Assessment of Lesser Prairie-Chicken Lek Density Relative to Landscape Characteristics in Texas

Report

Title: Assessment of Lesser Prairie-Chicken Lek Density Relative to Landscape Characteristics in Texas
Authors: Timmer, J.
Publication Date:
August 31, 2012
Document Number: DE - EE0000530.000
Pages: 26
Affiliation:
Sponsoring Organization:

Document Access

Website: External Link
Attachment: Access File
(641 KB)

Citation

Timmer, J. (2012). Assessment of Lesser Prairie-Chicken Lek Density Relative to Landscape Characteristics in Texas. Report by Texas Tech University. pp 26.
Abstract: 

My 2.5-yr Master's project accomplished the objectives of estimating lesser prairie-chicken (LPC) lek density and abundance in the Texas occupied range and modeling anthropogenic and landscape features associated with lek density by flying helicopter lek surveys for 2 field seasons and employing a line-transect distance sampling method. This project was important for several reasons. Firstly, wildlife managers and biologists have traditionally monitored LPC populations with road-based surveys that may result in biased estimates and do not provide access to privately-owned or remote property. From my aerial surveys and distance sampling, I was able to provide accurate density and abundance estimates, as well as new leks and I detected LPCs outside the occupied range. Secondly, recent research has indicated that energy development has the potential to impact LPCs through avoidance of tall structures, increased mortality from raptors perching on transmission lines, disturbance to nesting hens, and habitat loss/fragmentation. Given the potential wind energy development in the Texas Panhandle, spatial models of current anthropogenic and vegetative features (such as transmission lines, roads, and percent native grassland) influencing lek density were needed. This information provided wildlife managers and wind energy developers in Texas with guidelines for how change in landscape features could impact LPCs. Lastly, LPC populations have faced range-wide declines over the last century and they are currently listed as a candidate species under the Endangered Species Act. I was able to provide timely information on LPC populations in Texas that will be used during the listing process.

 

Line-transect distance sampling is a common technique used to estimate density and abundance of wildlife populations. Aerial surveys allow a larger area to be sampled in less time and access to remote or privately-owned land and helicopters in particular allow for reduced air speeds, sharper and safer turns between transects, and better vision directly below the aircraft as compared to fixed-wing aircraft. A recent Texas Tech graduate student evaluated the use of helicopters for surveying LPC leks and he found that lek detectability from an R-22 helicopter (2 observers) was 72.3% and there was minimal disturbance to the LPCs (McRoberts et al. 2011 ). He also found that the use of R-22 helicopters was a cost-effective technique for finding new leks as compared to driving roads and listening for leks. I followed the survey protocol of McRoberts et al. (2011 ) (i.e., transects spaced 400 - m apart, flight altitude of 15 m above ground-level, target speed of 60 km/hr, survey bet ween sunrise until ≈2.5 hr post-sunrise) and also concluded that the use of an R-22 helicopter is an efficient and effective method for monitoring LPC populations. My surveys cost $350/hr of flight time (for an average of 3hr of flight time per survey) plus gas mileage for the pilots, myself, and technicians. This may cost more than driving county roads for 3 hr listening for leks, but I found new leks that had not been previously detected by other graduate students or biologists and I was able to cover more area in less time with fewer personnel.

 

Spatial models relate landscape features, such as percent grassland and road density, with animal abundance, density, or occurrence. These models can identify suitable habitat and predict species occurrence or abundance. In particular, hierarchical distance sampling models rely on data collected by distance sampling methods and then relate spatial covariates to animal density or abundance through regression techniques. These methods model spatial variation associated with density or abundance and the resulting estimates are often more precise. These methods also estimate a detection probability for the sampled population, which many other spatial techniques, such as occupancy modeling, do not.

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