Biology subjects

Rabajante, J. F.

Publications and source records attributed to Rabajante, J. F..

5 recordsLinked to original sources

bioRxiv2024

Bee pasture as a buffer against flower-mediated disease transmission

Bees play a crucial role as pollinators in ecosystems, yet they face the risk of flower-mediated diseases that can spread among their communities. Infected bees can inadvertently transmit infective agents, such as microbial spores, to other bee colonies through shared floral resources during foraging. To address this challenge, we propose a solution involving the strategic planting of bee pasture as buffer zones around foraging areas, coupled with the careful placement of beehives to segregate bee colonies. However, this strategy presents dual potential outcomes: the buffer zone could act as a protective barrier, reducing disease transmission; or attract more bees from other colonies, thereby increasing infection risk. Employing mathematical modeling, we explore the intricate dynamics of this strategy to minimize negative outcomes, considering factors such as colony strength, optimal foraging behavior, and foraging area locations. Our results recommend implementing the following measures to safeguard the target bee colony against disease: (i) ensuring ample food sources are available in the vicinity of the target bee colony, and (ii) establishing bee pasture in a distinct, outlying area to serve as a buffer zone. While the bee pasture buffer zone cannot guarantee complete immunity from infection, it can effectively delay the spread of inter-colony diseases. This proposed strategy should be combined with other sound beekeeping practices for comprehensive disease management. Our analysis aims to provide insights into optimizing practices to protect the health of both managed and wild bee populations, and bolster ecological resilience. Significance StatementBees, vital pollinators in ecosystems, face the risk of flower-mediated diseases transmitted between colonies. Flower-mediated disease transmission occurs when bees spread pathogens to each other through sharing of flowers during foraging. To mitigate this, we propose planting bee pasture buffer zones around foraging areas. Using mathematical modeling, we recommend ensuring nearby food sources for the target bee colony and establishing bee pasture buffers. While not guaranteeing immunity, this strategy can delay disease spread. Combining this with other beekeeping practices can comprehensively manage diseases. This research underscores the importance of bee pasture areas for protecting both managed and wild bee populations and strengthening ecosystem resilience. MSC Classification92-10, 92D40, 92D30

ecology↗

bioRxiv2019

Dynamical Systems Modeling of Bt Resistant Cornborer Population Dynamics

Mathematical models provide insights for the design and optimization of strategies to control disease epidemics and evolution of pesticide resistance in crops. Here, we present a simple mathematical model to investigate the population dynamics of non-Bt resistant and Bt resistant cornborers in a field with refuge. In the presence of refuge, it is expected that the population of Bt resistant pests will decline due to the dilution effect (non-Bt resistant cornborers mate with Bt resistant pests). We have found that increasing the refuge size can be effective in reducing Bt resistant pests as long as there exists a relatively huge population of non-Bt resistant cornborers at the start of the simulation. This implies that refuge is useless in inhibiting the evolution of cornborers if a sufficient initial population of non-Bt resistant cornborers is absent.

evolutionary biology↗

bioRxiv2019

Agent-based Modeling of Asian Corn Borer Resistance to BT Corn

1.Overview and PurposeWe present ABM-IRM, an agent-based modeling approach to insect resistance management. ABM-IRM is one of the agent-based models that we have developed to simulate the insect resistance of Asian Corn Borer (ACB) to BT corn. The model was implemented using NetLogo, an agent-based programming environment (Wilensky, 1999). We created our model using simple rules to find emergent patterns (Wilensky and Rand, 2015) based on refuge types and pyramid BT events that may aid in controlling the resistance of ACB to BT Corn. Following how the Overview, Design concepts, and Details (ODD) protocol was used in presenting a related ABM paper as guide (Anderson and Dragi[c]evi[c], 2015), the following sections are organized as follows: Section 2 covers the typologies of the agents, Section 3 discusses the process overview, and Section 4 highlights our results and emergent patterns.

evolutionary biology↗

bioRxiv2019

On a mechanistic process of macroparasite aggregation

Parasite aggregation, a recurring pattern in macroparasite infections, is considered one of the "laws" of parasite ecology. Few hosts have a large number of parasites while most hosts have a low number of parasites. Phenomenological models of host-parasite systems thus use the negative-binomial distribution. However, to infer the mechanisms of aggregation, a mechanistic model that does not make any a priori assumptions is essential. Here we formulate a mechanistic model of parasite aggregation in hosts without assuming a negative-binomial distribution. Our results show that a simple model of parasite accumulation still results in an aggregated pattern, as shown by the derived mean and variance of the parasite distribution. By incorporating the derived statistics in host-parasite interactions, we can predict how aggregation affects the population dynamics of the hosts and parasites through time. Thus, our results can directly be applied to observed data as well as can inform the designing of statistical sampling procedures. Overall, we have shown how a plausible mechanistic process can result in the often observed phenomenon of parasite aggregation occurring in numerous ecological scenarios, thus providing a basis for a "law" of ecology.

ecology↗

bioRxiv2018

Antibiotic-driven Escape of Host in a Parasite-induced Red Queen Dynamics

Winnerless coevolution of hosts and parasites could exhibit Red Queen dynamics, which is characterized by parasite-driven cyclic switching of expressed host phenotypes. We hypothesize that the application of antibiotics to suppress the reproduction of parasites can provide opportunity for the hosts to escape such winnerless coevolution. Here, we formulate a minimal mathematical model of host-parasite interaction involving multiple host phenotypes that are targeted by adapting parasites. Our model predicts the levels of antibiotic effectiveness that can steer the parasite-driven cyclic switching of host phenotypes (heteroclinic oscillations) to a stable equilibrium of host survival. Our simulations show that uninterrupted application of antibiotic with high-level effectiveness (> 85%) is needed to escape the Red Queen dynamics. Intermittent and low level of antibiotic effectiveness are indeed useless to stop host-parasite coevolution. This study can be a guide in designing good practices and protocols to minimize risk of further progression of parasitic infections.

evolutionary biology↗