Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate...
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ftzenodo:oai:zenodo.org:5713239 2023-06-06T11:56:18+02:00 Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps Bedson, Carlos 2021-11-19 https://zenodo.org/record/5713239 https://doi.org/10.5061/dryad.3r2280gg0 unknown doi:10.2981/wlb.00802 https://zenodo.org/communities/dryad https://zenodo.org/record/5713239 https://doi.org/10.5061/dryad.3r2280gg0 oai:zenodo.org:5713239 info:eu-repo/semantics/openAccess https://creativecommons.org/publicdomain/zero/1.0/legalcode info:eu-repo/semantics/other dataset 2021 ftzenodo https://doi.org/10.5061/dryad.3r2280gg010.2981/wlb.00802 2023-04-13T22:54:42Z Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (Lepus timidus) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km-2 (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km-2 (95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km-2 (95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. We discuss the merits and limitations of each method with respect to the estimation of population density in the field. META DATA DESCRIPTIONS Data are prepared ready for loading in to Software Distance. _____DAYLIGHT VISUAL SAMPLING_____ STUDY AREA = HOLME MOSS for all sites SITE = Sites within the study, numbered 1 through 6 as per the manuscript. AREA KM^2 = 8.17KM^2 per site square which sums to 49 KM^2 for the whole area of Holme Moss. LINE_LENGTH _KM = Per site what distance was walked in kilometres. PERP_DISTANCE_M = Perpendicular distance to the transect line in metres. CLUSTER_SIZE = How many mountain hares were ... Dataset Lepus timidus mountain hare Zenodo Holme ENVELOPE(23.340,23.340,69.960,69.960) |
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Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (Lepus timidus) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km-2 (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km-2 (95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km-2 (95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. We discuss the merits and limitations of each method with respect to the estimation of population density in the field. META DATA DESCRIPTIONS Data are prepared ready for loading in to Software Distance. _____DAYLIGHT VISUAL SAMPLING_____ STUDY AREA = HOLME MOSS for all sites SITE = Sites within the study, numbered 1 through 6 as per the manuscript. AREA KM^2 = 8.17KM^2 per site square which sums to 49 KM^2 for the whole area of Holme Moss. LINE_LENGTH _KM = Per site what distance was walked in kilometres. PERP_DISTANCE_M = Perpendicular distance to the transect line in metres. CLUSTER_SIZE = How many mountain hares were ... |
format |
Dataset |
author |
Bedson, Carlos |
spellingShingle |
Bedson, Carlos Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
author_facet |
Bedson, Carlos |
author_sort |
Bedson, Carlos |
title |
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
title_short |
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
title_full |
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
title_fullStr |
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
title_full_unstemmed |
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
title_sort |
estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps |
publishDate |
2021 |
url |
https://zenodo.org/record/5713239 https://doi.org/10.5061/dryad.3r2280gg0 |
long_lat |
ENVELOPE(23.340,23.340,69.960,69.960) |
geographic |
Holme |
geographic_facet |
Holme |
genre |
Lepus timidus mountain hare |
genre_facet |
Lepus timidus mountain hare |
op_relation |
doi:10.2981/wlb.00802 https://zenodo.org/communities/dryad https://zenodo.org/record/5713239 https://doi.org/10.5061/dryad.3r2280gg0 oai:zenodo.org:5713239 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.3r2280gg010.2981/wlb.00802 |
_version_ |
1767963725793329152 |