DOI: 10.5061/dryad.p5hqbzkwt
title: Neotropical mammal responses to forest fires in Serra do Amolar, Brazil

description: The increasing frequency and severity of human-caused fires likely have
 deleterious effects on species distribution and persistence. In 2020,
 megafires in the Brazilian Pantanal burned 43% of the biome’s unburned
 area and resulted in mass mortality of wildlife. We investigated changes
 in habitat use or occupancy for an assemblage of eight mammal species in
 Serra do Amolar, Brazil, following the 2020 fires using a pre- and
 post-fire camera trap dataset. Additionally, we estimated density for two
 naturally marked species, jaguars Panthera onca and ocelots Leopardus
 pardalis. Of the eight species, six (ocelots, collared peccaries Dicotyles
 tajacu, giant armadillos Priodontes maximus, Azara’s agouti Dasyprocta
 azarae, red brocket deer Mazama americana, and tapirs Tapirus terrestris)
 had declining occupancy following fires, and one had stable habitat use
 (pumas Puma concolor). Giant armadillo experienced the most precipitous
 decline in occupancy from 0.431 ± 0.171 to 0.077 ± 0.044 after the fires.
 Jaguars were the only species with increasing habitat use, from 0.393 ±
 0.127 to 0.753 ± 0.085. Jaguar density remained stable across years (2.8 ±
 1.3, 3.7 ± 1.3, 2.6 ± 0.85 / 100km2), while ocelot density increased from
 13.9 ± 3.2 to 16.1 ± 5.2 / 100km2. However, the low number of both jaguars
 and ocelots recaptured after the fire period suggests that immigration may
 have sustained the population. Our results indicate that the megafires
 will have significant consequences for species occupancy and fitness in
 fire affected areas. The scale of megafires may inhibit successful
 recolonization, thus wider studies are needed to investigate population
 trends.
description: We used camera traps (Bushnell 119876, Panthera V4 and Cuddeback
 1279) to survey the study area in December 2019 (session 1; year 1 –
 pre-fires) and December 2020 (session 2, year 2 – 2 months post-fires).
 Due to logistical constraints, we installed cameras in February 2022
 (session 3, year 3 – 15 months post-fires) for an average duration of 53
 trap nights (range 1-136, see SI_1 for complete details). All three
 surveys took place in the rainy season. Thirty-five stations were active
 in session 1, 43 stations in session 2, and 31 stations in session 3.
 Cameras were placed at a distance of 1.5 ± .5 km between stations and were
 located in different land covers (primary, secondary and gallery forest,
 savannah). Minimum convex polygons for each survey were 189.68 km2 in year
 1, 272.26 km2 in year 2, and 245.95 km2 in year 3. We placed double
 stations to enable photographing both sides of each passing individual,
 thus enabling identification for naturally marked species like jaguars and
 ocelots. Each sampling station had 24-hour motion-triggered camera
 operation with a period of 30 seconds between photograph triggers.
 Geographic coordinates, camera serial number, date and time of camera
 installation, canopy cover, habitat, and whether the camera was on or off
 trail were recorded.  Our survey design complied with
 methodological assumptions to estimate jaguar (Foster et al., 2020; Tobler
 et al., 2013) and ocelot densities (Boron et al., 2021; Satter, Augustine,
 Harmsen, Foster, Sanchez, et al., 2019; Wolff et al., 2019), and we kept a
 discrete distance between stations to obtain data for the wider mammal
 community (Boron et al., 2021; de Martins et al., 2006; Rovero et al.,
 2020; Rovero & Ahumada, 2017). Our survey was limited to less than
 100 days per year, and fulfills overall capture-recapture model
 assumptions: a) the population needs to be considered closed and stable,
 and b) all individuals should have a chance of being captured (Otis et
 al., 1978; White, 1982). Covariate selection and
 extraction We selected a set of covariates to test our
 hypotheses related to pre- and post-fire habitat use/occupancy as well as
 density. Covariates were Normalized Difference Vegetation Index (NDVI),
 often used to assess habitat quality for mammals (Pettorelli et al., 2005;
 White et al., 2022), area burned derived from Normalized Burn Ratio (ΔNBR)
 which measures fire severity (Escuin et al., 2008), and distance from
 water (Boron et al., 2019). We additionally included effort as the total
 of trap nights per station; year, included to account for differences
 related to time variation as field staff and camera type on p
 (Gutiérrez-González et al., 2015; Kotze et al., 2012); and whether the
 camera station was on a trail or not for the probability of detection (p).
 Year or session was also used as a way to account for the heterogeneity of
 the detection probability, like seasonal activity of species and the
 possible loss of camera quality (Kotze et al., 2012; MacKenzie et al.,
 2003; Tobler et al., 2015). NDVI was obtained for each
 study session from Copernicus-Sentinel-II sensors via Google Earth Engine
 (code here). NDVI calculates vegetation greenness on a normalized scale with denser vegetation approaching one and barren areas or water bodies closer to a value of zero (Pettorelli et al., 2005). Annual NDVI rasters were obtained on days with less than 10 percent cloud cover during the period of one month before camera installation with a grain size of 10 meters. We then extracted the mean NDVI value for a 500-meter buffer around each station. We calculated the area burned (AB) as the area within a 500-meter buffer of each camera station that presented moderate-low severity or higher according to ΔNBR. For AB, we included only ΔNBR values that represent moderate-low burn severity and higher (ΔNBR = 270+) (Keeley, 2009; Key & Benson, 2006) in order to differentiate from areas that may have had low affectation from the fires or that may have presented false-positive values where fires may have burned due to the lack of an NBR system for the region. We obtained surface water data from MapBiomas (https://brasil.mapbiomas.org) and calculated the Euclidean distance of each camera station from surface water. All geoprocessing was conducted in ArcMap Desktop 10.8 (ESRI Inc., 2020). We used Spearman’s correlation test to check for highly correlated covariates (&gt;0.6) with the function ggcorr in the package “GGally v2.1.2” (Schloerke et al., 2022) in Program R v 4.2.2 (R Core Team, 2022). As covariates AB and ΔNBR were correlated (&gt;|0.6|), we fit two global models , each including one of this covariates and selected the best model using Akaike Information Criteria corrected (AICc) for small sample sizes  (Burnham & Anderson, 2002). The model with AB covariate performed better than ΔNBR for most species and thus was used in the analysis.  Dynamic occupancy We determined the habitat use or initial occupancy probability of eight mammal species in the study area: jaguars (Panthera onca), ocelots (Leopardus pardalis), pumas (Puma concolor), giant armadillo (Priodontes maximus), lowland tapir (Tapirus terrestris), red brocket deer (Mazama americana), collared peccaries (Dicotyles tajacu), and Azara’s agouti (Dasyprocta azarae). We determined initial occupancy probability when we could assume closure (individual’s home range is less than the radius between camera trap stations) between camera trap locations (MacKenzie et al., 2002), for ocelots (Crawshaw & Quigley, 1989), red brocket deer (Varela et al., 2010), Azara’s agouti (Cid et al., 2013) and giant armadillo (Desbiez et al., 2020). And determined habitat use for jaguars (Kantek et al., 2021; Soisalo & Cavalcanti, 2006), pumas (Silveira, 2004), collared peccaries (Desbiez et al., 2009) and tapirs (Medici et al., 2022), whose home range surpassed the distance (1.5 km) between stations. Detection histories were created for each species, grouping camera data into a 7-21 days survey occasions based on the results of goodness of fit (GOF) tests (MacKenzie & Bailey, 2004) (SI_3,4). Dynamic occupancy models (DOM) estimate the probability of occupancy and detection and are particularly useful for monitoring changes in occupancy status over time (MacKenzie et al., 2018), allowing us to detect if certain variables were influencing the colonization (Ɣ) and extinction (Ɛ) trends. We scaled covariates before analysis for interpretability. We used “unmarked” package v 1.2.5 (Fiske & Chandler, 2011) in Program R v 4.2.2 (R Core Team, 2022) for all occupancy analysis. The parameters used in DOM were Ψ = initial probability of a site being occupied; p = probability of a species being detected if it is present, Ɣ = probability of a new area to pass from unoccupied to occupied (or to unused to used) in the next year, Ɛ = probability that a species stops occupying an area, or to pass from used to unused. We fit models for species individually, and selected models according to AICc (Burnham & Anderson, 2002). We used a stepwise method (Doherty et al., 2012) for model selection. We first fit models for detection (p) with all other parameters constant. We included survey effort, whether cameras were on a trail, and year as covariates for detection, and selected the best detection model based on AICc value. We proceeded with this best detection model and subsequently fit models using covariates describing occupancy, colonization, and, finally, extinction. We included NDVI and distance to water as covariates for occupancy, whereas area burned was applied to colonization and extinction. We considered there to be satisfactory statistical evidence for an effect if the 95% confidence interval of logit scale coefficient estimates did not include zero (Muff et al., 2022). The β estimates were back-transformed to obtain model parameter estimates (MacKenzie & Bailey, 2004). We tested model fit by using a parametric bootstrap GOF test based on Pearson’s X2 where p&gt;0.05 indicates adequate model fit (Fiske & Chandler, 2011) (SI_3). Finally, we derived annual probability for each year, and calculated standard errors for the derived values using a bootstrap method (Kéry & Chandler, 2012). To assess whether differences in occupancy were statistically significant, we used a nonparametric bootstrap with 1000 iterations and calculated the difference between 95% confidence intervals between years one and three. If the difference in confidence intervals did not contain zero, we considered it significant (Kéry & Royle, 2021). Density Jaguar and ocelot individuals were visually identified by their spots patterns and sex based on external genitalia (Boron et al., 2016). Individuals of unidentified sex were classified as unknown (SI_1). We generated three files to input in “secr” in R (M. G. Efford, 2020) for density estimation a) a capture history per species per session with trap number, individual ID, date and time occasion number of each record; b) a trap deployment file consisting of all traps locations and a binary record file of activity per occasion (defined here as a 24 hours period/day); c) a mask file to represent the state space habitat area, at a 500m spacing and within a buffer of 20 km using the suggest.buffer that corresponds to ~4 (Efford, 2018) that also matched the largest buffer suggested for jaguars previously (Greenspan et al., 2020; Noss et al., 2012). We used the half-normal (HN) detection function, which considers that the probability of capture (p) of any individual (i) decreases with the distance (d) from the activity center such that: pij = g0exp(−dij2/2σ2), g0 represents the probability of capture or detection when a trap (j) is located right at the center of the home range; and sigma (σ) is a proxy parameter related to home range size (Efford, 2004). Felid species have varying home ranges size according to sex (Massara et al., 2015; Satter, Augustine, Harmsen, Foster, & Kelly, 2019); however, as sex was not identified for all individuals, this was not included in the models. Felids are also known to use unpaved roads to varying degrees and jaguars may be more detectable on them (Sollmann et al., 2011). We therefore enabled detection (g0) to vary by whether the station was located on a trail, while sigma (σ) varied according to the different sessions to assess if the value is constant or varies across years. We additionally considered distance to water, NDVI and ΔNBR as covariates for density and selected models using AICc (Burnham & Anderson, 2002).
description: # Neotropical mammal responses to forest fires in Serra do Amolar, Brazil
 [https://doi.org/10.5061/dryad.p5hqbzkwt](https://doi.org/10.5061/dryad.p5hqbzkwt) The data and scripts contained in this folder are from data collected by Instituto Homen Pantaneiro and Panthera collected between 2019-2021 in the Serra do Amolar, Mato Grosso do Sul, Brazil. The purpose of the study was to evaluate occupancy and density of mammal species before and after forest fires in 2020. These data and the attached scripts are from the article Bardales et al. (2024): DOI : 10.1111/gcb.17278. ## Description of the data and file structure The data are detections (presence and absence) of eight mammal species in the Serra do Amolar for occupancy analysis and recaptures of jaguars and ocelots for density analysis.. The scripts attached are for dynamic occupancy models for three years of data and multi-year density for jaguars and ocelots. List of files:  **Dynamic occupancy (19 files total)** * MS_Occupancy_allsppSA.R is the R script for all eight species. * SA_TE1 is the file with the trapping effort and start and end date for each camera. * SiteCov_St has all the site covariates for all the cameras. Trail (Y/N), normalized data vegetation index (NDVI), normalized burn ratio (NBR), Area burned (AB), Distance from water in meters (Dwater), and percent area burned (BP). Details for sources and how to derive covariates is included in the methodology with links to Google Earth Engine code.  * There is a .csv file for each species (*Dasyprocta azarae, Dicotyles tajacu, Leopardus pardalis, Mazama americana, Panthera onca, Priodontes maximus, Puma concolor, Tapirus terrestris*) that has a detection history for the species. The numbers in the title, ex. "Tapirus_14d" indicate the length of the secondary sampling period, adjusted per the goodness-of-fit test for each species capture history. The columns in the csv files represent the secondary sampling occasion, and ex. "o1" and they repeat for each year. 1s represent detection and 0s represent non-detection.  * There is an accompanying .csv for each species with the trapping effort for the species adjusted for sampling occasion length. The numbers in the title indicate the length of the secondary sampling period, adjusted per the goodness-of-fit test for each species capture history. **SECR Density (7 files total)** * MS Density Jag & Oc.R is the R script for jaguar and ocelot density for the three years. * Capture histories for years 1, 2, and 3 for jaguars and ocelots. ID: Jaguar ID, OCC: Occasion number, Det: Detector number, Site: camera on road, Prey: number of prey species recorded. * Masks and effort files for years 1, 2, and 3 have been removed for security purposes of camera locations. These files can be made available via request to the authors. ## Code/Software All code is run in R software. Information on necessary packages are in the scripts. Scripts can be downloaded or forked via Github: [https://github.com/matt-hyde-s/Amolar](https://github.com/matt-hyde-s/Amolar)