Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential

The damaging effects of invasive organisms have triggered the development of Invasive Species Predictive Schemes (ISPS). These schemes evaluate biological and historical characteristics of species and prioritize those that should be the focus of exclusion, quarantine, and/or control. However, it is not clear how commonly these schemes take microevolutionary considerations into account. We review the recent literature and find that rapid evolutionary changes are common during invasions. These evolutionary changes include rapid adaptation of invaders to new environments, effects of hybridization, and evolution in recipient communities. Strikingly, we document 38 species in which the specific traits commonly associated with invasive potential (e.g. growth rate, dispersal ability, generation time) have themselves undergone evolutionary change following introduction, in some cases over very short (≤ 10 year) timescales. In contrast, our review of 29 ISPS spanning plant, animal, and microbial taxa shows that the majority (76%) envision invading species and recipient communities as static entities. Those that incorporate evolutionary considerations do so in a limited way. Evolutionary change not only affects the predictive power of these schemes, but also complicates their evaluation. We argue that including the evolutionary potential of species and communities in ISPS is overdue, present several metrics related to evolutionary potential that could be incorporated in ISPS, and provide suggestions for further research on these metrics and their performance. Finally, we argue that the fact of evolutionary change during invasions begs for added caution during risk assessment

Abstract Exploiting the Rootkit Paradox with Windows Memory Analysis

Rootkits are malicious programs that silently subvert an operating system to hide an intruder's activities. Although there are a number of tools designed to detect rootkits, these programs are competing with the rootkit for system resources and allowing the rootkit to actively evade detection. By taking a memory image of the system, a forensic examiner can conduct a more thorough search for rootkits and even without discovering one directly, infer the presence of one. This paper explores how an examiner can create such a memory image and use the inherent properties of rootkits to find them in those memory images. Background Rootkits are programs designed to hide processes, files, and activity from the operating system and legitimate users of a computer. Normally used only by intruders, they subvert the operating system and prevent it from functioning normally. The rootkit can modify, delete, or insert data into any of the operating system's processes, and as a result, have complete control over what the operating system does or does not see. Intruders use rootkits to hide malicious activity such as opening back doors for unauthorized access, recording keystrokes, or launching attacks against other systems. By their very nature, rootkits are difficult to detect because they hide their own activities. For example, the Hacker Defender rootkit offers its owner the ability to hide itself, selected files, processes, and registry keys from the operating system and thus any user [HOLY]. Traditional malware detection techniques are not effective against rootkits as these programs cannot flag processes that they cannot see. The Rootkit Paradox All rootkits obey two basic principles: 1. They want to remain hidden. 2. They need to run. Taken together, these rules create a paradox. In order to remain hidden, the rootkit needs to minimize its footprint on the system. However, in order to run, the operating system, a deterministic process, has to be able to find and execute the rootkit. If a deterministic process like the operating system can find the rootkit, then an examiner www.ijde.or

Comparison of scoring systems for invasive pests using ROC analysis and Monte Carlo simulations

Different international plant protection organisations advocate different schemes for conducting pest risk assessments. Most of these schemes use structured questionnaire in which experts are asked to score several items using an ordinal scale. The scores are then combined using a range of procedures, such as simple arithmetic mean, weighted averages, multiplication of scores, and cumulative sums. The most useful schemes will correctly identify harmful pests and identify ones that are not. As the quality of a pest risk assessment can depend on the characteristics of the scoring system used by the risk assessors (i.e., on the number of points of the scale and on the method used for combining the component scores), it is important to assess and compare the performance of different scoring systems. In this article, we proposed a new method for assessing scoring systems. Its principle is to simulate virtual data using a stochastic model and, then, to estimate sensitivity and specificity values from these data for different scoring systems. The interest of our approach was illustrated in a case study where several scoring systems were compared. Data for this analysis were generated using a probabilistic model describing the pest introduction process. The generated data were then used to simulate the outcome of scoring systems and to assess the accuracy of the decisions about positive and negative introduction. The results showed that ordinal scales with at most 5 or 6 points were sufficient and that the multiplication-based scoring systems performed better than their sum-based counterparts. The proposed method could be used in the future to assess a great diversity of scoring systems