GEOG 566






         Advanced spatial statistics and GIScience

June 11, 2018

Using correlation-based techniques to investigate population trends in Bull Kelp (Nereocystis luetkeana) in southern Oregon

Filed under: Final Project @ 9:52 am

The Question: I was initially looking to explore correlation between a kelp canopy coverage data set and a suite of environmental variables. My question morphed into examining the correlation between canopy cover and two temperature data sets.

The Data: I used three datasets to investigate this question. The first was a 35-year time series of kelp canopy cover in southern Oregon derived from Landsat satellite imagery. This dataset was shared with me by my colleague Tom Bell and reported the percent cover of each 30mx30m pixel whenever cloud free imagery was available. The other two datasets were two time series of temperature on the Oregon coast, one derived from satellite imagery and the other from direct measurements of intertidal temperature near Port Orford. The satellite-derived data set was over 35 years long, but came at a resolution of 0.1 degree raster cells, meaning that it represented offshore sea temperatures more so than local, nearshore temperatures. The local, intertidal dataset was measuring local, nearshore water temperatures that were within 20 miles of most of my kelp beds. That time series only covered about half of my time series however, from late 1999 to the present.

 

The Hypotheses: Worldwide, warming temperatures have been implicated in global declines of kelp populations (Wernberg et al.,2016; Filbee-Dexter et al., 2016). Perhaps even more important than the direct effects of temperature is the fact that ocean temperature is closely correlated with nutrient availability, which is of crucial importance to these fast-growing primary producers. However, in many local or regional studies, temperature does not necessarily emerge as one of the top drivers of kelp growth and biomass. For example, in southern California giant kelp biomass was not significantly impacted by the extreme 2013-2015 warm water anomalies that impacted the Pacific coast of North America (Reed et al., 2016). Furthermore, several studies on giant kelp (Macrocystis pyrifera) in southern California have found that wave intensity as one of the primary factors controlling giant kelp biomass (Bell et al., 2015, Parnell et al., 2010). Biotic interactions, such as competition with understory kelp and herbivory by urchins, can also tightly control kelp populations (Dayton and Tegner, 1984). Overall, untangling the relative importance of abiotic drivers of kelp populations, is a highly context dependent business. In southern Oregon, I expect temperature/nutrient availability to have an effect on kelp biomass but for that effect to be moderated by the presence of urchins, wave events, and climate oscillations.

 

The Methods: To investigate the relationship between kelp and temperature, I employed three methods: 1) Autocorrelation and cross correlation to investigate temporal correlation within and between the two biggest kelp patches in southern Oregon.
2) Graphical representations and cross correlation to examine patterns between satellite-derived sea surface temperatures and local, directly-measured nearshore temperatures.

3) Interpolation of my kelp time series to monthly temperatures using polynomial splines in order to utilize wavelet and cross-wavelet analysis to look for areas of shared power within and between the kelp time series and the two temperature time series.

The Results and the Significance

Exercise 1: Autocorrelation and cross correlation were quite limited in the kelp patches I looked at, even at the yearly scale (e.g. Fig 1). This suggests that a) the species responds quickly to changing environmental conditions rather than to holding on to momentum from previous population sizes and 2) that local factors (less than 20km) are more important in driving kelp population size than local factors

Figure 1: Cross Correlation between maximum annual kelp cover at Rogue Reef and Orford Reef over a 35 year time series. The lag is in years.

While this finding was one of the simplest, it was also one of the most significant for me. This finding suggests that focusing on local, patch-specific dynamics will be important in untangling environmental drivers of kelp in Oregon. I’ve already utilized this finding when responding to feedback from managers. An ODFW employee suggested that my satellite-derived kelp time series was incorrect because it showed moderately sized populations since 2014. He said that Orford Reef, one of the largest kelp beds in the state, had been practically non-existent in past years, so there was no way regional populations were at anything other than historical lows. I looked into patch-specific dynamics over the past 4 years and found that, while Orford Reef had indeed gone essentially to zero since 2014, other patches in Oregon had recovered in that same time period and were bolstering regional kelp cover numbers.

Exercise 2: Overall, the relationship between SST and nearshore temperatures was fairly consistent. The temperatures matched very well in the winter, but then nearshore temps warmed up less and more slowly in the summer months. The average difference between mean annual temperature for the two datasets was consistent as well, usually staying between 1.1-1.5 degrees (Fig 2). The rankings of the warmest versus coolest years were also very similar between the datasets.

Figure 2: Difference in annual mean temperature between satellite derived sea surface temperature (black) and measured, nearshore temperatures (red) from 2000-2016. Note how consistent the difference between the two is.

While I can use satellite SST to infer average nearshore temperatures, it will not give me good insight into the variability of nearshore temperature, which could be important to kelp population regulation. If I want to try to incorporate this variability into my analyses, it may be useful to look further into the effect of upwelling and terrestrial water cycles on nearshore temperatures. However, considering a) that much of the interannual variability in temperatures (hottest to coolest years) is similar between satellite and nearshore temperatures and b) how much time it might take to more finely predict local temperatures from satellite temperatures, I think that the satellite temperatures may be good enough to use for most of my analyses.

Exercise 3: According to wavelet analysis, strong annual summer peaks in canopy were not a consistent pattern in my kelp time series, but rather only in the 1985-1991, 1999-2000, and 2014-2017 periods (Fig 3). This inconsistency extends to the cross wavelet analysis of kelp and temperature, where only certain years have high-power, annual cycles. Another important finding from this exercise was that sometimes there appear to be high-power interannual oscillations between kelp and temperature, suggesting climate oscillations may also be influencing kelp cover.

For me, there are two important takeaways from this analysis. First, it is particularly interesting to see a high degree of power in the 2014-2017 period because in these years a) we have had a boom in urchin populations, b) we have had anomalously high water temperatures and c) kelp in northern California has collapsed during this period. All of these other factors suggest that annual kelp population oscillations would have a smaller amplitude, not a larger. And second, the high power periods and years for temperature (both satellite and intertidal) were not necessarily the same as those for kelp. To me, this indicates something other than a 1:1 relationship between temperature and kelp. This suggests that I need to explore other environmental variables and that I might need to explore them in multivariate analysis rather than multiple univariate analyses.

Figure 3: A) Wavelet analysis of the kelp canopy time series and B) Cross-Wavelet analysis of kelp canopy and satellite temperature. Areas of high-power are in red. The black line in A represents the power ridge and the black arrows in B represent whether the two are in phase (right), out of phase (left), x leading (down) or x lagging (up).

I think my next steps with this project will be to use PCA to try and identify particular environmental factors that are most strongly correlated with kelp canopy cover. Incorporating my findings from Exercise 1, I will do it utilize PCA not only on the regional Oregon kelp time series, but also using the local population time series from individual patches. In this way, I will be able to look at regional drivers of kelp canopy cover as well as how the extent to which local drivers may differ a) between patches and b) from the regional drivers.

 

The Learning: I almost exclusively utilized R for this class, and gained familiarity with interpolation techniques as well as a number of very user-friendly, useful packages for performing correlation analyses. Most of the techniques I utilized in this class dealt with autocorrelation, in one form or another, but I learned about a number of other spatio-temporal statistics via the student presentations. In particular, I’m excited to utilize Geographically Weighted Regression to better understand local kelp patch correlations with various environmental variables and PCA to identify environmental variables that may be mostly closely correlated with kelp cover.

The References:

Bell, T. W., Cavanaugh, K. C., Reed, D. C., & Siegel, D. A. (2015). Geographical variability in the controls of giant kelp biomass dynamics. Journal of Biogeography, 42(10), 2010-2021.

Dayton, Paul K., and Mia J. Tegner. “Catastrophic storms, El Niño, and patch stability in a southern California kelp community.” Science 224.4646 (1984): 283-285.

Filbee-Dexter, K., Feehan, C. J., & Scheibling, R. E. (2016). Large-scale degradation of a kelp ecosystem in an ocean warming hotspot. Marine Ecology Progress Series, 543, 141-152.

Reed, D., Washburn, L., Rassweiler, A., Miller, R., Bell, T., & Harrer, S. (2016). Extreme warming challenges sentinel status of kelp forests as indicators of climate change. Nature communications, 7, 13757.

Wernberg, T., Bennett, S., Babcock, R. C., de Bettignies, T., Cure, K., Depczynski, M., … & Harvey, E. S. (2016). Climate-driven regime shift of a temperate marine ecosystem. Science, 353(6295), 169-172.

 

 

 

 

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4 Comments

  1.   leatherl — June 15, 2018 @ 3:04 pm    

    Nice job, Sara! And super cool that you’ve already been able to discuss these results anecdotally with ODFW managers. I’m curious if the Landsat data for Port Orford specifically corroborate the decline that the manager noted? Are there environmental data there to suggest what might have caused the decline in that specific region, especially since you have local temperature data? It seems like that reef could be a super interesting focal site! On larger spatial scales, I have a similar question to Sam– which additional environmental factors do you predict will be most influential on local kelp cover? Also, Landsat data are available less frequently than MODIS data. Even though you get the long-term record using Landsat, and MODIS has coarser spatial resolution, it seems like a shorter return interval from MODIS (every 8-16 days, depending on the product) could be beneficial– especially in such a cloudy system.

  2.   jonesju — June 15, 2018 @ 7:27 am    

    Nice work! Next steps: Can you better clarify what you were hoping to get from the wavelet analysis? A comparison of the wavelet plots with the cross-wavelet plots could produce some very good insights. Also, please consider extending the wavelet analysis to the field measured temperature, and do a cross-wavelet analysis of measured vs. satellite temperature, and measured temp vs. kelp. If you have kelp time series from more than one location, it would be cool to do cross-site comparisons of kelp. Keep up the good work!

  3.   swanssam — June 15, 2018 @ 7:08 am    

    Whoops, I meant “Sara”, not “Sarah” – sorry!

  4.   swanssam — June 15, 2018 @ 7:07 am    

    Hi Sarah, really cool work. I especially like the visualizations produced by the wavelet analysis. I’m curious what environmental factors you’ll be exploring next, beyond temperature. I don’t know very much about kelp – what other environmental factors influence their abundance? Do tides or the presence/absence of predators or competitors play a role? What about human influence, like pollution input at the mouth of streams or near shipping lines? Either way, great job and good luck in your future endeavors!

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