A scientific approach to agent selection

The prioritisation of potential agents on the basis of likely efficacy is an important step in biological control because it can increase the probability of a successful biocontrol program, and reduce risks and costs. In this introductory paper we define success in biological control, review how agent selection has been approached historically, and outline the approach to agent selection that underpins the structure of this special issue on agent selection. Developing criteria by which to judge the success of a biocontrol agent (or program) provides the basis for agent selection decisions. Criteria will depend on the weed, on the ecological and management context in which that weed occurs, and on the negative impacts that biocontrol is seeking to redress. Predicting which potential agents are most likely to be successful poses enormous scientific challenges. 'Rules of thumb', 'scoring systems' and various conceptual and quantitative modelling approaches have been proposed to aid agent selection. However, most attempts have met with limited success due to the diversity and complexity of the systems in question. This special issue presents a series of papers that deconstruct the question of agent choice with the aim of progressively improving the success rate of biological control. Specifically they ask: (i) what potential agents are available and what should we know about them? (ii) what type, timing and degree of damage is required to achieve success? and (iii) which potential agent will reach the necessary density, at the right time, to exert the required damage in the target environment?

Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.