If you do not have any statistical analysis in the article, it is an easy task for a reviewer to reject the article. You should have thought of the analysis before you started to do the research, but in case you did not, now it is the time to think.
The basis of the scientific research is that you should be able to say how probable it is that your results are true also elsewhere or maybe in the next year. This means that the result is not random, but it is real and reliable.
Although statistical analyses may not be so extremely important in the development of ecological sciences, you still, in practice, must do some analyses. Although you think that the results can be seen without any analysis, in practice, you must do at least one analysis.
Because the peer-review system is many times arbitrary, you cannot know in forehand what the reaction of the reviewer will be. However, one thing is almost sure. The reviewers want to see at least one statistical analysis. It does not need to be any complicated analysis, most often reviewers are satisfied with a simple analysis of variance, correlation analysis or principal component analysis.
Researchers in ecology are often not extremely interested in statistics, and they accept any statistics they see. However, they know that the scientific style is that some analysis should be done. That is why you better have at least one statistical analysis or some kind of a data-analysis in the article. Presenting the variation around the treatment means with SD or SE is a must, but it is not enough.
You really can improve the possibilities to get the article accepted with a statistical analysis – even if the statistics really do not improve the research nor its presentation. I know many examples, where the value of an article has been managed to be increased with complicatedly looking statistics. It certainly is possible to get a poor data accepted with complicated statistics. However, I am not recommending it here. I recommend that you plan and perform experiments with care and get good data and finally, do simple and common analyses that most people can understand.
In general, the statistical analyses are performed to show if the values observed are statistically significant. In many cases, in practice, the significance test helps readers to see the result easily without reading and comparing all values so carefully. The statistical significance tests help to write the results, in practice.
The tests also help authors to assess if the values or differences matter. However, the significance test itself does not tell about the ecological importance. The ecological meaning and relevance should be done separately from the statistical testing, but as a complex issue, it is a theme of another post.