Mammal responses to tourism 857
for five of the six target species, as we recorded pacas only rarely during the daytime. The generalized linear mixed models implemented here
FIG. 2 Estimates of species richness (jackknife 1) in Cavernas do Peruaçu National Park (Fig. 1) before and after tourism was allowed at each survey site (connected by lines). The 95% CIs of the estimates for all pairwise comparisons overlapped, indicating that changes in species richness were not statistically significant (CIs not shown for presentation purposes; Supplementary Table 7).
We used both visitation period and trail category as vari-
ables representing tourism in our models. We included the interaction between these factors as we anticipated that any potential responses to visitation period would be stronger on tourist trails. We also included vegetation type and sea- sonality as covariates because of their potential influence on probability of trail use by the target species, and we included camera-trap site as a randomfactor (SupplementaryTable 4). Because our main objective was to assess the effects of tour- ism, we built alternative models that varied in their in- clusion of vegetation type and seasonality covariates but holding tourism-related variables fixed (including their in- teractions). We used the Akaike information criterion with a correction for small sample sizes (AICc) to assess model support (Burnham & Anderson, 2002). Wepresent the results for only the best-supported model
for each species, as the effect of tourism-related variables in other concurrent models with ΔAICc ,6 did not change (Supplementary Tables 5&6). Wefollowed standard proce- dures to assess model fit (Zuur et al., 2009; Hartig, 2020)by plotting standardized residuals vs model predictions as well as observed vs expected distribution of residuals, which in- dicated adequate model fit for all species (Supplementary Figs 1&2).Werepeated the modelling procedures described above using a subset of the data to estimate the effect of visi- tors on the probability of trail use between 9.00 and 17.00, representing the core visitation hours when tourists are allowed in the Park. We conducted this additional analysis
do not account for any potential variation in detection prob- ability. Statistical adjustments for imperfect detection can improve monitoring programmes (Mackenzie et al., 2002) but the covariates influencing the detection probability can also be controlled prior to data collection through care- ful planning of the survey design (Banks-Leite et al., 2014). Although adequate survey design might not fully eliminate imperfect detection, it can minimize variation in the detec- tion probability that would affect the results. In our design, two features limited variation in the detection probability between sampling sites and survey periods: (1) we surveyed only pre-existing trails, avoiding the variation in detection between on- and off-trail sites, which is known to affect mammals in the region (Ferreira et al., 2017), and (2)at each site, camera traps were always deployed in the same tree, at the same height and facing the same direction during every survey, limiting the spatial and deployment effects on detection probability. Furthermore, we do not claim that a change in probability of trail use is driven necessarily by a change in animal abundance; instead, we interpret this as a metric reflecting the intensity of trail use by the species assessed, an approach that has been adopted in similar studies (Muhly et al., 2011; Blake et al., 2017; Kays et al., 2017; Ngoprasert et al., 2017). Finally, we investigated the effect of tourism on the activ-
ity of ocelots and rock cavies. We selected these species be- cause theywere amongst the most recorded species and were active during the daytime, and were thus more likely to be affected by visitors. To assess the effects of tourism on ac- tivity, we used all camera-trap records obtained for both species, not only the independent records. We estimated overall activity levels (proportion of time active) by fitting a flexible circular kernel distribution to time-of-detection data and we performed a Wald test to investigate whether the estimates before and after tourism differed significantly. Additionally, we conducted a Watson’s two-sample test to compare the activity patterns of these species before and after tourism was allowed (Jammalamadaka & SenGupta, 2001; Oliveira-Santos et al., 2013). To limit the potential ef- fects of spatial variation on activity patterns, we conducted these pairwise comparisons independently for tourist and non-tourist trails. Similarly, to avoid the influence of vegeta- tion type on activity,we restricted the comparisons to gallery forest sites, where we installed more survey sites on tourist trails. Analyses were conducted in R 3.6.3 (R Core Team, 2020) using packages activity (Rowcliffe et al., 2014), overlap (Meredith&Ridout, 2014) and circular (Agostinelli&Lund, 2017); we also used packages lme4 and MuMIn (Bates et al., 2015; Barton, 2016) for modelling and DHARMa (Hartig, 2020) for model checking. We estimated species richness with EstimateS 9.1.0 (Colwell, 2013).
Oryx, 2022, 56(6), 854–863 © Crown Copyright, 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321001472
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