Densities, Lengths and Vertical Limits of intertidal predator species at 33 sites on the US West Coast

Abstract

Predator SurveysWe conducted predator surveys in spring and summer 2018 at 33 rocky intertidal field sites from Orange County in southern California to Lincoln County in central Oregon (Fig. 1, Appendix S1 Table S1.1). The lower latitude sites represented the coastline at or nearing the southern range boundaries of the focal taxa (Frontana-Uribe et al., 2008; Marko et al., 2003; Morris et al., 1980). We surveyed five transects per site that extended from the high to low intertidal zones and spanned mussel beds (2-50 meters in length depending on site topography). We surveyed 0.5 x 0.5-meter quadrats placed at 0.5 to 2 meter intervals along each transect. Quadrat intervals were closer (every 0.5 m) near the lower limits of the mussel beds for finer spatial resolution where predators tended to be more abundant. We avoided surveying large tidepools (>~50 cm wide or >~20cm deep), deep cracks (> ~20 cm deep) and highly rugose surfaces.In each quadrat, we counted and measured individuals of all accessible predatory sea star and snail species, including Pisaster ochraceus, Leptasterias spp., Acanthinucella spp., Nucella canaliculata, N. emarginata, N. lamellosa, N. ostrina, and Paciocinebrina circumtexta. Size data were not collected at the two southernmost sites, Crystal Cove and Shaw’s Cove (CRCO and SHCO). Whelk size was maximum shell length (mm) from apex to the siphonal notch. Sea star size was length (mm) from the center of the central disk to the straightest arm tip (Leptasterias, P. ochraceus in California) or from madreporite to the opposite arm tip (P. ochraceus in Oregon, keeping with legacy practice). See Conversion for P. ochraceus Arm Length Measurements for more information.Species groupingsSince some predator taxa were rare or only occurred at some sites (Acanthinucella spp., N. lamellosa, Paciocinebrina circumtexta), we focused only on the predator taxa that were common, including Pisaster ochraceus, Leptasterias spp., N. canaliculata, N. ostrina and N. emarginata. Identification of P. ochraceus and the channeled whelk N. canaliculata in the field is straightforward. Field identification for species in the Leptasterias spp. complex (Flowers & Foltz, 2001; Foltz et al., 2008; Melroy & Cohen, 2021) is not possible, however. Similarly, the visually indistinguishable congeners N. emarginata and N. ostrina overlap from San Francisco Bay to Point Conception (Fig.1, Marko et al., 2003). Hence, we grouped these taxa as Leptasterias spp. and emarginate whelks, respectively.Vertical Zonation of PredatorsWe assigned each individual predator a shore level according to the center of the quadrat in which it was found (see Calculating Shore Levels in for more information). To determine the vertical limits of each predator, we estimated the upper and lower limits of each predator for each transect as the 0.5% quantile and 99.5% quantile, respectively, the median limit of the zone as the 50% quantile, and the vertical span as the difference between the upper and lower limits. Since P. ochraceus, Leptasterias spp. and N. canaliculata extend into the subtidal zone, we did not analyze the lower limits nor vertical span of these predators. Emarginate whelks are exclusively intertidal, so we were able to analyze their lower limits and vertical spans. For each of the 4 predator’s statistical models, we fit mixed effects linear models in JMP (v16.0, SAS Institute) by restricted maximum likelihood (REML) and included site as a random variable to account for the non-independence of transects within a given site. We tested the main and interactive effects of latitude and the 4 predator taxa on upper and median limits and the effect of latitude on the lower limit and vertical span of the emarginate whelk species complex.Densities of PredatorsWe calculated densities of species at the quadrat, transect, and site level only from quadrats in the ‘potential habitable zone’ of each species so that we excluded quadrats that were not within their vertical range at a given latitude (see Potential Habitable Zone for more information). We analyzed density with latitude using a generalized linear model (glm() in stats package v4.0.0) in R (R Core Team, 2020) and specified a Poisson distribution. We tested the main and interactive effects of latitude and species on counts at each site, using the log10 of total area surveyed as an offset. We analyzed the effects of species, region, and site nested within region on counts using a generalized linear mixed model (glmer in lme4 R package v 1.1-34 (Bates et al., 2015), and again used a Poisson distribution and the log10 of total area surveyed as an offset. Southern California sites could not be included in this model since there were zero individuals of all species except N. ostrina and emarginata.Conversion for P. ochraceus Arm Length Measurements To enable P. ochraceus size comparisons between California and Oregon and to allow other researchers to easily convert between arm lengths using different published methods, we measured arm lengths of 25 sea stars ranging in arm length from ~5-200mm at each of 6 sites in Oregon (Fig. 1) using both measurement approaches. To determine whether site influenced this conversion, we first ran a linear model with madreporite measure as the response variable and site and center measure as the interacting factors. No interacting effect of site and arm measurement from center to arm tip was found (F5,138 = 0.64, P = 0.672), suggesting this metric is consistent geographically, at least within Oregon. We had no data for California. So, we used a simple linear regression (R2 = 0.97, P Calculating Shore Levels At each site, we determined the height above mean sea level (MSL) of at least one reference point (a permanent lag screw) using a Trimble system with decimeter accuracy (cutoff was ± 0.25 m) (Trimble ® Zephyr™ Model 2 external Global Navigation Satellite System antenna, Trimble® GPS Pathfinder® ProXRT receiver and Trimble® Nomad® 900 series computer, https://geospatial.trimble.com/products-and-solutions/gnss-systems). Before using the Trimble, we identified the time windows with the best satellite configuration and lowest dilution of precision values using http://gnssmissionplanning.com/. We then calculated the MSL of every quadrat relative to this lag bolt, using surveying equipment (rotating laser level and stadia rod, TopCon RL-H4C). MSL measures were converted to the more ecologically-relevant meters above Mean Lower Low Water (MLLW), the tidal datum for this coast, by correcting for the meters of offset between the two measures for each site (https://vdatum.noaa.gov/vdatumweb/). By calculating the shore level above MSL for every quadrat, we were able to compare vertical distributions of predators between sites and within sites regardless of rock topography. Potential Habitable Zone Our transects spanned from the upper to lower intertidal, so we often sampled areas that were outside the zone of a given predator. To calculate accurate densities only within the habitable zone of a predator, we established the “potential habitable zone” for each species, which clearly changed with latitude (Fig. S2). We first analyzed how the shore level of individuals varied with latitude for each species using a regression. We then calculated the confidence interval around the line of fit at alpha = 0.005. We used this 99% confidence limit formula to calculate the upper and lower limits of the ‘potential habitable zone’ at a given latitude (Fig. S2). Note that this is different than the upper and lower limits above because 1) it spans a larger vertical area to capture the entire habitable zone (99% CI instead of the 95% CI), 2) it is a potential zone based on the latitude of the site, not the realized zone based on the position of individuals at a site, 3) it is not influenced by site-level density and 4) it should be independent of site topography (e.g. rocky habitat availability). By doing this, we were able to calculate species densities only within appropriate habitat in which we can reasonably expect to find a given predator at a given site.</p