Biology subjects

Ellner, S.

Publications and source records attributed to Ellner, S..

5 recordsLinked to original sources

bioRxiv2025

Individual bumble bees have small, unique, and persistent foraging repertoires: implications for disease transmission

Generalist species forage on a variety of resources, but individual members may specialize on a smaller subset of those resources. Previous attempts to incorporate intraspecific resource specialization in models of disease spread by pollinators have assumed specialization on one flower species, completely partitioning the plant-pollinator network, and very gradual changes in specialization, over days or weeks. These assumptions have not been demonstrated empirically but are critical to model conclusions. Here, we used palynology from sequential pollen loads of individual common eastern bumble bees (Bombus impatiens) to explore their foraging preferences and how they change over time. We found individuals in a bumble bee colony exhibited unique repertoires of two to three flower species while the full-colony repertoire was up to three times larger and varied by time of year. Floral constancy of individual bees lasted roughly ten days before turning over. We developed a disease transmission model parametrized with these empirical results, finding that R0 decreased with each additional flower species in a repertoire, which reduces the separation between theorized disease transmission subnetworks. Our results provide novel insight into the foraging behaviors of social bumble bees and improves understanding of how disease transmission occurs in complex plant-pollinator networks.

ecology↗

bioRxiv2024

Navigating the Landscape of Fear

O_LIAnimals searching for food must navigate complex landscapes with varying terrain, food availability, predator activity, and shelter. Where and when should they gather food? To what extent should they engage in anti-predator behaviors such as vigilance or seeking refuge if a predator is detected? Optimal foraging theory (OFT) posits that animals balance potentially conflicting goals (such as feeding versus escaping predation) by making decisions that maximize some expected utility or reward. However, OFT models have generally considered highly simplified landscapes, either ignoring spatial variability or assuming that the habitat consists of discrete, internally uniform habitat patches. As a result, OFT has largely avoided the question of how animals should move from one potential feeding area to another, or between feeding areas and refuges. C_LIO_LIWe develop methods based on stochastic dynamic programming to find optimal foraging strategies, including optimal movement paths, in a continuous landscape with spatially varying predation risk. Our approach accounts for switching from foraging to escape behavior when pursued by a predator. Because contingent escape paths are considered for all visited locations, they influence the optimal foraging path even before threats are encountered. The optimal strategy thus depends on the animals level of hunger, the distribution of food, and the perceived threat distribution. The realized path is further influenced by actual predator encounters. C_LIO_LIWe illustrate our approach with two numerical examples: the first hypothetical with two food-abundant regions accessible only via high-risk areas, the second based on empirical studies on foraging Samango monkeys, Cercopithecus albogularis schwarzi. We find that the shape of the foragers utility function (risk-averse, risk-neutral, or with state-dependent risk sensitivity) affects not only its choices of where to feed, but also the optimal paths to and from each feeding ground. C_LIO_LIOur methods make it possible to compare properties of observed foraging trajectories with those predicted for different goal functions. Foraging trajectories can then provide additional information, along with other behavioral choices, about what quantity, if any, animals aim to optimize while foraging. C_LI

ecology↗

bioRxiv2023

A time for every purpose: using time-dependent sensitivity analysis to help understand and manage dynamic ecological systems

Sensitivity analysis is often used to help understand and manage ecological systems, by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time--for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act upon. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life-history events, and endogenously-driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

ecology↗

bioRxiv2022

Modeling phytoplankton-zooplankton interactions: opportunities for species richness and challenges for modern coexistence theory

Many potential mechanisms can sustain biodiversity, but we know little about their relative importance. To compare multiple mechanisms, we modeled a two-trophic planktonic food-web based on mechanistic species interactions and empirically measured species traits. We simulated thousands of communities under realistic and altered trait distributions to assess the relative importance of three potential drivers of species richness: resource competition, predator-prey interactions, and trait trade-offs. Next, we computed niche and fitness differences of competing zooplankton to obtain a deeper understanding of how these mechanisms limit species richness. We found that predator-prey interactions were the most important driver of species richness and that fitness differences were a better predictor of species richness than niche differences. However, for many communities we could not apply modern coexistence theory to compute niche and fitness differences due to complications arising from trophic interactions. We therefore need to expand modern coexistence theory to investigate multi-trophic communities.

ecology↗

bioRxiv2021

Host-pathogen Immune Feedbacks Can Explain Widely Divergent Outcomes from Similar Infections

A longstanding question in infection biology is why two very similar individuals, with very similar pathogen exposures, may have very different outcomes. Recent experiments have found that even isogenic Drosophila melanogaster hosts, given identical inoculations of some bacterial pathogens at suitable doses, can experience very similar initial bacteria proliferation but then diverge to either a lethal infection or a sustained chronic infection with much lower pathogen load. We hypothesized that divergent infection outcomes are a natural result of mutual negative feedbacks between pathogens and the host immune response. Here we test this hypothesis in silico by constructing process-based dynamic models for bacterial population growth, host immune induction, and the feedbacks between them, based on common mechanisms of immune system response. Mathematical analysis of a minimal conceptual model confirms our qualitative hypothesis that mutual negative feedbacks can magnify small differences among hosts into life-or-death differences in outcome. However, explaining observed features of chronic infections requires an extension of the model to include induced pathogen modifications that shield themselves from host immune responses at the cost of reduced proliferation rate. Our analysis thus generates new, testable predictions about the mechanisms underlying bimodal infection outcomes.

systems biology↗