I have performed the experiment where I fed green tomato fruits (natural host) to the Helicoverpa armigera under six different alternating temperatures as treatments viz. 25:10, 25:13, 25:16, 30:10, 30:13 and 30:16 degree celsius (Max: min). The objective of the experiment is to understand the effect of alternating temperatures on development duration of immature stages of H. armigera . I have recorded different development duration of various insect stages. Also I have fitted the avg. temperature calculated from alternating temp. and development rate of this insect using simple linear and non-linear models. However, from the discussion it can be inferred that host (tomato fruit) also has a marked effect on larval duration of insect although no separate experiment was performed to understand the effect of host on development duration of H. armigera. I have submitted the related manuscript to a journal where after review process they have asked me to perform the partitioning of variance in order to understand the contribution of temperature, host and temperature exposure period on the development period of insect. As I have no idea about this statistical procedure so Kindly help.
Thanks in advance
I don't think there's any obvious way to do this if your experiment didn't include different hosts, except in a qualitative way. E.g. "Reviewing other experiments, it appears that host is a more important factor than temperature for the range of temperatures we explored."
Perhaps the reviewer didn't understand that your experiment didn't include different hosts?
I don't know what temperature and temperature exposure means. But in your experiment, it might be possible to separate the effects of maximum temperature and minimum temperature, via a two-way anova or analogous analysis. I don't know if this makes sense for your experiment, but it might be a way to address the reviewer's comments.
I recommend reading by Schoen et al. (2011) for Joint-vairance computation.
Matt
- Badges
- Science topic
- Similar topics
- Mathematical Sciences
- Statistics
- Statistical Modeling
- Analyses of Variance