Final answer:
Using a mixed model with individual plants as random effects is suitable for repeated measures over time, and addressing pseudo-replication due to shared rainfall data might necessitate clustered standard errors or a generalized estimating equation.
Step-by-step explanation:
When correlating the abscisic acid content of plants with rainfall measurements, using a mixed model with individual plants as a random effect is indeed statistically appropriate. This approach accounts for the fact that each plant is measured multiple times, introducing a random variation among the individual plants. However, you also identified a potential issue of pseudo-replication because the rainfall data is a single measurement applied to all 10 plants on each day. This could violate the assumption of independence in a standard linear regression model.
To address this, you may consider using clustered standard errors or a generalized estimating equation that accounts for the fact that observations within a cluster (in this case, measurements from the same day) may be correlated. This can give a better estimate of the uncertainty around your regression coefficients when dealing with repeated measures on the same subjects (plants). To analyze the relationship between abscisic acid content and rainfall, a mixed model should be used with the individual plant as a random effect to account for the repeated measurements over time. Additionally, there is a concern about pseudo-replication due to the fact that rainfall has only been measured once for all 10 abscisic acid measurements. Therefore, the abscisic acid vs. rainfall measurements are not truly independent on each day.