DOI: 10.5061/dryad.jsxksn0kp
title: Using camera traps to estimate site occupancy of invasive Argentine black and white tegus (Salvator merianae) in South Florida

description: The introduction of nonnative species is a leading cause of biodiversity
 loss. Many invasive species are cryptic or elusive in nature and therefore
 often evade detection, complicating their management. Occupancy modeling
 can reveal the presence and spread of invasive species over time and
 therefore has important management implications. Camera traps can be used
 to estimate occupancy, or the proportion of sites that are occupied by a
 target species. During a 4-year study (2016–2020), we used camera traps
 both with and without lures to detect the presence of invasive Argentine
 Black and White Tegus (Salvator merianae) at sites in Miami-Dade County,
 Florida. Our results from a multi-season occupancy model revealed that a
 quadratic effect of ordinal day was the best predictor of detection, with
 a peak in June, while occupancy was correlated with distance to landscape
 features that may facilitate tegu movement.  We did not detect
 any large-scale changes in occupancy over the course of our study. We also
 discovered that the use of lures with camera traps did not impact
 detection, which requires fewer resources from invasive species managers
 for continued monitoring. Understanding the factors that impact occupancy
 and detection probabilities can inform surveillance and removal efforts,
 leading to more efficient management strategies.
description: <strong>Field-site Description</strong>
 We conducted our research in Miami-Dade County in southern
 Florida, USA (Figure 1). Most of our sampling effort was concentrated in
 the Southern Glades Wildlife and Environmental Area (25°19'54"N,
 80°30'56"W); however, we also sampled north of the Southern
 Glades in the Redlands Agricultural Area, along the eastern boundary of
 Everglades National Park, just west of Biscayne National Park, and along
 US Highway 1, north of Key Largo. Our study area is mainly characterized
 by marshland intersected by canals, vegetated levees, and berms.
 Agricultural and residential areas as well as hardwood hammocks also fall
 within the boundaries of our study site. South Florida experiences a
 subtropical climate with a wet season occurring from late spring into fall
 and a dry season spanning from fall to spring (Obeysekera et al.
 1999).  <strong>Camera Trap
 Surveillance</strong> Between 15 February 2016
 and 31 December 2019, we placed camera traps (models: Moultrie M-880,
 Moultrie M-880 Gen2, Moultrie M-888, Moultrie M-888i, Browning Dark Ops
 Elite HD and HD 940) at 87 unique sites throughout Miami-Dade County. We
 placed camera traps on trees or attached to stakes driven into the ground
 when attaching them to trees was not possible. We placed cameras at
 approximately 0.5 m high and angled them slightly downwards to ensure
 cameras were positioned to capture photos of Tegus. We programmed cameras
 to operate 24 hours per day and to capture 1 photo upon detecting movement
 with a 30-second delay between subsequent photos. We baited approximately
 half of our camera traps (n = 43) using a bait cage containing a chicken
 egg and attached to the same tree or stake as the camera trap. Our camera
 trapping effort varied between sampling years, with 67, 65, 77, and 70
 cameras operating per year, respectively. 
 Due to access limitations and variation
 of suitable landscape types within study sites, we did not select our
 camera trap locations randomly. We typically placed camera traps along
 levees and roads bordering habitats and movement corridors suitable for
 Tegus as informed by resource use documented by Klug et al. (2015), where
 the authors found that the vegetated levees within aquatic habitats (i.e.,
 marshes) facilitated Tegu activity and movement. This meant that many
 cameras were spatially aligned in a relatively linear fashion. We also
 placed camera traps in residential and agricultural areas to assess
 potential variation of Tegu occupancy with regards to land use. The
 average straight-line distance between a camera trap and the nearest
 camera trap was 954 meters (range: 24–6014 m; SD ± 983 m). There were
 cases when cameras placed in proximity (i.e., &lt;30 m) were
 positioned on opposite sides of canals at minimal straight-line distances.
 However, the actual distance an animal would have to travel between camera
 traps was likely much greater, as canals may serve as a semi-permeable
 barrier to Tegu movement (Klug et al. 2015). Our camera trap placement
 also facilitated trap checking and maintenance, as levees provided a means
 of access within sites despite water inundation during the wet season,
 while deep-water canals often otherwise necessitate specialized equipment
 for access. Additionally, concurrent research examining spatial ecology of
 Tegus through radio telemetry suggested that Tegus avoid inundated marsh
 (Mason 2022). We collected SD cards from our unbaited
 camera traps approximately once per month. Upon retrieval, we examined
 camera traps to ensure they were in working order, removed vegetation or
 other obstructions of the camera view, if present, and replaced camera
 trap batteries as needed. For baited camera traps, we visited camera traps
 and collected SD cards approximately once every 2 weeks. Upon these
 visits, we repeated the same procedure, but also replaced lures (i.e.,
 chicken eggs) as needed. Following SD card collection, a member of our
 team manually checked camera trap photos to detect whether a Tegu was
 present in the photo (1 = yes, 0 = no). For data quality and assurance, we
 also had a different person “proof” or visually inspect each photo a
 second time. <strong>Occupancy
 Analysis</strong> <em>Data preparation.
 </em>In many cases, our cameras were active year-round. However, we
 excluded photos taken on camera traps between 1 November and 15 February
 of each year. Tegus generally brumate during cooler months, starting as
 early as September and ending in February or early March in southern
 Florida (Currylow et al. 2021, McEachern et al. 2015), so we selected
 these sampling periods to encompass the entirety of the Tegu active season
 in our study site. This is further supported by our own camera trap data,
 as we observed detections decrease in frequency during September and
 October, with the latest detection occurring on 7 November. Our earlier
 detections in the calendar year began to increase in late February, with
 our earliest detection observed on 20 February. During brumation, we
 suspect that the probability of detection is near 0 if not 0, even though
 occupancy of our selected sites may not change. We
 generated binary detection histories in R (v.4.3.1) (R Core Team 2021)
 using the package <em>camtrapR</em> (v 2.0.3) (Niedballa et
 al. 2016). We delineated our camera trap effort into sampling occasions
 consisting of 14 consecutive days, as we found that Tegus had low daily
 detection probabilities and this length of time allowed us to reduce
 non-detections and optimize modelling (Sollmann 2018). We included all
 camera trapping effort, meaning that if a camera was operating at any
 point during the 14-day period, we treated this period as a sampling
 occasion (see Supplemental Figure 1 in Supplemental File X, available
 online at
 http://www.eaglehill.us/SENAonline/suppl-files/nXX-X-XXX-Xxxxxx-sX.)).  <strong>Covariate Data Collection</strong> <em>Site covariates. </em>For each camera trap, we measured habitat type and distance (m) to landscape features (Table 1) using shapefile layers informed by the Florida Cooperative Landcover Map (CLC). We calculated the Euclidean distance (m) from each camera trap to the boundary of polygons representing urbanization, agricultural lands, and to 2 areas we deemed relevant to the invasion history of Tegus in South Florida. One of these areas is a suspected introduction site for Tegus in South Florida (hereafter referred to as “source population”). The source population site is characterized by a quarry bordered by an agricultural matrix and is north of many of the removal efforts targeting Tegus in South Florida. The second area or landmark of interest (hereafter referred to as the “UF core trapping area”) included a parcel of land where several live traps were being operated by University of Florida to capture Tegus. This area was located at an underpass of US Highway 1 and is where linear habitat features (i.e., Highway US1 and vegetated shrubland along a road) intersect within a largely inhospitable landscape (i.e., aquatic). The UF core trapping area is located at the northeastern boundary of an area deemed the “core management area” by Udell et al. (2022), where live trapping conducted by various agencies (i.e., University of Florida, United States Geological Survey, and the National Park Service) has proved productive for Tegu removal, and we assumed that relative abundance in this area was high. The core management area is made up of several levee systems in the Southern Glades and is approximately 4 km south of the source population. We suspected that the linear habitat features (i.e., levees or roads) connecting the core management area and the source population could aid in the dispersal of Tegus. The UF core trapping area, however, may serve as one of few dispersal pathways that would allow for Tegus to cross Highway US 1. Additionally, the intersection of linear habitat features may act as a large-scale drift fence, funneling Tegu movement within the Southern Glades to this area, potentially increasing occupancy probability.   We classified habitat into 3 categories: levee, terrestrial, and disturbed. We classified levee sites as cameras that were on a levee that was bordered by aquatic habitats (i.e., marsh or canals) on both sides. For cameras placed on levees that had aquatic features on one side, but terrestrial landscape features (i.e., forested area, naturally, or semi-naturally occurring vegetative cover) on the other side that would aid Tegu dispersal or movement, we classified this land use type as “terrestrial”. Classification of disturbed habitat type included sites where camera traps were placed in, or directly adjacent to, anthropogenic features (i.e., public roads, houses, and agricultural fields) that may hinder Tegu movement. We grouped camera trap sites into these 3 categories by visually inspecting satellite imagery to identify habitat characteristics and dominant landscape features of the areas adjacent (i.e., within a buffer of ~50 m) to the camera sites. <em>Detection covariates. </em>We suspected that whether the camera trap was baited, temperature, rainfall, the number of days a camera trap was active during the 14-day sampling occasion, and the ordinal date would influence detection probability of Tegus. As ectotherms, Tegus are highly susceptible to environmental conditions, which likely influence their activity patterns and detectability. We sourced temperature and rainfall data from the Florida Automated Weather Network station located in Homestead (25˚30’45”N, 80˚30’11”W). For each sampling occasion, we aggregated mean daily temperature and rainfall data to coincide with our 14-day sampling occasions and calculated the mean for each sampling occasion. We used the mid-point of each 14-day sampling occasion to assign an ordinal date to each sampling occasion. <em>Yearly site covariates. </em>Many multi-season or multi-year occupancy studies track occupancy probabilities and detection for conservation-based information (e.g., using occupancy as a proxy to track variation in population size and distribution of vulnerable species) (De Wan et al. 2009, Farris et al. 2017, Gojiman et al. 2015). In these cases, using year or other variables that may change between primary sampling seasons (e.g., canopy cover or forest cover) is often appropriate. However, working with invasive species often requires their detection and subsequent removal, so including information about removal efforts may be better applied to understanding how occupancy changes over time in response to management. For this reason, we used data from live traps that were operated by collaborating agencies at the same time as our camera traps to calculate seasonal covariates. We calculated the Euclidean distance (m) to the nearest known live trap for each camera trap in each sampling season and the capture per unit effort (CPUE) of the nearest live trap to investigate changes in occupancy and detection probabilities over time as a response to removal efforts. We had knowledge of and capture data for a total of 1,129 livetraps in our study area, with an average of 282 traps operating per year (range = 249–314; see Supplemental Figure 2 in Supplemental File X, available online at http://www.eaglehill.us/SENAonline/suppl-files/nXX-X-XXX-Xxxxxx-sX.). Live traps were distributed by collaborating agencies along levees, berms, and other areas suitable for Tegus with many of them aggregated in the core management area (n = 690), with 18 of these traps located at or within a 100 m radius of the UF core trapping area. We calculated live trap CPUE by dividing the number of Tegus captured in a trap by the total number of trap nights for that trap. <strong>Modeling Framework</strong> Occupancy models are applied to estimate occurrence (i.e., occupancy) of species at a site while taking into account imperfect detection (Kéry et al. 2010). In a traditional occupancy model, sites must be sampled on multiple occasions because non-detections do not necessarily mean that a site is not occupied (MacKenzie et al. 2002). Occupancy models can incorporate variables that may influence detection (detection covariates) and change between sampling occasions or between sites (site covariates) to account for heterogeneity (MacKenzie et al. 2003). Dynamic occupancy models attempt to answer similar questions (i.e., what proportion of sites are occupied, and what is the probability of detecting a species if it is present?), but over time. In addition to detection covariates and site covariates, a dynamic or multi-season occupancy model may include covariates that are not expected to change in between sampling occasions but may change between sampling seasons to account for local colonization (i.e., a site becoming occupied that was not in previous seasons) or local extinction (i.e., a site that was once occupied was not found to be occupied in following seasons). We created dynamic occupancy models using the R package <em>unmarked </em>(v.1.2.5) (Fiske and Chandler 2011) to see how occupancy of Argentine Black and White Tegus changed over the course of 4 sampling seasons (2016–2020). Prior to model creation, we standardized our detection covariates (temperature, rainfall, ordinal day, camera trapping effort per 14-day sampling occasion), site covariates (distances to landscape features and areas of interest, specifically urbanization, agriculture, the UF core trapping area and the source population), and colonization and extinction covariates (distance to nearest live trap and the CPUE of that trap). We created models applying a stepwise procedure (e.g., Stewart et al. 2022) which entails creating models at several steps where covariates are either added to or removed from the model based on their performance (i.e., AIC<sub>c</sub> score) as demonstrated by Button et al. (2019). At step 1, we created 9 models, including a null model and a model for each detection covariate including camera trap bait status, temperature, precipitation, camera trapping effort for each 14-day sampling occasion, and ordinal day with a quadratic effect to determine which covariate best explained detection probability. At this step, we also created a model for each of our seasonal covariates (distance to nearest live trap and CPUE of nearest trap), as these could be considered factors that may also influence detection. For example, the presence of a live trap with bait could act as a lure and thus increase the probability of detection. Additionally, the presence of a very productive live trap may decrease detection locally as individuals are removed from the landscape. We used the R package <em>AICcmodavg </em>(v.2.3-1) (Mazerolle 2023) to select top performing models using Akaike’s Information Criterion corrected for small sample sizes (AIC<sub>c</sub>), and we considered models with ∆ AIC<sub>c </sub> ≤ 2 to be top performing models (Burnham and Anderson 2002). This step allowed us to identify 1 or more covariates that best explain detection to be included in models in step 2. Once we identified which model best predicted the probability of detection (<em>p</em>), we moved on to step 2 and built 6 models that included covariates retained in the top performing detection model. This included a model for each of our site covariates (habitat type, distance to UF core trapping area, distance to source population, distance to urbanization, and distance to agriculture) as well as one model where occupancy was assumed to be constant across sites and seasons (i.e., intercept only). In the third and final step, we included covariates included in the top performing detection model (step 1) as well as those included in the top performing occupancy model (step 2).  At this step we included our seasonal covariates (CPUE and distance to nearest live traps) to identify which seasonal covariate best explains colonization and extinction of Tegus at our sites. These 7 models included various combinations of our seasonal covariates, as well as 1 model where colonization and extinction were assumed to be constant across sites and sampling seasons (i.e., intercept only) (Table 2). We calculated annual estimates of site occupancy using the “smoothed” function in <em>unmarked</em> to use the finite sample estimate of site occupancy.
description: # Using camera traps to estimate site occupancy of invasive Argentine
 black and white tegus (Salvator merianae) in South Florida
 [https://doi.org/10.5061/dryad.jsxksn0kp](https://doi.org/10.5061/dryad.jsxksn0kp) ## Description of the data and file structure Dataset to support 'Using Camera Traps to Estimate Site Occupancy of Invasive Argentine Black and White Tegus (<i>Salvator merianae</i>) in South Florida'. Samantha N. Smith, Melissa A. Miller, Hardin Waddle, Sarah Cooke, Amy A. Yackel Adams, Andrea Currylow, Kevin Donmoyer and Frank Mazzotti. 2024. Southeastern Naturalist These data are used to estimate occupancy, detection, colonization and extinction probabilities of invasive Argentine Black and White Tegus at 87 unique sites in South Florida, USA over the course of 4 primary sampling periods (2016, 2017, 2018, 2019). This dataset includes detection matrices, detection covariates, site-level covariates and yearly covariates that were used to create several dynamic occupancy models. Detection matrices 2016_detectionhistory(scaled).csv 2017_detectionhistory(scaled).csv 2018_detectionhistory(scaled).csv 2019_detectionhistory(scaled).csv There is one detection matrix for each primary sampling period. The first column corresponds to a unique camera trap location ID while the remaining columns (2-20) correspond to a secondary sampling period, which are consecutive 14 day periods between February 15 and November 1st each primary sampling period. Each row contains a binary value that indicates whether or not a tegu was detected in each secondary period. ``` 1= Tegu detected 0= Tegu not detected NA= Camera trap not operating during secondary sampling period ``` Detection covariates The following files correspond to several covariates that may impact detection of invasive tegus at our study site. **Temperature** 2016Temp.csv 2017Temp.csv 2018Temp.csv 2019Temp.csv There is a file with the aggregated average daily temperatures for each secondary sampling period (14 days) for each primary sampling period. Temperatures are recorded in degrees Fahrenheit. Each row corresponds to a unique camera trap location ID while each column corresponds to a secondary sampling period. **Precipitation** 2016Precip.csv 2017Precip.csv 2018Precip.csv 2019Precip.csv There is a file with the aggregated average daily rainfall for each secondary sampling period (14 days) for each primary sampling period. Rainfall is recorded in inches. Each row corresponds to a unique camera trap location ID while each column corresponds to a secondary sampling period. **Camera trap bait status** Baited4yr.csv This file indicates the bait status for each camera trap over the entire sampling period (4 primary sampling periods). Each row corresponds to a unique camera trap location ID while each column corresponds to a secondary sampling period. Each row contains a binary response regarding the bait status for the camera traps during each secondary sampling period. ``` 1= Camera trap is baited 0= Camera trap was not baited ``` **Ordinal (Julian) day** Juliandate2016_brum.csv Juliandate2017_brum.csv Juliandate2018_brum.csv Juliandate2019_brum.csv There is a file with the midpoint ordinal day within each secondary sampling period for each primary sampling period. Each row corresponds to a unique camera trap location ID while each column corresponds to a secondary sampling period. **Camera trap effort** Camera trap effort_2016.csv Camera trap effort_2017.csv Camera trap effort_2018.csv Camera trap effort_2019.csv There is a file with the number of days that a camera trap was active during each secondary sampling period for each primary sampling period. Each row corresponds to a unique camera trap location ID while each column corresponds to a secondary sampling period. **Site covariates** SiteData.csv This file contains several covariates that were measured at each unique camera trap site that may influence Tegu occupancy. Each row corresponds to a unique camera trap location ID and the measurements taken at that location. These measurements include: Euclidean distances (m) to agriculture (disttoag), an underpass at US Highway 1 that may be facilitating tegu movement (disttocore), urbanization (disttourban), and the alleged source population or introduction site of tegus in South Florida (disttorrp). The final column indicates the simplified, three-level habitat classification that the camera trap site is found in. **Yearly site covariates** CPUEnt4yr.csv This file contains data that were collected once per primary sampling period for each camera trap site. These covariates (i.e., live trapping/removal efforts) are used to determine which factors that change between primary sampling periods may influence colonization and/or extinction of camera trap sites by invasive tegus. This file includes the camera trap ID (CamTrap), the name of the nearest known live trap (Trap), the Euclidean distance (m) between the camera trap and the live trap (Dist), the year that this covariate was collected (Year), the agency affiliation of the live trap (Affiliation), and the capture per unit effort of the live trap (CPUE). The aforementioned datasets are used together to create dynamic occupancy models. We have provided R code used with these datasets to support our findings in our manuscript Using Camera Traps to Estimate Site Occupancy of Invasive Argentine Black and White Tegus (<i>Salvator merianae</i>) in South Florida' TeguDynamicOccupancyModeling.R The code is annotated to facilitate the analysis and provide information about the aforementioned files.