How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities

Vegetation structure quantified by light detection and ranging (LiDAR) can improve understanding of wildlife occupancy and species-richness patterns. However, there is often a time lag between the collection of LiDAR data and wildlife data. We investigated whether a time lag between the LiDAR acquis...

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Bibliographic Details
Published in:Remote sensing letters 2014-02, Vol.5 (2), p.185-193
Main Authors: Vierling, Kerri T, Swift, Charles E, Hudak, Andrew T, Vogeler, Jody C, Vierling, Lee A
Format: Article
Language:English
Subjects:
Acquisitions
Applied geophysics
biogeography
birds
canopy
case studies
Collection
coniferous forests
data collection
Earth sciences
Earth, ocean, space
ecosystems
Exact sciences and technology
Forests
Internal geophysics
Lidar
Marketing
nesting
probability
remote sensing
species diversity
Time lag
understory
wildlife
Wildlife management
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Summary:Vegetation structure quantified by light detection and ranging (LiDAR) can improve understanding of wildlife occupancy and species-richness patterns. However, there is often a time lag between the collection of LiDAR data and wildlife data. We investigated whether a time lag between the LiDAR acquisition and field-data acquisition affected mapped wildlife distributions ranging from an individual species distribution to total avian species richness in a conifer forest. We collected bird and LiDAR data in 2009 across a 20,000 ha forest in northern Idaho. Using the 2009 LiDAR data, we modelled the probability of occurrence for the brown creeper (Certhia americana). Using the same 2009 LiDAR data, we additionally modelled total avian species richness and richness of three different bird nesting guilds (ground/understory, mid/upper canopy and cavity). We mapped brown creeper occupancy probability and species richness using the 2009 models, and then compared these maps with maps based on the same models applied to a 2003-LiDAR dataset. A prior study identified areas harvested between 2003 and 2009. There was on average a 5% absolute decrease in mapped probabilities of brown creeper occurrence in non-harvest areas between 2003 and 2009. Species richness changed by less than one species in all cases within non-harvest areas between the 2003 and 2009 maps. Although these comparisons were statistically significant at the p
ISSN:2150-7058
2150-704X
2150-7058
DOI:10.1080/2150704X.2014.891773