Here are my thoughts:

1. First, before you do any statistics, you should determine if the data is meaningful from a subject matter perspective. How much of a change in the response distance/hour is of practical value to you the SME?

2. How was the data acquired? (e.g., Is this data from a directed sampling plan, randomly acquired or from an experiment?)

3. Visualize the data using graphical methods (e.g., Graph Builder). Are there any unusual data points? It appears there is more variation within Day/Night than between and the within Day is more than within Night? Why?

4. When you run fit model, you can look at the Prediction Profiler. Grab the vertical Red line for the Day factor and move it to night, watch how the effects on Depth change. This is a visual of the interaction.

5. Always assess your model. Look at residuals. They appear to be quite "funky" (Non-constant variance, and potentially some outliers) meaning you should re-evaluate your model.

Be careful of just using p-values. They are meaningless unless you understand the sources of variation used to estimate the mean squares for the terms in the model and most importantly the MSerror.

I re-attached your data table with some scripts to get you started.

"All models are wrong, some are useful" G.E.P. Box