CURRENT PROJECTS

Investigating the Link Between Personality and Problem-Solving in North American Gray Wolves

Graduate student: Yasmeen Ghavamian

Date: 2022-present

Goals: To evaluate the personality of captive gray wolves living in packs and assess individual differences in problem-solving strategies to understand how an individual’s personality may predict their ability to cope with changes in their environment.

 

 

Impacts of Enrichment and Personality on Cognitive Performance in Sows

 

Graduate student: Claire Jones

Date: 2022-Present

Goals: To explore how personality traits and environmental enrichment affect associative learning and problem-solving in domestic sows, in order to gain insights into why individuals differ in their speed and success of learning cognitive tasks.

 

 

Personality Assessment in African painted dogs: Inter-Individual Variability in Aggression, Boldness, and Exploration

Graduate student: Francesca Whitehead
Date: 2024-present

Goals: To assess the personality of APDs (focusing on the traits of aggression, boldness, and exploration) to help optimize pack composition, predict social rank and reproductive pairings, and design enrichment strategies.

 

COMPLETED PROJECTS

Personality Traits in Beef Cattle and How they Relate to Grazing Distribution on Extensive Rangelands

Graduate Student: Maggie Creamer

Date: 2019 – 2024

Goals:

  1. Identify reliable multidimensional personality traits in beef cattle across several contexts
  2. Relate personality measures to foraging patterns and movement via GPS data

Publications:

Creamer, M., & Horback, K. (2024). Consistent individual differences in behavior among beef cattle in handling contexts and social-feed preference testing. Applied Animal Behaviour Science, 106315.

Creamer, M., & Horback, K. (2024). Consistent individual differences in cattle grazing patterns. Applied Animal Behaviour Science, 271, 106176.

Creamer, M., & Horback, K. (2021). Researching human-cattle interaction on rangelands: challenges and potential solutions. Animals, 11(3), 725.

Creamer, M. L., Roche, L. M., Horback, K. M., & Saitone, T. L. (2020). Optimising cattle grazing distribution on rangeland: a systematic review and network analysis. The Rangeland Journal, 41(5), 441-455.

 

Investigation of Consistent Individual Differences During Pre- and Post-Natal, Human-Animal Interactions in Rangeland Ewes.

 

Graduate student: Kaleiah Schiller

Date: 2019 – 2023

Goals: Assess consistency of behavioral responses towards a human stimulus during and outside of the lambing season to understand context specificity or domain generalit

y of phenotypic traits in rangeland ewes.

 

Publications:

Schiller, K., & Horback, K. (2024). Varying degrees of human-animal interaction elicit weak evidence of a temporally stable behavioral trait in rangeland breeding ewes. Applied Animal Behaviour Science, 275, 106269.

Schiller, K., McVey, C., Doyle, S., & Horback, K. (2020). Chute scoring as a potential method for assessing individual differences in arousal among ewes. Applied Animal Behaviour Science, 230, 105073.

Livestock Informatics Toolkit (LIT)

Graduate Student: Catie McVey

Date: 2019 – 2022

Goals:

  1. Developing Unsupervised Machine Learning (UML) algorithms to visualize and quantify complex behavioral patterns.
  2. Using Information Theoretic Approaches to reveal the complex relationships between performance on controlled behavioral assays, home pen behaviors recorded by precision manage-met tools, and outcome measures such as health and productivity.

Open Source Code:
https://cgmcvey.github.io/LIT/#references

Publications:
McVey C, Hsieh F, Manriquez D, Pinedo P and Horback K (2020) Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques. Front. Vet. Sci. 7:523. doi: 10.3389/fvets.2020.00523

McVey, C., Hsieh, F., Manriquez, D., Pinedo, P., & Horback, K. (2022). Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches. Sensors, 22(1), 1.

McVey, C., Hsieh, F., Manriquez, D., Pinedo, P., & Horback, K. (2023). Invited Review: Applications of unsupervised machine learning in livestock behavior: Case studies in recovering unanticipated behavioral patterns from precision livestock farming data streams. Applied Animal Science, 39(2), 99-116.

Facial Inference Toolkit (FIT)

Graduate Student: Catie McVey

Date: 2019 – 2022

Goals:

  1. Develop algorithmic tools (projective biometrics) to quantify facial structure in livestock
  2. Developing algorithmic tools to correct for the impact of camera angle on measurements from 2D images of minimally restrained livestock
  3. Developing algorithmic tools to quantify facial expressions in livestock

Publications:

McVey, C., Egger, D., & Pinedo, P. (2022). Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning. Sensors, 22(21), 8347.

Effect of rearing environment on the development of depth perception in egg-laying hens

Graduate Student: Claire Jones

Date: 2019 – 2021

Goals: Assess depth perception in egg laying hens using a Y-maze task and visual cliff apparatus.

Publications:

Jones, C. T., Pullin, A. N., Blatchford, R. A., Makagon, M. M., & Horback, K. (2023). Effects of rearing with vertical structures on the ontogeny of depth perception in laying hens. Applied Animal Behaviour Science, 105837.