Three methods for outlier identification are described. A multivariate outlier need not to be extreme in any of its components. Generally, the outliers are searched among the extreme values and if this notion is easy to define in the univariate case, it is not so straightforward in the multivariate case. It depends how the outlier achieved its unusual value. There is no substitute for careful checking.
Mistakes often occur when data are being entered or copied.
Try to reduce the number of occasions that this happens. If it was due to a data entry error, make the correction. If you suspect that an observation was associated with an unusual event then you can probably delete it. If the outlier was not a consequence of a mistake then you must assume that its value is correct. Unusual observations can provide interesting biological information so do not dismiss it.