Estimating Occurrence and Detectability of a Carnivore Community in Eastern Botswana using Baited Camera Traps

Satterfield, Lauren; Thompson, Jeffrey J.; Snyman, Andrei; Candelario, Luis; Rode, Brian; Carroll, John P.

doi:10.3957/056.047.0032

Cited by 14 publications

(8 citation statements)

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“…First, as in all occupancy frameworks, we assume that detection probability ( p ) of our sampling device is either constant, or all sources of heterogeneity in p are controlled or explicitly modeled (MacKenzie et al 2017). For camera traps, an increasingly large list of local site factors has been shown to influence p , many of which impact (1) the ability to predict or funnel animal movement to the camera (e.g., placement on/off trails, Cusack et al 2015, Kolowski and Forrester 2017; roads, Sollmann et al 2013, Mann et al 2015; presence of logs, Kolowski and Forrester 2017), (2) the ability to physically detect or see the focal species (e.g., vegetation density, Hofmeester et al 2017, Kolowski and Forrester 2017; camera detection distance, Hofmeester et al 2017), or (3) the general favorability of the local site for the species of interest (e.g., local resource availability, Brassine and Parker 2015; presence of bait/lure, Satterfield et al 2017, Suarez‐Tangil and Rodriguez 2017). Although efforts have been made to summarize current knowledge about the factors influencing detection probability of a species (Hofmeester et al 2019), the ever‐growing list of potentially important factors should raise doubts as to our ability to identify and measure all the key factors in any given scenario.…”

Section: Introductionmentioning

confidence: 99%

High‐density camera trap grid reveals lack of consistency in detection and capture rates across space and time

2021

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Counts of independent photo events from camera traps are commonly used to make inference about species occupancy, the density of unmarked populations, and the relative abundance of species across time and space. These applications rest on the untested assumption that data collected from individual cameras are representative of the landscape location in which they are placed, and that nearby cameras would record similar data when any additional micro‐site differences are accounted for. We established a high‐density camera trapping grid (100 × 100 m; 27 cameras) in Virginia, USA, to explicitly test these assumptions, investigating variation in capture rates and detection probabilities for a range of terrestrial mammals during four 2‐month seasonal surveys. Despite controlling for numerous habitat and placement factors, we documented, across all 5 focal species, large ranges and coefficients of variation in both capture rate and detection probabilities, which were similar to those seen across 2 sets of independent forest sampling sites from a larger, more typical camera trap sampling design. We also documented a lack of spatial autocorrelation in capture rate at any distance. Measured local covariates relevant to the camera viewshed (stem density, camera height, log presence, effective detection distance [EDD], total dbh of oak trees) rarely explained any significant portion of observed variation in capture rates or detection probabilities across the grid. The influence of EDD, measured here for the first time for individual camera stations, was inconsistently important and varied in direction of effect depending on species and season. Our study indicates single‐camera stations may fail to sample animal presence and frequency of use in a robust and repeatable way, primarily resulting from the influence of both idiosyncrasies in animal movement and measured and unknown micro‐site characteristics. We recommend spatial replication within sites (e.g., small‐scale shifting of cameras or use of multiple stations) should be considered to minimize impacts of relevant micro‐site characteristics, some of which may be difficult to identify.

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Section: Introductionmentioning

confidence: 99%

High‐density camera trap grid reveals lack of consistency in detection and capture rates across space and time

2021

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“…Meek, Dixon, Dickman and Leung, 2016;Satterfield et al, 2017; Suárez-Tangil and Rodríguez, Reis et al, 2017;Lesmeister et al, 2015;Pease, Nielsen and Holzmueller, 2016;Welbourne et al, 2016 Welbourne et al, ) al., 2017aHowe et al 2017; Rowcliffe et al.al., 2007;Wearn et al, 2017) Landscape features channeling animal movement (e.g.,…”

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confidence: 99%

Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi‐species comparison

Hofmeester

Cromsigt

Oddén

et al. 2019

Ecology and Evolution

102

106

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Obtaining reliable species observations is of great importance in animal ecology and wildlife conservation. An increasing number of studies use camera traps (CTs) to study wildlife communities, and an increasing effort is made to make better use and reuse of the large amounts of data that are produced. It is in these circumstances that it becomes paramount to correct for the species‐ and study‐specific variation in imperfect detection within CTs. We reviewed the literature and used our own experience to compile a list of factors that affect CT detection of animals. We did this within a conceptual framework of six distinct scales separating out the influences of (a) animal characteristics, (b) CT specifications, (c) CT set‐up protocols, and (d) environmental variables. We identified 40 factors that can potentially influence the detection of animals by CTs at these six scales. Many of these factors were related to only a few overarching parameters. Most of the animal characteristics scale with body mass and diet type, and most environmental characteristics differ with season or latitude such that remote sensing products like NDVI could be used as a proxy index to capture this variation. Factors that influence detection at the microsite and camera scales are probably the most important in determining CT detection of animals. The type of study and specific research question will determine which factors should be corrected. Corrections can be done by directly adjusting the CT metric of interest or by using covariates in a statistical framework. Our conceptual framework can be used to design better CT studies and help when analyzing CT data. Furthermore, it provides an overview of which factors should be reported in CT studies to make them repeatable, comparable, and their data reusable. This should greatly improve the possibilities for global scale analyses of (reused) CT data.

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“…However, with this type of study design, it is difficult to separate the effect of lure from other factors that influence detectability, such as species abundance (McCarthy et al 2013) and local habitat conditions. Furthermore, most studies determine the effect of lure with a single metric—the number of days a species was detected in the presence or absence of lure (Burki et al 2010, Gerber et al 2012, Satterfield et al 2017, Ferreras et al 2018). Such analyses preclude other ways lure may influence species detectability, such as decreasing time to first detection (i.e., latency to detection; Bischof et al 2014) or increasing the number of images collected (Rocha et al 2016).…”

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Effect of Lure on Detecting Mammals with Camera Traps

Fidino

Barnas

Lehrer

et al. 2020

Wildlife Society Bulletin

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Motion-triggered camera traps are subject to imperfect detection and thus camera-trapping surveys often try to increase species detectability as part of the study design. One possible way to increase detectability is to use lures, which may encourage a species to investigate a given area. Yet the effectiveness of lures is primarily grounded in anecdotal support. We quantified the effect of a common olfactory carnivore lure on the detectability of mammals near Chicago, Illinois, USA, during 27 August 2018-25 September 2018. We deployed 2 camera traps per site, spaced apart by 100 m, to assess whether lure can modify detectability both within and between sites. At each camera location, we changed lure treatments every 7 days and placed either a lure or a non-lure control in view of each camera following a fully crossed design. For analysis, we developed single-season occupancy models with 3 distinct observational models to quantify whether lure increased the number of days a species was detected, decreased the amount of time to first detection, and increased the number of images collected. Lure induced a subtle change in detectability. Virginia opossum (Didelphis virginiana) responded most to the presence of lure; their daily detection probability rose by roughly 5% and the number of opossum images nearly doubled. However, the effect of lure was often negative for prey species. When lure was present, eastern cottontail (Sylvilagus floridanus) daily detection probability decreased by 5% and they were photographed 63% less often. Likewise, eastern gray squirrel (Sciurus carolinensis) arrived 70% later to a camera trap if lure was present and were photographed 14% less. By using multiple criteria, we were able to better understand how wildlife respond to lure while camera-trapping more thoroughly than would be possible with a single metric. Our results show that lure may not be as effective as expected in terms of increasing detectability, but the choice to use lure should depend on several factors, including the density of the study species, species life history, and the dynamics between the species studied.

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Estimating Occurrence and Detectability of a Carnivore Community in Eastern Botswana using Baited Camera Traps

Cited by 14 publications

References 91 publications

High‐density camera trap grid reveals lack of consistency in detection and capture rates across space and time

High‐density camera trap grid reveals lack of consistency in detection and capture rates across space and time

Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi‐species comparison

Effect of Lure on Detecting Mammals with Camera Traps

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