Coexistence and habitat use of the South American coati and the mountain coati along an elevational gradient

Mammalian Biology 98 (2019) 119–127

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Original investigation

Coexistence and habitat use of the South American coati and the mountain coati along an elevational gradient José Luis Mena a,b,∗ , Hiromi Yagui b a b

Museo de Historia Natural Vera Alleman Haeghebaert, Universidad Ricardo Palma, Lima 33, Peru World Wildlife Fund, Perú, Trinidad Moran 853, Lima 14, Peru

a r t i c l e

i n f o

Article history: Received 30 March 2019 Accepted 9 September 2019 Available online 10 September 2019 Handled by Luca Corlatti Keywords: Coatis Co-occurrence Montane forest Nasua nasua Nasuella olivacea Occupancy modelling Paramo

a b s t r a c t The South American coati Nasua nasua is a relatively common species throughout the Neotropical region. Despite this, ecological information on the species, including its biological interactions and habitat use, is scarce, especially for the Andes. In some regions, Nasua nasua is sympatric with other closely related species of the Procyonidae family, including the mountain coati Nasuella olivacea. Here, we assess the influence of environmental and anthropogenic factors on the occupancy of these two species and the spatial and temporal bases of their co-occurrence along an elevational gradient. Camera trapping (with 85 camera-trap stations) was conducted during the dry season of 2016 along elevations from 1600 to 3600 m above sea level (m. a. s. l.) in northern Peru. We observed a total of 244 detections for Nasua nasua and 17 for Nasuella olivacea over 9457 cumulative camera-days. Occupancy modelling (Royle-Nichols model) showed that Nasua nasua occupancy was significantly and negatively related to elevation but positively related to forest cover. In contrast, Nasuella olivacea occupancy was significantly and positively related to elevation. In addition, Nasuella olivacea was detected in only 5 of the 45 total sites occupied by Nasua nasua; therefore, spatial overlap was low. Consequently, co-occurrence modelling based on a Bayesian approach showed no evidence of avoidance between the two coati species. Additionally, activity patterns suggest low levels of temporal overlap; however, we consider this a preliminary finding due to the limited number of detections for Nasuella olivacea. Our results not only increase the understanding of the ecology of both Nasua nasua and Nasuella olivacea but also provide information towards their conservation in this part of their distribution range. ¨ Saugetierkunde. ¨ © 2019 Deutsche Gesellschaft fur Published by Elsevier GmbH. All rights reserved.

Introduction Coatis are a gregarious group of animals with complex social behaviors that comprise a distinct clade within the Procyonidae family (Balaguera-Reina et al., 2009; Silva-Caballero et al., 2017). This clade consists of two genera: Nasua (3 species) and Nasuella (2 species) (Glaston, 1994; Helgen et al., 2009). These species inhabit forests, ranging from tropical rainforests to dry forests, from the southern United States to Argentina (Emmons and Feer, 1997; Gompper and Decker, 1998). The South American coati Nasua nasua weights 2–7.2 kg, and lives in bands of up to 30 individuals (Tirira, 2017; Wilson and Mittermeier, 2009). It occurs east of the Andes from Colombia to the southernmost extent of the genus’s range in Argentina and throughout the Amazon from sea level

∗ Corresponding author at: Museo de Historia Natural Vera Alleman Haeghebaert, Universidad Ricardo Palma, Lima 33, Peru. E-mail address: [email protected] (J.L. Mena).

up to 2500 m (Glaston, 1994; Gompper and Decker, 1998; Tirira, 2017). The mountain coati Nasuella olivacea weights 1–1.5 kg, and is occasionally seen in small groups of 6–8 individuals (Tirira, 2017; Wilson and Mittermeier, 2009), although, some authors reported bands reaching up to 60 individuals (Balaguera-Reina et al., 2009; ˜ et al., 2003; Sánchez and Alvear, 2003). Nasuella Rodríguez-Bolanos olivacea, which has the most restricted range compared with other members of the Procyonidae family, is found along the Andes from Venezuela to Peru at elevations above 2000 m (Balaguera-Reina ˜ et al., 2009; Glaston, 1994; Pacheco et al., 2009; Rodríguez-Bolanos et al., 2003). Coatis are plant consumers, invertebrate and small vertebrate predators, and seed dispersers (Alves-Costa et al., 2004; RochaMendes et al., 2010). The ecological importance of Nasua nasua as a seed disperser has been hypothesized to increase in degraded or fragmented forests where large herbivores or apex predator species have disappeared (Alves-Costa and Eterovick, 2007), thus making the species essential for forest regeneration. They are also important prey to large carnivores like felines and wild canids

https://doi.org/10.1016/j.mambio.2019.09.004 ¨ Saugetierkunde. ¨ Published by Elsevier GmbH. All rights reserved. 1616-5047/© 2019 Deutsche Gesellschaft fur

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(Gompper and Decker, 1998). Nevertheless, although Nasua nasua is a common species with a wide geographic distribution (Wilson and Mittermeier, 2009), information about its natural history and population ecology in several parts of its geographical range (e.g., the Andes and the western Amazon) is limited. Likewise, information about Nasuella olivacea is scarce and restricted to some behavioral reports, mainly of populations in Colombia (BalagueraReina et al., 2009; Helgen et al., 2009). Camera traps have been a useful method to improve detectability for elusive and cryptic species (Balme et al., 2009; Bischof et al., 2014; Caravaggi et al., 2017; Kelly, 2008). Indeed, the use of a noninvasive technique such as camera trapping allows the observation of behaviors that would not otherwise be possible. Also, for some species, distinguishing individuals is nearly impossible due to the lack of fur patterns or specific characteristics. In those cases, with camera traps, species occupancy instead of abundance can be estimated. Occupancy ( ) is defined as the fraction of landscape units in which the species is present (MacKenzie et al., 2018). Occupancy models, which are some of the most widely used hierarchical models, combine a model for species occurrence (occupancy) and a model for imperfect detection (i.e., when a species is present but not detected), and include covariates for both detectability and occupancy (Kéry and Royle, 2016). Here, we analyze the co-occurrence of Nasua nasua and Nasuella olivacea using occupancy models and camera trapping data. Specifically, we assess hypotheses related to habitat use and cooccurrence of both species along an elevational gradient in northern Peru. Nasua nasua appears to be widely tolerant of a variety of vegetation types, from scrubs to tropical forests, and is distributed up to 2000 m. a. s. l. (Tirira, 2017; Wilson and Mittermeier, 2009). Given that, in general, abundance and occupancy often decline toward the edge of a species’ geographic range (Gaston, 2003) and the intuitive relationship between abundance and occupancy (MacKenzie and Nichols, 2004; MacKenzie et al., 2018), we expect to observe a negative relationship between occupancy and elevation for Nasua nasua (hypothesis 1). Nasuella olivacea, in contrast, inhabits montane forests and paramos at elevations between 1300 and 4000 m, with greater abundances above 3000 m (Balaguera-Reina et al., 2009; Glaston, 1994; Wilson and Mittermeier, 2009). Therefore, we expect to observe a positive relationship between mountain coati occupancy and elevation (hypothesis 2). Sympatry of both species along the elevational gradient is also possible. If Nasua nasua and Nasuella olivacea have a similar diet of predominantly invertebrates and fruits (Balaguera-Reina et al., 2009), we hypothesize temporal segregation as a strategy to avoid competition in areas of co-occurrence (hypothesis 3). Nasua nasua is considered a diurnal species; however, little is known about the activity patterns of Nasuella olivacea, though it is suspected to also be diurnal (Tirira, 2017; Wilson and Mittermeier, 2009). Our study not only provides new insights on the ecology of these two coati species but also is the first to report on the habitat use of Nasuella olivacea along its southern geographical range.

Material and methods Study area Our study area was in the Tabaconas Namballe National Sanctuary (TNNS), and its buffer zone (79◦ 24 –79◦ 06 W, 5◦ 00 –5◦ 20 S), located within the San Ignacio Province of the Cajamarca Department in the northern Andes of Peru (Fig. 1). The TNNS is a protected area of 32,124.87 ha (ha) with an elevation between 1600 and 3500 m. Annual average temperatures range from 11.2 to 24.6 ◦ C, and annual rainfall fluctuates between 1490 and 1770 mm, with the rainy season usually occurring from November to March. The TNNS

is characterized by two main vegetation types: paramo (> 3000 m) and montane forest (1600–3000 m). Authors such as Josse et al. (2007) have further defined the area as consisting of three ecological systems, each with different vegetation: (1) the upper montane paramo grassland and shrubland (> 3000 m); (2) the upper montane evergreen forest of the northern Andes (2900–3400 m), with 10–15 m of canopy height; and (3) the montane pluvial forest of the northern Andes (1900–2900 m), with 15–25 m of canopy height and daily cloud coverage according to the slope. The buffer zone (1200–2100 m) is characterized by a low montane, humid pluviseasonal forest of the Yungas, with seasonal evergreens, diverse and multi-stratified forest relicts, agricultural lands, and cattle ranching that dominates the area. Camera trap survey We conducted a camera trap survey during the dry season, between May and October of 2016. The survey consisted of an 85-site grid with a spacing of 1˜ km between sampling sites and a systematic design stratified by vegetation type (Fig. 1, Supplementary data). The locations of camera traps were selected to cover all principal vegetation types found within the TNNS (i.e., paramo and montane forest). Given the strong elevational gradient of the area, we aligned camera traps according to this gradient, a standard protocol for studies of this type (Ahumada et al., 2013; Rovero and Zimmermann, 2016). One camera trap (Bushnell® Trophy CamTM) was set at each site, and each camera was strapped to a tree or stake approximately 40 cm above ground. Cameras were active 24 h per day. All photos and species identifications were processed using Camera Base 1.7 (Tobler, 2015). Aside from body size and color, Nasua nasua was identified primarily by its ring pattern and longstanding tail and Nasuella olivacea by its shorter robust blackish tail and shorter legs in comparison to those on Nasua nasua (Fig. 2). For more photos, see Fig. A1 of Supplementary data. Covariates To estimate the occupancy probability of each species, we used the following quantitative site covariates: paramo cover, forest cover, elevation, distance to villages (as a proxy of disturbance), and distance to water sources (Table A2 of Supplementary data). Distances were obtained from GIS based on official national data. We quantified the covariates in a circular buffer (250-m radius) around each sampling site using ArcGIS (ESRI*ArcMap 10.6, Redlands, California-ESRI 2018). Covariates for detection included distance to villages, distance to water sources, and the number of days each camera station was functional. Variables were standardized to have a zero mean and unit variance to facilitate the interpretation of relative effect sizes (Kéry and Royle, 2016). We also tested covariates for collinearity (Kéry and Royle, 2016; Zuur et al., 2013) using the variance inflation factor (VIF) in the HH library package in R (Heiberger and Holland, 2015). All covariates, except paramo cover, were minimally correlated (VIF < 3, Table A3 of Supplementary data) and, therefore, used in the models (Heiberger and Holland, 2015; Zuur et al., 2010). Occupancy modelling Each camera-trap station constituted a sampling unit. We grouped the data into six-day sampling intervals (for a total of 23 occasions), which decreased the number of zeroes in the dataset, thus providing a more reliable detection history. Detection data were formatted into a matrix: six-day intervals with no detections were scored as 0 and positive detections were scored as 1 (a posi-

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Fig. 1. A map of the study area showing the Tabaconas Namballe National Sanctuary (TNNS), and its buffer zone, and the detections (events) of each coati species.

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Fig. 2. Camera trap photos of Nausa nasua (a) and Nasuella olivacea (b) in the TNNS (2016 field campaign).

tive detection was defined as a record of at least one individual or a group). We conducted single-season, single-species occupancy modelling (MacKenzie et al., 2018) based on the Royle-Nichols model (RN model) (Royle and Nichols, 2003) to assess the response of coati occupancy to site covariates. The RN model performs well at sam-

˜ ple sizes ≤ 100 and with > 5 replicates, although bias (10–15%) is expected for low values of detection probability (p) (Royle and Nichols, 2003). Camera trap detection data often show high heterogeneity, and in these cases, the RN model is an appropriate option (Li et al., 2018; Tobler et al., 2009). Moreover, the RN model is often described as an occupancy model that accounts for the

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site-specific heterogeneity in detectability that is derived from variation in abundance (Kéry and Royle, 2016; MacKenzie et al., 2018). In this model, occupancy ( ) is derived from the average number of individuals at each site or local abundance () as = 1 − e− (MacKenzie et al., 2018). Although in some cases,  can be considered an index of abundance, we only used it to estimate the occupancy of both species and not their abundances. We considered that the general assumptions of occupancy models, including the RN model, have been met in our study. These include independent detection, a stable occupancy status at each site for the duration of the sampling period (in our case, the dry season), and a distribution of individuals according to a Poisson process (MacKenzie et al., 2018). We also interpreted our results in terms of site used and not area occupied (MacKenzie et al., 2018) given that the home range diameter of Nasua varies from 0.4 to 7.44 km2 (Beisiegel, 2001; Guilherme Trovati et al., 2010), greater than the distance between camera trap stations. This is less likely to be an issue for Nasuella olivacea whose home range varies from ˜ et al., 2003). 0.09 to 0.11 km2 (Rodríguez-Bolanos Occupancy modelling (using the RN model) based on maximum likelihood was conducted in the R programming environment (R Core Team, 2019) using the unmarked library (Fiske and Chandler, 2011). We followed a two-step process for model selection. For each coati species, we first identified the best detection probability model while holding occupancy constant (Kéry and Royle, 2016). For this, we also included the effect of each of the following covariates individually: the number of days each camera station was functional within the six-day intervals (for a total of 12–141 days), distance to villages and distance to water sources. Although, the covariates “distance to villages” and “distance to water sources” are probably more related to occupancy, we included these covariates due to their potential effect on detectability as “natural attractants” (Burton et al., 2015), reported in several carnivore species studies (Boron et al., 2019; Curveira-Santos et al., 2019; Ferreira et al., 2017; Mann et al., 2015). Then, we included the occupancy covariates (i.e., forest cover, elevation, distance to villages, and distance to water sources), assessing their individual and combined effects based on our hypotheses (Table A4 of Supplementary data). We used the Akaike information criterion (AIC ≤ 2) to select the bestfit models (Burnham and Anderson, 2020) and then performed goodness-of-fit (GOF) tests using parametric bootstrapping with 10,000 pseudo-replicates, as implemented in the R package AICcmodavg with the mb.gof.test function (Mazerolle, 2019). We refitted the best maximum-likelihood fitted model for each coati species using a Bayesian approach with JAGS (Plummer, 2003), as implemented in the jagsUI package in R (Kellner, 2015). Although results from maximum-likelihood (performed in unmarked) and Bayesian approaches are usually similar, the latter is recommended for datasets with small sample sizes, few detections, and low detection and occupancy probabilities (Ahumada et al., 2013; Kéry and Schaub, 2012), as in the case of Nasuella olivacea. We adapted the code used by Chen et al. (2019) to implement the RN model following a Bayesian approach. The Bayesian models were run with three chains of 300,000 iterations each, a thinning rate of 100, and a burn-in of 50,000 iterations. The priors (Hobbs and Hooten, 2015) used for all model parameters followed Northrup and Gerber (2018) recommendations. Bayesian P-values were used to test the GOF of each model: a value close to 0.5 indicated a good model (Kéry and Royle, 2016). Model convergence was assessed by checking that the Gelman-Rubin diagnostic statistic was < 1.1 for each parameter (Gelman et al., 2014). Co-occurrence We used the same detection history for Nasua nasua and Nasuella olivacea and fitted the model using a Bayesian formulation devel-

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oped by Waddle et al. (2010) and adapted by Haidir et al. (2018). This formulation is used to estimate the co-occurrence of interacting species independent of the effects of imperfect detection and sampling. Using this model, which requires a dominant and a subordinate species to be defined (Mackenzie et al., 2018), we analysed the behavioural responses of the co-occurring species. We defined Nasua nasua as the dominant species and Nasuella olivacea as the subordinate species. In this model, the probability of occurrence of the subordinate species is conditional on the presence of the dominant one (Waddle et al., 2010). We did not include any effect of Nasua nasua on the detectability of Nasuella olivacea; however, for both species, we included the covariates used in the best-fit, single-species occupancy models. Assumptions of this model are similar to those of occupancy models (MacKenzie et al., 2018). The parameter describing the interspecific effect was considered significant if the 95% credible interval (CI) of the posterior mean did not include zero (Kéry and Royle, 2016). The model was fitted using the jagsUI package in R. The priors used for all parameter distributions followed Northrup and Gerber (2018), and the model was run with three chains of 100,000 iterations each, a thinning rate of 100, and a burn-in of 50,000 iterations. As described above, the Gelman-Rubin diagnostic statistic and the Bayesian P-value were used to assess model convergence and GOF, respectively. Activity patterns We described the circadian cycle of the two species on the basis of the recordings obtained from the 85 camera-trap stations. For each photograph, we recorded the date, time, and camera site ID (Rowcliffe et al., 2014). We defined an independent record as all coati photos within a 60-minute period (Farris et al., 2015). We used kernel density functions to compare activity patterns between the two species. Similarity and bootstrap confidence intervals were quantified with the “overlap” package in R (Meredith and Ridout, 2014). We estimated the overlap coefficient (), which varies from 0 (no overlap) to 1 (total overlap), following Ridout and Linkie (2009). Results We observed 244 detections for Nasua nasua and 17 for Nasuella olivacea over 9457 cumulative camera-days (and a six-day sampling interval) across the 85 camera-trap stations installed in the TNNS study area. Nasua nasua was detected at 45 stations and Nasuella olivacea at 10, yielding a naïve occupancy estimate of 0.53 and 0.12, respectively. Using a two-step process for model selection based on a maximum-likelihood approach (fitted with unmarked), we first identified the best detection models for each species (1 model for Nasua nasua and 2 for Nasuella olivacea). We next developed 17 and 27 occupancy models for Nasua nasua and Nasuella olivacea, respectively, by using the best detection covariates for each species (Table A4 of Supplementary data). One occupancy model tested for Nasua nasua, which included elevation and forest cover as occupancy covariates, had a AIC ≤ 2 with high support, as indicated by the model weights (w= 0.99, see Table 1). For Nasuella olivacea, three models were retained (AIC ≤ 2), which together were moderately supported (wall = 0.49). Elevation was retained in all models. These models exhibited evidence of adequate fit (P > 0.05) (Table 1). The Bayesian p-value of the single-season, single-species occupancy model for Nasua nasua and Nasuella olivacea was 0.72 and 0.54, respectively, indicating an acceptable fit in both cases. Occupancy ( ) of Nasua nasua was significantly and negatively related to elevation (= -1.00; 95% CI: -1.43 – -0.59) but positively to forest cover (ˇ = 0.95; 95% CI: 0.40–1.65) (Fig. 3a). Nasuella olivacea

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Table 1 Occupancy models fitted (AIC < 2) with the Royle-Nichols model for the South American coati (Nasua nasua) and the mountain coati (Nasuella olivacea). : average abundance per site, r: per-individual detection probability; AIC: Akaike information criterion; AIC: difference in AIC values between each model and the best model; w: AIC model weight; N Par: number of parameters; -2l: twice the negative log-likelihood; P: Goodness-of-fit (GOF) of the selected models. Model weight (w) may be interpreted as model probabilities (Mackenzie et al., 2018). Model

AIC

AIC

w

N par

−2l

P

South American Coati  (elevation + forest) r(water)

1035.72

0.00

0.99

5

−512.48

0.08

Mountain Coati  (elevation) r(days)  (elevation + water) r(days)  (elevation + forest) r(days)

159.86 160.79 160.97

0.00 0.93 1.11

0.22 0.14 0.13

4 5 5

−75.68 −75.01 −75.11

0.22 0.25 0.26

was low. Notably, Nasuella olivacea was a rare species along the elevational gradient surveyed: it was recorded only at 10 of the 85 total sites, the majority of which were located at elevations higher than 2400 m. The Bayesian posterior probability of this model was 0.49 and 0.46 for Nasua nasua and Nasuella olivacea, respectively, indicating an acceptable fit in both cases. Nasua nasua showed diurnal habits with 95% of its photocaptures taken during the day, and with a homogeneous distribution between 6:00 and 18:00 (n = 263) (Fig. 5). Nasuella olivacea, in contrast, displayed predominantly nocturnal habits: 76.5% of its photo-captures were taken at night (n = 17), with most occurring between 19:00 and 23:00 (Fig. 5). The overlap estimator ˆ1 (for small samples) was 0.25 (0.06–0.36), indicating a low level of temporal overlap. Discussion

Fig. 3. Regression (ˇ) coefficients for site and detection covariates related to the local abundance of Nasua nasua (a) and Nasuella olivacea (b) based on single-season, single-species occupancy modelling under a Bayesian approach. Horizontal line within the bar indicates the mean; narrow and wide bars indicate the 95% and 90% CI, respectively.

occupancy was positively related to elevation (ˇ = 0.92; 95% CI: 0.1–1.76) (Fig. 3b). A negative but non-significant relationship was found between detectability of Nasua nasua and distance to water sources, while Nasuella olivacea detectability increased significantly with effort (the number of days each camera station was functional) (ˇ = 1.27; 95% CI: 0.09–2.31) (Fig. 3b). The estimated average occupancy ( ) for Nasua nasua and Nasuella olivacea was 0.71 (SD 0.06, 95% CI: 0.58–0.83) and 0.39 (SD 0.13, 95% CI: 0.17–0.66), respectively. The detection probability (p) of Nasua nasua and Nasuella olivacea was 0.13 (SD 0.02, 95% CI: 0.11–0.14) and 0.01(SD 0.01, 95% CI: 0.005–0.013), respectively. According to the model of spatial co-occurrence, the presence of Nasua nasua does not influence the occupancy of Nasuella olivacea, as indicated by the beta coefficient (ˇ = 0.25; 95% CI: -1.80 - 2.68) (Fig. 4, Table A5 of Supplementary Data). Indeed, Nasuella olivacea was detected at only 5 of the 45 sites in which Nasua nasua was present; therefore, spatial overlap between the two species

Using a hierarchical modelling approach to assess the influence of environmental factors on occupancy, we found that Nasua nasua occupancy was negatively related to elevation but positively to forest cover (Table 1, Fig. 3a, and Table A4 of Supplementary data). In contrast, Nasuella olivacea occupancy appears to be positively related to elevation (Table 1, Fig. 3b, and Table A4 of Supplementary Data). Thus, our findings support our first two hypotheses (1 and 2). González-Maya et al. (2015) characterized the places where both coati species were detected as those with high forest cover, which is consistent with our results for Nasua nasua. However, our analyses do not support the importance of this covariate on Nasuella olivacea occupancy. We also observed differences in site use of the coati species along an elevational gradient. Nasuella olivacea was recorded at elevations of 1700–3300 m, with most detections recorded between 2500 and 3000 m (including the paramo). Nasua nasua was detected exclusively in the montane forest (1700–2700 m), with most detections occurring between 2000 and 2400 m (see Supplementary data, Fig. A2). Moreover, our results suggest that Nasuella olivacea, despite being a rare species (in terms of occupancy), at least in our study area, has a larger distribution range along the elevational gradient than the more common Nasua nasua. Studies have shown that Nasua nasua uses different types of vegetation, including forest and human-altered landscapes (Beisiegel, 2001; Cove et al., 2013; Dechner et al., 2018; Emmons and Feer, 1997), and can be positively affected by low levels of hunting and fragmentation (Kosydar et al., 2014) but not by intensive deforestation (Bisbal, 1993). We did not observe any positive effect of disturbance on Nasua nasua or Nasuella olivacea. Indeed, the ecological characteristics of Nasuella olivacea make the species more susceptible to habitat modification: it has a restricted distribution range and narrow habitat requirements. It mainly inhabits Andean forests and the lower limits of the paramo characterized by epiphytic flora and Podocarpus trees (Bisbal, 1993). Nevertheless, other disturbance sources should be considered. For instance, according to a recent study from Ecuador (Zapata-Ríos and Branch, 2016),

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Fig. 4. Regression (ˇ) coefficients for site (elevation and forest) and detection (effort and distance to water sources) covariates for Nasua nasua, the South American coati (SA), and Nasuella olivacea, the mountain coati (Mc), based on co-occurrence Bayesian modelling. The ˇ is indicated for the following conditions: spatial co-occurrence of Mc and SAc (Mc–SAc) and the effect of elevation on Mc (Mc–elev) and SAc (SAc–elev), forest cover on SAc (SAc–forest), effort on Mc (Mc–effort), and distance to water sources on SAc (SAc–water). Horizontal line within the bar indicates the mean; narrow bars and wide bars indicate the 95% and 90% CI, respectively.

Fig. 5. Kernel density estimates of the daily activity pattern of Nasua nasua and Nasuella olivacea in the Tabaconas Namballe National Sanctuary in 2016. The shaded area represents the overlap between the activity periods of the two coati species.

an increase in feral dogs encounters has negatively affected the abundance of both the mountain paca (Cuniculus taczanowskii) and Nasuella olivacea. Feral dogs were recorded within the TNNS and its buffer zone, especially near farming areas; however, due to the low number of detections, we did not include this data in our modelling. Although some studies have described the diet of Nasua nasua ˜ (Aguiar et al., 2011; Rodríguez-Bolanos, 1995), the diet of Nasuella ˜ olivacea has been poorly documented (Rodríguez-Bolanos, 1995). Based on the data available, the two species seem to have a similar diet or at least consume similar resources, such as plants, invertebrates, and small vertebrates (Alves-Costa et al., 2004; Gompper and Decker, 1998; Ramírez-Mejía and Sánchez, 2016). More studies on coati diets, particularly in areas of sympatry, will help to clarify how food resources are shared among species, which will also

shed light on how closely related, morphologically similar species can coexist. Temporal segregation, especially in carnivores like coatis, seems to be a very important strategy (Bianchi et al., 2016). In fact, mesocarnivores are certainly known to minimize overlaps in order to counteract or reduce interspecific competition by changing their diet (resources) or having differential spatial and/or temporal activity patterns (Barrientos and Virgós, 2006; Jonathan Davies et al., 2007; Massara et al., 2016; Schoener, 1974). Our results confirm that Nasua nasua is mainly diurnal and crepuscular. According to ˜ Rodríguez-Bolanos et al. (2003), who based their conclusion on telemetry data, Nasuella olivacea is also strictly diurnal. However, it has been suggested that this species might vary its activity depending on geographical location or interactions with other species

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(Ramírez-Mejía and Sánchez, 2016). Consistent with this idea, our results indicate that Nasuella olivacea may be mainly nocturnal in our study area, similarly to the results given by Ramírez-Mejía and Sánchez (2016) in an Andean forest and a Eucalyptus reforestation area. However, due to the low number of detections (17), likely due to its rarity, we treat our estimates of the temporal activity of Nasuella olivacea with caution. Indeed, a minimum sample size of at least 100 detections is recommended in order to obtain unbiased estimates of temporal activity overlap (Lashley et al., 2018). The study of niche partitioning and resources use in sympatric species, for instance, in carnivorous/omnivorous with similar morphological characteristics (Medel and Jaksic, 1988), will help elucidate the effect of environmental changes and anthropogenic factors on community structure. This issue is especially critical for species management to ensure long-term conservation goals (Dechner et al., 2018; Frey et al., 2017; Rocha-Mendes et al., 2010). The results of our study support a relationship between the occupancy of both coati species and elevation (hypotheses 1 and 2). In addition, our results, although preliminary, suggest a low level of temporal overlap between the two species, providing some support for our third hypothesis on temporal segregation. In order to propose tenable conservation strategies, more information is clearly needed on the rarer species, Nasuella olivacea. Modelling co-occurrence in both spatial and temporal scales will improve our understanding of how species react to environmental changes, which will then help to direct management decisions (Brodie et al., 2018). This will prove to be particularly important in the coming years: according to the magnitude of climate change predicted by climatic models, the high tropical Andes will be one of the most severely affected ecosystems worldwide (Herzog et al., 2011). In summary, our results provide new insights on the habitat use and the co-occurrence of these two understudied coati species in a part of their distributional range where climate and land use changes will likely challenge their conservation in the (not so) long-term. Acknowledgements We are grateful to the institutions that made this study possible, the administration office of the Tabaconas Namballe National Sanctuary and the National Service of Natural Protected Areas (SERNANP). We especially thank Douglas Cotrina, Head manager of the TNNS in 2016, and the park rangers for their excellent field assistance. We also thank Fabiola la Rosa, Luis Hiyo, and Carina Huaman for their invaluable help in the field phase of the project. Pamela Pastor provided support with the GIS. Finally, we recognize the assistance of Jorge Rivero and Johanna Bindels in the processing of data, and Laura Cancino for comments and an English revision of the manuscript. Funds from the WWF-Germany supported this study. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.mambio.2019. 09.004. References Aguiar, L.M., Moro-Rios, R.F., Silvestre, T., Silva-Pereira, J.E., Bilski, D.R., Passos, F.C., Sekiama, M.L., Rocha, V.J., 2011. Diet of brown-nosed coatis and crab-eating raccoons from a mosaic landscape with exotic plantations in southern Brazil. Stud. Neotrop. Fauna Environ. 46, 153–161. Ahumada, J.A., Hurtado, J., Lizcano, D., 2013. Monitoring the status and trends of tropical forest terrestrial vertebrate communities from camera trap data: a tool for conservation. PLoS One 8, e73707. Alves-Costa, C.P., Da Fonseca, G.A., Christófaro, C., 2004. Variation in the diet of the brown-nosed coati (Nasua nasua) in southeastern Brazil. J. Mammal. 85, 478–482.

Alves-Costa, C.P., Eterovick, P.C., 2007. Seed dispersal services by coatis (Nasua nasua, Procyonidae) and their redundancy with other frugivores in southeastern Brazil. Acta Oecol. 32, 77–92. Balaguera-Reina, S.A., Cepeda, A., Zárrate-Charry, D., González-Maya, J.F., 2009. The state of knowledge of Western Mountain Coati Nasuella olivacea in Colombia, and extent of occurrence in the Northern Andes. Small Carniv. Conserv. 41, 35–40. Balme, G.A., Hunter, L.T., Slotow, R., 2009. Evaluating methods for counting cryptic carnivores. J. Wildl. Manage. 73, 433–441. Barrientos, R., Virgós, E., 2006. Reduction of potential food interference in two sympatric carnivores by sequential use of shared resources. Acta Oecol. 30, 107–116. Beisiegel, B., 2001. Notes on the coati, Nasua nasua (Carnivora: Procyonidae) in an Atlantic forest area. Braz. J. Biol. 61, 689–692. Bianchi, Rd.C., Olifiers, N., Gompper, M.E., Mourão, G., 2016. Niche partitioning among mesocarnivores in a Brazilian Wetland. PLoS One 11, e0162893. Bisbal, F.J., 1993. Impacto humano sobre los carnívoros de Venezuela. Stud. Neotrop. Fauna Environ. 28, 145–156. Bischof, R., Ali, H., Kabir, M., Hameed, S., Nawaz, M.A., 2014. Being the underdog: an elusive small carnivore uses space with prey and time without enemies. J. Zool. 293, 40–48. ˜ Boron, V., Deere, N.J., Xofis, P., Link, A., Quinones-Guerrero, A., Payan, E., Tzanopoulos, J., 2019. Richness, diversity, and factors influencing occupancy of mammal communities across human-modified landscapes in Colombia. Biol. Conserv. 232, 108–116. Brodie, J.F., Helmy, O.E., Mohd-Azlan, J., Granados, A., Bernard, H., Giordano, A.J., Zipkin, E., 2018. Models for assessing local-scale co-abundance of animal species while accounting for differential detectability and varied responses to the environment. Biotropica 50, 5–15. Burnham, K.P., Anderson, D.R., 2020. Model Selection and Multimodal Inference: A Practical Information-Theoretic Approach. Springer, New York. Burton, A.C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J.T., Bayne, E., Boutin, S., 2015. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685. Caravaggi, A., Banks, P.B., Burton, A.C., Finlay, C.M., Haswell, P.M., Hayward, M.W., Rowcliffe, M.J., Wood, M.D., 2017. A review of camera trapping for conservation behaviour research. Remote Sens. Ecol. Conserv. 3, 109–122. Cove, M.V., Spínola, R.M., Jackson, V.L., Sáenz, J.C., Chassot, O., 2013. Integrating occupancy modeling and camera-trap data to estimate medium and large mammal detection and richness in a Central American biological corridor. Trop. Conserv. Sci. 6, 781–795. Curveira-Santos, G., Pedroso, N.M., Barros, A.L., Santos-Reis, M., 2019. Mesocarnivore community structure under predator control: unintended patterns in a conservation context. PLoS One 14, e0210661. Chen, C., Quan, R.-C., Cao, G., Yang, H., Burton, A.C., Meitner, M., Brodie, J.F., 2019. Effects of law enforcement and community outreach on mammal diversity in a biodiversity hotspot. Conserv. Biol. 33, 612–622. Dechner, A., Flesher, K.M., Lindell, C., de Oliveira, T.V., Maurer, B.A., 2018. Determining carnivore habitat use in a rubber/forest landscape in Brazil using multispecies occupancy models. PLoS One 13, e0195311. Emmons, L., Feer, F., 1997. Neotropical Rainforest Mammals: A Field Guide. The University of Chicago Press. Farris, Z.J., Gerber, B.D., Karpanty, S., Murphy, A., Andrianjakarivelo, V., Ratelolahy, F., Kelly, M.J., 2015. When carnivores roam: temporal patterns and overlap among Madagascar’s native and exotic carnivores. J. Zool. 296, 45–57. Ferreira, G.B., Ahumada, J.A., Oliveira, M.J.R., de Pinho, F.F., Barata, I.M., Carbone, C., Collen, B., 2017. Assessing the conservation value of secondary savanna for large mammals in the Brazilian Cerrado. Biotropica 49, 734–744. Fiske, I., Chandler, R., 2011. Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23. Frey, S., Fisher, J.T., Burton, A.C., Volpe, J.P., 2017. Investigating animal activity patterns and temporal niche partitioning using camera-trap data: challenges and opportunities. Remote Sens. Ecol. Conserv. 3, 123–132. Gaston, K.J., 2003. The Structure and Dynamics of Geographic Ranges. Oxford University Press, New York, NY U.S.A. Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., 2014. Bayesian Data Analysis, third ed. CRC Press. Taylor & Francis. Glaston, A., 1994. The Red Panda, Olingos, Coatis, Raccoons, and their Relatives. IUCN Status Survey and Conservation Action Plan for Procyonids and Ailurids. Gompper, M.E., Decker, D.M., 1998. Nasua nasua. Mamm. Species 580, 1–9. González-Maya, J.F., Vela-Vargas, I.M., Jiménez Alvarado, J.S., Hurtado-Moreno, A.P., Moreno, C., Aconchaabril, I., Zárrate-Charry, D.A., Vela-Vargas, I.M., Schipper, J., 2015. First sympatric records of Coatis (Nasuella olivacea and Nasua nasua; Carnivora: Procyonidae) from Colombia. Small Carniv. Conserv. 52, 93–100. Guilherme Trovati, R., Alves de Brito, B., Barbanti Duarte, J.M., 2010. Habitat use and home range of brown-nosed coati, Nasua nasua (Carnivora: Procyonidae) in the Brazilian Cerrado biome. Rev. Biol. Trop. 58, 1069–1077. Haidir, I.A., Macdonald, D.W., Linkie, M., 2018. Assessing the spatiotemporal interactions of mesopredators in Sumatra’s tropical rainforest. PLoS One 13, e0202876. Heiberger, R.M., Holland, B., 2015. Statistical Analysis and Data Display: An Intermediate Course with Examples in R, second ed. Springer, New York. Helgen, K.M., Kays, R., Helgen, L.E., Tsuchiya-Jerep, M.T.N., Pinto, C.M., Koepfli, K.-P., Eizirik, E., Maldonado, J.E., 2009. Taxonomic boundaries and geographic distributions revealed by an integrative systematic overview of the mountain coatis, Nasuella (Carnivora: Procyonidae). Small Carniv. Conserv. 41, 65–74.

J.L. Mena, H. Yagui / Mammalian Biology 98 (2019) 119–127 Herzog, S.K., Martinez, R., Jørgensen, P.M., Tiessen, H., 2011. Climate Change and Biodiversity in the Tropical Andes. Inter-American Institute for Global Change Research (IAI) and Scientific Committee on Problems of the Environment (SCOPE), pp. 348. Hobbs, N.T., Hooten, M.B., 2015. Bayesian Models: A Statistical Primer for Ecologists. Princeto University Press. Jonathan Davies, T., Meiri, S., Barraclough, T.G., Gittleman, J.L., 2007. Species co-existence and character divergence across carnivores. Ecol. Lett. 10, 146–152. Josse, C., Navarro, G., Encarnación, F., Tovar, A., Comer, P., Ferreira, W., Rodríguez, F., Saito, J., Sanjurjo, J., Dyson, J., Rubin de Celis, E., Zárate, R., Chang, J., Ahuite, M., Vargas, C., Paredes, F., Castro, W., Maco, J., Reátegui, F., 2007. Sistemas Ecológicos de la Cuenca Amazónica de Perú y Bolivia. Clasificación y mapeo. EE UU, NatureServe, Arlington, Virginia. Kellner, K., 2015. Jags UI: A Wrapper Around Rjags to Streamline JAGS Analyses. R Package Version 1.4.2. https://cran.r-project.org/web/packages/jagsUI/jagsUI. pdf. Kelly, M.J., 2008. Design, evaluate, refine: camera trap studies for elusive species. Anim. Conserv. 11, 182–184. Kéry, M., Royle, J.A., 2016. Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS, Vol. 1. Prelude and Static Models. Academic Press, Boston. Kéry, M., Schaub, M., 2012. Bayesian Population Analysis Using WinBUGS. A Hierarchical Perspective. Elsevier. Kosydar, A.J., Rumiz, D.I., Conquest, L.L., Tewksbury, J.J., 2014. Effects of hunting and fragmentation on terrestrial mammals in the Chiquitano forests of Bolivia. Trop. Conserv. Sci. 7, 288–307. Lashley, M.A., Cove, M.V., Chitwood, M.C., Penido, G., Gardner, B., DePerno, C.S., Moorman, C.E., 2018. Estimating wildlife activity curves: comparison of methods and sample size. Sci. Rep. 8, 4173. Li, X., Bleisch, W.V., Jiang, X., 2018. Using large spatial scale camera trap data and hierarchical occupancy models to evaluate species richness and occupancy of rare and elusive wildlife communities in southwest China. Divers. Distrib. 24, 1560–1572. MacKenzie, D.I., Nichols, J.D., 2004. Occupancy as a surrogate for abundance estimation. Anim. Biodivers. Conserv. 27, 461–467. MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L., Hines, J.E., 2018. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, 2nd edition ed. Academic Press, Boston. Mann, G.K.H., O’Riain, M.J., Parker, D.M., 2015. The road less travelled: assessing variation in mammal detection probabilities with camera traps in a semi-arid biodiversity hotspot. Biodivers. Conserv. 24, 531–545. Massara, R.L., Paschoal, A.M.O., Bailey, L.L., Doherty, P.F., Chiarello, A.G., 2016. Ecological interactions between ocelots and sympatric mesocarnivores in protected areas of the Atlantic Forest, southeastern Brazil. J. Mammal. 97, 1634–1644. Mazerolle, M.J., 2019. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c). R Package Version 2.2-2. http://CRAN.R-project.org/ package=AICcmodavg. Medel, R.G., Jaksic, F.M., 1988. Ecología de los cánidos sudamericanos: una revisión. Rev. Chil. Hist. Nat. 61, 67–79. Meredith, M., Ridout, M., 2014. Overlap: Estimates of Coefficient of Overlapping for Animal Activity Patterns. R Package Version 0.2 4. Northrup, J.M., Gerber, B.D., 2018. A comment on priors for Bayesian occupancy models. PLoS One 13, e0192819. Pacheco, V., Cadenillas, R., Salas, E., Tello, C., Zeballos, H., 2009. Diversidad y endemismo de los mamíferos del Perú. Rev. Peru. Biol. 16, 5–32. Plummer, M., 2003. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd international workshop on

127

distributed statistical computing (DSC 2003) https://www.r-project.org/ conferences/DSC-2003/Proceedings/Plummer.pdf. R Core Team, 2019. R: a language and environment for statistical computing. In: R Foundation for Statistical Computing URL, URL https://www.R-project.org/. Vienna, Austria. Ramírez-Mejía, A.F., Sánchez, F., 2016. Activity patterns and habitat use of mammals in an Andean forest and a Eucalyptus reforestation in Colombia. Hystrix Ital. J. Mammal. 27 (2), 1–7. Ridout, M.S., Linkie, M., 2009. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337. Rocha-Mendes, F., Mikich, S.B., Quadros, J., Pedro, W.A., 2010. Feeding ecology of carnivores (Mammalia, Carnivora) in Atlantic forest remnants, southern Brazil. Biota Neotrop. 10, 21–30. ˜ Rodríguez-Bolanos, A., 1995. Rango de acción y hábitos alimenticios del coatí de ˜ Nasuella olivacea en la Reserva Biológica Carpanta (Cundinamarca). montana Trabajo de grado, Departamento de Biología. Universidad Distrital Francisco José de Caldas, Bogotá. ˜ Rodríguez-Bolanos, A., Sánchez, P., Cadena, A., 2003. Patterns of activity and home range of Mountain Coati Nasuella olivacea. Small Carniv. Conserv. 29, 16–19. Rovero, F., Zimmermann, F., 2016. Camera Trapping for Wildlife Research. Exeter. Pelagic Publishing, UK. Rowcliffe, J.M., Kays, R., Kranstauber, B., Carbone, C., Jansen, P.A., Fisher, D., 2014. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5, 1170–1179. Royle, J.A., Nichols, J.D., 2003. Estimating abundance from repeated presence–absence data or point counts. Ecology 84, 777–790. Sánchez, F., Alvear, M., 2003. Comentarios sobre el uso de hábitat, dieta y conocimiento popular de los mamíferos en un bosque andino de Caldas, Colombia, Vol. 7. Boletín Científico, Museo de Historia Natural, Universidad de Caldas, pp. 121–144. Schoener, T.W., 1974. Resource partitioning in ecological communities. Science 185, 27–39. Silva-Caballero, A., León-Ávila, G., Valenzuela-Galván, D., Maldonado, J.E., Ortega, J., 2017. Patterns of genetic diversity of the white-nosed coati reveals phylogeographically structured subpopulations in Mexico. Nat. Resour. 8, 31. Tirira, D., 2017. A Field Guide to the Mammals of Ecuador. Editorial Murciélago Blanco, Quito, Ecuador. Tobler, M.W., 2015. Camera Base Version 1.7, Botanical Research Institute of Texas. http://www.atrium-biodiversity.org/tools/camerabase. Tobler, M.W., Carrillo-Percastegui, S.E., Powell, G., 2009. Habitat use, activity patterns and use of mineral licks by five species of ungulate in south-eastern Peru. J. Trop. Ecol. 25, 261–270. Waddle, J.H., Dorazio, R.M., Walls, S.C., Rice, K.G., Beauchamp, J., Schuman, M.J., Mazzotti, F.J., 2010. A new parameterization for estimating co-occurrence of interacting species. Ecol. Appl. 20, 1467–1475. Wilson, D.E., Mittermeier, R.A., 2009. Handbook of the Mammals of the World, Vol. 1. Lynx Edicions, Barcelona, Carnivores. Zapata-Ríos, G., Branch, L.C., 2016. Altered activity patterns and reduced abundance of native mammals in sites with feral dogs in the high Andes. Biol. Conserv. 193, 9–16. Zuur, A.F., Hilbe, J.M., Ieno, E.N., 2013. A Beginner’s Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists. Highland Statistics Newburgh. Zuur, A.F., Ieno, E.N., Elphick, C.S., 2010. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14.