P Bocher
email: bocher@cebc.cnrs.fr

F Caurant

P Miramand

Y Cherel

P Bustamante

Influence of the diet on the bioaccumulation of heavy metals in small burrowing petrels from the Kerguelen Islands

Concentrations of cadmium, mercury, copper and zinc were measured in liver, muscle, kidney and stomach contents and the preys of five species of small burrowing petrels : South Georgian (Pelecanoides georgicus) and common (P. urinatrix) diving petrels, thin-billed (Pachyptila belcheri) and Antarctic (P. desolata) prions, and blue petrel (Halobaena caerulea). Inter-specific differences were significant for zinc concentrations in liver and kidney and for copper concentrations in the muscle. Blue petrel exhibited the highest mercury concentrations in the liver, but no significant difference were found for cadmium. Since they are very closely related species, concerning moult schedule, body size and life span (at least for four of them), their diet has been considered in order to evaluate its influence on heavy metal bioaccumulation. Both instantaneous exposure (with stomach content concentrations) and long term exposure -by the heavy metals analysis in the most frequent preys of their diethave been evaluated. The most evident result is the influence of fish in the diet on the mercury levels. Diet does not seem to be discriminant for cadmium concentrations in the seabirds.

Introduction

The use of seabirds as monitors of the marine environment has largely been discussed [START_REF] Furness | Seabirds as monitors of the marine environment[END_REF]Furness & Greenwood, 1993), and it has been shown that they are a suitable choice to play a role as bioindicators of food supplies [START_REF] Cairns | Seabirds as indicators of marine food supplies[END_REF][START_REF] Barrett | Recent responses to changes in stock of prey species by seabirds breeding in the southern Barents Sea[END_REF]Cherel & Weimerskirch, 1995 ;[START_REF] Montevecchi | Seabird indication of squid stock conditions[END_REF][START_REF] Montevecchi | Centurial and decadal oceanographic influences on changes in northern gannet populations and diets in the north-west Atlantic : implications for climate change[END_REF][START_REF] Regehr | Interactive effects of food shortage and predation on breeding failure of black-legged kittiwakes : indirect effects of fisheries activities and implications for indicator species[END_REF], but also of pollution, and especially oil pollution [START_REF] Burger | Estimating the mortality of seabirds following oil spills : effects of spill volume[END_REF][START_REF] Camphuysen | The value of beached bird surveys in monitoring marine oil pollution[END_REF]. The fact that seabirds are most often top predators and long-lived species means also that they are useful as biomonitors of pollutants such as organochlorines or heavy metals. Nevertheless, since they are entirely anthropogenic, the biological significance of organochlorines is best understood, compared to heavy metals which occur naturally in marine systems and so are always present in the tissues of marine vertebrates [START_REF] Thompson | Metal concentrations in marine vertebrates[END_REF]. Among the heavy metals, cadmium and mercury have the ability to bioaccumulate in food webs and most of the long-lived predatory species exhibit high concentrations of these both toxic elements. This process is particularly exacerbated in high latitudes [START_REF] Bustamante | Cephalopods as a vector for the transfer of cadmium to top marine predators in the north-east Atlantic Ocean[END_REF]Dietz et al., ;1996[START_REF] Dietz | Have arctic marine mammals adapted to high cadmium levels ?[END_REF][START_REF] Elliot | Heavy metal and metallothionein concentrations in seabirds from the Pacific coast of Canada[END_REF][START_REF] Wagemann | Heavy metals and selenium in tissues of beluga whales, Delphinapterus leucas, from the Canadian Arctic and the St Laurent Estuary[END_REF] where the baseline concentrations are probably higher than in the temperate latitudes. This implies that it is difficult to assess the anthropogenic influence on the levels of trace elements found in the top predators of the polar and sublue petrelolar areas even if anthropogenic inputs for most of them -and especially cadmium and mercury -are considered more important than natural sources in their biogeochemical cycle (Walker et al., 1995). This is all the more difficult that a great number of factors -such as phylogeny, moult pattern, sex, life span, diet-are liable to influence trace elements bioaccumulation in seabird tissues, cadmium and mercury levels showing the largest variations among seabird species [START_REF] Furness | Birds as monitors of pollutants[END_REF][START_REF] Stewart | Influence of species, age and diet on mercury concentrations in Shetland seabirds[END_REF]1999 ;Stewart & Furness, 1998 ;[START_REF] Walsh | The use of seabirds as monitors of heavy metals in the marine environment[END_REF]. Among all these factors, it has been supposed that dietary was one of the more discriminant factor for the differences in mercury and cadmium concentrations. Thus cephalopods have been determined to be an important vector for the transfer of cadmium to top marine predators [START_REF] Bustamante | Cephalopods as a vector for the transfer of cadmium to top marine predators in the north-east Atlantic Ocean[END_REF][START_REF] Caurant | Cadmium contamination in pilot whales Globicephala melas : source and potential hazard to the species[END_REF][START_REF] Honda | Heavy metal concentrations in muscle, liver and kidney tissue of striprd dolphin, Stenella coeruleoalba, and their variations with body length, weight, age and sex[END_REF][START_REF] Muirhead | Heavy metal concentrations in the tissues of seabirds from Gough Island, South Atlantic Ocean[END_REF][START_REF] Muirhead | Heavy metal concentrations in the tissues of seabirds from Gough Island, South Atlantic Ocean[END_REF][START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF] have shown that seabird species including appreciable amount of crustacea in their diet had lower cadmium and mercury concentrations than those which tended to rely predominantly on squid and fish alone. Nevertheless, trace element concentrations found in crustaceans from the Antarctic Ocean also exhibit considerable interspecific heterogeneity [START_REF] Petri | Trace metals in crustaceans in the Antarctic Ocean[END_REF], which implies that the interpretation of the concentrations in predators probably require a sharper knowledge of the diet.

This study records the cadmium, mercury, zinc and copper concentrations in individuals of five species of small burrowing petrels from Kerguelen Islands (South Indian Ocean) : South Georgian (Pelecanoides georgicus) and common (P. urinatrix) diving petrels, thin-billed (Pachyptila belcheri) and Antarctic (P. desolata) prions, and blue petrel (Halobaena caerulea), including their stomach contents. Their diet has been deeply investigated [START_REF] Bocher | Complete trophic segregation between South Georgian and common diving petrels during breeding at Iles Kerguelen[END_REF](Bocher, , 2001) ) and consequently the same trace elements have been determined in their main prey species. This provided a unique opportunity to investigate the importance of the influence of the diet on trace element levels in closely related species, and the suitability of using these trace elements as tracers of the diet of species and /or populations.

Materials and methods

Sampling

Fieldwork was carried out at Kerguelen Islands, Southern Indian Ocean. Study colonies of birds were located in two adjacent islands of the "Golfe du Morbihan", a large gulf (about 700 km 2 ) located in the eastern part of the archipelago, where large populations of petrels breed (Weimerskirch et al. 1989). Blue petrels, thin-billed prions and common diving petrels were studied on Mayes Island (49°28'S, 69°57'E), and South Georgian diving petrels and Antarctic prions on Verte Island (49°31'S, 70°04'E).

Seabirds were collected during several breeding seasons mainly on Mayes and Verte Islands but also in others places on the archipelago. In order to not kill bird, we collected freshly dead birds as the result of predation by subantarctic skua or collisions of petrels at night with lights of buildings or ships. The number of birds so obtained varied greatly for each species and no birds were sampled unless they were largely intact and freshly dead. The birds collected could include both breeding adults and immature non-breeders which were generally not possible to discriminate.

Stomach samples were collected during the middle of the chick-rearing period in 1995 except for thin-billed prion which was sampling in summer 1999. Because breeding cycles are not synchronised between species (Weimerskirch et al. 1989), the time of food sampling varied according to the species and the whole study period occurring roughly from mid-January to mid-March. Breeding adult petrels and prions were caught at night by mist netting or in burrows fitted with trap doors at the entrance to retain the adult before the chick was fed.

Food samples from blue petrel, thin-billed prion and antarctic prion were collected by spontaneous regurgitation, and those from common diving petrel and South Georgian diving petrel by stomach lavage following Gales (1987) and [START_REF] Bocher | Complete trophic segregation between South Georgian and common diving petrels during breeding at Iles Kerguelen[END_REF]. No individual bird was sampled more than once.

Macrozooplankton and micronekton samples were collected on cruises of the R/V La Curieuse during austral summer. The amphipods (Themisto gaudichaudii) and the copepods (Paraeuchaeta antarctica) were sampling inside the golfe du Morbihan in March 1997 with an ORI-net (2 m2, 1 mm mesh Antarctic prionerture). The amphipods (Themisto gaudichaudii), the euphausiids (Euphausia vallentini and E. triacantha) and myctophids were collected outside the gulf in eastern part of the peri-insular shelf in February 1998, using a IYGPT trawl (International Young Gadoid Pelagic Trawl; opening: 12 x 7 m) with 10-mm meshsize in the codend.

Birds, stomach samples, macrozooplankton and micronekton samples were weighed, immediately frozen and stored at -20°c on fieldwork before to be sent to the Laboratory of Biology and Environment in La Rochelle (France), for analysis. All birds were defrosted overnight, sexed and samples of liver, kidney and pectoral muscles removed. Each stomach sample was thawed and placed in a flat-bottomed tray for visual estimation of volume of main prey. For each main crustacean species from zooplanktonic samples, three sets of 5 to 40 individuals were constituted and the smallest and the tallest individual measured in order to establish the size range. The euphausiid Thysanoessa sp. was absent in zooplankton samplings and provided from stomach samples of South Georgian diving petrel which were constituted only from this species. Myctophids and squids were individually analysed.

Heavy metal analysis

Stomach contents, samples of preys and samples of bird tissues were analysed in the same way. For cadmium (Cd), copper (Cu) and zinc (Zn) determination, two aliquots of aprox. 200 or 300 mg of each homogenised dried samples were digested in 5 ml of supra-pure 65% nitric acid at 80°C on a hot plate until the solution was clear. After evaporation, the residues were dissolved in 10ml of 0.3N supra-pure nitric acid. Cd, Cu and Zn were determined by Flame Atomic Absorption Spectrophotometry (AAS) with a Varian spectrophotometer Spectra 250 Plus with a deuterium background correction.

Mercury analysis were carried out with an Automatic Mercury Analyser spectrophotometer, ALTEC AMA 254, which does not require an acid-digestion of the samples. Aliquots ranging from 10 to 50 mg of dried samples were directly analysed after they have been inserted in the oven of the apparatus. After drying, the samples is heated under oxygen atmosphere for 3 minutes and subsequently amalgamated on Au-net. Then the net was heated after a 45 sec.

waiting time to liberate the collected Hg, which was then measured by absorption spectrophotometry.

Accuracy and reproducibility of the methods were tested by using dogfish liver (DOLT-2) and muscle (DORM-2) (National Research Council, Canada) reference standards. Standard and blanks were analysed along with each set of samples. Measurements were also validated by the IAEA Intercalibration Exercise [START_REF] Coquery | The analytical performance study for the Medpol region : determination of trace elements and methylmercury in estuarine sediment sample IAEA-405[END_REF].

Concentrations are expressed in µg.g -1 wet weight (w.w.), in order to interpret our results in the context of the transfer of metals between the preys and their predators. Nevertheless, the percentage of humidity is included in the tables in order to facilitate the comparison with other studies.

Results

Diet of the different species

The diet of these species have been studied in details elsewhere on a greater number of individuals [START_REF] Bocher | Complete trophic segregation between South Georgian and common diving petrels during breeding at Iles Kerguelen[END_REF]Bocher, 2001), but the composition by estimated volume of the stomach contents of the birds analysed for trace elements is shown in Fig. 1. Crustaceans dominated in the stomach contents of all the species except the blue petrels. One species predominated in common diving petrel, South Georgian diving petrel and antarctic prion, whereas crustaceans were more diversified in thin-billed prion. The amphipod Themisto gaudichaudii was the main prey in common diving petrel, whereas it was the euphausiid Thysanoessa sp. in South Georgia diving petrels, and the euphausiids Euphausia vallentini in Antarctic prion, which also exhibited a few percentage of T. gaudichaudii. E. vallentini and Thysanoessa sp were the two main preys in thin-billed prion, but T. gaudichaudii and the copepod Paraeuchaeta antarctica were also present. Fish was the main prey and represented more than 50% of the estimated volume of the stomach contents of blue petrel. Fish were also present and accounted for more than 25% and for a few percentage (by volume) of the stomach contents of thin-billed prion and Antarctic prion respectively (Fig. 1).

Heavy metal in bird tissues

Comparison between tissues

Cadmium, copper, zinc and mercury concentrations in liver, muscle and kidney tissues of the five species are presented in Table 1.

In all species, cadmium concentrations were higher in kidney than in liver and muscle tissues, and mercury concentrations were higher in liver than in kidney and muscle tissues.

For the essential metals, zinc and copper, the highest levels varied between tissues according to the species. Nevertheless, copper concentrations were always lower in kidney tissue, and zinc concentrations were always lower in muscle tissue.

The variability of coefficients of variation reflected the individual variations typical of each element in one species : a low coefficient reflects limited individual variations as a consequence of homeostasis processes, which is usually the case for the essential elements as copper and zinc. If we only consider blue petrels and common diving petrels, the species with larger sample sizes, there is no such a big difference of coefficients of variation between essential and non essential elements in some tissues and they did not follow the same pattern between both species. Thus blue petrels exhibited relatively high coefficients of variation for copper in the liver (as much as cadmium) compared to common diving petrels, and common diving petrels exhibited particularly low coefficients of variation for mercury in the three tissues.

Comparison between species

Since the number of individuals were low in species, statistical comparisons have been carried out by using the Kruskal-Wallis non parametric test. Significant differences have been found for zinc concentrations in the liver (T = 11.8 ; p = 0.019), and zinc concentrations in the kidney (T = 12.7 ; p = 0.013). Blue petrels exhibited the highest zinc concentrations in the liver (62 µg.g -1 ), followed by thin-billed prions (51 µg.g -1 ), common diving petrel (42 µg.g -1 ), South-Georgia diving petrel (34 µg.g -1 ) and Antarctic prions (31 µg.g -1 ) (Table 1). For the same element, in the kidney the order was modified, since common diving petrel exhibited the highest value (44 µg.g -1 ), and the blue petrel one of the lowest value (33 µg.g -1 ) with the Antarctic prions (30 µg.g -1 ) (Table 1). Copper concentrations in the muscle were also significantly different between species, the blue petrels exhibiting the lowest levels (3.71 µg.g -1 ), compared to the other species which all exhibited concentrations between 5 and 6 µg.g -1 (Table 1). Concerning cadmium and mercury no significant differences were found between the five species. Nevertheless Hg concentrations were much higher in the tissues of the blue petrels (Table 1), and the low values exhibited by one individual (see the concentrations minimum of 0.10, 0.01 and 0.05 in liver, muscle and kidney respectively) was responsible for the non significance of the difference.

Relationships between cadmium concentrations in kidney tissue and mercury concentrations in liver tissue -the organ where they are long term stored respectively -showed the bioaccumulation of these both non essential elements and the differences between species (Fig. 2). The higher mercury concentrations in the liver of blue petrels compared to the other species were thus evident. This was also the case in muscle and kidney (Table 1).

Trace elements in stomach contents and in preys

The exposure to trace elements for the different species have been estimated from the analysis of stomach contents, and from the analysis of preys collected on cruises. Mean concentrations of trace elements in stomach contents and in preys are shown in Table 2 andTable 3 respectively. Concerning the stomach contents, the Kruskal-Wallis test showed significant differences between species for Cu (T = 24.6, p < 0.001), for Zn (T = 10.1, p = 0.039) and for Hg (T = 26,4, p < 0.001). Zinc and copper concentrations were lower in stomach contents of the common diving petrel with 3.76 and 1.23 µg.g -1 , respectively (Table 2). Copper concentrations were higher in stomach contents of the thin-billed prions (11.4 µg.g -1 ), and mercury concentrations were particularly high in stomach contents of the blue petrel (0.026 µg.g -1 ), and not negligible (0.016 µg.g -1 ) in the stomach contents of the South Georgia diving petrels compared to the other species (Table 2).

Although the number of preys analysed was low, the main differences between species Antarctic prionpeared for Cd concentrations which were higher in the amphipod Themisto gaudichaudii (the difference between outside and inside the "Golfe du Morbihan" being linked to the size of the individuals which were larger outside the gulf), and in the squids (Table 3). Only squids exhibited higher Cu concentrations, which seemed to be equivalent in the other species. The copepod Paraeuchaeta antarctica exhibited the highest Zn concentrations which were at least two times higher than in the other species. Mercury concentrations were higher in myctophids (0.039 µg.g -1 ) and to a lesser extent in squids (0.022 µg.g -1 ) compared to the other species. Among crustaceans, the amphipods exhibited the lowest Hg concentrations, compared to the other species.

Discussion

Cadmium and mercury

There are several papers that deal with the concentrations of selected trace elements in Southern Hemisphere seabirds [START_REF] Hindell | Mercury and cadmium concentrations in the tissues of three species of southern albatrosses[END_REF][START_REF] Kim | Distribution of 14 elements in tissues and organs of oceanic seabirds[END_REF][START_REF] Muirhead | Heavy metal concentrations in the tissues of seabirds from Gough Island, South Atlantic Ocean[END_REF][START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF]Szefer et al., 1993a ;Szefer et al., 1993b), but most of them concern albatrosses, storm petrels or other species of petrels different from small burrowing species. The 5 species of small burrowing petrels investigated in this study are closely related, and very similar in their body size and moult schedule. The life span is also very similar for 4 of them, since thin-billed prion, Antarctic prion, South Georgian diving petrel, common diving petrel can live about 10 years, blue petrel life span being significantly longer, with about 20 years. This makes the study of bioaccumulation of trace elements quite interesting since the main differences between these 5 species is the diet which has been supposed to be a very discriminant factor for trace element bioaccumulation [START_REF] Monteiro | Relationship between mercury levels and diet in Azores seabirds[END_REF][START_REF] Stewart | Influence of species, age and diet on mercury concentrations in Shetland seabirds[END_REF][START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF][START_REF] Thompson | Seabirds as monitors of mercury inputs to epipelagic and mesopelagic marine food chains[END_REF]. Thus the exposure of these seabirds has been evaluated by the trace element analysis in the stomach contents and also directly in the preys.

The most interesting tissues to consider for inter-specific comparisons of cadmium and mercury are liver and kidney which are the main sites for long-term storage in high vertebrates. Only two studies included very similar species in their analysis : recently, 2 individuals of fairy prion (Pachyptila turtur) were included in a study on seabirds from New Zealand [START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF]. They exhibited mean Cd concentrations of 24 ± 1.4 and 76 ± 9.2 µg.g -1 dry weight respectively in liver and kidney, which is very similar to our results in prions after conversion to dry weight (32 and 24 ± 7.6 in liver of antarctic prion and thinbilled prion respectively ; 102 and 100 ± 46 in kidney of Antarctic prion and thin-billed prion respectively). Mean mercury concentrations were slightly lower in the liver of Antarctic prion and thin-billed prion (Table 1) compared to the concentrations found by [START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF] in Pachyptila turtur. Moreover mean mercury concentrations were similar in liver and kidney of fairy prions (2.6 ± 0.8 and 2.5 ± 0.8 µg.g -1 d.w., respectively) [START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF], whereas they were 2 to four times lower in the kidney compared to the liver in our samples (Table 1). This different pattern of Hg distribution between both organs could reflect a difference in mercury detoxification between the species. Thus, the demethylation of mercury from its more toxic methyl form to the less toxic inorganic one is often associated with the displacement of mercury from kidney to the liver a less vulnerable organ [START_REF] Ma | Mercury and selenium interaction : a review[END_REF]. The levels and the physico-chemical form of mercury and selenium in the preys could influence this detoxification [START_REF] Caurant | Mercury in pilot whales : possible limits to the detoxification process[END_REF]. However in both studies, sample sizes were small, and since the individual variation is high in toxic element concentrations, these mean values should be considered with caution. [START_REF] Muirhead | Heavy metal concentrations in the tissues of seabirds from Gough Island, South Atlantic Ocean[END_REF] analysed cadmium in liver and kidney and mercury in liver of 17 common diving petrels and 31 broadbilled prions (Pachyptila vittata). Mean Cd concentrations reported in their study (32 ± 12 and 7 ± 2.9 µg.g -1 w.w. , in kidney and liver of common diving petrels respectively ; 33 ± 10 and 16 ± 5.2 µg.g -1 w.w. in kidney and liver of broad-billed prions respectively), as well as mean mercury concentrations (0.54 ± 0.34 and 0.38 ±0.21 µg.g -1 w.w. in common diving petrels and broad-billed respectively) were similar to our own results (Table 1). [START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF] have classified different seabird species according to their pattern of accumulation of mercury in the liver and cadmium in the kidney. They found that 3 broad categories existed : 1° extremely high mercury and high cadmium levels (with range of means varying from 350 to 450 µg.g -1 d.w. for mercury and from 125 to 132 µg.g -1 d.w for cadmium) which included wandering and royal albatross, 2° moderately high mercury (from 79 to 143 µg.g -1 d.w.) and high cadmium levels (from 86 to 190 µg.g -1 d.w.), which included black-browed albatross, white-chinned petrel, black petrel and grey petrel, and 3°low or very low mercury (from 2.5 to 35 µg.g -1 d.w.) but high cadmium levels (from 74 to151 µg.g -1 d.w.), this last category including Buller's albatross, shy albatross, sooty shearwater, greyfaced petrel, and cape petrel and fairy prion. The 5 species of small burrowing petrels we analysed, which included 2 prions belonged to this third categories (Table1). However, inside this third category, it does exist some differences, which are shown in Fig. 2, blue petrel exhibiting higher mean mercury concentrations in the liver, compared to the other species. Mean mercury concentrations were significantly higher in the blue petrel stomach contents (Table 2), which is in accordance with the fact that they contained a high percentage of fish (Fig. 1), which also exhibited the highest mercury concentrations (Table 3). Considering the long-term exposure, the global study of the diet carried out on all the sampled stomach contents showed that fish was the main prey and represented 57% of the reconstituted mass (including myctophids : 14%) in the diet of blue petrel (Bocher, 2001). Blue petrel fed also on cephalopods, which represented 2% of the reconstituted mass in their diet, and participated in the mercury exposure (Table 3). The high mercury levels in this species was also expected due to the longer exposure time available to long-lived species. The few reported results on age-related variation of mercury levels in soft tissues are contradictory (Monteiro & Furness, 1995), but it would not be surprising that in the older individuals of this species, the elimination to the plumage during the annual moult was insufficient to balance the dietary intake.

Although the amphipod, Themiso gaudichaudii exhibited the highest cadmium concentrations (either inside or outside the gulf) compared to the other crustaceans (Table 3), Cd concentrations in stomach contents were not significantly different between species (Table 2, Fig. 1). However, the results shown in Fig. 1 were in accordance with the global study of the diet carried out on this seabird species (Bocher, 2001) and the theoretical exposure to cadmium would be higher for common diving petrel, which fed almost exclusively on this amphipod which predominated by number (61%) and by mass (91%) in their diet. The exposure to cadmium was under-estimated by the analysis of stomach contents of Antarctic prions and thin-billed prions. The theoretical exposure was probably higher for these species, since considering the global study on the diet (Bocher, 2001), the amphipod Themisto gaudichaudii (and not the euphausiid, Euphausia vallentini and Thysanoessa sp. as shown in Fig. 1) predominated by number (70% and 76%), respectively in antarctic prion and thin-billed prion and by mass (57%) in both species (Bocher, 2001). According to the estimated exposure of the species, significantly lower levels of cadmium would have been expected in blue petrel and South Georgian diving petrel liver and kidney. This was not the case and this implies that either cadmium in the amphipods would not be bioavailable for the birds or that elimination processes balance the cadmium dietary intake. The relatively low coefficients of variation of cadmium concentrations in kidney seabirds (Table 1) would support this hypothesis. It must also be noticed that diet has been studied during the breeding season, but it is not during the inter-breeding season. [START_REF] Bocher | Complete trophic segregation between South Georgian and common diving petrels during breeding at Iles Kerguelen[END_REF] have shown for example that the stable isotopic composition (δ 13 C and δ 15 N) of South Georgian and common diving petrels adult feathers were identical in both species, indicating no trophic segregation during the moulting period when birds feed in offshore waters. This could imply that the exposure to Cd would be very similar during most of the time.

Copper and zinc

For the essential elements copper and zinc, concentrations are supposed to be regulated in the body of vertebrates [START_REF] Underwood | Trace elements in human and animal nutrition[END_REF][START_REF] Walsh | The use of seabirds as monitors of heavy metals in the marine environment[END_REF], and this is normally shown by low coefficients of variation. In our study copper concentrations in liver and kidney were not significantly different between seabirds with mean concentrations ranging from 4.8 to 8.7 µg.g -1 wet weight (Table 1). Moreover diet can't be a discriminant factor for the accumulation of this element in the predators since only the cephalopods exhibited higher concentrations, compared to the other preys. According to the global study of the diet, blue petrel, thin-billed prion and Antarctic prion exhibited cephalopods in their diet, but they represented only 2%, 6% and 3% of the reconstituted mass respectively.

There was a fair degree of variation in zinc levels in the seabirds analysed in this study, but if the lowest levels were in Antarctic prions in the liver as well as in the kidney, the order of the species changed between the hepatic and renal levels (Table 1). The presence of the copepod Paraeuchaeta antarctica in the diet could influence Zn levels in the predators since they exhibited higher concentrations (55 ± 32 µg.g-1 wet weight), compared to the other prey species (Table 2). The analysis of thin-billed prion and South Georgian diving petrel stomach contents, which included the copepod in a significant amount (Fig. 1), were in accordance with this, since Zn levels were higher with 6.94 ± 1.82 and 7.13 ± 3.42 µg.g -1 wet weight respectively (Table 3). However, the global study of the diet have shown that Paraeuchaeta antarctica was only represented in the diet of common diving petrel and only accounted for 21% of the total number of prey and 9% of the food by estimated reconstituted mass. This was in accordance with the higher Zn concentrations in common diving petrel kidney, but blue petrel exhibited the highest zinc concentrations in the liver (Table 1). It is not surprising that zinc and copper concentrations did not reflect exactly dietary intake, since an homeostatic control exists as a consequence of a regulation of their absorption in vertebrates, which depends on the nutritional requirements of the individual [START_REF] Underwood | Trace elements in human and animal nutrition[END_REF]. However, inter specific variations have often been shown in zinc and copper levels in seabirds [START_REF] Elliot | Heavy metal and metallothionein concentrations in Atlantic Canadian seabirds[END_REF][START_REF] Kim | Distribution of 14 elements in tissues and organs of oceanic seabirds[END_REF][START_REF] Muirhead | Heavy metal concentrations in the tissues of seabirds from Gough Island, South Atlantic Ocean[END_REF][START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF]Szefer et al., 1993) as well as correlations between zinc, copper and cadmium in liver or in kidney [START_REF] Elliot | Heavy metal and metallothionein concentrations in Atlantic Canadian seabirds[END_REF][START_REF] Stewart | Influence of phylogeny, diet, moult schedule and sex on heavy metal concentrations in New Zealand procellariiformes[END_REF]Wenzel et al., 1996). Zinc and copper were correlated with cadmium in blue petrel kidney and copper was correlated with cadmium in common diving 16 petrel (unpubl. data). Metallothioneins have been shown in seabirds [START_REF] Elliot | Heavy metal and metallothionein concentrations in Atlantic Canadian seabirds[END_REF][START_REF] Elliot | Heavy metal and metallothionein concentrations in seabirds from the Pacific coast of Canada[END_REF][START_REF] Stewart | Relationships between heavy metal and metallothionein concentrations in lesser black-backed gulls, Larus fuscus, and Cory's shearwater, Calonectris diomedea[END_REF] and molecular interactions between the essential elements Cu and Zn and the toxic metals Cd and Hg may be envisaged as a consequence of their affinities for metallothioneins, as it has been shown in mammals [START_REF] Webb | Toxicological significance of metallothionein[END_REF]. Another hypothesis would explain that Cu and Zn levels in seabirds would not be reflected by dietary intake. The antagonism between Cd, Zn and Cu implies that the levels of one of them in the diet compared to the others, would modify the absorption, the retention, and the distribution in the organism of the less abundant element in the diet [START_REF] Underwood | Trace elements in human and animal nutrition[END_REF].

Conclusions

The small burrowing petrels analysed in this study had low or very low mercury levels compared to many seabirds, but cadmium levels were comparable to numerous seabird species from the Antarctic. Mercury levels in the seabirds reflected well the different dietary intake, and especially the presence of fish in the diet. But despite a different Cd dietary intake -at least during the breeding period-between these very closely related species no significant different Cd concentrations were found. Even if high levels of Cd are reached in Antarctic seabirds, the interactions of this element with Cu and Zn and the similar behaviour of the three metals through the binding to the metallothioneins could be responsible for a kind of "regulation" of Cd. It is well established that squid contribute to high levels of Cd, but it will probably more difficult to evaluate the influence of other preys on Cd accumulation in seabirds.

Weimerskirch (1989) Wenzel C, Adelung D, Theede H. (1996). Distribution and age -related changes of trace elements in kittiwake Rissa tridactyla nestlings from anisolated colony in the German Bight, Table 2. Mean mass (in g) and mean concentrations of trace elements (in µg.g -1 wet weight) ± SD of the stomach contents of five species of burrowing petrels collected in Kerguelen Island, Southern Indian Ocean.

Fig. 2 .

 2 Fig. 2. Relationship between mercury concentrations in the liver and cadmium concentrations in the kidney (µg.g -1 wet weight) of 5 species of burrowing petrels from the Kerguelen Island,

Table 3. Mean concentrations of trace elements (in µg.g -1 wet weight) ± SD in macrozooplankton, micronekton, fish and cephalopods collected on cruises of the R/V La Curieuse during the austral summer in Kerguelen Island, Southern Indian Ocean.


Rami Albasha

Christian Fournier

Christophe Pradal

Michaël Chelle

Jorge Alejando Prieto

Gaëtan Louarn

Thierry Simonneau
email: thierry.simonneau@inra.fr

Eric Lebon

HydroShoot: a functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit -application to grapevine (Vitis vinifera L.)

Keywords: Functional-structural plant model (FSPM), grapevine (Vitis vinifera L.), water deficit, hydraulic structure, energy budget, gas-exchange

come   L'archive ouverte pluridisciplinaire

Introduction

Climate change is seriously challenging viticulture sustainability in its current areas of production [START_REF] Hannah | Climate change, wine, and conservation[END_REF]van Leeuwen et al., 2013;[START_REF] Duchêne | Grapevine and climate change: what adaptations of plant material and training systems should we anticipate[END_REF]. One efficient shortterm solution for hampering the projected adverse effects of water and heat stress on viticulture, is to reconsider training systems so that they allow maximizing the ratio of carbon assimilation ( the design of canopy structures that are adapted to adverse environmental conditions is most efficiently performed with the aid of models able to accurately predict the influence of canopy architecture on its gas exchange rates and leaves temperature under combined water and heat stress.

That is what functional-structural plant models (FSPMs) offer [START_REF] Vos | Functional-structural plant modelling: a new versatile tool in crop science[END_REF].

FSPMs received nevertheless little attention in grapevine scientific literature to assess the impact of shoot architecture on plant gas-exchange rates (Medrano et al., 2015 a). This is probably due to the inherent complexity in scaling up eco-physiological processes from the leaf to the canopy level, as strong variability in gas exchange rates (CO 2 versus water vapor) exists inside the canopy driven by variations in micrometeorological conditions and leaf functional traits Hence, adequately predicting the intra-canopy variability of both leaf stomatal conductance and temperature stands out as the key challenge to using FSPMs to predict plant gas-exchange rates under water deficit conditions. Yet, the remaining question is how this variability can be accurately described and what are their main determinants.

Describing the intra-canopy variability of both leaf stomatal conductance and temperature requires from the one hand to explicitly describe their drivers as a function of leaf position inside the canopy, and from the other hand to adequately account for their mutual interactions (how stomatal aperture affects leaf energy budget and vice versa) [START_REF] Chelle | Phylloclimate or the climate perceived by individual plant organs: What is it? How to model it? What for?[END_REF]. The main drivers for both processes are commonly determined as incident shortwave irradiance, air temperature, air humidity and leaf "water status" which determines stomatal closure (Damour et al., 2010). Among these drivers, leaf water status (which controls stomatal aperture and consequently both leaf transpiration and temperature) still makes no consensus in the scientific literature when it comes to determining what it refers to. A basal approach considers that leaf water status is equal to the soil water status (e.g. A c c e p t e d M a n u s c r i p t

the rhizosphere impacts uniformly all leaves regardless of their position. By contrast, leaf water status may also be considered as a "local" water status specific to each individual leaf (e.g. [START_REF] Tuzet | A coupled model of stomatal conductance, photosynthesis and transpiration[END_REF][START_REF] Buckley | A hydromechanical and biochemical model of stomatal conductance[END_REF] which results from the interplay between water demand (transpiration) and offer (xylem flow) at the leaf-scale, which are notably determined by the shoot hydraulic structure. This "local" approach mostly agrees with observations whereby stomatal closure is uneven across the canopy. Sunlit leaves, for instance, experience stronger water deficit than shaded leaves, and are therefore the first to undergo reductions in gas exchange rates under water deficit [START_REF] Escalona | Distribution of leaf photosynthesis and transpiration within grapevine canopies under different drought conditions[END_REF]. This "local" approach seems hence as the most adequate in FSPM's, however, the "remote" modelling approaches also proved satisfactory when individually considered. (e.g. were built at the leaf-cluster scale which does not allow accounting for the location of individual leaves in plant hydraulic structure necessary to calculate local leaf water status. In addition, both models consider the "remote" action of the rhizosphere on stomatal aperture instead of the "local'

water status, assuming again as negligible the potential contribution of shoot hydraulic structure on shaping the intra-canopy variability of leaf stomatal conductance and temperature.

In this paper, it is postulated that intra-canopy variability in both leaf water potential and leaf temperature are the main drivers for adequately predicting photosynthesis and transpiration fluxes at the plant scale under water deficit conditions using FSPMs. This paper has threefold objective. The first is to describe HydroShoot, a leaf-scale-based FSPM that allows predicting whole plant transpiration A c c e p t e d M a n u s c r i p t

and photosynthesis rates by scaling-up these processes from the leaf-level. This model uses as lever for this scaling process the simulation of the interactions between hydraulic structure of the shoot, the energy balance, and gas-exchange rates of individual leaves. The second objective is to evaluate the performance of the model using both virtual and real canopies with data collected on photosynthesis and transpiration rates (plant scale), and stomatal conductance and temperature (leaf scale). Finally, the third objective is to examine how detailed hydraulic structure and energy budget simulations determine the predicted gas-exchange rates at the plant scale under water deficit conditions.

Materials and methods

Model structure and basic assumptions

HydroShoot is a static FSPM (with regards to plant structure) that takes plant shoot architecture, weather, and soil water conditions as inputs, and returns transpiration and net photosynthesis rates both of individual leaves and the whole plant at hourly time steps as outputs. It is conceived as a set of three modules which simulate water potential (hydraulic module), energy budget (energy module), and C 3 -type gas-exchange rates (exchange module). These three modules run jointly, having leaf xylem water potential and temperature at the leaf level as pivots (cf. implementation and numerical solution section). The formalisms used in each module are developed in the following sections.

Hydraulic module

The hydraulic module computes water potentials of plant segments (output of the module) as a function of water flow in the plant and water potential of the soil (input to the module). The whole plant is compartmentalized in elementary conducting elements corresponding to petioles, internodes of the current-year stems, and elements of previous-year trunk and branches (internodes or pruning complexes). Leaves are treated in this system as nodes letting water flow but having no gradient in their water potential ( ). A c c e p t e d M a n u s c r i p t

Water transfer across the hydraulic segments is simulated by analogy to Ohm's law in electrical circuits (Figure 1). 2002).

Equations 1 to 3 apply to all conducting segments (not leaves blades). Water potential of the upper extremity of the petiole is assumed equal to that of the lumped leaf water potential .

Exchange module

The exchange module computes the rates of net carbon assimilation and transpiration per unit surface area (respectively and ) for each individual leaf as a function of micrometeorological Eq. 5a does not account for stomatal sensitivity to soil water deficit ("remote" approach) or local leaf water potential ("local" approach). It is noteworthy that only the forced convective heat transfer is currently considered in HydroShoot since forced convection dominates free convection once wind speed exceeds roughly 0.1 [START_REF] Nobel | Temperature and energy budgets[END_REF]). This wind speed threshold is generally exceeded during diurnal hours. However, under low wind conditions heat transfer may be underestimated.

Since the resolution of the last equation is highly time-consuming, we assumed the energy gain from the neighbouring leaves through thermal longwave as a lumped term whereby average leaf temperature is considered instead of individual leaves [START_REF] Dauzat | Simulation of leaf transpiration and sap flow in virtual plants: model description and application to a coffee plantation in Costa Rica[END_REF]. In this case, the lumped form factor ∑ is simply taken as ( ) (that is the solid angle where neither the sky nor the soil are seen by a single leaf). The former equation becomes:

( [ ( )] ) (Eq. 14) ( )

Implementation and numerical resolution

HydroShoot is developed using the Python programming language (Python Software Foundation The resolution of HydroShoot equations is performed by an iterative procedure that is schematized in Figure 2. Iterations have three levels. The first is in the hydraulic module and concerns calculating xylem water potential of plant segments in interaction with their hydraulic conductivity (interdependent processes, cf. equations 1 and 2). The second level is between the exchange and hydraulic modules in order to calculate jointly gas-exchanges rates and leaf water potential values (transpiration affects the hydraulic structure Eq. 1 while the latter affects stomatal aperture Eq. 5b). The third level is between the energy module and both exchange and hydraulic modules, so that at each time new

Model evaluation

Model evaluation was performed in three steps. Firstly, the coherence between expected and simulated gas-exchange, temperature, and xylem water potential dynamics for different canopy architectures was assessed. Secondly, the precision was assessed by comparing model outputs to measured plant gas-exchange rates and leaf stomatal conductance, water potential and temperature.

Finally, the required complexity level was evaluated, whereby we sought at determining whether simulating the hydraulic structure and energy balance were (both) required in order to obtain accurate predictions of gas-exchange rates at the plant scale. For all the following simulations, parameter values are given in Appendix IV.

Coherence

HydroShoot was run on 3 virtual grapevine canopies which share the same soil type, soil initial water content, weather conditions, and total leaf area, and differ only in their shoot architecture (Figure 4). The objective was to examine whether the model reflects the differences of shoot architecture on gas and energy exchange rates as may be expected. For instance, whether higher photosynthesis and transpiration rates are obtained for canopies absorbing higher solar irradiance flux densities. Similarly, whether higher transpiration rates trigger steeper drops in leaf water potential.

The 3 canopies were trained on three different training systems: Vertical Shoot Positioning (VSP), Geneva Double Curtain (GDC) and Lyre systems (Figure 4a). All canopies had the same leaf area (5.7 ), internode diameter distribution (Figure 4b), planting density (inter-and intra-row spacing of 3.6 and 1.0 respectively), soil type (Sand Loam) and initial collar water potential (-0.6 ).

The simulations were run using weather data extracted from the database of the weather Data on VSP grapevines (2009) were collected during a period of 4 days under well-watered and water deficit conditions. Water deficit was created by cutting off the irrigation system on the first day of the experiment (July 29 th ). Whole plant transpiration and net assimilation were monitored using open portable gas-exchange chambers (Perez Peña and Tarara, 2004). Temperature of individual leaves were monitored using thermocouples inserted into the primary veins of 10 fully developed individual leaves positioned on different heights from the top of the canopy to the inside, so that temperature gradient resulting from different irradiance conditions was captured.

Data on GDC grapevines (2012) were also collected during a 4 days experiment (starting on August 1 st ), but only under water deficit conditions. Only rate was monitored by measurements of sap flow installed on the two cordons of the GDC plants. Stomatal conductance and leaf water potential measurements were performed for a number of leaves on GDC grapevines during the experiment, but the exact position of leaves was not reported with measurements.

For both VSP and GDC grapevines, shoot architecture was constructed based on digitisation data, using a grapevine-specific shoot architecture module following a Multiscale Tree Graph (MTG)

approach [START_REF] Godin | A multiscale model of plant topological structures[END_REF][START_REF] Pradal | OpenAlea: a visual programming and component-based software platform for plant modelling[END_REF][START_REF] Balduzzi | Reshaping plant biology: qualitative and quantitative descriptors for plant morphology[END_REF], in which organs topological connections and geometry were associated to shoot architecture (Figure 5). Plant mock-ups were produced so that the simulated vertical and horizontal profiles of leaf surface area fitted those observed.

A c c e p t e d M a n u s c r i p t

Complexity

In order to explore the contribution of HydroShoot's hydraulic and energy modules components to the final simulation output, a sensitivity analysis was performed by plugging/unplugging each of these components and observing the resulting difference on simulated outputs. This procedure aims in fine at evaluating whether adding complexity to an FSPM would improve its performance in predicting gas-exchange dynamics at the plant-scale. The following simulation combinations are used: sim0: the reference (complete) version of HydroShoot having the ensemble of its components;

sim1: stomatal conductance varies with (as described by Leuning, 1995 in Eq. 5a)

regardless of leaf water potential; sim2: the hydraulic structure is disregarded (water potential of all leaves is forced equal to water potential at the collar) and stomatal conductance varies with collar water potential Eq. 5c;

sim3: energy balance is disregarded, that is all leaves have the same temperature as that of the air; sim4: the same case of sim1 but using tighter control of on stomatal conductance ( in Eq. 5a is set to 1 instead of 30 as proposed by Prieto et al. ( 2012).

Evaluation criteria

The overall adequacy between observed and simulated variables was assessed based on the estimation of the mean bias error ( ) and root mean square error ( ):

∑ ( ) (

Results and discussion

Outputs coherence

Simulation outputs for virtual canopies are illustrated in Figure 6 (plant-scale outputs) and The simulations at the plant scale (Figure 6) show that GDC canopies had the highest absorbed irradiance rates, followed by Lyre and VSP canopies (Figure 6a), reflecting the higher exposure to solar irradiance using the GDC system. This trend was reflected on carbon assimilation (Figure 6b), temperature (Figure 6c), and transpiration (Figure 6d), whereby highest values were obtained for GDC then Lyre followed by VSP canopies.

Midday depression in was simulated for the three canopies proportionally to the absorbed irradiance, that is, highest for GDC and lowest for VSP (Figure 6b). The higher transpiration rates of GDC led to simulate lower leaf water potential values (Figure 7) around midday, which, combined with higher absorbed irradiance, led also to higher leaf temperatures (Figure 8). The combined effects of lower leaf water potential and higher temperature in GDC led to simulate a higher effect of midday depression in compared to Lyre and VSP canopies as may be expected. This first illustrative example on virtual canopies shows that the effect of canopy architecture on its gas-exchange and temperature behaviour is captured in HydroShoot. The comparison to measurements in the following section will show how the observed dynamics on both plant and leaf scales are accurately reproduced using HydroShoot for two real canopies.

Comparison to observed data

Leaf-scale

Simulation results at the leaf-scale are shown in Figures 9, 10 and 11, respectively for water potential, stomatal conductance, and temperature. The dynamics of these variables were adequately reproduced but with some discrepancies regarding the onset timing of the effect of water deficit.

Figures 9 and 10 show that the simulated and of the three GDC canopies decreased progressively as the soil water deficit increased, consistently with observations, but with an earlier onset of water stress which is probably due to inadequate parametrization either of the response function of to (cf. Eq. 5b) or of the soil hydrodynamic model (cf. Coupling with irradiance and soil models). Upon the onset of water stress, when water deficit was still mild in the first day (date 01 08 in Figure 10), HydroShoot simulated higher for sunlit leaves than for shaded leaves. Later, as water deficit increased, this trend was inverted, whereby sunlit leaves had the lowest rates (dates Aug 2 nd and Aug 3 rd in Figure 10). This inversion was due to a lower for sunlit leaves as a consequence of higher potential transpiration withdrawal per unit leaf surface area (cf. Eq. 1).

Moreover, Figure 10 shows that, by the end of the water deficit period (day Aug 3 rd ), leaf position had merely no more effect on its water vapor conductance . At this stage, water potential of all leaves reached low values at which stomata were almost closed. This uniformization of stomatal closure through the canopy is consistent with the observations reported by Escalona et al.

( 2003,2016). Both studies reported a progressive homogenization of gas-exchange rates of grapevine leaves (cv. Tempranillo, Manto Negro and Grenache) as soil water deficit increased. Ngao et al.

(2017) reported similar results on apple trees (Malus pumila Mill.), showing that intra-canopy variability in decreased significantly under the effect of soil water deficit. The effect of soil water deficit on leaf temperature was efficiently captured by HydroShoot (Figures 11 and 12). The diurnal trends of leaf temperature were adequately reproduced (Figure 11) whereby water-deficit leaves (Figure 11b, d) had higher temperature than those well-watered (Figure 11a,c). The comparison between simulated and observed temperatures at the leaf level (Figure 12)

shows that the model simulated an increase in leaf-to-air temperature of approximately 2 , in agreement with observations (Figure 12a). Furthermore, HydroShoot reproduced the observed magnitude between minimum and maximum leaf temperatures across the canopy (Figure 12b) and how this magnitude increased with soil water deficit (Figure 12c).

The overall adequation between simulated and observed leaf temperature was high when comparing median values (Figure 11) having an of -0.5 and -0.8 and an of 0.8 and 1.0 under well-watered and water deficit conditions, respectively. A greater of 2.4 was obtained when leaf-to-leaf comparison was performed (Figure 12), yet, remained at -0.45 .

It is noteworthy that leaf temperature was systematically underestimated during nocturnal hours (Figure 11, Figure 12).

Plant-scale

The observed daily patterns of and were accurately reproduced under both well-watered and water deficit conditions (Figure 13 for VSP and Figure 14 for GDC) .

For VSP canopies (Figure 13), the reduction in soil-water availability was reflected by the severe reductions in and rates, consistently with observations but with a slight overestimation of (Figure 13) under well-watered conditions. and totalled respectively -0.1 and 5.5 for under well-watered conditions (Figure 13a), compared to 1.0 and 3.8 , respectively, under water deficit conditions (Figure 13b). For , and were respectively 89.5 and 141.7 under well-watered conditions (Figure 13c), compared to 27.7 and 67.6 , respectively under water deficit conditions (Figure 13d).

A c c e p t e d M a n u s c r i p t

For the three water-deficit GDC canopies (where only rates were observed),

HydroShoot reasonably reproduced the diurnal patterns in (Figure 14). Yet, rates were underestimated upon the onset of water stress (day 1 Aug in Figure 14) and were slightly overestimated thereafter. fall between -22.3 and 29.2 and between 13.7 and 105.4 which were similar to values obtained for VSP. It is noteworthy that the impact of the imbalance in leaf area between both cordons of Canopy1 (cf. Figure 5) was reflected in the simulated (Figure 14, Canopy1) whereby a noticeable differences in the simulated fluxes was obtained between both cordons of the canopy consistently with the observed sap flow rates. This example further demonstrates how the impact of canopy is adequately reflected on its eco-physiological functioning in HydroShoot. To which extent is modelling complexity needed?

We show in Figure 15 together with Table 1 that the best fit between simulated and observed gas-exchange rates was obtained using the complete HydroShoot model (i.e. sim0) which yielded, in almost all cases, the least values of and for both and .

However, the simulated hydraulic structure and spatialized leaf temperature values had unequal contributions to prediction's accuracy. Notwithstanding, it is noteworthy that omitting energy balance calculations in HydroShoot allowed saving up to 75% compared to the case where all processes combination was considered (Table 2). Using the complete combination (sim0) is indeed quite costly in time (between 7 and 12 seconds for each simulation time step, i.e. hour). Removing the energy balance reduced considerably time cost (by up to 75% in the case of VSP) and reduced non-convergence risk (data not show).

Removing the hydraulic structure (i.e. stomata are only function of VDP, sim4) increased considerably the time required for convergence (60% for VSP and up to 250% for the water-deficit GDC Canopy 1) and non-convergence risk. The considerable economy in calculation cost is an argument that should be considered when simulating large-scale plant scenes.

  optimal leaf temperature conditions (Medrano et al., 2012; Duchêne et al., 2014; Palliotti et al., 2014). However, training systems present a wealth of possibilities (Reynolds and Vanden Heuvel, 2009) that cannot be compared experimentally. Therefore,

  This scaling-up task is even more complex under water deficit conditions, as stomatal aperture is likely to reduce under water deficit in a non-uniform pattern across the canopy (e.g. Gonzalez-Dugo et al., 2012; Ngao et al., 2017) further distorting intra-canopy gas-exchange and leaf temperature variability (Reynolds and Vanden Heuvel, 2009). Bauerle et al. (2007) indicated that disregarding this variability may lead to strongly overestimate the predicted whole canopy daily transpiration flux, up to 25% greater than observed values as they found on a study on Red Maple (Acer rubrum L.)

  Prieto et al. (2012) were probably the first to use an FSPM to examine the effects of canopy architecture on gas-exchange in grapevine (cv. Syrah). The authors coupled the grapevine-specific structural plant model proposed by Louarn et al. (2008) to leaf-level models of photosynthesis (Farquhar et al., 1980) and stomatal conductance (Leuning, 1995) but did not incorporate the effects of (soil) water deficit. More recently, Zhu et al. (2017) developed a model similar to that proposed by Prieto et al. (2012) including the effect of water deficit on gas-exchange and leaf temperature. Nevertheless, this model assumed a uniform xylem water potential across the shoot, that disregarded how shoot hydraulic structure affects leaf-scale gas-exchange rates, which is an unrealistic assumption for large plants when considering the substantial hydraulic resistances observed in the stems of mature grapevine (Jacobsen et al., 2012). In addition, longwave energy exchange among leaves from the one hand, and between leaves and the surrounding elements from the other hand, were disregarded, which makes the application of this model to open field conditions not suitable since sky and soil longwave energies substantially affect leaves temperature (Nobel, 2005). This has been solved in two models that link a complete energy budget with gas-exchange in perennials (Bailey et al., 2016 for Vitis vinifera L. and Acer x fremanii; Ngao et al., 2017 for Malus pumila Mill.) Yet again, both models

Figure 1 :

 1 Figure 1: (a) Illustration of the parameters required to calculate the hydraulic structure: , rate of water flow [ ], , hydraulic conductivity per unit length of the conducting element [ ], , length of the conducting element [ ] and

  http://www.python.org) in the OpenAlea platform[START_REF] Pradal | OpenAlea: a visual programming and component-based software platform for plant modelling[END_REF][START_REF] Pradal | OpenAlea: scientific workflows combining data analysis and simulation[END_REF]. The code of the model can be freely accessed through its public depository (https://github.com/openalea/hydroshoot).It uses theMultiscale Tree Graph (MTG) method (Godin and Caraglio, 1998; Balduzzi et al., 2017) as a central data-structure in order to allow indirect communication between the different models which favours modularity (Fournier et al., 2010; Garin et al., 2014). Each process has been implemented as a reusable component in OpenAlea and can be reused independently in other models and composed in various ways, provided that the other models are written in the Python language and provide the adequate inputs.

Figure 2 :

 2 Figure 2: Schematic representation of the numerical resolution of HydroShoot. Meteorological inputs that are common to all leaves are air temperature (

  transpiration fluxes are calculated, leaf temperature values are updated, and the new temperature values are used to update gas-exchange rates which in their turn impose new xylem water potential distribution. The details on the numerical resolution are given in Appendix III. Coupling with irradiance and soil models HydroShoot needs irradiance absorption by individual leaves and soil water potential as inputs. It is therefore coupled in this work to Caribu irradiance model (Chelle and Andrieu, 1998) and to a simple soil water-budget model in order to calculate respectively irradiance absorption () and soil water potential ( ) values on an hourly basis (Figure3).The soil module links transpired water rates to the transpirable soil water volume () in order to predict the hourly variations in . At the beginning of each calculation step, transpired water volume from the previous step is withdrawn from the . The soil volumetric water content is then determined by dividing by the effective soil porosity. is then obtained from from the water retention curve (van Genuchten, 1980) and used as an input for the hydraulic module. This procedure is referred to as the Root-Shoot loop in Figure3.

Figure 3 :

 3 Figure 3: Flowchart of the modelling frame used in this application example. Meteorological inputs that are common to all leaves are air temperature (

Figure 5 :

 5 Figure 5: 3D mockups of grapevines plants trained to Vertical Shoot Positioning system (VSP, a and b) under water deficit (VSP WD ), well water conditions (VSP WW ) and Geneva Double Curtain (GDC, c, d and e). The mockups were reconstructed from measured leaf surface profiles using the architecture module (input to HydroShoot). Numbers below canopies indicate leaf area per cordon [ ].

Figures 7

 7 Figures 7 and 8 (leaf-scale outputs).

Figure 6 :

 6 Figure 6: Simulation of absorbed irradiance (a), net carbon assimilation (b), temperature (c) and transpiration (d) at the plant-scale for three contrasted grapevine canopies (VSP, GDC and Lyre); is air temperature. Temperature curves in (c) trace the hourly values of the median of leaves temperatures.

Figure 7 :

 7 Figure 7: Snapshot at solar midday (14:00hs) of water potential distribution across the shoot (left column) and only for leaves (boxplots, right column) for three contrasted grapevine canopies (VSP, GDC and Lyre).

Figure 8 :

 8 Figure 8: Snapshot at solar midday of individual leaf temperature values (left column) and leaf temperature distribution (boxplots, right column) for three contrasted grapevine canopies (VSP, GDC and Lyre).

Figure 9 :

 9 Figure9: Snapshots of the simulated hydraulic structure of the three GDC canopies considered in this study, referred to as 'Canopy1', 'Canopy2' and 'Canopy3', respectively, prior to solar noon, during the first three days

Figure 10 :

 10 Figure 10: Snapshots of the stomatal conductance of the three GDC canopies considered in this study, referred to as 'Canopy1', 'Canopy2' and 'Canopy3', respectively, prior to solar noon, during the first three days following the onset of soil water deficit, respectively 1 Aug, 2 Aug and 3 Aug. Soil predawn water potential of the three days was equal to -0.19, -0.38, -0.61 respectively. Filled circles represent stomatal conductance to water ( ), their colors represent absorbed value per unlit leaf surface area; sunlit leaves are yellow while shaded leaves are red. Due to uncertainties in measurements locations, the observed stomatal conductance values of sunlit leaves are indicated by the dark grey patches which cover minimum and maximum values.Observed data were collected from experiments conducted in 2012.

  Luquet et al. (2003) and Bailey et al. (2016) reported similar trends that they explained by the frequent dysfunction of thermocouples during the night. From a modelling perspective however, such discrepancies between simulated and observed temperatures may be explained by three possibilities. From the one hand, HydroShoot assumes flat leaves having uniform temperature across their surface, whereas strong temperature gradients occur across the surface of each individual leaf due to their three-dimensional structure (Saudreau et al., 2017). Thermocouples measure the temperature of only a limited fraction of leaf surface, and it is likely that the measured

Figure 11 :

 11 Figure 11: Comparison between simulated and observed individual leaf temperature for VSP canopies under wellwatered (a, b) and water deficit (c and d) conditions. Soil predawn water potential of the four days was equal to -0.37, -0.50, -0.40, and -0.32 respectively. (a and b) diurnal variation in leaf temperature: red zones indicate the extension between maximum and minimum simulated values, black curves indicate simulated mean values while blue boxplots indicate observed leaf temperature; (c and d) are 1:1 plots between observed (x-axis) and simulated (y-axis) leaves median temperatures for each hour with error bars representing minimum and maximum temperature values.

Figure 12 :

 12 Figure 12: Comparison between the observed and simulated (a) differences between leaf and air temperatures, (b) leaf temperatures, and (c) magnitude between minimum and maximum leaf temperatures across the canopy of VSP plants. Maximum and minimum leaf temperatures during each hour are represented by circles and triangles, respectively (a and b plots), well-watered and water deficit conditions are represented by blue red colours, respectively.

Figure 13 :

 13 Figure 13: Comparison between simulated and observed plant net carbon assimilation ( ) and transpiration ( ) rates of VSP canopies. The subplots a, b, c and d trace the temporal trends of and (grey circles are for observed values and blue lines are for those simulated) conducted under well-watered (a, c)and water deficit (b, d) conditions. Subplots e and f compare simulated to observed rates for both water conditions (blue dots for well-watered and red dots for water deficit). Soil predawn water potential of the four days was equal to -0.13, -0.15, -0.15, -0.08 respectively under well-watered conditions and -0.19, -0.30, -0.37, and -0.50 respectively under water-stress. and indicate respectively mean bias error and root mean squared error (same units of the y-axes).

Figure 14 :

 14 Figure 14: Comparison between the observed (circles) and simulated (curves) transpiration rates ) for each cordon of three grapevines plants trained to GDC under water deficit conditions. Soil predawn water potential of the three canopies was equal to -0.19, -0.38, -0.61, and -0.51 respectively. Red and blue colors indicate fluxes through east-and west-exposed cordons respectively. and indicate respectively mean bias error and root mean squared error (same units of the y-axes).

  accounted for the impact of microclimate inside the canopy on leaf photosynthetic traits. In their study, Bauerle et al. (2007) used leaf temperature as the primer driver for intra-canopy variability in leaf photosynthetic traits. In our study, we linked leaf photosynthetic traits to the 10-days cumulative absorbed (cf. Eq. 12) while leaf temperature was used only to affect directly , and indirectly . This conceptual difference may explain the lower sensitivity to leaf temperature in our study compared to that performed by Bauerle et al. (2007). Bailey et al. (2016) showed furthermore that the importance of accounting for the intra-canopy distribution of leaf temperature inFSPMs is a matter of canopy size. The authors reported that for grapevines, leaf temperature distribution had a negligible impact on the simulated plant-scale emitted thermal longwave irradiance. In contrast, on Freeman maple (Acer × Freemanii) which have notably higher leaf area per plant, simulating the spatial distribution of leaf temperature reduced by 50% prediction errors of the emitted thermal longwave irradiance. The low sensitivity of HydroShoot to leaf temperature may thus be linked to the simulated canopy size.

Figure 15 :

 15 Figure 15: Impact of different modelling details of HydroShoot on the simulated plant carbon assimilation ( ) and transpiration ( ) rates: the reference (complete) HydroShoot version is indicated as sim0, sim1 indicates the version whereby leaf stomatal conductance varies with vapor pressure deficit instead of leaf water potential (i.e. original model of Leuning, 1995,

  Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019

Misson et al., 2004; van Wijk et al., 2000), considering that the "remote" action of available water in

Dauzat et al., 2001; Bailey et al., 2016; Ngao et al., 2017).

  It is hence unclear in literature how the simulation of leaf water status of individual leaves affects the predicted gas-exchange rates and leaf temperature distribution in FSPMs under water deficit and this matter needs to be assessed if FSPMs are to be used under water deficit conditions.

The few existing grapevine FSPM's in literature do not account for the interactions between water status, energy budget and gas-exchange rates at the leaf scale [see Supporting Information S1].

Tyree and Sperry, 1989). This

				Each segment is characterized by its length (	) and hydraulic
	conductivity (	), and is crossed by a water flux (	) which, together with
	conductivity modifies water head at its upper (downstream,	) and lower (upstream,	) extremities
	[	]:
							(	)	(Eq. 1)
	where	denotes the segment identifier,	and	are respectively xylem water potential at the
	upper and lower extremities,	and	are elevations of upper and lower extremities [ ],
	respectively,	is water density [	] and	is the gravitational acceleration [	].
			Xylem conductivity varies with water potential as a result of xylem cavitation under water
	deficit (relationship is described using a sigmoidal function:
					(	)	(Eq. 2)
	where		is the maximum segment conductivity [	],	is the arithmetic mean of
	water potential values of the segment	(	),	[	] and	[ ] are shape
	parameters.	is estimated empirically as proposed

by Tyree and Zimmermann (2002):

				(Eq. 3)
	where	is segment average diameter [ ] and	and	are dimensionless shape parameters,
	mostly given within the ranges of [2.5, 2.8] and [2.0, 5.0], respectively (

Tyree and Zimmermann,

by Yin et al. (2009) for coupling the photosynthesis model of Farquhar et al. (1980) to the stomatal conductance model of Ball et al. (1987). This

	conditions and leaf water status. The calculations use the analytical solution proposed coupling allows stomatal conductance (to both CO 2 and water vapor)
	to respond to environmental stimuli (temperature and irradiance) via photosynthesis [see Supporting
	Information S2]. It is based on Fick's first law of diffusion, whereby	, the stomatal conductance to
	CO 2 (	), and the mesophyll conductance ( ) are used. However, as Farquhar's model has been
	thoroughly detailed in literature, its description is given in Appendix I. The focus of this section is
	given instead to the stomatal conductance formulae which are a key element in this work.
	is calculated according to Yin et al. (2009) as: ( is the residual stomatal conductance to ) respiration in the light [ where ], is a n u s c r i p t (Eq. 4) [ ], is mitochondrial compensation point in the absence of mitochondrial respiration [ ], is a dimensionless shape parameter, and is a M dimensionless function representing the response of to air water vapor deficit ( ). is
	( A c c e p t e d where is a scaling parameter [ ].	)	(Eq. 5a)

deduced from the stomatal conductance model of Leuning (1995) as:

Correia et al., 1990; Flexas and Medrano, 2002; Lovisolo et al., 2010).

  Finally, this module was provided with a photoinhibition model as this phenomenon is frequently reported to affect grapevines under combined heat and water stresses (The simple photoinhibition model implemented in HydroShoot is detailed in Appendix II and assumes that combined heat and water stresses inhibit photosynthesis by reducing the electron transport rate (cf. in Eq. A6) as the result of an increase

	where	is a critical leaf water potential threshold [	] at which stomatal conductance is
	reduced by 50%, and dependency of Intra-canopy variability in photosynthetic capacity is a shape parameter [ ]. The same last equation is used to express the on the remote soil water potential ( ):
	Leaf photosynthetic traits (maximum carboxylation rate	, maximum electron transport
	( , triose-phosphate transport rate vary within the plant canopy so that to increase light-saturated net assimilation rate with increasing ( ) ) (Eq. 5c) rate and ; cf. Appendix I) have been shown to strongly of deactivation energy (cf. equations A9 and A10).
	M a n u s c r i p t ] is calculated as: ( ) (Eq. 6) ] and is the boundary layer conductance to water ], derived from Nobel (2005) as: The transpiration rate [ is the atmospheric pressure [ ( ) (Eq. 7) solar irradiance availability throughout the canopy (Niinemets et al., 2014). HydroShoot accounts for where vapor [ with this variability by considering leaf nitrogen content per unit leaf surface area ( Energy module ) as the pivotal trait to determine the photosynthetic capacity of leaves (Prieto et al., 2012) as follows: (Eq. 10) where is the value at 25 for any of the rates , , and (given as inputs), and [ ] and M The energy module computes the temperature of individual leaves based on a detailed energy balance model [see Supporting Information S3]. This module will be briefly described hereafter a n u s c r i p t for the sake of simplicity.
	A c c e p t e d ( ) where is the diffusion coefficient of H 2 O in the air at 0 ( ) ambient air pressure at 0 temperature [ ], ∑ ( ( )) ( ) ( ∑ ( ) where refers to leaf identifier, is lumped leaf absorptance in the shortwave band [ ], (Eq. 8) (2.13*10 -5 ), (Eq. 11) (Eq. 12) ) (Eq. 13) is the is flux density of shortwave global irradiance [ ], , and are emissivity-absorptivity A c c e p t e d coefficients of the leaf, sky and soil, respectively [ ], is the Stefan-Boltzmann constant
	Nikolov (1995): since budburst, [ ], linear relationship between [ , , and all taken as input parameters for HydroShoot; ( ( ] and are respectively the sky, soil, and air absolute temperatures [ ] ) ) (Eq. 5b) [ ] are the slope and intercept of the is temperature of neighbouring leaf (solved by and accumulated thermal time since budburst, [ convergence, see Implementation and numerical resolution section), is latent heat for vaporization ] is the cumulative photosynthetic photon flux density irradiance intercepted by the leaf (output of the energy module) averaged over the past 10 days, [ ] and [ ], is the thermal conductivity of air [ ], and finally, , and are
	[	] are the slope and intercept of the linear relationship between	and the
	logarithm of	.

Tuzet et al. (2003) and Leuning et al. (2004) suggested to express as a function of the local . This function is implemented in HydroShoot following is water vapor partial pressure [MPa], and is the thickness of the boundary layer [ ] which is defined as (Nobel 2005): √ (Eq. 9) where is the mean length of the leaf in the downwind direction [ ], set to 70% of blade length, and is the ambient wind speed [ ]. Finally, mesophyll conductance to CO 2 is assumed to simply depend on bulk leaf temperature (Evers et al., 2010) following an Arrhenius equation trend (as for photosynthetic parameters, cf. Eq. A8) with a basal value at 25 set to 0.1025 [ ].

A c c e p t e d M a n u s c r i p t [ ] are the slope and the intercept of the linear relationship with specific to each rate. is calculated as the product of nitrogen content per unit leaf dry mass [ ] and leaf dry mass per area [ ]. linearly varies with plant age, expressed as the thermal time cumulated from budburst (input of the model), and is determined by leaf exposure to light during the last past days (Prieto et al., 2012). This is expressed respectively in the two following equations: where is the mean temperature of the day [ ] and is the base temperature (minimum required for growth) [ ], set to 10°C for grapevine and used for the calculation of thermal time Each leaf is represented as a group of solid flat triangles. It gains energy from the absorbed shortwave (solar irradiance) and thermal longwave irradiance from the sky, the soil, and the neighbouring leaves (indexed ). It loses energy through its own emission in the thermal longwave band and through latent heat due to transpiration (output of exchange module). Finally, it exchanges energy with the surrounding air by thermal conduction-convection. The resulting leaf-scale energy balance equation writes: the form factors of the sky, soil and canopy elements in the sphere surrounding the leaf (Chelle et al., 1998).

, and are input parameters considered as uniform for all leaves.

  Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019

	the absorbed photosynthetic photon flux density (	,		) and	the absorbed
	during the last 10 days.		is xylem water potential at the nodes between each pair of stem elements [	].
	is initial	[	].	is soil water potential.	is leaf temperature [ ].	is initial	[ ].
	[	] is initial hydraulic conductivity of each segment,	is the maximum allowable error of
	the estimation of xylem water potential [	] and	is the maximum allowable error of the estimation of leaf
	temperature [ ]. Circles inside module boxes indicate internal iteration loops. Symbols between curly brackets
	represent spatially structured variables.
					M a n u s c r i p t
	A c c e p t e d

). Inputs per individual leaves are

  Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019 ] is initial hydraulic conductivity of each segment. Symbols between curly brackets represent spatially structured variables. architecture, CARIBU (Chelle and Andrieu, 1998) and soil are external modules used to simulate canopy architecture, irradiance interception, and soil water potential, respectively.

	the absorbed photosynthetic photon flux density (	,	) and	the absorbed
	during the last 10 days.	is the initial xylem water potential at the nodes between each pair of stem
	elements [	].	is soil water potential.		is initial temperature of individual leaves [ ].
			M a n u s c r i p t
	A c c e p t e d

). Inputs per individual leaves are [

  Mock-ups of three virtual grapevine canopies trained respectively to Vertical Shoot Positioning (VSP),

	between 32 and 44%, wind speed at 2	height going from 0 to 2	, and a clear sky with a
	maximum	of 1670	at solar midday.
	Geneva Double Curtain (GDC) and Lyre systems. (a) Canopies mock-ups, (b) conducting segments (primary and
	A c c e p t e d secondary internodes, petioles) diameters distribution, and (c) cumulative leaf area with height. M Precision The precision of simulated outputs was evaluated by running HydroShoot on real canopies using collected data from experiments conducted in 2009 and 2012 on grapevine (cv. Syrah, grafted on SO4) at INRA, in Montpellier (same above-mentioned station). Five grapevines trained with two contrasting training systems were considered (cf. Figure 5): GDC in 2009 and VSP in 2012. Grapevine rows were oriented 140° from North on a shallow sandy loam soil with a low water holding capacity. Inter-row spacing was 3.6 for GDC and 1.8 for VSP. Intra-row spacing was 1 [see a n u s c r i p t Supporting Information S4].	Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019

station of the National Institute for Agricultural Research (INRA) in Montpellier (3°53" E, 43°37" N, 44 m alt) on July 29 th , 2009 (DOY 210). Weather conditions corresponded to a warm day having minimum and maximum air temperature of 19 and 34 °C respectively, relative humidity oscillating

Figure 4:

Brunt et al., 1932).

  temperature differs from the uniform one simulated. From the other hand, HydroShoot considers only forced heat convection driven by wind speed (cf. Eq. 14), which may lead to underestimate convective heat transfer during nights with low wind speed (0.02 to 0.2 were recorded). The overall result is an underestimate leaf temperature during nocturnal hours. Finally, the error in simulated temperature may result from the assumption of a constant sky emissivity, while the latter is well known to vary with air humidity and temperature (

	A c c e p t e d M a n u s c r i p t	Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019

Table 1 :

 1 Precision estimators of simulated carbon assimilation ( ) and transpiration () rates of the plant of well-watered and water-deficit VSP grapevines using five versions of HydroShoot: sim0, the reference (complete) HydroShoot version, sim1, the version whereby leaf stomatal conductance varies with vapor pressure deficit instead of leaf water potential (i.e. original model of Leuning, 1995, = 5 in Eq. 5a), sim2, shoot hydraulic structure omitted (i.e. all leaves have the same water potential which is equal to that of the collar), sim3, energy balance of individual leaves omitted (i.e. leaves temperature equal to air temperature) and sim4, the same as sim1 but with an increased impact of the vapor pressure deficit on ( = 1 in Eq. 5a).

			[	]	[	]	[	]	[	]
	WW	sim0	-0.05		5.49		89.47		141.66
		sim1	1.26		7.43		225.00		361.18
		sim2	1.20		5.27		170.15		247.45
		sim3	-0.26		5.58		109.18		154.60
		sim4	0.50		5.40		100.04		156.68
	WD	sim0	1.01		3.76		27.65		67.57
		sim1	3.03		6.95		193.89		315.41
		sim2	1.36		3.86		35.41		71.13
		sim3	1.05		3.82		34.12		70.81
		sim4	3.16		6.07		127.40		196.02

Table 2 :

 2 Estimation of computation cost for two canopies and three HydroShoot versions (sim0, the complete HydroShoot version, sim3, energy balance of individual leaves omitted (i.e. leaves temperature equal to air temperature) and sim4, leaf stomatal conductance varies with vapor pressure deficit instead of leaf water

	potential)
	Training system	Water condition	[	Leaf area	]	Computation time (per time step) [ ]
						sim0	sim3	sim4
	VSP ww	well-watered		5.2		11.96	2.98	19.87
	GDC (Canopy 1)	water deficit		3.56		7.45	2.29	26.57

Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019

Acknowledgments

This research was funded by the European Community's Seventh Framework Program (FP7/2007-2013) under the grant agreement no. FP7-311775, Project INNOVINE. It was also partly funded by the Environment and Agronomy department of the French National Institute for Agricultural Research (INRA).

The authors greatly acknowledge Mr. Gerardo Lopez for his concise and constructive notes on this manuscript. They also thank Dr. Junqi Zhu for his valuable feedback on the gas-exchange code.

The authors dedicate this work to their beloved colleague Eric Lebon, who left us just before the closure of the INNOVINE project. Downloaded from https://academic.oup.com/insilicoplants/advance-article-abstract/doi/10.1093/insilicoplants/diz007/5519776 by guest on 30 June 2019

Data

All data used in this paper are available from HydroShoot open-access depository through the OpenAlea platform (https://github.com/openalea/hydroshoot).

A c c e p t e d M a n u s c r i p t

When stomatal aperture was dissociated from soil water status (i.e. sim1 and sim4), the results indicated a substantial increase in prediction errors, resulting in particular in the highest values for both and for . For instance, increased from 142 to 361 under well-watered conditions, and from 68 to 315 under water deficit conditions, when sim0 is compared to sim1. An improvement in prediction quality was obtained when stomatal aperture was linked to the collar water potential (i.e. sim2), yet, a considerable error still existed compared to the reference case. 2017) on the role of leaf water potential variability on apple tree gas-exchange rates. The authors firstly reported that a reliable prediction of plant-scale gas-exchange fluxes in apple trees was allowed when stomatal closure was simulated as a function of soil water potential. However, they postulated that further improvements are yet expected when the hydraulic structure of the shoot is simulated.

Regarding the contribution of simulating leaf-scale energy balance to the predicted plant-scale fluxes, its effect was shown weak (Table 1). Indeed, disregarding energy balance calculations (i.e.

sim3) merely affected the predicted rates under both water conditions. For the well-watered case, and changed from of -0.05 to -0.26 and from 5.5 to 5.6 , respectively, using sim0 compared to sim3. For the water deficit case, changed from of 1.01 to 1.05 while merely changed. Similar results were also obtained for .

Our results disagree with those reported by

Conclusions

We presented in this paper the functional-structural plant model (FSPM) HydroShoot. This model was built in order to allow simulating the effect of plant shoot architecture on its gas-exchange dynamics under soil water deficit conditions. In order to achieve this objective, we constructed HydroShoot on the base of three interacting processes: leaf-scale gas-exchange, leaf-scale energy balance, and internode-scale xylem transport (i.e. the hydraulic structure of the shoot). The produced model was evaluated using both virtual and real grapevine canopies of three strongly contrasting shoot architectures, under both well-watered and water deficit conditions. We showed that HydroShoot reproduced efficiently the effect of canopy architecture on plant-scale gas-exchange processes under the observed gradient of water deficit conditions, fulfilling thus the objectives for which the model was built. We showed furthermore that both hydraulic structure and energy balance simulations were required for a precise prediction of plant-scale gas-exchange rates under soil water deficit. However, our results indicate that under the given grapevine architecture, soil type, and meteorological conditions, the hydraulic structure has, by far, the largest effect on simulated net photosynthesis and transpiration rates while simulating leaf-scale energy balance improves minorly prediction results.

A c c e p t e d M a n u s c r i p t