Ecologgers... We have regularly censused populations of several different plant species throughout the growing season and categorized the observed individuals into one of 7 different phenological stages (e.g., stage 1 = initial greening, stage 4 = peak flowering, stage 6 = seed drop, etc.). These numeral IDs for the different stages are ordinal data that, by coincidence, tend to scale linearly with day of the growing season. Although using ordinal data is not permitted (and makes no sense) in regression analyses, we've done it anyway! By running regressions we are able to get slopes (change in phenological stage vs. day of year) which, in essence, quantifies the seasonal rates of development for the different species. Taking it one step further, Analyses of Covariance confirm that some species progress through these phenological stages at rates that are significantly different from that of other species. So if this tells me what I want to know, what is the problem? The problem, of course, is that this approach treats these phenological stage IDs (1-7) as quantitative values when, in fact, they are nothing more than category labels. Can anyone suggest an alternative way to use these data to quantify seasonal development rates and test for differences among species?
BTW, we censused different individuals within each population haphazardly (~10 individuals per population per census date) and did NOT follow the same individuals over the season. John Skillman
