DOI: 10.5061/dryad.4mt00
title: Data from: Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

description: It is common to use multiple field sampling methods when implementing
 wildlife surveys to compare method efficacy or cost-efficiency, integrate
 distinct pieces of information provided by separate methods, or evaluate
 method-specific biases and misclassification error. Existing models that
 combine information from multiple field methods or sampling devices permit
 rigorous comparison of method-specific detection parameters, enable
 estimation of additional parameters such as false-positive detection
 probability, and improve occurrence or abundance estimates, but with the
 assumption that the separate sampling methods produce detections
 independently of one another. This assumption is tenuous if methods are
 paired or deployed in close proximity simultaneously, a common practice
 that reduces the additional effort required to implement multiple methods
 and reduces the risk that differences between method-specific detection
 parameters are confounded by other environmental factors. We develop
 occupancy and spatial capture-recapture models that permit covariance
 between the detections produced by different methods, use simulation to
 compare estimator performance of the new models to models assuming
 independence, and provide an empirical application based upon American
 marten (Martes americana) surveys using paired remote cameras,
 hair-catches, and snow tracking. Simulation results indicate existing
 models that assume that methods independently detect organisms produce
 biased parameter estimates and substantially understate estimate
 uncertainty when this assumption is violated, while our reformulated
 models are robust to either methodological independence or covariance.
 Empirical results suggested that remote-cameras and snow-tracking had
 comparable probability of detecting present martens, but that
 snow-tracking also produced false-positive marten detections that could
 potentially substantially bias distribution estimates if not corrected
 for. Remote cameras detected marten individuals more readily than passive
 hair-catches. Inability to photographically distinguish individual sex did
 not appear to induce negative bias in camera density estimates; instead,
 hair-catches appeared to produce detection competition between individuals
 that may have been a source of negative bias. Our model reformulations
 broaden the range of circumstances in which analyses incorporating
 multiple sources of information can be robustly used, and our empirical
 results demonstrate that using multiple field-methods can enhance
 inferences regarding ecological parameters of interest and improve
 understanding of how reliably survey methods sample these parameters.
description: Fisher
 DetectionsThe binary detection
 history of fisher via camera at non-invasive detection stations. Fisher
 detections were used as a covariate for the probability of falsely
 detecting American marten; the dimensions of the file represent the number
 of detection stations (238 rows) and the maximum number of sampling
 occasions (9 columns) for any given station; 0 indicates no fisher
 detection, 1 indicates fisher detection, and NA indicates the station was
 not active for a particular check.Fisher.csvMartenOccupancyEncountersEncounter history for American martens at 238 detection stations in Maine, used to model marten occurrence. Rows represent unique stations (238), columns represent sampling occasions (maximum of 9). Cell values correspond to detection outcomes: 1 indicates a marten was detected by a camera only, 2 indicates a marten was detected via snow-tracking, 3 indicates that both a camera image and marten snow tracks were observed, and 4 indicates no marten detection.Detection Station LocationsThis file provides the x and y coordinates of 238 detection stations used to survey American marten in Maine, USA. Units are UTM/1000 (thus, km units); original projection of the data was UTM Zone 19N.TrapLocations.csvDetection Station Activity HistoryThe detection station activity history used for Spatial Capture-Recapture modeling of American marten density. 238 rows correspond to distinct station, with 22 total checks corresponding to columns. Cell values of 1 indicate that a detection station was active during a specific check period, and cell values of 0 indicate it was not active.TrapHist.csvMarten site covariates, occupancy modelsTwo site covariates used for occupancy modeling. each row corresponds to 1 of 238 detection stations: "Early" is a binary variable indicating whether a station was active during early winter (Jan. & Feb.), "MNEVC" corresponds to the z-standardized proportion of existing Tree vegetation cover within a 1km buffer surrounding the detection station location.MartenCovariates.csvIndividual Marten CapturesIndividual marten encounter history used for Spatial Capture-recapture modeling of marten density in 'flat format': data must be coerced to a 3-dimensional array for model fitting using the BUGS language. Individual represents the identity of the marten encountered, trap represents the trap number at which the individual was encountered, check corresponds to the check # during which the individual marten was detected at the specific detection station, and detection type corresponds to the type of detection: 2 represents detection by camera only, 3 represents detection by non-invasive genetic sampling only, and 4 represents detection by both camera and non-invasive genetic sampling.MartenCaptures.csv