Contrasting effects of ocean warming on different components of plant-herbivore interactions

Abstract

There is increasing uncertainty of how marine ecosystems will respond to rising temperatures. While studies have focused on the impacts of warming on individual species, knowledge of how species interactions are likely to respond is scant. The strength of even simple two-species interactions is influenced by several interacting mechanisms, each potentially changing with temperature. We used controlled experiments to assess how plant-herbivore interactions respond to temperature for three structural dominant macrophytes in the Mediterranean and their principal sea urchin herbivore. Increasing temperature differentially influenced plant-specific growth, sea urchin growth and metabolism, consumption rates and herbivore preferences, but not movement behaviour. Evaluating these empirical observations against conceptual models of plant-herbivore performance, it appears likely that while the strength of herbivory may increase for the tested macroalga, for the two dominant seagrasses, the interaction strength may remain relatively unchanged or even weaken as temperatures rise. These results show a clear set of winners and losers in the warming Mediterranean as the complex factors driving species interactions change.

Full text

Contrasting effects of ocean warming on different 1
components of plant-herbivore interactions 2
3
4
Jordi F. Pagèsa* 5
Timothy M. Smithb,c 6
Fiona Tomasd,e 7
Neus Sanmartíf
8
Jordi Boadac 9
Harriet De Baric 10
Marta Pérezf 11
Javier Romerof 12
Rohan Arthurc,g 13
Teresa Alcoverroc,g
14
15
16
aSchool of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, United Kingdom 17
bDeakin University, Centre of Integrative Ecology, School of Life and Environmental Sciences, Geelong, Australia 18
cCentre d'Estudis Avançats de Blanes (CEAB-CSIC), Accés a la cala Sant Francesc, 14, Blanes, Catalunya, Spain 19
dInstitut Mediterrani d’Estudis Avançats, IMEDEA (CSIC-UIB), Miquel Marquès 21, Esporles, Illes Balears, Spain 20
eDepartment of Fisheries and Wildlife, Oregon State University, OR, United States 21
fDepartament d'Ecologia, Facultat de Biologia, Universitat de Barcelona, Diagonal 643, Barcelona, Catalonia, Spain 22
gOceans and Coasts Program, Nature Conservation Foundation, 3076/5, 4th Cross, Gokulam Park, Mysore, India
23
24
25
*Corresponding author: j.pages@bangor.ac.uk 26
27

Abstract 28
There is increasing uncertainty of how marine ecosystems will respond to rising 29
temperatures. While studies have focused on the impacts of warming on individual 30
species, knowledge of how species interactions are likely to respond is scant. The strength 31
of even simple two-species interactions is influenced by several interacting mechanisms, 32
each potentially changing with temperature. We used controlled experiments to assess 33
how plant-herbivore interactions respond to temperature for three structural dominant 34
macrophytes in the Mediterranean and their principal sea urchin herbivore. Increasing 35
temperature differentially influenced plant-specific growth, sea urchin growth and 36
metabolism, consumption rates and herbivore preferences, but not movement behaviour. 37
Evaluating these empirical observations against conceptual models of plant-herbivore 38
performance, it appears likely that while the strength of herbivory may increase for the 39
tested macroalga, for the two dominant seagrasses, the interaction strength may remain 40
relatively unchanged or even weaken as temperatures rise. These results show a clear set 41
of winners and losers in the warming Mediterranean as the complex factors driving 42
species interactions change. 43
44
Keywords 45
climate change; macroalgae; Mediterranean; seagrass; sea urchin; temperature 46
47
48

Highlights 49
•!Multiple mechanisms influence interactions, each likely modified by temperature. 50
•!Mediterranean macrophyte-herbivore interactions show complex contrasts. 51
•!Herbivory on the two main Mediterranean seagrasses is expected to decrease. 52
•!A key canopy-forming macroalgae however is likely to suffer increased herbivory. 53
•!Warming is creating winners and losers in temperate waters as interactions change. 54
55
56

Introduction 57
Over the coming decades, the ecological impacts of global warming are expected to 58
increase as temperatures rise (IPCC, 2013). Global average sea surface temperatures are 59
predicted to rise by 0.75ºC by 2035 (Kirtman et al., 2013) and between 1ºC and more than 60
3ºC by 2100 (Collins et al., 2013), relative to the reference period 1986-2005. While a 61
large body of research has focused on the direct effects of global change on population 62
abundances, community composition, and organismal physiology (e.g. Sala et al., 2000), 63
global change may cause less obvious alterations to the networks of interactions among 64
species (Tylianakis et al., 2008). Indeed, biotic interactions such as predation, herbivory, 65
parasitism or mutualism are key in maintaining ecosystems’ biodiversity, resilience and 66
services (Bascompte et al., 2006; Dobson et al., 2011; Ives and Carpenter, 2007). The 67
historical lack of research on the effects of warming on biotic interactions, especially in 68
marine ecosystems (Wernberg et al. 2012, but see recent advances, e.g. Gutow et al., 69
2016; Hernán et al., 2017), likely stems from difficulties in quantifying modifications in 70
interactions compared to documenting changes in single species abundance, biodiversity 71
or individual physiological processes (McCann, 2007; Somero, 2012; Wernberg et al., 72
2012). Even a simple two-species interaction is ridden with complexities, driven by a host 73
of biological, behavioural and ecological mechanisms that can all interact in often 74
surprising ways (Boada et al., 2017). Unravelling these mechanisms and understanding 75
how they are likely to respond to change is far from trivial. Indeed, interactions may be 76
particularly susceptible to warming, since they are sensitive to the relative abundances of 77
the set of interacting species, their physiology, phenology and behaviour (Parmesan, 78
2006; Suttle et al., 2007; Tylianakis et al., 2007). 79
80

The interaction between a primary producer and its consumer can be used as a basic 81
model to explore the complexity inherent in understanding the effects of changing 82
temperatures at the community level. Plant-herbivore interactions are crucial for the 83
evolution of both plant and herbivore traits (e.g. Fritz and Simms, 1992), and are critical 84
in determining the abundance of primary producers globally (Cebrián, 1999). They 85
structure both terrestrial and marine food webs and ultimately determine whether the 86
world is dominated by producers or consumers (Polis, 1999). Plant-herbivore interactions 87
play a central role in driving marine ecosystem dynamics (e.g. Bakker et al., 2016), and 88
it is far from clear how the strength of these interactions will respond to a changing 89
climate. 90
91
For a start, trophic interactions are regulated by the autoecology of the intervening species. 92
Temperature can alter plant and animal growth and survival rates, which influence their 93
population abundance, playing a crucial role in determining trophic interactions (Bale et 94
al., 2002; O’Connor, 2009; Post and Pedersen, 2008). In addition, nonlethal temperature 95
rises tend to increase growth and production of plants (Nemani et al., 2003; Post and 96
Pedersen, 2008; Way and Oren, 2010), given that biochemical reaction rates accelerate 97
with temperature fuelled by an increase in kinetic energy (Janssens et al., 2015). Similarly, 98
moderate warming will also likely result in increased growth rates of ectothermic animals 99
(Kordas et al., 2011), decreased development time, increased herbivore population sizes 100
and expanded geographic ranges (Bale et al., 2002; O’Connor et al., 2011). Moreover, 101
both animal and plant respiration rates show higher thermal sensitivity compared to 102
photosynthetic rates (Allen et al., 2005; Padilla-Gamiño and Carpenter, 2007). In addition, 103
higher temperatures may also imply changes in animal behaviour, such as faster and 104
longer animal movements and also increased feeding rates as metabolic needs increase 105

(Gibert et al., 2016; Kordas et al., 2011). This raises the question whether warming will 106
expand the spatial scale over which key species exert their influence (Welsh and 107
Bellwood, 2012). In addition, movement patterns have been linked to the feeding 108
capacity of some animals, with individuals that display restricted mobility having a lower 109
impact on their resources (Hereu, 2005). 110
111
Plants respond to herbivory using a range of strategies. While some plants are well-112
adapted to tolerate herbivory pressure (Strauss and Agrawal, 1999), herbivory often 113
triggers compensatory growth (Sanmartí et al., 2014; Vergés et al., 2008), or an increase 114
in deterrent secondary metabolites (Tomas et al., 2015; Vergés et al., 2007a), thus 115
influencing herbivore feeding choices. How each of these individual mechanisms will 116
work together to influence the overall outcome of plant-herbivore interactions in a 117
warming environment is an open question (Post and Pedersen, 2008). For a start, it would 118
help to understand how the different mechanisms influencing the strength of the 119
interaction respond to warming. Synthesizing these responses could give us a better sense 120
of how plant-herbivore interaction strength is likely to change as temperatures increase. 121
122
As a simple heuristic, we propose a model to assess how warming is likely to change the 123
impacts of herbivory on vegetation. At its simplest, it is possible to conceive three 124
potential responses derived from the interplay between the individual responses of plant 125
and herbivores to warming (see Fig. 1 and see Supplementary Material): (i) if plant and 126
herbivores respond equally to warming (in terms of individual growth, termed 127
“performance” for the sake of simplicity), herbivore pressure will remain unchanged (Fig. 128
1a); (ii) if the plant’s optimal performance range extends to higher temperatures than the 129
herbivore’s performance range, then herbivore pressure will decrease (Fig. 1b); (iii) and 130

if the optimum temperature for plant performance is lower than that of the herbivore, then 131
herbivore pressure will increase with warming (Fig. 1c). We define herbivore pressure as 132
the fraction of primary production removed by an individual herbivore – obtained by 133
dividing herbivore performance by plant performance. 134
135
Our study aims to explore which of the many factors that could potentially influence 136
plant-herbivore interactions are likely to change given projected temperature scenarios in 137
three important Mediterranean macrophytes and their sea urchin common consumer. We 138
focus on plant growth, herbivore growth and respiration, and herbivore behaviour 139
(movement patterns, feeding choices and rates). We integrate these responses and 140
compare them to the heuristic models presented above, to assess how the strength of 141
herbivory is likely to shift as temperatures increase depending on plant species identity 142
and characteristics. As an enclosed temperate sea, the Mediterranean is experiencing 143
rapid temperature change (Coma et al., 2009; Garrabou et al., 2009) but we know very 144
little of how herbivory processes are likely to be affected in these waters. We aim to fill 145
this gap. 146
147
Materials and Methods 148
Study system 149
Our study focuses on the subtidal photophilic environments of the Mediterranean, 150
examining interactions between the main invertebrate herbivore in these systems and the 151
principal canopy-forming macrophyte species in sandy and rocky bottoms. Sandy areas 152
are typically dominated by the seagrasses Posidonia oceanica (L.) Delile and Cymodocea 153
nodosa (Ucria) Ascherson, while rocky areas are dominated by macroalgal communities 154
(largely Cystoseira mediterranea (Sauvageau)). These primary producers are all 155

consumed by the sea urchin Paracentrotus lividus (Lam.), which is the most important 156
invertebrate herbivore in the Mediterranean (Boudouresque and Verlaque, 2001). 157
158
P. oceanica is a stenohaline seagrass species with high thermal sensitivity (Gacia et al., 159
2007; Tomasello et al., 2009); shoot mortality is known to increase by 2% year-1 for each 160
additional degree of annual maximum temperature (Marbà and Duarte, 2010), with some 161
studies arguing it might become functionally extinct in the Mediterranean during this 162
century as a result of warming (Jorda et al., 2012). C. nodosa is the second most abundant 163
seagrass species occupying soft bottoms, and occurs mostly in coastal lagoons and 164
sheltered bays, where it can endure a wide range of temperatures and salinities (Pagès et 165
al., 2010; Pérez and Romero, 1992). Rocky littoral and infralittoral environments are 166
dominated by a diverse assemblage of canopy-forming macroalgae, of which C. 167
mediterranea is among the most dominant (Ballesteros, 1992). To our knowledge, little 168
is known of its response to warming. The sea urchin, P.lividus is a key herbivore both in 169
algal-dominated rocky bottoms, where it can produce barren overgrazed areas (e.g. Boada 170
et al., 2017), and in seagrass meadows, where it can consume up to 20% of annual 171
seagrass production (Prado et al., 2007; Tomas et al., 2005). In addition, in the presence 172
of predators, P. lividus shows very restricted movements, and when released from 173
predation pressure, browses much more extensively, which can have important 174
consequences for the plant resources they feed on (Hereu, 2005). Despite its ecological 175
importance, the response of this sea urchin species to warming is not clear, with adult 176
skeletons remaining unaffected by warming (Collard et al., 2016), while larval fitness 177
being reduced at high temperatures (García et al., 2015). 178
179
Study design 180

We conducted a series of modular laboratory experiments to explore the influence of 181
temperature on different components of the interaction between macrophytes and 182
herbivorous sea urchins. This included testing the effects of temperature on plant growth, 183
sea urchin growth and respiration, movement behaviour, plant consumption and plant 184
choice. The results of these controlled experiments were used to inform empirical 185
performance curves for the three dominant macrophyte species and their principal 186
invertebrate herbivore. We used these empirical performance curves to evaluate the 187
direction plant-urchin interactions will likely take as temperatures increase for each of 188
the studied plant species. We used different temperature conditions that aimed at 189
capturing current mean and maximum summer temperatures present in the NW 190
Mediterranean plus potential extremely warm temperatures. The analysis of the longest 191
data series available for sea surface temperature in the Catalan coast (l’Estartit, 1975–192
present, data provided by J. Pascual) shows that the mean summer sea surface temperature 193
is 22ºC, with maximum temperatures in August being 23.8ºC on average and with 194
temperatures above 28ºC being extremely rare (J. Pascual unpublished data, Garrabou et 195
al., 2009). Using these known ranges, we determined the different temperature treatments 196
for each of the manipulative experiments described below. 197
198
All the urchins, C. mediterranea, and P. oceanica samples used in the manipulative 199
experiments were collected near Blanes (41°40' N, 2°48' E). C. nodosa samples were 200
collected in a bay in the Ebre delta (40°35 'N, 0°37' E). To minimise inter-seasonal 201
influences all the sampling was done in spring or early summer between 2014 and 2016 202
depending on the experiment (the average SST at that time is 13-16ºC). Water 203
temperature treatments in all of the aquaria were achieved by increasing or decreasing 204

water temperatures by 1ºC every 6 hours until treatment temperatures were reached, to 205
prevent plants or animals from suffering a thermic shock. 206
207
Plant growth 208
The effect of increasing temperature on plant growth was assessed by determining either 209
leaf elongation or biomass change in each of the three plant species under different 210
temperature conditions. We collected 30 P. oceanica shoots in the field and placed them 211
in 6 aerated flow-through 200 L aquaria within an hour (5 shoots per aquarium). We 212
randomly assigned each aquarium to two growing temperature treatments (18ºC or 25ºC). 213
Aquaria were placed in full sunlight and the shoots were weighted down to ensure they 214
remained submerged. All shoots were marked near the ligula with a needle to assess leaf 215
elongation over 15 days (modified Zieman method, see e.g. Pérez and Romero, 1994). A 216
similar procedure was used for C. nodosa seagrass shoots. 45 shoots were harvested from 217
the field and placed in 9 aquaria (5 shoots per aquarium). We randomly assigned each 218
aquarium to 3 temperature treatments (20, 30, 35ºC). Again, all shoots were marked near 219
the ligula with a needle to assess leaf elongation over 15 days as described above. Note 220
that we used higher temperature treatments for C. nodosa, given this species lives in 221
shallower, often enclosed bays. Finally, for C. mediterranea, we collected 10 thalli and 222
randomly allocated each of them to one of two aerated flow-through temperature 223
treatment aquaria (18ºC or 25ºC) (5 thalli per treatment). 200 L aquaria were placed in 224
full sunlight and the thalli were weighted down to ensure they remained submerged. 225
Growth of C. mediterranea, was estimated as the change in biomass (as fresh weight, g) 226
of each alga from the start to the end of the experiment (5 weeks). Even if all thalli from 227
the same treatment were placed in the same aquarium, aquaria were big enough (200L) 228

to allow sufficiently spatial heterogeneity (i.e. differences in temperature of 0.2ºC) to 229
avoid pseudoreplication (Hurlbert, 1984). 230
231
Plant growth data was analysed in R with linear models containing the response variable 232
‘plant growth’ and the predictor variable ‘temperature’ coded as a fixed factor with 2 233
levels for P. oceanica and C. Mediterranea, and with 3 levels for C. nodosa. We tested if 234
the random grouping variable ‘aquarium’ should be added to the linear models, but 235
Akaike Information Criterion (AIC) and Log Likelihood Ratio recommended dropping 236
random effects (Zuur et al., 2009) from all the models except for the analysis of C. nodosa 237
growth. Assumptions of normality and homoscedasticity were checked graphically and 238
fulfilled in all cases (in the case of C. nodosa growth, data was square root transformed). 239
240
Herbivore growth and respiration 241
The effect of temperature on sea urchin growth was assessed by comparing the growth of 242
urchins at different water temperatures. Sea urchins of different sizes were collected in 243
the field, randomly allocated to different aquaria for each temperature treatment (16, 19, 244
22, 25, 28 and 31 °C treatments, 6 aquaria per treatment) and fed ad libitum a mix of 245
algae every three days, for the entire duration of the experiment. Each aquarium had two 246
small (<3 cm), two medium (3-5 cm) and two large (>5 cm) individuals. We 247
photographed all individuals from each aquarium and temperature treatment at the start 248
of the experiment (216 individuals) and after two months (<200). Some individuals did 249
not survive for the entire duration of the experiment and were excluded from the analyses. 250
Images were taken with the aboral side of each individual facing upwards and with a ruler 251
as measure reference. We used imageJ to estimate urchin test diameter to the nearest 252

millimetre. Growth was calculated as the increase in test diameter of each individual sea 253
urchin from the start to the end of the experiment. 254
255
The effect of temperature on sea urchin respiration was assessed by comparing oxygen 256
concentration before and after a 90-minute incubation of three replicate individuals per 257
temperature treatment (16, 19, 22, 25 and 28ºC) and for three different sea urchin sizes 258
(small [<3 cm], medium [3-5 cm], large [>5 cm]), placed in hermetic 1L glass containers. 259
Sea urchins were collected from the field and fed ad libitum a mix of algae for the entire 260
duration of the experiment. An incubation time of 90 minutes was determined in pilot 261
studies to assess the kinetics of decline in dissolved oxygen levels in the container. 262
Oxygen concentration (mg/l) was measured at the start and the end of the experimental 263
period with an optical dissolved oxygen meter (YSI, ProOBOD) placed inside the 264
container. Sensor calibration and salinity corrections were done following manual 265
instructions. Oxygen saturations below 80% were not observed in the trials. Shaking 266
avoided temperature and oxygen gradients developing within the container during 267
measurements. Oxygen consumption was calculated following the equation: 268
Oxygen consumption (mg ind-1 h-1) = [(O0-Ot)*V / T] 269
Where Oo and Ot are the initial and final oxygen concentrations (mg O2 l-1) measured, T 270
is the incubation time (h) and V is the volume (l) of the container. 271
272
The response variables ‘sea urchin growth’ and ‘sea urchin respiration’ were analysed in 273
R with linear models. Given that in this case we had 5-6 levels of the predictor variable 274
temperature, we treated it as a continuous variable instead of a factor. This allowed us to 275
test not only the linear effect of temperature on growth and respiration rates, but also the 276

quadratic term. Sea urchin size was used as a covariate. Assumptions of normality and 277
homoscedasticity were checked graphically and fulfilled in both cases. 278
279
Herbivore movement behaviour 280
A separate laboratory experiment was performed to assess the effect of temperature on P. 281
lividus movement patterns. Sea urchins of a similar size (between 2-3 cm) were collected 282
and placed in large aquaria with seawater either at 18ºC or at 25ºC for acclimation, and 283
fed a mix of P. oceanica leaves and macroalgae. To test their movement patterns at 284
different temperatures, we placed urchins in 1-metre circular tanks (void of food) either 285
at 18ºC (n=21) or 25ºC (n=14). Each sea urchin was tested only once and urchins were 286
transferred from the acclimating aquaria to tanks of the same temperature. The arenas 287
were lit with fluorescent light sources and urchin movements were recorded using stop-288
motion filming (one image taken every 30 seconds) from above. Urchins were placed at 289
the centre of the arena at the beginning of each trial and their movement was tracked until 290
they reached 10 cm from the edge of the tank. The tank was emptied, and carefully 291
cleaned at the end of each day of tests to ensure that cues from the previous trial did not 292
influence subsequent trials (e.g. Yerramilli and Johnsen, 2010). 293
294
The movement response of sea urchins to warming was determined by analysing a total 295
of 3292 images that resulted from the experiment. The x and y coordinates of each urchin 296
were obtained using an image processing toolbox in Matlab (Mathworks Ltd) and then 297
analysed with the adehabitatLT package in R (Calenge, 2011). This package computes 298
the increments in the x and y axis for each step of the trajectory (time interval = 30 299
seconds). The x and y coordinates of each individual trajectory were used to assess the 300
movement behaviour of sea urchins in each condition. We used a general numerical 301

approach based on the analysis of the qth order long-range correlations in sea urchin 302
displacements (for more information see Supplementary Information and Seuront and 303
Stanley, 2014). 304
305
Finally, for each replicate sea urchin we calculated the mean sea urchin speed and the 306
straightness index. The straightness index (Is), a measure of path tortuosity, is a 307
dimensionless number that ranges from 1 (maximum straightness) to 0 (maximum 308
tortuosity). It is the ratio of the Euclidian distance between the initial and final point of 309
the trajectory, and the sum of Euclidian distances between pairs of points separated by a 310
given time. Since different windows of time result in different Is (Benhamou 2004), we 311
calculated this index for a range of window widths. Comparisons between experiments 312
were consistent regardless of window width and, we only present the Is for a window of 313
1 step (30 seconds). 314
315
The significance of the differences between the empirical values of the function ζ(q) was 316
analysed with a linear model, considering as a response variable the ‘slope of the 317
exponents of the qth order moments (ζ(q))’ and the fixed factor ‘temperature’ (2 levels: 318
18ºC and 25ºC) as the predictor. Each individual sea urchin was considered a replicate. 319
The response variables mean sea urchin speed and tortuosity were analysed with a linear 320
model to assess the effects of the predictor temperature (fixed factor, 2 levels). Normality 321
and homoscedasticity were assessed graphically and fulfilled in all cases. 322
323
Herbivore consumption 324
The effect of temperature on consumption was assessed by comparing the amount of 325
seagrass P. oceanica, C. nodosa and algae C. mediterranea eaten by the urchin P. lividus 326

at different water temperatures in separate experiments. In each experiment, 200 L 327
aquaria were divided into 6 compartments, 5 of which contained a sea urchin with plant 328
biomass and the 6th compartment was maintained as control, with only plant material to 329
account for plant losses not due to consumption by sea urchins. Two aquaria were 330
allocated to one of 3 treatments in experiments using C. nodosa and P.oceanica , 15, 20 331
or 25°C and 4 treatments for C. mediterranea, 15, 22, 25 or 28°C. Urchins were starved 332
for 3 days before a known amount of plant material was placed in each compartment. 333
After 2-8 days (depending on the plant species) all remaining plant material in each 334
compartment was removed and weighed to estimate the biomass eaten. This was repeated 335
twice for P. oceanica and C. Mediterranea, and three times for C. nodosa. While the 336
possibility of changes in plant palatability in the course of the feeding experiment cannot 337
be ruled out, we think it very unlikely given the short duration of our feeding trials 338
compared to the rate of change in plant metabolites and toughness (i.e. in the order of 339
weeks to months, Hernán et al., 2017). 340
341
The effects of the fixed factor ‘temperature’ (3 levels: 15, 20, 25ºC) on the response 342
variable ‘sea urchin consumption’ of the seagrass P. oceanica was analysed with a 343
generalised linear mixed effects model with a Poisson distribution, due to the high number 344
of zeros of the response variable. ‘Sea urchin consumption’ was the result of subtracting 345
the initial plant biomass by the final biomass in each compartment and corrected by 346
subtracting any autogenic change (estimated from the biomass change in control 347
compartments). We used the function glmer from the package lme4 (Bates et al., 2017). 348
The random effect ‘aquarium’ could not be dropped from the model according to the 349
Akaike Information Criterion (AIC) and the Log Likelihood Ratio (Zuur et al., 2009). We 350
used a similar generalised linear model to assess the effect of the fixed factor ‘temperature’ 351

(3 levels: 15, 20, 25ºC) on the consumption of C. nodosa. However, in this case we used 352
a negative binomial distribution due to the response variable being overdispersed (Zuur 353
et al., 2009). Again, we could not drop the random effect ‘aquarium’ according to AIC 354
and the Log Likelihood Ratio. Finally, to analyse the effects of temperature on the 355
consumption of C. mediterranea, we used a simple linear model. Assumptions of 356
normality and homoscedasticity were checked graphically and fulfilled in all cases. 357
358
Herbivore choice experiments 359
An herbivore choice experiment was undertaken to determine if changes in water 360
temperature affected plant defence mechanisms. Shoots of the seagrasses P. oceanica and 361
C. nodosa were collected and stored in either 22°C or 30°C treatment aquaria for 3 weeks 362
to allow changes to plant metabolites. Seagrass traits generally respond within these time 363
frames to changes in environmental conditions (Hernán et al., 2017, 2016; Jordi F Pagès 364
et al., 2010; Ruiz et al., 2001). The alga C. mediterranea was collected and stored in 365
aquaria at 18°C and 25°C, since thalli could not survive the 30ºC treatment. Experiments 366
were conducted by placing 20 cm of seagrass or 1 g of algae from each temperature 367
treatment at either end of 5 L aquaria containing an urchin and ambient flow through 368
water. This was done for 36 aquaria containing P. oceanica treatments, 23 containing C. 369
nodosa incubated at 22 and 30°C treatments and 25 aquaria containing C. mediterranea 370
incubated at 15 and 25°C treatments. Seagrass and algae were measured or weighed to 371
determine the amount consumed by urchin after half of all the plant material in each 372
aquarium had been eaten or 10 days had elapsed. Each aquarium was treated as a replicate 373
but aquaria where no plants were eaten after 10 days were removed from the analysis. 374
For each plant species 5 aquaria containing plant material but no urchins were used as 375
controls for autogenic change. However, we did not need to correct for any autogenic 376

change, given that there was no difference in length or weight of plant material in any of 377
the controls at the end of each experiment. 378
379
To assess if there was a preference for plants incubated at each temperature treatment, we 380
calculated the difference between consumption at lower and higher temperature 381
treatments. We then checked the normality of these differences and applied a T-test or a 382
Wilcoxon rank test depending on whether normality was fulfilled or not respectively. 383
Both statistical analyses test whether the vector of differences in consumption are 384
significantly different from zero (alpha = 0.05). A significant difference indicates a 385
preferred choice. 386
387
Plant performance, herbivore performance and herbivore pressure conceptual curves 388
In order to model both plants’ and urchins’ thermal performance curves, we used 389
modified Gaussian functions obtained from Angilletta (2006). We parameterised each 390
function with values chosen to best reflect the empirical optima observed in our 391
experiments (using data from Fig. 2 for the plants, and from Fig. 3 and 4 for the 392
herbivores). These parameter values do not bear biological meaning, but were used to 393
observe the shape of the resulting curves (see supplementary information), using the web 394
app Geogebra (www.geogebra.org). For the herbivorous sea urchins, we modelled two 395
types of performance curves depending on whether sea urchin feeding preferences were 396
influenced by the incubation temperature of their feeding source (see supplementary 397
information): a continuous modified Gaussian function was used when sea urchins did 398
not modify their preference when offered plants incubated at warm temperatures; while a 399
stepwise function was used to impose a truncation of the thermal performance curves of 400
sea urchins, to mimic the effect of offering them plants incubated at warm temperatures 401

(i.e. less preferred). The stepwise function behaves as a modified Gaussian for x<2, but 402
otherwise it quickly drops to 0 (and then negative values, with no biological meaning in 403
this case). Finally, to obtain the herbivore pressure curve, we divided the thermal 404
performance function of sea urchins by the thermal performance function of each plant 405
(see supplementary). 406
407
Results 408
Plant Growth 409
Temperature significantly affected the growth rates of the three plants studied. P. 410
oceanica and C. mediterranea displayed significantly lower growth rates at warmer 411
temperatures (25 vs 18ºC; Fig. 2a,c, Table 1). In contrast, C. nodosa displayed higher 412
growth rates at temperatures as high as 30ºC, compared to cooler and warmer treatments 413
(20 and 35ºC) (Fig. 2b, Table 1). 414
415
Herbivore growth and respiration 416
Temperature significantly affected both the growth and respiration of the herbivorous sea 417
urchin P. lividus (Fig. 3, Table 1). The best model fitting our data included the quadratic 418
term of temperature, highlighting a temperature that maximises both processes at ca. 22ºC. 419
Sea urchin size also significantly affected both growth and respiration rates (see 420
supplementary Fig. S1a,b). 421
422
Herbivore behaviour 423
Sea urchin movement patterns in the lab did not change significantly between temperature 424
treatments. Their trajectories were similar in terms of tortuosity (Fig. 4a), and long range 425
correlations (Fig. 4c). There was a faint trend of slower velocities at warmer temperatures 426

(Fig. 4b), but this was not significant at ! = 0.05. As is typical for this species (Pagès, 427
2013) their trajectories were in the realm of superdiffusive movements, nearer to ballistic 428
than Brownian motion (Fig. 4c). 429
430
Herbivore consumption and feeding choice experiments 431
Sea urchin feeding rates on both seagrass species were maintained from 15 to 20ºC, but 432
then plunged at the warmest treatment (25ºC) (Fig. 5a,c, Table 1). Moreover, for both 433
seagrass species, sea urchins preferred seagrass leaves that had been incubated at cooler 434
temperatures (Fig. 5b,d, Table 1). In contrast, sea urchin feeding rates on the alga C. 435
mediterranea were sustained even at higher temperatures, although with a negative trend 436
towards the warmest treatments (Fig. 5e, Table 1). Sea urchins did not display any 437
preferences between algae incubated at cool or warm treatments (Fig. 5f). 438
439
Discussion 440
Increasing temperatures are likely to trigger a complex suite of responses in the dynamics 441
of plant-herbivore interactions, with potentially far-reaching consequences for 442
Mediterranean macrophyte communities. While it is clear that some plant species, like 443
Posidonia oceanica and Cystoseira mediterranea will be pushed beyond their optima and 444
show decreased growth, Cymodocea nodosa may actually benefit due to its high thermal 445
optimum. Together with the other responses to temperature evidenced here, which 446
include sea urchin growth, respiration, feeding rates and plant susceptibility to 447
consumption, it appears that while the strength of the plant-sea urchin interaction may 448
weaken for seagrass species – quite considerably in the case of C. nodosa – herbivory 449
pressure may actually increase on the macroalga (see these results using the framework 450
of our heuristic models in Fig. 6). 451

452
As plant-herbivore interactions are the outcome of several processes acting together, 453
changes in any one of these processes could influence the interaction. The picture further 454
gains in complexity because as found elsewhere (Sentis et al., 2015; Van De Velde et al., 455
2016), not all processes are equally influenced by temperature. While growth and feeding 456
showed clear directional responses, plant susceptibility to being consumed exhibited 457
contrasting responses, and urchin movement did not change. In addition, these responses 458
were highly species specific, dependent on the inherent tolerance limits of each species 459
(Kordas et al., 2011). Thus, while both P. oceanica and C. mediterranea showed higher 460
growth at lower temperatures (as is typical for most temperate species, Lee et al., 2007), 461
C. nodosa grew best at 30°C. The responses of their common sea urchin consumer to 462
increasing temperatures varied. Surprisingly, while growth and respiration were highest 463
at intermediate temperatures (ca. 22ºC, see Fig. 3), P. lividus did not modify its movement 464
behaviour with increasing temperatures. Consumption rates did not correspond well with 465
urchin growth either; at 25°C, urchins had practically stopped eating. Mismatches 466
between consumption and metabolism/growth are common in many species including 467
urchins, likely representing physiological limits to plasticity (Lemoine and Burkepile, 468
2012). In addition, while the palatability of the two seagrass species apparently declined 469
(possibly as a result of increased production of secondary compounds (Vergés et al., 470
2007b), but see Hernán et al., 2017), this was not true for the macrophyte C. 471
mediterranea. These differences can lead to differential susceptibilities of species to 472
herbivory pressure across the seascape as temperature increases (Peñuelas and Staudt, 473
2010; Poore et al., 2013). 474
475

Global warming is changing the odds for Mediterranean macrophytes by creating clear 476
‘winners’ and ‘losers’ among the species that dominate these waters at present. What is 477
interesting, though, is that these patterns arise not as a result of a single mechanism or 478
process that changes with temperature, but because of the interplay between several 479
mechanisms that together shape the plant-herbivore interaction. Thus, the expected 480
decrease in herbivory pressure with temperature for C. nodosa (see Fig. 6b), results not 481
merely from a faster growth, and thus increased productivity, but also because it reduces 482
its palatability to urchins (Figs. 5d) and because sea urchins consume much less at higher 483
temperatures (Fig. 5c, independent of seagrass palatability). Consequently, C. nodosa is 484
likely to be released from herbivory pressure as temperatures increase (Fig. 6b). 485
Similarly, while the growth of P. oceanica decreases at higher temperatures (Fig. 2a), 486
given that in parallel urchin growth decreases (Fig. 3a), as does consumption (Fig. 5a) 487
and palatability is reduced (Fig. 5b), the impact of herbivory may still decrease or remain 488
unchanged for this species (Fig. 6a). In sharp contrast, the canopy-forming macroalga, C. 489
mediterranea is probably most at risk from increasing temperatures, once again as a result 490
of a suite of changes in mechanisms affecting plant-herbivore interactions. Thus, while it 491
reduces its growth in elevated temperature conditions (Fig. 2c), urchin consumption 492
remains high until 25 ºC (Fig. 5e), while palatability does not decrease at the highest 493
temperatures (Fig. 5f). If anything, the strength of this algae-herbivore interaction is set 494
to increase with ocean warming (Fig. 6c). This is particularly worrying, given that of all 495
the systems we studied, benthic macroalgal systems are most prone to state shifts, often 496
precipitated by urchin overgrazing (Boada et al., 2017; Pinnegar et al., 2000). 497
498
In interpreting these results, it is essential to remember that there are several additional 499
mechanisms that we have not considered. Our laboratory experiments and the 500

performance curves test the current tolerance limits of the species in question to changing 501
conditions. Of course, as temperatures change, it is quite possible for species to acclimate 502
within the limits of their phenotypic plasticity, or genetically adapt to increasing 503
temperatures by selection of the fittest genotypes (Lee et al., 2007). While most plants 504
show considerable capacity to adjust their photosynthetic traits to enhance their 505
performance, this ability varies considerably between species (Lee et al., 2007). 506
Consumers, in contrast, tend to be more sensitive to warming (Voigt et al., 2003). The 507
consumer P. lividus, however, is a thermal generalist that experiences a wide range of 508
environmental temperatures, ranging from 10 to 30ºC (Boudouresque and Verlaque, 509
2001), and is potentially exposed to extremes of temperatures in shallow coastal bays. 510
How plants and animals acclimate or adapt to increasing temperatures will significantly 511
change performance optima and result in further changes in the plant-animal interaction. 512
As species are pushed to the edge of their tolerance limits, we should expect a host of 513
individual and population-level consequences that will also be critical to ecosystem 514
functioning (Bennett et al., 2015; Tylianakis et al., 2008). However, in a field experiment 515
using a thermal plume, Garthwin et al. (2014) showed that a meadow of the seagrass 516
Zostera muelleri that had been exposed to sustained higher temperatures for 30 years had 517
similar levels of growth and herbivory than un-impacted meadows nearby. Similarly, 518
Morelissen and Harley (2007) found that even though individual species may be 519
influenced by temperature, plant–herbivore interactions may not necessarily be. Other 520
studies, in contrast, have found that warming tends to increase interaction strength 521
between producers and consumers (O’Connor et al., 2009; Poore et al., 2013). Our 522
heuristic models help to explain why warming may or may not modify plant-herbivore 523
interaction strength by influencing some of the components of these interactions (see 524
rationale at the end of introduction). Moreover, our results show that the same amount of 525

warming might have opposing effects on Mediterranean macrophyte-herbivore 526
interactions contingent on species specific thermal performance. We must apply caution 527
when interpreting our heuristic models (Fig. 6), given the low number of temperature 528
levels used in the plant growth experiments (see Fig. 2). As a sensitivity exercise, we 529
examined the effect of shifting the plants’ thermal performance curves around their 530
optima, leaving the urchins’ performance curves unchanged (see results in the 531
supplementary, Figs. 8-10). The sensitivity analysis confirmed the results observed in 532
Fig. 6, given that the changes to the resulting herbivore performance curves changed 533
minimally. In the future, however, we might have to introduce more actors into the 534
picture, as the sparid Sarpa salpa (L.) and the thermophilous black sea urchin Arabacia 535
lixula (L.) will also likely be affected by warming (Gianguzza et al., 2011; Privitera et al., 536
2011). Moreover, warming is already causing a host of tropical species, such as the 537
herbivorous rabbitfish (Siganus luridus and S. rivulatus), to migrate to temperate areas 538
(Vergés et al., 2014), altering local interactions and potentially precipitating algal barrens 539
(Sala et al., 2011). 540
541
As far as we are aware, this is the first study to explicitly examine how warming mediates 542
key plant-animal interactions (that structure Mediterranean macrophyte communities in 543
this case) at this diversity of scales (from the behavioural, metabolic, to individual level). 544
Moreover, the inclusion of these responses in simple heuristic models demonstrates that 545
the complex effects of warming on plant-animal interactions are the result not merely of 546
their effect on each individual species’ survival, but also of temperature changing a suite 547
of plant and animal responses (including palatability and potentially behaviour [not in 548
this case]) that are difficult to predict a priori. This can lead to unexpected results. 549
Ecological interactions have developed over evolutionary time scales and are the 550

consequence of a dynamic interplay between each species attempting to adjust to 551
environmental changes as well as ensuring its own evolutionary success. Rapid 552
environmental changes are accelerating this dynamic process, stretching the ability of 553
species to cope with the rate of these changes. How these interactions play out in real-554
world scenarios, where several species interact both directly and indirectly in a dizzyingly 555
complex network of interactions, is difficult to conceive, especially given that warming 556
experiments generally assume that individual organisms that have been experimentally 557
warmed in short-term experiments, will respond in a similar way as individuals whose 558
ancestors have been exposed to the same level of warming over decades. In any case, our 559
results show that not all of these consequences are going to be negative, since some 560
species may be able to compensate for the effects of temperature, leaving the interaction 561
itself unchanged. Some structural species, like C. nodosa in the case of this study, may 562
even emerge as clear winners in these scenarios. Much will depend on the plasticity and 563
adaptive capacity of the individual actors within the interaction to this change. It may be 564
useful to think of interactions themselves as having an inherent plasticity, adapting in a 565
coupled way to changing conditions. There will be limits to this joint plasticity, breaking 566
down either as its individual actors cross tolerance thresholds, or when the interaction 567
itself becomes too strong or too weak (see Fig. 6). Clearly, as human-induced rapid 568
environmental change continues apace, it is pushing us to investigate more carefully what 569
governs species interactions, in order to understand how they will respond to change. 570
Knowing what to expect of these ecosystems in the near future, may help us manage them 571
more effectively. We believe we can be moderately optimistic for Mediterranean seagrass 572
communities given their expected unchanged or reduced herbivore pressure as warming 573
continues. However, our study should serve as an early warning for Mediterranean 574
macroalgal communities, which are already subject to strong top-down control due to the 575

loss of top-predators (Pinnegar et al., 2000), but which are likely to be subjected to even 576
higher herbivore pressure. 577
578
Acknowledgments 579
We would like to thank Mònica Vergés, Donatella Palomba, Ana Calvo, Lluís Casabona, 580
Sandra Muñoz, Elisabet Nebot and Irene Giralt for their help in setting up and following 581
the experiments in the lab. We thank Liliana Salvador for providing the Matlab script that 582
allowed us to perform the image analysis of sea urchin movements. We would also like 583
to thank Josep Pascual, observer from l’Estartit meteorological station, for kindly letting 584
us use his weekly seawater temperature time series; David Alonso for his help in 585
discussing the heuristic models, and two anonymous reviewers for their comments. The 586
Spanish Ministry of Science and Innovation funded this research (projects CMT2010-587
22273-C02-01-02 and CMT2013-48027-C03-R) and supported JB (scholarship BES-588
2011-043630). The Spanish National Research Council supported RA’s visitorship 589
(CSIC-201330E062). TS was supported by an Australian Government Endeavour 590
Fellowship. JFP acknowledges financial support from the Welsh Government and Higher 591
Education Funding Council for Wales through the Sȇr Cymru National Research Network 592
for Low Carbon, Energy and Environment. Support to FT was provided by the Ramón y 593
Cajal Programme (RYC-2011-08572). 594
595

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832
833

Figure legends 834
Fig. 1. Conceptual model of the potential outcomes of plant-animal interactions in a warming 835
Mediterranean. The arrow above the dashed vertical lines show the direction of warming. (a) 836
When both plant and herbivore thermal performance curves are of similar shape and display the 837
same optimal temperature, warming will not produce any changes to herbivore pressure1. (b) If 838
plant performs better at warmer temperatures compared to the herbivore, herbivore pressure1 will 839
decrease with warming. (c) In contrast, herbivore pressure1 will increase with warming, if the 840
herbivore performs better at warmer temperatures compared to the plant. See the supplementary 841
for more information on the shape of these theoretical curves. 842
1Here, we conceptually define herbivore pressure as the result of dividing herbivore performance by plant performance. 843
844
845
846
Herbivore
performance
Herbivore pressure
Temperature
a b c
Plant performance
Plant performance
Herbivore
performanceHerbivore pressure
Temperature
Plant performance
Herbivore
performanceHerbivore pressure
Temperature

Fig. 2. Plant growth at different incubating temperatures. (a) Posidonia oceanica seagrass, (b) 847
Cymodocea nodosa seagrass, (c) Cystoseira mediterranea macroalgae. Asterisks denote 848
significant differences. Significance codes p < 0.001 ‘***’, p < 0.05 ‘*’. 849
850
851
852
*
0
1
2
3
18 25
Temperature (ºC)
Plant growth (cm shoot−1 day−1)
Temperature (ºC)
Plant growth (mg DW shoot−1 day−1)
*
0.0
0.5
1.0
1.5
18 25
Temperature (ºC)
Plant growth (g WW plant−1 day−1)
a b c
●
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***
20 30 35
0
1
2
3
***

Fig. 3. Sea urchin thermal performance curves (a) for growth and (b) respiration rates. Solid lines 853
correspond to the predictions of a linear model applied to the data sets using the quadratic term 854
of temperature as a predictor, hence the parabolic shape of the curve. Shaded areas define the 855
95% confidence intervals around fitted values. Sea urchin size significantly affected both growth 856
and respiration curves as well (see Fig. S1 from the supplementary). 857
858
859
860
Temperature (ºC)
Sea urchin respiration rate (mg O2 h-1)
a b
0.00
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1.00
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Sea urchin growth (mm)
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Fig. 4. Sea urchin movement behaviour at cool and warm water temperatures. (a) Temperature 861
did not affect the tortuosity of sea urchin trajectories, (b) nor their mean speed. (c) The analysis 862
of sea urchin trajectories at different scales (see methods) did not find any differences between 863
the trajectories of urchins wandering in cool (blue solid line) or warm (red solid line) conditions. 864
The dotted line denotes a ballistic trajectory, while the dashed line represents Brownian motion. 865
866
867
868
18ºC 25ºC
Tortuosity
0.0 0.2 0.4 0.6 0.8 1
02468
02468
q
ζ(q)
25ºC
18ºC
18ºC 25ºC
Mean speed (cm minute-1)
0 2 4 6 8 10 12
a b c

Fig. 5. Sea urchin consumption rate at increasing temperatures and sea urchin choice of plants 869
incubated at cool and warm temperatures. (a, b) correspond to the seagrass Posidonia oceanica, 870
(c, d) to the seagrass Cymodocea nodosa, and (e, f) to the macroalgae Cystoseira mediterranea. 871
Significance codes p < 0.001 ‘***’, p <0.01 ‘**’, p < 0.05 ‘*’. For the preference plots (b,d,f), 872
effects are significant (P ≤ 0.05) where confidence intervals do not intercept 0. 873
874
875
●
●
**
0.00
0.25
0.50
0.75
1.00
15 20 25
Aquarium temperature (ºC)
Consumption (g WW day−1)
●
**
0.0
0.1
0.2
0.3
0.4
0.5
15 20 25
Aquarium temperature (ºC)
Consumption (g WW day−1)
●
***
0
1
2
3
15 22 25 28
Aquarium temperature (ºC)
Consumption (g WW day−1)
a
c d
e f
b
●
−20 −10 0 10 20
Preference (cm)
●
−20 −10 0 10 20
Preference (cm)
●
−20 −0.5 0 0.5 1.0
Preference (g)

Fig. 6. Conceptual model of the outcomes of plant-animal interactions in a warming 876
Mediterranean. The arrow above the dashed vertical lines show the direction of warming. (a) We 877
expect herbivore pressure on Posidonia oceanica seagrass to keep unchanged with warming, 878
given that both the sea urchin and the seagrass display similar optimal temperatures of 879
performance; however, sea urchins’ feeding rates plunge when offered P. oceanica seagrass 880
leaves from plants incubated at warm temperatures, hence the decrease in herbivore pressure 881
when warming increases (from the blue to the red dotted lines). (b) We expect the herbivore 882
pressure between urchins and the seagrass Cymodocea nodosa to decrease with warming given 883
the warmer optimal temperature of performance of the seagrass compared to the herbivore. The 884
herbivore pressure curve is expected to be especially steep at higher temperatures, given the lower 885
feeding rates of urchins when offered plants incubated at warm temperatures. (c) We expect the 886
herbivore pressure between the macroalga Cystoseira mediterranea and the sea urchin to increase 887
with warming, given the low performance of the macroalga at warm temperatures, while sea 888
urchins still display high feeding rates (see also Fig. 5). See methods and supplementary materials 889
for more information on the shape of these curves. 890
891
892
Plant growth
Plant growth
Herbivore
performance
Herbivore
performance
Herbivore
performance Plant growth
Herbivore pressure
Temperature
Herbivore pressure
Temperature
Herbivore pressure
Temperature
3426 30221814106 3426 30221814106 3426 30221814106
a b c

Table 1. Summary of the different analyses performed. Model: type of model used in R (either linear [lm()/lme()], generalized linear with Poisson 893
distribution [glmer()], generalized linear with negative binomial distribution [glmer.nb()] or non-parametric Kruskal-Wallis [kruskal.test()]). 894
Random: type of random effects introduced into the model. Resp. Transf.: Type of transformation applied to the response variable. Df: Degrees of 895
freedom. Statistic: Depending on the model used the statistic used was Fisher’s F, Chi-squared, or the Kruskal-wallis. 896
Significance codes p < 0.001 ‘***’, p <0.01 ‘**’, p < 0.05 ‘*’, p > 0.05 ‘ ’. 897
898
899
Response variable Model Random Resp. Transf. Effect Sum squares Df Statistic P-value 900
P. oceanica growth Linear - - Temperature 6.53 1 7.55 0.010 * 901
Residuals 24.23 28 902
903
C. nodosa growth Linear 1|Aquarium sqrt(x) Temperature - 2 21.12 2.6 10-5 *** 904
905
C. mediterranea Linear - - Temperature 1.14 1 7.48 0.026 * 906
growth Residuals 1.22 8 907
908
Sea urchin growth Linear - - Temperature 1.70 1 18.89 2.9 10-5 *** 909
I(Temperature^2) 1.74 1 19.33 2.4 10-5 *** 910
Size class 1.21 2 6.74 0.002 **
911
Residuals 10.99 122 912
913
Sea urchin respiration Linear - - Temperature 0.15 1 36.48 4.6 10-7 *** 914
I(Temperature^2) 0.17 1 41.69 1.2 10-7 *** 915
Size class 2.62 2 319.38 <2 10-16 **
916
Residuals 0.16 39 917
918
P. oceanica glm Poisson 1|Aquarium round(x*100) Temperature - 2 32.28 9.8 10-8 *** 919
consumption Size class - 1 11.48 0.0007 *** 920
921

C. nodosa glm negative 1|Aquarium round(x*10) Temperature - 2 9.95 0.007 ** 922
consumption binomial 923
924
C. mediterranea Linear - - Temperature 6.99 3 21.19 2.0 10-6 *** 925
consumption Residuals 2.20 20 926
927
Difference in t-test - - - - 21 2.10 0.047 * 928
consumption 929
P.oceanica 930
931
Difference in Wilcoxon - - - - 20 155.5 0.050 * 932
consumption 933
C. nodosa 934
935
Difference in t-test - - - - 23 0.517 0.610 936
consumption 937
C. mediterranea 938
939
940
941