Placing invasive species management in a spatiotemporal context

Baker, Christopher M.; Bode, M. F.

doi:10.1890/15-0095.1

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…This fact holds for emerging infectious diseases (EIDs) of plants, and the prevalence of invasive plant diseases is increasing due to trade and transport globalization (Hulme 2009;Dehnen-Schmutz et al 2010), as well as climate change (Gautam et al 2013). There is a clear need to develop strategies to manage the emergence, spread and impacts of these diseases (Baker and Bode 2016), but for many EIDs, novel environments or a general lack of data make predicting future distributions or rates of spread difficult. Despite this, modelling efforts can help to understand better the spread of new diseases as well as provide testable theory and guidance on effective control strategies.…”

Section: Introductionmentioning

confidence: 99%

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

et al. 2017

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Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector longdistance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally-suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal.Keywords Buffer zone Á CoDiRO Á Olea europaea Á Olive quick decline syndrome Á Pierce's disease Á X. fastidiosa Electronic supplementary material The online version of this article

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Section: Introductionmentioning

confidence: 99%

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

et al. 2017

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“…maximize net benefits); design management to best achieve management objective Apply dynamic optimization, optimal control, etc. within a bioeconomic modeling framework Olson and Roy (2002), Eiswerth and Johnson (2002), , (2014), Haight and Polasky (2010), Burnett et al (2007), Baker and Bode (2016), Sharov and Liebhold (1998) and Sims (2011); majority of studies cited in this review Optimal policy design Determine optimal policy parameters (e.g. tax level, inspection rate) to alter private behavior or decisionmaking to achieve management objectives Apply dynamic optimization, optimal control, etc.…”

Section: Overview Of Economic Concepts Applied To Invasive Species Mamentioning

confidence: 99%

“…When suppressing an invader population, Baker and Bode (2016) find that the rapidity of the program should depend on how the invader growth rate compares with the rate of diminishing returns to control expenditures. If growth rates are high or the returns to control investments diminish slowly, control should be completed more quickly, because faster control does not have a large cost penalty and gains can be realized from minimizing additional invader reproduction.…”

Section: Economics Of Invasive Species… 3343mentioning

confidence: 99%

Economics of invasive species policy and management

Epanchin‐Niell

2017

Biol Invasions

156

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This article examines the use of economic analysis to inform bioinvasion management, with particular focus on forest resources. Economics is key for understanding invasion processes, impacts, and decision-making. Biological invasions are driven by and affect economic activities at multiple scales and stages of an invasion. Bioeconomic modeling seeks to inform how resources can be optimally allocated across invasion management activities-including prevention, surveillance programs for early detection and management, and controlling invasion populations and spread-to minimize the long-term costs and damages. Economic analysis facilitates understanding of decisions by public and private decision-makers, gaps between these, and the design of policies to achieve socially desirable outcomes. Private decisionmakers may undercontrol invasions relative to socially optimal levels, because they generally account for their own costs and benefits of control but less often for broader ecosystem impacts or future spread across the landscape. Economic analysis considers approaches for increasing private invasion management and evaluates feedbacks between ecological and economic systems that can affect policy outcomes. Future research should continue evaluation and design of control strategies across the biosecurity continuum and across species to enhance cost-effectiveness, better incorporate uncertainty into policy design, increase focus on incentives and behavioral tools to influence private behaviors that affect invasion spread, and incorporate invasive species consideration within broader systems-focused science. In addition, challenges in valuing biodiversity and ecosystem service impacts and the costs and effectiveness of control measures are key data gaps. Greater collaboration between decision-makers and researchers will facilitate development and communication of usable economic research.

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“…Islands are global biodiversity hotspots (Veitch and Clout 2002;Dawson et al 2015) and are often under threat from invasive species (Courchamp et al 2003;Gurevitch and Padilla 2004;Bellard et al 2016). Fortunately, invasive species eradications often proceed on relatively small islands, making eradication achievable and economical (Pluess et al 2012;Baker and Bode 2015;Holmes et al 2015), with their isolation mitigating the threat of re-invasion (Veitch and Clout 2002). An eradication plan must consider many issues including economic, occupational health and safety, legislative requirements, and non-target impacts, as well as ultimately provide an assessment of the likelihood of eradication success (e.g.…”

Section: Introductionmentioning

confidence: 99%

Modelling tropical fire ant (Solenopsis geminata) dynamics and detection to inform an eradication project

et al. 2017

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Invasive species threaten endangered species worldwide and substantial effort is focused on their control. Eradication projects require critical resource allocation decisions, as they affect both the likelihood of success and the overall cost. However, these complex decisions must often be made within data-poor environments. Here we develop a mathematical framework to assist in resource allocation for invasive species control projects and we apply it to the proposed eradication of the tropical fire ant (Solenopsis geminata) from the islands of Ashmore Reef in the Timor Sea. Our framework contains two models: a population model and a detection model. Our stochastic population model is used to predict ant abundance through time and allows us to estimate the probability of eradication. Using abundance predictions from the population model, we use the detection model to predict the probability of ant detection through time. These models inform key decisions throughout the project, which include deciding how many baiting events should take place, deciding whether to invest in detector dogs and setting surveillance effort to confirm eradication following control. We find that using a combination of insect growth regulator and toxins are required to achieve a high probability of eradication over 2 years, and we find that using two detector dogs may be more cost-effective than the use of lure deployment, provided that they are used across the life of the project. Our analysis lays a foundation for making decisions about control and detection throughout the project and provides specific advice about resource allocation.

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Placing invasive species management in a spatiotemporal context

Cited by 4 publications

References 38 publications

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Economics of invasive species policy and management

Modelling tropical fire ant (Solenopsis geminata) dynamics and detection to inform an eradication project

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