Browsing by Author "Kinsley, Amy"

Now showing 1 - 5 of 5
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    Aquatic invasive species prevention: getting the best bang for the buck!
    (2023) Angell, Nichole; Bajzc, Alex; Brady, Valerie; Campbell, Tim; Doll, Adam; Dumke, Josh; Kinsley, Amy; Keller, Reuben; Phelps, Nicole
    Common AIS prevention efforts focus on public education, watercraft inspection, and watercraft decontamination. While these prevention efforts are currently widely implemented, little is understood about the cost-effectiveness of these methods.
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    Data in support of Quantifying the effectiveness of three aquatic invasive species prevention methods
    (2023-05-04) Angell, Nichole R; Campbell, Tim; Brady, Valerie; Bajcz, Alex; Kinsley, Amy; Doll, Adam; Dumke, Josh; Keller, Reuben; Phelps, Nicholas BD; nangell@glc.org; Angell, Nichole R; Minnesota Aquatic Invasive Species Research Center (MAISRC)
    Efforts to prevent the spread of aquatic invasive species (AIS) have been widely implemented at many scales to mitigate economic and environmental harms. Boater education, watercraft inspection, and hot water decontamination are popular strategies for prevention of AIS moving through the recreational boating pathway. However, few studies have actually quantified the effectiveness of these strategies under field conditions. We estimated their effectiveness based on the performances of boaters, watercraft inspectors, and hot water decontaminators. Participants (n=144) were recruited at 56 public water access sites in Minnesota and 1 in Wisconsin. Each participant was asked to inspect and remove AIS from a boat staged with macrophytes, dead zebra mussels, and spiny water fleas. The types and amounts of AIS removed were used to estimate the effectiveness of each prevention method. We observed that removal varied by type of AIS, with macrophytes being most commonly removed for all participants. There were also regional (metro and outstate) differences for some species perhaps due to awareness and education. Hot water decontamination was the most effective (83.7%) intervention but was not significantly better at reducing risk of spread than was watercraft inspection (79.2%). Boaters were less effective at AIS removal (56.4%). Our results suggest that watercraft inspection is an effective prevention method for most boats, and that hot water decontamination is an important tool for high-risk boats. However, robust decontamination protocols are difficult to effectively execute. Furthermore, our results provide insights into how to increase boater awareness of often-overlooked locations and help reduce risk when inspectors cannot be present at a public water access site.
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    Data in support of: AIS Explorer: Intervention Impact - An application for planning cost-effective AIS prevention programs
    (2024-01-22) Angell, Nichole R; Bajcz, Alex; Kinsley, Amy; Keller, Reuben; Phelps, Nicholas B. D.; nangell@glc.org; Angell, Nichole R.; Minnesota Aquatic Invasive Species Research Center (MAISRC)
    The movement of aquatic invasive species (AIS) between waterbodies is often facilitated by overland transport on recreational boats. Once established, AIS can have detrimental ecological effects that are difficult or impossible to manage. Prevention is the most cost-effective AIS intervention strategy, with many management agencies focused on implementing spread prevention techniques such as boater education, watercraft inspection, and hot water decontamination. Given resource constraints, deciding which spread prevention techniques to implement and where to place them is a decision fraught with uncertainty. In this study, we collected data for, developed, and tested a new application entitled “Intervention Impact” for the AIS Explorer, an online AIS program-planning dashboard (www.aisexplorer.umn.edu). The application assists AIS managers by simulating scenarios derived from user-defined lake-level budgets, effort, and effectiveness of interventions, enabling them to make comparisons. The outputs provide estimates for risk reduction and infestations averted for both zebra mussel and starry stonewort in Minnesota lakes. We demonstrate the utility of this application using the conditions of Cass County, Minnesota, USA as a case study. Our simulation outputs highlight the tradeoffs of each prevention strategy applied given budget constraints and demonstrate that value of a data-driven approach to guide the implementation of cost-effective prevention plans.
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    R Code, Data, and Output Supporting: Facilitating effective collaboration to prevent aquatic invasive species spread
    (2023-09-05) Bajcz, Alex, W.; Kinsley, Amy; Haight, Robert; Phelps, Nicholas; bajcz003@umn.edu; Bajcz, Alex W.; Minnesota Aquatic Invasive Species Research Center; Veterinary Population Medicine, College of Veterinary Medicine; USDA Forest Service, Northern Research Station; Department of Fisheries, Wildlife, and Conservation Biology
    This repository contains R code, raw and processed data, and associated outputs supporting the results reported in: Kinsley, A, Bajcz A, Haight R, and Phelps N. 2023. Facilitating effective collaboration to prevent aquatic invasive species spread. Biological Invasions [in press]. In brief, this repository provides the inputs, code, and documentation for our process of generating optimization models, using linear integer programming (LIP) in R, that would find optimal placement patterns for watercraft inspection stations to thwart the movement of boats at risk of carrying aquatic invasive species from one lake to another within the state of Minnesota, given certain assumptions about how jurisdictional authority operates within the state.
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    The use of movement data and network models to measure the effectiveness of control strategies for foot-and-mouth disease in swine
    (2018-11) Kinsley, Amy
    Foot-and-mouth disease has been considered a significant epidemic threat to livestock since the sixteenth century, hindering animal health and leading to direct and indirect economic losses through treatment, decreased productivity, trade restrictions, and disease control programs. The disease is caused by infection with foot-and-mouth disease virus (FMDV), which belongs to the Aphthovirus genus and the family Picornavirdae. There are several main serotypes circulating throughout the world, with numerous subtypes creating challenges for global eradication. In the event of a of foot-and-mouth disease (FMD) incursion into an FMD-free country, response strategies are required to control, contain and eradicate the pathogen as efficiently as possible. Simulation models have often been used to test the effectiveness and efficiency of alternative control strategies to mitigate the spread of infectious animal diseases and have contributed greatly to advancements in our understanding of disease transmission. However, quantitative values on the duration of the stages of FMD infection, within-farm transmission dynamics, and understanding between-farm movement patterns are all essential components in using simulation models in livestock populations. In this thesis, we quantified values associated with the duration of the stages of FMD infection (latent period, subclinical period, incubation period, and duration of infection), probability of transmission (within-herd and between-herd via spatial spread), and time to the diagnosis of a vesicular disease within a herd using a meta-analysis of peer-reviewed literature and expert opinion. We then assessed the impact of farm structure (different barns or rooms for breeding and gestation, farrowing, nursery, and finishing) and demography (piglet births and deaths, and animal movement within and off of the farm) by testing the impact of assuming a homogeneous mixing/closed population, a common assumption for within-farm models of highly contagious diseases of swine, such as foot-and-mouth disease (FMD), on predictions about disease spread. Looking beyond within-farm dynamics, we described the annual movement patterns between swine farms in three production systems of the United States and identified farms that may be targeted to increase the efficacy of infectious disease control strategies. We then used the results from the within-farm model and analysis of movement patterns to understand the impact of using empirical movement data compared to simulated movement data and compared targeted control strategies using metrics of the movement data to control strategies that are based on geographical factors such as zones and rings. The results worth highlighting from of our investigations include the following: Chapter 2: When quantifying the duration of the stages of FMD infection in swine, we found that the latent period and the incubation period ranged from 1 to 7 days and 1 to 9 days, respectively. Furthermore, we found that distribution of those values is dependent on the strain of FMDV, in which some strains have a shorter latent period and incubation period than others, which should be considered when modeling FMD transmission. Chapter 3: In this chapter, we incorporated farm structure and demography in to the within-herd model and observed transmission dynamics that differed in the latter portion of an outbreak in certain conditions. Specifically, we observed that farm structure and demography, which were included in the farrow to finish and farrow to wean farms, resulted in FMD virus persistence within the population, which can have significant impacts on between-farm spread. Chapter 4: Through our analysis of empirical movement data, we showed that targeting farms based on a metric that captured the temporal sequence of movements (mean infection potential), substantially reduced the potential for transmission of an infectious pathogen in the contact network and performed consistently well across production systems. This result highlights the importance of detailed movement data in understanding potential disease spread within production systems. Chapter 5: In this chapter we modeled the impact of alternative control strategies on between-farm transmission of FMD, we saw that control strategies, which preemptively targeted specific farms based on their spatial network, reduced the number of infected farms, duration of the epidemic, number of vaccinated farms, and the number of culled farms when compared to reactive scenarios that used the formation of rings and zones around infected-detected farms.