A framework for comparing pollinator performance: effectiveness and efficiency

Measuring pollinator performance has become increasingly important with emerging needs for risk assessment in conservation and sustainable agriculture that require multi-year and multi-site comparisons across studies. However, comparing pollinator performance across studies is difficult because of the diversity of concepts and disparate methods in use. Our review of the literature shows many unresolved ambiguities. Two different assessment concepts predominate: the first estimates stigmatic pollen deposition and the underlying pollinator behaviour parameters, while the second estimates the pollinator’s contribution to plant reproductive success, for example in terms of seed set. Both concepts include a number of parameters combined in diverse ways and named under a diversity of synonyms and homonyms. However, these concepts are overlapping because pollen deposition success is the most frequently used proxy for assessing the pollinator’s contribution to plant reproductive success. We analyse the diverse concepts and methods in the context of a new proposed conceptual framework with a modular approach based on pollen deposition, visit frequency, and contribution to seed set relative to the plant’s maximum female reproductive potential. A system of equations is proposed to optimize the balance between idealised theoretical concepts and practical operational methods. Our framework permits comparisons over a range of floral phenotypes, and spatial and temporal scales, because scaling up is based on the same fundamental unit of analysis, the single visit.

Innovation clusters: key concepts

Innovation is easier to describe than it is to systematically analyse, and easier to analyse than it is to effectively promote. Part of the problem, of course, is the imprecise way in which the activity of innovation itself is conceptualised. To achieve more precision, the logic of analysis suggests that innovation should be should be systematically analysed and then divided into rough categories to produce a working taxonomy based on a number of key dimensions. A major part of the purpose of this paper is to develop such a working taxonomy.

TRCM: a methodology for temporal analysis of evolving concepts in Twitter

The Twitter network has been labelled the most commonly used microblogging application around today. With about 500 million estimated registered users as of June, 2012, Twitter has become a credible medium of sentiment/opinion expression. It is also a notable medium for information dissemination; including breaking news on diverse issues since it was launched in 2007. Many organisations, individuals and even government bodies follow activities on the network in order to obtain knowledge on how their audience reacts to tweets that affect them. We can use postings on Twitter (known as tweets) to analyse patterns associated with events by detecting the dynamics of the tweets. A common way of labelling a tweet is by including a number of hashtags that describe its contents. Association Rule Mining can find the likelihood of co-occurrence of hashtags. In this paper, we propose the use of temporal Association Rule Mining to detect rule dynamics, and consequently dynamics of tweets. We coined our methodology Transaction-based Rule Change Mining (TRCM). A number of patterns are identifiable in these rule dynamics including, new rules, emerging rules, unexpected rules and ?dead' rules. Also the linkage between the different types of rule dynamics is investigated experimentally in this paper.