UM student research tests ways to reduce errors in wildlife surveys

October 22, 2019

MISSOULA - Research led by a University of Montana undergraduate student to identify less error-prone methods for performing wildlife surveys was published Oct. 20 in Ecological Applications.

Biologists around the world use a variety of boots-on-the-ground field methods to survey animal populations. When extrapolated, these data provide population counts and other scientific information used to study and manage species. But counting wildlife is rarely straightforward. Birds, for instance, are small or sometimes hard to see, and many species look and sound similar.

"Many biologists assume that false positives - either misidentifications or double-counts of animals - don't happen in their surveys. But research has shown that false positives happen quite a lot, and those false positives can have huge impacts on the reliability of population estimates that we calculate from those data," said first author Kaitlyn Strickfaden, a researcher with UM's Avian Science Center. "So we made bird call simulations in which we knew the true identity of every calling bird to test differences in false positive rates in a few survey scenarios."

Strickfaden and her co-authors tested different experience levels (expert and naive) and two survey methods. The first survey method used a single observer while the second used two collaborating observers.

Strickfaden and her team created call simulations featuring mashups of songs from 10 different bird species. The researchers knew when particular species were calling throughout each of the simulations. Their volunteer observers - six experts and six beginners - did not. These observers listened to the call simulations, either alone or with another observer, and recorded the birds they thought they heard.

The double-observer method reported significantly lower false-positive rates regardless of the observers' experience level.

Observer experience was also an important factor, reaffirming that proper training is crucial to minimizing misidentifications during data collection.

The researchers found that error rates varied widely by species. Species with more unique songs were not misidentified as often as other species in the study. There also was an uneven trade-off in misidentifications within similar-sounding pairs. For example, McCown's longspurs were often misidentified as horned larks, so horned larks were greatly overcounted in the study compared to how many truly occurred, while McCown's longspurs were greatly undercounted.

"We don't make any claims about what survey method researchers should use, since every researcher's situation is different, but our data do show that the double-observer method was less prone to errors than the single-observer method," Strickfaden said. "Collecting more accurate data gives us the ability to more accurately estimate population sizes. When we ignore false-positive errors, we may not know when populations are doing poorly and need conservation actions. Our research is a step forward in addressing this problem."

Strickfaden, who graduated from UM in 2018 with a wildlife biology degree, has worked in the Avian Science Center since 2017. She conducted this research as her undergraduate senior thesis project.

"Kaitlyn's persistence and tenacity is admirable. Publishing her undergraduate senior research in Ecological Applications is an outstanding accomplishment and demonstrates her abilities," said Vicky Dreitz, director of the Avian Science Center and paper co-author. "Kaitlyn had the foresight to develop a project that provides information to avian ecologists, and wildlife biologists and managers, about the level of false positives, a well-known nuance in count-based survey data. We are proud and excited to be part of her accomplishment."
Other co-authors include Danielle Fagre, Alan Harrington, Jessie Golding, Kaitlyn Reintsma and Jason Tack, all former or current UM students and part of the Avian Science Center.

The article "Dependent double-observer method reduces false positive errors in auditory avian survey data" is online at By Kasey Rahn, UM News Service

The University of Montana

Related Data Articles from Brightsurf:

Keep the data coming
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Astronomers are bulging with data
For the first time, over 250 million stars in our galaxy's bulge have been surveyed in near-ultraviolet, optical, and near-infrared light, opening the door for astronomers to reexamine key questions about the Milky Way's formation and history.

Novel method for measuring spatial dependencies turns less data into more data
Researcher makes 'little data' act big through, the application of mathematical techniques normally used for time-series, to spatial processes.

Ups and downs in COVID-19 data may be caused by data reporting practices
As data accumulates on COVID-19 cases and deaths, researchers have observed patterns of peaks and valleys that repeat on a near-weekly basis.

Data centers use less energy than you think
Using the most detailed model to date of global data center energy use, researchers found that massive efficiency gains by data centers have kept energy use roughly flat over the past decade.

Storing data in music
Researchers at ETH Zurich have developed a technique for embedding data in music and transmitting it to a smartphone.

Life data economics: calling for new models to assess the value of human data
After the collapse of the blockchain bubble a number of research organisations are developing platforms to enable individual ownership of life data and establish the data valuation and pricing models.

Geoscience data group urges all scientific disciplines to make data open and accessible
Institutions, science funders, data repositories, publishers, researchers and scientific societies from all scientific disciplines must work together to ensure all scientific data are easy to find, access and use, according to a new commentary in Nature by members of the Enabling FAIR Data Steering Committee.

Democratizing data science
MIT researchers are hoping to advance the democratization of data science with a new tool for nonstatisticians that automatically generates models for analyzing raw data.

Getting the most out of atmospheric data analysis
An international team including researchers from Kanazawa University used a new approach to analyze an atmospheric data set spanning 18 years for the investigation of new-particle formation.

Read More: Data News and Data Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to