Effects of climate change on purple urchin feeding behavior in the presence and absence of California spiny lobsters.

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Mitchell Johnson, Matthew Edwards This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6149444/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Grazing by sea urchins can dramatically alter the structure of kelp forest communities, but this can be moderated through both direct and indirect effects from their predators. For example, in southern California, USA, the presence of spiny lobsters, Panulirus interruptus , can dramatically increase the time it takes for purple urchins, Strongylocentrotus purpuratus , to emerge from their shelters to feed, reduce the total time that the urchins spend foraging, and consequently decrease the amount of kelp they consume. The mechanisms driving this, however, may change as the oceans become warmer and more acidic. To examine this, we quantified three measures of purple urchin grazing behavior (latency to emerge from shelters, time spent feeding, and kelp mass consumed) in the presence and absence of spiny lobsters under present day (Current), ocean warming (OW), ocean acidification (OA), and OW + OA (Future) conditions. Specifically, we placed purple urchins in laboratory mesocosms reflecting these conditions with shelters and known quantities of kelp, and then allowed them to graze in both the presence and absence of lobsters for three days. Urchin feeding activity was quantified using time-lapse photography and by recording the amount of kelp eaten over each three-day period. Our results revealed that urchins took longer to emerge from their shelters, grazed for less time, and consumed less kelp when in the presence of spiny lobsters under Current conditions, but these differences largely disappeared under OW, OA and Future conditions. These results reveal possible implications for how urchins will graze when in the presence of predators and thus affect kelp forest communities in the future. Climate change grazing kelp forest purple urchins spiny lobsters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The anthropogenic release of CO 2 is altering the earth’s ecosystems and raising atmospheric temperatures. As the oceans are responsible for absorbing 80% of this excess heat (Levetus 2005; Johnson 2020), this has resulted in sea surface temperatures increasing by approximately 1°C over the past century, a pattern that is expected to continue if CO 2 emissions are not controlled (Levitus 2009). Indeed, predictions for ocean temperature suggest a 3 to 4° o C increase by the end of the century under high CO 2 emission scenarios (IPCC 2014). In addition to ocean warming (OW), the oceans have absorbed about one-third of the excess CO 2 released into the atmosphere (Sabine et al. 2004), resulting in ocean acidification (OA) (Doney et al. 2009 ). The current average pH of the oceans, which is approximately 8.1, is expected to decrease by another 0.287–0.290 pH units by the end of the century under high CO 2 emission scenarios (Bindoff 2019). These stressors (OA and OW), either in combination or alone, have the potential to affect the physiology, growth and behavior of marine organisms across a wide range of taxa (e.g., Nowicki et al. 2012 ; Kroeker et. al. 2013 ; Brown et al. 2014 ; Kim et al. 2016 , 2020 ; Shukla and Edwards 2017; Edwards 2022 ), and in turn alter the structure and functioning of marine communities. Both OA and OW have been shown to have detrimental effects on marine invertebrates by altering a wide range of physiological functions, many of which are responsible dictating their behavior (Widdicombe 2008 ; Wicks et al 2012; Brown et al. 2014 ). For example, OA can inhibit neural signaling processes (Watson et al. 2014 ; Melzner 2020) and reduce the ability to detect chemical stimuli ( (Leduc et al. 2013 ), and thereby affect behaviors related to reproduction (Seuront 2010 ; Borges 2018), foraging (De la Haye 2012), and predator avoidance (Watson et al. 2014 ; Jellison et al. 2016 ). Likewise, OW can induce higher metabolic rates, which can also lead to altered foraging activity and risk-taking behaviors (Biro et al. 2007 , 2013 ; Pörtner and Peck 2010 ). Further, OA and OW can both alter the structure of the chemical cues themselves, which can lead to detrimental behaviors related to predator avoidance and foraging activities (Arnold et al. 2012 ; Leduc et al. 2013 ). However, despite these important effects, the interaction between OA and OW remains understudied and previous research has produced conflicting results (e.g., Sundin et al. 2017 ; Welch et al. 2016), especially regarding predator avoidance behavior (e.g., Zittier et al. 2013; Schram et al. 2014). Indeed, a review by Kroeker et al. ( 2013 ) suggests there is no clear consistency among organisms in how they will respond to OA and OW, either when they occur alone or together. However, understanding how these ocean change stressors affect predator-prey relationships can be important to predicting how ocean ecosystems may respond in the future, especially in coastal kelp forests where these relationships can alter grazing, which is known to be integral in shaping community structure and function (Estes et al. 1998 ; Vicknair 1997 ; Gabara et al. 2021 ; Jenkinson et al. 2021). Kelp forests along the Pacific coast of the United States are some of the most diverse and productive ecosystems on the planet (Schiel and Foster 2015 ). These underwater forests are dominated by high densities of canopy-forming kelps that provide shelter and food to numerous marine invertebrates, fishes, and mammals (Mann 1973; Graham 2004 ; Schiel and Foster 2015 ), and support a broad range of ecosystem services. For example, they reduce current and wave energy and thereby can buffer coastlines from hydrodynamic activity (Jackson and Winant 1983 ; Hondolero and Edwards 2017 ; Elsmore et al. 2024 ). They alter seawater chemistry (Evans and Edwards 2011 ; Gonzales et al. 2017; Pfister et al. 2019 ; Corrano et al. 2020), support enhanced biodiversity (Torres-Moye et al. 2013 ; Konar et al. 2015 ; Metzger et al., 2019 ; Gabara et al. 2021 ), and they are harvested for a variety of food, medical, and industrial products (Borras-Chaves et al. 2012, 2016). They also support high rates of primary production (Reed and Brezinski 2009; Edwards et al. 2020 ; Spector and Edwards 2020 ) and sequester and store carbon in their standing biomass (Wilmers et al. 2012 ) thereby playing important roles in global carbon cycles (Reed and Brezinski 2009). Consequently, the loss of these forests can lead to the loss of these important ecosystem services. In southern California, USA, kelp forests are dominated by the largest of the kelp species, the giant kelp, Macrosystis pyrifera , whose populations are strongly affected by both ocean conditions (i.e., bottom-up control: Edwards and Estes 2006 ; Foster et al. 2006 ) and trophic dynamics (i.e., top-down control: Halpern et al. 2006 : Jenkinson et al. 2020 ). As with kelps in other regions worldwide (Konar et al 2015 ; Krumhansl et. al. 2016 ; Edwards and Konar 2020 ; Smale 2019 ; Wernberg et al. 2018), giant kelp forests are experiencing long-term declines and/or periodic losses over large geographic areas due to both natural and anthropogenic forces (Edwards and Hernández-Carmona 2005 ; Byrnes et al. 2011; Edwards 2019 ). This has resulted in altered spatial structuring (Edwards 2004 ; Edwards and Konar 2020 , Jenkinson et al., 2020 ), simplified trophic dynamics (Graham 2004 ; Gabara et al 2021 ), and reduced primary production (Edwards et al. 2020 ; Spector and Edwards 2020 ; Sullaway and Edwards 2020 ; Gabara et al. 2021 ) in the coastal zone. Declines in kelp forest condition are particularly relevant to Point Loma, San Diego, CA, USA, which is home to one of the largest contiguous giant kelp forests in California that supports intense sport and commercial fishing for sea urchins, spiny lobsters, and finfish. Many kelp forest communities are strongly influenced by a single type of herbivore, the stronglyocentroid sea urchin (Ebeling et al. 1985 ; Harrold and Reed 1985 ; Estes et al 1998 ; Steneck et al. 2013 ). In southern California, one of the most abundant urchin species is the purple urchin, Strongylocentrotus purpuratus . These subtidal echinoderms are voracious grazers of giant kelp, especially drift kelp that has become dislodged from the substrate or fragmented from nearby sporophytes (Harrold and Reed 1985 ). However, in the absence of drift kelp, urchins will often actively feed on the kelp’s holdfast, ultimately dislodging its thallus from the rocky substrate. As with urchins and kelps in many other regions (e.g., Estes et al. 1998 , Steneck et al. 2013 ; Konar et al. 2014; Jeon et al. 2015 ), when large populations of urchins begin to forage this way, they can decimate entire kelp forests and create urchin barren grounds that can persist as a stable state for years to decades (Mann 1977 ; Ebeling et al. 1985 ; Estes et al. 1998 ). The urchins in these barren grounds can then survive in a state of reduced metabolic activity until drift kelp becomes available again (Dolinar and Edwards 2021 ). While urchin barrens may still contain abundant marine life, the biodiversity and food web connectivity are dramatically lower than that found in kelp forest habitats (Graham 2004 ; Metzger et al. 2019 ; Gabara et al. 2021 ). Under the right conditions, rocky reef predators such as the California spiny lobster, Panulirus interruptus , have the potential to reduce urchin abundance (Lafferty 2004; Pearse 2006 ; Nicols 2009; Dunn 2019 ; Jenkinson et. al. 2020 ) and alter their feeding behaviors. Indeed, they are one of the primary predators of purple urchins and their consumption of urchins serves a functional role in maintaining healthy benthic communities and kelp forest ecosystems (Cascorbi 2004 ; Jenkinson et. al. 2020 ). For example, Matassa ( 2010 ) found that urchins reduced grazing by almost 44% when in the presence of spiny lobsters, while Knight ( 2020 ) found that urchins collected from kelp forests reduced kelp consumption by about 73% in the presence of spiny lobster chemical cues. These effects, however, may vary if ocean change reduces the ability of the urchins to recognize the spiny lobster predators or increases risk taking behavior, and thereby alters these predator-prey interactions. Here, we examine how OA and OW affect the ability of purple urchins to detect and/or react to California spiny lobsters, and how this in turn affects their grazing activity on giant kelp. Specifically, we examined how purple urchin behavior is altered by spiny lobster predatory cues when exposed to the individual and combined effects of OA and OW, with the overarching goal of understanding how this may alter ecosystem-level impacts within kelp forest ecosystems. Materials and methods Mesocosm set up To examine the effects of OA, OW, and Future (OA + OW) ocean conditions on how spiny lobsters affect purple urchin feeding behavior, four 350-liter shallow aquaculture tanks (hereafter, “mesocosms”) were used to establish four orthogonal treatment combinations that represented present day ocean conditions and the individual and combined effects of OA, OW, and Future ocean conditions that are expected by the year 2100. Specifically, the four mesocosms had either 1) seawater that represented present-day (i.e. “Current”) conditions of 15°C and pH 8.1; 2) acidified (i.e. “OA”) conditions of 15°C and 7.8 pH; 3) warmed (i.e. “OW”) conditions of 18°C and pH 8.1; and 4) OA + OW (i.e. “Future”) conditions of 18°C and 7.8 pH. The parameters established for the Current conditions reflected the current average temperature and acidity for the Point Loma kelp forest, and parameters for the OA, OW and Future conditions reflected IPCC projections for how these will change by the end of the century under current CO 2 emissions (IPCC 2014). All spiny lobsters and purple urchins were collected by hand on SCUBA from the Point Loma Kelp Forest (32.42.564⁰N, 117.16.633⁰W) in San Diego, California between January and August 2023. Purple urchins were all between 4–6 cm test diameter to ensure consistency with grazing rates among different runs of the experiments, while spiny lobsters were all at least 64 mm total carapace length to ensure they had reached sexual maturity. Giant kelp, Macrocystis pyrifera , blades were collected at the surface by hand from university research vessels. All organisms were transported to the San Diego State University Coastal and Marine Institute Laboratory (CMIL) immediately after collection and held in flow-through aquaria under ambient conditions until being used in laboratory experiments. To establish the four ocean change conditions (Current, OA, OW, Future), a mesocosm array was built to house four separate recirculating systems, with the procedure of pumping the water into the mesocosms done in a manner to reduce disturbance to the study organisms and eliminate bubbles that may affect seawater chemistry. Due to logistical constraints, only one mesocosm could be established per ocean change treatment. Therefore, replication was established by running the experiment 14 times (hereafter “trials”) with the mesocosms emptied and cleaned after each trial. Thus, the variable Trial was considered as a temporal blocking factor for statistical analyses. In total, the 14 trials were run over the course of six months, from January to June 2023, however the third trial’s behavior data became unusable due to video system malfunctions that occurred during a strong winter storm, resulting in 13 usable trials (i.e. n = 13). pH and temperature data, however, were still retained from this trial to demonstrate the mesocosms system’s ability to maintain the targeted ocean change conditions. To reduce the chances of mesocosm identity or position bias, the mesocosms were emptied, cleaned, and randomly re-assigned to each of the four ocean change treatments prior to the start of each trial. Seawater pH within the mesocosms was established using similar methods to those described for previous ocean acidification experiments conducted by our group (Brown et al. 2014 ; Kim et al. 2016 , 2020 ; Shukla and Edwards 2017). Here, to achieve the acidified conditions within both the OA and Future treatments, pure CO 2 gas (Airgas, San Diego) was slowly bubbled directly into sump tanks and the resulting acidified water was pumped into the experimental mesocosms where it then flowed via gravity back into the sump tanks. The injection of CO 2 gas was balanced by bubbling ambient air into the seawater, with the flow rates of the two gasses closely controlled through both a primary regulator and a secondary gas manifold to maintain the desired seawater pH. The ambient air was also bubbled into the seawater within the non-acidified (Current and OW) treatments. Seawater pH in each of the mesocosms was then measured twice daily using a Thermo Scientific Orion Portable pH meter (Star A121) with ROSS Ultra pH/ATC Triode (Orion 8157BNUMD) calibrated with Orion ROSS pH buffers. While pH values in the four mesocosms did experience some temporal variation over each three-day trial, as they would experience naturally with coastal kelp forests (Hoffman et al. 2011), this method created acidified (OA and Future) seawater that was consistently maintained at 0.3 pH units below the non-acidified (Current and OW) seawater (Fig. 1A). To achieve the desired temperatures for the ocean change treatments, each mesocosm was equipped with an individual flow-through chiller system (1/3 hp flow-through chillers, Aqualogic, San Diego) that maintained our target temperatures. Here, the sump water was pumped through the chillers and then back into the sump before being pumped into the experimental mesocosms as described above. Each chiller was equipped with a thermometer and a controller that allowed for precise and constant temperatures in each mesocosm, which was measured twice daily in conjunction with pH measurements. Specifically, temperatures in the non-warmed (Current and OA) mesocosms were maintained at 15°C, and temperatures in the warmed (OW and Future) mesocosms were maintained at 18°C. As with pH, seawater temperatures experienced some temporal variation in the mesocosms over each three-day trial, and they increased slightly with seasonal changes as the trials progressed from January to June as would be expected in the Point Loma kelp forest, but the OW and Future treatments were consistently maintained at 3°C above the Current and OA treatments (Fig. 1B). Evaluating purple urchin feeding behaviors To evaluate the effects of the different ocean change treatments on purple urchin grazing and foraging behavior in the presence and absence of spiny lobsters, all purple urchins were acclimated to their respective treatment conditions for three days before each experimental trial began. Additionally, the purple urchins were starved during this time to standardize them and facilitate grazing behavior once the experimental trial began. Two scenarios were then tested under each ocean condition: spiny lobster (i.e., predator) presence and spiny lobster absence. Here, each of the four mesocosms was divided in half using a permeable barricade made from 1 cm Vexar mesh that was anchored to the bottom with a length of chain. This created a seamless barricade that prevented lobster and urchin movement between mesocosm sides but allowed for free flow of chemical cues from one half to the other. Thus, the mesh allowed for urchins to be fully aware of the presence of the predator but did not allow for predation to occur. An artificial shelter consisting of a cinder block was placed inside the mesocosm one side of the barricade, which provided a place for urchins to seek refuge from the lobsters. Three urchins were placed in the mesocosm on the side of the barricade with the shelter and allowed to acclimate to the conditions for three days. After the acclimation period, a lobster was placed on the opposite side of the barricade for predator-present trials, while this area was left empty for the predator-absent trials. At the start of each trial, a pre-weighed bundle of giant kelp (approximately 20 g wet weight) that served as urchin food was patted dry, weighed, and placed on the side of the mesocosms with the urchins at a set distance of 45cm from the urchin shelters by zip-tying the bundles to a length of chain, which anchored them to the bottom. To record urchin feeding behavior under each ocean change treatment, a GoPro camera was mounted to a wooden frame 60 cm above each mesocosm and pointed downward so that its field of view captured the entire half of the tank where the urchins were placed. Feeding behaviors were quantified from visual analyses of 72 h time-lapse images, with images taken once every 60 seconds during each three-day experimental trial. Foraging behavior was assessed by quantifying the movement of each urchin and then averaged to provide an estimate for each trial. While viewing the GoPro footage to quantify the movement of the urchins, the observer was blind to which treatment was being evaluated to remove any viewer bias. Foraging behavior was divided into one of two distinct behaviors; latency to emerge from shelter and time spent feeding. Latency to emerge from the shelter was quantified as the time it took for an individual urchin to completely exit its cinder block shelter, while time spent feeding was quantified as the amount of time an individual urchin spent in contact with the kelp placed in its mesocosm. For each of these variables, the total number of time-lapse frames was recorded and then scaled to per-hour estimates. Additionally, the total amount of kelp consumed was used as a metric for measuring grazing activity. For this, the kelp bundles were collected from the mesocosms upon completion of each trial, blotted dry, and re-weighed. The change in mass over the three-day period was recorded as the total mass consumed by urchins during the experiment. Statistical Analyses Univariate and multivariate statistical analyses were conducted using R (R Core Team 2021), Primer/PERMANOVA (ver. 7), and SYSTAT (ver. 12). Evaluation of urchin behaviors were analyzed in a two-step process. First, behavior data for all three urchins in each mesocosm were averaged and then square root transformed. Data for kelp consumed were also square root transformed. Then, the three urchin feeding behaviors (latency to emerge, time spent feeding, kelp mass consumed) were combined and evaluated together to determine if they collectively varied among the four ocean change (Current, OA, OW, Future) and two predator (Present, Absent) treatments using a three-factor Model I nested permutational analysis of variance (PERMANOVA) on a Euclidean distance-based resemblance matrix as described by Clark et al. (1993). Here, trial was considered a fixed factor that was nested within predator treatment (i.e., the predator present and the predator absent treatments were examined within different trials and were thus non-orthogonal). Trial was also considered as a blocking factor for the ocean change variable, as there was only one replicate of each treatment during each trial. The resulting data were then plotted using non-metric Multidimensional Scaling (nMDS) to visually examine similarities in collective behaviors among the different treatments. Following this, individual one factor PERMANOVAs and nMDS plots were used as a priori-defined post-hoc tests to evaluate whether the urchin feeding behaviors collectively differed between predator present and predator absent treatments within each ocean change treatment separately. Second, differences in each feeding behavior alone were compared separately among ocean change and predator treatments, and among trials using similarly constructed three-factor nested ANOVAs (for each feeding behavior), with predator treatments nested within trials, and trial considered as a blocking factor for the ocean change treatments. Prior to these analyses, data were checked for normality using Shapiro-Wilk tests and for homogeneity of variance using Levene’s test. Here, data for latency to emerge were non-normal for the OW treatment (Shapiro-Wilk = 0.557, p < 0.001) and therefore log-transformed, which corrected the problem (Shapiro-Wilk = 0.864, p = 0.056). Data for time spent feeding were non-normal for both Predator treatments (Predator Absent: Shapiro-Wilk = 0.908, p = 0.032; Predator Present: Shapiro-Wilk = 0.503, p < 0.001) and therefore also log transformed, which corrected the problems (Predator Absent: Shapiro-Wilk = 0941, p = 0.172; Predator present: Shapiro-Wilk = 0.905, p = 0.206). Lastly, data for kelp consumed were non-normal for the OW treatment (Shapiro-Wilk = 0.830, p = 0.012) and were therefore square root transformed, which corrected the problem (Shapiro-Wilk = 0.940, p = 0.426). All other data met the assumptions of ANOVA following their respective transformations, except for kelp consumed data within the Predator Absent treatment, which remained non-normal (Shapiro-Wilk = 0.909, p = 0.029) and could not be corrected by transformation. However, graphical interpretation of these data using a Q-Q probability plot revealed that this departure from normality was minor and we consider the ANOVA robust for comparison of the means among treatments (e.g. Sawyer 2009 ). These ANOVAs were each followed by independent t-tests as post hoc tests to evaluate a priori defined hypotheses regarding differences in each behavior between the predator treatments within each ocean change treatment separately. All data for each feeding behavior were then graphed using Box Plots to visualize differences among ocean change and predator treatments. Results Behavior Visual assessments of the collective urchin behaviors did not initially reveal clear differences among the ocean change treatments (PERMANOVA: pseudo-F 3,33 = 1.189, P(perm) = 0.315). In contrast, significant differences in urchin behaviors were observed between the two predator treatments (pseudo-F 1,33 = 1.189, P(perm) = 0.010), which appeared consistent across the four ocean change treatments (Ocean Change x Predator interaction: pseudo-F 3,45 = 1.285, P(perm) = 0.277, Table 1A, Fig. 2), and no significant differences were observed among the 13 trials (pseudo-F 12,33 = 1.228, P(perm) = 0.243). Interestingly, when differences in urchin behaviors were compared between the predator treatments within each ocean change condition separately, significant differences in behavior were observed within the Current ocean conditions (PERMANOVA: pseudo-F 1,33 = 1.285, P(perm) = 0.014), but no differences were observed between the predator treatments under any of the other three ocean change conditions (Table 2, Fig. 3). Further, when each of the urchin grazing behaviors were examined individually, different patterns emerged. Specifically, latency to emerge did not differ among the ocean change treatments (ANOVA: F 1,32 = 1.669, p = 0.193) but it did differ between the two predator treatments (F 1,32 = 5.716, p = 0.023, Table 3, Fig. 4), a pattern that was consistent across all four ocean change treatments (Ocean Change x Predator interaction: F 3,32 = 0.416, p = 0.743). However, although no differences were observed between the predator present and predator absent scenarios under OA, OW, or Future conditions, urchins emerged from their shelters 37.5 ± 12.4% faster (mean proportion of change in time ± se) when predators were absent than when they were present under current conditions (Table 4, Fig. 4), which represented a marginally significant difference (t 11 = 1.769, p = 0.105). Time spent feeding did not differ among either the ocean change (ANOVA: F 3,33 = 0.850, p = 0.477) or predator (F 1,33 = 1.046, p = 0.314) treatments or among trials (F 11,33 = 0.237, p = 0.993), but the ocean change and predator treatments did appear to interact with each other (Ocean Change x Predator interaction: F 3,44 = 2.475, p = 0.079) (Table 5). Specifically, a priori defined post hoc tests revealed that time spent feeding was reduced by 75.4 ± 22.0% when predators were present under current conditions (post hoc t-test: t 11 = 3.093, p = 0.010), but it did not differ under any of the other three ocean change scenarios (Table 6, Fig. 5). This finding is consistent with the results of previous studies showing urchins alter their behavior when in the presence of predators, but also suggests that this difference disappeared under ocean change conditions. Not surprisingly, the amount of kelp consumed by urchins followed a similar pattern to the time they spent feeding. Specifically, although the amount of kelp consumed did not differ among either the ocean change (ANOVA: F 3,33 = 1.551, p = 0.222) or predator (F 1,33 = 1.303, p = 0.290) treatments, these factors again interacted with each other (Ocean Change x Predator interaction: F 3,33 = 2.729. p = 0.060) and varied among trials (F 12,44 = 2.018, p = 0.055) (Table 7). Further, a priori defined post hoc analyses revealed that the urchins consumed 55 ± 6.18% less kelp when predators were present under current conditions (post hoc t-tests: t 11 = 0.269, p = 0.021), but no differences in kelp consumption were observed under any of the other three ocean change scenarios (Table 8, Fig. 6). Together, our results suggest that under current ocean conditions, urchins increased the time it took to emerge from their shelters, reduced the time they spend feeding, and consumed less kelp when predators were present, but these differences were not observed under any of the ocean change conditions, which may have significant implications for urchin foraging behavior under changing ocean conditions. Table 1 Results of a three-factor nested permutational analysis of variance (PERMANOVA) testing for differences in urchin grazing behaviors (latency to emerge, time spent feeding, kelp mass consumed) among Ocean Change (Current, OA, OW, Future) and Predator (presence, absence) treatments. Similarities are based on a Euclidean distance resemblance matrix using square root transformed data. Source SS df MS Pseudo-F P(perm) Predator 16.032 1 16.032 4.642 0.010 Ocean Change 12.320 3 4.107 1.189 0.315 Predator*Ocean Change 13.312 3 4.437 1.285 0.277 Trial(Predator) 50.884 12 4.240 1.228 0.243 Residuals 113.980 33 3.454 Table 2 Results of separate one-factor permutational analyses of variance (PERMANOVAs) testing for differences in urchin feeding behaviors between predator presence and predator absence treatments within each Ocean Change treatment. A) Current, B) Warming (OW), C) Acidification (OA), and D) Future (OW + OA) conditions. Similarities are based on a Euclidean distance resemblance matrix using square root transformed data. A) Current Source df SS MS Pseudo-F P(perm) Predator 1 12.813 12.813 5.939 0.014 Residuals 11 23.732 2.157 B) Warming Source df SS MS Pseudo-F P(perm) Predator 1 0.449 0.449 0.090 0.898 Residuals 12 59.894 4.991 C) Acidification Source df SS MS Pseudo-F P(perm) Predator 1 1.008 1.008 0.604 0.652 Residuals 10 16.686 1.669 D) Future Source df SS MS Pseudo-F P(perm) Predator 1 5.274 5.274 1.436 0.256 Residuals 11 40.387 3.672 Table 3 Results of three-factor nested analysis of variance (ANOVA) testing for differences in latency to emerge by purple urchins among ocean change and predator treatments, and among trials. Data for the three urchins in each treatment tank were averaged prior to analysis and Log transformed to correct problems with Normality. Due to logistic constraints in the experimental design, Trial was considered fixed and nested within the Predator treatment and as a blocking factor for ocean change treatments. Source Type III SS df Mean Squares F-ratio p-value Ocean Change 1.691 3 0.564 1.480 0.234 Predator 1.875 1 1.875 4.922 0.032 Ocean Change*Predator 0.273 3 0.091 0.239 0.869 Error 16.379 43 0.381 Table 4 Results of a prior defined t-tests examining differences in purple urchin latency to emerge between predator absent and predator present scenarios within each ocean change treatment. Data were log transformed prior to analysis to correct problems with Normality, and reported p-values are uncorrected. Ocean Change treatment t statistic df p-value Current 1.769 11 0.105 Warming 0.558 10 0.588 Acidification 1.174 11 0.265 Future 1.908 11 0.296 Table 5 Results of three-factor nested analysis of variance (ANOVA) testing for differences in time spent feeding by purple urchins among ocean change and predator treatments, and among trials. Data for the three urchins in each treatment tank were averaged prior to analysis and Log transformed to correct problems with Normality. Due to logistic constraints in the experimental design, Trial was considered fixed and nested within the Predator treatment and as a blocking factor for Ocean change treatments. Source Type III SS df Mean Squares F-ratio p-value Ocean Change 2.298 3 0.766 1.051 0.380 Predator 0.942 1 0.942 1.293 0.262 Ocean Change * Predator 6.690 3 2.230 3.058 0.038 Error 32.083 44 0.729 Table 6 Results of a prior defined t-tests examining differences in purple urchin feeding rates between predator absent and predator present scenarios within each ocean change treatment. Data were log transformed prior to analysis to correct problems with Normality, and reported p-values are uncorrected. Ocean Change treatment t-statistic df p-value Current 3.093 11 0.010 Warming -1.332 11 0.209 Acidification -0.232 11 0.821 Future 1.383 11 0.194 Table 7 Two-factor Model I analysis of variance (ANOVA) testing for differences in kelp consumed by urchins across Ocean Change and Predator treatments. Data were square root transformed to correct problems with Normality. Due to logistic constraints in the experimental design, Trial was considered fixed and nested within the Predator treatment and as a blocking factor for ocean change treatments. Source Type III SS df Mean Squares F-ratio p-value Ocean Change 6.600 3 2.200 0.847 0.475 Predator 3.093 1 3.093 1.191 0.281 Ocean Change*Predator 13.019 3 4.340 1.671 0.187 Error 116.886 45 2.597 Table 8 Results of a prior defined t-tests examining differences in kelp mass consumed by purple urchins between predator absent and predator present scenarios within each ocean change treatment. Data were square root transformed prior to analysis to correct problems with Normality, and reported p-values are uncorrected. Ocean Change treatment t-statistic df p-value Current 0.269 11 0.021 Warming -0.438 11 0.669 Acidification -0.808 11 0.436 Future 0.275 11 0.788 Discussion Our primary finding was that purple urchins alter their feeding behavior in the presence of spiny lobsters under current ocean conditions, but they do not alter them under ocean change conditions. Specifically, purple urchins took 37% more time to emerge from their shelters, spent 75% less time feeding, and consumed 56% less kelp and when lobsters were present in their environment under current ocean conditions, but these feeding behaviors were not affected by the presence of lobsters under OA, OW or Future (OW + OA) ocean conditions. This suggests that urchins can detect and respond to the presence of lobster predators under current ocean conditions, but they may lose this ability or exhibit more risky behavior in the future as the ocean becomes warmer and more acidic. While these results are consistent with recent studies on other invertebrate species (e.g. Biro et al. 2007 , 2013 ; Pörtner and Peck 2010 ; Watson et al. 2014 ; Jellison et al. 2016 ), they present an exciting advance in our understanding of how purple urchins may be affected by climate change. This lack of a response to the presence of lobster predators under ocean change conditions suggests that when subjected to climate change stressors, purple urchins either lost the ability to detect the presence of predator cues (e.g., Leduc et al. 2103) or they increased their risk-taking behavior to account for higher energy demands (e.g., Biro et al. 2007 , 2013 ; Pörtner and Peck 2010 ), both of which can lead to altered behavior related to predator avoidance. Similarly, other studies have found that urchins exhibit diminished righting and covering behaviors when subjected to OW conditions that are consistent with IPCC-predicted changes in ocean temperatures (Brothers 2016), which also mimics the compromised predator-avoidance behaviors we observed. While few studies have examined how the combination of these two ocean change stressors affects urchin behavior, some evidence has shown climate change scenarios induce weakened physiological mechanisms resulting in compromised grazing behaviors (Brothers 2016), which is again consistent with our results. Further, information on the individual and combined effects of OA and OW on other marine invertebrates have been mixed (reviewed in Kroeker et al. 2013 ). For example, Horwitz (2020) found that the sea hare, Stylocheilus striatus , exhibits a 1.5 to 2-fold reduction in foraging, locomotion speed, and time needed to locate food under individual OA and OW stressors, which mirrors our results for purple urchins. However, Horwitz (2020) also found that the sea hares exhibited up to a 3-fold reduction in these behaviors when they were subjected to the combination of both OA and OW climate change stressors (i.e. Future conditions). Likewise, Baure (2023) found that the sea cucumber, Stichopus cf. horrens , and marine gastropod, Trochus maculatus , both initially increased feeding activities under OW and Future conditions, suggesting a metabolic response to warming, but these changes were diminished after five days of acclimation to these conditions. Results such as these indicate that some marine organisms may be able to adjust to rapid physical changes in their environment, though this may not be likely not be true for purple urchins, at least over a six-day (3-day acclimation plus 3-day trial duration) period. Indeed, while studies examining individual effects of climate change stressors (OA or OW) on marine invertebrates seem to show more consistent responses, these responses appear are more varied when these stressors are combined (OA + OW) as they would be under future ocean change. Overall, our study paints a potentially interesting picture for the future of purple urchins and the habitats they help to shape. Initially, our primary takeaway is that climate change will alter how lobster predators affect purple urchin grazing behavior and thus may ultimately alter kelp forest condition (Jenkinson et al. 2020 ). Whether this was due to changes in purple urchin metabolic demands or altered predator perception, the urchins appear lose their ability to fully respond to the presence of predators under ocean change conditions (OA, OW, and/or Future). Instead, the urchins appear to continue to behave as they would if predators were not present, which may lead to population-level effects especially if the predators are less affected by these changes. It is also unclear if these changes are more strongly linked to how OW might alter urchin metabolic demands (e.g. Biro et al. 2007 , 2013 ; Pörtner and Peck 2010 ) or how OA might alter how urchins perceive their predators (e.g. Leduc et al. 2013 ). Regardless, we pose that these behavioral changes can result in two potential outcomes. First, the urchins’ reduced ability to detect predators or their disregard for them can lead to increased levels of mortality from predation, which could lead to populations being controlled more effectively. This type of top-down control might then improve overall kelp forest health (e.g., Halpern et al. 2006 ; Jenkinson et al. 2020 ). On the other hand, if urchin populations are grazing more aggressively, they could potentially overgraze kelp forests faster than predators can reduce their populations and thereby reduce kelp forest health. Given that algal biomass within kelp forests is strongly affected by forces such as grazing and competition (e.g. Clark et al. 2004 ; Edwards and Connell 2012 ), losing this prominent ecosystem-level control could and raise the importance of bottom-up forcing (Foster et al. 2006 ) and thereby have dramatic consequences for overall kelp forest health. What remains unclear is whether other marine invertebrates share a similar response to climate stressors, or if these responses ultimately lead to changes in kelp forest condition. 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Treatment types are represented by shading. SD’s are based on two measurements made per day in each mesocosm over each three-day trial.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/3ae1dea9c7a4585cdf948eac.png"},{"id":79301877,"identity":"3adcf160-fe45-4e80-83f9-3f0e64fb4371","added_by":"auto","created_at":"2025-03-26 19:15:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188783,"visible":true,"origin":"","legend":"\u003cp\u003enMDS plot showing similarities in all three feeding behavior variables (latency to emerge, time spent feeding, kelp consumed) among ocean change and predator treatments. Predator treatments are represented by shading and ocean change treatments are represented by symbol shape. Similarities are based on a Euclidean distance resemblance matrix using square root transformed data.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/bcb61ccb88cad7f43f8c763a.png"},{"id":79301519,"identity":"46b4d518-603c-4c41-9606-cb0ecc6de6b1","added_by":"auto","created_at":"2025-03-26 19:07:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58325,"visible":true,"origin":"","legend":"\u003cp\u003enMDS plots showing the similarities in all three feeding behavior variables (latency to emerge, time spent feeding, kelp consumed) between predator present and predator absent mesocosms within each ocean change treatment type. Similarities are based on a Euclidean distance resemblance matrix using square root transformed data.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/da1ccaaa2afca4ae97ec2fd7.png"},{"id":79301515,"identity":"07527d91-992f-4be3-9de5-3b4196a107c9","added_by":"auto","created_at":"2025-03-26 19:07:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41448,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot showing the average latency to emerge by urchins. Data are categorized by treatment type along the x-axis. Predator scenarios are represented by color (absent: red, present: blue). Mean values for each are represented with a black circle.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/9c0231c9a7d9578902abc354.png"},{"id":79301881,"identity":"6f2e9856-aaef-4bff-8d56-02eb9c3898a9","added_by":"auto","created_at":"2025-03-26 19:15:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42688,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot showing the average time urchins spent feeding. Data are categorized by treatment type along the x-axis. Predator scenarios are represented by color (absent: red, present: blue). Mean values for each are represented with a black circle.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/34e83bf1b0b7ea9a8f3dbd01.png"},{"id":79302027,"identity":"3c735a5f-a032-4f1c-ae13-d9ba51d147b6","added_by":"auto","created_at":"2025-03-26 19:23:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":41596,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot showing the average kelp mass consumed by urchins. Data are categorized by treatment type along the x-axis. Predator scenarios are represented by color (absent: red, present: blue). Mean values for each are represented with a black circle.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/e846ea3bf895778e9abfaef9.png"},{"id":86404256,"identity":"8afe474d-f7cf-4f7a-b44a-1f69f5d24987","added_by":"auto","created_at":"2025-07-10 09:36:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6149444/v1/b83a04f7-6452-4f98-84b4-3b8b7542785a.pdf"}],"financialInterests":"","formattedTitle":"Effects of climate change on purple urchin feeding behavior in the presence and absence of California spiny lobsters.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe anthropogenic release of CO\u003csub\u003e2\u003c/sub\u003e is altering the earth\u0026rsquo;s ecosystems and raising atmospheric temperatures. As the oceans are responsible for absorbing 80% of this excess heat (Levetus 2005; Johnson 2020), this has resulted in sea surface temperatures increasing by approximately 1\u0026deg;C over the past century, a pattern that is expected to continue if CO\u003csub\u003e2\u003c/sub\u003e emissions are not controlled (Levitus 2009). Indeed, predictions for ocean temperature suggest a 3 to 4\u0026deg;\u003csup\u003eo\u003c/sup\u003eC increase by the end of the century under high CO\u003csub\u003e2\u003c/sub\u003e emission scenarios (IPCC 2014). In addition to ocean warming (OW), the oceans have absorbed about one-third of the excess CO\u003csub\u003e2\u003c/sub\u003e released into the atmosphere (Sabine et al. 2004), resulting in ocean acidification (OA) (Doney et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The current average pH of the oceans, which is approximately 8.1, is expected to decrease by another 0.287\u0026ndash;0.290 pH units by the end of the century under high CO\u003csub\u003e2\u003c/sub\u003e emission scenarios (Bindoff 2019). These stressors (OA and OW), either in combination or alone, have the potential to affect the physiology, growth and behavior of marine organisms across a wide range of taxa (e.g., Nowicki et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kroeker et. al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Brown et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shukla and Edwards 2017; Edwards \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and in turn alter the structure and functioning of marine communities.\u003c/p\u003e \u003cp\u003eBoth OA and OW have been shown to have detrimental effects on marine invertebrates by altering a wide range of physiological functions, many of which are responsible dictating their behavior (Widdicombe \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wicks et al 2012; Brown et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For example, OA can inhibit neural signaling processes (Watson et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melzner 2020) and reduce the ability to detect chemical stimuli \u003csup\u003e(\u003c/sup\u003e(Leduc et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and thereby affect behaviors related to reproduction (Seuront \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Borges 2018), foraging (De la Haye 2012), and predator avoidance (Watson et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jellison et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Likewise, OW can induce higher metabolic rates, which can also lead to altered foraging activity and risk-taking behaviors (Biro et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; P\u0026ouml;rtner and Peck \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Further, OA and OW can both alter the structure of the chemical cues themselves, which can lead to detrimental behaviors related to predator avoidance and foraging activities (Arnold et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Leduc et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, despite these important effects, the interaction between OA and OW remains understudied and previous research has produced conflicting results (e.g., Sundin et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Welch et al. 2016), especially regarding predator avoidance behavior (e.g., Zittier et al. 2013; Schram et al. 2014). Indeed, a review by Kroeker et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) suggests there is no clear consistency among organisms in how they will respond to OA and OW, either when they occur alone or together. However, understanding how these ocean change stressors affect predator-prey relationships can be important to predicting how ocean ecosystems may respond in the future, especially in coastal kelp forests where these relationships can alter grazing, which is known to be integral in shaping community structure and function (Estes et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Vicknair \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Gabara et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jenkinson et al. 2021).\u003c/p\u003e \u003cp\u003eKelp forests along the Pacific coast of the United States are some of the most diverse and productive ecosystems on the planet (Schiel and Foster \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These underwater forests are dominated by high densities of canopy-forming kelps that provide shelter and food to numerous marine invertebrates, fishes, and mammals (Mann 1973; Graham \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Schiel and Foster \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and support a broad range of ecosystem services. For example, they reduce current and wave energy and thereby can buffer coastlines from hydrodynamic activity (Jackson and Winant \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Hondolero and Edwards \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Elsmore et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They alter seawater chemistry (Evans and Edwards \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Gonzales et al. 2017; Pfister et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Corrano et al. 2020), support enhanced biodiversity (Torres-Moye et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Konar et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Metzger et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gabara et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and they are harvested for a variety of food, medical, and industrial products (Borras-Chaves et al. 2012, 2016). They also support high rates of primary production (Reed and Brezinski 2009; Edwards et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Spector and Edwards \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and sequester and store carbon in their standing biomass (Wilmers et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) thereby playing important roles in global carbon cycles (Reed and Brezinski 2009). Consequently, the loss of these forests can lead to the loss of these important ecosystem services.\u003c/p\u003e \u003cp\u003eIn southern California, USA, kelp forests are dominated by the largest of the kelp species, the giant kelp, \u003cem\u003eMacrosystis pyrifera\u003c/em\u003e, whose populations are strongly affected by both ocean conditions (i.e., bottom-up control: Edwards and Estes \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Foster et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and trophic dynamics (i.e., top-down control: Halpern et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e: Jenkinson et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As with kelps in other regions worldwide (Konar et al \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Krumhansl et. al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Edwards and Konar \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Smale \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wernberg et al. 2018), giant kelp forests are experiencing long-term declines and/or periodic losses over large geographic areas due to both natural and anthropogenic forces (Edwards and Hern\u0026aacute;ndez-Carmona \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Byrnes et al. 2011; Edwards \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This has resulted in altered spatial structuring (Edwards \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Edwards and Konar \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Jenkinson et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), simplified trophic dynamics (Graham \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Gabara et al \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and reduced primary production (Edwards et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Spector and Edwards \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sullaway and Edwards \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gabara et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in the coastal zone. Declines in kelp forest condition are particularly relevant to Point Loma, San Diego, CA, USA, which is home to one of the largest contiguous giant kelp forests in California that supports intense sport and commercial fishing for sea urchins, spiny lobsters, and finfish.\u003c/p\u003e \u003cp\u003eMany kelp forest communities are strongly influenced by a single type of herbivore, the stronglyocentroid sea urchin (Ebeling et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Harrold and Reed \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Estes et al \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Steneck et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In southern California, one of the most abundant urchin species is the purple urchin, \u003cem\u003eStrongylocentrotus purpuratus\u003c/em\u003e. These subtidal echinoderms are voracious grazers of giant kelp, especially drift kelp that has become dislodged from the substrate or fragmented from nearby sporophytes (Harrold and Reed \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). However, in the absence of drift kelp, urchins will often actively feed on the kelp\u0026rsquo;s holdfast, ultimately dislodging its thallus from the rocky substrate. As with urchins and kelps in many other regions (e.g., Estes et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Steneck et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Konar et al. 2014; Jeon et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), when large populations of urchins begin to forage this way, they can decimate entire kelp forests and create urchin barren grounds that can persist as a stable state for years to decades (Mann \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Ebeling et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Estes et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The urchins in these barren grounds can then survive in a state of reduced metabolic activity until drift kelp becomes available again (Dolinar and Edwards \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While urchin barrens may still contain abundant marine life, the biodiversity and food web connectivity are dramatically lower than that found in kelp forest habitats (Graham \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Metzger et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gabara et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Under the right conditions, rocky reef predators such as the California spiny lobster, \u003cem\u003ePanulirus interruptus\u003c/em\u003e, have the potential to reduce urchin abundance (Lafferty 2004; Pearse \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nicols 2009; Dunn \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jenkinson et. al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and alter their feeding behaviors. Indeed, they are one of the primary predators of purple urchins and their consumption of urchins serves a functional role in maintaining healthy benthic communities and kelp forest ecosystems (Cascorbi \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Jenkinson et. al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, Matassa (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that urchins reduced grazing by almost 44% when in the presence of spiny lobsters, while Knight (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that urchins collected from kelp forests reduced kelp consumption by about 73% in the presence of spiny lobster chemical cues. These effects, however, may vary if ocean change reduces the ability of the urchins to recognize the spiny lobster predators or increases risk taking behavior, and thereby alters these predator-prey interactions. Here, we examine how OA and OW affect the ability of purple urchins to detect and/or react to California spiny lobsters, and how this in turn affects their grazing activity on giant kelp. Specifically, we examined how purple urchin behavior is altered by spiny lobster predatory cues when exposed to the individual and combined effects of OA and OW, with the overarching goal of understanding how this may alter ecosystem-level impacts within kelp forest ecosystems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMesocosm set up\u003c/span\u003e\u003c/h2\u003e\n \u003cp\u003eTo examine the effects of OA, OW, and Future (OA\u0026thinsp;+\u0026thinsp;OW) ocean conditions on how spiny lobsters affect purple urchin feeding behavior, four 350-liter shallow aquaculture tanks (hereafter, \u0026ldquo;mesocosms\u0026rdquo;) were used to establish four orthogonal treatment combinations that represented present day ocean conditions and the individual and combined effects of OA, OW, and Future ocean conditions that are expected by the year 2100. Specifically, the four mesocosms had either 1) seawater that represented present-day (i.e. \u0026ldquo;Current\u0026rdquo;) conditions of 15\u0026deg;C and pH 8.1; 2) acidified (i.e. \u0026ldquo;OA\u0026rdquo;) conditions of 15\u0026deg;C and 7.8 pH; 3) warmed (i.e. \u0026ldquo;OW\u0026rdquo;) conditions of 18\u0026deg;C and pH 8.1; and 4) OA\u0026thinsp;+\u0026thinsp;OW (i.e. \u0026ldquo;Future\u0026rdquo;) conditions of 18\u0026deg;C and 7.8 pH. The parameters established for the Current conditions reflected the current average temperature and acidity for the Point Loma kelp forest, and parameters for the OA, OW and Future conditions reflected IPCC projections for how these will change by the end of the century under current CO\u003csub\u003e2\u003c/sub\u003e emissions (IPCC 2014).\u003c/p\u003e\n \u003cp\u003eAll spiny lobsters and purple urchins were collected by hand on SCUBA from the Point Loma Kelp Forest (32.42.564⁰N, 117.16.633⁰W) in San Diego, California between January and August 2023. Purple urchins were all between 4\u0026ndash;6 cm test diameter to ensure consistency with grazing rates among different runs of the experiments, while spiny lobsters were all at least 64 mm total carapace length to ensure they had reached sexual maturity. Giant kelp, \u003cem\u003eMacrocystis pyrifera\u003c/em\u003e, blades were collected at the surface by hand from university research vessels. All organisms were transported to the San Diego State University Coastal and Marine Institute Laboratory (CMIL) immediately after collection and held in flow-through aquaria under ambient conditions until being used in laboratory experiments.\u003c/p\u003e\n \u003cp\u003eTo establish the four ocean change conditions (Current, OA, OW, Future), a mesocosm array was built to house four separate recirculating systems, with the procedure of pumping the water into the mesocosms done in a manner to reduce disturbance to the study organisms and eliminate bubbles that may affect seawater chemistry. Due to logistical constraints, only one mesocosm could be established per ocean change treatment. Therefore, replication was established by running the experiment 14 times (hereafter \u0026ldquo;trials\u0026rdquo;) with the mesocosms emptied and cleaned after each trial. Thus, the variable Trial was considered as a temporal blocking factor for statistical analyses. In total, the 14 trials were run over the course of six months, from January to June 2023, however the third trial\u0026rsquo;s behavior data became unusable due to video system malfunctions that occurred during a strong winter storm, resulting in 13 usable trials (i.e. n\u0026thinsp;=\u0026thinsp;13). pH and temperature data, however, were still retained from this trial to demonstrate the mesocosms system\u0026rsquo;s ability to maintain the targeted ocean change conditions. To reduce the chances of mesocosm identity or position bias, the mesocosms were emptied, cleaned, and randomly re-assigned to each of the four ocean change treatments prior to the start of each trial.\u003c/p\u003e\n \u003cp\u003eSeawater pH within the mesocosms was established using similar methods to those described for previous ocean acidification experiments conducted by our group (Brown et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kim et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shukla and Edwards 2017). Here, to achieve the acidified conditions within both the OA and Future treatments, pure CO\u003csub\u003e2\u003c/sub\u003e gas (Airgas, San Diego) was slowly bubbled directly into sump tanks and the resulting acidified water was pumped into the experimental mesocosms where it then flowed via gravity back into the sump tanks. The injection of CO\u003csub\u003e2\u003c/sub\u003e gas was balanced by bubbling ambient air into the seawater, with the flow rates of the two gasses closely controlled through both a primary regulator and a secondary gas manifold to maintain the desired seawater pH. The ambient air was also bubbled into the seawater within the non-acidified (Current and OW) treatments. Seawater pH in each of the mesocosms was then measured twice daily using a Thermo Scientific Orion Portable pH meter (Star A121) with ROSS Ultra pH/ATC Triode (Orion 8157BNUMD) calibrated with Orion ROSS pH buffers. While pH values in the four mesocosms did experience some temporal variation over each three-day trial, as they would experience naturally with coastal kelp forests (Hoffman et al. 2011), this method created acidified (OA and Future) seawater that was consistently maintained at 0.3 pH units below the non-acidified (Current and OW) seawater (Fig. 1A). To achieve the desired temperatures for the ocean change treatments, each mesocosm was equipped with an individual flow-through chiller system (1/3 hp flow-through chillers, Aqualogic, San Diego) that maintained our target temperatures. Here, the sump water was pumped through the chillers and then back into the sump before being pumped into the experimental mesocosms as described above. Each chiller was equipped with a thermometer and a controller that allowed for precise and constant temperatures in each mesocosm, which was measured twice daily in conjunction with pH measurements. Specifically, temperatures in the non-warmed (Current and OA) mesocosms were maintained at 15\u0026deg;C, and temperatures in the warmed (OW and Future) mesocosms were maintained at 18\u0026deg;C. As with pH, seawater temperatures experienced some temporal variation in the mesocosms over each three-day trial, and they increased slightly with seasonal changes as the trials progressed from January to June as would be expected in the Point Loma kelp forest, but the OW and Future treatments were consistently maintained at 3\u0026deg;C above the Current and OA treatments (Fig. 1B).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluating purple urchin feeding behaviors\u003c/h3\u003e\n\u003cp\u003eTo evaluate the effects of the different ocean change treatments on purple urchin grazing and foraging behavior in the presence and absence of spiny lobsters, all purple urchins were acclimated to their respective treatment conditions for three days before each experimental trial began. Additionally, the purple urchins were starved during this time to standardize them and facilitate grazing behavior once the experimental trial began. Two scenarios were then tested under each ocean condition: spiny lobster (i.e., predator) presence and spiny lobster absence. Here, each of the four mesocosms was divided in half using a permeable barricade made from 1 cm Vexar mesh that was anchored to the bottom with a length of chain. This created a seamless barricade that prevented lobster and urchin movement between mesocosm sides but allowed for free flow of chemical cues from one half to the other. Thus, the mesh allowed for urchins to be fully aware of the presence of the predator but did not allow for predation to occur. An artificial shelter consisting of a cinder block was placed inside the mesocosm one side of the barricade, which provided a place for urchins to seek refuge from the lobsters. Three urchins were placed in the mesocosm on the side of the barricade with the shelter and allowed to acclimate to the conditions for three days. After the acclimation period, a lobster was placed on the opposite side of the barricade for predator-present trials, while this area was left empty for the predator-absent trials. At the start of each trial, a pre-weighed bundle of giant kelp (approximately 20 g wet weight) that served as urchin food was patted dry, weighed, and placed on the side of the mesocosms with the urchins at a set distance of 45cm from the urchin shelters by zip-tying the bundles to a length of chain, which anchored them to the bottom.\u003c/p\u003e\n\u003cp\u003eTo record urchin feeding behavior under each ocean change treatment, a GoPro camera was mounted to a wooden frame 60 cm above each mesocosm and pointed downward so that its field of view captured the entire half of the tank where the urchins were placed. Feeding behaviors were quantified from visual analyses of 72 h time-lapse images, with images taken once every 60 seconds during each three-day experimental trial. Foraging behavior was assessed by quantifying the movement of each urchin and then averaged to provide an estimate for each trial. While viewing the GoPro footage to quantify the movement of the urchins, the observer was blind to which treatment was being evaluated to remove any viewer bias. Foraging behavior was divided into one of two distinct behaviors; latency to emerge from shelter and time spent feeding. Latency to emerge from the shelter was quantified as the time it took for an individual urchin to completely exit its cinder block shelter, while time spent feeding was quantified as the amount of time an individual urchin spent in contact with the kelp placed in its mesocosm. For each of these variables, the total number of time-lapse frames was recorded and then scaled to per-hour estimates. Additionally, the total amount of kelp consumed was used as a metric for measuring grazing activity. For this, the kelp bundles were collected from the mesocosms upon completion of each trial, blotted dry, and re-weighed. The change in mass over the three-day period was recorded as the total mass consumed by urchins during the experiment.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eUnivariate and multivariate statistical analyses were conducted using R (R Core Team 2021), Primer/PERMANOVA (ver. 7), and SYSTAT (ver. 12). Evaluation of urchin behaviors were analyzed in a two-step process. First, behavior data for all three urchins in each mesocosm were averaged and then square root transformed. Data for kelp consumed were also square root transformed. Then, the three urchin feeding behaviors (latency to emerge, time spent feeding, kelp mass consumed) were combined and evaluated together to determine if they collectively varied among the four ocean change (Current, OA, OW, Future) and two predator (Present, Absent) treatments using a three-factor Model I nested permutational analysis of variance (PERMANOVA) on a Euclidean distance-based resemblance matrix as described by Clark et al. (1993). Here, trial was considered a fixed factor that was nested within predator treatment (i.e., the predator present and the predator absent treatments were examined within different trials and were thus non-orthogonal). Trial was also considered as a blocking factor for the ocean change variable, as there was only one replicate of each treatment during each trial. The resulting data were then plotted using non-metric Multidimensional Scaling (nMDS) to visually examine similarities in collective behaviors among the different treatments. Following this, individual one factor PERMANOVAs and nMDS plots were used as a priori-defined post-hoc tests to evaluate whether the urchin feeding behaviors collectively differed between predator present and predator absent treatments within each ocean change treatment separately. Second, differences in each feeding behavior alone were compared separately among ocean change and predator treatments, and among trials using similarly constructed three-factor nested ANOVAs (for each feeding behavior), with predator treatments nested within trials, and trial considered as a blocking factor for the ocean change treatments. Prior to these analyses, data were checked for normality using Shapiro-Wilk tests and for homogeneity of variance using Levene\u0026rsquo;s test. Here, data for latency to emerge were non-normal for the OW treatment (Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.557, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and therefore log-transformed, which corrected the problem (Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.864, p\u0026thinsp;=\u0026thinsp;0.056). Data for time spent feeding were non-normal for both Predator treatments (Predator Absent: Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.908, p\u0026thinsp;=\u0026thinsp;0.032; Predator Present: Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.503, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and therefore also log transformed, which corrected the problems (Predator Absent: Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0941, p\u0026thinsp;=\u0026thinsp;0.172; Predator present: Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.905, p\u0026thinsp;=\u0026thinsp;0.206). Lastly, data for kelp consumed were non-normal for the OW treatment (Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.830, p\u0026thinsp;=\u0026thinsp;0.012) and were therefore square root transformed, which corrected the problem (Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.940, p\u0026thinsp;=\u0026thinsp;0.426). All other data met the assumptions of ANOVA following their respective transformations, except for kelp consumed data within the Predator Absent treatment, which remained non-normal (Shapiro-Wilk\u0026thinsp;=\u0026thinsp;0.909, p\u0026thinsp;=\u0026thinsp;0.029) and could not be corrected by transformation. However, graphical interpretation of these data using a Q-Q probability plot revealed that this departure from normality was minor and we consider the ANOVA robust for comparison of the means among treatments (e.g. Sawyer \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). These ANOVAs were each followed by independent t-tests as post hoc tests to evaluate a priori defined hypotheses regarding differences in each behavior between the predator treatments within each ocean change treatment separately. All data for each feeding behavior were then graphed using Box Plots to visualize differences among ocean change and predator treatments.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eBehavior\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eVisual assessments of the collective urchin behaviors did not initially reveal clear differences among the ocean change treatments (PERMANOVA: pseudo-F\u003csub\u003e3,33\u003c/sub\u003e = 1.189, P(perm)\u0026thinsp;=\u0026thinsp;0.315). In contrast, significant differences in urchin behaviors were observed between the two predator treatments (pseudo-F\u003csub\u003e1,33\u003c/sub\u003e = 1.189, P(perm)\u0026thinsp;=\u0026thinsp;0.010), which appeared consistent across the four ocean change treatments (Ocean Change x Predator interaction: pseudo-F\u003csub\u003e3,45\u003c/sub\u003e = 1.285, P(perm)\u0026thinsp;=\u0026thinsp;0.277, Table\u0026nbsp;1A, Fig.\u0026nbsp;2), and no significant differences were observed among the 13 trials (pseudo-F\u003csub\u003e12,33\u003c/sub\u003e = 1.228, P(perm)\u0026thinsp;=\u0026thinsp;0.243). Interestingly, when differences in urchin behaviors were compared between the predator treatments within each ocean change condition separately, significant differences in behavior were observed within the Current ocean conditions (PERMANOVA: pseudo-F\u003csub\u003e1,33\u003c/sub\u003e = 1.285, P(perm)\u0026thinsp;=\u0026thinsp;0.014), but no differences were observed between the predator treatments under any of the other three ocean change conditions (Table\u0026nbsp;2, Fig.\u0026nbsp;3). Further, when each of the urchin grazing behaviors were examined individually, different patterns emerged. Specifically, latency to emerge did not differ among the ocean change treatments (ANOVA: F\u003csub\u003e1,32\u003c/sub\u003e = 1.669, p\u0026thinsp;=\u0026thinsp;0.193) but it did differ between the two predator treatments (F\u003csub\u003e1,32\u003c/sub\u003e = 5.716, p\u0026thinsp;=\u0026thinsp;0.023, Table\u0026nbsp;3, Fig.\u0026nbsp;4), a pattern that was consistent across all four ocean change treatments (Ocean Change x Predator interaction: F\u003csub\u003e3,32\u003c/sub\u003e = 0.416, p\u0026thinsp;=\u0026thinsp;0.743). However, although no differences were observed between the predator present and predator absent scenarios under OA, OW, or Future conditions, urchins emerged from their shelters 37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4% faster (mean proportion of change in time\u0026thinsp;\u0026plusmn;\u0026thinsp;se) when predators were absent than when they were present under current conditions (Table\u0026nbsp;4, Fig.\u0026nbsp;4), which represented a marginally significant difference (t\u003csub\u003e11\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.769, p\u0026thinsp;=\u0026thinsp;0.105). Time spent feeding did not differ among either the ocean change (ANOVA: F\u003csub\u003e3,33\u003c/sub\u003e = 0.850, p\u0026thinsp;=\u0026thinsp;0.477) or predator (F\u003csub\u003e1,33\u003c/sub\u003e = 1.046, p\u0026thinsp;=\u0026thinsp;0.314) treatments or among trials (F\u003csub\u003e11,33\u003c/sub\u003e = 0.237, p\u0026thinsp;=\u0026thinsp;0.993), but the ocean change and predator treatments did appear to interact with each other (Ocean Change x Predator interaction: F\u003csub\u003e3,44\u003c/sub\u003e = 2.475, p\u0026thinsp;=\u0026thinsp;0.079) (Table\u0026nbsp;5). Specifically, a priori defined post hoc tests revealed that time spent feeding was reduced by 75.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.0% when predators were present under current conditions (post hoc t-test: t\u003csub\u003e11\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.093, p\u0026thinsp;=\u0026thinsp;0.010), but it did not differ under any of the other three ocean change scenarios (Table\u0026nbsp;6, Fig.\u0026nbsp;5). This finding is consistent with the results of previous studies showing urchins alter their behavior when in the presence of predators, but also suggests that this difference disappeared under ocean change conditions. Not surprisingly, the amount of kelp consumed by urchins followed a similar pattern to the time they spent feeding. Specifically, although the amount of kelp consumed did not differ among either the ocean change (ANOVA: F\u003csub\u003e3,33\u003c/sub\u003e = 1.551, p\u0026thinsp;=\u0026thinsp;0.222) or predator (F\u003csub\u003e1,33\u003c/sub\u003e = 1.303, p\u0026thinsp;=\u0026thinsp;0.290) treatments, these factors again interacted with each other (Ocean Change x Predator interaction: F\u003csub\u003e3,33\u003c/sub\u003e = 2.729. p\u0026thinsp;=\u0026thinsp;0.060) and varied among trials (F\u003csub\u003e12,44\u003c/sub\u003e = 2.018, p\u0026thinsp;=\u0026thinsp;0.055) (Table\u0026nbsp;7). Further, a priori defined post hoc analyses revealed that the urchins consumed 55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18% less kelp when predators were present under current conditions (post hoc t-tests: t\u003csub\u003e11\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.269, p\u0026thinsp;=\u0026thinsp;0.021), but no differences in kelp consumption were observed under any of the other three ocean change scenarios (Table\u0026nbsp;8, Fig.\u0026nbsp;6). Together, our results suggest that under current ocean conditions, urchins increased the time it took to emerge from their shelters, reduced the time they spend feeding, and consumed less kelp when predators were present, but these differences were not observed under any of the ocean change conditions, which may have significant implications for urchin foraging behavior under changing ocean conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eResults of a\u003c/b\u003e three-factor nested permutational analysis of variance (PERMANOVA) testing for differences in urchin grazing behaviors (latency to emerge, time spent feeding, kelp mass consumed) among Ocean Change (Current, OA, OW, Future) and Predator (presence, absence) treatments. Similarities are based on a Euclidean distance resemblance matrix using square root transformed data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePseudo-F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP(perm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator*Ocean Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrial(Predator)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of separate one-factor permutational analyses of variance (PERMANOVAs) testing for differences in urchin feeding behaviors between predator presence and predator absence treatments within each Ocean Change treatment. A) Current, B) Warming (OW), C) Acidification (OA), and D) Future (OW\u0026thinsp;+\u0026thinsp;OA) conditions. Similarities are based on a Euclidean distance resemblance matrix using square root transformed data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA) Current\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePseudo-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP(perm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB) Warming\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePseudo-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP(perm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC) Acidification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePseudo-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP(perm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD) Future\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePseudo-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP(perm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResiduals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of three-factor nested analysis of variance (ANOVA) testing for differences in latency to emerge by purple urchins among ocean change and predator treatments, and among trials. Data for the three urchins in each treatment tank were averaged prior to analysis and Log transformed to correct problems with Normality. Due to logistic constraints in the experimental design, Trial was considered fixed and nested within the Predator treatment and as a blocking factor for ocean change treatments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType III SS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change*Predator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of a prior defined t-tests examining differences in purple urchin latency to emerge between predator absent and predator present scenarios within each ocean change treatment. Data were log transformed prior to analysis to correct problems with Normality, and reported p-values are uncorrected.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of three-factor nested analysis of variance (ANOVA) testing for differences in time spent feeding by purple urchins among ocean change and predator treatments, and among trials. Data for the three urchins in each treatment tank were averaged prior to analysis and Log transformed to correct problems with Normality. Due to logistic constraints in the experimental design, Trial was considered fixed and nested within the Predator treatment and as a blocking factor for Ocean change treatments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType III SS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change * Predator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of a prior defined t-tests examining differences in purple urchin feeding rates between predator absent and predator present scenarios within each ocean change treatment. Data were log transformed prior to analysis to correct problems with Normality, and reported p-values are uncorrected.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo-factor Model I analysis of variance (ANOVA) testing for differences in kelp consumed by urchins across Ocean Change and Predator treatments. Data were square root transformed to correct problems with Normality. Due to logistic constraints in the experimental design, Trial was considered fixed and nested within the Predator treatment and as a blocking factor for ocean change treatments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType III SS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change*Predator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of a prior defined t-tests examining differences in kelp mass consumed by purple urchins between predator absent and predator present scenarios within each ocean change treatment. Data were square root transformed prior to analysis to correct problems with Normality, and reported p-values are uncorrected.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcean Change treatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur primary finding was that purple urchins alter their feeding behavior in the presence of spiny lobsters under current ocean conditions, but they do not alter them under ocean change conditions. Specifically, purple urchins took 37% more time to emerge from their shelters, spent 75% less time feeding, and consumed 56% less kelp and when lobsters were present in their environment under current ocean conditions, but these feeding behaviors were not affected by the presence of lobsters under OA, OW or Future (OW\u0026thinsp;+\u0026thinsp;OA) ocean conditions. This suggests that urchins can detect and respond to the presence of lobster predators under current ocean conditions, but they may lose this ability or exhibit more risky behavior in the future as the ocean becomes warmer and more acidic. While these results are consistent with recent studies on other invertebrate species (e.g. Biro et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; P\u0026ouml;rtner and Peck \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Watson et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jellison et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), they present an exciting advance in our understanding of how purple urchins may be affected by climate change.\u003c/p\u003e \u003cp\u003eThis lack of a response to the presence of lobster predators under ocean change conditions suggests that when subjected to climate change stressors, purple urchins either lost the ability to detect the presence of predator cues (e.g., Leduc et al. 2103) or they increased their risk-taking behavior to account for higher energy demands (e.g., Biro et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; P\u0026ouml;rtner and Peck \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), both of which can lead to altered behavior related to predator avoidance. Similarly, other studies have found that urchins exhibit diminished righting and covering behaviors when subjected to OW conditions that are consistent with IPCC-predicted changes in ocean temperatures (Brothers 2016), which also mimics the compromised predator-avoidance behaviors we observed. While few studies have examined how the combination of these two ocean change stressors affects urchin behavior, some evidence has shown climate change scenarios induce weakened physiological mechanisms resulting in compromised grazing behaviors (Brothers 2016), which is again consistent with our results. Further, information on the individual and combined effects of OA and OW on other marine invertebrates have been mixed (reviewed in Kroeker et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For example, Horwitz (2020) found that the sea hare, \u003cem\u003eStylocheilus striatus\u003c/em\u003e, exhibits a 1.5 to 2-fold reduction in foraging, locomotion speed, and time needed to locate food under individual OA and OW stressors, which mirrors our results for purple urchins. However, Horwitz (2020) also found that the sea hares exhibited up to a 3-fold reduction in these behaviors when they were subjected to the combination of both OA and OW climate change stressors (i.e. Future conditions). Likewise, Baure (2023) found that the sea cucumber, \u003cem\u003eStichopus\u003c/em\u003e cf. \u003cem\u003ehorrens\u003c/em\u003e, and marine gastropod, \u003cem\u003eTrochus maculatus\u003c/em\u003e, both initially increased feeding activities under OW and Future conditions, suggesting a metabolic response to warming, but these changes were diminished after five days of acclimation to these conditions. Results such as these indicate that some marine organisms may be able to adjust to rapid physical changes in their environment, though this may not be likely not be true for purple urchins, at least over a six-day (3-day acclimation plus 3-day trial duration) period. Indeed, while studies examining individual effects of climate change stressors (OA or OW) on marine invertebrates seem to show more consistent responses, these responses appear are more varied when these stressors are combined (OA\u0026thinsp;+\u0026thinsp;OW) as they would be under future ocean change.\u003c/p\u003e \u003cp\u003eOverall, our study paints a potentially interesting picture for the future of purple urchins and the habitats they help to shape. Initially, our primary takeaway is that climate change will alter how lobster predators affect purple urchin grazing behavior and thus may ultimately alter kelp forest condition (Jenkinson et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Whether this was due to changes in purple urchin metabolic demands or altered predator perception, the urchins appear lose their ability to fully respond to the presence of predators under ocean change conditions (OA, OW, and/or Future). Instead, the urchins appear to continue to behave as they would if predators were not present, which may lead to population-level effects especially if the predators are less affected by these changes. It is also unclear if these changes are more strongly linked to how OW might alter urchin metabolic demands (e.g. Biro et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; P\u0026ouml;rtner and Peck \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) or how OA might alter how urchins perceive their predators (e.g. Leduc et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Regardless, we pose that these behavioral changes can result in two potential outcomes. First, the urchins\u0026rsquo; reduced ability to detect predators or their disregard for them can lead to increased levels of mortality from predation, which could lead to populations being controlled more effectively. This type of top-down control might then improve overall kelp forest health (e.g., Halpern et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Jenkinson et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, if urchin populations are grazing more aggressively, they could potentially overgraze kelp forests faster than predators can reduce their populations and thereby reduce kelp forest health. Given that algal biomass within kelp forests is strongly affected by forces such as grazing and competition (e.g. Clark et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Edwards and Connell \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), losing this prominent ecosystem-level control could and raise the importance of bottom-up forcing (Foster et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and thereby have dramatic consequences for overall kelp forest health. What remains unclear is whether other marine invertebrates share a similar response to climate stressors, or if these responses ultimately lead to changes in kelp forest condition. Addressing these uncertainties will contribute to our understanding of how our changing climate can lead to greater changes in coastal ecosystems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnold T, Leahey H, Hall-Spencer JM, Maers K (2012) Mealey C, Leahey H, Miller AW, Hall-Spencer JM, Milazzo M, Maers K. Ocean acidification and the loss of phenolic substances in marine plants. PLoS ONE, 7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA.F. (2004) The Oceanic Sink for Anthropogenic CO\u003csup\u003e2\u003c/sup\u003e. Science 305: 367\u0026ndash;371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaure JG, Roleda MY, Juinio-Me\u0026ntilde;ez MA (2023) Short-term exposure to independent and combined acidification and warming elicits differential responses from two tropical seagrass-associated invertebrate grazers. 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Hyas Araneus Mar Biology 160:2049\u0026ndash;2062.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, grazing, kelp forest, purple urchins, spiny lobsters","lastPublishedDoi":"10.21203/rs.3.rs-6149444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6149444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGrazing by sea urchins can dramatically alter the structure of kelp forest communities, but this can be moderated through both direct and indirect effects from their predators. For example, in southern California, USA, the presence of spiny lobsters, \u003cem\u003ePanulirus interruptus\u003c/em\u003e, can dramatically increase the time it takes for purple urchins, \u003cem\u003eStrongylocentrotus purpuratus\u003c/em\u003e, to emerge from their shelters to feed, reduce the total time that the urchins spend foraging, and consequently decrease the amount of kelp they consume. The mechanisms driving this, however, may change as the oceans become warmer and more acidic. To examine this, we quantified three measures of purple urchin grazing behavior (latency to emerge from shelters, time spent feeding, and kelp mass consumed) in the presence and absence of spiny lobsters under present day (Current), ocean warming (OW), ocean acidification (OA), and OW\u0026thinsp;+\u0026thinsp;OA (Future) conditions. Specifically, we placed purple urchins in laboratory mesocosms reflecting these conditions with shelters and known quantities of kelp, and then allowed them to graze in both the presence and absence of lobsters for three days. Urchin feeding activity was quantified using time-lapse photography and by recording the amount of kelp eaten over each three-day period. Our results revealed that urchins took longer to emerge from their shelters, grazed for less time, and consumed less kelp when in the presence of spiny lobsters under Current conditions, but these differences largely disappeared under OW, OA and Future conditions. These results reveal possible implications for how urchins will graze when in the presence of predators and thus affect kelp forest communities in the future.\u003c/p\u003e","manuscriptTitle":"Effects of climate change on purple urchin feeding behavior in the presence and absence of California spiny lobsters.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 19:06:57","doi":"10.21203/rs.3.rs-6149444/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef7b67e2-fde3-4aed-9276-daf998cc19cb","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-10T09:28:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 19:06:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6149444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6149444","identity":"rs-6149444","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}