The research question that you asked (provide one question for each exercise).

How is gray whale foraging distribution related to zooplankton abundance, life history and community composition across sampling sites due to visibility (secchi depth)? (for A)

What is the probability gray whales are foraging in a given location in Port Orford? (for one part of B, using kernel density) and Are the annual values of factors correlated to themselves at some point in time? (for the second part of B, using time series ACF/PACF).

How are upwelling and zooplankton abundance correlated in time during the 2017-2021 seasons? Are there patterns at more than one scale? (for exercise C, using CCF/wavelet)

A description of the dataset you examined, with spatial and temporal resolution and extent.

For the first part of exercise B, I used the GPS points of foraging whales in the Port Orford study site for all years. This was the only spatial analysis I conducted.

For the second part of exercise B, I used the number of GPS points, secchi depth, and zooplankton abundance (both net tow and GoPro abundance). These were mean annual values for site-wide occurrences.

For exercise C, I used the daily upwelling index (CUTI) and daily zooplankton CPUE (gopro) at each station for the 2017-2021 sampling seasons. 

Hypotheses: predictions of patterns and processes you looked for.

For the first part of exercise B, I hypothesized that there would be higher probabilities of foraging in areas close to the rocky reef structures. 

For the second part of exercise B, I hypothesized that the value of each variable would be more related to itself at a closer point in time.

For C, I hypothesize that upwelling will be cross-correlated with zooplankton abundance at a certain lag time. I also hypothesized that zooplankton abundance and upwelling would have variability at more than one temporal scale.

Approaches: analysis approaches you used.

For the first part of exercise B, I used the kernel density approach for spatial analysis using the adehabitatUD package in R.

For the second part of exercise B, I used the acf/pacf function.

For C, I used the ccf function and cross-wavelet analysis in Passage software.

Results: what did you produce — maps? statistical relationships? other? Present the key, important results you created.

For the first part of B, I produced a kernel density map.

For the second part of B, I produced several time series plots with acf/pacf plots.

For C, I produced a time series plot, a ccf plot, and two wavelet plots.

What did you learn from each of the analyses you conducted (i.e., from each exercise)? 

For the first part of B, I learned so much about kernel density. First, I learned the “nuts and bolts” of the code. Then, I learned more about what is behind the calculations for density probability and understood home range estimation better.

For the second part of B, it was reinforced how just 5 data points may not be sufficient to find significant patterns in a dataset. I also learned that we saw very small size classes in 2018 compared to any other year (by comparing the net tow vs. gopro abundances). 

For C, I learned that there are certain lags that are correlated between upwelling and zooplankton. I also found that variability may be scale dependent for both zooplankton and upwelling.

Significance. How are these results important to science? to resource managers?

My preliminary results for the first part of B are not quite yet significant for science and resource managers. However, when I refine that analysis and potentially overlay a benthic map I may be able to uncover the statistical relationship between habitat and probability density. If significant, resource managers would be able to determine which areas (bull kelp reefs, etc.) should be targeted for monitoring/restoration.

Similarly, my second part of B was not particularly significant, however, when I incorporate daily/weekly values instead of just annual mean I may be able to uncover correlations and understand the statistical relationships between sampling years for each of those variables.

For C, it is important to know how timings of upwelling impact zooplankton abundance. While there is not much managers can (or should) do to intervene with upwelling, it is important to gain a better holistic understanding of the ecosystems that gray whales forage to better allocate resources for conservation considerations.

Software learning. Your learning: what did you learn about software (a) Arc-Info, (b) GIS programming in Python, (c) programming in R, (d) Modelbuilder in Arc,or (e) other?

For the first part of B, I had the opportunity to hone my skills in R more by learning a brand new package and conducting kernel density analysis

For the second part of B, I got to understand time series analysis more. Overall, however, I was able to learn to wrangle my dataset more than ever before and feel much more organized than when I started this term. 

For C, I learned the ccf function and worked in the Passage software more for the wavelet analysis.

Statistics learning. What did you learn about statistics, including (a) hotspot, (b) spatial autocorrelation (including correlogram, wavelet, Fourier transform/spectral analysis), (c) cross-correlation/regression (cross-correlation, geographically weighted regression [GWR], regression trees, boosted regression trees), (d) multivariate methods (e.g., PCA, multiple component analysis), (e) other techniques (change detection/confusion matrices, other)?

I learned much more about how PACF actually works, and how kernel density functions are calculated.

I also learned much more about my own dataset and my own workflow as a coder. I learned more about data input requirements and interpretation of the wavelet process. And that I might need to use an R package instead of the Passage software in order to do a more customized analysis.

Evolving question. How did the results of each analysis lead you to change/refine your question?  Write out the original question you stated at the beginning of the class, and restate the question(s) you now plan to address. 

 I think my final question for C is a much more honed question than the previous questions I was asking. This whole process has allowed me to realize I need to scale down the spatial and temporal extent of my questions, and for data management purposes – start with a smaller dataset and learn the methods before I progress.

Future techniques. What techniques would you like to explore to answer your research questions in the future?

I would like to actually continue with the wavelet analysis but conduct a more customized analysis using an R package. I also would like to either try boosted regression trees or GAMs so I can assess the impact of multiple environmental variables on my biological response metrics.