The Exploration-Exploitation Dilemma: A Multidisciplinary Framework

Berger‐Tal, Oded; Nathan, Jonathan; Meron, Ehud; Saltz, David

doi:10.1371/journal.pone.0095693

Cited by 186 publications

(148 citation statements)

References 49 publications

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“…We hypothesize that the temporal inter-class intervals (ICIs) distribution will converge for stationary versus non-stationary behaviors. The empirical cumulative distribution function (eCDF) for time between the same actions demonstrates that the difference between non-stationary and stationary ICIs distributions diminished during late exploration (Fig.7), consistent with the idea that exploration-exploitation trade-off depends on experience in the environment (16). More surprisingly, we observed that the our extracted stereotypical grooming sequence appear to be affected by experience as well.…”

Section: Exploration Versus Exploitationsupporting

confidence: 84%

An Open Source Unsupervised Algorithm for Identification and Fast Prediction of Behaviors

Hsu

Yttri

2019

Preprint

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The motivation, control, and selection of actions comprising naturalistic behaviors remains a tantalizing but difficult field of study. Detailed and unbiased quantification is critical. Interpreting the positions of animals and their limbs can be useful in studying behavior, and significant recent advances have made this step straightforward (1, 2). However, body position alone does not provide a grasp of the dynamic range of naturalistic behaviors. Behavioral Segmentation of Open-field In DeepLabCut, or B-SOiD ("B-side"), is an unsupervised learning algorithm that serves to discover and classify behaviors that are not pre-defined by users. Our algorithm segregates statistically different, sub-second rodent behaviors with a single bottom-up perspective video camera. Upon DeepLabCut estimating the positions of 6 body parts (snout, the 4 paws, and the base of the tail), our software performs novel expectation maximization fitting of Gaussian mixture models on t-Distributed Stochastic Neighbor Embedding (t-SNE). The original features taken from dimensionally-reduced classes are then used build a multi-class support vector machine classifier that can decode millions of actions within seconds. We demonstrate that the highly reproducible, independently-classified behaviors can be used to extract kinematic parameters of individual actions as well as broader action sequences. This open-source platform enables the efficient study of the neural mechanisms of spontaneous behavior as well as the performance of disease-related behaviors that have been difficult to quantify, such as grooming and stride-length in Obsessive-Compulsive Disorder (OCD) and stroke research. Behavioral Analysis | Open-field | DeepLabCut | Unsupervised LearningCorrespondence: eyttri@andrew.cmu.edu

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Section: Exploration Versus Exploitationsupporting

confidence: 84%

An Open Source Unsupervised Algorithm for Identification and Fast Prediction of Behaviors

Hsu

Yttri

2019

Preprint

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“…captive or wild origin), which will govern decisions about dispersing from the release site and control the rate of learning and adapting to differences between the source and release environments (Stamps & Swaisgood, ; Le Gouar et al , ). However, it is highly likely that these initial behavioral responses will not be optimal in the new environment, and should therefore change as the animal gains experience in its new habitat, becoming more adaptive with time (Berger‐Tal et al , ). This change can be termed ‘post release behavioral modification’, and knowledge of this behavioral change can assist wildlife managers in evaluating the state of the translocated animals (Berger‐Tal & Saltz, ).…”

Section: Introductionmentioning

confidence: 99%

Conservation translocations: a review of common difficulties and promising directions

Berger‐Tal

Blumstein

Swaisgood

2019

Animal Conservation

261

269

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Translocations are a common conservation and management strategy, but despite their popularity, translocations are a high‐cost endeavor with a history of failures. It is therefore imperative to maximize their success by learning from our collective experience. The Global Re‐introduction Perspectives Series is a collection of conservation translocation case studies, generated by the IUCN’s Conservation Translocation Specialist Group, and presented in a structured format with an emphasis on practical information. All 293 animal translocation case studies to date include a section in which the authors list the difficulties they have faced during the translocation project, with over 1200 difficulties described so far. We reviewed all difficulties reported in the series to get insights into the common perceived difficulties faced by wildlife managers during animal translocations. The most reported‐upon problems had to do with animal behavior, followed by monitoring difficulties, lack of funding, quality of release habitat, lack of baseline knowledge and lack of public support. We scrutinized each of these difficulties to highlight future research directions that are most likely to improve translocation success, and put a special emphasis on difficulties stemming from animal behavior, and on solutions that may alleviate these problems and improve conservation translocation success world‐wide.

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“…These reserves are entirely fenced, leading to intense competition for resources and territories (Ferreira & Hofmeyr, 2014). Introduced animals also face the additional challenge of being in an unfamiliar environment where they need to explore and learn the locations of resources (Berger-Tal et al, 2014). The fitness and survival of introduced large carnivores are therefore directly affected by their spatial responses to competitors and resource distribution.…”

Section: Introductionmentioning

confidence: 99%

Resource selection in reintroduced lions and the influence of intergroup interactions

et al. 2018

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Optimal resource selection is crucial for maximising fitness and survival. Animals introduced to a new area need time to explore the environment, which could result in a time lag before optimal selection occurs. Furthermore, intra‐specific interactions (particularly in territorial species) also play a role in shaping resource selection patterns, with weaker individuals being displaced from optimal resources. We assessed within‐home range habitat selection of lions (Panthera leo) introduced to a South African wildlife reserve, to examine changes in their resource selection patterns across 3 years from the time of introduction, and the effect of intra‐specific interactions on these selection patterns. After initial exploration, lions selected low altitudes and flat slopes that facilitate movement, high tree densities that provide cover and refuge, and areas close to water sources and flood plains that have high prey density. Responses to human disturbances differed between males and females, with males being more tolerant to high densities of roads and buildings. These patterns were however disrupted by deaths due to intergroup conflicts and by introductions of new individuals, with the dominant groups remaining within favourable resources and subordinate groups shifting to suboptimal resources. Our findings support the spatio‐temporal dynamic nature of habitat selection processes for reintroduced large carnivores and emphasize the role of intra‐specific interactions in influencing the landscape determinants of habitat selection.

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The Exploration-Exploitation Dilemma: A Multidisciplinary Framework

Cited by 186 publications

References 49 publications

An Open Source Unsupervised Algorithm for Identification and Fast Prediction of Behaviors

An Open Source Unsupervised Algorithm for Identification and Fast Prediction of Behaviors

Conservation translocations: a review of common difficulties and promising directions

Resource selection in reintroduced lions and the influence of intergroup interactions

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