Which glm family to use for soil respiration data? I'm trying to fit a GLM on my dataset which consists of soil respiration data (RS), soil temperature (TEMP), soil water content (SWC), biomass (BIOM), day of the year when the sampling was done (DOY) and the vegetation type (grasslands, old fields, ploughland and oversewn grassland). The measurement was done along a 15 m long circular transsect of consecutive quadrats, in every 20 cm, so there are 75 measurements in a transsect.

The question is the relationship between soil respiration (RS) and the other variables (SWC, TEMP, BIOM, DOY and type of vegetation) so how the changes of the related variables influence soil respiration (e.g. if the temperature is increasing, will soil respiration also increase?). I am thinking about a model like this: glm(RS~SWC+TEMP+DOY+type).
The values of RS, TEMP and DOY are all above zero, but SWC and BIOM have zero values, and there are NAs in the BIOM variable. None of the variables are normally distributed and there is an order of magnitude difference between the variables.

How can I decide which family to use?
Thank you for the suggestions!
Edit: boxplot and histogram of the variables



Related question:
Do I need to transform my variables for GLM?
 A: Your dependent variable RS could likely be handled with a log transformation or modeled as conditionally Gamma-distributed.
In this case, there would be a question with how to handle zero values. One thing to consider is if these values are actually zero, or if they are simply below some nominal detection level.  I imagine that soil respiration in a natural system would unlikely to be precisely zero, but there could be situations where this is true.  One approach for left-censored data is to simply substitute small values for zero values.  It's clear that the zeros account for a relatively small proportion of your observations.  You might see section 4.7 in the USEPA document below for some simple guidelines for substituting values for observations below the detection limit.
Also, ordinary least squares (OLS) regression may work for your situation.  You might construct the model and examine the residuals. It may be that RS is close enough to conditionally normal for this to work fine.
The question as to whether to transform other variables is a separate question.
USEPA. 2000. Guidance for Data Quality Assessment: Practical Methods for Data Analysis, EPA QA/G-9, QA00 Update. https://www.epa.gov/sites/default/files/2015-06/documents/g9-final.pdf
