Domain Editor-in-Chief: Jody W. Deming; Department of Biological Oceanography, University of Washington, US
Associate Editor: Julie E. Keister; School of Oceanography, University of Washington, US


Introduction

Ocean acidification (OA) is known to adversely affect the calcification, survival, metabolism, growth, development, and pH balance of many marine benthic (Orr et al., 2005; Kroeker et al., 2013; Gazeau et al., 2013) and pelagic (Iglesias-Rodriguez et al., 2008; Riebesell and Tortell, 2011) organisms. Calcifying organisms are especially sensitive as they rely heavily on the carbonate saturation state (Ω), the concentration of carbonate (CO32–) ions in the water column, to form their skeletons, shells, and other structures (e.g., radulae). Much of the northeastern Pacific Ocean is already experiencing a low Ω for the biologically important and more soluble form of calcium carbonate, aragonite (Feely et al., 2008), which is evident in both field assessments (Bednaršek et al., 2014) and aquaculture production (Barton et al., 2012) in the region. Given a continued increase in anthropogenic CO2 absorbed by the world’s oceans and a concomitant decrease in ocean pH (Gruber et al., 2012; Takeshita et al., 2015), many marine organisms will experience climate stress that could outstrip their abilities to acclimate or adapt.

Washington’s outer coast has been identified as a particularly vulnerable location for OA due to a combination of natural upwelling events (Feely et al., 2008) and anthropogenic processes (Feely et al., 2012). Washington’s numerous rivers can introduce relatively low pH freshwater to the nearshore (Salisbury et al., 2008) as well as deliver nutrients that influence pH via photosynthesis and respiration processes (Borges and Gypens, 2010; Cai et al., 2011). Photosynthesis increases coastal pH through CO2 uptake, and respiration reduces coastal pH and contributes to the creation of hypoxia zones that compound OA effects on coastal marine life (Grantham et al., 2004; Keeling et al., 2010). In addition to natural processes, the contribution of anthropogenic CO2 to the ocean has increased since the Industrial Revolution (Le Quére et al., 2016) and accounts for roughly 70% of the dissolved inorganic carbon in upwelled surface water in the California Current (Feely et al., 2016). Taken together, these natural and human-derived stressors amplify the impacts of OA on the exceptionally biodiverse outer Washington coast.

The Olympic National Park (ONP) coastline (Figure 1) is the longest stretch of wilderness shoreline in the lower 48 states and is identified as a significant resource under the park’s general management plan (NPS, 2007). The 65-mile coastline is a UNESCO Biosphere Reserve and World Heritage Site that hosts one of the most biodiverse assemblages of marine invertebrates and seaweeds along the west coast of North America (Schoch et al., 2006). The intertidal zone is comprised of rocky reefs, sandy beaches, and cobble boulder fields and is valued regionally and nationally for its ecological, economic, and cultural significance. The loss of species in the intertidal zone that rely on a particular pH threshold or the availability of CO32– could fundamentally alter one or all of these interests (Cooley et al., 2009; Lynn et al., 2013) and have resounding reverberations for marine food webs (Gaylord et al., 2015). Over the past decade, ocean pH off the Olympic coast has declined an order of magnitude faster than predicted by accepted conservative climate change models (Feely et al., 2004; Wootton and Pfister, 2012) and has impacted marine aquaculture in the region (Barton et al., 2015). These observations spurred the Governor of Washington to form a Blue Ribbon Panel on Ocean Acidification. The panel’s report (Washington Department of Ecology, 2012) recommended expanded pH monitoring and an assessment of marine resource sensitivity to OA.

Figure 1 

Map of Washington State, USA, with a cutout of Olympic National Park and long-term community monitoring sites. The ONP boundary, identified in green, extends along the outer coast of the Olympic Peninsula down to the mean lower low-water line. Long-term community monitoring sites are marked by stars and cover the latitudinal gradient of the outer coast. DOI: https://doi.org/10.1525/elementa.312.f1

Although OA-associated impacts are documented for several cosmopolitan species, a major limitation for resource managers is determining how those impacts will be experienced at a local level. The number of laboratory and mesocosm experiments has grown exponentially since OA was widely recognized as a global concern, but these examinations are limited in both their replication of in situ conditions and taxonomic coverage of intertidal organisms. In many cases, the best information available for determining OA vulnerability can only be extrapolated from taxonomically (Kroeker et al., 2010) or functionally (Busch and McElhany, 2016) similar species despite evidence of species-specific responses to OA (Langer et al., 2006; Price et al., 2011; Movilla et al., 2014). In the absence of directly testable response metrics across a broad range of species, managers are tasked with logistically prioritizing certain communities or species for long-term monitoring without a good understanding of which taxa will be susceptible or not susceptible to change. Poor investment in monitoring only OA-tolerant species or only OA-intolerant species could be avoided with better sensitivity forecasting tools.

Several models are currently available for identifying resource vulnerability for particular organisms or regions of interest. Organisms of high economic value (Barton et al., 2015; Mathis et al., 2015; Ou et al., 2015; Richards et al., 2015) or those characterized by high a priori sensitivity due to vulnerable life stages (Barton et al., 2012; Cooley et al., 2012) have been studied disproportionately more than those that may have more surreptitious sensitivities (e.g., metabolic pH imbalance). Furthermore, a select number of organisms, such as pteropods (Bednaršek et al., 2017) and oysters (Waldbusser et al., 2011; Barton et al., 2012), have received closer examination due to their role as bellwethers for OA-derived impacts. There is good reason to study organisms of high economic importance or anticipated sensitivity to better understand the overall impact of OA on marine systems, but there is also a need for resource managers to better understand the effects of OA for the specific regions they manage. Examples of place-based analyses exist for determining vulnerability derived from habitats (Okey et al., 2015), state delineated fisheries (Mathis et al., 2015), and marine ecosystems (Teck et al., 2010) to better forecast OA effects for a region of interest. These evaluations take into account details of local geography to identify factors that may compound inherent organism risk. Understudied biodiversity hotspots, such as ONP, are not easily assessed with current methods for two reasons: 1) the majority of the ONP taxa have not been studied at a detailed taxonomic level and 2) the intertidal zone is an ecosystem not easily modeled for OA exposure. Ultimately, an assessment that accounts for both species-specific sensitivity and ecosystem-specific exposure could serve as a management tool for inquiries into regional OA vulnerability.

A major challenge for identifying regionally specific vulnerability is the resolution of OA sensitivity information across large groups of marine taxa. Taxonomic relationships or functional groupings (Kroeker et al., 2010; Mathis et al., 2015; Busch and McElhany, 2016; Hare et al., 2016) can be used to characterize broader communities for which information is lacking at lower taxonomic levels. However, these methods are not intended to characterize species-specific sensitivities, but rather to predict organism vulnerability based on extrapolations from shared anatomical structures (Kroeker et al., 2010), life histories (Kurihara, 2008), phylogenetic relationships (Jennings et al., 1999) and habitats (Hall-Spencer et al., 2008). Here, we use a recently developed literature-based method for scoring species sensitivity to OA in the California Current (Busch and McElhany, 2016) in combination with an intertidal habitat exposure metric to predict the OA vulnerability of ONP taxa. We then take this evaluation one step further by using the resulting vulnerability scores to address how the ONP intertidal ecosystem may be affected at the community level given a disproportionate effect of OA on the dominant, space-occupying intertidal taxa.

Materials and methods

Species vulnerability assessment

Vulnerability was calculated for the species level assessment only and is comprised of two components: sensitivity, as calculated from the revised rScore values; and exposure, a categorical determination of species primary habitat elevation. The taxonomic group and community level assessments included determination of sensitivity only. To determine OA sensitivity, we used a modified version of the methodology of Busch and McElhany (2016) that uses natural and laboratory experiment-derived sensitivity scores. We determined OA exposure based on ONP tidal elevation data and the vertical distribution of each ONP species. Species found in the lower intertidal zone are bathed in seawater for longer periods of time than higher intertidal species and presumably have higher long-term exposure to OA-induced corrosive seawater. The ONP coastal landscape is dominated by exposed surfaces (e.g., long flat benches, rocky platforms, and boulder habitat) and not the characteristic tidal pool landscape often associated with intertidal ecosystems. Although individuals of some species do inhabit tidepools, a relatively low pH environment, most individuals of those species do not exclusively use this niche pool habitat. Our exposure analysis does not include tidepool-obligate taxa and instead aims to interpret trends across the larger ONP coastal landscape as a whole.

NOAA database

Sensitivity was derived from a literature database (hereafter referred to as the NOAA database) of more than 3,000 experimental field, laboratory, and mesocosm OA-related studies published before January 1, 2015 (Busch and McElhany, 2016). Every experimental response from the NOAA database was assigned a population persistence score and relevance score (rScore for short). Population persistence, defined as an organism’s ability to maintain abundance in succeeding generations through survival and reproductive success, was grouped into categories: net positive, net negative, neutral, or no score due to confounding variables. The rScore is a weighted measure from 0 to 1 calculated as an average of eight ecological relevance factors. These factors include the study environment, ability to measure population persistence, experimental pH treatment values, relatedness to taxa within the California Current, distribution within or outside of the California Current, site of organism collection, response type, and duration of treatment exposure (Busch and McElhany, 2016). A large rScore with an associated negative population persistence indicator represents a highly relevant response with a negative impact on organism survival, respectively. Busch and McElhany (2016) went on to use their rScore values to compute directional and confidence scores. Directional scores indicate the net directional effect of CO2, either antagonistic or beneficial to organism survival. Confidence scores indicate the amount of support provided in the literature contributing to the calculation of the directional effect. When multiplied, these two scores equal what Busch and McElhany (2016) termed the survival scalar. In our analysis the survival scalar is synonymous with our determination of organism sensitivity and represents the degree to which pH will affect the predicted survival success of a given taxon.

The rScore scoring system from Busch and McElhany (2016) was modified to be applicable to the ONP intertidal zone. Specifically, the relatedness, distribution, and collection categories were narrowed in geographic range, from the California Current to the ONP intertidal zone (Table 1). The ONP species list was derived from a variety of inventory and monitoring efforts (ONP, unpublished data; see Acknowledgments) and consists of over 700 species of marine invertebrates and algae. Each ONP species received a single sensitivity score derived from cumulative database evidence at the closest relevant taxonomic levels. This relatedness factor explicitly weighted the results of literature studies with taxonomic phylogeny similar to ONP more heavily than those that used taxa of greater phylogenetic distance. The number of responses included in the sensitivity calculation ranged from taxon to taxon, but in general, sensitivities calculated from higher taxonomic levels included more responses than those derived from lower taxonomic levels. Fifteen species in the phyla Sipuncula, Nemertea, Brachiopoda, and Platyhelminthes were represented by database evidence only at the kingdom level. These species were excluded from the analysis because we considered kingdom-level sensitivity calculations to be too broad to be meaningful for a region-specific examination. In the NOAA database, species found or collected in the California Current received the highest rScore, but in our assessment species found or collected in ONP scored the highest with similar rules applying for distance thereafter. This modification necessitated a recalculation of the original NOAA database rScores such that species native to ONP would exhibit a higher relevance score and likewise reduce the rScores for taxa not directly related to ONP. A regionally explicit rScore recalculation reflects the degree to which local OA adaptation and acclimatization drive genetic variation in intertidal populations (Hofmann et al., 2014).

Table 1

Recalculated rScore metrics for ONP modified from Busch and McElhany, 2016 rScore calculations. DOI: https://doi.org/10.1525/elementa.312.t1

Rule type Rule set Score

Relatedness to species in ONP If species lives in ONP 1.00
If the genus but not species lives in ONP 0.75
If the family but not genus lives in ONP 0.50
If the order but not family lives in ONP 0.25
All else 0.10
Distribution in ONP If the species occurs in ONP 1.00
If the species does not occur in ONP 0.25
Collection location If study subjects were collected in ONP 1.00
If study subjects were collected in California Current 0.75
If study subjects were not collected in California Current or not specified 0.25

Species sensitivity analysis

Sensitivity and confidence scores were calculated from the modified rScores for every species in the ONP intertidal zone. Sixty nine percent of the ONP species sensitivity scores corresponded with the NOAA database species at least at the order taxonomic level (Table S1). Where there was correspondence at the species level, the rScore values were greatest and sensitivity was calculated based only on data for that species. Where there was no direct correspondence at the species level, the lowest common taxonomic level was used as the basis for calculating sensitivity using rScores for all species within the common taxonomic level. For example, a sensitivity score calculated at the family taxonomic level would be applied to all ONP species in that family for which there was no other evidence at the genus or species levels. The taxonomic level at which conclusions can be drawn for each species will be refined over time as more experimental evidence becomes available. The confidence score calculated as a component of sensitivity gives an overall sense of how much evidence is included and to what degree that evidence agrees. Taken together, these scores present a best estimate of species-specific sensitivity and a degree of confidence in that conclusion given a lack of experimental evidence across a wide range of species.

Taxonomic group sensitivity analysis

Species were combined into taxonomic groups to determine the mean effect that OA may have on ecologically important taxonomic groups. For this analysis, exposure was not considered, as contributing species ranged across intertidal elevations. Twenty-two taxonomic groups, from the family to phylum levels, encompassing 548 of all 712 ONP species, were selected based on their respective roles in the intertidal ecosystem. The sensitivity and confidence scores were averaged for all ONP species belonging to these 22 taxonomic groups to determine the overall sensitivity of the respective group. Taxonomic level divisions were chosen based on anatomical and physiological differences between levels. For example, sea stars, urchins, and sea cucumbers are all echinoderms and have similar anatomical and physiological structures within their respective orders, but differ markedly at the class level (e.g., presence of calcium carbonate skeleton). The differences at the class level are meaningful for interpreting OA effects for this example and therefore served as an appropriate level to split dissimilar taxonomic groups.

Community group sensitivity analysis

The taxonomic levels used in ONP long-term community monitoring (Fradkin and Boetsch, 2012) were used to assess rocky intertidal community sensitivity in the high and mid intertidal zones. Rocky intertidal monitoring has been conducted annually since 2009 at four sites: Point of Arches (POA), Sokol Point (SOK), Taylor Point (TAY), and Starfish Point (SFP) that span the latitudinal gradient of the ONP outer Olympic coast (Figure 1). Percent area cover of sessile organisms and abundance of mobile organisms was estimated using random point counts and quadrats in fixed plots 10 m long horizontally along the shore that span an elevational gradient from the mid to high intertidal zone. Taxa were generally identified at the species level; however, some taxa were grouped together at higher taxonomic levels to aid in field identification. An average sensitivity score for each sessile (n = 22) and mobile (n = 10) community taxon was calculated from the sensitivity scores for all species that make up the respective taxon.

Characterizing exposure

Exposure is the second component of species vulnerability and has been defined by ecosystem exposure to environmental change (Metzger et al., 2005; Halpern et al., 2008; Okey et al., 2015), expert opinion (Teck et al., 2010; Hare et al., 2016) and economic risk (Mathis et al., 2015). We calculated exposure as a function of intertidal elevation to determine which taxa are likely to be most physically exposed to OA. Low intertidal zone taxa will interact with low pH surface ocean and low pH upwelled water more frequently and high intertidal taxa are less frequently (total number of hours over a given tidal cycle) exposed to ambient corrosive waters. Zonation ranged from the high intertidal (0.76 m or above) to the low intertidal (0 m or below) as defined by Ricketts et al. (1985). Three exposure categories, high, mid and low, were applied to the ONP intertidal zone according to these elevation boundaries. Measured tidal heights were then used to calculate the percent of total tidal time each elevation category was inundated (exposed) to surface ocean water, as estimated from NOAA Tides and Currents tidal station hourly data (https://tidesandcurrents.noaa.gov; station id: La Push 9442396; data range 2008–2016). The low intertidal zone category included all times for which the tidal height fell below 0 m and all times when the mid and high intertidal were also inundated. The percent time the low intertidal zone was covered was thus 100%. We assigned the low zone an exposure score of 1.00, and the mid and low zones exposure scores of 0.95 and 0.76 (95% and 76% relative exposure time), respectively. As a multiplier equal to or less than 1.00, elevational exposure can only decrease vulnerability, as inherently insensitive organisms remain insensitive when immersed in lower pH waters (Calosi et al., 2013), especially when food sources are available (Thomsen et al., 2013). In our vulnerability calculations, a highly sensitive organism at a lower, more exposed elevation remains highly vulnerable (exposure score = 1.0), while a highly sensitive organism at a higher, less exposed elevation is scored as less vulnerable (exposure score = 0.95 or 0.76). An OA-insensitive organism cannot be made more sensitive by greater exposure.

Results

Species vulnerability

We characterized the OA vulnerability, sensitivity and exposure of 697 ONP intertidal species (Table S2). The majority of all ONP species are located in the low intertidal zone (71%), followed by the mid (21%) and high (8%) intertidal zones respectively. We found no apparent elevational pattern of vulnerability (p = 0.0749). The most and least vulnerable species are listed in Table S2. Eighty percent of ONP species are vulnerable to OA, with scores less than 0, and the other 20 percent are relatively invulnerable to OA, with scores ranging from 0 to 0.55 (Figure 2). The majority of the highly vulnerable species belong to the phylum Mollusca, specifically the classes Bivalvia and Polyplacaphora. The most vulnerable species was the red sea urchin, Strongylocentrotus franciscanus (taxon ID: 697). S. franciscanus had the most negative sensitivity score and the highest exposure score. The least vulnerable species belonged to the red algal order Ceramiales in the family Rhodomelaceae (taxon id: 1–5). Exposure reduced the vulnerability score of a limited number of species that dwell at higher elevation and had little and no effect on vulnerability at mid and low intertidal zones, respectively (Figure S1).

Figure 2 

Vulnerability scores for all ONP species. Vulnerability scores are a product of sensitivity and exposure for each species, where exposure is a function of intertidal elevation. Bar color represents the intertidal elevation for each taxon. Taxon identification (ID) numbers correspond to species names listed in Table S2. DOI: https://doi.org/10.1525/elementa.312.f2

Taxonomic group sensitivity

At the broader taxonomic group level, 87% of groups exhibit some negative sensitivity to OA ranging from –0.59 to –0.54 (Table S3). With the exception of soft-bodied anthozoans and nereidid worms, the most positively sensitive groups are all non-calcareous algal taxa. The number of species represented in each taxonomic group ranged from three (Tracheophyta) to 96 (Gastropoda). For calculated group sensitivities (Figure 3), the ratio of the total number of NOAA database sensitivity scores per group to the number of unique NOAA database sensitivity scores per group represents an index of how much species-level information is available to characterize the group overall. A unique sensitivity score is one that does not exactly share the same base sensitivity information as another score. For example, if the sensitivity scores of two ONP species within a taxonomic group are averaged to arrive at a group score and both contributing scores are based on the same underlying NOAA database evidence, then there is only one unique contributing score or a ratio of 2:1. This ratio ranged from 41:1 (Isopoda, information poor) to 4:4 (Echinoidea, information rich). There are no significant relationships between taxonomic group sensitivity and either the ratio of unique sensitivity scores (p = 0.3879) or the number of species within a taxonomic group (p = 0.4552). Bivalvia and Ceramiales are the most and least negatively sensitive taxonomic groups representing 38 and 42 species, respectively (Figure 3). Bivalvia is highly negatively sensitive and has been relatively well studied with 13 unique values at lower taxonomic levels. Conversely, the order Ceramiales is not negatively sensitive, but the high agreement within this group is attributed more to a lack of evidence than agreement between individual studies as scores were primarily assigned based on evidence at the order and family levels.

Figure 3 

Mean taxonomic group sensitivity and confidence scores. The total number of species in each group and the number of unique responses are represented parenthetically next to the group name. A unique species response is determined by the source of the information contributing to group score, where unique responses are determined by different source sensitivity evidence and non-unique species responses rely on the same base sensitivity evidence. Error bars represent the standard deviation about the mean for the total number of species. Taxonomic groups with a sensitivity score based on a single unique score were not assigned a confidence score. DOI: https://doi.org/10.1525/elementa.312.f3

Community sensitivity

Sessile and mobile taxa were separated into 32 unique taxonomic groups ranging from species to phylum. The major sessile community taxa, as represented by percent cover, varied by site. The dominant sessile taxa at the POA and TAY sites are Mytilus californianus and Balanus glandula. The dominant taxa at the SFP and SOK sites are B. glandula and M. trossulus (Figure 4). The dominant mobile taxa across all sites are Littorina spp. and Lottia spp. In our analysis, mobile organisms are more negatively sensitive to OA than the sessile taxa. Six of the 22 sessile species are negatively sensitive to OA with sensitivity scores ranging from –0.25 to –0.42 and six of the ten mobile species are highly negatively sensitive with scores ranging from –0.50 to –0.75 (Table S4). The negatively sensitive sessile taxa include the dominant community species M. californianus (Sensitivity = –0.33), but not B. glandula, which is only somewhat negatively sensitive (Sensitivity = –0.16) (Figure 4). Both of the most common mobiles species, Littorina spp. and Lottia spp., are highly negtaively sensitive (Sensitivity = –0.64 and –0.62). Seven of the sessile species are not negatively sensitive and the most positively sensitive taxonomic group is the family of red algae, Rhodomelaceae (Sensitivity = 0.55). No mobile taxa are considered positively sensitive, but Nucella spp. are the least sensitive (Sensitivity = –0.07; Figure 4).

Figure 4 

Relative percent cover and abundance of sessile and mobile taxa collected from long-term community monitoring surveys. Four ONP intertidal sites are represented here from North to South including Point of Arches (POA), Sokol Point (SOK), Taylor Point (TAY), and Starfish Point (SFP) (See Figure 1 for locations). Pie chart wedges represent either percent cover (Sessile taxa) or abundance (Mobile taxa) for data collected from 2009 to 2016, except SOK which includes 2008 data as well. The dominant taxa are labeled and all other taxa are listed clockwise in the following order: 1) Lithothamnion spp., 2) Corallina spp., 3) Pollicipes polymerus, 4) Mytilus californianus, 5) non-specified Rhodophyta, 6) Semibalanus cariosus, 7) Hildenbrandia spp., 8) Chthamalus dalli, 9) Balanus glandula, 10) M. trossulus, 11) Cryptosiphonia woodii, 12) Cladophora spp., 13) Mazaella spp., 14) non-specified Gigartinales, 15) Phyllospadix spp., 16) Sachharina sessilis, 17) Ulva spp., 18) Acrosiphonia spp., 19) non-specified Phaeophyceae, 20) Anthopleura spp., 21) non-specified Ceramiales, 22) Rhodomelaceae, 23) Polyplacaphora, 24) Littorina spp., 25) Lottia spp., 26) Onchidella borealis, 27) Diodora aspera, 28) Isopoda, 29) Petrolisthes spp., 30) Amphipoda, 31) Polychaeta, 32) Nucella spp. Colors represent OA sensitivity: blue, positively sensitive or not sensitive (0.55 to 0.00); tan, somewhat negatively sensitive (–0.01 to –0.24); orange, moderately negatively sensitive (–0.25 to –0.49); and red, highly negatively sensitive (0.50–1.00). DOI: https://doi.org/10.1525/elementa.312.f4

Confidence scores

Every species and taxonomic group received a confidence score in addition to its respective sensitivity scores. Very few ONP taxa have a high degree of confidence at the species level due to a lack of experimental observation (Table S2). A few exceptions are the species Cancer magister, Strongylocentrotus purpuratus, S. droebachiensis, Mytilus edulis, Corallina officinalis, Petrolithes cintipes, and Crassostrea gigas. Taxonomic groups for which there is relatively high confidence include Bivalvia, Isopoda, Decapoda, Porifera, and Ceramiales (Figure 3). Conversely, the taxonomic groups for which there is little confidence include Tracheophyta, Holothuroidea, Ulvophyceae, and Ophiuroidea (Figure 3). The standard deviation of the confidence score represents the variation in contributing confidence scores among the species in that group. Taxonomic groups with low confidence score standard deviation are either comprised of species for which there is good agreement and evidence (e.g., Sessilia) or few unique species observations and therefore good agreement (e.g., Isopoda) (Figure 3).

Discussion

OA vulnerability at ONP

Our assessment indicates that the majority of ONP intertidal species (Figure 2), broader taxonomic groups (Figure 3) and rocky intertidal communities (Figure 4) are both sensitive and vulnerable to OA. Although the most sensitive species in our analysis is an echinoderm (Table S2), the majority of ONP-sensitive species belong to the calcareous phyla Mollusca and Arthropoda, a trend observed in other vulnerability assessments as well (Fabry et al., 2008; Kroeker et al., 2010; Hare et al., 2016). Calcareous coralline algae are also ranked among the most sensitive taxonomic groups in our analysis, which is consistent with earlier work (Hall-Spencer et al., 2008; Kroeker et al., 2010), but may have specific implications for intertidal zone ecology. Coralline algae are key ecological players in intertidal processes such as CO32– production, larval settlement, fish nurseries, and habitat formation (McCoy and Kamenos, 2015). Conversely, non-calcareous algae from the phyla Ochrophyta, Ceramiales, and Ulvophyceae appear to benefit from OA and are represented by 99 species at ONP (Table S3). Potential benefits of OA have been observed in experiments with autotrophic algae (Hendriks et al., 2010; Cornwall et al., 2012). However, better characterization of additive temperature effects and carbon sequestration pathway mechanisms across marine algae may ultimately moderate perceived benefits derived from OA (Koch et al., 2013).

Data limitations

Calcareous species are especially vulnerable to OA, but a few notable exceptions in our analysis include members of the class Asteroidea, sea stars. Five of the six responses reported for the class Asteroidea differ in magnitude and direction, and the deviation in these responses extends from possible positive to negative responses to OA (Figure 3). Unlike other taxonomic groups where there is high variation between species but a clear response direction (e.g., Decapoda), the effect of OA on the group Asteroidea is unclear. We suspect that, in this instance, the limited amount of evidence and slight positive effects found in a few experiments are compounding to make a summary response less clear for this taxonomic group. Specific examples include the family Solasteridae and species Pisaster ochraceus, which exhibit slightly positive OA effects from increased growth rates (Gooding et al., 2009; Dupont et al., 2010). The ONP star, Solaster stimpsoni from the family Solasteridae, derives its sensitivity score from its family member Crossaster papposus (Dupont et al., 2010) and, similarly, the sensitivity of P. ochraceus is derived from a single study (Gooding et al., 2009) that reports both negative and positive OA fitness responses. This limited experimental evidence and directionality of results illustrates potential pitfalls of our approach to characterizing OA vulnerability for a given species.

Ulvophyceae sensitivity, in contrast, is not clearly understood due to an abundance of information, rather than a lack of it. Both within and between species, research on different mechanisms shows both positive and negative responses. OA may be represented as a benefit or deficit depending on whether chlorophyll a content, photosynthesis, quantum yield, biomass, electron transport, dry weight, or growth is considered (Porizo et al., 2011; Hofmann et al., 2012; Liu et al., 2012; Olischläger and Wiencke, 2013). More species-specific information is needed to make a determination for location-specific trends, but resource managers are able to use sensitivity assessments and a systematic weighting of experimental response variables (Busch and McElhany, 2016) to better understand where those knowledge gaps exist until that information becomes available.

Extrapolations in evaluating species or taxonomic group sensitivities must be utilized with a degree of caution, as phylogenetic similarities are only one predictive variable contributing to organism survivorship. Anatomical structures (Kroeker et al., 2010), life histories (Kurihara, 2008), and habitats (Hall-Spencer et al., 2008) are other important factors that also contribute to organism OA vulnerability and could be considered as well. Our analyses are fundamentally limited by the availability of data to evaluate the sensitivity of both species and taxonomic groups. As such, the information available to describe a single species may stem from phylum to species level evidence and the confidence score is as important as the sensitivity score when interpreting our results. Sensitivity or vulnerability scores that have low confidence scores are of particular note and should receive the most scrutiny as more experimental evidence becomes available (Figure 2). Caution is therefore warranted in the application of these analyses to the broader intertidal environment, as our conclusions are driven by the published findings of primarily lab-based experimentation and not empirical observations of sensitivity from the field.

Rocky intertidal community sensitivity

Long-term data on the rocky intertidal community show that dominant community members are negatively sensitive to OA (Figure 4). M. californianus dominates the mid intertidal zone at the POA and TAY sites and has a high sensitivity to OA. OA conditions can reduce mussel larval size (Frieder et al., 2014; Kelly et al., 2015) as well as shell strength and thickness (Gaylord et al., 2011), which could have consequences for development time or predator avoidance (Gaylord et al., 2011). A reduction in the percent cover of M. californianus would have major implications for intertidal landscapes (Wootton et al., 2008), as mussels provide biogenic habitat for a diverse community of organisms (Smith et al., 2006). The less abundant, but closely related M. trossulus is also impacted by OA through weakened byssal thread attachment (O’Donnell et al., 2013) and a decrease in larval size (Sunday et al., 2011). The second largest contributor to sessile percent cover is the acorn barnacle, B. glandula, which dominates the SFP and SOK monitoring sites (Figure 4). Although less sensitive to OA than M. californianus, this species is also negatively impacted by OA, though there is no direct observational evidence below the family level. A potential decrease in major space-occupying mussels could lead to greater space availability favorable for barnacle settlement (Gaines and Roughgarden, 1985) and therefore possibly increase the prevalence of B. glandula over the short term (Wootton et al., 2008). Evidence from other Balanus species, however, suggests that OA decreases growth rates (Kroeker et al., 2013), leading to a long-term decline favoring other successional species. Likely OA-favored successional species include brown algae (Phaeophyceae) and red algae (Rhodomelaceae). These taxa are positively sensitive to OA and have the highest percent cover at ONP rocky intertidal monitoring sites (Figure 4). Experimental laboratory treatments (Johnson et al., 2014), modeling exercises (Wootton et al., 2008), and volcanic reef field surveys (Enochs et al., 2015) support this taxonomic transition from calcareous taxa to fleshy macroalgae when exposed to decreasing pH, but this trend has not yet been detected in the ONP community survey data.

In contrast, mobile organisms almost exclusively rely upon calcareous structures and demonstrate some degree of OA sensitivity (Figure 4). The most sensitive belong to the phylum Mollusca, a taxonomic group with known OA sensitivity (Gazeau et al., 2013; Parker et al., 2013). Chitons (Polyplacaphora), for example, have eight articulated dorsal plates comprised entirely of aragonite (Runnegar, 1989) and are highly negatively sensitive (S = –0.75). Experimental testing of one chiton species showed evidence of anatomical robustness to OA conditions, possibly due to metabolic trade-offs between physiological processes such as growth, reproduction, shell maintenance, or acid-base regulation (Sigwart and Carey, 2014). Littorina and Lottia species account for the largest mean number of organisms across all monitoring sites and both exhibit OA sensitivities, though with evidence from different taxonomic levels. A number of physiological and anatomical deficiencies such as lower heart rates, decreased respiration, and altered shell morphology were observed for two species of Littorina, L. obtusata (Ellis et al., 2009) and L. littorea (Melatunan et al., 2011; Melatunan et al., 2013) grown experimentally at low pH. In addition to direct negative effects, indirect effects such as reduced predator avoidance may increase the risk of this group under acidified conditions (Bibby et al., 2007). A lack of literature evidence for the genus Lottia precluded a high-resolution determination of sensitivity, but field observations for other limpet taxa show signs of shell corrosion (McClintock et al., 2009) as well as higher increased calcification rates near CO2 vents (Rodolfo-Metalpa et al., 2011).

Impacts to biodiversity

OA can reduce biodiversity through the loss of individual taxa, taxonomic groups (Hale et al., 2011; Kroeker et al., 2013), or the loss of biogenic habitat provided by major space-occupying taxa (Sunday et al., 2016). Several of the major space-occupying taxa characteristic of the rocky intertidal zone (mussels, barnacles, coralline algae) are negatively impacted by OA (Kroeker et al., 2013; Kroeker et al., 2014; Ordoñez et al., 2014), which could have significant implications for community succession (Wootton et al., 2008). Our analysis of the ONP intertidal taxa is consistent with the paradigm that taxa with calcium carbonate structures are more vulnerable to OA than their non-coralline, fleshy algal neighbors (Guinotte and Fabry, 2008; Kroeker et al., 2010). Bivalves and gastropods are particularly sensitive taxa (Figure 3) and dominate the ONP intertidal community (Figure 4). At the other extreme, fleshy algal species and sea grasses may benefit from OA conditions (Falkenberg et al., 2013; Hendriks et al., 2014) and could replace calcareous taxa leading to a simplification of the marine ecosystem (Kroeker et al., 2013). Our results support this hypothesis, as fleshy algae were the least OA-sensitive taxa (Table S3). The only non-algal taxa in our analysis that benefit from OA are non-tube-building polychaete worms and anemones. Both of these groups are non-calcareous and have a high resilience to OA (Calosi et al., 2013; Ventura et al., 2016), although this response varies by species and may direct further reductions in biodiversity among OA-tolerant taxa as pH declines (Gambi et al., 2016). Alternatively, indirect OA impacts on organismal life history (Widdicombe and Spicer, 2008), development time (Dupont et al., 2008; Walther et al., 2010), predator avoidance (Bibby et al., 2007), and food web structure (Fabry et al., 2008) may ultimately have a greater impact than anatomy or physiology in directing biodiversity loss in the intertidal zone. For example, taxa that are relatively insensitive to OA may still be impacted indirectly if their food supply or habitat is directly impacted by OA (Marshall et al., 2017). Conversely, negatively sensitive taxa may receive some degree of physiological relief from the deleterious effects of OA if food availability increases (Thomsen et al., 2013; Pansch et al., 2014).

Quantifying exposure

Exposure to OA is an ecologically important component in determining overall ecosystem risk, but is difficult to model in the intertidal zone. Several studies have approached the quantification of coastal exposure at varying degrees of resolution (Mathis et al., 2015; Okey et al., 2015; Hare et al., 2016; Marshall et al., 2017); however, no studies to date have focused exclusively on intertidal exposure parameters, though Wootton et al. (2008) examined some parameters related to pH exposure in the intertidal zone. Notable differences in literature estimations of exposure result from the definition of exposure (i.e., physical, commercial, or anthropocentric), and idiosyncratic differences between marine habitats (i.e., pelagic, benthic, intertidal). Exposure across organism life stages has not been explicitly included in past vulnerability assessments (Small-Lorenz et al., 2013); however, Hodgson et al. (2016) demonstrate stage-dependent vulnerabilities for several California Current taxa. Our analysis used the Busch and McElhany (2016) methodology to incorporate life-stage exposure by weighting rScore values by the number of exposed days for early life stages. This methodology could be improved by incorporating seasonality, upwelling intensity, or in-situ pH observations to better quantify species-specific exposure.

We chose intertidal elevation as an additional component of exposure in our analysis because elevation directly affects intertidal composition as a function of the period of time that organisms are immersed in seawater. We found that for the ONP intertidal zone, however, the difference in exposure time between mid and low intertidal time was relatively small (5% difference) and contributed to the lack of observed exposure effect between these zones (Figure S1). The nearshore environment along the West Coast of North America is predicted to experience a decrease of 0.20 pH units by the middle of the century under a high emissions scenario (Gruber et al., 2012). The Washington coast already experiences large swings in coastal pH due to upwelling with CO2 rich waters rising to the surface closer to land (Feely et al., 2008). The entire ONP intertidal assemblage is exposed to OA conditions; however, most intertidal taxa are meroplankton, with early life-history stages that live in the nearshore pelagic zone rather than in the intertidal zone. These taxa likely experience a higher degree of exposure, at potentially more sensitive life stages than their obligate intertidal counterparts, which could alter our interpretation of their OA vulnerability (Kurihara, 2008). Uncertainties in weighing the effect of exposure as we defined it led us to more heavily weight sensitivity in our calculation of species-specific vulnerabilities, and only use sensitivity to determine OA impact at taxonomic groups above the species level.

Management implications

A number of published vulnerability studies seek to explain the biological (Hare et al., 2016; Marshall et al., 2017) and social (Mathis et al., 2015; Colburn et al., 2016) impacts of OA on various coastal ecosystems. These assessments use a common language for establishing vulnerability based on sensitivity, adaptive capacity, and exposure (IPCC, 2012). The definition of these core elements, however, is not consistent across studies or ecosystems. In some instances, adaptive capacity and sensitivity are treated as the same value (Busch et al., 2016; Hare et al., 2016); in others, sensitivity is derived from habitat (Okey et al., 2015) or ecosystem (Teck et al., 2010) type. Exposure represents a similar issue and has been quantified through expert opinion (Hare et al., 2016), Regional Ocean Modeling Systems (ROMS) (Marshall et al., 2017), or some combination of both applied to a fixed area of ocean habitat (Halpern et al., 2008; Okey et al., 2015; Hodgson et al., 2016). Spatially explicit evaluations are necessary to best approach spatially explicit habitats, biological assemblages, and research questions. Methodology differences, however, make it difficult to compare results across species lists and habitats and present hurdles for resource managers with questions on how to approach site-specific biodiversity loss and exposure.

Our assessment had three primary goals: to identify which species in ONP are most vulnerable to OA as a function of their biological sensitivities and exposure, to identify which broader taxonomic groups are most sensitive to OA, and to apply these results to ONP long-term rocky intertidal monitoring to identify how site-specific communities are likely to be impacted by OA. We used a regionally relevant method based on an open access database of current literature-based sensitivity values to determine how OA might affect the invertebrates and algae in the ONP intertidal zone. Our approach identified vulnerable species and sensitive taxa as well as limitations and absences in species-level information. There are a small number of dominant, space-occupying taxa in the ONP intertidal for which we have literature evidence for species-level OA vulnerability. The bulk of the ONP intertidal biodiversity consists of species across multiple taxonomic groups for which we have little OA sensitivity evidence at the species level.

In the absence of this sort of assessment, OA-related declines may go undetected by resource managers in the intertidal zone or its detection may come more slowly than if OA monitoring was included in the design of long-term monitoring studies (Klinger et al., 2017). OA is one of many environmental challenges that resource managers will face with the onset of climate change, and it is critical that these and other stressors be considered in future vulnerability assessments. OA research is a relatively young field, but OA is a stressor that marine invertebrates and algae are currently experiencing (Feely et al., 2008; Bednaršek et al., 2014), warranting a swift response from resource managers. Here we have demonstrated how previously developed tools can be adapted for distinct coastal management zones to better understand site-specific OA impacts. We found that the majority of ONP intertidal taxa are at risk to OA and that vulnerability differs in magnitude across taxonomic groups; we identified OA-sensitive taxa that should be included in long-term monitoring efforts to detect the potential ecosystem impacts of OA. Furthermore, our findings suggest that intertidal OA exposure needs better characterization to determine the extent and magnitude of high and low risk environments. Future efforts should be directed at expanding our experimental knowledge of OA sensitivity for ecologically important intertidal species, the effect of OA sensitivity on community structure, and how different intertidal habitats positively or negatively contribute to species vulnerability.

The potential for organism resiliency to OA is also an area worthy of additional investigation. The Olympic coast is part of the California Current Large Marine Ecosystem, which contains a mosaic of pH exposure regimes due to differences in upwelling intensity and other anthropogenic and oceanographic factors (Chan et al., 2017). Within this ecosystem, areas that historically have experienced periodic low pH conditions, such as the Olympic coast (Chan et al., 2017; S.F., unpublished data) may contain species genotypes that are more resilient to future pH declines. Additionally, concurrent changes in environmental variables, such as temperature and resource availability, have the potential to mitigate low pH exposure impacts on intertidal organisms (Kroeker et al., 2016).

ONP resource managers conduct long-term monitoring to detect change in intertidal communities over time. Our tool is informative not only for interpreting historical biological data, but also as a guide for monitoring the effects of climate change into the future. Our work represents a direct application of the database provided by Busch and McElhany (2016), which takes into account over 3,000 organismal responses to OA treatments across a large number of taxonomic groups. Our method also represents a novel approach in using combined experimental evidence and long-term monitoring data to provide information on OA sensitivities in understudied ecosystems or regions at risk to OA. Through our assessment we were able to calculate specific vulnerability scores at various taxonomic levels for ONP taxa; however, we recognize that as OA science evolves, new insights into species-level OA sensitivities may require revisions to our vulnerability scores. We anticipate future revisions to our assessment, but view this effort as an important first step in an iterative process.

Data Accessibility Statement

The NOAA database data are provided at https://doi.org/10.1371/journal.pone.0160669.s001. All original ONP database data, including rScores, can be found at https://doi.org/10.6084/m9.figshare.5726047.v1. All other data are available from the authors upon request.

Supplemental files

The supplemental files for this article can be found as follows: