A review of 28 free animal tracking software: current features and limitations

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s41684-021-00811-1[Abstract]: Well-quantified laboratory studies can provide a fundamental understanding of animal behavior in ecology, ethology and ecotoxicology research. These types of studies require observation and tracking of each animal in well-controlled and defined arenas, often for long timescales. Thus, these experiments produce long time series and a vast amount of data that require the use of software applications to automate the analysis and reduce manual annotation. In this review, we examine 28 free software applications for animal tracking to guide researchers in selecting the software that might best suit a particular experiment. We also review the algorithms in the tracking pipeline of the applications, explain how specific techniques can fit different experiments, and finally, expose each approach’s weaknesses and strengths. Our in-depth review includes last update, type of platform, user-friendliness, off- or online video acquisition, calibration method, background subtraction and segmentation method, species, multiple arenas, multiple animals, identity preservation, manual identity correction, data analysis and extra features. We found, for example, that out of 28 programs, only 3 include a calibration algorithm to reduce image distortion and perspective problems that affect accuracy and can result in substantial errors when analyzing trajectories and extracting mobility or explored distance. In addition, only 4 programs can directly export in-depth tracking and analysis metrics, only 5 are suited for tracking multiple unmarked animals for more than a few seconds and only 11 have been updated in the period 2019–2021

Automated tracking reveals the social network of beach mice and their burrows

Evolutionary biologists have long sought to understand the selective pressures driving phenotypic evolution. While most experimental data come from the study of morphological evolution, we know much less about the ultimate drivers of behavioral variation. Among the most striking examples of behavioral evolution are the long, complex burrows constructed by oldfield mice (Peromyscus polionotus ssp.). Yet how these mice use burrows in the wild, and whether burrow length may affect fitness, remains unknown. A major barrier to studying behavior in the wild has been the lack of technologies to continuously monitor – in this case, nocturnal and underground – behavior. Here, we designed and implemented a novel radio frequency identification (RFID) system to track patterns of burrow use in a natural population of beach mice. We combine RFID monitoring with burrow measurements, genetic data, and social network analysis to uncover how these monogamous mice use burrows under fully natural ecological and social conditions. We first found that long burrows provide a more stable thermal environment and have higher juvenile activity than short burrows, underscoring the likely importance of long burrows for rearing young. We also find that adult mice consistently use multiple burrows throughout their home range and tend to use the same burrows at the same time as their genetic relatives, suggesting that inclusive fitness benefits may accrue for individuals that construct and maintain multiple burrows. Our study highlights how new automated tracking approaches can provide novel insights into animal behavior in the wild

Modern Telemetry

Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

High-Throughput Automated Olfactory Phenotyping of Group-Housed Mice

Behavioral phenotyping of mice is often compromised by manual interventions of the experimenter and limited throughput. Here, we describe a fully automated behavior setup that allows for quantitative analysis of mouse olfaction with minimized experimenter involvement. Mice are group-housed and tagged with unique RFID chips. They can freely initiate trials and are automatically trained on a go/no-go task, learning to distinguish a rewarded from an unrewarded odor. Further, odor discrimination tasks and detailed training aspects can be set for each animal individually for automated execution without direct experimenter intervention. The procedure described here, from initial RFID implantation to discrimination of complex odor mixtures at high accuracy, can be completed within <2 months with cohorts of up to 25 male mice. Apart from the presentation of monomolecular odors, the setup can generate arbitrary mixtures and dilutions from any set of odors to create complex stimuli, enabling demanding behavioral analyses at high-throughput