTM system with a radius error of up to 8 m.

During the nocturnal period, we recorded most locations via triangulation, which entailed one researcher recording three consecutive positions and bearings, spending the shortest time possible between successive recordings (Carey et al. 1989, White and Garrot 1990). We estimated the triangulated locations in the program Locate II (Nams 2002) and corrected all bearings according to the magnetic declination in the study area during the survey periods. To assess the triangulation error, we collected simulated triangulation data, recording the locations of hidden transmitters at different known sites in EEI. Thus, using program ArcGis 9.3 (ERSI, Redlands, California, U.S.A.), we measured the distance between the real point and the triangulation-estimated point (Barros et al. 2011). The mean (±SD) error in triangulation locations was 15.3 (11.3) m (n  =  10). During the night, we occasionally established visual contact with tagged owls, mainly when they were perched close to roads or trails. In this case, we recorded the exact location where the owl was perched. In diurnal periods, we recorded locations only through direct observation.

We radio-tracked each owl for 5–6 mo. Because this species is more active from the late afternoon to the first hours of the night (Braga and Motta-Junior 2009), we recorded all nocturnal locations during this period (1800–2300 H). To ensure temporal independence of locations, we allowed at least 40–50 min between consecutive nocturnal locations. We considered this amount of time to be sufficient for the owls to move to any site within their home range (Lair 1987, Barros et al. 2011). As the Tropical Screech-Owl tends to be inactive throughout the day (Motta-Junior 2002, König and Weick 2008), we obtained only one roosting location per day during this period.

Analysis of Telemetry Data.

We calculated individual home-range sizes via the fixed kernel (FK) method (Worton 1989) using a least-squares cross-validation (Horne and Garton 2006). To compare these results with those of other surveys, we used the 95% minimum convex polygon method (Mohr 1947). We calculated the core areas (center of activity) within the home ranges using a 65% FK with a least-squares cross-validation. We performed all home-range analyses in the program Biotas 2.2 alpha (Biotas 2011). We assessed the sample sufficiency for home-range size estimation based on individual area-observation curves, using the 95% MCP method, which does not consider location density.

To assess habitat use and selection, we generated a map of the study site from a satellite image (1-m spatial resolution) from the program Google Earth (Google 2012). Then, using ArcGis 9.3 (ERSI, Redlands, California, U.S.A.), we classified all habitat types present in EEI. To ensure the accuracy of image classification, we checked all habitats in the field as references for image interpretation.

We classified the habitats as follows: wetlands (Wet): open and semi-open floodable areas with moist soil and a predominance of grasses; campo limpo (Cli): grasslands containing native grasses and some patches of exotic grass (Brachiaria spp.); campo sujo (Csu): grassland savannas containing native and exotic grasses with some shrubs and scattered small trees; campo cerrado (Cce): semi-open areas similar to Csu, but with a higher density of shrubs and small trees; cerrado sensu stricto (Css): semi-closed woodland savanna with spatially dense shrubs and trees; gallery forest (Gfo): closed forests near streams with dense tree vegetation and hydromorphic soils; and invasive pine (Ipi): closed and dense woodlot with a predominance of pine (Pinus elliottii) and with some typical cerrado shrubs and small trees. In EEI, Ipi is associated with wetlands that were colonized by dispersive pines from nearby pine woodlots (see Zanchetta and Diniz 2006). We classified the gradient of cerrado physiognomies (campo limpo to cerrado sensu stricto) according to Coutinho (1978; Fig. 1).

To test whether habitat use differed between diurnal and nocturnal periods, we used the chi-square test (χ²) of independence, comparing the frequency of locations in each habitat for all individuals. To test habitat selection, we compared used versus available habitats within each owl home ranges using Bailey's confidence interval (Cherry 1996). To estimate habitat availability, we considered the proportional area of each habitat within each individual 100% MCP home range, which included all of the sites used and made no probabilistic assumptions (Boal et al. 2001). To identify more general habitat use patterns, we analyzed habitat selection for all the studied owls together, by summing all habitat area available (within each individual home range, MCP 100%) and all locations in the habitats used by the four owls. We estimated the individual available habitats in Biotas 2.2 alpha (Biotas 2011).

Home-range Size.

The mean (±SD) home-range size of the four male Tropical Screech-Owls was estimated to be 51.2 (26.9) or 80.8 (40.2) ha according to 95% MCP and 95% FK, respectively (Table 1, Fig. 2). The mean (±SD) core area (FK 65%) was 22.4 (8.8) ha (Table 1). The home-range area observation curves for three individuals reached asymptotes at between 15 and 55 locations (Fig. 3). Although the home-range area observation curve for M4 did not clearly reach an asymptote, its curve tended toward stabilization; hence, we considered the data for this individual to be sufficient.

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Table 1.  Home-range sizes of four male Tropical Screech-Owls at Estação Ecológica de Itirapina, southeastern Brazil (2009–2011).

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Habitat Use and Habitat Selection.

In both periods (diurnal and nocturnal, n  =  335), the Tropical Screech-Owls together mainly used campo cerrado (50.9%) and campo sujo (28.4%) habitats. When only nocturnal locations were considered (n  =  238), we found that the same two habitats were used most by the owls (campo cerrado: 54.1%, campo sujo: 36.4%). Considering only diurnal locations (n  =  97), the habitats that were used the most by the owls were campo cerrado (43.0%) and invasive pine (23.7%; Table 2). Habitat use differed significantly between diurnal and nocturnal periods (χ²  =  82.159; df  =  6; P < 0.001).

Table 2.  Habitat selection by male Tropical Screech-Owls at Estação Ecológica de Itirapina, southeastern Brazil (2009–2011). (Wet) wetlands, (Csu) campo sujo, (Cce) campo cerrado, (Css) cerrado sensu stricto, (Gfo) gallery forest, and (Ipi) invasive pine. (NU) not used, (+) selected, (−) avoided, ( = ) used according to the availability.

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During the nocturnal periods, the four owls selected campo cerrado and cerrado sensu stricto and avoided wetland and campo sujo, while during the diurnal periods, the owls selected semi-closed and closed physiognomies (cerrado sensu stricto, gallery forest and invasive pine) and avoided open ones (campo sujo; Table 2). We excluded the campo limpo land-cover type from our analysis because among the 335 locations recorded for all individuals in both periods, only one was situated in this land-cover type (M1, at night); thus, we did not consider campo limpo to be a significant habitat for the Tropical Screech-Owls in this study.

Habitat Use and Habitat Selection.

In general, during the night, most owls hunt, rest, engage in social interactions, and defend territories (Boxall and Lein 1989, Sick 1997, König and Weick 2008, Campioni et al. 2010), whereas during the day, nocturnal owls (such as Tropical Screech-Owls) only rest (König and Weick 2008), although some species may sometimes hunt opportunistically (Sovern et al. 1994, Herter et al. 2002). In the present study, the habitats were not used equally by the owls in the diurnal and nocturnal periods, suggesting that their habitat use is modulated by the activities performed during each period. In the early evening, owls typically move from roost sites to hunting areas, which may be far apart (König and Weick 2008), and this may also be the case for Tropical Screech-Owls in EEI. Except for M1, which seemed to be a non-territorial floater, all the owls appeared to roost at determined sites and hunt at other sites (Fig. 2).