Ecology

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Climate models need to capture a full spectrum of data from forests such as the Brazilian Amazon.Credit: Florence Goisnard/AFP/Getty

Humanity’s understanding of how forests are responding to climate change is disconcertingly fragile. Take carbon fertilization, for example — the phenomenon by which plants absorb more carbon dioxide as its concentration in the atmosphere increases. This is one of the principal mechanisms by which nature has so far saved us from the worst of climate change, but there’s little understanding of its future trajectory. In fact, researchers don’t fully understand how climate change interacts with a multitude of forest processes. Complex, unsolved questions include how climate warming affects forest health; how it affects the performance of forests as a carbon sink; and whether it alters the ecosystem services that forests provide. Forests are our life-support system, and we should be more serious about taking their pulse.Six papers in this week’s Nature provide important insights into those questions. They also underline some of the challenges that must be overcome if we are to fully understand forests’ potential in the fight against climate change. These challenges are not only in the science itself, but also relate to how forest scientists collaborate, how they are funded (especially where data collection is concerned) and how they are trained.Forest science is an amalgam of disciplines. Ecologists and plant scientists measure tree growth, soil nutrients and other parameters in thousands of forest plots around the world. Physical scientists monitor factors such as forest height and above-ground forest biomass using remote-sensing data from drones or satellites. Experimental scientists investigate how forests might behave in a warming world by artificially altering factors such as temperature or carbon dioxide levels in experimental plots. Some of the data they generate are absorbed by yet another community: the modellers, who have created dynamic global vegetation models (DGVMs). These simulate how carbon and water cycles change with climate and, in turn, inform broader earth-system and climate models of the type that feed into policymaking.Different DGVMs make different predictions about how long forests will continue to absorb anthropogenic CO2. One reason for these differences is that models are sensitive to assumptions made about the processes in forests. There are many influences — including temperature, moisture, fire and nutrients — that are generally studied in isolation. Yet they interact with each other.Not all DGVMs account for the dampening effect that a lack of soil phosphorus can have on carbon fertilization, for example. Much of central and eastern Amazonia is poor in phosphorus, and research has shown that introducing phosphorus limitation into DGVMs can cut the carbon-fertilization effect1. This week, Hellen Fernanda Viana Cunha at the National Institute for Amazonian Research in Manaus, Brazil, and her colleagues report2 a powerful experimental demonstration of how the soil’s poor phosphorus content limits carbon absorption in an old-growth Amazonian forest.Models simulating the northward spread of boreal forest as temperatures rise are also missing key drivers3, according to Roman Dial at Alaska Pacific University in Anchorage and his colleagues. They report today that a white-spruce population has migrated surprisingly far north into the Arctic tundra. To explain this, it is necessary to take into account winter winds (which facilitate long-distance dispersal) along with the availability of deep snow and soil nutrients (which promote plant growth).Models are often based on a small number of ‘functional tree types’ — for example, ‘evergreen broadleaf’ or ‘evergreen needle leaf’. These are chosen as a proxy for the behaviour of the planet’s more than 60,000 known tree species. Yet ecologists are discovering that the biology of individual species matters when it comes to a tree’s response to climate change.David Bauman at the Environmental Change Institute at the University of Oxford, UK, and his co-workers reported in May that tree mortality on 24 moist tropical plots in northern Australia has doubled in the past 35 years (and life expectancy has halved), apparently owing to the increasing dryness of the air4. But that was an average of the 81 dominant tree species: mortality rates varied substantially between species, a variation that seemed to be related to the density of their wood.Peter Reich at the Institute for Global Change Biology at the University of Michigan in Ann Arbor and his colleagues now report that modest alterations in temperature and rainfall led to varying rates of growth and survival5 for different species in southern boreal-forest trees. The species that prospered were rare.Failure to examine multiple factors simultaneously means that scientists are making findings that challenge the assumptions in models. Spring is coming earlier for temperate forests and most models assume that, by prolonging the growing season, this increases woody-stem biomass. However, observational work carried out in temperate deciduous forests by Kristina Anderson-Teixeira at the Smithsonian Conservation Biology Institute in Front Royal, Virginia, and her colleagues found no sign of this happening6.Modellers are all too aware of the need to incorporate more complexity into their models, and of the potential that increasing amounts of computing power have to assist them in this endeavour. But they need more data.Continuity problemTo obtain comprehensive, valuable data for the models, continuous, long-term observations need to be made, and that depends on the availability of long-term funding. Achieving such continuity is a problem for both remote-sensing and ground-based operations. The former can cost hundreds of millions of dollars, but the value of its long-term data sets is immense, as demonstrated by a team led by Giovanni Forzieri at the University of Florence in Italy. The authors used 20 years of satellite data to show that nearly one-quarter of the world’s intact forests have already reached their critical threshold for abrupt decline7. But even field-based data collection, which costs a pittance by comparison, struggles to achieve financial security.Important ground-based operations include the Forest Global Earth Observatory (ForestGEO), part of the Smithsonian Tropical Research Institute, which is headquartered in Washington DC. This monitors 7.5 million individual trees in plots around the world. The amount of work that goes into this monitoring is formidable. For example, at present, ForestGEO is conducting the eighth five-yearly census of a plot in Peninsular Malaysia. This involves determining the species for each of the 350,000 trees (there are some 800 species growing there) and measuring the circumference of each trunk. It will take 16 skilled people a year to measure all the trees. Delays in the provision of funding to ForestGEO have held up similar censuses at plots in countries including Papua New Guinea, Vietnam, Brunei and Ecuador.

A ForestGEO researcher making tree measurements at a forest plot in Barro Colorado Island, Panama.Credit: Jorge Aleman, STRI

The future of the plots in North Queensland, which supplied Bauman with a rare 49 years’ worth of continuous data, is uncertain. They have been monitored since the mid-1970s by the Australian public research-funding agency CSIRO — initially every two years, then, more recently, every five years. In 2019, monitoring of the plots was switched to every 50 years because of funding shortages at CSIRO, leaving scientists searching for new sources of funding.Without continuity of funding, organizations such as ForestGEO can’t equip researchers with the requisite skills or collect data over periods longer than an individual’s time in a specific post or a funder’s cycle. “We have trained people and then lost them due to job insecurity,” says Stuart Davies, who leads ForestGEO.Different groups of forest researchers are trying to address these problems. ForestGEO is coordinating the Alliance for Tropical Forest Science in an effort to make it easier to share data, and to bolster the morale and careers of the skilled technicians and scientists — many of whom live in low- and middle-income countries — who do the bulk of the data collection.But we also need more-imaginative funding mechanisms that lift long-term observational plots out of three- to five-year funding cycles. Space agencies that fund remote-sensing satellites could collaborate with other funding agencies, for example, so that earth-observation missions include a fully funded component for ground-based data collection — which is, after all, crucial for calibrating their results. Journals, too, could do more to value and incentivise the production of long-term data sets.And there is a need for more interdisciplinarity. The US Department of Energy is funding a project called NGEE–Tropics (Next-Generation Ecosystem Experiments–Tropics) in which modellers will work with empirical researchers, both observational and experimental, who study tropical forests to create a full, process-rich model of such forests. This is encouraging, and the idea could be pushed further. What is needed is an initiative that pulls the disciplines together towards a goal of building a better understanding of forest processes. Among other things, such an initiative would encourage researchers in different disciplines to take each other’s data needs into account when planning their projects.For this to work, we need to remember that the edifice of forest science relies on the long-term data that scientists wring from forests over decades. Our chances of overcoming climate change are small, but they will diminish further if we forget the basics of monitoring our home planet. More

Shotgun sequencing and generation of metagenome-assembled genomesIn our previous study, 78 Holstein Friesian dairy cows were sampled for rumen content, metagenomic shotgun sequencing was carried out, and raw Illumina sequencing reads were assembled into contigs using megahit assembler using default settings [7]. We used a pooled assembly of the original 78 samples to increase the quality of the metagenome-assembled genomes (MAGs) with the syntax: megahit [14] -t 60 -m 0.5 −1 [Illumina R1 files] −2 [Illumina R2 files]. Next, the assembled contigs were indexed using BBMap [15]: bbmap.sh threads = 60 ref = [contigs filename]. Thereafter, reads from each sample were mapped to the assembled contigs using BBTools’ bbwrap.sh script. In order to determine the depth (coverage) of each contig within each sample, the gi_summarize_bam_contig_depths tool was applied with the parameters: gi_summarize_bam_contig_depths –outputDepth depth.txt –pairedContigs paired.txt *.bam –outputDepth depth.txt –pairedContigs paired.txt.Using the depth information, metabat2 [16] was executed to bind genes together into reconstructed genomes, with parameters: metabat2 -t40 -a depth.txt.To evaluate genomic bin quality, we used the CheckM [17] tool, with parameters: checkm lineage_wf [in directory] [out directory] -x faa –genes -t10.Preparing proteomic search libraryWe generated 93 unique high-quality MAGs, and further increased our MAG database by including phyla that were not represented in our set of MAGs. In order to do so, we used the published compendium of 4,941 rumen metagenome-assembled genomes [18] and dereplicated those MAGs using dRep [19]. We then selected MAGs from phylum Spirochaetes, Actinomycetota, Proteobacteria, Firmicutes, Elusimicrobia, Bacillota, Fibrobacteres and Fusobacteria, which had the highest mean coverage in our samples as calculated using BBMap and gi_summarize_bam_contig_depths as described above [15]. This strategy minimized the false discovery rate (FDR), that would have been obtained if larger and unspecific databases would have been employed [20] and allowed the addition of 14 MAGs to our database.In order to create the proteomic search library, genes were identified along the 107 MAGs using the Prodigal tool [21], with parameters: prodigal meta and translated in silico into proteins, using the same tool. Replicates sequences were removed. Protein sequences from the hosting animal (Bos taurus) and common contaminant protein sequences (64,701 in total) were added to the proteomic search library in order to avoid erroneous target protein identification originating from the host or common contaminants. Finally, in order to subsequently assess the percentage of false-positive identifications within the proteomic search [22], the proteomic search library sequences were reversed in order and served as a decoy database.Proteomic analysisThe bacterial fraction from rumen fluid of the 12 selected animals selected from extreme feed efficiency phenotypes, were obtained at the same time as the samples analyzed for metagenomics and stored at −20 °C until extraction. To extract total proteins, a modified protocol from Deusch and Seifert was used [23]. Briefly, cell pellets were resuspended in 100 µl in 50 mM Tris-HCl (pH 7.5; 0.1 mg/ml chloramphenicol; 1 mM phenylmethylsulfonyl fluoride (PMSF)) and incubated for 10 min at 60 °C and 1200 rpm in a thermo-mixer after addition of 150 µl 20 mM Tris-HCl (pH 7.5; 2% sodium dodecyl sulfate (SDS)). After the addition of 500 µl DNAse buffer (20 mM Tris-HCl pH 7.5; 0.1 mg/ml MgCl2, 1 mM PMSF, 1 μg/ml DNAse I), the cells were lysed by ultra-sonication (amplitude 51–60%; cycle 0.5; 4 × 2 min) on ice, incubated in the thermo-mixer (10 min at 37 °C and 1,200 rpm) and centrifuged at 10,000 × g for 10 min at 4 °C. The supernatant was collected and centrifuged again. The proteins in the supernatant were precipitated by adding 20% pre-cooled trichloroacetic acid (TCA; 20% v/v). After centrifugation (12,000 × g; 30 min; 4 °C), the protein pellets were washed twice in pre-cooled (−20 °C) acetone (2 × 10 min; 12,000 × g; 4 °C) and dried by vacuum centrifugation. The protein pellet was resuspended in 2× SDS sample buffer (4% SDS (w/v); 20% glycerin (w/v); 100 mM Tris-HCl pH 6.8; a pinch of bromophenol blue, 3.6% 2‑mercaptoethanol (v/v)) by 5 min sonication bath and vortexing. Samples were incubated for 5 min at 95 °C and separated by 1D SDS-PAGE (Criterion TG 4-20% Precast Midi Gel, BIO-RAD Laboratories, Inc., USA).As previously described, after fixation and staining, each gel line was cut into 10 pieces, destained, desiccated, and rehydrated in trypsin [24]. The in-gel digest was performed by incubation overnight at 37 °C. Peptides were eluted with Aq. dest. by sonication for 15 min The sample volume was reduced in a vacuum centrifuge.Before MS analysis, the tryptic peptide mixture was loaded on an Easy-nLC II or Easy-nLC 1000 (Thermo Fisher Scientific, USA) system equipped with an in-house built 20 cm column (inner diameter 100 µm; outer diameter 360 µm) filled with ReproSil-Pur 120 C18-AQ reversed-phase material (3 µm particles, Dr. Maisch GmbH, Germany). Peptides were eluted with a nonlinear 156 min gradient from 1 to 99% solvent B (95% acetonitrile (v/v); 0.1% acetic acid (v/v)) in solvent A (0.1% acetic acid (v/v)) with a flow rate of 300 ml/min and injected online into an LTQ Orbitrap Velos or Orbitrap Velos Pro (Thermo Fisher Scientific, USA). Overview scan at a resolution of 30,000 in the Orbitrap in a range of 300-2,000 m/z was followed by 20 MS/MS fragment scans of the 20 most abundant precursor ions. Ions without detected charge state as well as singly charged ions were excluded from MS/MS analysis. Original raw spectra files were converted into the common mzXML format, in order to further process it in downstream analysis. The spectra file from each proteomic run of a given sample was searched against the protein search library, using the Comet [25] search engine with default settings.The TPP pipeline (Trans Proteomic Pipeline) [26] was used to further process the Comet [25, 27] search results and produce a protein abundance table for each sample. In detail, PeptideProphet [28] was applied to validate peptide assignments, with filtering criteria set to probability of 0.001, accurate mass binning, non-parametric errors model (decoy model) and decoy hits reporting. In addition, iProphet [28, 29] was applied to refine peptide identifications coming from PeptideProphet. Finally, ProteinProphet [28,29,30] was applied to statistically validate peptide identifications at the protein level. This was carried out using the command: xinteract -N[my_sample_nick].pep.xml -THREADS = 40 -p0.001 -l6 -PPM -OAPd -dREVERSE_ -ip [file1].pep.xml [file2].pep.xml.. [fileN].pep.xml  > xinteract.out 2  > xinteract.err. Then, TPP GUI was used in order to produce a protein table from the resulting ProtXML files (extension ipro.prot.xml).Subsequently, proteins that had an identification probability < 0.9 were also removed as well as proteins supported with less than 2 unique peptides (see Supplementary Table 1).Quantifying metagenomic presence of MAGsA reference database containing all 107 MAGs’ contigs was created (bbmap.sh command, default settings). Then, the paired-end short reads from each sample (FASTQ files) were mapped into the reference database (bbwrap.sh, default settings), producing alignment (SAM) files, which were converted into BAM format. Subsequently, a contig depth (coverage) table was produced using the command jgi_summarize_bam_contig_depths --outputDepth depth.txt --pairedContigs paired.txt *.bam. As each of the MAGs span on more than one contig, MAG depth in each sample was calculated as contig length weighted by the average depth. Finally, to account for unequal sequencing depth, each MAG depth was normalized to the number of short sequencing reads within the given sample.Correlating metagenomic and proteomic structuresIn order to compare metagenomic and proteomic structures, we first calculated the mean coding gene abundance and mean production levels of each of the 1629 detected core proteins over all 12 cows. Both mean gene abundance and mean production level were translated into ranks using the R rank function. The produced proteins were ranked in descending order and the coding genes in the gene abundance vector were reordered accordingly. The two reordered ranked vectors then plotted using the R pheatmap function, and colored using the same color scale.Selection of proteins for downstream analysisAs our goal was to analyze plasticity in microbial protein production in varying environments, e.g., as a function of host state, only MAGs that were identified in all of the 12 proteomic samples were kept for further analysis. Consequently, only proteins that were identified in at least half of the proteomic samples (e.g., in at least six samples) were selected. This last step aimed to reduce spurious correlation results. These filtering steps retained 79 MAGs coding for a total of 1,629 measurable proteins.Feed efficiency state prediction and ordinationIn order to calculate the accuracy in predicting host feed efficiency state based on the different data layers available (16S rRNA (Supplementary Table 2), metagenomics, metaproteomics), the principal component analysis (PCA) axes for all the samples based on the microbial protein production profiles were calculated. Then, twelve cycles of model building and prediction were made. Each time, the two first PCs of each of five cows along with their phenotype (efficiency state) were used to build a Support Vector Machine (SVM) [R caret package] prediction model and one sample was left out. The model was then used to perform subsequent prediction of the left-out animal phenotype (feed efficiency) by feeding the model with that animal’s first two PCs. This leave-one-out methodology was then repeated over all the samples. Finally, the prediction accuracy was determined as the percent of the cases where the correct label was assigned to the left-out sample. For the proteomics data, this procedure was applied on both the raw protein counts, and the protein production normalized based on MAG abundance, which enabled us to compare the prediction accuracies of the microbial protein production to that of the raw protein counts.Identification proteins associated with a specific host stateIn order to split the proteomics dataset into microbial proteins that tend to be produced differently as a function of the host feed efficiency states, each microbial protein profile was correlated to the sample’s host feed efficiency measure (as calculated by RFI) using the Spearman correlation (R function cor), disregarding the p value. Proteins that had a positive correlation to RFI were grouped as inefficiency associated proteins. In contrast, proteins that presented a negative correlation to RFI were grouped as efficiency associated proteins. To test for equal sizes of these two protein groups, a binomial test was performed (R function binom.test) to examine the probability to get a low number of feed efficient proteins from the overall proteins under examination, when the expected probability was set to 0.5.Functional assignment of proteinsProtein functions were assigned based on the KEGG (Kegg Encyclopedia of Genes and Genomes) [31] database. The entire KEGG genes database was compiled into a Diamond [32] search library. Then, the selected microbial proteins were searched against the database using the Diamond search tool. Significant hits (evalue < 5e-5) were further analyzed to identify the corresponding KO (KEGG Ortholog number). Annotations of glycoside hydrolases were performed using dbcan2 [33].Protein level checkerboard distribution across the feed efficiency groupsThe checkerboard distribution in protein production profiles was estimated separately within the feed efficient and inefficient animal groups. To enable the comparison between the two groups’ checkerboardness level, we chose a standardized C-score estimate (Standardized Effect Size C-score - S.E.S C-Score), based on the comparison of the observed C-score to a null-model distribution derived from simulations. The S.E.S C-score was estimated using the oecosimu function from R vegan package with 100,000 simulated null-model communities.Calculating functional redundancyThe functional redundancy within a given group of proteins was measured as the mean number of times a given KO occurred within a given group, while neglecting proteins that have not been assigned a KO level functional annotation.In order to test whether a given group of proteins exhibits more or less functional redundancy than would have been expected, a null distribution for functional redundancy was created, based on the number of proteins in the given group. A random group of proteins was drawn from the entire set, keeping the same sample size as in the tested group, and the process was repeated 100 times. Then, the functional redundancy for each random protein group was calculated. Thereafter, the null distribution was used to obtain a p value to measure the likelihood of obtaining such a value under the null.Examining functional divergenceExamining the functional divergence between the two groups of proteins, e.g. the feed efficiency and inefficiency associated proteins, was done by first counting the amount of shared functional annotations, in terms of KOs between the two groups. Thereafter, a null distribution for the expected count of KOs was built by randomly splitting in an iterative manner the proteins into groups of the same sizes and calculating the number of shared KOs. A p value for the actual count of shared proteins was obtained by ranking the actual count over the null distribution.Calculating average nearest neighbor ratio (ANN ratio)ANN Ratio analysis was carried out independently for each protein function (KO), containing more than 14 proteins with at least 5 proteins within each feed efficiency group. Initially, all proteins assigned to a given KO were split into two sets, in accordance to their feed efficiency affiliation group. Thereafter, proteins within each set were independently projected into two-dimensional space by PCA applied directly to Sequence Matrix [34]. Average nearest neighbor ratio within each set was then calculated within the minimum enclosing rectangle defined by principal component axes PC1 and PC2, as defined by Clark and Evans [35].MAG feed efficiency score calculationMicroorganism feed efficiency score was calculated for each MAG individually by first ranking each protein being produced by the given microbe along the 12 animals, based on the normalized protein production levels. Thereafter, a representative production value for the microbe in each animal was calculated as the average of the ranked (normalized) protein production levels in that animal (using R rank function). This ranking allowed us to alleviate the potential skewing effect of highly expressed proteins. The microorganism’s Feed Efficiency Score was calculated as the difference between its mean representative production value within feed efficient animals to that within feed inefficient animals. Values close to zero will reflect similar distribution between the two animal groups, positive values will indicate higher expression among efficient animals, and negative values will indicate higher expression among inefficient animals. To calculate significance, the actual feed efficiency score was compared to values in a distribution derived from a permutation based null model. Each of the permuted Feed Efficiency Scores (10,000 for each microbe) was obtained by independently shuffling each of the proteins produced by the MAG between the animals, prior to calculating the actual microorganism feed efficiency score. By positioning the absolute score value over its distribution under permuted assumptions (absolute values), we obtained a significance p value.MAG phylogenetic tree construction and phylogenetic signal estimationIn order to assess the link between phylogenetic similarity between the MAGs and their association with feed efficiency, phylogenetic tree estimating evolutionary relationships between the MAGs was constructed using the PhyloPhlAn pipeline [36]. The phylogenetic signal for Microorganism Feed Efficiency Score was estimated by providing the phylogSignal function from R phylosignal [37] package with MAGs phylogenetic tree and respective values. Pagel’s Lambda statistics was chosen for the analysis, owing to its robustness [38].Plot generationAll bar plots, scatter plots and other point plots were generated with R package ggplot2. Heatmaps were produced by either ggplot2 [39] or pheatmap [https://cran.r-project.org/web/packages/pheatmap/index.html] R packages. KEGG map was produced using the online KEGG Mapper tool [40]. Phylocorrelogram was produced with phyloCorrelogram function from R package phylosignal [37].MAG differential production analysisMAGs that contain a minimal number of proteins (50 functions) were selected for differential protein production analysis, in order to have sufficient data to perform statistical tests. For each MAG, the relative production was used in order to calculate the Jaccard pairwise dissimilarity for core protein production between feed efficient and inefficient cows using the R vegan package. Analysis of similarity between efficiency and inefficiency associated proteins for each MAG (ANOSIM) values and p values were then calculated using the same package.Predicting animal feed efficiency state according to GH family countsUsing all GH annotated proteins, a feature table that sums the count of each GH family within each sample was produced. Thereafter a leave-one-out cross-validation (LOOCV) [R caret package] was performed, each time building a Random Forest (RF) prediction model from the GH family counts and efficiency state of 11 samples, leaving one sample outside. Each one of the RF models, in its turn, was applied on the left-out animal to predict its efficiency state. Model accuracy and AUC curve were calculated based on the LOOCV performance. More

Healthy foods are green foodsAn analysis of 57,000 foods reveals which have the best and worst environmental impacts. A team of researchers used an algorithm to estimate how much of each ingredient was in thousands of products sold in major UK supermarket chains. The scientists then gave food items an environmental-impact score out of 100 — with 100 being the worst — by combining the impacts of the ingredients in 100 grams of each product. They considered several factors, including greenhouse-gas emissions and land use.Healthier foods tended to have low environmental impacts, the team found. Products containing lamb and beef — such as ready-made meat pies — had the most serious environmental impact. The lowest-impact foods tended to be made with plants and included bread products, fruits, vegetables, grains and sugar-rich drinks. There were some notable exceptions: both nuts and seafood had a good nutrition score but relatively high environmental impacts.

Source: M. Clark et al. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).

COVID-19 reboundAfter the COVID-19 antiviral Paxlovid began to be used in late 2021, researchers noticed that some people experience a rebound in the virus and symptoms after taking the drug. Two recent studies suggest that it is surprisingly common for SARS-CoV-2 to return in untreated cases of COVID-19 too. To determine the frequency of rebound, Jonathan Li, a physician-scientist at Brigham and Women’s Hospital in Boston, Massachusetts, and his team analysed data from hundreds of people who were randomized to receive a placebo in a large-scale trial of COVID-19 antibody drugs. More than one-quarter of participants who were infected with SARS-CoV-2 reported a rebound in their symptoms, according to the study, which has not yet been peer reviewed. “The main take-home message is that recovery from COVID-19 is not going to be a linear process,” Li says.

Source: Deo, R. et al. Preprint at medRxiv https://doi.org/10.1101/2022.08.01.22278278 (2022).

How trees grow in a warmer worldThis chart shows how the early arrival of spring, due to climate change, affects the growth of trees and the amount of carbon they sequester. In a paper in Nature, researchers investigated the consequences of an early start to the growing season in deciduous forests. Leaf emergence is followed by carbon uptake through the process of photosynthesis. Over time, carbon can be captured for long-term sequestration if it contributes to the radial growth of stems or to wood formation. The areas under the curves represent annual growth in terms of: the amount of carbon captured by leaves (top curves, brown); annual radial growth (lower left curve, blue); and increase in woody biomass (lower right curve, red).The authors report that the early arrival of spring, which shifts the margins of the growing season (lighter curves), had little impact on the final annual tree-ring width or the amount of woody biomass produced, whereas high temperatures in summer had a negative effect on radial growth (dotted curve). Other studies (plotted here as dotted curves) indicate that high temperatures and related drought can suppress carbon capture and woody-biomass production.The study provides evidence that warmer springs have advanced the leaf emergence of temperate deciduous forests but have not substantially increased their wood production. This suggests that the extra uptake of carbon dioxide does not contribute to sustainable carbon sequestration in the trunks of long-lived trees, as our News & Views article explains. More

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Taxonomy, variation, and biochronologyThe fossils described herein represent one of the most valuable and best-preserved samples of “Dama-like” deer from the European Early Pleistocene. The systematics of these forms has been essentially based on the morphology of the antlers and teeth, with less attention paid to the skull (due to the rarity of well-preserved finds) and postcranial bones.The Pantalla sample shows a combination of characters allowing an unambiguous attribution to ‘P.’ nestii, a species reported confidently so far in the early Late Villafranchian of Italy (several sites) and in the Georgian Homo-bearing locality of Dmanisi (Supplementary Table S1). Based on the literature6,8,12,38, these characters include: four-pointed antler with elongated, slender, and tubular beam; basal tine branching off at a certain distance from the burr forming an acute angle; well-developed middle tine; terminal bifurcation oriented normal to the sagittal plane; cranium with large orbits, preorbital fossae, and ethmoidal vacuities; relatively elongated neurocranium with flat parietals; caudally-oriented pedicles; molarized P2-P3; presence of cingula in upper molars; enlarged i1; un-molarized p4. However, some characters observed in the Pantalla specimens (e.g., rostral edge of the orbit reaching the level of M2; elongated metapodials) do not fit the revised diagnosis of ‘P.’ nestii by Croitor12. The latter author considers nestii as the earliest species of the genus Cervus based on similarities with the extant red deer especially in cranial morphology12,22,23. However, in our opinion, his conclusions are biased by relying mostly on the skull IGF 243 of ‘P.’ nestii from Upper Valdarno6,8, which is heavily deformed and belongs to a juvenile individual (see below for details on ontogenetic variation in ‘Pseudodama’).A broader look at the entire record of ‘P.’ nestii reveals that this species displays a mosaic of characters between Dama and Cervus, but also that the shared characters with Dama are prevalent (as already pointed out by Azzaroli8). The Pantalla sample allows to substantiate these conclusions very well. Our CT-based comparisons between the crania from Pantalla and those of extant red deer and fallow deer (Fig. 3) highlight some morphological similarities with the former, including a relatively longish neurocranium with steep forehead and deep preorbital fossa. On the other hand, ‘P.’ nestii from Pantalla clearly shows Dama-like cranial characters, such as a marked interfrontal crest, horizontal zygomatic arch, high maxilla below the orbit, muzzle more inclined ventrally and less cylindrical in overall shape, sub-horizontal upper cheek tooth row (i.e., the occlusal margin of the row is approximately straight in buccal view), apical surface of the pedicle more inclined dorsocaudally, and overall morphology of the antlers, which in rostral view diverge, rather than converge as in the red deer (Fig. 2).Likewise, the teeth from Pantalla, have a mixture of Dama and Cervus characters although the former are prevalent. All the premolar characters (the complete absence of a lingual grove on P4, the presence of a cingulum on the distolingual wall of P4, the presence of a small paraconid in p2, the entoconid more aligned with the mesiodistal axis in p3-p4, and a weak mesial cingulum on p4) and most of the lower molar characters are Dama-like. The upper molar features are instead more reminiscent of Cervus being either intermediate between the morphology of the latter and that of extant Dama or even matching Cervus (see Supplementary Table S6 and below).The postcranial remains from Pantalla appear more similar to Dama than to Cervus. Of the 23 morphological characters by Lister39 which are present in the preserved bones (axis, metacarpal, tibia, astragalus, calcaneum, cubo-navicular, metatarsal, phalanx I, and phalanx II), 21 scores as fallow deer and only two as red deer (details in Supplementary Table S7).A mixed character suite between Dama and Cervus are revealed also by our palaeoneurological analysis. The brain of ‘P.’ nestii shows Dama-like size and Cervus-like morphology with a prominent cerebellum and a dorsoventrally flattened cerebrum. The latter character is clearly noticeable in ‘Pseudodama’ and Eucladoceros, is less evident in extant Cervus, and is missing in Dama. The hypothesis that depressed and longish cerebra represent a primitive character in Cervini (at least in Pleistocene European forms) is supported by our preliminary data and agree with Azzaroli8.Most interestingly, the two crania from Pantalla actually show some remarkable morphological differences. The neurocranium of 337643 is more lengthened (i.e., more Cervus-like), albeit this shape might be taphonomically modified by the lateral compression of the specimen. This morphology fits that observed in some other ‘P.’ nestii specimens such as IGF 1403 from Olivola (Italy), while the relatively shorter and more rounded neurocranium of 337655 resembles that of other specimens such as IGF 1404 also from Olivola. Moreover, 337643 shows a stronger nuchal crest than 337655. These differences may be related to ontogenesis (see the advanced age of 337643 based on tooth wear). In several cervid species including fallow deer, aging leads to morphological changes in the neurocranium, which tends to elongate and flatten and shows a more developed nuchal crest, probably as a response to the support of larger and heavier antlers18,38. Similarly, in 337643, the pedicles are apparently closer to one another due to their thickening—an expected condition for an old individual as the distance between the pedicles tends to decrease with age8—and markedly shorter than wide. Our comparative data on European Dama-like deer show that the pedicle section can be highly variable both within and between species, although a general trend of laterolateral flattening (i.e., oval shape with major axis oriented anteroposteriorly) can be traced through time (Supplementary Fig. S3), probably as a result of the development of wide, laterally-projecting palmated antlers (in extant deer, D. dama is among those with the heaviest antlers relative to body size40,41). Therefore, the Pantalla sample on the one hand confirms the variation in cranial morphology already observed for ‘P.’ nestii6,8, on the other hand it supports the affinities between this species and the fallow deer. The presence of Cervus-like features especially in cranial morphology may be interpreted as plesiomorphic characters which, associated with some characters of the dentition and of the brain, suggest a basal position of ‘Pseudodama’ in the evolutionary history of the Cervini. This hypothesis may be tested in the future through phylogenetic analyses, currently made difficult by the lack of sufficiently well-preserved material of some species of ‘Pseudodama’ (e.g., ‘P.’ lyra, ‘P.’ perolensis).Compared with other specimens of ‘P.’ nestii6,8, the sample from Pantalla shows some plesiomorphic characters including a high ratio between the premolar and molar lengths, i.e., 0.77–0.82 (n = 2) for upper teeth (LP/LM) and 0.68–0.69 (n = 3) for lower teeth (Lp/Lm). These values are closer to the basal forms of ‘Pseudodama’, such as ‘P.’ lyra from Montopoli (LP/LM = 0.73, n = 1; Lp/Lm = 0.64, n = 2) and ‘P.’ rhenana from Saint Vallier (LP/LM = 0.75, n = 9; Lp/Lm = 0.68, n = 18; data from Valli42), than to ‘P.’ nestii from Olivola and Upper Valdarno (LP/LM = 0.72, n = 10; Lp/Lm = 0.63, n = 17). Other putatively plesiomorphic features of the sample from Pantalla are all those that approach it morphologically to Cervus (see Supplementary Table S6), i.e., the strong development of lingual conids and stylids in lower molars (Char. 439) and of buccal cones and styles in upper molars (Char. 139), the lack of a clear step between 2nd and 3rd lobe of m3 (Char. 1139), the strong lingual cingulum on upper molars (Char. 339), and the lack of the horizontal turning of the buccal columns of upper molars (Char. 439—the so-called buccal “cingulum”43). The strong lingual cingulum on upper molars is constantly present in the earliest species of the ‘Pseudodama’ group, ‘P.’ pardinensis9, and still present, although extremely rare, in ‘P.’ lyra from Montopoli, ‘P.’ rhenana from Saint Vallier and Senèze, ‘P.’ perolensis from Peyrolles, and ‘P.’ nestii from Olivola. However, this feature is back less rare in ‘P.’ nestii from Upper Valdarno and ‘P.’ farnetensis from Selvella, suggesting a certain polymorphism at this stage. The lack of buccal “cingulum” is a constant in the earliest ‘Pseudodama’ populations (Lower Valdarno, Saint Vallier, Senèze), the buccal “cingulum” appearing, although rare, in ‘P.’ perolensis from Peyrolles and ‘P.’ nestii from Olivola and Upper Valdarno but becoming more common only in later ‘P.’ farnetensis, ‘P.’ vallonnetensis, and constant in Dama.The above affinities between the Pantalla deer and the early representatives of ‘Pseudodama’ support the idea that the age of the assemblage may be close to the beginning of the Late Villafranchian (ca. 2.1–2.0 Ma), as already suggested based on the occurrence of Leptobos merlai44 and a primitive form of Equus stenonis35. Thus, the ‘P.’ nestii sample described herein may represent one of the earliest occurrences of the species in Europe.Palaeoecological and palaeoethological inferencesThe Pantalla sample is also noteworthy as it allows opening a window into the behaviour of these extinct deer. The anomalies found on the two male crania are probably the result of different traumas during their life.Deer are well known for the intense fights they engage in during the rutting season using their antlers, as a result of an escalation of a broad repertoire of threats and displays45. Mineralized antlers are solid structures able to withstand the vehemence of the fight46, whereas growing antlers are extremely fragile and any contact with a solid object may result in a serious injury47,48 that may jeopardize the bearer’s ability to compete with conspecifics and, consequently, its dominance status49. Accidents are inevitable in the life of a deer and, in case of the suffered damage not leading to the breakage of the growing beam and consequent loss of its distal part, the antler may continue its growth although, in case of a severe lesion, at a crooked angle45. Thus, if the antler was just cracked and the broken part was held together by the velvet and periosteum, with the blood supply still being guaranteed, the damaged beam would just present a conspicuous swelling around the area of fracture (i.e., a fracture callus)45,50 and a change in the axis of orientation. These features match those seen in the left beam of 337655, which shows a fracture callus between the basal and middle tines corresponding to a change in the orientation of the beam.The supernumerary tine of the right antler of the same individual can be interpreted as the result of a trauma, too. Considering the delicate nature of the growing antlers and the non-negligible risks of occurrence of an injury, it is safe to believe that the right antler has undergone a light traumatic event (most likely concerning the pedicle) at some early stage of its growth. In fact, it is known that limited injuries could result in the growth of supernumerary tines, even in atypical positions51, as it has been documented in other deer species (e.g., reindeer52, sambar53). It is therefore reasonable to hypothesize that both antler anomalies of 337655 derive from traumas suffered by the deer during the antler growth, when the velvet was still present. It is not known whether the two injuries happened at the same time or in two different events. In fact, it cannot even be said that the two events took place during the same season. While the breakage of the left beam must have occurred in the year of the animal’s death (i.e., during the velvet period preceding the period of hard antler in which the individual died), the development of the supernumerary tine on the right may be the result of a trauma suffered in a previous year. This is due to the fact that when unilateral trauma affects the generative region of the antler (i.e., the pedicle area), abnormalities such as supernumerary tines can reappear in next antler cycles even in more intensified forms54, as in the case of 337655 in which the extra-tine is extremely long.The bone anomaly on the right squamosal of 337643 is also likely the outcome of an injury. Although the external portion was artificially smoothed during the preparation of the specimen, the outer and inner morphology matches that of a callus related to the healing of a major lesion and probable intracranial abscessation. Post-traumatic inflammatory processes are known to cause erosion or pitting of cranial bones in deer55 and can be triggered by many factors (e.g., wounds and abrasions of the pedicle56), among which violent sexual competition among males with hard antlers is considered one of the most common55,57. The advanced healing of the injury shown by 337643 suggests that it was not the cause of death, but rather that the individual survived a long time after the trauma albeit with the brain partially compressed by the callus.The six mandibles recovered at Pantalla, all coming from the same bone accumulation hence reasonably referable to a single deer population, represent several age classes, from calves as young as a few months up to very old individuals (i.e., over 15 years; Supplementary Table S4). Unfortunately, no mandible can be safely associated with the two male crania, although 337631 may belong to the same individual as 337643 based on advanced wear and size. Interestingly, the three most significant cranial remains (crania 337655 and 337643 and frontal bone fragment with basal antler base 337625) belong to adult males, which probably died during the hard antler period (i.e., rutting season: 337655 and 337625) or shortly after (i.e., 337643). The absence of females (at least among the remains with certain sex attribution) contrasts with the population structure in the extant fallow deer, in which females represent on average 75% of the herd58. However, the relative abundance of males may increase up to 50% in the rutting season59,60. Therefore, in spite of the relatively low number of fossils available, based on the age and sex structure of the palaeopopulation and by analogy with the extant fallow deer, the most plausible hypothesis is that the Pantalla deer died during or immediately after the rutting season (Fig. 5).Figure 5Life appearance of ‘Pseudodama’ nestii represented during the rutting season. The reconstruction is based on the cranial and postcranial material from the Early Pleistocene of Pantalla (Italy) and on literature data. Artwork by D.A. Iurino.Full size image More

Experimental set-upFour vegetation species were selected: Spartina maritima, Salicornia europaea, Halimione portulacoides and Juncus maritimus. These species were chosen for a broad representation of the biomechanical properties and morphological characteristics of saltmarsh species42,43. Plants were collected in Cantabrian estuaries in late summer and early autumn (from early September to late October) during low tide (please refer to the “Methods” section). A total of 105 boxes were collected, of which 94 boxes were used to build a 9.05 m long and 0.58 m wide meadow in a flume (Fig. 1). Five boxes were used to directly estimate the meadow standing biomass in the field (Sample 1 in Table 1), leaving 6 extra boxes for possible contingencies.Figure 1(A) Shows a sketch of the experimental flume, where the vegetation box distribution in the 100% and 50% density cases is displayed in the two upper panels and a lateral view in the bottom panel. The green boxes indicate the vegetated area in each case. Free surface sensors are displayed by blue lines and numbers. (B) Shows the four species within the flume. From left to right: view of the Spartina sp. frontal edge, aerial view of Salicornia sp., frontal view of Juncus sp. and top view of the Halimione sp. rear edge.Full size imageTable 1 Standing biomass (g/m2) and plant height (m) for the four species.Full size tableExperiments were conducted in a flume 20.71 m long and 0.58 m wide at the University of Cantabria. The flume is equipped with a piston wave maker at its left end and a dissipation beach at the rear end. The 94 vegetation boxes used to create a meadow were introduced into the flume following the pattern shown in panel A of Fig. 1 to minimize any edge effects along the edges of the boxes. To ensure a smooth transition from the bottom of the channel to the vegetated area, two false bottoms were constructed with wood, and a thin sediment layer was glued to the wood to mimic the field roughness.Three meadow densities per species were considered. The meadow density directly determined in the field was chosen under the 100% density scenario. To consider a second meadow density, and therefore a second standing biomass value, plants were removed from half of the boxes following the pattern shown in Panel A of Fig. 1 to prevent creating preferential flow channels along the meadow. This case was considered the 50% density scenario. The study of these two biomass scenarios for each vegetation species is carried out with the aim of covering a wide range of standing biomass values, including low values that may be more representative of meadow winter conditions, thus facilitating the applicability of obtained results. Finally, a second cut was made, in which all plants were removed, resulting in the final scenario with a zero density. Plants were cut from above to avoid any damage along the meadow surface (as shown in Supplementary Fig. S2). In each cut, plants in 5 boxes along the leading edge and in 5 boxes at the center of the meadow were collected to quantify the standing biomass (Samples 2 and 3 for the first cut and Sample 4 and 5 for the second cut in Table 1). Therefore, the standing biomass could be monitored throughout the entire duration of the experiments, from the field until the second cut, when all plants were removed.Once located in the flume, the meadow was evaluated under regular and random wave conditions considering three water depths, i.e., h = 0.20, 0.30 and 0.40 m. Regular waves were generated using Stokes II-, III- and V-order and Cnoidal theories when applicable. Wave heights ranging from 0.05 to 0.15 m and wave periods varying between 1.5 and 4 s were considered. Random waves were generated using a Jonswap spectrum with a peak enhancement factor of 3.3, a significant wave height varying between 0.05 and 0.15 m and a peak wave period ranging from 1.8 to 4.8 s (please refer to Supplementary Table S1). Additionally, all wave conditions were considered under the zero-density scenario with bare soil for each species. The wave height evolution along the flume was recorded using 15 capacitive free surface gauges, as shown in Fig. 1 (please refer to Supplementary Table S2 for detailed coordinates).Meadow characteristics analysisThe characteristics of the vegetation meadows were analyzed by measuring the standing biomass throughout the full duration of the experiments and by measuring the individual plant height (please refer to the “Methods” section). The mean standing biomass value obtained for each species was considered the value associated with the 100% density scenario. Then, half of the standing biomass value was considered under the 50% density scenarios since half of the boxes was randomly cut, and the standing biomass values obtained after the second cut agreed with those obtained after the first cut and in the field, as indicated in Table 1. The plant height for each species was also measured (please refer to the “Methods” section), and the resultant mean value detailed in Table 1 was considered.Wave height attenuation analysisWave height attenuation analysis was performed following previous studies reported in the literature assessing the capacity by fitting a damping coefficient6,7,35,44. The18 formulation was used for regular waves, and that of19 was used for random waves (please refer to the “Methods” section). Cases with a zero density were also considered in this analysis to quantify the influence of bare soil friction by determining the corresponding damping coefficient, ({beta }_{B}). Consequently, β was obtained in the 100% and 50% density cases and the cases without vegetation (please refer to Supplementary Tables S3, S4 and S5 to find the obtained coefficients for all cases). This allowed the determination of a new damping coefficient isolating the effect of the standing biomass, ({beta }_{SB}), following24 (please refer to the “Methods” section). Figure 2 shows an example of wave height attenuation analysis for the four species and the different densities under wave condition JS07 (Supplementary Table S1).Figure 2Analysis of wave attenuation under wave condition JS07 for Spartina sp. 100% (S100), 50% (S050) and zero density (S000); Salicornia sp. 100% (L100), 50% (L050) and zero density (L000); Juncus sp. 100% (J100), 50% (J050) and zero density (J000); and Halimione sp. 100% (H100), 50% (H050) and zero density (H000). The damping coefficients for the bare soil cases, ({beta }_{B}), are displayed in blue. The damping coefficients for the 100% and 50% density cases, (beta ), are displayed in dark and light green, respectively. The damping coefficients obtained after subtracting the dissipation obtained in the bare soil cases, ({beta }_{SB}), are displayed in black and dark gray. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe damping coefficients for the bare soil cases shown in Fig. 2, ({beta }_{B}), are consistent with the soil properties observed in the field. Spartina sp. was collected in a muddy area, whereas the other three species were collected in areas with coarser sediments and exhibited a mixture of sand and mud. For all species, wave dissipation was significantly higher under the 100% density scenario than that under the 50% density cases, as expected, highlighting the importance of the standing biomass in wave energy dissipation. It was also observed that bottom friction-induced dissipation plays a more important role for the pioneer species, i.e., Spartina sp. and Salicornia sp., than for the upper marsh species, i.e., Juncus sp. and Halimione sp., which can dissipate wave energy to a greater extent.The importance of wave parameters in the resultant wave attenuation has been highlighted by several works in the literature. Therefore, not only vegetation characteristics but also incident wave conditions determine the coastal protection capacity. Figure 3 shows a comparison of the obtained wave height attenuation due to Halimione sp. under the different wave conditions.Figure 3Analysis of wave attenuation under the different irregular wave conditions for the Halimione sp. 100% (H100) and zero-density (H000) cases. The top panel shows two cases with different h but equal Hs and Tp values (JS01 and JS08), the middle panel shows two cases with different Tp but equal h and Hs values (JS10 and JS11), and the bottom panel shows two cases with different Hs but equal h and Tp values (JS09 and JS12). 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe top panel in Fig. 3 shows two cases where Hs and Tp are equal, i.e., JS01 and JS08 in Supplementary Table S1, and two water depths are considered, namely, h = 0.2 and 0.3 m. As can be observed, wave damping is higher for the smallest water depth, where most of the water column is covered by vegetation since the mean vegetation height for Halimione sp. reaches 0.187 m (Table 1). The importance of the water depth with respect to the plant height in terms of wave height attenuation has been reported by several authors44,45,46 who have highlighted this aspect based on the submergence ratio, i.e., the plant height divided by the water depth, revealing higher attenuation at lower submergence ratios on a consistent basis. Bottom friction attenuation is also higher for the smallest water depth, as expected.The middle panel of Fig. 3 shows two cases with equal h and Hs but different Tp values, namely, JS10 and JS11 in Supplementary Table S1. Wave height attenuation is higher for the shortest wave period, as well as the damping produced by bottom friction. This is in line with previous studies, such as35 and44, who conducted experiments involving simulated and real saltmarshes, respectively. Finally, the bottom panel of Fig. 3 shows two cases with different Hs but equal h and Tp values, i.e., JS09 and JS12 in Supplementary Table S1. As widely reported in the literature, e.g.,7,47,48, wave height attenuation increases with the wave height, as shown in the bottom panel of Fig. 3. Bottom friction also increases with the wave height, as expected.A set of damping coefficients was obtained via the 288 tests conducted in the laboratory, 144 tests involving regular waves and 144 tests involving random waves. Additionally, in all cases, the damping coefficient considering the isolated effect of the standing biomass, ({beta }_{SB}), was determined. The relationship of these damping coefficients to the measured standing biomass is explored in the next section with the aim of establishing a new relationship to estimate the wave damping effect of the different saltmarsh species based on the standing biomass, without the need for data fitting.Wave damping coefficient as a function of the standing biomassThe mean standing biomass obtained for the different species, Table 1, is considered here to analyze the relationship with the wave damping coefficients obtained by fitting18 formulation to wave heights measured along the meadow for regular waves and19 formulation for random waves. The plant height was highly variable among the different species (Table 1), ranging from 0.170 m for Spartina sp. to 0.714 m for Juncus sp. Then, some species were submerged at all tested water depths, while other species remained above water in all tests. In the latter cases, there remained a portion of each plant above the water level, thus not contributing to wave attenuation. To consider the actual interaction between the standing biomass and flow conditions and assuming a uniform vertical distribution, the effective standing biomass, (ESB), can be defined as follows:$$ESB=DryWeight*frac{minleft{{h}_{v},hright}}{{h}_{v}}$$
(1)
where (DryWeight) denotes the measured dry weight for each species (g/m2), ({h}_{v}) is the mean plant height and (h) is the water depth. Additionally, in the submerged cases, the same (ESB) value will impact flow differently depending on the submergence ratio, (SR), as defined in Eq. (2). To consider this effect, the standing biomass ratio, (SBR) in Eq. (3), can be defined as follows:$$SR=frac{{h}_{v}}{h}, ;;where ;; SR=1 ;;for ;;{h}_{v} >h$$
(2)
$$SBR=ESB*SR$$
(3)
Figure 4 shows the relationship between (SBR) and the measured wave damping coefficient, (beta ). The results for regular and random waves are displayed for each water depth, and a linear fit was found under each condition.Figure 4Wave damping coefficient, (beta ), as a function of the standing biomass ratio, (SBR), under all regular (left panels) and random (right panels) wave conditions. Each panel shows the wave trains assessed at each water depth, h = 0.20, 0.30 and 0.40 m. The results for the 100% density case are marked with circles and those for the 50% density case are marked with squares. The linear fitting results obtained under each wave condition are also displayed.Full size imageUnder each wave condition, a linear fitting relationship between (beta ) and (SBR) was obtained for the eight (SBR) values, as shown in Fig. 4. For similar (SBR) values, the highest (beta ) values were consistently obtained at the smallest water depth, highlighting the notable influence of this parameter on the obtained wave attenuation. Following previous works, such as those of24 and25, who considered the vegetation submerged solid volume fraction to estimate the resulting wave attenuation and established a common relationship for different water depths, the volumetric standing biomass, (VSB), can be defined as follows:$$VSB= SBR*frac{1}{h}$$
(4)
(VSB) is expressed in units of g/m3, which is the weight per unit volume. Exploring the relationship of (beta ) with this new parameter, it was found that the results for the three water depths could be fitted with a single linear relationship, as shown in Fig. 5. However, despite the linear trend observed in Fig. 5, notable data scatter was observed for each (VSB) value. Each of these groups corresponds to a certain water depth and (SBR) value, which were determined under different wave heights and wave periods.Figure 5Wave damping coefficient, (beta ), as a function of the volumetric standing biomass, (VSB), under all regular (top panel) and random (bottom panel) wave conditions. The obtained linear fitting results are displayed in both panels. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageFinally, to account for the characteristics of the incident wave conditions, including the wave height and period, two nondimensional parameters were considered. The first parameter, considering the wave height, is the relative wave height, defined as the ratio of the incident wave height to the water depth, (H/h). Previous studies have highlighted the importance of this parameter in the resultant wave attenuation (e.g.24,44). Under random wave conditions, the considered wave height is ({H}_{rms}), according to wave attenuation analysis. The second parameter, considering the effect of the different wave periods and the importance of the number of wave lengths inside the vegetation length49, is the relative meadow length, defined as the ratio of the meadow length to the wave length, ({L}_{v}/L). To ensure consistency with the above wave attenuation analysis, in which the wave damping amount per unit length was obtained, the unit meadow length was considered here. Thus, the hydraulic standing biomass, (HSB), can be defined as:$$HSB=VSB*frac{H}{h}*frac{{L}_{v}}{L}$$
(5)
Figure 6 shows the relationship obtained between (beta ) and this new variable under all regular and random conditions following the linear fitting relationship of (beta =A*HSB+B), where (A) and (B) are fitting constants with units of (g/m2)−1 and m−1, respectively.Figure 6Wave damping coefficient, (beta ), as a function of the hydraulic standing biomass, (HSB), under all regular (top panel) and random (bottom panel) wave conditions. Both panels show linear fitting results obtained without considering the saturation point, indicated by the black solid line, and those obtained considering the saturation point, indicated by the gray solid line. The black dashed line indicates the saturation point. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageThe linear fitting results obtained between (beta ) and (HSB) under regular and random wave conditions are shown in Fig. 6 as solid black lines and expressed as Eqs. (6) and (7), respectively, where values between brackets are the 95% confidence interval for each coefficient.$$beta =9.206cdot {10}^{-4} left(9.006cdot {10}^{-5}right)*HSB+0.103 (0.021)$$
(6)
$$beta =1.192 cdot {10}^{-3} left(9.124 cdot {10}^{-5}right)*HSB+0.071 (0.016)$$
(7)
The inclusion of incident wave condition characteristics reduces the resulting data scatter, highlighting the role of the wave height and period in the obtained wave attenuation, as described in the previous section. An interesting aspect observed in Fig. 6 is that the four cases with the highest wave damping coefficients yielded similar values for the different (HSB) values. Under regular wave conditions, the mean (beta ) value for these four cases is 0.76, and under random wave conditions, the value reaches 0.68. This may indicate that the damping coefficient has reached its maximum value and no longer increases with increasing (HSB) value. To analyze this aspect in more detail, the wave height evolution measured for the four tests in which (beta ) reaches its maximum value are plotted (as shown in Supplementary Fig. S3). These tests correspond to Halimione sp. with a density of 100% and the shallowest water depth, h = 0.20 m. This species achieved the highest standing biomass value among the species considered in these experiments, and for h = 0.20 m, almost the entire water column was covered by vegetation. For these tests, a notable wave height attenuation was observed, where the wave height strongly decayed along the first 5 m of vegetation, and the wave height entirely dissipated along the last 4 m (as shown in Supplementary Fig. S3). The wave damping equation cannot suitably reproduce the strong wave decay within this few meters. Then, an almost constant wave damping coefficient value is reached under the different considered wave conditions, and a saturation regime is observed, in which the wave height beyond the meadow can be assumed to be negligible. To consider this phenomenon, a two-section fitting relationship is proposed, as shown in Fig. 6. The value of the saturation damping coefficient, chosen as the mean value of the four cases analyzed, is plotted as a dashed gray line, and a linear fit is obtained for the remaining data. The two-section fitting relationship is expressed in Eqs. (8) and (9) for both regular and random waves, respectively, where values between brackets are the 95% confidence interval for each coefficient.$$beta =left{begin{array}{ll}1.020 cdot {10}^{-3}left(1.112 cdot {10}^{-4}right)*HSB+0.088 ; (0.020) \ 0.758; (0.027)end{array}right. begin{array}{l} ;;0 < HSB < 659\ ;; HSB > 659end{array}$$
(8)
$$beta =left{begin{array}{l}1.310cdot {10}^{-3}left(1.232cdot {10}^{-4}right)*HSB+0.059; (0.017) \ 0.684 ;(0.066)end{array}right. begin{array}{l};;0474end{array}$$
(9)
All damping coefficients considered in the previous analysis were obtained without subtracting any additional source of dissipation such as bottom and wall friction. Previous works, such as24, highlighted the high importance of considering any other sources of wave dissipation besides the effect of vegetation elements when quantifying the wave height attenuation capacity. In this case, the flume walls were made of glass, and the friction induced by these walls could be considered negligible. However, bottom friction could be significant, as observed in tests run after removing all vegetation stems. Then, the wave damping coefficient obtained after subtracting the bottom friction contribution, ({beta }_{SB}), is studied here. Figure 7 shows the relationship obtained between this damping coefficient, ({beta }_{SB}), and hydraulic standing biomass, (HSB).Figure 7Wave damping coefficient, ({beta }_{SB}), as a function of the hydraulic standing biomass, (HSB), under all regular (top panel) and random (bottom panel) wave conditions. Both panels show linear fitting results obtained without considering the saturation point, indicated by the black solid line, and those obtained considering the saturation point, indicated by the gray solid line. The black dashed line indicates the saturation point. 95% confidence interval is shown in brackets and correlation coefficient (({rho }^{2})) for each fit is also displayed.Full size imageA linear relationship was also obtained for ({beta }_{SB}), revealing correlation coefficients similar to those obtained when analyzing (beta ). The obtained linear relationships under regular and random wave conditions are expressed as Eqs. (10) and (11), respectively, where values between brackets are the 95% confidence interval for each coefficient. A two-section fitting relationship, Eqs. (12) and (13), was also included considering the saturation regime obtained in the Halimione sp. 100% density and h = 0.20 m cases with a ({beta }_{SB}=) 0.69 and 0.63 under regular and random wave conditions, respectively.$${beta }_{SB}=1.051*{10}^{-3} left(7.063cdot {10}^{-5}right)*HSB$$
(10)
$${beta }_{SB}=1.296*{10}^{-3} left(6.894cdot {10}^{-5}right)*HSB$$
(11)
$${beta }_{SB}=left{begin{array}{l}1.151cdot {10}^{-3} left(7.445cdot {10}^{-5}right)*HSB \ 0.685 ;(0.047)end{array}right. begin{array}{l} ;; 0599end{array}$$
(12)
$${beta }_{SB}=left{begin{array}{l}1.396cdot {10}^{-3}left(7.919cdot {10}^{-5}right)*HSB \ 0.631 ;left(0.055right)end{array}right. begin{array}{l};; 0451end{array}$$
(13)
As can be noted, the ({beta }_{SB}) values are significantly lower than those obtained for (beta ), especially in the shallowest water depth cases where bottom friction is the highest, as discussed above. The estimation of (beta ) and ({beta }_{SB}) allows two possible approaches to determine the wave damping effect of a saltmarsh. The first approach, based on (beta ), includes wave damping induced by the combined effect of vegetation and bottom friction. Therefore, the consideration of (beta ) in analytical or numerical analysis could provide the total dissipation induced by the species under study, and sediment characteristics are not necessary for analysis. Considering that saltmarsh species grow in muddy to sandy environments and that the major contribution to the obtained wave attenuation is associated with vegetation, this approach may be the best option if soil properties are not thoroughly characterized.The second approach relies on the definition of ({beta }_{SB}). In this case, the wave damping contributions of vegetation drag and bottom friction are separated. Then, ({beta }_{SB}) can be used in cases where the effect of both momentum sinks can be separately evaluated. To quantify the wave damping contribution of vegetation drag only, ({beta }_{SB}) can be used, and then, the additional friction due to the bottom effect can be added considering the soil properties in each case. This second approach assumes a linear sum of both momentum sinks and could be applicable when soil properties are thoroughly characterized. More