Ecology

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In the present work, we performed quantitative metabolomic analysis for eleven biological tissues of S. lucioperca. The advantage of the quantitative approach over commonly used semi-quantitative measurements is that the obtained data on the metabolite concentrations expressed in nmoles per gram of a tissue can be directly used by any researcher as a reference to the baseline level of metabolites in that tissue. Quantitative data also allow for the comparing the tissues with very dissimilar metabolomic compositions.
The metabolomic analysis performed in the present work demonstrates that although the majority of metabolites are common for all tissues, their concentrations in tissues may vary at a large scale. Moreover, there are some tissue-specific compounds with very high abundance in only 1–2 types of tissue. The examples of such metabolites are glycine, histidine, creatine, and betaine in muscle, ovothiol A in lens, NAA in lens and brain, glucose in liver. Apparently, these compounds are important for biological functions specific for these particular tissues.
Two groups of metabolites, osmolytes and antioxidants, play the key role in the cell protection against osmotic and oxidative stresses. In this work, the following compounds were conventionally assigned to osmolytes: taurine, myo-inositol, NAH, NAA, betaine, threonine-phosphoethanolamine (Thr-PETA), and Ser-PETA. Obviously, this assignment is rather arbitrary: some of these compounds, besides osmotic protection, perform other cellular functions, including cell signaling, providing substrate for biosynthesis, and so on17,18,19. At the same time, the tissues under study contain metabolites with concentrations of the same level or even higher than the concentrations of compounds assigned to osmolytes: lactate, glucose, acetate, creatine. These metabolites are mostly related to the reactions of cellular energy generation, and their concentrations should strongly depend on the fish activity. For that reason, in this work we did not include them into the list of osmolytes. Figure 6 shows the concentrations of osmolytes in different fish tissues (excluding acellular tissues AH and VH), and demonstrates that the composition of osmolytes in tissues strongly depends on the cell type.
Figure 6

Concentrations of major osmolytes (in µmol/g) in S. lucioperca tissues.

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We found in the fish tissues the following compounds with antioxidative properties: glutathione (GSH), ascorbate, OSH, and NADH. The detection of minor amounts of one more well-known thiol antioxidant, ergothioneine, in the gills of another freshwater fish, R. rutilus lacustris, has recently been reported15; however, in the present work ergothioneine was not found neither by NMR nor by LC–MS in any of the studied tissues of S. lucioperca. NADH was found only in NMR spectra of liver, muscle, heart, and lens, and its concentration in these tissues does not exceed 15 nmol/g. GSH, OSH, and ascorbate are present in much higher concentrations in the majority of the fish tissues, so these three compounds play the main role in the cellular defense against the oxidative stress. The presence and the relative abundance of GSH and OSH in the fish tissues were confirmed by LC–MS data.
The metabolomic features of particular fish tissues are discussed below.
AH and VH
AH and VH are acellular fluids with minimal metabolic activity. AH is produced in the ciliary epithelium through both the active secretion and the passive diffusion/ultrafiltration of blood plasma20,21,22,23. Consequently, the metabolomic composition of AH is similar to that of plasma10. VH is also connected with blood via the hematoophthalmic barrier24 and with AH, and one can see (Table 1, Fig. 2) that the metabolomic compositions of AH and VH are close to each other. Thus, it is safe to assume that the levels of metabolites in AH and VH reflect their levels in blood plasma, which circulates through the majority of fish tissues. Significant deviations of metabolite levels in tissue as compared to plasma should be attributed to the intracellular metabolic activity specific for this particular tissue.
Lens
The eye lens is one of the most anatomically isolated tissues. The lens mostly consists of metabolically inert fiber cells without nuclei and organelles with the exception of metabolically active epithelial monolayer. The data present in Table 1 indicate that the lens contains very high levels of proteinogenic amino acids: for some amino acids (for example, branched-chain amino acids, glutamine, aspartate) their levels in the lens are more than ten-fold higher than that in AH. Moreover, the concentrations of the majority of amino acids in the lens are higher than in any other fish tissue. The elevated levels of amino acids in the lens has been noticed many years ago25, and it was attributed to the active amino acid transport from AH to lens26,27,28,29. These amino acids are presumably needed to synthesize high protein content (up to 40% of the total lens weight), which is in turn needed to provide high refraction coefficient.
The fish lens contains a unique set of osmolytes and antioxidants. The lens osmolytes are myo-inositol, NAH, NAA, Thr-PETA, and Ser-PETA. We have previously shown15 that the concentrations of osmolytes in the fish lens undergo significant seasonal variations. At the late winter time, when the fish was caught for this study, the most abundant lens osmolyte is myo-inositol. High concentrations of this compound are also found in other fish tissues, including brain, gill, and spleen. Thr-PETA and Ser-PETA are also among the most abundant metabolites in the majority of the fish tissues. In opposite, NAH and NAA are present in high concentrations only in the fish lens and brain. At the same time, the concentration of taurine, which is the most abundant osmolyte in all other fish tissues, in the lens is rather low.
The major antioxidant of the fish lens is OSH12. It has been shown that the level of OSH in S. lucioperca lens vary from 3 µmol/g at autumn to 1.5 µmol/g at winter15, which is in a good agreement with our present data (Table 1). The concentration of the second most abundant lens antioxidant, GSH, is 3–4 times lower than that of OSH. Taking into account the properties of OSH30,31,32,33, it has been proposed12,34 that OSH is a primary protector against the oxidative stress, while the main function of GSH in the lens is the maintenance of OSH in the reduced state. It should be noticed that although OSH was also found in other fish tissues (Table 1, Fig. 2), its concentration in these tissues is significantly lower than in the lens. Therefore, in respect to fish, OSH can truly be called “lenticular antioxidant”.
High concentrations of amino acids, osmolytes, antioxidants and some other compounds in the lens indicate that these metabolites are either synthesized in metabolically active epithelial cells, or pumped into the lens from AH against the concentration gradient with the use of specific transporters also located in the epithelial layer. The fiber cells of the lens are metabolically passive, and fresh metabolites can appear in these cells only due to the diffusion from the epithelial layer toward the lens center. Therefore, one can expect that the concentrations of the most important metabolites decrease from the lens cortex toward the lens nucleus. To check this assumption, we measured the metabolomic profiles for cortex and nucleus separately. The measurements were performed for three lenses; then the ratios of the metabolite concentrations in the cortex to that in the nucleus were calculated and averaged. The results of the calculations are shown in Fig. 7 (only for metabolites with the highest and the lowest cortex/nucleus ratios) and Supplementary Table S2 (for all metabolites). Indeed, the levels of the majority of metabolites in the lens nucleus are significantly lower than in the cortex. For five metabolites, namely ATP, NAA, inosinate, ADP, and GSH the difference exceeds the factor of thirty; that means that these compounds are almost completely depleted during their diffusion toward the lens nucleus.
Figure 7

Barplot for statistically significant differences in the metabolomic content of lens cortex and nucleus. Bars show the averaged ratio of metabolite concentrations in the cortex to that in the nucleus of the S. lucioperca lens.

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Brain
The brain tissue similarly to the lens is isolated from the vascular system by means of the hematoencephalic barrier. However, in opposite to the lens, brain is very metabolically active tissue, as in particular indicated by the high level of lactate (14 µmol/g). Similar lactate concentrations were found only in muscle and heart (Table 1). Besides lactate, the most abundant metabolites of the fish brain are osmolytes myo-inositol, Ser-PETA, taurine, Thr-PETA, NAH, and NAA; the concentrations of these compounds in brain are in the range from 2.5 to 13 µmol/g (Table 1). The brain tissue also contains high levels of glutamate and creatine, which are used by brain cells for the cellular energy generation. The level of antioxidant ascorbate in brain (400 nmol/g) is significantly higher than in other fish tissues, which indicates the importance of ascorbate for the brain correct operation. Besides ascorbate, the brain tissue also contains OSH and GSH, but at significantly lower concentrations (100–200 nmol/g).
Blood-rich organs: liver, spleen, milt, muscle, heart, gill, kidney
Figure 4 demonstrates that from the metabolomic viewpoint, gill, kidney, milt, and spleen are the most similar tissues. However, the quantitative analysis indicates significant differences. In particular, spleen does not contain measurable by NMR amounts of antioxidants OSH and GSH. The levels of osmolytes are also different: the concentration of taurine in spleen is threefold higher than in gill and milt, while the level of myo-inositol in milt is much lower than in spleen and gill (Fig. 6). Significant differences are also found for some amino acids (alanine, creatine), organic acids (lactate, GABA), and nucleosides (ATP, ADP, AMP, inosine).
One of the important liver functions is the maintaining the glucose level in blood regulated by producing glucose from stored glycogen. Correspondingly, the level of glucose in liver is extremely high (40 µmol/g), which is higher than in any other tissue by at least an order of magnitude. Liver also contains elevated (as compared to other tissues) concentrations of threonine, glutamate, succinate, fumarate, AMP, and nicotinamide.
The biological functions of muscle and heart are relatively similar; however, the metabolomic compositions of these tissues differ significantly. The main osmolyte in muscle cells is taurine (30 µmol/g), while in heart the osmotic protection is shared between taurine (15 µmol/g) and Ser-PETA (12 µmol/g). Muscle contains very high levels of glycine and histidine. Glycine is known to protect muscles from wasting under various wasting conditions35,36, while histidine and histidine-related compounds were reported to play the role of intracellular proton buffering constituents in vertebrate muscle37. Very likely that both glycine and histidine also participate in the osmotic protection of the muscle cells. The level of creatine—the energy source—in muscle (23 µmol/g) is five-fold higher than in heart. More

Quality control of the full-length transcriptomes
The FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti were constructed based on sequencing data of three separated libraries on the PacBio Sequel platform. Specifically, a total of 3,444,947 subreads with 6,448,987,299 nucleotides, 3,255,638 subreads with 6,504,282,447 nucleotides and 3,403,451 subreads with 7,190,237,257 nucleotides were generated for R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After quality control, we obtained 137,159 circular consensus sequencing (CCS) reads for R. a. hainanus, 137,160 CCS reads for R. a. himalayanus and 152,251 CCS reads for Myotis ricketti. With the standard IsoSeq. 3 classification and clustering pipeline, we identified 111,806 FLNC for R. a. hainanus, 105,713 FLNC for R. a. himalayanus and 122,222 FLNC for Myotis ricketti. After isoform-level polishing, 10384, 9984 and 10932 high quality isoforms were retained in R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After removing redundancy with CD-HIT-EST and filtering isoforms shorter than 200 bp, the final FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti (FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively) contain 10103, 9676 and 10504 FL isoforms with an average length of 2251, 2370 and 2530 bp, respectively (Table 2). Finally, the FL transcriptome from both CF and FM bats (FL-CF-FM) contains 26,342 transcripts with an average length of 2,405 bp (Table 2). BUSCO analysis revealed that a total of 2,354 (57.4%) BUSCOs were included in FL-CF-FM. We also found 39.9%, 38.1% and 41.9% BUSCOs in FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively (Table 4). Given the highly specialized function of the cochlea, we should not expect a high level of BUSCO value in FL transcriptome of cochlea. A recent single cell RNA-seq study has identified a similar number of genes expressed in the murine cochlea (a total of 12,944)30.
Table 4 Completeness of each of the four FL transcriptomes assessed by benchmarking universal single-copy ortholog (BUSCO) analysis.
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Quality control of annotation
Four FL transcriptomes (FL-CF-Rhai, FL-CF-Rhim, FL-FM-Myo, and FL-CF-FM) were functionally annotated by performing DIAMOND and BLASTx searches against the Nr and UniProt databases separately. For FL-CF-FM, 24,793 and 24,198 transcripts were annotated by Nr database and UniProt database, respectively (Table 3). After combining the annotation results from the two databases, a total of 24,833 transcripts were annotated in at least one database. We obtained similar annotation results for FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo (Table 3). Transcripts without annotations might be novel isoforms of echolocating animals or due to the lack of representative sequences for cochlea in public databases. More

These methods are an expanded version of those in our related work, Boakes et al.15.
The database was compiled over the period 2005–2008. Data collection equates to around 1500 person-days and data were gathered by a team of 21 people. Between them, team members were fluent in English, French, German, Mandarin, Russian, Spanish and Swedish. These languages were extremely helpful in transcribing museum specimen labels and in translating publications. However, the majority of publications were in English and we acknowledge that the database will be biased toward records published in English-language publications.
Our study focuses on the 130 galliform species that occur within the Palaearctic and Indo-Malay biogeographic realms22 (see Online-only Table 1). We have additionally included records of the Imperial Pheasant (Lophura imperialis) although it is now recognised that this is a hybrid and not a species. The geographic range of two of the species in the database, the Red Grouse (Lagopus lagopus) and the Rock Ptarmigan (Lagopus muta), extends to North America. North American data was often included in the information which museums sent us and in these instances we entered those records into the database since we thought they might be of use to researchers studying these species. However, it should be noted that we did not search exhaustively for records of these species in North America, we have merely included those that we came across.
We attempted to gather all species distribution data that could be accessed from five different sources; museum collections, literature records, banding (ringing) data, ornithological atlases and birdwatchers’ trip report websites. For each data source, exhaustive and systematic search strategies were adopted.
Museum collections
Using web-based searches and Roselaar23, 377 natural history collections were identified. We found contact details for 338 of these collections and requested by email or letter a list of the Galliformes in their holdings along with collection localities and dates. Non-respondents were recontacted. 135 museums were able to share data with us (see Online-only Table 2). Museum records were obtained through publicly available online databases e.g. ORNIS, electronic or paper catalogues sent to us by the museums or by visiting the museums and transcribing data directly from specimens or card catalogues. Almost half of the museums we contacted did not respond despite at least one follow-up enquiry, and there was substantial variation in the amount and format of data contributed by those that did reply. Altogether, over 50% of the records came from just six museums (Natural History Museum, London; Zoological Institute of the Russian Academy of Sciences, St Petersburg; Zoological Museum of Lomonosov Moscow State University; Field Museum of Natural History, Chicago; American Museum of Natural History, New York; National Museum of Natural History, Leiden), a single museum (the Natural History Museum, London) contributing nearly 20% of the museum records that could be georeferenced and dated15. Following databasing and/or georeferencing, records were returned to larger collections and to those who had requested the data.
Literature
Data from the literature were added to those previously collected by McGowan24. Entire series of key English-language international and regional ornithological journals such as Ibis, Bird Conservation International, Journal of the Bombay Natural History Society, and Kukila were scanned for relevant information, availability allowing. We began at the library of the Zoological Society of London and followed up missing journal issues at the BirdLife International library, Cambridge UK; the British Library, London, UK; the Edward Grey Institute, University of Oxford, UK. Relevant Chinese literature was also scanned. Additionally, data were obtained from regional reports, personal diaries, letters, newsletters etc stored in the archives of BirdLife International, Cambridge, UK; the World Pheasant Association, Newcastle, UK; the Edward Grey Institute, University of Oxford, UK. Several of the species/regional experts we consulted also contributed their personal records which were recorded in the database as ‘personal communications’. As far as it were possible, records were classed as primary or secondary data within the ‘dynamicProperties’ field of GalliForm14. It is important to note that some primary records or museum specimens will be duplicated within the database in the secondary data.
Banding records
Eighty-three ornithological banding groups were identified using web-based searches and were contacted via email. Thirty of these groups replied and only seven were able to provide us with data (see Table 1). The majority of galliform species tend not to be banded due to their large body sizes and spurs. Additionally, many of the banding groups kept their records on paper and were not able to send them to us. Nevertheless, we were able to access and georeference 15,152 banding records.
Table 1 The ringing groups that shared data with GalliForm.
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Ornithological atlases
We digitised location data from 20 ornithological atlases (see Table 2). Data from several other atlases were not used since the range of dates for the records was wider than 20 years.
Table 2 The atlases that were digitised to be included in GalliForm.
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Trip report website data
We used the two trip report websites that were popular with birders during the data recording period (2005–2008), www.travellingbirder.com and www.birdtours.co.uk. At that time, eBird (probably the most relevant current online source today) did not cover the majority of the countries within our study region, and our intention with the deposition of this dataset is to focus on pre-eBird data that are more difficult and time consuming to access. We extracted data from all trip reports of birdwatching visits to European, Asian and North African countries. Care was taken to enter reports that featured on both websites once only.
Criteria for data inclusion
To be included in the database, records had to meet the following criteria:
1.
The record identified the species of the bird concerned.

2.
The record contained either a verbal description of the locality at which the bird concerned was observed or the co-ordinates at which the bird was observed.

Records of captive birds were excluded. Records relating to non-native occurrences were included but were flagged in the ‘establishmentMeans’ field as “introduced”.
Data entry
GalliForm14 was originally compiled in the programme Microsoft Access 2003. To maximise uniformity in data entry, all data recorders were given thorough and consistent training and each was provided with a set of database guidelines. An Access Database form was created to standardise data entry and to enable multiple members of the team to collect data simultaneously.
Each entry in GalliForm14 corresponds to a single record of a single species recorded in a specific location. The data fields of GalliForm14 are described in Online-only Table 3. The taxonomy used has been updated to be consistent with the BirdLife International 2019 taxonomy (datazone.birdlife.org). All information was entered exactly as it was described in the data source, with as much information extracted as possible. Multiple records from different sources which recorded the same information were still included in the interest of completeness. The only exception to this is the trip report data in which we did not enter identical records which occurred on both the Travelling Birder and Bird Tours websites.
The source of the data, i.e. literature, museum, atlas, ringing or website trip report is recorded in the ‘dynamicProperties’ field under the code “dataSource”. For literature data, (where known) the nature of the record, i.e. primary or secondary, is recorded under the code “datatype”.
Taxonomy has of course changed considerably over time. To allow for this we recorded the taxonomy as it was described in the data source in the ‘originalNameUsage’ field. The current taxonomy was then selected from a look-up table. If at the time of data entry, the data compiler was unsure which species the synonym referred to, the species was tagged as “unknown” and the species was designated at a later date following further research on the synonym.
Identical localities can also be described in multiple ways. We recorded the locality as it was given in the data source in the ‘verbatimLocality’ field. If the ‘verbatimLocality’ clearly tallied with a locality already within the database, the record was linked to that locality in order to increase georeferencing efficiency.
It was rare for a source to record absence of evidence, i.e. a survey for a species at a particular locality which failed to find that species. However, in the few cases where we did come across such records, the locality and date of the survey were recorded and “absent” was recorded in the ‘occurrenceStatus’ field.
Each record refers to an independent observation. For museum and ringing records, this means a single individual. For literature, atlas or trip report records this may refer to a group of birds observed in one particular locality, on one particular day. If given, the number of total individuals is recorded in the ‘individualCount’ field. The number of males and females is recorded in the ‘sex’ field and the number of juveniles and adults in the ‘lifeStage’ field. If the ‘lifeStage’ field is blank, it is reasonable to assume the individual(s) is an adult.
Occasionally, additional information about the observation might be included in the data source, for example the habitat the bird was observed in or whether the bird was common or rare in that locality. These data are recorded in the ‘habitat’ and ‘organismQuantity’ fields, respectively. Any additional information which did not fit within the structure of the database was recorded in the ‘occurrenceRemarks’ field, along with any notes found on museum labels.
For the purposes of data deposition, the database was converted to a tab-delimited CSV file with all fields following Darwin Core format. A full summary of these fields is given in Online-only Table 3.
Georeferencing
Locality descriptions were converted to geographic co-ordinates using a wide range of atlases and gazetteers, co-ordinates generally only being assigned if accurate to one degree (although in the majority of cases the locations were accurate to within 30 minutes, Table 3). We would initially search for a locality within the gazetteers available to us at the time. If the locality was not listed within those gazetteers we would search for the locality using atlases. Since this fieldwork was conducted, MaNIS standards have become widely used for studies of this kind, but these weren’t fully developed at the time of data collection25. Named places, e.g. towns or counties, were georeferenced using their geographic centre and georeferencing uncertainty measured from the centre to the edge of the named place. Often localities were given simply as the name of a river, mountain or Protected Area. In these instances we used the midpoint of the river between source and mouth (uncertainty measured as distance from midpoint to source/mouth), the summit of the mountain (uncertainty measured as distance from summit to approximate mountain foot) and the rough centre of the Protected Area (uncertainty measured as distance from centre to Protected Area edge). If a particular locality description matched two or more places their midpoint was taken (uncertainty measured as distance from midpoint to place). Offsets from localities (e.g. “50 km N of Kuala Lumpur”; “8 miles along the road from Sheffield to Chesterfield”) were measured using a digital atlas (uncertainty was approximated at the georeferencer’s discretion in these instances, usually between 3 and 10 arc-minutes, depending on the vagueness of the offset.) For georeferencing done ‘in house’, the gazeteer/atlas used was recorded.
Table 3 Georeference and date completeness of the records.
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When possible, localities we could not georeference ourselves were sent to regional experts.
92% of our localities are georeferenced to an accuracy of 30 minutes, corresponding to 82% of occurrence records (see Table 3).
We had less success at georeferencing museum records than literature records15, due in part to difficulties in reading hand-writing on specimen labels. Older records were also harder to georeference, presumably due to changes in place names over time, and to some early ornithologists failing to document the collection locality. As might be expected, localities from countries that do not use the Roman alphabet were also harder to georeference.
Some records were excluded from the database based on their locality: records which we thought were trading localities, notably Malacca in Malaysia and Leadenhall Market in the UK; records from captive specimens, e.g. zoological gardens.
Dating
49% of records are dated to within an accuracy of one year. Where possible, we assigned date ranges to undated records. For example, if the name of the collector was given on a museum specimen and we knew when that collector was active in that region, we assigned a date range covering that period. There remain undated records which could perhaps be dated in this way. Undated literature records were designated as occurring before their publication date. We were able to date 89% of records to within 10 years. More

Affiliations

School of Geosciences, University of Edinburgh, Edinburgh, UK
Andrew T. Nottingham & Patrick Meir

Smithsonian Tropical Research Institute, Panama City, Panama
Andrew T. Nottingham, Esther Velasquez & Benjamin L. Turner

Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
Patrick Meir

Authors
Andrew T. Nottingham

Patrick Meir

Esther Velasquez

Benjamin L. Turner

Corresponding author
Correspondence to Andrew T. Nottingham. More

Our starting point is a two-dimensional shallow water model that solves the hydromorphodynamic problem by integrating numerically the depth-averaged shallow water equations coupled with the Exner equation, which describes the time evolution of riverbed elevation41. When paired with a description of the vegetation dynamics and its effect on flow and sediment transport, such a model was demonstrated to reproduce key ecomorphodynamic processes34. In this study, we model feedbacks between hydromorphodynamic processes and vegetation including a description of both above- and below-ground plant traits and their dynamics. In particular, we consider that vegetation impacts flow and sediment transport by modifying the flow resistance, the bed shear stresses, and the threshold for the onset of bed load transport. In turn, morphodynamic processes occurring during flood events are responsible for two main vegetation mortality mechanisms, which are, plant uprooting and sediment burial, while water level fluctuations during low flow periods between floods control the vegetation growth.
Hydromorphodynamic processes
River hydromorphodynamics is simulated using the numerical model BASEMENT (freeware software)41 and the computational domain is discretised using a triangular unstructured grid. First, the model solves the hydrodynamic problem by using the Manning-Strickler approach for evaluating the hydraulic roughness, in which the bottom shear stress, τ, reads as

$$begin{aligned} mathbf{{tau }} = frac{rho g mathbf {u}|mathbf {u}|}{K_s^2h^{1/3}}quad , end{aligned}$$
(1)

where (rho) is the water density, g is the gravitational acceleration, (mathbf {u}) is the flow velocity vector, h the water depth and (K_s) the Strickler coefficient (the inverse of the Manning coefficient n). Second, the sediment continuity equation (Exner equation) is solved to obtain the evolution of the bottom elevation (z_b) of a riverbed composed of a uniform sediment. The bed load transport intensity is evaluated using the standard Meyer-Peter and Müller formula42, as a function of the excess of the Shields shear stress (theta) above a threshold value ((theta _{cr}=0.047)), where

$$begin{aligned} theta = frac{|mathbf{{tau }}|}{(rho _s -rho ) g d_s} end{aligned}$$
(2)

and (rho _s) and (d_s) are the sediment density and diameter, respectively.
Vegetation description
Vegetation is described by a total dimensionless biomass density, B, which is partitioned into two components, above-ground, (B_c) (subscript c stands for canopy) and below-ground, (B_r) (subscript r stands for roots) (Fig. 2a). The above-ground biomass is considered to be evenly distributed along the plant height, H. Conversely, the below-ground biomass is characterised by a dimensionless vertical density distribution (b_r(zeta )) that extends downward in the (zeta)-direction, from the riverbed surface ((zeta =0)) to the rooting depth (zeta _r(t)), i.e. the maximum depth reached by roots at a generic time, t (Fig. 2a). (b_r(zeta )) is calculated via the stochastic model developed by Tron et al.43. The model assumes that fluctuations of the water table level follow the water level in the channel, which produce an alternating sequence of root growth and decay periods at each riverbed depth (zeta). This behaviour is then described as a stochastic process and solved to obtain a steady-state solution for (b_r(zeta )) (see Appendix for the formulation43), which depends on the mean frequency, magnitude, and decay rate of water table fluctuations and represents a key component in our model, controlling plant growth rate and vegetation resistance to uprooting.
Vegetation growth dynamics
The fluctuation of the water table level is one of the main driving factors controlling growth performances of riparian plants22. The ability of species to rapidly grow roots tracking the water table was found to be key for determining the growth rate of plants and their potential establishment success on bars44. Highly variable water table levels during growth periods tend to produce water stress that reduces plant growth because of reduced root respiration, while long free-inundation periods may be similarly harmful for plants that fail to grow roots deep enough to secure a connection with the groundwater. To model this link, we use the function (b_r) as a proxy for the ability of the plant to tolerate inundations, which depends on the riverbed depth reached by the roots a certain time, and relate that to the plant growth rate. We consider that the total biomass density B (above- and below-ground components) grows in time (t) following a logistic function:

$$begin{aligned} frac{dB}{dt} = sigma _B B biggl (1-frac{B}{B_{max}}biggr ) quad , end{aligned}$$
(3)

where (B_{max}) is the maximum biomass value (set to 1 in our model) and (sigma _B) is the growth rate assumed to vary depending on the dimensionless root density distribution, (b_r), as

$$begin{aligned} sigma _B = phi _{B} int _{0}^{1}b_{r}(z)dz end{aligned}$$
(4)

where (phi _{B} [-]) is a scaling factor and (int _{0}^{1}b_{r}(z) dz) represents the steady-state dimensionless root biomass. With Eq. (4), we assume that vegetation grows faster (higher (sigma _B)) when plant roots are more developed (at the steady-state). This assumption is largely used in modelling dryland vegetation where plant species act as phreatophytes45, similarly to riparian plants. Biomass density decay due to waterlogging is included considering that B decreases exponentially with a constant rate of 0.1 when the riverbed level falls below the mean water table level.
We assume that the rooting depth changes over time following an exponential function as

$$begin{aligned} frac{dzeta _r}{dt} = sigma _{r}( zeta _{r,max}-zeta _r) quad , end{aligned}$$
(5)

where (sigma _r) is the root deepening rate, which is constant, and (zeta _{r,max}) represents the distance between the riverbed surface and the minimum water table level (Fig. 2a). Below such level, riverbed matrix is saturated with pore water and roots cannot grow due to the resulting anoxic conditions43. Equation (5) implies that roots grow faster as farther they are from the groundwater and linearly with (zeta _{r,max}). This behaviour is representative of phreatophytes plant species that uses groundwater as main source of water and tend to elongate roots to keep pace with the receding rate of the water table level21,46.
The proportion of the total biomass growth allocated to each plant component is derived using a mass balance model47 by introducing two constant partitioning coefficients, (lambda _i) with (i in (c,r)) (i.e. canopy and roots). These define the fraction of the total biomass growth allocated above- and below-ground and satisfy (lambda _c + lambda _r = 1) for all times. As a consequence, the growth rates of the two plant components can be written as

$$begin{aligned} frac{dB_i}{dt} = lambda _i frac{dB}{dt} ;. end{aligned}$$
(6)

Here we consider (lambda _i) constant, assuming no plasticity in biomass partitioning. The canopy height depends on the above-ground biomass through the function48

$$begin{aligned} H(t)=aB_c^b(t) end{aligned}$$
(7)

where parameters a and b are constant in our model and can be used to modulate the plant height growth.
Feedbacks between vegetation and hydromorphodynamics
We consider that the above-ground biomass changes the bed roughness by modifying the Strickler coefficient (K_s), which is used to calculate the flow resistance [Eq. (1)], such as

$$begin{aligned} K_s = K_{s,g}+(K_{s,v}-K_{s,g})frac{B_{c}(t)}{B_{c,max }} end{aligned}$$
(8)

where (K_{s,g}) represents the roughness of the bare bed, which depends on the sediment grain size, while (K_{s,v}) (( H_{bur}), where (H_{bur}=beta _{bur}H) with (beta _{bur}in [0,1]) (see Fig.  2a) . The parameter (beta _{bur}) accounts for the ability of the plant to withstand sediment burial and the reduction of the canopy height due to bending caused by water flow25. The uprooting is modelled by defining a critical below-ground biomass (B_{r,cr}) that has to be excavated by flow erosion until vegetation is uprooted19. This is defined as32

$$begin{aligned} B_{r,cr}= int _{0}^{hat{zeta }_{upr}}b_r(z)dz = beta _{upr} int _{0}^{1}b_r(z)dz end{aligned}$$
(10)

where (hat{zeta }_{upr}=zeta _{upr}/zeta _r) represents the ratio between the uprooting depth (zeta _{upr}) and the rooting depth (zeta _r). (beta _{upr}) is a constant parameter that defines the strength of the root system to withstand erosion32. Uprooting can occur during the flood event at any time. B and (zeta _r) are set to their initial values when burial or uprooting occurs.
Riverbed erosion and aggradation processes alter the proportion of above and below-ground biomass during floods. According to the mass balance adopted, vegetation is able to re-allocate biomass at a rate that depends on the partitioning coefficients, (lambda _i) [Eq.  (6)]. We consider that buried part of above-ground biomass can convert to roots, while exposed part of below-ground biomass caused by erosion can transform in above-ground biomass. Canopy height is then re-calculated using Eq.  (7) and the rooting depth is adjusted depending on bed level changes. This assumption is justified by the great plasticity observed in riparian species, which are able to easily resprout from buried stems and grow new tissues from exposed roots49. When no morphological changes occur, the proportion of biomass allocated above- and below-ground can be calculated as (B_i=lambda _iB).
Model workflow
The model workflow is shown in Fig. 2b and considers an alternating sequence of floods and low flow periods. During each flood event morphological changes occur as a result of the two-way interaction between riparian vegetation and river morphodynamic processes. Vegetation can be uprooted during the entire flood event and/or can die by burial at the end of the falling limb of the discharge. Low flow periods are comprised between two consecutive flood events and may last from months to years. In this phase the riverbed is inactive ((theta < theta _{cr})) and vegetation is allowed to grow and develop above- and below-ground traits. As a first step, we simulate vegetation growth during a low flow period, given a vegetation cover and a riverbed topography. The biomass growth rate [Eq. (4)] is dynamically computed through the evaluation of the function (b_r) [see Eq.  (11) in the Appendix], which varies in space depending on the local (cell-wise) variability of the water table level during the growth period. Here we assume that the water table level changes locally with the water surface elevation. This assumption holds true for gravel, uniform substrates where hydraulic conductivity is high43. Discharge variability during low flow periods is responsible for changes in water surface elevations, and thus in water table levels. To evaluate the water surface elevation associated with a certain discharge, we derive a water level-discharge h-Q relation for each computational cell by running one hydrodynamic (fixed bed) simulation for a series of discharges. When the water surface elevation is zero, namely when the cell becomes dry at a certain discharge, we assume that the water table level can be calculated by interpolating the water surface elevations of the nearest wet cells. This allow us to transform discharges in water table level time series and to obtain the frequency distribution of water table levels for each cell during low flow periods. The h-Q relation is then used to calibrate the parameters (lambda _w), (eta _w), (gamma _w) required to compute the function ({b}_r) [see Eqs. (12) and (13) in the Appendix for details] and calculate the growth rate, (sigma _B). By numerically integrating Eqs. (3) and (5) for the duration of the specific growth period, we update the vegetation variables and the associated feedbacks. This procedure introduces a link between plant growth rate and low flow regime characteristics, which in turn influences plant traits. As a second step, we simulate the riverbed evolution during floods, where feedback mechanisms between vegetation and hydromorphological processes are activated. In particular, the uprooting mechanism [Eq. (10)], the correction of the bed shear stress and flow resistance [Eq. (8)], and the critical Shield parameter [Eq. (9)] are updated every time step. After the flood, we used the new river bed topography for updating the vegetation cover including mortality by burial. The resulting vegetation cover and plant traits are then used as initial conditions for the subsequent growth period. More

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