@article{11447,
abstract = {Empirical essays of fitness landscapes suggest that they may be rugged, that is having multiple fitness peaks. Such fitness landscapes, those that have multiple peaks, necessarily have special local structures, called reciprocal sign epistasis (Poelwijk et al. in J Theor Biol 272:141–144, 2011). Here, we investigate the quantitative relationship between the number of fitness peaks and the number of reciprocal sign epistatic interactions. Previously, it has been shown (Poelwijk et al. in J Theor Biol 272:141–144, 2011) that pairwise reciprocal sign epistasis is a necessary but not sufficient condition for the existence of multiple peaks. Applying discrete Morse theory, which to our knowledge has never been used in this context, we extend this result by giving the minimal number of reciprocal sign epistatic interactions required to create a given number of peaks.},
author = {Saona Urmeneta, Raimundo J and Kondrashov, Fyodor and Khudiakova, Kseniia},
issn = {1522-9602},
journal = {Bulletin of Mathematical Biology},
keywords = {Computational Theory and Mathematics, General Agricultural and Biological Sciences, Pharmacology, General Environmental Science, General Biochemistry, Genetics and Molecular Biology, General Mathematics, Immunology, General Neuroscience},
number = {8},
publisher = {Springer Nature},
title = {{Relation between the number of peaks and the number of reciprocal sign epistatic interactions}},
doi = {10.1007/s11538-022-01029-z},
volume = {84},
year = {2022},
}
@article{9128,
abstract = {This paper reviews recent important advances in our understanding of the response of precipitation extremes to warming from theory and from idealized cloud-resolving simulations. A theoretical scaling for precipitation extremes has been proposed and refined in the past decades, allowing to address separately the contributions from the thermodynamics, the dynamics and the microphysics. Theoretical constraints, as well as remaining uncertainties, associated with each of these three contributions to precipitation extremes, are discussed. Notably, although to leading order precipitation extremes seem to follow the thermodynamic theoretical expectation in idealized simulations, considerable uncertainty remains regarding the response of the dynamics and of the microphysics to warming, and considerable departure from this theoretical expectation is found in observations and in more realistic simulations. We also emphasize key outstanding questions, in particular the response of mesoscale convective organization to warming. Observations suggest that extreme rainfall often comes from an organized system in very moist environments. Improved understanding of the physical processes behind convective organization is needed in order to achieve accurate extreme rainfall prediction in our current, and in a warming climate.},
author = {Muller, Caroline J and Takayabu, Yukari},
issn = {1748-9326},
journal = {Environmental Research Letters},
keywords = {Renewable Energy, Sustainability and the Environment, Public Health, Environmental and Occupational Health, General Environmental Science},
number = {3},
publisher = {IOP Publishing},
title = {{Response of precipitation extremes to warming: What have we learned from theory and idealized cloud-resolving simulations, and what remains to be learned?}},
doi = {10.1088/1748-9326/ab7130},
volume = {15},
year = {2020},
}
@article{10794,
abstract = {Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different initial conditions or to test how a system responds to external perturbations. For this analysis to be meaningful in real applications, however, it is of paramount importance to choose an appropriate model structure and to infer the model parameters from measured data. While many parameter inference methods are available for models based on deterministic ordinary differential equations, the same does not hold for more detailed individual-based models. Here we consider, in particular, stochastic models in which the time evolution of the species abundances is described by a continuous-time Markov chain. These models are governed by a master equation that is typically difficult to solve. Consequently, traditional inference methods that rely on iterative evaluation of parameter likelihoods are computationally intractable. The aim of this paper is to present recent advances in parameter inference for continuous-time Markov chain models, based on a moment closure approximation of the parameter likelihood, and to investigate how these results can help in understanding, and ultimately controlling, complex systems in ecology. Specifically, we illustrate through an agricultural pest case study how parameters of a stochastic individual-based model can be identified from measured data and how the resulting model can be used to solve an optimal control problem in a stochastic setting. In particular, we show how the matter of determining the optimal combination of two different pest control methods can be formulated as a chance constrained optimization problem where the control action is modeled as a state reset, leading to a hybrid system formulation.},
author = {Parise, Francesca and Lygeros, John and Ruess, Jakob},
issn = {2296-665X},
journal = {Frontiers in Environmental Science},
keywords = {General Environmental Science},
publisher = {Frontiers},
title = {{Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study}},
doi = {10.3389/fenvs.2015.00042},
volume = {3},
year = {2015},
}
@article{9146,
abstract = {The factors governing the rate of change in the amount of atmospheric water vapor are analyzed in simulations of climate change. The global-mean amount of water vapor is estimated to increase at a differential rate of 7.3% K − 1 with respect to global-mean surface air temperature in the multi-model mean. Larger rates of change result if the fractional change is evaluated over a finite change in temperature (e.g., 8.2% K − 1 for a 3 K warming), and rates of change of zonal-mean column water vapor range from 6 to 12% K − 1 depending on latitude.
Clausius–Clapeyron scaling is directly evaluated using an invariant distribution of monthly-mean relative humidity, giving a rate of 7.4% K − 1 for global-mean water vapor. There are deviations from Clausius–Clapeyron scaling of zonal-mean column water vapor in the tropics and mid-latitudes, but they largely cancel in the global mean. A purely thermodynamic scaling based on a saturated troposphere gives a higher global rate of 7.9% K − 1.
Surface specific humidity increases at a rate of 5.7% K − 1, considerably lower than the rate for global-mean water vapor. Surface specific humidity closely follows Clausius–Clapeyron scaling over ocean. But there are widespread decreases in surface relative humidity over land (by more than 1% K − 1 in many regions), and it is argued that decreases of this magnitude could result from the land/ocean contrast in surface warming.},
author = {O’Gorman, P A and Muller, Caroline J},
issn = {1748-9326},
journal = {Environmental Research Letters},
keywords = {Renewable Energy, Sustainability and the Environment, Public Health, Environmental and Occupational Health, General Environmental Science},
number = {2},
publisher = {IOP Publishing},
title = {{How closely do changes in surface and column water vapor follow Clausius–Clapeyron scaling in climate change simulations?}},
doi = {10.1088/1748-9326/5/2/025207},
volume = {5},
year = {2010},
}