Introduction
The loss of species to human pressures is a worldwide and, especially in tropical forests, accelerating phenomenon (Ceballos et al., Reference Ceballos, Ehrlich, Barnosky, Garcia, Pringle and Palmer2015). The effects of such pressures tend to be worse when several, such as hunting and habitat loss, act together (Brook et al., Reference Brook, Sodhi and Bradshaw2008). Large forest-dwelling gamebirds, including curassows (genera Crax, Mitu and Pauxi of the family Cracidae), are among the vertebrate species most affected by the combination of hunting pressure and declines in forest quantity and quality (Collar et al., Reference Collar, Wege, Long and Remsen1997; Thornton et al., Reference Thornton, Branch and Sunquist2012; Kattan et al., Reference Kattan, Muñoz and Kikuchi2016). Curassows appear to be sensitive to the proximity of human communities (Begazo & Bodmer, Reference Begazo and Bodmer1998; Thornton et al., Reference Thornton, Branch and Sunquist2012) and the presence of roads (Martónez-Morales, Reference Martónez-Morales1999; Srbeck-Araujo et al., Reference Srbeck-Araujo, Silveira and Chiarello2012), both of which are associated with hunting pressure.
Although they can be abundant where not exploited (Kattan et al., Reference Kattan, Muñoz and Kikuchi2016), curassows are highly vulnerable to hunting pressure because of their low productivity (maximum output of two chicks per female per year; Butcher, Reference Butcher, Harrisson and Lightfoot2006). Seven of the eight species of the genus Crax are categorized as threatened on the IUCN Red List: two are Critically Endangered, two are Endangered and three are Vulnerable (BirdLife International, 2018). The red-billed curassow Crax blumenbachii (Endangered; downlisted from Critically Endangered in 2000) is the only representative of the genus in South America's Atlantic Forest, to which it is endemic. Extensive habitat loss and high hunting pressure are the main threats to the species. It originally occurred from near the city of Rio de Janeiro to central Bahia, Brazil, in what was once continuous habitat (IBAMA, 2004), but natural populations now survive in < 20 remnant forests in Espírito Santo and Bahia, with reintroduced populations in three forest patches in Minas Gerais and one in Rio de Janeiro (IBAMA, 2004; Alvarez & Develey, Reference Alvarez and Develey2010; Bernardo & Locke, Reference Bernardo and Locke2014).
The Brazilian Atlantic Forest is a high priority region for global biodiversity conservation (Myers et al., Reference Myers, Mittermeier, Mittermeier, Da Fonseca and Kent2000). Reduced to 12.5% of its original area, it is highly fragmented, with most remnants < 50 ha and c. 1.5 km from the nearest forest patch (Ribeiro et al., Reference Ribeiro, Metzger, Martensen, Ponzoni and Hirota2009). Over 60% of Brazil's human population live in this region (Scarano & Ceotto, Reference Scarano and Ceotto2015), and wildlife species with poor dispersal capabilities and/or high exposure to human persecution struggle to persist in such conditions (Ribeiro et al., Reference Ribeiro, Metzger, Martensen, Ponzoni and Hirota2009; Scarano & Ceotto, Reference Scarano and Ceotto2015).
Red-billed curassows favour tall forest but with moderate tree girths, conditions found mostly in secondary forest tracts (Alves et al., Reference Alves, López-Iborra, Stojanovic and Silveira2017). Knowledge regarding such habitat requirements is critical to improve management for the species in a fragmented landscape. Here, we examined the relative importance of habitat quality and hunting pressure on the persistence of red-billed curassows in 14 forest patches in the Brazilian Atlantic Forest. We predicted that the species would be more persistent in forest patches surrounded by landscapes with higher habitat quality, which we defined as areas with (1) higher availability of water resources (river length), (2) higher proximity of rivers and other forest patches and (3) higher density of forest patches within the fragmented landscape (higher proportion of native forest cover). We also predicted that curassows would persist better in forest patches in landscapes with lower hunting pressure, as reflected by (1) lower proportion of shaded cacao agroforest and farmland (crops and livestock; the region's two main land uses), (2) fewer settlements, (3) lower incidence of unpaved roads and (4) greater distance from unpaved roads and settlements.
Study area
We conducted the study in 14 protected and non-protected forest patches of varying size (275–24,084 ha; Table 1) within the geographical range of the red-billed curassow in southern Bahia, Brazil (Fig. 1, Supplementary Tables 1 & 2). We selected these forest patches because they were identified as holding, potentially holding or having held populations of curassows, including locations mentioned in the species action plan (IBAMA, 2004), locations where curassows were recorded by CSSB or locations of which CSSB had been notified informally by researchers. The forest patches are surrounded mostly by farmland (crops and livestock) but also eucalyptus monoculture, open ground, urban areas, sandbank vegetation and/or mangroves (Landau et al., Reference Landau, Hirsch, Musinsky, Prado, Landau, Moura, Pinto, Fonseca and Alger2003). Except for sites 8 and 10 (Table 1, Fig. 1), protected and unprotected sites employ guards who offer a degree of safeguarding against hunting and habitat loss.
Fig. 1 Location of 14 forest remnants within the range of the red-billed curassow Crax blumenbachii and the number of species records in each forest remnant of the Brazilian Atlantic Forest, southern Bahia, Brazil.
Table 1 Records and persistence scores of the red-billed curassow Crax blumenbachii in 14 forest patches in southern Bahia, Brazil, assessed using a literature review, camera traps, line transects and interviews. Data before 2007, and for 2010 and 2011, were only available for line transects at Michelin Ecological Reserve (≥ 3 records for each year). They had no effect on the ordinal logistic regression score and are not shown here.
1 Records from: LT, line transects; CT, camera traps; I, interviews.
2 ID, identification of focal forest patch in Fig. 1.
3 Persistence scores for populations of the curassow: 3 = persistent, 2 = precarious, 1 = extirpated (see Methods for details). These values were used in the ordinal logistic regression models as the response variable.
Methods
Diversity of data sources
We adopted a pragmatic approach to data collection because of the urgent need to better understand the threats to the red-billed curassow, the logistic difficulties of fieldwork across the species’ range, and low encounter rates. Ideally we would have gathered data using a single method (e.g. line transects or camera traps) across all survey sites, but for reasons of practicality (e.g. hunter hostility to camera traps in some parts of Bahia) we needed to collate data from a diversity of qualitatively different methods to gather sufficient material in the short time frame available.
Data collection
We compiled information on each forest patch from the literature and interviews, to determine the curassow's status (i.e. persistence level). This was based on the number of records in a 1-year period derived from (1) line-transect surveys, (2) camera-trap studies, or (3) interviews (Table 1). The time frame for the 1-year period was any year after 2007 when camera-trap and line-transect data were available for the species. We categorized a population as persistent when one of these sources (sightings, photographs or interviews) yielded ≥ 3 records of curassows within a 1-year period (score 3, n = 4 forest patches), precarious if there were 1–2 records (score 2, n = 5 forest patches), or extirpated (score 1, n = 5 forest patches) if curassows had only been sighted prior to 2007 (Table 1, Fig. 2).
Fig. 2 Flow chart for the allocation of persistence level scores (response variable) for an ordinal logistic regression. For each forest patch, the flowchart was used no more than three times, which is equivalent to the maximum number of sampling methods (line transects, camera traps and interviews). If two or more studies had been conducted in the same forest patch and different scores were obtained, we chose the score from the most recent study period. The time frame for the 1-year period was any year after 2007, when camera-trap and line-transect data for the red-billed curassow were available. Therefore, the base year related to score 1 (2007) varied depending on data availability for each forest patch (e.g. the base year for the study area Capitao is 2013, no data available from 2007 to 2013; Table 2).
Table 2 Best models of the ordinal logistic regression analysis of the persistence level of the red-billed curassow in the Atlantic Forest, southern Bahia, Brazil. The Table shows the determination coefficient R 2 of each model, the range of each independent variable and the parameter estimates with standard errors.
We used the persistence scores (3, 2, 1) in an ordinal logistic regression in which the response variable obeys an ordinal rank order (such as high, medium, low), because the use of binary data (records present/absent) and multiple logistic regression would oversimplify the analysis. The few data available from line transects, camera traps and interviews allowed us to classify the response variable (persistence level) into distinct categories and adopt a statistical approach based on the ordinal response variable (see Data analysis).
After obtaining permission (licenses in protected areas and verbal permission for private lands), we conducted interviews to confirm the red-billed curassow's presence, and estimated encounter rates using line transects in six forest patches and camera traps in four of these patches (Table 1).
Interviewees, identified by researchers, landowners and/or park staff as familiar with the local fauna, included protected area rangers and managers, local people and/or researchers. To ensure that responses were independent, we did not interview members of the same family and conducted interviews with one person at a time (Canale et al., Reference Canale, Peres, Guidorizzi, Gatto and Kierulff2012). To confirm their familiarity with the red-billed curassow, we asked interviewees to identify the species in colour plates, which included species that do not occur in Brazil, and to describe its calls, behaviour and the colour of male and female plumages. Approved interviewees were then asked to report the number of visual records of the species within a 1-year period, indicate the location of records and provide the date of the most recent sighting. In total, we conducted 47 semi-structured interviews (2–4 interviewees per forest patch; Table 1) in 2015, spending 3–4 days per forest patch. Interviews lasted ≤ 30 minutes.
We performed a χ 2 test and a Spearman correlation to verify that persistence levels of curassows were not associated with sampling method (χ 2 test, χ 2 = 1.88, P = 0.59) or sampling effort (Spearman correlation, Rs = 0.73, P = 0.09). We thus assumed that the data from each method were equally reliable.
Landscape features
To assess the association between landscape features and curassow persistence, we extracted patch and landscape metrics for the 14 forest patches by considering multiple buffer radii (500 m, 1 km and 2 km) from the edge of each forest patch. Radial sizes were based on the evidence that, 2 years after being reintroduced, adult red-billed curassows maintained a mean distance of 2.8 km from the centre of each other's home ranges (Bernardo et al., Reference Bernardo, Lloyd, Olmos, Cancian and Galetti2011). Assuming circular home ranges and no overlap between them, the mean radius of a single home range is 1.4 km; thus, we adopted a maximum radius size of 2 km.
Using QGIS (QGIS Development Team, 2009) and Google Earth (Google, Mountain View, USA), we obtained five variables associated with habitat quality at the landscape scale and five associated with hunting pressure. We re-projected all vector and raster datasets from a geographical coordinate system to a projected coordinate system before using any other geoprocessing tool. The variables related to habitat quality were: (1) Proportion of native forest cover within each buffer (Landau et al., Reference Landau, Hirsch, Musinsky, Prado, Landau, Moura, Pinto, Fonseca and Alger2003), derived from Land Cover Statistics (LecoS) in QGIS (Jung, Reference Jung2016). We assumed that surrounding landscapes with higher forest cover offer higher habitat quality at the landscape scale (Thornton et al., Reference Thornton, Branch and Sunquist2012). (2) Total length of rivers within the focal forest patch and within the buffers, and the distance from the focal forest patch to the nearest river within the buffers. We used the Brazilian hydrography shapefile (ANA, 2010) to calculate the length and distance from rivers by using both the length function within the geometry group from the field calculator tool in QGIS and the v.distance tool available in the GRASS plugin. We assumed that forest patches in a landscape with more and nearer rivers represent a higher habitat quality for red-billed curassows, because they were frequently recorded in forests in close proximity to rivers (Sick, Reference Sick1997; IBAMA 2004; Bernardo et al., Reference Bernardo, Lloyd, Olmos, Cancian and Galetti2011). (3) Forest patch density (i.e. number of forest patches divided by the area within the buffer), by using Land Cover Statistics (LecoS) in QGIS (Jung, Reference Jung2016). We assumed that, in an already fragmented but still well-forested region, a higher density of patches contributes to habitat quality at the landscape scale because it maintains higher connectivity, which is important for recolonization dynamics (Tambosi et al., Reference Tambosi, Martensen, Ribeiro and Metzger2014). (4) Distance from the edge of the focal forest patch to the nearest patch within the buffers (SOS Mata Atlântica, 2012), assuming nearby forest patches improve connectivity and contribute to habitat quality at the landscape scale (Tambosi et al., Reference Tambosi, Martensen, Ribeiro and Metzger2014; Supplementary Table 1). We obtained this variable by using the v.distance tool available in the GRASS plugin.
The variables related to hunting pressure were: (1) Proportion of cacao agroforests and farmland within each buffer, which we obtained by using Land Cover Statistics (LecoS) in QGIS (Jung, Reference Jung2016). (2) Total area occupied by settlements within both focal forest patch and buffers. We obtained this variable by creating a shapefile to draw polygons that corresponded to settlements within a Google Maps satellite image visualized with the OpenLayers plugin. (3) Mean distance from the edge of the focal patch to settlements, using the v.distance tool available in the GRASS plugin. (4) Total length of all unpaved roads (hereafter roads) within the patch and the buffers, using the length function within the geometry group from the field calculator tool in QGIS; we used a shapefile available from DNIT (2015) and a shapefile we created to draw lines that corresponded to roads within a Google Maps satellite image visualized with the OpenLayers plugin. (5) Distance from the focal forest patch to the nearest road within the buffers, using the v.distance tool available in the GRASS plugin. We assumed that all these parameters are associated with higher numbers of and increased access by hunters and domestic dogs (Canale et al., Reference Canale, Peres, Guidorizzi, Gatto and Kierulff2012; Thornton et al., Reference Thornton, Branch and Sunquist2012; Cassano et al., Reference Cassano, Barlow and Pardini2014; Supplementary Table 1). We showed the map of unpaved roads in the relevant forest patch to key interviewees, to confirm that the network was essentially unchanged for at least 20 years.
Data analysis
We first selected the appropriate spatial scale to be used in further analyses, by comparing the determination coefficients (R 2) of habitat quality and hunting pressure variables as a function of forest cover at multiple scales (i.e. considering the amount of forest cover within the focal forest patch and at 500 m, 1 km and 2 km from its edge). The spatial scale with the highest R 2 values was thus established as the standard for subsequent analysis.
We checked for multicollinearity among the five variables related to habitat quality, and among the five variables related to hunting pressure, and selected those with values of the variance inflation factor < 5 (O'Brien, Reference O'Brien2007). Then we ran all possible combinations of variables related to habitat quality (hereafter referred to as habitat quality submodels), and applied the same procedure for variables relating to hunting pressure (hereafter referred to as hunting pressure submodels). We selected the best combinations of variables based on the lowest Akaike information criterion (AIC; Burnham & Anderson, Reference Burnham and Anderson2004) values, with ΔAIC < 2. Finally, we built a model containing variables presented in the best submodels of habitat quality and hunting pressure, following Thomas (Reference Thomas2017). We selected the best model based on AIC values for the variables presented in the final model, as for the submodels. We excluded collinear variables from the final model.
Because of the ordinal nature of the response variable, we conducted ordinal logistic regression to evaluate which landscape variables were associated with the persistence levels of red-billed curassows. Ordinal logistic regression preserves information about the ordering (e.g. high, medium, low), whereas for other types of logistic regression (e.g. binomial models) information must be binary (i.e. 0 = absence, 1 = presence), which would oversimplify the analysis for our research question (Harrell, Reference Harrell2015). We calculated values of variance inflation and performed ordinal logistic regression using the package rms (Frank & Harrell, Reference Frank and Harrell2017) in R 3.1.1 (R Development Core Team, 2015).
Results
The 2 km buffer radius best explained most of the habitat quality and hunting pressure variables, as indicated by the highest determination coefficients (Supplementary Table 2). The best combination of habitat quality variables resulted in two submodels with relatively low explanatory power (R 2 = 0.22–0.26). The hunting pressure variables resulted in two well-fitted submodels with higher explanatory power (R 2 = 0.54–0.56; Table 2).
Four final best models containing both habitat quality and hunting pressure variables had a high explanatory power (R 2 = 0.36–0.63), which comprised four hunting pressure variables and two habitat quality variables (Table 2). Distance from the focal forest patch to settlements occurred in all final models, and area occupied by settlements in three models. In contrast, proportion of native forest cover and distance from the nearest forest patch occurred in one final model (Table 2). Specifically, curassow populations persist better in patches located (1) more distant from settlements, (2) in landscapes with few settlements, (3) in landscapes with high incidence of unpaved roads, (4) in a mosaic with high proportion of forest, cacao agroforests and farmland, and (5) more distant from other forest patches (Table 2).
These outputs suggest that curassow persistence is more affected by hunting pressure than habitat quality, because (1) the hunting pressure submodels presented higher explanatory power than habitat quality submodels, (2) the final models included four hunting pressure variables in contrast to only two habitat quality variables, and (3) hunting pressure variables appeared in all models (e.g. distance from the focal forest patch to settlements) or in the majority of models (area occupied by settlements), whereas habitat quality variables appeared in only one final model.
Discussion
We used a variety of methods to gather information on factors affecting the persistence of the red-billed curassow in 14 sites in southern Bahia, in the threatened Brazilian Atlantic forest. Although we are confident that this served our purpose of providing an insight into the relative roles of habitat change and hunting in influencing persistence of the species, additional, more consistent data would be helpful. This is a challenge for threatened species that occur at low densities (naturally or as a result of anthropogenic declines) in habitats where access can be difficult and detection rates are low. Nonetheless, a systematic approach to both gathering data and maximizing the value of data from planned and ongoing studies would yield significant benefits for informing management decisions in such situations (see also Grainger et al., Reference Grainger, Ngoprasert, McGowan and Savini2017).
Our findings suggest that the most important factor to consider in conservation strategies for the red-billed curassow is hunting pressure. As predicted, red-billed curassows are more likely to persist in forest landscapes with low human density (i.e. forest patches further away from settlements). Several other cracids show similar patterns: the great curassow Crax rubra and crested guan Penelope purpurascens were less likely to occupy densely settled areas in the Yucatán Peninsula, Mexico (Urquiza-Haas et al., Reference Urquiza-Haas, Peres and Dolman2009), the black curassow Crax alector exhibits high occupancy rates in forest patches unaffected by hunting pressure in central Amazonia (Benchimol & Peres, Reference Benchimol and Peres2015), and the razor-billed curassow Mitu tuberosa, Spix's guan Penelope jacquacu, blue-throated piping guan Pipile cumanensis and speckled chachalaca Ortalis guttata have much lower population densities within 5 km of villages in the Peruvian Amazon (Begazo & Bodmer, Reference Begazo and Bodmer1998).
We hypothesized that red-billed curassow persistence is inversely related to the incidence of unpaved roads, because roads facilitate access by hunters (Canale et al., Reference Canale, Peres, Guidorizzi, Gatto and Kierulff2012). However, we found red-billed curassows are more likely to persist in landscapes with higher incidence of roads. One possible explanation is that curassows utilize roads to forage (Srbeck-Araujo et al., Reference Srbeck-Araujo, Silveira and Chiarello2012), thereby increasing the chances of people recording them. We also found red-billed curassows more likely to persist in a mosaic of forest, cacao agroforest and farmland as long as the proportion of native forest cover is high. Cacao agroforests are known to support a high diversity of forest species when located near large forest remnants (Faria et al., Reference Faria, Paciencia, Dixo, Laps and Baumgarten2007).
Contrary to our expectations, we found that red-billed curassows are more likely to persist in isolated forest patches (> 1 km distant from others). However, these isolated forest patches are relatively large (> 3,700 ha) and located in landscapes with a high proportion of forest cover (> 65%). Thus it is possible that curassows do not attempt to leave such patches. The few data available on dispersal of curassows suggest poor dispersal capability: no gap-crossing movements of bare-faced curassows Crax fasciolata were recorded in Amazonian forest patches (Lees & Peres, Reference Lees and Peres2009) and none of 25 reintroduced red-billed curassows monitored over a 25-month period moved across > 750 m in open areas (Bernardo, Reference Bernardo2010). In our studied landscape, the minimum distance from the focal forest patch to another patch was 760 m, which may be too wide a gap to be crossed successfully (Table 2). If curassows attempt to cross such gaps, searching for areas with higher resource availability, they may be more vulnerable to natural predators, hunters and/or dogs.
Because hunting pressure compromises curassow persistence, future protected areas will serve the species better in regions with low human density. Reintroduction initiatives for red-billed curassows (Bernardo & Locke, Reference Bernardo and Locke2014) need to take this factor into account (Alves et al., Reference Alves, López-Iborra, Stojanovic and Silveira2017). Hunting pressure (and the feasibility of reducing it) should be assessed at potential reintroduction sites before any such reintroductions proceed.
Acknowledgements
We acknowledge Coordenação de Aperfeiçoamento de Pesquisa e Ensino Superior (CAPES) for a Master's studentship to ERS and post-doc studentship to CSSB, and Universidade Estadual do Sudoeste da Bahia- campus Jequié for logistical support.
Author contributions
Study design: ER, CSSB; fieldwork: ER; data analysis: CSSB; writing: all authors.
Conflicts of interest
None.
Ethical standards
This research abided by the Oryx guidelines on ethical standards and was approved by the Ethics Committee of Universidade Estadual do Sudoeste da Bahia—Brazil (CEUA n. 332013) and the Brazilian Environmental Agency (SISBIO-ICMBio n. 487391).
Open access
