Lethal and Sublethal Effects of Toxicants on Bumble Bee Populations: A Modelling Approach

Abstract

Pollinator decline worldwide is well-documented; globally, chemical pesticides (especially the class of pesticides known as neonicotinoids) have been implicated in hymenopteran decline but the mechanics and drivers of population trends and dynamics of wild bees is poorly understood. Declines and shifts in community composition of bumble bees (Bombus sp.) have been documented in North America and Europe, with a suite of lethal and sub-lethal effects of pesticides on bumble bee populations documented. We employ a mathematical model parameterized with values taken from the literature that uses differential equations to track bumble bee populations through time in order to attain a better understanding of toxicant effects on a developing colony of bumble bees. We use a delay differential equation (DDE) model, which requires fewer parameter estimations than agent-based models while affording us the ability to explicitly describe the effect of larval incubation and colony history on population outcomes. We explore how both lethal and sublethal effects such as reduced foraging ability may combine to affect population outcomes, and discuss the implications for the protection and conservation of ecosystem services.

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Document Details

Document Type
Technical Report
Publication Date
Feb 14, 2020
Accession Number
AD1133308

Entities

People

  • A. N. Laubmeier
  • H. Thomas Banks
  • J. E. Banks
  • N. Myers
  • R. Bommarco

Organizations

  • North Carolina State University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Agent-Based Simulations
  • Computational Science
  • Differential Equations
  • Ecology
  • Ecosystems
  • Equations
  • Hymenoptera
  • Insecticides
  • Interdisciplinary Science
  • Mathematical Models
  • North America
  • Pesticides
  • Pollinators
  • Risk Analysis
  • Simulations
  • Toxicity
  • United States

Fields of Study

  • Environmental science

Readers

  • Aquatic Ecology
  • Computational Modeling and Simulation