We used a systematic sampling design consisting of km x km grid cells, plotted on a digital landscape coverage in ArcGIS ArcGis Within each cell, we deployed a fixed sampling site which remained in place for the season. We subjectively deployed sampling sites generally at mid-elevation, in drainages or other travel corridors, with evidence of animal movement Fig 1. Subjectivity at the site level maximizes probability of detection, but does not affect the probabilistic design as statistical inference is at the scale of the grid-cell.
In some cells where known grizzly bear activity was concentrated, we divided the grid cell into 4 equal sections, and surveyed each of these smaller-scale grid cells to serve management objectives. Exploratory analysis showed detectability did not differ between these cells and the main grid so we pooled all sample sites.
We surveyed 50 sites in , and 76 sites in , monthly between April den emergence and November den re-entry. Specific sampling sites differed among years to achieve other management objectives , so we analyzed each year's data separately. We used two concurrent methods to sample grizzly bear occupancy: non-invasive genetic tagging NGT via hair sampling, and camera trapping Fig 2A.
We smeared ca. Grizzly bears investigating the tree rubbed and left hair samples with some degree of error, which we aimed to quantify Fig 2B. We collected hair from the traps monthly, using sterile techniques.
Sampling sites consisted of a hair trap—a scent-lured tree wrapped with barbed wire—and a camera trap placed 6—10 m away to image the hair trap and the surrounding area a. A grizzly bear encountering the trap could be imaged by the camera, but might not leave a hair sample with viable DNA b.
Camera data were downloaded monthly in conjunction with hair data collection. Images were analysed and summarised for species presence within day periods; each period constituted a single survey.
Likewise, the hair collection during each day period was considered as a single survey. The final data frame was comprised of 50 sites and 76 sites , with 8 repeated monthly visits and 2 methods per site.
This research was conducted in part on public land in provincial protected areas. The Government of Alberta, Ministry of Environment and Parks—who also collected data as legal designated authority under The Wildlife Act—granted research permission. Landowner permission was sought and granted for all sites on private land. All sampling procedures were reviewed and specifically approved as part of obtaining the government research permit. The noninvasive nature of sampling avoided distress to the designated "Threatened" grizzly bears we sampled.
We used the single-season, multi-method occupancy models of Nichols, Bailey [ 41 ] to estimate i the probability of grizzly bear occupancy at a site, ii the conditional probability that a present bear would leave a viable hair sample, and iii and the probability of detecting grizzly bears, if present at a site, within each year. These models assume that sites are closed changes in occupancy at the species level among years, or rather, that any such changes are non-Markovian random among sites and among surveys.
For mobile animals, we assume that a species available for sampling has a non-zero probability of being present at the sample unit within the sampling period. Month-long surveys were designed to satisfy this condition, as grizzly bear is expected to traverse its home range in much less than a month.
Though we use the term "occupancy" for consistency, for mobile animals occurrence at a site should be interpreted as "site use" rather than permanent residence [ 33 , 47 ].
It is important to note that the definition of sampling units or plot sizes is an area of debate and ongoing research [ 44 , 47 ] and so is the interpretation of conditional and large-scale occupancy parameters.
In our multi-method survey protocol, animals at the hair trap were fully exposed to the camera trap Fig 2. The detection area of the cameras was greater than point-detection at the hair trap; barring camera failures treated as missing data , there were no occasions where a bear was sampled at a hair trap without being sampled by a camera. This differs from the Nichols, Bailey [ 41 ] scenario wherein either device could fail to detect a species at a site.
Here, one method cameras drives large-scale occupancy and the other method NGT is subset of those detections. Nichols et al. There are many possible causes of missed detections among surveys. Here, we explicitly acknowledge that probability of detection p is a function of both grizzly bear movement and missed detections at a sampling device. Consider for example a hair-trap detection history , which may arise from 2 processes. First, a bear may occur at a site in one month, but not the next, and then re-appear; in the '0' case the bear was present on its territory but moving about elsewhere rather than at our trap.
Second, the bear may have been present at the site on all three occasions, but failed to leave a hair at the second occasion. In this case p conflates both the probability that a bear available for surveying does not appear at a site due to this vagility, as well as missed detections due to behaviour, environment, or sampling device failure. This key distinction between detectability and availability is not typically explicitly acknowledged in occupancy studies [ 47 ].
We constructed multiple competing single-season models to weigh the evidence in support of five hypotheses: detectability was either 1 constant, 2 differed between methods, 3 varied with each survey period, 4 varied as a trend through time, or 5 varied through time independently for each method.
For comparison, we also constructed single-season single-method occupancy models for each device to compare detectability and occupancy estimates from each sampling approach, though Nichols, Bailey [ 41 ] explain why this is not advocated. Grizzly bear detection was generally consistent among years. Probability of detecting grizzly bears p was either constant. Grizzly bears were most likely to occupy hair traps in spring and summer in , and in summer in In , conditional occupancy at the scale of the hair trap was at best 0.
In , conditional occupancy ranged from 0. Notably, there was a brief reduction in conditional occupancy at the hair trap in mid-summer in both years, roughly occurring in June and August Hair traps were less likely to detect grizzly bears than were camera traps. These are per-survey estimates; when compounded through time the probability of false absence declines Fig 4.
After three monthly surveys there is a less than 0. PFA is 1-p per survey probability of detection , compounded monthly, in a and b Single-season, single-method occupancy models corroborate our findings.
Genetic data are remarkably valuable for identifying individuals, mapping distribution, estimating density, assessing relatedness, and investigating gene flow through landscape genetics—provided that biases in genetic analysis [ 11 ] and in the detection process [ 42 ] can be modelled and accounted for.
We show that independent validation of NGT-based sampling via cameras reveals sometimes substantial detection bias in this important mode of ecological inquiry. Unmodelled heterogeneity in detection hence capture rates can violate the assumptions of statistical models using NGT data, such as density estimation models [ 51 — 53 ]. If sampling design—specifically, the timing and duration of sampling—imparts sampling error by sampling for too short a duration, or moving sites under the assumption that all sampling periods provide equal detectability, then resulting density estimates may be biased, with implications for conservation decisions relying on those data.
We found that monthly hair-trap NGT surveys underestimated grizzly bear occupancy by a widely fluctuating margin, depending on the month.
Variability in p was in part a result of survey-to-survey differences in the rate at which species appeared at a trap—as many past studies have acknowledged, even if not explicitly [ 17 , 42 , 44 , 54 , 55 ]. The difference in detection error is presumably due to variability in bears' willingness to rub on the hair trap Fig 2 , the degree to which pelts retain or release hairs, or the decay rate of DNA in hair samples due to ambient temperature and moisture [ 14 , 39 , 56 ].
Co-occurrence of other species at the hair trap can also reduce or facilitate hair deposition [ 42 ]. The mechanisms require further examination, but regardless, we demonstrate that this rate of error can be substantial and varies through time.
The timing of sampling matters. This fact can impart significant error if sampling sites are moved around but pooled and analysed as a single season, a natural design choice when seeking to maximum sampling sites n [ 57 — 61 ], but with unknown consequences.
On the other hand, repeated monthly sampling can reduce this error to negligible margins, which is fine for occupancy studies; but density models are heavily influenced by per-survey detections to estimate numbers of unknown individuals, so missed detections may influence these estimates to an unknown degree.
It is important to note that occupancy modelling is not a panacea to the problem of detection error; models are based on multiple assumptions that may or may not be met in any given repeat-sampling design [ 47 , 62 ], and camera trapping is a special subset of this question [ 44 ]. Occupancy models do offer an explicit framework for formulating and testing hypotheses about process errors. We also note that although we used monthly samples, weekly or any other temporal schedule could be used, and this will change estimated p for mobile animals as p depends greatly on the frequency of site use.
Finally note that although the wrapped-tree sampling method is gaining popularity it differs from the "wire corral" typically used in grizzly bear NGT surveys [ 63 ]. Corrals rely on a hair capture from a bear as it enters or exits to get bait. Our lured-tree method stimulates a repeated rub response Fig 2 , thus multiplying chances for a hair capture but also potentially entraining error from age-sex differences in rubbing behaviour. Repeating monthly surveys four times reduces this error to near zero.
Moreover, p at cameras was 0. The extent of genetic sampling underestimation cannot be known without camera traps; such independent validation and multi-state modelling provides an empirical lens through which to view the accuracy of NGT estimates. Missed detections are a non-trivial problem inherent in all surveys, and we show that missed detections in NGT hair-trapping surveys can bias occupancy estimates markedly, and through time.
Reliable scientific inference requires that survey methods measure and account for this heterogeneity. NGT surveys should be conducted for a minimum of 3 repeated monthly surveys at fixed sampling points, and aim for four surveys where logistics permit.
Fewer surveys results in high probabilities of false absence missing grizzlies where they do occur , and risk negatively biasing occupancy estimates. Second, NGT surveys should be conducted when the probability that a visiting bear will leave viable DNA is the greatest. Finally, NGT surveys should be validated with camera surveys. Camera data are vital in quantifying the bias associated with hair sampling, and correcting for this bias. Further, cameras provide data on reproductive success across space [ 54 ] and behaviour at the hair trap [ 42 ], data not available from NGT surveying alone.
Cameras need not be deployed at every survey station, but should be deployed at a random subsample of survey sites. A final question remains: How do missed detections translate into potentially biased abundance and density estimates?
A great deal of effort has gone into understanding sources of heterogeneity in bear NGT surveys, and this is an ongoing area of research [ 35 — 37 , 65 — 67 ]. We suggest that density models be subject to a sensitivity analysis, wherein random samples are dropped as missed detections to determine how missed hairs translate into missed bears. With ecological inference and conservation actions relying so heavily on NGT surveys for bears and many other species, understanding the consequences of detection error is vital to making effective conservation and management decisions.
For each survey session month 1—8, a bear was either detected 1 or undetected 0 at a hair trap H and a camera trap C. Vital help was given by J. Honeyman, J. Jorgenson, B. Boukall, S. Jevons, K. Richardson, T. Clevenger, J. Gould, M. Wheatley, M. Schwartz, J. Primer3 on the WWW for general users and for biologist programmers. Methods in Molecular Biology — Smith D. Assessing reliability of microsatellite genotypes from kit fox faecal samples using genetic and GIS analyses.
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