Quantitative Methods for Controlling the Spread of Invasive Species

• Author / Creator

  Fischer, Samuel

• Invasive species and infectious diseases cause significant ecological and economic harm all over the world. Therefore, substantial effort is made across the globe to prevent the spread and decrease the impact of biological invasions and epidemicsby implementing early detection and rapid response measures. To optimize policy and make control efforts more effective, managers need risk assessment and decision support tools providing them with practical management advice. The development of such tools is the objective of this thesis.
  A major vector for many invasive species and infectious diseases is human traffic and trade through road networks. Therefore, reliable predictions of road traffic are needed to facilitate optimal invasion and disease control. Traffic estimates can be used to determine where new invasions and infections are most likely to occur and to optimize prevention measures reducing the introduction of propagules and pathogens to uninfested areas. A challenge, however, is the vast number of potential routes that road travellers could take to reach their destinations. This challenge can make both traffic estimates and effective spread control difficult.
  In this thesis, I develop a set of tools to both assess the traffic of potential invasive species or disease vectors and to optimize road-side control measures hindering the propagation of biological invasions and epidemics. I introduce a novel method to compute routes that potential vectors might reasonably take and incorporate the resulting paths in a hybrid gravity and route choice model for vector traffic. The hybrid model accounts for both the travel incentive and the route choice of potential vectors. This hybrid approach makes it possible to fit the model to survey data collected at roads and to determine the major pathways of potential vectors. Fitting the model to road-side survey data facilitates more accurate traffic estimates and permits the construction of large-scale traffic models, which were difficult to fit with traditional methods. The road-specific traffic estimates, in turn, can be used to determine the best locations to control potentially infested vectors, and I develop a management support tool for this task. The decision support tool can account for location-specific management constraints and provides specific management advice.
  I introduce a number of statistical tools to test model assumptions and to assess the credibility of parameter estimates and predictions. In particular, I develop a robust and efficient algorithm to compute profile likelihood confidence intervals. The new algorithm is applicable even in situations in which earlier methods regularly fail or return erroneous results.
  I apply all methods developed in this thesis to prediction and control of the transport of zebra and quagga mussels (Dreissena spp.) to the Canadian province British Columbia. Dreissenid mussels are invasive in North America and have various negative effects on both ecosystems and human economy and well-being. A major spread mechanism for zebra and quagga mussels is traffic of boaters transporting their watercraft from invaded to uninvaded waterbodies. I apply the newly developed management support tools to optimize placement and operation of watercraft inspection stations, where watercraft are screened for invasive mussels and decontaminated if potentially infested. Considering different management scenarios, I identify general principles for optimal invasive species and disease management.

• Subjects / Keywords

  • optimal management
  • alternative paths
  • infectious disease
  • linear integer programming
  • route choice
  • spatially explicit
  • gravity model
  • profile likelihood
  • invasive species
  • choice set
  • hierarchical model
  • zebra mussel
  • confidence interval
  • road network
  • propagule pressure
  • local optimality

• Graduation date

  Spring 2020

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-bk53-mq42

• License

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