I have two datasets from different years (2020 and 2021) that I needed to merge because my sample was too small. I'm investigating which morphological features (sex, age, weight, fat reserves and muscle mass) from a migratory bird species affect the number of days they're staying in a determined area before leaving for migration (they have GPS tags on them).

The problem is that the 2020 data was collected by someone else and 2021 was collected by me. While all the variables are equal and measured right, the person before me captured and measured birds in different months while I opted to capture and measure all birds on the same month (as soon as birds start to aggregate in the study area). Because it wasn't my initial plan to merge the datasets, this caused problems.

I'm using negative binomial distribution because my response variable is count data (number of days) and on R the model is glm.nb = days ~ age + sex + fat + muscle + weight. The summary of this model presents me significant values for muscle, fat and age, which match my initial hypothesis but as soon as a put year on the mix (a variable I feel I can't ignore), every other variable loses it's significance and year is the only one significant. Nagelkerke's R² is bigger on the model with the year variable and the AIC is lower, but I'm sure the way data was collected is causing this.

Is there something I can do to eliminate this effect of "year" or is it just a lost cause? I have the exact dates of each capture so this may be better than year? I'm new here so sorry if more information is needed or I'm not being clear. Thanks in advance.

I have two datasets from different years (2020 and 2021) that I needed to merge because my sample was too small. I'm investigating which morphological features (sex, age, weight, fat reserves and muscle mass) from a migratory bird species affect the number of days they're staying in a determined area before leaving for migration (they have GPS tags on them).

The problem is that the 2020 data was collected by someone else and 2021 was collected by me. While all the variables are equal and measured right, the person before me captured and measured birds in different months while I opted to capture and measure all birds on the same month (as soon as birds start to aggregate in the study area). Because it wasn't my initial plan to merge the datasets, this caused problems.

I'm using negative binomial distribution because my response variable is count data (number of days) and on R the model is glm.nb = days ~ age + sex + fat + muscle + weight. The summary of this model presents me significant values for muscle, fat and age, which match my initial hypothesis but as soon as a put year on the mix (a variable I feel I can't ignore), every other variable loses it's significance and year is the only one significant. Nagelkerke's R² is bigger on the model with the year variable and the AIC is lower, but I'm sure the way data was collected is causing this.

Is there something I can do to eliminate this effect of "year" or is it just a lost cause? I have the exact dates of each capture so this may be better than year? I'm new here so sorry if more information is needed or I'm not being clear.