Looking beyond wildlife: using remote cameras to evaluate accuracy of gridded snow data

Abstract

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS) gridded predictions using data from three winters (2014)(2015)(2016). Snow depth observations were correlated with SNODAS predictions for sites (R 2 = 0.20) and regions (R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer-dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species' responses to climate change.

Local climate determines vulnerability to camouflage mismatch in snowshoe hares

“…Snow was present at a camera site when SWE > 0, and absent when SWE = 0, based on daily gridded 1 km × 1 km resolution data (SNODAS, for validation of dataset see Sirén et al, 2018). Next, we defined white hares as when mean p white ≥ 60% and brown hares as p brown > 60% as these thresholds included mostly white or brown hares, respectively, when compared to observations.…”

Using by‐catch data from wildlife surveys to quantify climatic parameters and timing of phenology for plants and animals using camera traps

“…; Sirén et al. ). Combining camera traps with external temperature loggers would reduce this issue (Sirén et al.…”

“…Combining camera traps with external temperature loggers would reduce this issue (Sirén et al. ). Although our estimates of midday temperature were biased, they did show the same temporal pattern as data obtained from the weather station, which suggests that data from multiple study sites using the same camera type could be used as a covariate studying differences in animal phenology at these sites.…”

“…), which are often used to test camera functioning, but not for image‐analysis related to ecological questions (but see Sirén et al. ). The advantage of a PIR‐sensor triggered camera trap over a phenocam taking time‐lapse images is that rare events, such as animals at low densities moving past the camera, can be recorded (Hamel et al.…”

“…), although care has to be taken about the usability of these data (Sirén et al. ). Thus, camera traps provide the opportunity to study plant phenology and climatic variables, and relate these to the phenology of animals at low densities at the same spot simultaneously through a single device.…”