918 R. Costa-Araújo et al.
PLATE 1 A typical group of Callicebus melanochir, observed in the study area (Fig. 2). Photo: Rodrigo Costa-Araújo.
Callicebus barbarabrownae calls during a short pilot study prior to the survey, to detect C. melanochir and to record the species’ calls, as these were not formerly available. In the absence of standard protocols for playback surveys of titi monkeys in fragmented landscapes, we developed an approach to avoid any potential detection bias from dif- ferential propagation of sound as a result of the variable vegetation structure of forest fragments. We modulated the distance between playbacks (50–150 m) in each fragment to allow sound overlap between adjacent sampling points and improve our detection rates, controlling for sound over- lap with the aid of a field assistant. At each sampling point one long-call (2 minutes 45 seconds) was played; when titi monkeys responded with vocalization only, the same long- call was played up to three subsequent times to stimulate their approach. Informal interviews were conducted with local people in the vicinity of forest fragments and with citizens in nearby communities, to collect additional data on the species’ occurrence. The long-call of C. melanochir is distinctive from any other primate in the region, can be heard at a distance of several kilometres from the source. Recent reports (,5 years) of the species’ vocalization were therefore considered as accurate and included in our data.
Patch and landscape metrics We adopted a patch–land- scape perspective to investigate the responses of our mod- el species to forest loss, degradation and fragmentation (following Arroyo-Rodríguez & Mandujano, 2009; Arroyo- Rodríguez & Fahrig, 2014), considering that the spatial scale within which populations are affected in a fragment relates to the dispersal ability of individuals (Jackson & Fahrig, 2015). We delimited forest fragments by interpreting high- resolution (,1 m) images from the online World Imagery available through ArcGIS 10.2 (Esri, Redlands, USA) and the Open Layer plugin of QGIS 2.14 (QGIS, 2014). Fragments within 100 m of each other were considered a unique patch, assuming that titi monkeys can move across this
distance in pastures (Souza-Alves et al., 2019). We defined the scale of the effect of forest loss and fragmentation by set- ting a buffer of 500 m around the edges of study fragments, assuming a conservative estimate of the maximum dispersal recorded for titi monkeys in open areas (400 m; Mason, 1968), thus ensuring independence of the landscapes ana- lysed. We calculated three patch-level metrics and one landscape-level metric (Supplementary Fig. 1) using the packages raster, rgeo and gtools in R 3.5.0 (Hijmans & Etten, 2012;Warnes et al., 2015; Bivand et al., 2017). The for- est fragments surveyed vary in area, quality and connec- tivity (Supplementary Table 1), and therefore our study area is representative of landscapes in the Atlantic Forest and other tropical forest hotspots. All four metrics (Table 1) were first tested for autocorrelation (Pearson r,0.7; Supplementary Fig. 2).
Data analysis We first defined a set of univariate models considering species occurrence as a response variable and each patch–landscape metric (patch area, quality and visibil- ity, and landscape connectivity) as a predictor variable, using linear generalized models with a binomial distribution. Because we hypothesized that patch area would have a major, but not ubiquitous, influence on the occurrence of arboreal mammals,we also defined a set of compound mod- els combining patch area plus each of the other metrics, again with species occurrence as the response variable. We avoided models with more than two predictors because of potential difficulties with interpretation. We compared the performance of competing models within univariate and within univariate plus compound models, including also a null model representing random occurrence, using the Akaike information criterion corrected for small sample size (AICc), using the bbmle package in R. We considered univariate and compound models with ΔAICc #2.0 as equally plausible for explaining titi monkey occurrence; additionally we verified model weights to identify the best model among equally plausible models (Burnham & Anderson, 2002). To identify which predictor variables are more informative within each plausible compound model, we estimated the beta coefficients of the predictor variables using the confint function of the bbmle package. Absence of zero within the estimated 95% confidence interval of a predictor variable indicates a strong effect of that variable (Gelman & Hill, 2007).
Results
Considering the data from playback surveys and interviews, we recorded C. melanochir in 15 of the 38 study fragments. During playback censuses we recorded groups of 2–3 indivi- duals in areas of native forest and, for the first time, in areas of shaded cocoa crops embedded in the forest fragments.
Oryx, 2021, 55(6), 916–923 © The Author(s), 2021. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319001522
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164
