This dissertation compares the reproductive ecology of three species of intertidal barnacles in the context of their life histories. Emphasis is placed on the patterns of repro­duction shown by similar species exploiting different but overlapping ranges of the intertidal zone. The intertidal environment imposes stringent gradients of greater physical stress in the high range of tidal exposure and increasing biological complexity at the low range of tidal exposure. Selection for differing adaptive modes of reproduction, re­cruitment, growth, and survivorship may be expected in species occupying different positions along these environmental gradi­ents. Ideally, the problem is not just to measure how partic­ular aspects of these modes differ between species facing vary­ing degrees of environmental stress, competition, and preda­tion but it is also to examine how components of the reproduc­tive ecology of the species interact with important parameters of growth and demography throughout their life histories.

Plants have internal stress response mechanisms. These mechanisms are activated in response to environmental stimuli. Measuring the activation of these mechanisms directly gives a clearer picture of how an environmental stimulus is actually affecting the stress state of the plant. This information is valuable in an agricultural context because these internal mechanisms are generally upstream of observable physiological manifestations of stress. But measuring the activity of these pathways is often expensive and time-consuming. We will demonstrate an optical biosensor system reporting the activity of a heat- and drought- sensitive gene pathway in Arabidopsis thaliana. The marginal cost per data point of this system is very low, and the rate of data acquisition is faster than the dynamics of the gene pathway, so the biosensor system can easily and quickly detect transient changes in pathway activation, opening a real-time window into the heat- and drought-stress state of the plant. With this real-time information in hand, we proceed to close the loop around the biosensor output using a computer-based feedback policy to maintain a constant biosensor expression level using the temperature in a small greenhouse as our control input. Along the way we will discover that as complicated and unknown as the internal dynamics of this process may be, a mechanistic model is not necessary to design a feedback policy that is robust to biological variability in the plants.