Researchers at the University of Utah Health have used machine learning to start making links between seemingly instinctive, random behaviors and the genetic factors that shape such behaviors. Using machine learning to study mice with differences in their genetics and age, the team found that these differences influenced the behavioral sequences the animals expressed while they foraged for food. The researchers believe the methodology could one day be applied to help understand the genomic elements that may shape complex behaviors in humans, including those that lead to disease or psychiatric disorders. Patterns of complex behavior, like searching for food, are composed of sequences that feel random, spontaneous and free. Using machine learning, we are finding discrete sequences that are reproduced more frequently than you would expect by chance and these sequences are rooted in biology." Gregg and colleagues are venturing into what has previously been considered a controversial new territory called behavioral sequencing. The aim is to understand the architecture of complex behavior and how genetics shape these patterns. The concerns surrounding behavioral genetics research are based on fears that it could lead to eugenic policies. Literally meaning "well-born," eugenics refers to the improvement of humanity using scientific methods such as selective breeding. As outlined by the Nuffield Council on Bioethics, the use of "negative eugenics" has led to some of the worst atrocities in recent history such as the segregation and sterilization of hundreds of thousands of people in the United States and Europe. However, members of the council point out that contemporary research into the area is not necessarily pursuing eugenics-based goals and that the devastating events that have occurred in the past could be learned from to prevent such abuse in the future. The council acknowledges that there are certain concerns that need to be addressed if research into the field is going to be encouraged. Defining and measuring behaviors can be challenging and there is a risk of misinterpreting or misapplying statistical estimates of heritability. Other concerns include the lack of replicated findings and difficulties in predicting how behavior develops, given how complex the interaction between genes and the environment is. However, the council concludes that despite these concerns, identifying and investigating the genes that influence behavior is still practicable and worthwhile. "There are currently no practical applications of research in the genetics of behavior within the normal range.

Blogging birds is a novel artificial intelligence program that generates creative texts to communicate telemetric data derived from satellite tags fitted to red kites -- a medium-size bird of prey -- as part of a species reintroduction program in the U.K. We address the challenge of communicating telemetric sensor data in real time by enriching it with meteorological and cartographic data, codifying ecological knowledge to allow creative interpretation of the behavior of individual birds in respect to such enriched data, and dynamically generating informative and engaging data-driven blogs aimed at the general public. Geospatial data is ubiquitous in today's world, with vast quantities of telemetric data collected by GPS receivers on, for example, smartphones and automotive black boxes. Adoption of telemetry has been particularly striking in the ecological realm, where the widespread use of satellite tags has greatly advanced our understanding of the natural world.14,23 Despite its increasing popularity, GPS telemetry involves the important shortcoming that both the handling and the interpretation of often large amounts of location data is time consuming and thus done mostly long after the data has been gathered.10,24 This hampers fruitful use of the data in nature conservation where immediate data analysis and interpretation are needed to take action or communicate to a wider audience.25,26 The widespread availability of GPS data, along with associated difficulties interpreting and communicating it in real time, mirrors the scenario seen with other forms of numeric or structured data. It should be noted that the use of computational methods for data analysis per se is hardly new; much of science depends on statistical analysis and associated visualization tools. However, it is generally understood that such tools are mediated by human operators who take responsibility for identifying patterns in data, as well as communicating them accurately.

Artificial Intelligence techniques such as agent-based modeling and probabilistic reasoning have shown promise in modeling complex biological systems and testing ecological hypotheses through simulation. We develop an agent-based model of Arctic foxes from Medniy Island while utilizing Probabilistic Graphical Models to capture the conditional dependencies between the random variables. Such models provide valuable insights in analyzing factors behind catastrophic degradation of this population and in revealing evolutionary mechanisms of its persistence in high-density environment. Using empirical data from studies in Medniy Island, we create a realistic model of Arctic foxes as agents, and study their survival and population dynamics under a variety of conditions.