I am currently running some linear models and generalized least squares in R to detect latitudinal effects in the body size of a group of marine invertebrates.
The response variable is a measurement of the body size per each species included in the original dataset. The explanatory variables that I included in the models are the following ones: - Latitude (both absolute and squared values, to detect both linear and hump-shaped relationships). - Species records (a likely confounding variable that reports the number of records of species per latitudinal band of 1º). - Species richness (a likely confounding variable that reports the number of species per latitudinal band of 1º) - Sea surface temperature (an environmental variable that varies through the latitudinal gradient). - Net primary productivity (an environmental variable that varies through the latitudinal gradient). - Ocean (it's a categorical variable with six values: W Atl, E Atl, W Pac, E Pac, W Ind, E Ind). It represents the main coastline where the species is present.
I perform a first model, only including latitude (both absolute and squared values), the confounding variables (species richness, species records) and the ocean. Then, I perform a second model including latitude, confounding variables, ocean and environmental variables (sea surface temperature and net primary productivity).
I initially performed the models with the whole dataset (data from both the northern and the southern hemispheres), and I got statistically significant results. However, when repeating the models separating the original dataset per hemisphere (analyses of the northern and the southern hemisphere separately, to detect in the observed trends are present in both or only in a single hemisphere), I didn't get significant results. How is it possible?
Is it possible to obtain results for the whole dataset but nothing when separating per hemisphere?