Landscape structure and the speed of adaptation

Claudino, Elder S.; Campos, Paulo R. A.

doi:10.1016/j.physleta.2014.07.019

Cited by 2 publications

(1 citation statement)

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“…In the present context, the mean of this distribution represents the selection strength of beneficial mutations in the population. In accordance with earlier literature, we have set α = 20 [4,51] under the presumption of high influx of beneficial mutations [51][52][53][54]. Now, to assign correlations in the fitness landscape, the selective effect of s j of the neighboring node is drawn from a distribution such that s j is conditional on s i of the seed node.…”

Section: Network and Topologically Inspired Walks On Fitness Landscapesmentioning

confidence: 99%

Topologically Inspired Walks on Randomly Connected Landscapes With Correlated Fitness

2018

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Strictly adaptive walks on uncorrelated and correlated fitness landscapes have been a subject of intense research. However, some experimental findings tend to advance the notion of non-adaptive evolution in terms of epistasis. To address such evolutionary paths, herein we introduce the concept of topologically inspired walks on connected and correlated landscapes with complex topologies. These walks are dictated solely by the topology of connections and are not explicitly dependent on the underlying fitness values. In the biologically significant regime of sparse randomness, we observe that such topologically inspired walks might carry a population to a local optimum even faster than strictly adaptive walks. This effect becomes more pronounced with increasing correlations in fitness. We observe interesting tradeoffs between topologically inspired walks governed by the minimum and maximum value of a set of given network metrics.

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Section: Network and Topologically Inspired Walks On Fitness Landscapesmentioning

confidence: 99%

Topologically Inspired Walks on Randomly Connected Landscapes With Correlated Fitness

2018

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Quantifying the behavioural consequences of shark ecotourism

Gayford

Pearse

Venegas³

et al. 2023

Preprint

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Shark populations globally are facing catastrophic declines. Ecotourism has been posited as a potential solution to many of the issues facing shark conservation, yet increasingly studies suggest that such activity may negatively influence aspects of shark ecology and so further pressure declining populations. Here we combine UAV videography with deep learning algorithms, multivariate statistics and hidden Markov models (HMM) to quantitatively investigate the behavioural consequences of ecotourism in the whale shark (Rhincodon typus). We find that ecotourism increases the probability of sharks being in a disturbed behavioural state, likely increasing energetic expenditure and potentially leading to downstream ecological effects. These results are only recovered when fitting models that account for individual variation in behavioural responses and past behavioural history. Our results demonstrate that behavioural responses to ecotourism are context dependent, as the initial behavioural state is important in determining responses to human activity. We also suggest that the responsiveness of R. typus to human activity hints at a previously neglected resilience to environmental change. Finally, we argue that complex models incorporating individuality and context-dependence should, wherever possible, be incorporated into future studies investigating the ecological impacts of shark ecotourism, which are only likely to increase in importance given the expansion of the industry and the dire conservation status of many shark species.

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Landscape structure and the speed of adaptation

Cited by 2 publications

References 46 publications

Topologically Inspired Walks on Randomly Connected Landscapes With Correlated Fitness

Topologically Inspired Walks on Randomly Connected Landscapes With Correlated Fitness

Quantifying the behavioural consequences of shark ecotourism

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