Markov models of territory occupancy: implications for the management and conservation of competing species

Abstract Background Understanding fish movements and migrations are paramount for management and conservation efforts. By applying Hidden Markov Models (HMMs) on records from electronic tags, migration routes of tagged fish can be reconstructed and new insights to the movement ecology of a species can be gained. We demonstrate the usability of HMMs on a widespread, iteroparous salmonid (sea trout, Salmo trutta) in a complex marine area with highly variable temperatures and salinities within small geographic distances. Understanding how the highly adaptable sea trout cope with these complex conditions could shed new light on factors driving the movement ecology of salmonids. Migration tracks of fish migrating at sea are reconstructed by applying an HMM on temperature and depth records from eight wild post-spawned sea trout from four Danish rivers. Results The fish migrated at sea for 47–142 days. Estimated positions of all fish were close to the coast (< 100 km) throughout the marine period, but migrations along coastlines up to 580 km away from the natal river occurred. Seven of eight individuals resided in or actively migrated into stratified or shallow marine areas that heat up fast during spring, while all eight individuals resided in deeper and more heterogeneous areas that heat up slow during summer. All fish entered the Skagerrak (located between Denmark and Norway) at some stage during summer. Migrations were directed into less saline areas during the first 15 days at sea for all individuals. Mean linear progression of the fish was 16 km day−1 (range 0–58 km day−1). Conclusions The results corroborate the expectation that sea trout are more coastally orientated than other salmonids, but also suggest that longer migrations occur in the seas surrounding Denmark compared to elsewhere. This could be a consequence of the fish seeking out habitats with optimal conditions (e.g. salinity, temperature, predation and foraging options) for growth in different parts of the year. The coinciding movement from shallow or stratified marine areas that heat up fast during spring to deeper, more well-mixed areas that heat up slow during summer suggested that some habitat selection had occurred. These results shed new light on factors influencing marine migrations in salmonids and demonstrate how HMMs can expand our knowledge on behaviour and movement ecology of marine fishes.

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Movement of Rehabilitated African Elephant Calves Following Soft Release Into a Wildlife Sanctuary

Frontiers in Conservation Science ◽

10.3389/fcosc.2021.720202 ◽

2021 ◽

Vol 2 ◽

Author(s):

Shifra Z. Goldenberg ◽

Nathan Hahn ◽

Jenna Stacy-Dawes ◽

Stephen M. Chege ◽

David Daballen ◽

...

Keyword(s):

Social Factors ◽

Markov Models ◽

Arid Environments ◽

Wildlife Sanctuary ◽

Activity Budgets ◽

Northern Kenya ◽

Ecological Conditions ◽

Human Care ◽

Natal Family ◽

Management And Conservation

The ability to locate essential resources is a critical step for wildlife translocated into novel environments. Understanding this process of exploration is highly desirable for management that seeks to resettle wildlife, particularly as translocation projects tend to be expensive and have a high potential for failure. African savannah elephants (Loxodonta africana) are very mobile and rely on large areas especially in arid environments, and are translocated for differing management and conservation objectives. Thus, research into how translocated elephants use the landscape when released may both guide elephant managers and be useful for translocations of other species that adjust their movement to social and ecological conditions. In this study, we investigated the movement of eight GPS tracked calves (translocated in three cohorts) following their soft release into a 107 km2 fenced wildlife sanctuary in northern Kenya and compared their movement with that of five tracked wild elephants in the sanctuary. We describe their exploration of the sanctuary, discovery of water points, and activity budgets during the first seven, 14, and 20 months after release. We explored how patterns are affected by time since release, ecological conditions, and social factors. We found that calves visited new areas of the sanctuary and water points during greener periods and earlier post-release. Social context was associated with exploration, with later release and association with wild elephants predictive of visits to new areas. Wild elephants tended to use a greater number of sites per 14-day period than the released calves. Activity budgets determined from hidden Markov models (including the states directed walk, encamped, and meandering) suggested that released calves differed from wild elephants. The first two cohorts of calves spent a significantly greater proportion of time in the directed walk state and a significantly lower proportion of time in the encamped state relative to the wild elephants. Our results represent a step forward in describing the movements of elephant orphan calves released to the wild following a period of profound social disruption when they lost their natal family and were rehabilitated with other orphan calves under human care. We discuss the implications of the elephant behavior we observed for improving release procedures and for defining success benchmarks for translocation projects.

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Transition (markov) models for the analysis of survival times in clinical research

Communications in Algebra ◽

10.1080/00927879908826833 ◽

1999 ◽

Vol 28 (1) ◽

pp. 165-176

Author(s):

Sati Mazumdar ◽

Kenneth Liu ◽

Sang Ahnn ◽

Patricia R. Houck ◽

Charles F. Reynolds

Keyword(s):

Clinical Research ◽

Markov Models ◽

Survival Times

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Markov models—Markov chains

Nature Methods ◽

10.1038/s41592-019-0476-x ◽

2019 ◽

Vol 16 (8) ◽

pp. 663-664 ◽

Cited By ~ 1

Author(s):

Jasleen K. Grewal ◽

Martin Krzywinski ◽

Naomi Altman

Keyword(s):

Markov Chains ◽

Markov Models

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Estimating Personality Impression from Speech Record Using Hidden Markov Models

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.135.1517 ◽

2015 ◽

Vol 135 (12) ◽

pp. 1517-1523 ◽

Cited By ~ 1

Author(s):

Yicheng Jin ◽

Takuto Sakuma ◽

Shohei Kato ◽

Tsutomu Kunitachi

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov

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Hidden Markov Processes

10.23943/princeton/9780691133157.001.0001 ◽

2014 ◽

Cited By ~ 2

Author(s):

M. Vidyasagar

Keyword(s):

Hidden Markov Models ◽

Markov Processes ◽

Viterbi Algorithm ◽

Markov Models ◽

Hidden Markov ◽

Local Alignment ◽

Biological Applications ◽

Standard Material ◽

Hidden Markov Processes ◽

Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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