We are interested in a stochastic model of trait and age-structured population undergoing mutation and selection. We start with a continuous time, discrete individual-centered population process. Taking the large population and rare mutations limits under a well-chosen time-scale separation condition, we obtain a jump process that generalizes the Trait Substitution Sequence process describing Adaptive Dynamics for populations without age structure. Under the additional assumption of small mutations, we derive an age-dependent ordinary differential equation that extends the Canonical Equation. These evolutionary approximations have never been introduced to our knowledge. They are based on ecological phenomena represented by PDEs that generalize the Gurtin-McCamy equation in Demography. Another particularity is that they involve an establishment probability, describing the probability of invasion of the resident population by the mutant one, that cannot always be computed explicitly. Examples illustrate how adding an age-structure enrich the modelling of structured population by including life history features such as senescence. In the cases considered, we establish the evolutionary approximations and study their long time behavior and the nature of their evolutionary singularities when computation is tractable. Numerical procedures and simulations are carried.
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http://dx.doi.org/10.1007/s00285-008-0202-2 | DOI Listing |
Heliyon
February 2025
Discipline of Agricultural Economics, School of Agriculture, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa.
The adoption of Integrated Striga Management (ISM) technology has been recommended as an effective approach to mitigating Striga infestation, a significant challenge to maize productivity and food security. Despite substantial efforts by stakeholders to promote its deployment, adoption levels remain low. This study utilized data from 643 smallholder farmers sampled across Bauchi and Kano states in Northern Nigeria.
View Article and Find Full Text PDFCan J Rural Med
January 2025
Department of Safe Medical Care, Canadian Medical Protective Association, Ottawa, Ontario, Canada.
Introduction: Anaesthesiologist medico-legal risk is well reported in the literature, however, there is little data regarding the medico-legal risk of family practice anaesthetists (FPAs) in Canada. We aimed to describe the expert criticisms from medico-legal cases involving family physicians providing care within the scope of anaesthesia.
Methods: Medico-legal cases involving FPAs providing anaesthesia-related care were identified from a national repository at the Canadian Medical Protective Association.
Microb Pathog
March 2025
Department of Epidemic Disease Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. Electronic address:
Acinetobacter baumannii, acritical nosocomial pathogen, is one of the leading causes of human mortality, globally. The extraordinary genetic plasticity of A. baumannii leads to a high propensity antimicrobial resistance trait that demands urgent attention for alternative therapeutics.
View Article and Find Full Text PDFFront Plant Sci
February 2025
School of Agriculture, Jilin Agricultural Science and Technology University, Jilin, China.
Introduction: Soybean seeds have a protein content of about 40% and are widely used due to their unique nutritional value. Research has found that drought and nitrogen fertilizer environments are conducive to the formation and accumulation of grain protein. Nitrogen is an essential element for soybean growth and development, and is converted into grain protein through a series of pathways such as the soybean root nodule system.
View Article and Find Full Text PDFHortic Res
February 2025
Fruit Breeding, Agroscope, Mueller-Thurgau-Strasse 29, 8820 Waedenswil, Switzerland.
Genomic prediction for multiple environments can aid the selection of genotypes suited to specific soil and climate conditions. Methodological advances allow effective integration of phenotypic, genomic (additive, nonadditive), and large-scale environmental (enviromic) data into multi-environmental genomic prediction models. These models can also account for genotype-by-environment interaction, utilize alternative relationship matrices (kernels), or substitute statistical approaches with deep learning.
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