ECOP-06 Contributed Talks

Vincenzo Luongo

University of Naples Federico II

"Modeling photo-fermentative bacteria evolution for H2 production in a bio-reactor"

We propose a mathematical model describing the dynamics of photo fermentative bacteria leading to hydrogen production and polyhydroxybutyrate accumulation in an engineered environment. The model is derived from mass balance principles and consists of a system of differential equations describing the biomass growth, the substrate degradation and conversion into hydrogen and other catabolites, such as intracellular polyhydroxybutyrate, a precursor for bioplastics. The model accounts for crucial inhibiting phenomena and catabolic interactions affecting the evolution of the process. The study of the model has been performed also in terms of calibration with real experimental data related to specific photo-fermentative species, and it is supported by a sensitivity analysis study. The effective application of photo-fermentation for the concomitant hydrogen production and polyhydroxybutyrate accumulation was investigated.

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Ryan Palmer

University of Bristol, UK

"Modelling electrostatic sensory interactions between plants and polinators: a guide from AAA to Bee"

Plant-arthropod relationships are crucial to the health of global ecosystems and food production. Through co-evolution, arthropods have acquired a variety of novel senses in response to the emergence of floral cues such as scent, colour and shape. The recent discovery that several terrestrial arthropods can sense electrical fields (e-fields) motivates the investigation of floral e-fields as part of their wider sensory ecology. That is, how does a flower's morphology and material properties produce and propagate detectable, ecologically relevant electrical signals? To investigate this, we modelled the e-field interior and exterior of a flower using a novel modification of the popular AAA-least squares algorithm, extending it to two domain boundary value problems. Physically, flowers typically act as dielectrics that inductively charge in the presence of a background electrical field, e.g. charged pollinators or the Earth's atmospheric potential gradient. We therefore present the development and application of this new method for these cases and discuss the biological relevance of the results for sensory and ecological studies. Our adapted AAA algorithm gives accurate and rapid results dependent on only three parameters: the relative permittivity of the flower, flower shape and the location of the pollinator(s). The results show how flowers display distinct information about their morphology, pollen availability and nearby pollinators, at distance, through the perturbed e-field. As well as how predators, such as the crab spider, can use flowers to mask their own electrical presence and draw in unsuspecting prey. The results of the two-dimensional AAA method also shows good qualitative agreement with equivalent three-dimensional finite element models. Biologically, our results highlight the significant role floral electrics may play in plant-pollinator and predator-prey relationships, unveiling previously unstudied facets.

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Tamantha Pizarro

Arizona State University

"Impacts of Social Organization and Competition on Social Insect Population Dynamics"

In this study, we utilize the species Pogonomyrmex californicus, a type of queen ant that exhibits two distinct behavioral subtypes, as inspiration for our mathematical model. The first, known as solitary queens, establish colonies independently and represent the ancestral lineage. The second subtype, cooperative queens, form groups that collectively found a single colony—an evolutionary adaptation. Laboratory experiments have revealed that these queen types display distinct behavioral traits, or personalities. To better understand the ecological implications of these differences, we develop an ordinary differential equations (ODE) model that incorporates the effects of resource availability and social organization among adult ants, particularly in relation to brood care and foraging behaviors. Our model allows for Hopf bifurcations, enabling us to analyze the conditions under which colony coexistence is promoted or collapses. With this framework, we seek to address the following key questions: How does dependency on resource availability impact the survival of each queen type? How do social organization and resource dependency together influence queen survival, and what new conditions must be met for their persistence?

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Kayode Oshinubi

Northern Arizona University

"Forecasting Mosquito Population in Maricopa County Using Climate Factors and Filtering Techniques"

Mosquito-borne diseases pose a significant public health challenge, and effective prevention requires accurate forecasting of mosquito populations. In this study, we developed a statistical forecasting framework that leverages climate factors, such as temperature and precipitation, to improve mosquito population predictions in Maricopa County, Arizona. Our approach combines adaptive modeling techniques and filtering methods to infer precise model parameters and address previously observed limitations, particularly the inability to capture spring dynamics. By incorporating an Ensemble Kalman Filter (EnKF) method, we estimated time-varying parameters (baseline population growth rate) and static parameters while resolving the spring problem observed in prior models. Using Generalized Additive Models (GAMs), we forecasted the baseline population growth rate on a weekly basis, integrating precipitation and temperature data as covariates. These forecasts were further used to run a mechanistic ordinary differential equation (ODE) model to predict mosquito abundance and estimate associated uncertainties. Our iterative framework was applied weekly over a 52-week period, successfully capturing seasonal variations in mosquito populations from 2014 to 2015. The EnKF demonstrated superior performance compared to traditional Markov Chain Monte Carlo (MCMC) approaches for fitting mosquito abundance data. This enhanced methodology provides actionable insights for public health decision-makers, supporting resource allocation and improving outcomes in mosquito-borne disease prevention. Our findings underscore the value of integrating climate data and adaptive filtering techniques to address forecasting challenges, ultimately enabling more effective responses to emerging or reemerging pathogens of mosquito-borne disease risks, which can be driven by human behavior to become a pandemic.

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