Help interpreting strange pattern in PCA This is likely a naive question about principle coordinate analysis, but my googling skills are failing me in this instance. I've conducted PCA on a multidimensional dataset, and got this result:

My experience working with data makes me immediately suspicious of that long trailing line of points, but I'm new to PCA and am not sure how/if it is an issue or what might be causing it. I've already removed co-correlated variables and have z-score standardized the remaining variables prior to conducting the PCA. Trying min/max normalization also did not change this result. Does anyone have any insight about what might be going on here?
Here are the loadings of the PCs:

And here is a header of the z-score standardized data used to create the PCA:

 A: As the arrows and principal-component loadings indicate, a change of one unit in $k$ represents different directions of change in PC1 and PC2, with the magnitude of the change about 3 times more for PC2 than for PC1. That describes your "long trailing line of points."
As values of $k$ change while other things are held constant (or while there are offsetting changes in change_abund and end_abund, whose arrows are pretty much opposite in direction to each other and orthogonal to the $k$ direction in the plot), an increase of 0.1 units in PC1 due to a change in $k$ will be associated with a decrease of about 0.3 units in PC2. That's what you see.
The data would probably show that those points in the line either have similar values for predictors other than $k$ or offsetting differences in change_abund and end_abund.
With so few predictors and data points, it's not clear that working with principal components analysis is going to be of much help. You are probably better off doing a standard regression rather than working with principal components. Then coefficients would also have interpretations that are easier to understand and to explain to others.
