**Question asked**

Following exercise 2, it seems that the relationship between distance to kelp and density of zooplankton cannot appropriately be explained via a linear function. Therefore, I wanted to explore whether certain stations are correlated with one another to continue to try and figure out a way to create a spatial layer of zooplankton density across my entire study area that is ecologically informed. Therefore, my questions for exercise 3 were:

*“Are there correlations between zooplankton density values at different stations?”**“If so, what is the nature of these correlations – positive/negative?”**“How does distance factor into these relationships?”*

**Approaches/tools used, methods & results**

I first visualized the zooplankton density at all of my stations for 2018 across time (Fig 1). Since this plot looks a little convoluted, I also decided to split it out by the two sites (Mill Rocks and Tichenor Cove; Fig 2) as well as by station (Fig 3). Just by visual inspection, it does look like there may be peaks and troughs in zooplankton density occurring at the same time across several stations.

To investigate this, I then ran a bivariate correlation matrix on my data to compare prey density between all pairs of stations on the same days (Fig 4), which resulted in only 3 pairs being significantly correlated (Fig 5).

Fig 4 Fig 5

While this was interesting to see, these plots did not help elucidate where these correlations are occurring in space. Therefore, to add the spatial element into this, I plotted the correlation coefficients of each pair by the distance between that pair of stations (Fig 6). Looking at the plot, there is no obvious, discernable trend. Interestingly, there appear to be some stations that are strongly positively correlated but far apart, while there are also stations that are positively correlated that are close to each other. The same trend applies for negative correlations, though there is not as strong of a negative correlation as there is a positive one. However, a majority of the stations also fall in the middle, indicating that for a lot of stations, zooplankton density at one station does not relate to density at another station.

Although this plot did bring the distance between stations into context, it is hard to visualize that this actually looks like. Therefore, I visualized this plot in two ways. One was by drawing color-coded lines of the strongest positive and negative correlations on my site map (Fig 7), while the second was the same plot as Fig 6, however the points were color-coded by the habitat type (Fig 8) of the two stations (since habitat type is likely also a determinant of where we find zooplankton).

Fig 7 Fig 8

**Critique**

It has become very clear to me that trying to figure out a way to interpolate prey density across my sites is not as straightforward as I thought it was going to be, which can feel very frustrating at times. However, I simply need to remind myself that the marine environment is incredibly complex and dynamic and that it would be near miraculous if the relationships between habitat, environment, prey, and predator could be easily defined by one variable. For now, exercise 3 has continued to uncover the patterns that do and don’t exist in my data and has led to more analyses to try to keep disentangling the patterns. My next step for the final project will be to investigate temporal cross-correlation to see whether the density at station *x*_{1} at time *t* is related to the density at station *x _{2} *at time

*t*. I am a little doubtful about whether or not this will work because my sampling frequency varies between stations (sometimes we can’t do certain stations due to unworkable conditions) and there are sometimes 3 day gaps in sampling. However, I shall persevere and see how it goes!

_{-1}