Estimating Parameters From Multiple Time Series of Population Dynamics Using Bayesian Inference

Rosenbaum, Benjamin; Raatz, Michael; Weithoff, Guntram; Fussmann, Gregor F.; Gaedke, Ursula

doi:10.3389/fevo.2018.00234

Cited by 33 publications

(43 citation statements)

References 60 publications

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“…For analysing the population growth dynamics of the ciliates, we implemented the Beverton-Holt population growth model [47] (electronic supplementary material, figure S3) using a Bayesian framework in RStan [48], following methods used by the authors in [49,50] . This function has the form of…”

Section: (Ii) Beverton-holt Model Fitmentioning

confidence: 99%

Evolution in interacting species alters predator life-history traits, behaviour and morphology in experimental microbial communities

et al. 2020

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Predator–prey interactions heavily influence the dynamics of many ecosystems. An increasing body of evidence suggests that rapid evolution and coevolution can alter these interactions, with important ecological implications, by acting on traits determining fitness, including reproduction, anti-predatory defence and foraging efficiency. However, most studies to date have focused only on evolution in the prey species, and the predator traits in (co)evolving systems remain poorly understood. Here, we investigated changes in predator traits after approximately 600 generations in a predator–prey (ciliate–bacteria) evolutionary experiment. Predators independently evolved on seven different prey species, allowing generalization of the predator's evolutionary response. We used highly resolved automated image analysis to quantify changes in predator life history, morphology and behaviour. Consistent with previous studies, we found that prey evolution impaired growth of the predator, although the effect depended on the prey species. By contrast, predator evolution did not cause a clear increase in predator growth when feeding on ancestral prey. However, predator evolution affected morphology and behaviour, increasing size, speed and directionality of movement, which have all been linked to higher prey search efficiency. These results show that in (co)evolving systems, predator adaptation can occur in traits relevant to foraging efficiency without translating into an increased ability of the predator to grow on the ancestral prey type.

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Section: (Ii) Beverton-holt Model Fitmentioning

confidence: 99%

Evolution in interacting species alters predator life-history traits, behaviour and morphology in experimental microbial communities

et al. 2020

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“…We adapted Bayesian statistical models from Rosenbaum et al. () to estimate parameter values for r 0 , α, d , and K using the rstan package and trajectory matching, that is, assuming pure observation error (see https://zenodo.org/record/2658131 for code). We chose vaguely informative priors, that is, we provided realistic mean estimates, but set standard deviation broad enough to not constrain the model too strongly, for the logarithmically (base e ) transformed parameters with

\ln false(r_{0} false) \sim n o r m a l false(- 2.3, 1 false)

\ln (d) \sim n o r m a l (- 2.3, 1)

, and

\ln (K) \sim n o r m a l (13.1, 1)

(see Section S4 for full information on priors; Fig.…”

Section: Methodsmentioning

confidence: 99%

Evolution under pH stress and high population densities leads to increased density‐dependent fitness in the protistTetrahymena thermophila

et al. 2020

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Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco‐evolutionary feedbacks, have been highlighted. Here, we experimentally investigated how populations adapt to pH‐stress under high population densities. Using the protist species Tetrahymena thermophila, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (r0). Overall, evolution at low pH led to the convergence of r0 and intraspecific competitive ability (α) across the four genotypes. Given the strong correlation between r0 and α, we argue that this convergence was a consequence of selection for increased density‐dependent fitness at low pH under the experienced high density conditions. Increased density‐dependent fitness was either attained through increase in r0, or decrease of α, depending on the genetic background. In conclusion, we show that demography can influence the direction of evolution under abiotic stress.

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“…The K parameter in equation (2) represents the equilibrium population density. We adapted Bayesian statistical models from Rosenbaum et al (2019) to estimate parameter values for r 0 , α, d, and K using the rstan package and trajectory matching, that is, assuming pure observation error (see https://zenodo.org/record/2658131 for code). We chose vaguely informative priors, that is, we provided realistic mean estimates, but set standard deviation broad enough to not constrain the model too strongly, for the logarithmi-…”

Section: Population Growth Model Fittingmentioning

confidence: 99%

Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protistTetrahymena thermophila

Moerman

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Merkli

et al. 2019

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Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco-evolutionary feedbacks, have been highlighted. Here, we experimentally investigated the interaction between adaptation to pH-stress, and ecological conditions, specifically demography. Using the protist species Tetrahymena thermophila, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (r 0 ). Overall, evolution at low pH led to the convergence of r 0 and intraspecific competitive ability (α) across the four genotypes. Given the strong correlation between r 0 and α, we argue that this convergence was a consequence of selection for increased density-dependent fitness at low pH under the experienced high density conditions. Increased density-dependent fitness was either attained through increase in r 0 , or decrease of α, depending on the genetic background. In conclusion, we show that demography can interact with abiotic stress, to impact the direction of evolution under abiotic stress.FM, FA and EAF designed the experiment. FM, AA and SM performed the experimental work. Statistical analyses were done by FM and EAF. FM, FA, AW and EAF interpreted the results. FM, FA and EAF wrote the first version of the manuscript and all authors commented the final version.

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Estimating Parameters From Multiple Time Series of Population Dynamics Using Bayesian Inference

Cited by 33 publications

References 60 publications

Evolution in interacting species alters predator life-history traits, behaviour and morphology in experimental microbial communities

Evolution in interacting species alters predator life-history traits, behaviour and morphology in experimental microbial communities

Evolution under pH stress and high population densities leads to increased density‐dependent fitness in the protistTetrahymena thermophila

Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protistTetrahymena thermophila

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