New Technology II

Contributed Paper
ROOM: Rooms 27 – Picuris, 29 – Sandia and 31 – Santa Ana Combined

10:30AM Beyond Capture: Development of a Visual Body Condition Index for Mule Deer Using Camera Traps
Rachel A. Smiley; Tony W. Mong; Kevin L. Monteith; Matthew J. Kauffman; Chadwick D. Rittenhouse
Advances in technology and availability associated with camera traps have resulted in a rapid rise in their use to monitor wildlife distribution, abundance, and behavior. We focus on assessing body condition, a new application of camera traps. Body condition indices must relate to the percent body fat if they are to be useful. Yet, most body condition indices require capture or mortality of animals to estimate, which has limitations when applied to free-ranging animals. We developed a non-invasive, visual body condition index (VBCI) to assess body condition of mule deer (Odocoileus hemionus) that can be applied to camera trap images or videos. The VBCI was based on the visibility of five bone regions, the scapula, spinal ridge, ribs, tuber ischium, and tuber ilium, which are covered in varying amounts of subcutaneous fat. We compared the VBCI to known values of ingesta-free body fat, obtained from ultrasonography and physical palpation of captured mule deer. Our VBCI was related positively to percent ingesta-free body fat (R2=0.22, p<.001). Additionally, all five bone regions evaluated were correlated with the percent ingesta-free body fat (p<.001). Based on the relationships between VBCI and ingesta-free body fat, we developed two visual body condition indices, one that requires a broadside orientation to the camera (VBCI-1) and one that is applicable when a deer is quartered towards the camera (VBCI-2). Potential applications of the VBCI include evaluating relationships between body condition and habitat enhancements, habitat disturbances, population performances, and weather.
10:50AM Public Geospatial Datasets as an Approach to Maximizing Efficiency in the Collection of Site Covariates in Wildlife-Vehicle Collision Studies
James Vance; Walter Smith; Gabrielle Smith
Wildlife-vehicle collisions (WVCs) are a major research focus because of increasing human health and safety concerns and the potential for biological impacts on wildlife. A key component of both understanding the causes of WVCs and designing mitigation measures is the collection and analysis of environmental and roadway data at WVC sites. However, collecting these site data can be logistically challenging and potentially dangerous to researchers. We studied the feasibility and accuracy of using public geospatial datasets, particularly Google Earth and Street View, as an alternative approach to assessing WVC on-site covariates. We randomly selected 50 sites from a larger WVC study and measured the topography, habitat type, width of the road median, and presence of fencing at each site as representatives of typical WVC site covariates. We compared the measurements recorded in the field to estimates obtained from public geospatial datasets in the lab. We determined that median topography had the lowest overall accuracy (60%), followed by presence of fencing with accuracy at 75% of sites. By contrast, median habitat type was identified correctly in almost all comparisons (96% overall accuracy). The root mean squared error for median width was 1.15 m overall. Our results suggest that Google platforms may serve as viable alternatives to field data collection for site covariates related to coarse measures of habitat type and some characteristics of road topography, thus reducing time requirements and potential safety risks to researchers in the field. However, there are several crucial caveats to consider when using geospatial platforms, particularly as they relate to three-dimensional depictions of roadway features. Thus, we urge caution when attempting to use digital platforms to collect data on these covariates.
11:10AM Developing and Testing a Software Defined Radio and UAV System for Wildlife Tracking
Kellan M. Rothfus; Matthew Robinson; Gabriel Vega; Michael Shafer; Paul Flikkema
Tracking wildlife using radio telemetry methods is laborious, time consuming, and in some cases dangerous. The ability to elevate radio receivers above local topography can greatly increase received signal strength and help to reduce ambiguity related to tag location. Our group has worked to integrate standard radio telemetry (RT) equipment with a multirotor unmanned aerial vehicle (UAV) to leverage the vehicle’s improved vantage and mobility.The UAV-RT system includes a standard handheld VHF receiver and the associated electronics to transmit the received signals to the user on the ground. The audio from this receiver is transmitted over a minimal latency wireless network link so the user can complete telemetry assessments in flight. Recordings are made on the vehicle and the ground station for post flight assessments. The vehicle developed for the UAV-RT system is a Pixhawk Mini controlled hexacopter with autonomous flight features that can be leveraged for radio tag localization efforts. When the flight controller is paired with a ground control computer software, such as Mission Planner, the UAV can be programmed to autonomously fly specific routes using onboard sensors for navigation. A goal of this effort is to utilize standard hardware and open source software to allow for customization and modifications by other radio telemetry users. This presentation will include an overview of the vehicle mechanical and avionics design, and will review two UAV-RT search methods currently used to locate VHF tags. The “tower search method” rotates the vehicle in place at a prescribed altitude and using a directional antenna to achieve estimates bearing. The “grid search method” employs an omnidirectional antenna pointed toward the ground and searches for signal nulls over a uniform or variable grid. These methods and the vehicle design have been developed to increase data collection capacity, while minimizing barriers to entry for users.
11:30AM Excel, Geodatabase, and Spatial Tool Workflows to Promote Rapid Organization and Analysis of Data Collected from Platform Transmitter Terminals
Krista Mougey; Cathy Nowak; Dan Collins; Blake Grisham
Modern advancements in wildlife tracking technologies have led to a substantial increase in both the volume and complexity of spatial ecology datasets. There are significant advantages to using relational databases to store and manage these types of data. However, because of the learning curve, time investment, and, in some cases, programming knowledge needed to operate or develop database platforms, many wildlife professionals continue to use spreadsheet programs like Microsoft Excel for spatial data management. Manipulating and formatting these data within Excel and then exporting them to other software packages for analysis is often a very time consuming and error-ridden process. We developed a series of shareable and customizable tools to: 1) help wildlife researchers more effectively use Excel spreadsheets for spatial data management, 2) automate data management processes that must be repeated each time new ARGOS PTT data are downloaded, and 3) mechanize certain Excel-to-R and Excel-to-ArcMap interface procedures for rapid data analysis. Herein, we present a summary of these toolsets using an example sandhill crane spatial dataset. We discuss the development of our file structures, describe the design of our ArcGIS geodatabase, and present custom ArcGIS geoprocessing sequences developed to reduce the workload of updating map layers in the absence of dynamic Excel-to-ArcGIS links. Our customized macros, tools, and workflows can save wildlife professionals significant amounts of time by improving the efficacy of data management techniques. Moreover, these tools can easily be adapted for other wildlife species and customized for additional research objectives.
11:50AM Spatial Mark-Recapture as a Conservation Tool for Monitoring Grizzly Bear Populations in Alberta
John Boulanger; Scott Nielsen; Gord Stenhouse
One of the challenges of grizzly bear conservation and management is determination of population size and distribution as it relates to habitat and anthropogenic influences. Spatial mark-recapture has recently emerged as a method for improving estimates of population density without the need for radio collaring animals to assess movement. We re-analyzed DNA mark-recapture data for grizzly bears collected between 2004-2008 from five bear management areas (BMA’s) in Alberta, Canada. We estimated density using spatially explicit mark-recapture methods and assessed factors influencing the detection of bears at hair snag sites. We also estimated density using telemetry and closed mark-recapture models. We used existing habitat and mortality risk models for grizzly bears to test for associations with density using density surface modelling. Results demonstrated sex-specific and BMA-specific factors, as well as the effects of surrounding habitat on detections of grizzly bears. Estimates from spatially explicit methods were similar to those from closed models and telemetry with similar or higher levels of precision. Habitat was most associated with areas of higher bear density in the north, whereas mortality risk was most associated (negatively) with density of bears in the south. Comparison of distribution of areas of risk and habitat reveals differences by BMA that in turn influence local abundance of bears. We suggest that density surface modelling increases the resolution of mark-recapture methods by directly inferring the effect of spatial factors on regulating local densities of animals.


Contributed Paper
Location: Albuquerque Convention Center Date: September 26, 2017 Time: 10:30 am - 12:10 pm