GEOG 566

         Advanced spatial statistics and GIScience

April 6, 2018

Associations between physical substrate complexity and spatiotemporal kelp forest dynamics

Filed under: 2018,My Spatial Problem @ 10:08 pm


Nearshore temperate kelp forests are structured by an interaction of physical forces and biological processes that produce patchy species assemblages across space and rapid shifts in community structure through time. At short temporal scales seasonal storm surge removes adult giant kelp (Macrocystis pyrifera) and can instigate community shifts, while longer-term periodicity (e.g., El Niño Southern Oscillation events, Pacific Decadal Oscillation) modulates the oceanographic conditions influencing macroalgal growth. Disturbance and herbivore driven community shifts are often transient; however, deforested regions do not always immediately recover, and urchin dominated communities may persist for a decade or more before reverting back to a macroalgal state. While the processes involved in both directions of the switch between urchin and macroalgal states have been repeatably observed and qualitatively described, it is less clear how biological and physical context at local scales may modify positive and negative feedback mechanisms to either perpetuate or dampen these shifts. To (1) investigate how variation in physical substrate complexity is associated with varying temporal kelp forest dynamics, and (2) map a future survey, I will incorporate a spatially explicit 38-year time series of community dynamics with 2m side scan sonar bathymetry around San Nicolas Island, CA, in the Channel Islands.


(1) Are spatial associations between physical habitat complexity (i.e., relief) and community structure consistent through time? Are varying levels of relief associated with (a) cyclical periodicity through time or (b) edges in community structure? Does increasing the spatial scale of bathymetry incorporated around each site influence the previous results? That is, does the composition or heterogeneity of an expanding spatial window of substrate provide insight into the nature or persistence of species-habitat associations?

(2) Create a pool of possible survey sites for fieldwork currently slated for summer 2019. Based on previous surveys, our inference into community structure is influenced by the complexity of the substrate sampled, and thus it is possible to obtain a misleading or incomplete snapshot of community structure if a completely random design is implemented irrespective of local heterogeneity. Therefore, my second objective is to create a sampling design that incorporates physical substrate complexity (e.g.,, a stratified sampling design, but not necessarily scaled by area per condition), allowing divers in situ to survey both low- and high-relief all around the island, providing an independent test of my hypotheses for how species-habitat relationships vary over time given broader context (e.g., sea otter distribution, storm exposure).


Benthic bathymetry

I will use existing side-scan sonar bathymetry data of the nearshore subtidal around San Nicolas Island (SNI), CA (Kvitek, 2011). These data have a 2m grain and extend approximately 1km offshore around the island. I will predominantly use a slope layer containing measures of substrate verticality.

Time series

I will use data from an ongoing 38-year biannual sampling program that has surveyed seven subtidal sites in the nearshore subtidal (35-40 feet sea water) around SNI (Kenner et al, 2011). Macroalgae and urchins species are surveyed within 10x2m2 transects (5 per site, 35 total), filamentous red algae and colonial species are recorded in 1m2 percent-cover quadrats (10 per site, 70 total), and fish are recorded in benthic and midwater 8x50m2 transects (5 per site, 35 total). As incorporating multiple community matrices sampled at different scales may require more time than the scope of this class, a few key indicator species will be retained and standardized for initial analyses, including 1) Giant kelp (Macrocystis pyrifera), 2) Purple urchins (Strongylocentrotus purpuratus), 3) the California Sheephead (Semicossyphus pulcher), and percent coverage of 4) fleshy red algae, and 5) suspension feeders.


1) I hypothesize sites predominantly comprised of low-relief will exhibit large shifts in community structure through time (e.g., large swings in local urchin densities), sites comprised of a mix of low- and high-relief will exhibit spatially explicit differences in species-habitat associations through time, and sites comprised of high-relief will exhibit relatively uniform species-habitat associations and minimal shifts in community structure over time (e.g., consistently low urchin densities).

2) I hypothesize low-relief sites that are homogenous across an increasing window of spatial scale will experience rapid and lasting shifts in community structure, while other sites comprised of a mix of low- and high-relief will exhibit a spatial patchwork of urchin barrens and kelp regions. I hypothesize homogenous high-relief sites will be associated with high Sheephead densities, an urchin predator whose spatial aggregation may locally increase the strength of top-down trophic regulation, limiting herbivory, and yielding macroalgal species that exhibit cycles with periodicities characteristic of populations governed by age-structured growth and senescence.


My objective is to increase my technical proficiency in ArcGIS and learn how to apply various spatiotemporal analyses, e.g., autocorrelation and spatial and temporal cross-correlation among transects within a site. Wavelet analysis has proven very useful, and perhaps those results could be related to the bathymetry. I would like to explore a variety of methods to motivate future efforts (e.g., incorporate the entire time series at a later date, perform more robust spatial autocorrelation analysis once the 2019 island-wide survey has taken place).


I would like to produce maps that show the increasing scales of benthos analyzed for the later part of question (1), along with various statistical output for analyzing patterns within- and among-sites (e.g., variograms, correlograms). For the sampling design I would like to map a pool of potential sample sites linked to GPS coordinates from which I’ll randomly select and sample 35.


Results from these analyses will provide insight into how local features are associated with varying temporal dynamics over time, potentially contextualizing experimental work planned for summer 2018 that will test a key mechanism hypothesized to vary with substrate complexity. Additionally, abrupt transitions in community structure often negatively affect ecosystem function over time; for example, sea star wasting has resulted in the domination of purple urchin populations and a crash in macroalgae, and as a consequence, for the first time since its creation, the recreational abalone fishery will not open this season. Results for the islandwide snapshot survey sampling design will directly inform questions of management interest, as the recent arrival of the invasive macroalgae Sargassum horneri threatens native species at SNI, and the current distribution is unknown.


I have a rudimentary working knowledge of ArcGIS, and can probably figure out how to do most tasks once I explicitly know what it is I need to do (and which tools or packages are required for the specific tasks). I conceptually know what I need to do, but I don’t know how that translates in terms of ESRI tools. I’ve used R to structure data, run analyses, and create figures, but I have yet to analyze spatial data.


Kvitek, R. 2012. Bathymetry data used in this study were acquired, processed, archived, and distributed by the Seafloor Mapping Lab of California State University Monterey Bay.

Kenner, M.C., Estes, J.A., Tinker, M.T., Bodkin, J.L., Cowen, R.K., Harrold, C.H., Hatfield, B.B., Novak, M., Rassweiler, A., Reed, D.C. 2013. A multi-decade time series of kelp forest community structure at San Nicolas Island, California (USA). Ecology 94(11): 2654

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1 Comment

  1.   jonesju — April 8, 2018 @ 10:43 pm    

    hi Zach,
    Thanks for your spatial problem blog post. Three requests: (1) please simplify your research questions. Each of your stated research questions is actually two or more questions. Try to rephrase them so that each question asks only one thing. (2) Please select a dependent variable for your initial analyses. Will this be kelp cover in the plots? Think it over and make a note of this in the blog post. (3) once you have selected a single dependent variable, create a GIS layer that shows the spatial locations where the variable was sampled and attribute that layer with kelp cover at the points for whatever date(s) each point was sampled.

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