DOI: 10.5061/dryad.5qfttdz3h
title: Detection histories of mesocarnivores in agricultural areas of Southern Chile

description: We obtained mesocarnivore detection/non-detection data from 180 sampling
 units (4 km2 each) located in the agricultural landscapes of southern
 Chile from January-April of 2019. We used single-species occupancy models
 to investigate the associations of forest fragmentation, forest loss, and
 private land ownership subdivision (as a measure of human use
 intensification) with the occurrence of four mesocarnivores (güiña, grey
 fox, culpeo fox, and Molina’s hog-nosed skunk), and extended this
 framework to two-species occupancy models to assess patterns of
 mesocarnivore co-occurrence with domestic carnivores. We also assessed
 whether co-occurrence of native and domestic carnivores led to shifts in
 species’ temporal activity. We hypothesized that the occurrence of native
 mesocarnivores was largely mediated by human use intensification variables
 only, the occurrence of domestic mesocarnivores only, or a combination of
 both. Our results largely supported the human use intensification
 hypothesis, with some influence of domestic mesocarnivores. Mesocarnivore
 occurrence shifted from a native to a domestic species composition as
 private land ownership subdivision increased, and native mesocarnivores
 shifted their behaviour temporally when co-occurring with domestics. In
 addition, the presence of domestic dogs was associated with an absence of
 native mesocarnivores, possibly driving a defaunation process in
 agricultural areas.
description: We
 obtained mesocarnivore detection/non-detection data via a camera trap
 survey from January to April of 2019 (i.e. summer months). Potential
 sampling units (hereafter SUs) were defined via a grid of 4
 km<sup>2</sup> cells across the study region. The size of the
 SUs was informed by the mean estimated home range size of güiña from
 collared individuals in the study area, a size comparable to home ranges
 from the other native mesocarnivores under study (Schüttler et al., 2017;
 Johnson & Franklin, 1994; Salvatori et al., 1999; Kasper, Soares,
 & Freitas, 2012). Detectability was modelled based on the
 assumption that a 5-day survey block is a separate independent sampling
 occasion, a survey period used previously for mesocarnivores in southern
 Chile (Moreira-Arce et al., 2016). Minimum survey
 effort requirements (i.e. number of SUs and sampling occasions) were
 determined following Guillera-Arroita, Ridout, and Morgan (2010), using
 species-specific parameter values from Gálvez et al. (2013) and a target
 statistical precision in occupancy estimation of SE &lt; 0.075. Our
 target was a minimum of 120 SUs and 10 sampling occasions. A total of 180
 SUs were selected at random from the grid of 2,243 cells (Fig. 2). Each
 camera was active 48.6 days on average (SD = 7.77), with 4 to 14 sampling
 occasions at each SU (average = 10.14 occasions, SD = 1.55).
 The
 extent of forest loss and fragmentation were evaluated using ecologically
 meaningful metrics reported in the literature as being relevant to
 mesocarnivores and our hypotheses (see below), using remotely‐sensed
 landcover data (Table 1). The metrics were measured at two scales:
 landscape-scale (400 ha circular buffer around each camera) and fine-scale
 (10 ha buffer) in order to examine the landscape-level and core home
 range-level effects, respectively, of habitat loss and fragmentation on
 species’ occupancy. FRAGSTATS 4.1 was used to create these buffers
 (McGarigal, Cushman, Neel, & Ene, 2002). These core area variables
 were also used to model detection (Guillera-Arroita, 2017). Importantly,
 land subdivision was assessed as the number of land properties within each
 400-ha or 10-ha buffer. Depending on
 the species, we used 5 to 7 covariates for modelling detection
 probability, including quadratic effects on continuous covariates that
 could have a non-monotonic effect on species detection (e.g. forest cover;
 Table 1). See Supporting Information Appendix S1 for methods used to
 confront the multicollinearity of our covariates.