Analyzing variability in nectar amino acids: composition is less variable than concentration.

Thirty species of flowering plants were analyzed for floral nectar amino acid composition. High-performance liquid chromatography (HPLC) was used in conjunction with AccQtag derivatization to produce accurate and precise data. For any one species, the total concentration of amino acids varies greatly (average coefficient of variation 0.65), but composition is much less variable (average correlation among samples from a single species 0.85). Absolute concentration of individual amino acids is much more variable than the relative abundance (coefficients of variation 0.98 and 0.77, respectively; N = 544, t = 16.98, P < 0.001). When amino acids that occur in only small relative abundance (<1%) are removed from the analysis, the difference is even more marked (0.78 and 0.51, respectively; N = 344, t = 15.13, P < 0.001). The results highlight the need for large sample sizes when making observations concerning the absolute amounts of amino acids in nectar and for sensitive analyses of the composition, as even small changes may be biologically significant.

A comparative evaluation of models of cinnabar moth dynamics.

1. A complex model of cinnabar moth dynamics proposed by Dempster and Lakhani (1979) with 23 parameters is reduced to a single equation with five parameters, and the behaviour of the reduced model shown to explain most features of the full model. 2. The efficiency of the full model is compared with the reduced model and with two even simpler models (the two parameter discrete logistic and a four parameter model based on a step-function for mortality) in their abilities to describe time series data of cinnabar moth population densities from Weeting Heath. Models with more parameters were not significantly better than few-parameter models in describing population trajectories. 3. Models that included a driving variable (in this case observed rainfall data) were no better at describing the data than simpler models without driving variables. It appears, therefore, that the routine inclusion of driving variables may be counterproductive, unless there is compelling empirical or theoretical evidence of their importance and the mode of action of the driving variables can be modelled mechanistically. For example, the regression model used to describe the relationship between rainfall and plant biomass in Dempster and Lakhani (1979), breaks down if rainfall is assumed to be constant, because there is no explicit model for the regulation of plant biomass. 4. The parameter values of the cinnabar-ragwort interaction suggest that cinnabar moth dynamics may be chaotic. Whether or not field data exhibit chaos or environmental stochasticity (or a mixture of both) is impossible to determine from inspection of time series data on population density. There is an urgent need for experimental and theoretical protocols to disentangle these two sources of population fluctuation.