Summer sea wrack spatial data; Central Coast, British Columbia, Canada (2015 - 2017)
This tabular dataset contains the biophysical environmental variables (climate, site characteristics, or amount of donor habitat) of each site along with the sum of dry weight wrack collected. Dead, shore-cast seaweeds and seagrasses (commonly called sea wrack) provide an important vector of marine-derived nutrients to low productivity terrestrial environments. However, little is known about the processes that facilitate wrack transport, deposition, and accumulation in coastal temperate British Columbia. Three broad factors affect the stock of wrack along a shoreline: climatic events which dislodge seaweeds and move them ashore, physical characteristics which retain wrack at a site, and amount of potential donor habitat nearby. To determine when, where, and what wrack was accumulating on shorelines I surveyed 455 sites across 101 islands. At each site, I recorded wrack biomass, shoreline biogeographical characteristics and weather conditions information. I returned to a subset of sites on a bi-monthly basis to document temporal changes in wrack biomass and species composition. Zostera marina, Fucus distichus, Macrocystis pyrifera, Nereocystis luetkeana, Pterygophora californica and Phyllospadix spp. were the six dominant species found across spatial and temporal scales. Detailed methods available in the MSc Thesis found in the linked to folder. My results indicate that sea wrack can accumulate along any shoreline that is not composed of rock substrate and that the presence of wrack is positively influenced by the amount of donor ecosystem habitat as well as the width and wave exposure of a shoreline. This demonstrates that of the three broad factors considered, physical site characteristics and the amount of donor habitat near a site have more of an influence on wrack accumulations than climate events. Additionally, I found that wrack biomass and species composition were similar throughout all four seasons. My results suggest that sea wrack is a consistent vector of potential nutrients from the marine to the terrestrial environment in British Columbia. Sara Wickham – University of Victoria, Brian Starzomski – University of Victoria, John Reynolds – Simon Fraser University, Chris Darimont – University of Victoria
https://hecate.hakai.org/geonetwork/srv/api/records/7381aff7-a4fe-4309-81f1-8eebe183b4d8/attachments/Sea Wrack Dataset Attributes_.pdf
description: Definitions and descriptions of the attributes in the Sea Wrack spatial dataset described here
name: Sea wrack spatial data
description: The sea wrack spatial data used in the analysis referenced by this record.
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Methodology Description: Detail of how the study was conducted, recorded, and quality controlled. Please provide a full description so future work can replicate your study using these details. Island Selection. Cluster analysis was used to identify study islands. Clustering is an unsupervised, multivariate technique that can be used to group observations or sample units (i.e. islands), that are similar with respect to the variables used to define them (Hill and Lewicki n.d.). Cluster analysis provides a method of data reduction that ensures that a range of island characteristics are sampled. Cluster variables were selected for their relevance to Island Biogeography theory (e.g., island size and distance to mainland, Wilson and MacArthur (1967)) and subsidized island biogeography theory (i.e. isolation and perimeter to area ratio, Polis and Hurd (1996), Appendix, Table 3). Five biogeographical descriptors for all islands within the study region (n = 1470) were derived. Biogeographical characteristics were extrapolated from the British Columbia (BC) ShoreZone dataset (Howes et al. 1994). The results of cluster analysis identified several clusters (Appendix, Table 4) where multiple islands were located within close proximity to each other (i.e. within a ‘node’), and for logistical reasons I chose to sample these nodes. Within a node, islands were selected to maximize variation across a range of island sizes and shoreline structure. The final dataset consisted of 101 islands within nine nodes. Wrack Biomass and Composition Measurements. During May, June, July and August of 2015, 2016, and 2017, I visited each island once, conducting four surveys per island, one at each of predetermined coordinates representing the furthest North, East, South, and West aspect of each island. Additionally, because different substrates have varying abilities to trap and hold wrack (Orr et al. 2005), extra surveys were performed on islands that had beaches with either a sand, gravel, cobble, or boulder substrate outside of the cardinal direction surveys. Therefore, each island had either a minimum of four or a maximum of ten survey sites for a total of 455 sites in the study area. Each survey entailed three 20 meter transects, centered on the pre-determined cardinal direction coordinates. To account for tidal range flux, transects were focused in and around the supralittoral zone which I could access during all tidal cycles. For each survey, one transect was placed at the most recent high tide wrack line, one at the spring/storm/surge wrack line (the highest wrack line visible on the shore), and one was placed just inside the shoreline’s terrestrial edge (towards the island interior). I used a random number generator to determine three locations along each transect line (n = 9 across three transects at each cardinal direction) to place a 1 m2 quadrat. All wrack that was visible within the quadrat was identified to the functional group (as per Steneck and Dethier 1994), genus, or species level, sorted, and weighed. Wrack that was unidentifiable was categorized as such and weighed. Wrack that was partially buried but still had a portion visible was uncovered, rinsed or wiped of sand, sorted, and weighed. Wrack was weighed with either a kitchen diet scale with accuracy (+/-) 2 g or a hanging spring scale with accuracy (+/-) 1 kg attached to a tarp. Prior to weighing I also assigned a wet/dry category to each species pile (desiccated = air or sun dried and fully desiccated, damp = partially air or sun dried but still retaining some moisture content, wet = appearing to be freshly washed ashore, wet, full moisture content). Following methods outlined in chapter three, I took subsamples from twelve of the most common seaweed species of each wet/dry scale category and dried them in a Fisher Scientific Isotemp drying oven at 80 degrees Celsius until the samples each reached a constant mass (weight within +/- 0.005 g for three consecutive measurements). Wet to dry mass calibrations were performed by deriving a correction factor from each species’ linear relationship between wet and dry conditions. The correction factor was applied to all wet and damp biomass measurements. Subsequently all biomass results for both spatial and temporal data are reported in dry estimates. Biophysical and Environmental Measurements. In addition to the wrack biomass and composition surveys, I also collected site data as per protocols outlined in the ShoreZone Coastal Habitat Mapping Protocol (Harper and Morris 2014). The ShoreZone Mapping Protocol describes methods to catalog geomorphic and biological coastal features of the Pacific Northwest (including BC, Alaska, Washington and Oregon). Site data I collected included shoreline slope, aspect, substrate, width, and biobands, which are patterns of identifiable biota observable in the intertidal and supralittoral zone (Howes et al. 1994). Biobands were used to classify the wave exposure of a site as per the ShoreZone Mapping protocol. Substrate categories were adapted from the Wentworth scale of grain size and included sand, gravel, cobble, boulder, and rock (Wentworth 1922). Shoreline slope, aspect, and width measurements were used to ground-truth the results of a shore zone morphology dataset generated by Unmanned Aerial Vehicle (UAV) imagery (Nijland et al. unpublished data). The UAV dataset generated slope, aspect, and width measurements at every five meters along every islands shoreline. These measurements were similar but more precise than my on the ground measurements (Nijland et al. unpublished data). Therefore, in my models I used the mean slope, aspect, and width measurements from the UAV dataset for each site. Methods of imagery analysis are outlined in Nijland et al. (2016) and are used with UAV imagery and elevation models at ten centimeter resolution. Wind direction, wind speed, wave height, and wave period measurements from the time period of six hours before every site visit were accessed from Environment Canada West Sea Otter Buoy archives (“West Sea Otter Archive Plot” n.d.). If data were unavailable for that specific time period a measurement was used from within +/- 2 hours. A site’s proximity to a source seaweed habitat was calculated by identifying the three main donor ecosystems: 1) kelp forests as donors of M. pyrifera and N. luetkeana, 2) eelgrass beds as donors of Z. marina, and 3) rocky intertidal shorelines as a donor of F. distichus. To determine the relative contribution of each ecosystem in explaining biomass measurements, I analyzed UAV and WorldView 2 satellite imagery in ArcGIS and estimated the extent of all forest/bed/rocky intertidal habitats. With the understanding that kelps such as M. pyrifera commonly wash ashore within a five kilometer radius of their detachment sites (Jenifer E. Dugan unpublished data), I positioned a set of radii around each survey site (length of radii = 25 m, 50 m, 100 m, 500 m, 1km, 2 km, 3 km, 4 km, 5 km, 7.5 km) and analyzed the strength of the relationship between the summed area of forest/bed/rocky intertidal habitat and kelp/eelgrass/F. distichus biomass using Spearman’s correlation analysis for non-normally distributed data in R Version 3.3.3 (R Core Team 2017). Following methods established by Leibowitz et al. (2016), the radius with the strongest relationship (from my analysis: 2 km) was used for subsequent analysis.
marine terrestrial subsidy
Detailed Methods are outlined in the attached methods document.
Appropriate credit must be given to the authors of the dataset.
University of Victoria
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