Habitat selection by the endangered Red-billed ...

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Habitat selection by the endangered Red-billed Curassow (Crax blumenbachii) in an Atlantic forest remnant Fernanda Alves , Germán M. López-Iborra, Dejan Stojanovic & Luís Fábio Silveira To cite this article: Fernanda Alves , Germán M. López-Iborra, Dejan Stojanovic & Luís Fábio Silveira (2017): Habitat selection by the endangered Red-billed Curassow (Crax blumenbachii) in an Atlantic forest remnant, Emu - Austral Ornithology To link to this article: http://dx.doi.org/10.1080/01584197.2017.1326010

Published online: 30 May 2017.

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Date: 31 May 2017, At: 15:38

EMU - AUSTRAL ORNITHOLOGY, 2017 https://doi.org/10.1080/01584197.2017.1326010

Habitat selection by the endangered Red-billed Curassow (Crax blumenbachii) in an Atlantic forest remnant Fernanda Alves

a

, Germán M. López-Iborrab, Dejan Stojanovicc and Luís Fábio Silveiraa

a

Seção de Aves, Museu de Zoologia da Universidade de São Paulo, São Paulo, Brazil; bDepartamento de Ecología/IMEM Ramon Margalef, Universidad de Alicante, Alicante, Spain; cFenner School of Environment and Society, Australian National University, Canberra, Australia ABSTRACT

ARTICLE HISTORY

Understanding habitat selection is important for informing conservation management actions. However, many endangered species are data deficient, especially in tropical forests. Wild populations of the endangered Red-billed Curassow are one such data-deficient species. We investigated habitat selection by Red-billed Curassows in an important Atlantic forest remnant in Espírito Santo state, Brazil. We sampled vegetation plots to test fine-scale habitat associations and used GIS tools to identify landscape-scale variables that may influence curassow habitat use. We modelled the occurrence of Red-billed Curassows to test the contribution of these variables using hierarchical partitioning analysis in R. Abundance of standing dead trees, decaying log and leaf litter depth had a negative effect on the occurrence of Red-billed Curassows. The species preferred tall forests and abundant trees with diameter at breast height of 11–30 cm. Our results indicated that the Red-billed Curassow can utilise some secondary forest habitats, and suggest a preference for more open forest habitats that may facilitate terrestrial foraging. This is the first scientific examination of habitat requirements of Red-billed Curassows and our results will aid conservation activities by improving site selection for reintroduction efforts.

Received 26 August 2016 Accepted 24 April 2017

Understanding animal habitat preferences provides fundamental ecological information that is critical for effective management (Manly et al. 2002; Jones 2004). However, for many species this information is either limited or unavailable, which impedes effective conservation (Possingham et al. 2001). This is especially problematic in tropical forests, where habitat loss and species extinction are severe and accelerating (Pimm and Jenkins 2010). For species guilds whose ecology affects ecosystem function (e.g. pollinators, seed dispersers, predators), detailed knowledge of habitat requirements is critical to preserving ecosystem function (Marsden and Pilgrim 2003; Cranmer et al. 2012). Where such keystone species are threatened, conservation action to preserve their populations can impact other species (Cardoso da Silva and Tabarelli 2000; Sekercioglu et al. 2004; Kakishima et al. 2015). For example, seed dispersers are a guild with a critical role in forest ecology, but are also increasingly affected by anthropogenic impacts (Turner 1996; Cardoso da Silva and Tabarelli 2000; Kakishima et al. 2015). Anthropogenic impact on forest cover in the Neotropics is an internationally recognised conservation challenge, but chronic data deficiency limits CONTACT Fernanda Alves © 2017 BirdLife Australia

[email protected]

KEYWORDS

Brazil; cracids; conservation; habitat use; management

understanding of the impact of forest disturbance on vulnerable species. In the Neotropics, cracids (curassows, guans and chachalacas) play an important role in tropical forests as seed dispersers/predators (Brooks and Strahl 2000). Cracids are sensitive to habitat loss and fragmentation (Delacour and Amadon 2004), and combined with hunting, cumulative anthropogenic changes have caused cracids to be recognised as one of the most threatened groups in the Neotropics (Brooks and Fuller 2006). Curassows (Crax spp., Mitu spp.) may require large areas of mature forest for their survival due to their dependence on forest plants for food (Del Hoyo 1994; Delacour and Amadon 2004; Muñoz and Kattan 2007), although studies on habitat requirements of cracids have shown that several species are able to use secondary (i.e. anthropogenically disturbed) forests at least to some extent (Borges 1999; Schmitz-Ornés 1999). For instance, chachalacas (Ortalis spp.) and some guan species are known to use secondary and relatively disturbed habitats (Silva and Strahl 1991; Schmitz-Ornés 1999). In contrast, curassows are comparatively scarce and more dependent on primary habitats, but they have also been recorded occurring in secondary forests (e.g. Crax alector, Borges 1999; Crax alberti, Moreno-Palacios and Molina-

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Martinez 2008). Given the contrasting ecology and conservation status of different cracids, it is unlikely that conservation managers could reliably apply patterns of sensitivity to disturbance and habitat use by one cracid species to other species. This lack of data is a particular challenge for managing conservation programs targeted at threatened cracids, which employ resource-intensive approaches such as reintroduction programs (Armstrong and Seddon 2008; WPA-IUCN 2009). In this study we consider the habitat preferences of the endangered Red-billed Curassow (Crax blumenbachii). The species is endemic to lowland Brazilian Atlantic forest (Delacour and Amadon 2004; IBAMA and Ministério do Meio Ambiente 2004) which has been severely deforested, and their remaining populations occur in relict habitat patches (IBAMA and Ministério do Meio Ambiente 2004). Red-billed Curassows are chronically data deficient and available information is contradictory about their dependence on mature (Sick 1970; Delacour and Amadon 2004) vs. secondary forest (Teixeira and Snow 1982; Collar and Gonzaga 1988; Bernardo et al. 2011). There is some habitat-use information available derived from individuals in a reintroduced population (Bernardo et al. 2011), but data from studies on wild populations are still unavailable to inform decision making (Armstrong and Seddon 2008). Given that reintroduction has been considered a feasible strategy for the recovery of the species (Bernardo and Locke 2014), detailed knowledge of their habitat preferences can be applied to select the best locations to release individuals and to inform the habitat management that should be undertaken to improve habitat quality. For relict wild populations this information may be used to limit impacts that could affect preferred habitat or to improve habitat quality. Therefore, we attempt to describe habitat use by a wild population of Red-billed Curassows in a major remnant of lowland Brazilian Atlantic forest, considering both fine-scale and landscape-scale characteristics of habitat. Our study is the first detailed investigation of habitat use by a wild population of this species and we discuss the management implications of these results for conservation of Red-billed Curassow populations and identify knowledge gaps and research priorities for their conservation.

Methods Study area We conducted field work from March 2012 to February 2013 at Vale Nature Reserve (VNR), one of the largest areas of remnant Atlantic forest. The study

area comprises ~23 000 ha of lowland Atlantic forest in Espírito Santo state, Brazil (19°06′–19°15′ S, 39°52′–40° 05′ W, elevation: 28–65 m; Figure 1).The reserve is covered by lowland Atlantic coastal forest which in the region is dominated by five vegetation formations: (1) tabuleiro forest: coastal plain forest on clay or sandy-clay soil, closed ~40 m tall canopy, greatest richness of tree species (Myrtaceae, Leguminosae, Annonaceae, Sapotaceae, Rubiaceae, and Bignoniaceae) and considered to be optimal Red-billed Curassow habitat (Delacour and Amadon 2004; IBAMA and Ministério do Meio Ambiente 2004); (2) mussununga: similar to tabuleiro forest, but lower and more open canopy; (3) transition zone between tabuleiro and mussununga (Arecaceae prevalent, especially Euterpe aff. edulis and Attalea humilis); (4) riparian forest: permanently/seasonally flooded areas associated with streams (predominance of palms); and (5) nativo: open fields with natural grassland and shrubs (Peixoto and Gentry 1990; Jesus and Rolim 2005; Peixoto et al. 2008). VNR is surrounded by a matrix of pasture, cropland (Jesus and Rolim 2005) and exotic Eucalyptus plantations. The temperature in this region ranges from 14.8°C to 34.2°C (mean 23.3°C) and mean annual precipitation is 1202 mm, with a wet season from October to March and a dry season from April to September (Jesus and Rolim 2005). Data collection and field protocol We used line transects (Buckland et al. 2001) to survey Red-billed Curassows at VNR from March 2012 to February 2013 (Alves et al. 2017). We surveyed transects every month, and we used sites where curassows were detected (see below) as points where habitat-use data were recorded (hereafter: curassow plots). A total of 13 transects designed as square circuits of approximately 4 km each (Figure 1) were placed randomly within tabuleiro forest (Alves et al. 2017), and sometimes transects intersected patches of riparian and mussununga forest, which are gradual transitional habitat within tabuleiro forest and therefore were included in our models. No transects were placed in nativo because this is not a potential habitat for Red-billed Curassows. Because little information is available on Red-billed Curassow ecology, we tested variables related to forest disturbance, availability of food resources and other habitat features that influence the ecology of other curassow species (e.g. Martinez-Morales 1999; Muñoz and Kattan 2007; Luna-Maira et al. 2013). To evaluate fine-scale vegetation characteristics, we compared vegetation characteristics in curassow plots to random plots. Locations where Red-billed Curassows were

EMU – AUSTRAL ORNITHOLOGY

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Figure 1. Vale Nature Reserve showing placement of line transects. Hollow circles indicate places where Red-billed Curassows were recorded, and stars indicate random plot locations.

detected during transect surveys (Alves et al. 2017) were used as curassow plot centroids. We selected random plots to quantify characteristics of available habitat. All plots were 10 m radius circles (314 m2). After all surveys had been completed, five random plots were situated along each transect within 60 m (i.e. the distance of the farthest Curassow detection) of the line. Random plots were located >100 m from one another and from curassow plots. Dirt roads (including road stretches where curassows were detected) were excluded from analysis. We recorded both visual and aural detections, but the latter were included in analysis only if they were confirmed visually. At each plot we recorded the following fine-scale habitat features: (1) number of trees in each of four categories of diameter at breast height (DBH: ≤10 cm, 11–30 cm, 31–60 cm, and >60 cm); (2) height of emergent trees; (3) canopy height; and (4) number of palms. These habitat-structure variables may be related to habitat quality for curassows because they can indicate the presence of keystone plant resources, characteristic of mature forests, and the presence of tree families that are known food for other curassows. We also collected variables that we believed to be related to abundance of invertebrate prey that are known to be part of other curassow diet (Bennett 2000), but that also relate to forest disturbance, specifically: (5)

number of standing dead trees (snags); (6) number of decaying logs; (7) canopy cover (measured using a convex spherical densitometer following Lemmon (1957); (8) mean leaf litter depth (measured at the plot centroid, 5 and 10 m intervals along each compass quadrant); and (9) abundance of lianas (ordinal categorical score: 0 = none, 1 = few, 2 = common, 3 = abundant, and 4 = very abundant). We also estimated additional landscape-scale variables for each plot using GIS software, specifically: (1) distance to streams; (2) distance to dirt roads; (3) distance to mussununga; (4) distance to nativo (open fields); and (5) distance to forest edge (Table 1). Data analyses First, we tested for collinearity among explanatory variables by using variance inflation factor (VIF) analysis. We removed the variables ‘emergent height’, ‘mean emergent height’ and ‘tree density’ which presented VIF values higher than 3 (Zuur et al. 2010). We used generalised linear models (GLM), with logit link and binomial distribution to model the probability that a plot was a curassow or random plot. We used the ‘bestglm’ package (McLeod and Xu 2014) to derive the 300 best models based on Akaike information criterion (AICc) criteria. We selected the models with ΔAICc ≤7 and calculated

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Table 1. Vegetation structure variables and landscape variables analysed in this study Random plots N = 65 Mean ± SE

Presence N = 37 Mean ± SE

Vegetation structure variables (1) Abundance of trees with DBH ≤10 cm 25.06 ± 0.77 26.72 ± (2) Mean DBH of trees with DBH ≤10 cm 6.97 ± 0.04 7.02 ± (3) Abundance of trees with DBH from 11 up to 30 cm 18.2 ± 0.5 19.18 ± (4) Mean DBH of trees with DBH from 11 to 30 cm 15.79 ± 0.12 15.64 ± (5) Abundance of trees with DBH from 31 to 60 cm 3.09 ± 0.24 2.57 ± (6) Abundance of trees with DBH >60 cm 0.78 ± 12 1.05 ± (7) Emergent height (m): estimated in every sub plot and then 42.67 ± 1.08 41.98 ± averaged (8) Canopy height (m): estimated in every sub plot and then 20.46 ± 0.72 24.87 ± averaged (9) Canopy opening (%): measured using a convex spherical 1.96 ± 0.17 1.87 ± densitometer (10) Leaf litter depth (cm): measured at the plot centre, 5 and 10 m 5.52 ± 0.14 4.95 ± from the centre facing north, east, south and west (11) Abundance of lianas using five categories: 0 = none; 1 = few; 1.56 ± 0.01 1.42 ± 2 = common; 3 = abundant; 4 = very abundant (12) Abundance of palms (with and without trunk) 9.85 ± 1.09 6.55 ± (13) Snag abundance 4.35 ± 0.36 2.33 ± (14) Decaying log abundance 10.11 ± 0.46 6.92 ± Landscape variables (15) Distance to the nearest stream (m) 1140.12 ± 80.75 1507.17 ± (16) Distance to the nearest internal road (m) 463.8 ± 34.43 512.29 ± (17) Distance to the nearest forest edge (m) 1632.28 ± 102.08 1756.5 ± (18) Distance to the nearest mussununga patch (m) 929.01 ± 67.36 1001.52 ± (19) Distance to the nearest nativo patch (m) 1580.21 ± 83.01 1594.72 ±

Percentage of models in which variables were included

Sum of AICc weights

1.41 0.05 0.81 0.21 0.29 0.18 1.7

30.7 14.2 100 14.2 94.9 27.7 -

0.21 0.10 0.99 0.10 0.93 0.21 -

1.22

100

0.99

0.26

17.6

0.13

0.15

99.3

0.99

0.11

42.9

0.35

1.3 0.32 0.53 103.7 63.02 126.24 82.55 98.33

100 100 100 18.2 26 13.9 31.1 13.9

0.99 0.99 0.99 0.13 0.21 0.10 0.24 0.10

For variables 7–11, the plot was subdivided into four sections and measures were taken in each section and then averaged. The mean value of each variable in random and presence plots is shown (SE = standard error). The last two columns present the percentage of the models (generated using the bestglm package; see methods) in which each variable was included, and the sum of AICc weights of these models.

their AICc weight (Burnham et al. 2011). Then for each variable, the AICc weights of the models where it is included were summed. This value was used to select variables entering hierarchical partitioning (HP) analysis (see below). To take into account spatial autocorrelation we included a spatial term of the form x + y + x2 + xy + y2 + x3 + x2y + xy2 + y3 (Legendre and Legendre 1998), where x and y are the Universal Transverse Mercator coordinates of the plot. We then performed HP analysis (Chevan and Sutherland 1991; MacNally 2000) with the variables selected from the above process. Hierarchical partitioning results are unstable when more than nine variables are included (Olea et al. 2010), so we selected the nine variables with the highest sum of AICc weights. We used the ‘hier.part’ package (Walsh and MacNally 2013) for HP using logistic regression and log-likelihood as the goodness-of-fit measure. The significance of the independent contribution of the environmental variables was evaluated by randomisation tests based on 999 randomisations (MacNally 2002).

Results We recorded 37 visual detections of Red-billed Curassows (including booming males that were detected both aurally and visually) and 12 aural

detections (which were excluded from further analysis). We collected data from 102 plots (n = 37 curassow plots, n = 65 random plots). We detected 13 curassows in the dry season and 24 in the wet season. The species was detected in all but two transects (Figure 1). The difference in AICc with the bestglm model was less than seven in 296 models. Eight vegetation variables and the spatial term were selected using the bestglm procedure and sum of AICc weights (Tables 1 and 2). None of the landscape variables considered were selected due to their low sum of AICc weights. According to HP (Table 2), all selected variables presented a significant contribution to explain the presence of the Red-billed Curassow at VNR, with the exception of abundance of lianas. These variables were also significant when included together in a GLM that explained 58% of the deviance (Table 2). Abundance of decaying logs had the greatest independent contribution to the model (28.61%) and was negatively related to the occurrence of Red-billed Curassows (Figure 2). Snags (15.23%), leaf litter depth (6.70%), trees in the DBH category 31–60 cm (5.62%) and palms (12.76%) were negatively related to curassow presence. We found significant positive relationships between curassow presence and mean canopy height (13.90%) and trees in the DBH category 11–30 cm (6.40%). Although significant, the effects of


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Table 2. Results of the hierarchical partitioning (HP) analyses performed with the best nine variables selected according to their AICc sum in bestglm models Hierarchical partitioning (Intercept) Spatial term Decaying log abundance Snag abundance Palm abundance Mean canopy height Mean leaf litter depth Abundance of trees with DBH from 10 to 30 cm Abundance of trees with DBH from 31 to 60 cm Liana abundance

I – 3.54 10.97 5.84 4.89 5.33 2.57 2.45 2.15 0.60

GLM J – 2.79 −1.72 2.23 −3.09 −0.10 0.61 −1.84 −1.25 0.01

%I – 9.23 28.61 15.23 12.76 13.90 6.70 6.36 5.62 1.56

z score – 4.27*** 15.42*** 7.67** 6.50*** 6.48** 2.52** 2.59** 2.59* 0.14

Estimate 1.40 2.96 −0.53 −0.57 −0.21 0.23 −0.77 0.27 −0.51 –

SE 2.96 2.24 0.13 0.22 0.07 0.08 0.33 0.10 0.26 –

z value 0.47 1.32 −3.91*** −2.57** −3.09*** 2.96** −2.32** 2.64** −1.96* –

Null deviance: 133.616 on 101 degrees of freedom. Residual deviance: 58.171 on 93 degrees of freedom. Percentage deviance explained: 58. A GLM fitted with the variables that were significant in HP is also shown. In HP results, I and J are, respectively, the independent and joint contribution of a variable. %I is the percentage of the total I accounted for by each habitat variable. z scores in HP test the independent contribution of each variable using 999 randomisations. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 2. Relationship between probability of Red-billed Curassow occurrence and environmental variables that were significant in HP analyses. Hollow circles represent raw data but their positions are slightly spaced along the vertical axis to avoid overlapping. The predicted regression line is represented in bold and confidence intervals (95%) are shown in grey around the line.

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some of these habitat characteristics on curassow occurrence were weak.

Discussion We present the first quantification of habitat selection by a wild population of Red-billed Curassows. We found some significant relationships between vegetation characteristics and curassow occurrence probability at VNR. Abundance of snags, decaying logs and leaf litter were the most important variables in our models of habitat use by curassows. Unexpectedly, though, our models show that these three habitat features were negatively related to the presence of Red-billed Curassows. Although decaying log, snags and litter are habitat features that favour the abundance of some invertebrates, particularly saproxylic insects (Grove 2002), given that invertebrates are less important in curassows’ diet than plants, it is likely that these results may be explained by some negative ecological consequences associated with these habitat features. Areas with a high abundance of snags had traits indicating forest disturbance (e.g. recovering forest gaps; Rodrigues and Peixoto 1993; Yamamoto 2000), and Red-billed Curassows were rarely found in plots with more than three snags. In contrast, up to 13 snags (mostly with small DBH) could be found in random plots. High snag abundance is not characteristic of mature tropical forests (Gibbs et al. 1993) and is related to previous disturbance caused by edge effects or canopy gaps (Rodrigues and Peixoto 1993; Yamamoto 2000; Laurance et al. 2007). The negative association with decaying log abundance and deep litter we found in our models may be attributable to impediment of terrestrial behaviours of Red-billed Curassows (e.g. reduced access to fallen fruits, heightened predation risk) and this result warrants further investigation to identify the mechanisms that underpin it. Among variables positively related to Red-billed Curassow habitat use, canopy height was the most important and ranked third highest among variables in the HP results. This relationship is explained by the presence of large keystone plant resources (Richards 1996; Curran and Leighton 2000; Peres 2000a; DiazMartin et al. 2014) such as Virola gardneri and Lecythis pisonis which are tall trees characteristic of mature forest and whose fruits are known curassow food (Sick 1970). We also found that tree size classes influenced the occurrence of Red-billed Curassows but this effect was relatively weak. Our data show some preference by Red-billed Curassows for areas with a high density of trees with DBH ranging from 11 to 30 cm. In contrast, areas dominated by trees of larger diameter

(DBH 31–60 cm) were less likely to be used. At VNR the diversity of plant families that produce fleshy fruits is high in the DBH class of 11–30 cm (Jesus and Rolim 2005). This is especially the case for Myrtaceae, which are usually dispersed by large birds (Pizo 2002; IBAMA and Ministério do Meio Ambiente 2004) and whose high abundance is characteristic of remnant forests where wild populations of Red-billed Curassows remain (IBAMA and Ministério do Meio Ambiente 2004). Trees from the families Fabaceae, Moraceae and Rubiaceae, which are food for other curassow species (IBAMA and Ministério do Meio Ambiente 2004; Muñoz and Kattan 2007) also occur in the medium to upper strata of the canopy of our study area, and their mean DBH falls within the 11–30 cm interval (Peixoto et al. 1995, 2008), and may explain our observations. Palms were negatively associated with Red-billed Curassow detections in our data, which might be explained in part because palms are particularly abundant in areas subject to flooding (Peixoto et al. 2008). Such areas may be avoided by ground-dwelling Redbilled Curassows. In addition, the species composition of palms could also explain this result because Redbilled Curassows feed on fruits of some palms (for instance Geonoma spp.; Sick 1970), but might avoid others. Vale Nature Reserve has a high diversity of palm species (24; Peixoto et al. 2008) and little information is available about which species are used as a food source by curassows. None of the landscape scale habitat variables were included in the best candidate explanatory variables. The transition between patches of mussununga and tabuleiro forest matrix can be difficult to distinguish due to their similar vegetation composition, and this may explain why distance to mussununga patches was not selected. Freshwater streams and small rivers have been reported to be important for other curassows (Martinez-Morales 1999; Hill et al. 2008; LunaMaira et al. 2013) and for a reintroduced population of Red-billed Curassows (Bernardo et al. 2011), but we did not detect an effect of distance to streams in our analysis. We recorded most curassows during the wet season in the northern portion of the reserve where water is abundant and hence is unlikely to act as a limiting resource. Another alternative is the presence of intermittent streams at VNR (Kierulff et al. 2014), which may reduce the likelihood of detecting a relationship between curassow detections and mapped water sources. However, factors other than water availability may drive the patterns of occupancy we recorded. For instance, most of the tabuleiro forest is concentrated in the north of


VNR and hunting pressure is less intense there (Srbek-Araujo et al. 2012). We did not detect an influence of the distance to forest edge on the probability of Red-billed Curassow occurrence. However we interpret this result cautiously because our transects were >550 m from forest edges and we may have been unable to detect edge effects (Laurance et al. 2002). Our findings demonstrate that some fine-scale habitat features are important for Red-billed Curassow occurrence, and these may be related to their diet and foraging behaviour. These findings have important applications on the selection of sites for the reintroduction of curassows. Our study indicates that tall canopy, including large, emergent food trees, is important for curassows, but they can also occur in areas where medium to small fruit-bearing trees are abundant. This suggests that secondary forests may still act as potential habitat for this species, provided that fruitbearing trees are present and the habitat structure is appropriate. Our study also highlights the need to better understand seasonal differences in habitat preferences, especially in relation to food resources and water availability. Further, landscape-scale patterns of habitat use in the few forest remnants where the species persists are still poorly understood. These fragments vary in their level of protection and habitat quality, thus understanding landscape patterns of curassow occurrence is essential to develop a successful protocol to manage isolated relict populations. Hunting is known to extirpate curassows from otherwise suitable habitats (Peres 2000b), and therefore the need to fully protect remnant populations from poachers is also of paramount importance. Our study shows that for elusive curassows, patterns of habitat selection may not always be easy to recognise. Although mature trees may be associated with higher food availability, other fine-scale habitat characteristics may exert an important influence on whether habitat is suitable, sometimes in unexpected ways. Anthropogenic disturbance has important impacts on the structure and composition of forests, and these factors may affect the likelihood of seeddispersing animals moving through and using disturbed locations. This in turn may have important ramifications for the capacity of forests to regenerate via natural seed dispersal after disturbance. Our study is a step towards understanding how a chronically understudied threatened species uses habitat, and highlights a need to better understand how landscape-scale processes affect habitat use by keystone species in Brazilian forests.

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Acknowledgements This research was funded by Conservation des Espèces et des Population Animales (CEPA), the Mohamed bin Zayed Species Conservation Fund (grant number: 11252465), Idea Wild and Vale Nature Reserve. F.A. and L.F.S. were supported by FAPESP and CNPq. A 4-month Research Internship Abroad (BEPE) provided by FAPESP enabled F. A. to carry out data analyses at Alicante University in Spain. We thank all the field assistants and Vale Nature Reserve for allowing us to conduct our research. We also thank two anonymous reviewers for their comments and suggestions to improve the manuscript.

Funding This work was supported by the Conservation des Espèces et des Population Animales (CEPA); the Mohamed bin Zayed Species Conservation Fund [11252465]; Idea Wild; Vale Nature Reserve; FAPESP; and CNPq.

ORCID Fernanda Alves

http://orcid.org/0000-0001-8825-6358

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