The Poisson model is about counts. Count values are positive natural numbers including zero (0, 1, 2, 3, ...). In your data seem to be non-integers, that is, floating point numbers with some non-zero decimal digits. The waring show these values (14.61 is not an integer; this cannot be a count value: how will one count 14.61 things?).

If your data should be counts, than it should not contain non-integers. If your data are not representing counts, then the Poisson model is wrong. You should consider a gamma model or a log-normal model instead.

The Poisson model is about counts. Count values are positive natural numbers including zero (0, 1, 2, 3, ...). In your data seem to be non-integers, that is, floating point numbers with some non-zero decimal digits. The waring show these values (14.61 is not an integer; this cannot be a count value: how will one count 14.61 things?).

If your data should be counts, than it should not contain non-integers. If your data are not representing counts, then the Poisson model is wrong. You should consider a gamma model or a log-normal model instead.

glm is for fitting generalized linear models which is different from general linear models. If you just have multiple predictors and a continuous approximately Gaussian distributed dependent variables, just use lm. Of course, you can specify Gaussian family in glm and fit the same model. But the only real difference is that you can specify a link funciton in glm.

and if you provide a reproducable example, we might be able to help further. Or do an internet search for R examples related to your specific model (looks like a poisson GLM?) to find examples you can follow.