I've been lurking on the side-lines. Much of this discussion can be simplified: 1) For anyone 'musing' about pesticides and how to study, you need to start with a proper background concerning variables, interactions,how traditional assays have been conducted, as well as the historical and yearly publications comparing pesticide toxicities, residual effects, field weathering, etc. which no longer occurs since Universities have gotten out of the pesticide testing for label registration and extension services no longer have easy access to the data needed to provide the comparative data updates. Go buy a copy of Carl Johansen and Dan Mayer's Pollinator protection: a bee & pesticide handbook, 1990. You can get it from Wicwas. Read all 200+ pages, then we can return to the discussion/musings. 2) Laboratory studies are fine, but one must always remember, small numbers of bees in a lab or not the same as the superorganism that a whole colony represents. I spent 20 years developing honey bees as pollution sentinels. In this discipline, we distinguish between indicator species and monitor species. We also talk about keystone species. An indicator species is characterized by organisms such as some lichens that are extremely sensitive to some air pollutants and virtually vanish as soon as an area is impacted. Also, many aquatic organisms are used as indicator species - my favorite, the daphnia immobolizatioon assay - in other words, the daphnia die. A true monitoring species has to persist for enough time to be able to measure a change. Hence, we used whole colonies of bees as pollution monitors - using the bees to collect chemicals of interest, or using bee counters that profiled forager bee flight activity and flagged any event that decreased the number of bees returning. For a healthy colony, we expect daily return rates to run about 94-96%. Obviously, some bees die each day of old age, get splattered on windshields, become food for birds, etc. With counters, we could detect events ranging from a slowly declining return rate (over days/weeks), a drop to a lower value - returns in 80% rates often indicated a low level toxic chemical in the environment, or a catastrophic event - drops below 50%, such as a spray incident. Honey bees represent one of the few species that is a keystone species (because they are part of a bigger picture, the pollination syndrome), an indicator (individual bees due to small body size may receive a high dose and quickly indicate a response, whether behavioral, physiological, death, or other assessment end point), and a monitor (the colony as a whole is remarkably resilient and adaptive). Lab trials are by definition usually limited to bees in a cage, small numbers, rarely whole colonies. As such, one is likely to see indicator responses. But, in the context of whole colonies, outdoors, the indicator function become a subset of the system as a whole or may even disappear due to compensatory mechanisms or simpy dilution (few bees out of tens of thousands). 3) For years, I've been surprised that many of my colleagues from the social sciences argue vehemently that the social sciences are true science. Given that many of our initial statistical programs became widely available as a consequence of social studies, I've never had a problem considering the social sciences a science. But, the recent discussion underscores a really important issue, one that I've seen increase dramatically with on-line publications and journals managed by journalists, not experienced scientists. Here, I'm showing my age. I'm used to a system of editorial oversight and peer-review, where a seasoned scientist or group of scientists act as the editors. They imposed a level of screening for objectivity, knowledge of the field, and appropriate design. And, I realize the down side was that some of these editors had an agenda or their own bias, but MOST were experienced. They knew the difference between a well designed study, the basics of statistical analysis, and most importantly the difference between Inductive and Deductive Reasoning. A paper like the Harvard Imidaclprid study would not have made it past the editors desk, much less be sent to review, and then get published. How many fails did that represent? Yet, I increasingly see authors and editors who do NOT understand this difference between Inductive and Deductive Logic, which to a scientist is critical - its a cornerstone of the scientific process. And, I'm amazed to see so many reviewers who don't know how to properly set up and analyze data from a statistical perspective, and don't recognize serious flaws when they occur. My guess - us old timers had to manually punch in numbers, do the math, when we learned our stats. I started with rotary calculators and punch cards. But now, anyone can grab an Excel spreadsheet or a canned stats program, crank in data, and get an answer out the other end. Whether the appropriate data transformations, replication, adequate degrees of freedom, proper stats were applied is any one's guess. My hat is off to Peter Borst and Randy - I don't have the energy or time to enter into every discussion, but once in a while, something gets my attention. Jerry *********************************************** The BEE-L mailing list is powered by L-Soft's renowned LISTSERV(R) list management software. For more information, go to: http://www.lsoft.com/LISTSERV-powered.html Guidelines for posting to BEE-L can be found at: http://honeybeeworld.com/bee-l/guidelines.htm