Abstract
Comparing populations across temperature gradients can inform how global warming will impact the structure and function of ecosystems. Shoot density, morphometry and productivity of the seagrass Posidonia oceanica to temperature variation was quantified at eight locations in Sardinia (western Mediterranean Sea) along a natural sea surface temperature (SST) gradient. The locations are spanned for a narrow range of latitude (1.5°), allowing the minimization of the effect of eventual photoperiod variability. Mean SST predicted P. oceanica meadow structure, with increased temperature correlated with higher shoot density, but lower leaf and rhizome width, and rhizome biomass. Chlorophyll a (Chl-a) strongly impacted seagrass traits independent of SST. Disentangling the effects of SST and Chl-a on seagrass meadow shoot density revealed that they work independently, but in the same direction with potential synergism. Space-for-time substitution predicts that global warming will trigger denser seagrass meadows with slender shoots, fewer leaves, and strongly impact seagrass ecosystem. Future investigations should evaluate if global warming will erode the ecosystem services provided by seagrass meadows.
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Introduction
Global warming is expected to have profound consequences on biodiversity and functioning of major systems on Earth1,2. The impact of temperature increase has been measured over the past two decades3,4,5,6, but understanding how this physical forcing affects ecosystems is unclear, particularly in the sea7,8,9. This, however, is critical for predicting the consequences of global warming and identifying mitigation and restoration actions.
Much of experimental temperate marine coastal ecology is focused on elucidating how temperature increases will impact the physiology, fitness and distribution of organisms. Two main approaches are being employed to examine warming effects: (I) experiments with artificial heating such as mesocosms10,11,12,13 and (II) monitoring the response of organisms to temporal or spatial variation in temperature, across years14,15,16 or latitude17,18,19,20. Each of these approaches has advantages and drawbacks. Manipulative experiments may examine responses to temperature or patterns not yet under natural conditions, such as intense, long lasting heat waves21,22,23. Experiments are typically done at small spatiotemporal scales and often ignore covarying abiotic conditions including light availability24, UV irradiation and acidification25,26, or biotic effects such as predation27,28. Conversely, comparing populations across sites with varying temperatures, such as latitudinal gradients, can provide information about the role of warming on the structure and function of future ecosystems, but it is often difficult to disentangle temperature from other covarying effects, such as photoperiod, light quality and quantity29. Moreover, marine sea surface temperature (SST) is commonly linked to chlorophyll-a (Chl-a), with high-temperature locations having low-nutrient availability and Chl-a30,31 and high light attenuation32,33. Problems between laboratory and field results are not surprising, since temperature, nutrients and irradiance effects may be cumulative or antagonistic depending on the species and system.
Therefore, uncertainties with warming effects on marine biota are also indirectly due to co-variation between SST and Chl-a. While there are latitudes where these patterns are predictable, regional anomalies are also found especially where upwelling occurs34. Nevertheless, SST increase does not necessarily imply decreasing Chl-a, suggesting that complex processes, such as advection, define sea water conditions34. Further variability of marine species response to warming comes from natural variation in physiological, morphological and life-history attributes (functional traits) among populations, as there is evidence of adaptation to spatial temperature gradients in many organisms and at different scales35,36,37,38,39. Species phenotypic gradients presumably can reflect patterns of genetic differentiation and local adaptation, making additional data potentially necessary to estimate how much of observed phenotypic differences are due to plastic responses versus adaptive differentiation between populations.
Understanding future warming effects on foundation species, as marine macrophytes, is pivotal to predict their distribution and physical structure40, as temperature is thought the most important range limiting factor41. Seagrasses are valuable providers of coastal ecosystem services including, carbon sinks, nursery grounds, habitat, nutrient cycling, sediment stabilization, trophic transfer to adjacent habitats42,43,44 and protection from erosion45,46. Posidonia oceanica (L.) Delile is a slow-growing seagrass, endemic to the Mediterranean, experiencing widespread decline due to multiple local anthropogenic stressors47. The abrupt decline experienced by P. oceanica from recent heatwaves48, however, has seriously questioned its persistence for the coming decades40. Due to its vulnerability in aquaria and slow growth, laboratory experiments have been limited and controversial. Nevertheless, plants from warm thermal environments were found to activate a suite of physiological49 and molecular mechanisms50,51,52 to tolerate simulated heatwave exposures, whereas phenological response to warming likely involves higher flowering53 and denser meadows54.
This is a space-for-time substitution, a method for studying slow ecological processes, where the relationships between ecological variables are studied at sites that are assumed to be at different stages of development55. This study is based on the assumption that plant functional traits vary along environmental gradients and potentially predict responses to environmental change. Thus, to examine the performance of P. oceanica to future temperature conditions, we measured shoot density, morphometry and productivity at eight locations in Sardinia (western Mediterranean Sea) along a natural gradient of water temperature. Despite similar latitude (minimum interference of photoperiod), the western locations are generally cooler than the eastern sites, with differences in SST comparable to climate change scenarios for the twenty-first century for the Mediterranean Sea (peaking at 2.6 °C in 210056,57), making this space-for-time substitution informative for projections of trait changes over the next decades. Chl-a, a proxy of light irradiance, was a further driver of seagrass structure. P. oceanica is currently in the EU Marine Strategy Framework Directive monitoring protocols58.
Results
Seagrass variability
Shoot density changed considerably between Sardinian coasts (Table 1 and Fig. 1) as well as leaf width which was larger on the west than on the east side (Table 1 and Fig. 2), although both variables were significant across locations and areas. All other morphometrical variables were significantly affected by location and area, except for necrotic leaf portion that was only dependent on the area (Table 1 and Fig. 2).
Posidonia oceanica mean (+ SE) shoot density (# of shoots/m2) at each location: in blue the western and in red the eastern. For each location data of the three areas are shown (n = 4).
Posidonia oceanica. Mean (+ SE) morphometry (left) and productivity (right) variables. Morphometry: # of leaves/shoot, leaf width (cm), length (cm), and necrotic leaf portion (%). Productivity: # of scales/shoot*year, rhizome elongation (cm/year), rhizome width (cm/year) and rhizome biomass (g/year) across locations, in blue the western and in red the eastern. For each location data of the three areas are shown (n = 20).
The reconstruction analysis showed that annual plant productivity changed between coasts only in terms of number of scales (remnant leaf sheats) and rhizome width, being lower on the east coast. Rhizome width and biomass were significantly dependent on the location, while all other variables were highly area dependent (Table 1 and Fig. 2).
Relationship between seagrass and environmental variables
Multiple regressions retained only mean temperature in four models indicating that leaf width, number of scales, rhizome width and rhizome biomass were negatively related with mean temperature. Shoot density was related to mean temperature and Chl-a, as well as the number of scales and rhizome width (Table 2, Figs. 3 and 4). Specifically: I) increased shoot density was correlated with increased mean temperature, while an opposite trend was found for the leaf width, number of scales and rhizome width (Fig. 3) and II) reduced shoot density, number of scales and rhizome width were correlated with increased Chl-a (Fig. 4). The response variables where models retained Chl-a as the only explanatory variable, were the number of leaves and rhizome length, which increased and decreased, respectively, with increasing Chl-a (Table 2, Figs. 3 and 4).
Plots from the multiple regression model of Posidonia oceanica response variables vs. mean temperature (°C).
Plots from the multiple regression model of Posidonia oceanica response variables vs. mean Chl-a (mg/m3).
Finally, the regression model indicated that shoot density was negatively related to the number of leaves and leaf width (Table 3).
Discussion
Posidonia oceanica morphometry and productivity were linked to the thermal environment. Increased temperature triggered higher shoot density, but lower leaf and rhizome width, fewer scales and lower rhizome biomass. Additionally, Chl-a was a temperature independent driver of the plant performance. Temperature strikingly affected shoot density, increasing gradually across the thermal gradient from 496.1 ± 21.6 to 829.9 ± 43.2 shoots/m2 (mean ± SE n = 12) at AHO and REI, respectively. Shoot density is the most common descriptor of P. oceanica meadows defining its conservation status (Marine Strategy Framework Directive) assuming that higher densities reflect lower human influence and better marine water conditions. However, the density classes distinguished by previous authors (reviewed by59), ignore natural environmental variation. Since our data were collected unaffected from local anthropogenic disturbances, our results highlight that thermal environment is critical factor in determining plant shoot density, providing evidence of the need to refer the seagrass density classes to the mean temperature environment.
Our results revealed a strong spatial association between plant traits and temperature across a gradient suggest that future warming is predicted to produce denser P. oceanica meadows. This finding is corroborated by long-term correlative data revealing that shoot density is a plant trait that varies with thermal environment54, providing evidence that the plant would rearrange (increasing the number of modules) the meadows structure with warming (Fig. 5). The fact that Chl-a is inversely related to the meadow density will sharpen this pattern, as this influence is disentangled from temperature effects and because both drivers work in the same direction, enhancing shoot density and potentially producing synergistic effects. In fact, numerical models of future Chl-a due to anthropogenic climate change, generally suggest a decrease in globally integrated primary productivity driven by a reduction in supply of macronutrients60,61,62,63. Nevertheless, predicting meadow structure based on the relationship between spatial pattern of plant traits and the environment assumes that the seagrass traits could change proportionally to climate change54, although the species may respond to finer-scale changes in environmental variables that cannot be predicted using averages64,65.
Summary of the results. Shoot density, morphometry and productivity was measured to examine the performance of the seagrass to the thermal gradient. Warming will trigger denser seagrass meadows with slender shoots (lower rhizome width and biomass) and fewer leaves (scales).
Regarding mechanisms regulating the Chl-a-shoot density interaction, our data support the hypothesis that different light conditions due to the phytoplankton density (not nutrient availability) are involved, although manipulative experiments are needed. In fact, evidence of reduction of P. oceanica shoot density with depth are commonly gained66,67,68, supporting the hypothesis that light extinction is pivotal69. observed that seagrasses growing in low light reduce shoot density and above-ground biomass as an acclimation response to reduce self-shading within the canopy.
Shoot density changes induced by the climate change, however, will involve other phenological traits, such as leaf width and number of leaves. Their dependence on shoot density has been interpreted as the result of self-organization to shading70,71,72,73,74. Reducing the size of ramets to attenuate intraspecific competition is a common pattern in clonal plants73,74. Productivity of P. oceanica was not directly dependent on shoot density, but it seems that it will be contrastingly affected by the temperature and Chl-a, so that predicting the number of scales and rhizome width in coming decades is not obvious and likely dependent on the strength of their associations. Therefore, the prediction about the productivity that can be made on the trait gradients (trait variation along environmental gradient) regards the decrease in rhizome biomass and length affecting the plant robustness through decades.
Future changes in temperature and Chl-a, may drive P. oceanica morphometry and productivity patterns that will affect the ecosystem services that seagrass meadows currently provide. Quantification of seagrass services, however, have never been provided on a structure-specific basis75,76 and we believe this might become a relevant issue. Indeed, in a future warmer Mediterranean Sea, where summer mean SST increase will likely peak 2.9 °C and 2.7 °C for the end of the century on the east and west Sardinia coasts, respectively56, P. oceanica leaf canopy, structured by higher shoot density with bundles of a lower number of leaves smaller in width, can create a different habitat and associated community. Similarly, whether the reduction in rhizome width and biomass has consequences on both the vulnerability of plants to storms and Carbon storage remains unanswered.
This study shed light on how seagrass systems could respond to climate change, independently of the effects of extreme events (such as heat waves), as the latter undoubtedly affect deleteriously the seagrass structure with die-offs47,77,78. Nevertheless, the extent the phenotypic gradients of the seagrass systems depend on acclimation versus adaptation processes should be measured. However, the analysis of processes involved in phenotypic plasticity and the possibility that such plastic responses might be adaptive is complex for both the long-life cycles and slow growth of most of the seagrasses that impede manipulative experiments and trans-generation assessments79. Further space-for-time substitutions to predict functional traits changes due to global warming in seagrasses are necessary. Future trait gradients analysis should consider wider thermal range to sharpen our prediction and establish how closely the highest mean temperature used in the model stands are to the tolerance limit of the seagrass.
Methods
Study locations and design
This study was done on the western and eastern coasts of Sardinia (Italy, western Mediterranean Sea, Fig. 6) where differences in water conditions are evident. The western coastline receives Atlantic waters directly through the Western Mid-Mediterranean Current and is also influenced by coastal upwellings80. In contrast, the eastern coast is affected by the warm Algerian Current81.
Study locations and areas along the Sardinian coasts. Left-hand map shows locations on the west (in blue) and east (in red) coasts: AHO Alghero, BOS Bosa, SIN Penisola del Sinis, GON Gonnesa, COM Capo Comino, CGO Cala Gonone, ARB Arbatax, REI Costa Rei. Right-hand inset maps show location of each study area within each location. Map produced with QGIS 3.16 software.
Seagrass meadows unaffected from local anthropogenic disturbances (e.g. harbour, fish farming, and urbanisation) were sampled in eight different locations (Fig. 6), with a hierarchical design: for both coasts of Sardinia, four locations were selected (Alghero = AHO, Bosa = BOS, Penisola del Sinis = SIN, and Gonnesa = GON for the west and Capo Comino = COM, Cala Gonone = CGO, Arbatax = ARB, and Costa Rei = REI for the east) from 40°34' to 39°15'N. At each location, three areas 100 s of m apart were randomly selected and sampled at a depth of 10 m.
Environmental data
For each location the SST for the years 2010–2019 were obtained by the Group for High Resolution Sea Surface Temperature (GHRSST) daily, 1 km resolution SST (G1SST) dataset produced by JPL NASA (https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.html) as a proxy of 10 m subtidal temperature82. Moreover, 1 Day Composite, 4 km resolution Chlorophyll-a data from NASA's Aqua Spacecraft (https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1chla1day.html) were extracted for the same years. For the warm season 1st May–31st October (the period of the largest differences between the two coasts), daily SST and Chl-a data were averaged through years (Fig. 7) and the mean, maximum and variance for both variables were calculated (Table 4).
Mean variation from 1st May to 31st October in (a) temperature (°C), in blue the western locations and in red the eastern, and (b) Chl-a (mg/m3), in green the western locations and in yellow the eastern). The y axis of the latter plot is log2 scale.
Seagrass data collection
From 20th June to the 10th July 2020 the density of Posidonia oceanica shoots was estimated using 40 × 40 cm quadrats haphazardly placed within meadows (n = 4) and 20 orthotropic shoots were collected at each area. A total of 480 shoots were collected, transported to the laboratory and stored frozen. Sampling was non-lethal and followed the guidelines approved by the Marine Strategy Framework Directive (EC 2008) for the monitoring program. P. oceanica shoots were deposited as voucher specimens at the University of Sassari Herbarium (SS, collection 2000/, ID number: SS#14159-SS#14166).
The leaf length, leaf width, number of leaves and necrotic leaf portion were measured following83 to estimate P. oceanica shoot morphometry. Furthermore, the age reconstruction technique based on the cyclic annual variation of the sheath thickness84 was used to estimate shoot productivity through years: therefore, the number of leaves (by counting the scales), rhizome elongation, rhizome width and biomass per year were measured on each shoot (after drying rhizomes for 48 h at 60 °C).
Data analysis
For each P. oceanica variable (shoot density, leaf length, leaf width, number of leaves, necrotic leaf portion, number of scales, rhizome elongation, rhizome width and rhizome biomass) a three-way anova was run to test the effect of ‘Coast’ (C, west vs east), ‘Location’ (L, 4 levels) random nested in C, and ‘Area’ (3 levels) random nested in L. Cochran’s test was used to test variance homogeneity.
With the aim of finding a relationship between the P. oceanica and the explanatory variables (mean temperature, maximum temperature, temperature variance, mean Chl-a, maximum Chl-a and Chl-a variance, Table 4), we ran separate multiple linear regression models for each P. oceanica response variables. No linear regression was run on leaf length since it is largely affected by herbivore pressure, and it cannot be evaluated unless controlled experiments are performed85. Data exploration followed86: outliers were inspected with Cleveland dotplots (and removed in four cases) and normality with histograms and Q–Q plots. Rhizome biomass was square root transformed. Collinearity between continuous explanatory variables was inspected with pair-plots, and variance inflation factors (VIFs) were calculated. Several significant correlations were found, particularly, mean temperature, maximum temperature and temperature variance were correlated to each other, as well as mean Chl-a, maximum Chl-a and Chl-a variance. Thus, only mean temperature and mean Chl-a (the variables with VIFs < 3) and their interaction were considered in the analyses, even though the results obtained for each of them can be extended to all the correlated descriptors.
The explanatory variables used in the final model were chosen with a backward selection process80. Model validation was run calculating and plotting: (I) standardized residuals against fitted values to assess homogeneity; (II) histogram of the residuals to verify normality; (III) residuals against each explanatory variable that was used in the model; (IV) residuals against each explanatory variable not used in the model. At the end, the model was assessed for influential observations using the Cook distance function.
Correlations between P. oceanica shoot density and all the other plant variables were explored at the scale of area to identify eventual plant traits that might derive from a compensatory performance of the plant to temperature and Chl-a. Thus, following the same methodological approach, another multiple linear regression was run to identify the relationship between shoot density and the other response variables. Since rhizome width was correlated to leaf width and rhizome elongation was correlated to rhizome biomass, the model was run using leaf width, number of leaves and scales and rhizome biomass as predictors. All the analyses were run in R Core Team87, using the package MASS88.
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Acknowledgements
We sincerely thank Andrea Crocco, Matteo Puddu and Alessio Sau for helping in processing plant material. The study has been funded by Italian Ministry of Education and Research: PRIN 2017 (MHHWBN) “Marine Habitats restoration in a climate change-impaired Mediterranean Sea (MAHRES)”.
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A.P. and G.C. conceived the ideas and designed methodology; A.P., F.P., and P.S. collected the data; G.L.M. analysed the data; A.P. and G.C. led the writing of the manuscript. All authors have contributed critically to the drafts, gave final approval for publication and agreed to be accountable for all aspects of the work.
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Pansini, A., La Manna, G., Pinna, F. et al. Trait gradients inform predictions of seagrass meadows changes to future warming. Sci Rep 11, 18107 (2021). https://doi.org/10.1038/s41598-021-97611-x
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DOI: https://doi.org/10.1038/s41598-021-97611-x
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