548 V. H. Luja et al.
(Karanth & Nichols, 1998). Camera trapping followed the protocol of the National Jaguar Census, a methodology created by Mexican scientists to standardize the collection of jaguar population data (Chávez et al., 2007). Using QGIS 3.4.4. (QGIS, 2020) and Google Earth (Google, Mountain View, USA), we divided the study area into seven quadrats of 9 km2 each. In each quadrat, we placed three camera-trap stations (two with a single camera each, and one with two cameras facing each other, to obtain photographs of both flanks of jaguars passing between them), in locations with signs of jaguar presence such as tracks and scrapes. We installed a total of 25 camera stations, with a minimum distance of 1 km between stations. We used Cuddeback Colour X-Change camera traps (Cuddeback, De Pere, USA), attached to trees 40–50 cm above ground level, placed perpendicular to wildlife trails and programmed to take one picture with a trigger speed of 0.5 seconds.
Jaguar population data
We derived occupancy and relative abundance of jaguars from the analysis of images obtained during both survey periods and following the methodology described by Sanderson &Harris (2012), which is detailed below for rela- tive abundance potential prey.We determined the number of individuals, age classes and sex ratio by identifying indi- viduals from their unique spot and rosette patterns (Karanth &Nichols, 1998). To estimate population size, we constructed a capture history matrix (1 = presence, 0 = absence) for each individual and each 10-day survey period (Chávez et al., 2013). We analysed the resulting matrix using CAPTURE (Rexstad & Burnham, 1992). To estimate population density, we divided the abundance estimated with CAPTURE by the effective trapping area (Silver et al., 2004). To estimate the effective trapping area, we generated a circular buffer area around each trapping station, with a radius equal to half the mean maximum distance moved. We estimated the maximum distance moved for each male individual cap- tured at more than one station, as their home ranges are much larger than those of females. We then calculated the total area covered by the stations and their buffer areas, thus estimating the effective trapping area, using QGIS (Silver et al., 2004), and calculated jaguar population density by dividing the estimated population size by the effective trapping area. To generate abundance estimates for the sampled area, CAPTURE uses several different models based on the number of individual animals captured and the frequency of recaptures. The models consider different sources of variation in the probability of capture, including the variation between individuals, their probability of being captured, and others. CAPTURE also offers model selection to determine which estimator best fits the data.
Relative abundance of potential prey
We determined potential prey species based on available literature (Hayward et al., 2016; Luja et al., 2020; Perera- Romero et al., 2021) and followed the protocol described by Sanderson & Harris (2012) for the organization and ana- lysis of camera-trap photographs. We calculated the relative abundance index (RAI) using the formula proposed by Maffei et al. (2004): RAI = (C/SE) × 100 where C is the number of photographic captures, SE is the sampling effort (number of cameras per monitoring day) per 100 camera days (standard correction factor). We considered photo- graphs of the same species at the same camera station as independent if they were taken at least 60 minutes apart (Sanderson & Harris, 2012). We calculated naïve occupancy as the proportion of cameras by which a species was regis- tered in relation to the total number of cameras used (O’Connell & Bailey, 2011).
Land-cover change
We analysed land-cover change across 6,276 ha, the ap- proximate area of the terrain covered by camera traps, by visually interpreting digital orthophotos from1999 (resolution of 1 m per pixel) obtained from the National Institute of Statistics, Geography, and Informatics (Instituto Nacional de Estadística, Geografía e Informatica, INEGI, Aguascalientes City,Mexico),and images from Google Satellite 2019 (Google, Mountain View, USA; resolution of 0.6–2.5 m per pixel), using QGIS. Firstly, we made a preliminary classification, distinguishing elements of the images by their shape, size, tone and colour, texture and distribution, supported by vegetation maps and the prior knowledge of the observer. Secondly, we verified land cover on the ground, validating or correcting the preliminary identification of land-cover types. We carried out an interpretation precision test and constructed a confusion matrix, obtaining the omission error values and the overall accuracy of the map (Cakir et al., 2006). We identified six land-cover types: water bodies, infrastruc- ture, agricultural land, mangrove, bare land and secondary vegetation.We obtained the total area (in ha) for each land- cover type for the years 1999 and 2019,and determined the per cent of the study area covered by each type in each year and the change in the area covered by each type in 1999 compared to 2019. We calculated the annual change rate (Tasa) in ha/ year for each land-cover type, using the equation proposed by the Food and Agriculture Organization of the United Nations in 1996 (Ruiz et al., 2013):
Tasa = 1/n
S2 S1
−1
where S2 is the area in year 2, S1 the area in year 1, n is the number of years between the two dates, multiplied by 100 to
Oryx, 2022, 56(4), 546–554 © The Author(s), 2022. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605321001617
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