Elevation as an occupancy determinant of the little red brocket deer ( Mazama rufina ) in the Central Andes of Colombia

We assessed the influence of terrain variables on the occupancy of the little red brocket deer ( Mazama rufina ) in the Central Andes of Colombia. Occupancy increased with elevation up to 3000, where it starts decreasing. This information is crucial to predict the potential effects of climate change on M. rufina and other mountain species.

bution of mountain-living species is important to predict their potential responses to climate change and other factors affecting their habitat availability and quality (Elsen and Tingley 2015). The little red brocket deer (Mazama ru la, Colombia, Ecuador, and Northern Peru (Lizcano et al. 2010). It is classified as vulnerable by IUCN due to the decrease in its populations associated to habitat transforma-na Pucheran, 1851) is found in Andean forests above 1500 m and goes up to paramos at 3800 m in Venezue tion and habitat loss (Lizcano and Alvarez 2016). In this study we estimated the influence of mountain attributes (i.e., terrain variables) on occupancy (ψ) and detection probability (p) of M. rufina.
Understanding the factors influencing the spatial distri fi tion ranged between 1800 and 3880 m. We installed 28 camera traps (Bushnell HD) in three arrays between March and June 2017 at the Quindio locality where they remained active for 35 days. In Risaralda we installed 60 -cameras (Bushnell Trophy Cam) in two arrays between March and July 2017. These remained active for 45 days. We installed the camera traps with at least 500 m distance from each other on a regular grid. Distance between cameras was based on travel distance estimates for the closely related species Mazama gouazoubira (Fischer, 1814), to ensure sampling independence (Grotta-Neto et al. 2019). Cameras were unbaited. We followed a variation of the TEAM Net work protocol (TEAM Network 2011). We organized and tagged pictures using the software WildID (Fegraus et al. This study took place in the Central Andes of Colombia, at the Biological Station Estrella de Agua and its surroundings in Salento, (Quindío department) and Ucumari Regional Park (Risaralda department). In these two localities eleva Figure 1. Functional relationships between covariates and detection probability and occupancy (left) and spatial predictions of detection probability and occupancy of M. rufina (right). a Detection probability as a function of roughness; b Predicted detection probability in a map; c Occupancy (ψ) probability as a function of elevation; d Predicted occupancy in a map. The blue line represents the predicted variable given the true value of the covariate coefficient with confidence intervals indicated by grey lines. Blue dots are camera traps 2011), using one-hour intervals to define independent sampling events (Meek et al. 2014). For more details on methods (see Supplementary material).

CONFLICT OF INTEREST
The authors declares that they do not have conflict of interest. Akaike H. 1973

Supplementary material of the short note: Elevation as an occupancy determinant of the little red brocket deer (Mazama rufina) in the Central Andes of Colombia
Material suplementario de la nota corta: Elevación como un determinante de la ocupación del venado soche (Mazama rufina) en los Andes centrales de Colombia

Methods details
Camera placement: The cameras were installed using a minimum distance of 500 meters between each other. This distance corresponds to an average home range for the closely related species Mazama gouazoubira (Fischer, 1814). The data set for the little red brocket deer was produced manipulating the output tables from WildID using the R code created by the TEAM network, available in the link: https:// github.com/ConservationInternational/teamcode/tree/ master/cameratrapping nization (Meek et al. 2014) was initially defined as one hour, meaning that all the little red brocket deer pictures in the same camera during one hour period were grouped and considered as one individual event. For the occupancy analysis, we grouped and coded all the events on the same day as one, and cero if there was no picture on that day. The data set was composed of 108 days and 87 sampling locations or camera sites. We did not collapse the data set to perform the analysis. We used the occupancy modeling framework to investigate possible relationships between occupancy and the geographical covariates et al. 2017;Dénes et al. 2015;Bajaru et al. 2019). We compared a set of nine models testing the effect of the covariates on occupancy and detection. In this framework, we son 2002; Johnson and Omland 2004).
olution) using the function getData of the package raster (Hijmans 2020). Slope aspect and roughness were computed using the function terrain of the package raster. The four covariates were stacked, and the values were extract-(MacKenzie used the Akaike Information Criterion (AIC) to rank all the nine candidate models and calculate their weights. We considered two models as different if the delta AIC was bigger than two (Akaike 1973;Burnham and Ander The models were coded using the package unmarked (Fiske and Chandler 2011). The spatial covariates used in the analysis were four; elevation, slope, aspect, and roughness (Fig. 1). The elevation was obtained from the CGIAR-SRTM (90 m res ed for each camera trap location using the function extract from the package raster (Hijmans 2020).
The definition of an event for the camera trap data orga