Junwen Guo
email: celiajunwenguo@gmail.com

Sonia Brugel

Agneta Andersson

Mehdi Cherif

C M Godwin

A Lewandowska

Carbon-nitrogen association influences response of the microplankton food web to enrichment

INTRODUCTION

Aquatic systems are facing multiple nutrient supplies simultaneously, which influences living organisms in the food web in a complex way. Part of the complexity comes from the nutrients themselves. For example, N occurs in 2 main forms: dissolved organic N (DON) (e.g. amino acids) and dissolved inorganic N (DIN) (e.g. ammonium and nitrate). As organisms have preferences for different forms of N, the N form could influence food web dynamics by promoting different types of organisms at the base of the food web. In general, phytoplankton contribute to most of the uptake of DIN, while bacteria can utilize both DIN and DON simultaneously. Noticeably, when bacteria utilize DON with a low C:N ratio, they regenerate DIN (mostly in the form of ammonium), which can then benefit phytoplankton growth [START_REF] Goldman | Regulation of gross growth efficiency and ammonium regeneration in bacteria by substrate C: N ratio[END_REF]). However, when DON is depleted in the ecosystem or when its C:N ratio is high, bacteria strongly compete for DIN with phytoplankton [START_REF] Joint | Competition for inorganic nutrients between phytoplankton and bacterioplankton in nutrient manipulated mesocosms[END_REF].

ABSTRACT: In aquatic ecosystems, there are 2 major forms of N available at the base of the planktonic food web: dissolved organic N (DON) and dissolved inorganic N (DIN). In DON, N is associated with organic C, which may promote both heterotrophs and autotrophs. In environments where DIN nitrate is the prevailing N form and dissociated dissolved organic C (DOC) is available, heterotrophs may also be promoted, but they may compete with the autotrophs for DIN. The influence of associated or dissociated CN nutrient sources on the interaction between organisms and the food web function is poorly known and has not been studied before. To approach this question, we performed a microcosm experiment with a coastal microbial food web, where N and C nutrient sources were provided either associated in 1 molecular compound (DON), or dissociated in 2 separate molecular compounds (DIN and DOC). The results showed that association or dis sociation of C and N input had marked effects on all trophic levels, most probably through its effect on bacteria-phytoplankton interaction, which switched between increased coupling and increased competition. The biomass of all components of the food web benefitted from the association of C and N in a single DON molecule. Our study indicated that the degree of association between C and N is an important factor affecting the productivity and efficiency of the microbial food web. Therefore, the C and N association should be considered when studying aquatic systems.

KEY WORDS: Organic nitrogen • Inorganic nitrogen • Organic carbon • Microbial food web • Ecological stoichiometry • Bacteria-phytoplankton interaction

In natural aquatic environments, dissolved N is either dissociated or associated with dissolved organic C (DOC). While DIN supplies solely N to osmotrophic organisms, DON allows for simultaneous assimilation of both C and N. Hence, when bacteria take up DIN, they need to acquire C from other sources, e.g. from DOC. In contrast, when bacteria consume DON, they simultaneously assimilate both C and N. These 2 types of association between N and organic C (DON or DIN + DOC) can strongly influence the interaction between bacteria and phytoplankton. Since bacteria have a higher affinity for DON than do phytoplankton and can mineralize the assimilated organic N as ammonium, utilization of DON can generate commensalism, a positive effect of bacteria on phytoplankton (Bratbak & Thingstad 1985, Bronk et al. 2007), which may promote a mutualistic interaction, leading to a relatively high autotroph to bacteria (AU:BA) biomass ratio (Danger et al. 2007). In contrast, if the bulk of DOC does not contain N, bacteria turn to DIN uptake, and both bacteria and phytoplankton compete for DIN (Daufresne & Loreau 2001). The interaction between bacteria and phytoplankton then turns into competition for N nutrition, in which both bacterial and phytoplankton growth are constrained.

Bacterial utilization of DON does not always result in commensalism, because the ammonium regeneration only occurs when the taken-up N exceeds the bacterial N demand. It is only when the C:N ratio of DON is lower than a specific threshold that the excess N is excreted as ammonium. Previous experimental and theoretical studies show that the threshold C:N ratio is around 10, which is very similar to the C:N ratio of aquatic bacteria (5-7) corrected with the respiration coefficient (around 0.6) [START_REF] Parnas | Model for decomposition of organic material by microorganisms[END_REF], Gold man et al. 1987, Goldman & Dennett 1991, Cherif & Loreau 2007). When the DON has a ratio larger than the bacterial C:N ratio (>10), net N uptake and C excretion by bacteria may occur; thus, the interaction between bacteria and phytoplankton may turn into competition. However, in some cases, the bacterial C:N ratio may also align with the C:N ratio of their resource, either by adapting their cellular physiology (e.g. Egli 1991) or through a change in the species composition of the whole bacterial community (Cotner et al. 2010[START_REF] Lee | Nutrient stoichiometry shapes microbial community structure in an evaporitic shallow pond[END_REF]). If such a stoichiometric alignment between bacteria and the C:N ratio of DON happens, then net N uptake by bacteria does not occur, and competition with phytoplankton cannot be promoted by DON with large C:N ratios. Hence, the interaction of bacteria and phytoplankton is greatly dependent on whether bacteria contribute to the pool of ammonium or not.

As fundamental basal producers, autotrophs and bacteria are both important food sources for higher trophic levels, and the complexity of bacteriaphytoplankton interactions results in changes in basal production and further creates alternative pathways of energy flow to higher trophic levels [START_REF] Legendre | Plankton and nutrient dynamics in marine waters[END_REF], Fonte et al. 2013). Under a commensalistic interaction, the consumers would gain energy channeled via both the bacterial and the phytoplankton pathways, while under a competitive interaction, the consumers would mostly gain energy from either the phytoplankton or the bacterial pathway. Given that both pathways often show different trophic transfer efficiency (Degerman et al. 2018), we may expect different food web energy transfer when the basal interaction is switched between commensalism and competition.

Studies within the field of ecological stoichiometry have focused on how the ratio of limiting chemical elements (e.g. P, N and C) affect bacteriaphytoplankton interactions and associated processes [START_REF] Mindl | Effects of phosphorus loading on interactions of algae and bacteria: reinvestigation of the 'phytoplankton-bacteria paradox' in a continuous cultivation system[END_REF], Cherif & Loreau 2007, Danger et al. 2007). However, these earlier studies considered the relative amounts of the various chemical elements but have not yet addressed the question of how association or dissociation of N and C affects microbial interactions and how these effects transfer to higher trophic levels, e.g. heterotrophic grazers. To increase the understanding of how dissociated or associated C and N affect the interaction between bacteria and phytoplankton and the microbial food web transfer efficiency, we performed a microcosm experiment. We isolated a natural microplanktonic food web (< 50 μm) from a coastal site in the northern Baltic Sea and enriched the food web in C and N with varying degrees of association. In the DON-addition treatment, C and N were associated in 1 organic molecule (glutamic acid). In contrast, C and N were fully dissociated in the DIN/DOC-addition treatment, consisting of nitrate and α-ketoglutaric acid. In Baltic Sea coastal waters, the microbial food web shows many features that complicate the prediction of re sponses to different N enrichments, as it is rich in mixotrophs and small heterotrophs (Andersson et al. 1996) and receives high supplies of terrestrial matter that contains DON and DIN in various proportions according to the season [START_REF] Savchuk | Large-scale nutrient dynamics in the Baltic Sea, 1970-2016[END_REF]. Thus, the balance of bacterial to phytoplankton biomass is likely to be key to its functioning. The main objectives of this study were not to look at the nutrient enrichment effects but to elucidate if the addition of dissociated and associated C and N results in different types of bacteria-phytoplankton interactions and if the food web structure is affected. Further, we addressed the impacts of DIN/DOC and DON uptake on the C:N:P stoichiometric response of different size fractions of the food web.

MATERIALS AND METHODS

Microcosm setup

Seawater for the experiment was collected from the northern Baltic Sea (63°34' N, 19°54' E, Bothnian Sea, Sweden) at a depth of 5 m on 12 October 2016. Before incubation, the water was pre-filtered through 50 μm mesh to exclude large plankton.

To compare the impacts of dissociated CN and associated CN addition on the microplankton food web, 12 microcosms were set up for 2 treatments (6 replicates in each treatment): DIN/DOC and DON (Table 1, Fig. 1). The DON treatment consisted of the addition of glutamic acid (C 5 H 9 NO 4 ), in which the C and N are fully associated in 1 organic molecular compound, and the C:N ratio of DON was smaller than the threshold ratio of 10 to ensure that ammonium regeneration occurred in this treatment [START_REF] Goldman | Regulation of gross growth efficiency and ammonium regeneration in bacteria by substrate C: N ratio[END_REF]). In the DIN/DOC treatment, in which the C and N are fully dissociated in 2 different molecular compounds, besides nitrate (NO 3 -), the DOC α-ketoglutaric acid (C 5 H 6 O 5 ) was added at the same C concentration as in the DON treatment (Table 1, Fig. 1). Glutamic acid and α-ketoglutaric acid were selected because these molecules commonly exist in nature and are highly bioavailable for bacteria and phytoplankton. Moreover, the molecular structures of these 2 organic compounds are similar to minimize potential utilization preference. Nitrate was selected as a source of DIN, because significant nitrification-deni-trification was unlikely to occur under experimental conditions. Hence, all increases in ammonium after the addition of nitrate were interpreted as a result of mineralization by organisms present in the microcosms. The purpose of the experiment was also to get the most contrasted situations, with one favoring mostly bacteria and one mostly phytoplankton, as is often observed in natural ecosystems, where the phytoplankton dominance and bacterial dominance alternate across seasons. The addition of nitrate in the DIN/DOC treatment would favor phytoplankton over bacteria since bacteria have a low affinity for nitrate [START_REF] Kirchman | The uptake of inorganic nutrients by heterotrophic bacteria[END_REF], while the addition of DON would favor bacteria over phytoplankton (Bronk et al. 2007).

The original dissolved C, N and P concentrations in the collected water amounted to 3800 μg C l -1 (DOC), 215 μg N l -1 (total dissolved N [TDN]) and 10 μg P l -1 (total dissolved P [TDP]). All the nutrients were added every second day (Days 0, 2, 4, 6, 8). Thus, the total addition reached 100 μM of N, 20 μM of P and 500 μM of C at the end of the experiment. The C:N:P molar ratio of added nutrients was 25:5:1 for all microcosms. To make sure that the microcosms were not limited by P, all microcosms were supplemented with the same amount of dissolved inorganic P (Table 1). We chose to add the nutrients in a protracted press perturbation fashion (Glasby & Underwood 1996) as a compromise. We wanted to ensure that the various organisms involved would still be growing at the end of the experiment, and not senescing, while at the same time avoiding the technical complexities of large-scale chemostat experiments. The effects of total N and C additions on planktonic food webs in mesocosms are well studied, notably in the Baltic Sea [START_REF] Sörensson | Flows of nitrogen in a mesocosm experiment in the Baltic Sea[END_REF][START_REF] Kivi | Nutrient limitation and grazing control of the Baltic plankton community during annual succession[END_REF], Balode et al. 1998[START_REF] Traving | The effect of increased loads of dissolved organic matter on estuarine microbial community composition and function[END_REF]). Hence, we did not focus our experiment on the effects of N and C additions per se but used a comparative approach between the 2 treatments that differ in their degree of association of C and N. We decided as well to set only the 2 most contrasted possible treatments (100% associated C and N vs. 100% dissociated C and N) to increase the possibility to capture any potential effect of this largely unexplored factor so far.

For each microcosm, 10 l of experimental water were incubated in high-density polyethylene cubitainers (total storage capacity 15 l). All the microcosms were placed in a temperature-controlled room (15°C) under 100 μmol photons m -2 s -1 light intensity and 12:12 h light:dark cycle to simulate natural conditions. Air (0.2 μm filtered) was gently bubbled to each microcosm to ensure mixing. The experiment lasted 10 d.

During the 10 d experiment, samples were taken for analyses of water chemistry, phytoplankton/bacterial abundance, and phytoplankton/zooplankton taxonomy and seston size fraction stoichiometry. Water chemistry and abundance samples were taken every second day (Days 0, 1, 3, 5, 7, 9). Taxonomy samples were taken at the beginning, middle and end of the experiment (Days 0, 5, 9). Plankton stoichiometry samples were filtered only from the original water and at the end of the experiment.

Water chemistry

Concentrations of phosphate, N and C were analyzed following standard analytical methods described in [START_REF] Grasshoff | Size-selective microzooplankton grazing on the phytoplankton in the Curonian Lagoon (SE Baltic Sea)[END_REF]. Briefly, concentrations of phosphate and DIN (nitrate + nitrite + ammonium) were analyzed using a Seal QuAAtro39 autoanalyzer. Samples for TDN and TDP were analyzed in the same way after filtration through 0.2 μm Supor membrane syringe filters (non-pyrogenic, Acro disc ® , Pall) and an oxidation step using peroxodisulfate. DON concentration was determined by the difference between TDN and DIN. DOC was analyzed on 0.22 μm filtered (Supor membrane syringe filter, non-pyrogenic, Acrodisc ® ) and acidified (18 mM HCl, final concentration) samples using a high-temperature combustion Shimadzu TOC-5000 analyzer.

Heterotrophic bacteria and picophytoplankton

Heterotrophic bacteria and picophytoplankton abundances were estimated by flow cytometry. The samples were preserved with 0.1% glutaral dehyde (final concentration) and immediately stored at -80°C. All frozen samples were quickly thawed in a 30°C water bath before analysis with a BD FACS-Verse™ flow cytometer (BD Biosciences). Heterotrophic bacteria samples were stained with SYBR Green I (Invitrogen) to a final concentration of 1:10 000 [START_REF] Marie | Phytoplankton cell counting by flow cytometry[END_REF]). The samples were run at a flow rate of 40 μl min -1 for 1 min. When necessary, samples were diluted with filtered sea water to avoid coincidence. Microspheres of 1 μm (Fluoresbrite Plain YG, Polysciences) were added to the samples as the internal standard. Picophytoplankton samples were analyzed at a flow rate of 100 μl min -1 for 3 min with 3 μm microspheres (Fluoresbrite Plain YG, Polysciences) as the internal standard. Pico eukaryotes and picocyanobacteria could be discriminated based on their red and orange fluorescence.

Samples for phytoplankton (> 3 μm) and microzooplankton identification and counting were preserved with 2% acidic Lugol's solution. Samples were settled for 24 h and counted using the inverted microscope method [START_REF] Utermöhl | Zur Vervollkommnung der quantitativen Phytoplankton-Methodik[END_REF]. Functional groups (autotrophs [AU], heterotrophs [HT] and mixotrophs [MX]) were assigned according to [START_REF] Olenina | Biovolumes and size-classes of phytoplankton in the Baltic Sea[END_REF]. The ciliate Mesodinium rubrum was included in the autotrophs. The plankton cells were divided into different size classes (< 3 μm [picophytoplankton], <10 μm [3-10 μm] and >10 μm) based on measurements of the longest cell axis. Phytoplankton and ciliate biomasses were calculated from the geometric shape of cells following [START_REF] Olenina | Biovolumes and size-classes of phytoplankton in the Baltic Sea[END_REF], and cell C content was calculated according to [START_REF] Menden-Deuer | Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton[END_REF]. The biomass of rotifers was calculated according to [START_REF] Pace | The relative importance of protozoans, rotifers, and crustaceans in a freshwater zooplankton community[END_REF] and Andersen & Hessen (1991).

CNP stoichiometry of different seston size fractions

Due to the technical restrictions that prevent the measurement of stoichiometry separately for each taxonomic group in natural communities, we used size fractionation to quantify the stoichiometry of different food web components. Water samples of seston were filtered into 3 different size fractions: (1) 0.3-0.7 μm, with mostly bacterioplankton; (2) 0.7-10 μm, with pico-/nanophytoplankton and heterotrophic nano flagellates; and (3) 10-50 μm, with larger nano-/ microphytoplankon, ciliates and rotifers. The description of phytoplankton and zooplankton composition in the 0.7-10 and 10-50 μm size fractions was based on microscopic examination of samples from this study and Baltic Sea phytoplankton size classification [START_REF] Olenina | Biovolumes and size-classes of phytoplankton in the Baltic Sea[END_REF]) and zooplankton taxonomy [START_REF] Telesh | Zooplankton of the open Baltic Sea: extended atlas[END_REF]). We collected the 0.7-10 and 10-50 μm size fractions by using GF/F glass fiber filters (25 mm, 0.7 μm pore size). The 0.3-0.7 μm size fraction was filtered on GF-75 glass fiber filters (Advantec, 25 mm, 0.3 μm pore size). Both GF/F and GF-75 filters were pre-combusted at 450°C in a muffle furnace (Nabertherm LT 5/11/P33) for 5 h before filtration. The volumes filtered depended on the particle concentration of the water and were between 500 and 1000 ml. Two replicate filters were prepared for CNP analyses. Filters for particulate organic C and particulate organic N were analyzed using a CHN elementary analyzer (Leco, TruSpec Micro CHN/CHNS Determinator), and particulate organic P was quantified using the ash-hydrolysis method [START_REF] Solórzano | Determination of total dissolved phosphorus and particulate phosphorus in natural waters[END_REF].

Bacteria-phytoplankton interactions and transfer efficiency to higher trophic level

Measuring the interactions between bacteria and phytoplankton is not straightforward in planktonic food webs. In this study, we used the biomass ratio of autotrophs to heterotrophic bacteria, AU:BA, as an index to estimate the relative competition and coupling between treatments. For example, if the AU:BA ratio is higher in one treatment compared to the other, the dominance of autotrophs over bacteria could be due to ammonium regeneration by bacteria in this treatment. Besides the ratio of the overall autotroph biomass to bacteria, we also calculated the biomass ratio of different autotroph size fractions (e.g. autotrophs <3 μm [Pico<3], autotrophs between 3 and 10 μm [AU<10] and autotrophs between 10 and 50 μm [AU>10]) to bacteria.

To assess food web transfer efficiency, we first calculated the biomass of the basal food web resource (Basal) by summing up the biomass of bacteria and picophytoplankton. We assumed that mixotrophs and heterotrophs were mainly grazing on small autotrophs (picophytoplankton) and bacteria. Then, we used the biomass ratios of mixotrophs and heterotrophs to the basal food web resource as the index of food web transfer efficiency. The heterotrophs were also classified into 2 size fractions, heterotrophs <10 μm (HT<10) and heterotrophs >10 μm (HT>10). For instance, if the resource ratio of heterotrophs >10 μm to the basal food web (HT>10:Basal) is higher in one treatment compared to the other, this means that heterotrophic consumers gain relatively higher biomass per unit of available resource, indicating a higher food web transfer efficiency in this treatment.

Statistical analyses

One-way ANOVAs were used to compare differences in the C:N, C:P and N:P ratios of each seston size fraction (0.3-0.7, 0.7-10 and 10-50 μm). We used a 3-way repeated-measure ANOVA to analyze how the AU:BA biomass ratios were affected by time (Days 5 and 9), treatment (DIN/DOC and DON addition) and size class of phytoplankton (Pico, AU<10 or AU>10). To test how the basal food resource could influence the bio mass of their grazers, we performed a 3-way repeated-measure ANOVA to analyze how the ratio of the biomass of grazers to the biomass of their assumed resource (picophytoplankton + bacteria) was affected by time (Days 5 and 9), treatment (DIN/DOC and DON addition) and size class of grazers (MX, HT<10 or HT>10). The samples were taken from each microcosm over time repeatedly; therefore, it was necessary to apply ANOVAs for repeated measures. The main models used for 3-way repeatedmeasure ANOVAs consist of 1 between-group factor (Treatment) and 2 within-group factors (Day and Size class) (of phytoplankton or grazers).

All statistical analyses above were conducted using R (version 3.5.2) (R Core Team 2019). All data for ANOVAs were ln transformed a priori, according to the recommended practice for ratio data [START_REF] Keene | The log transformation is special[END_REF], Isles 2020). The assumption for normality and homogeneity of variance was assessed on the residuals from the ANOVAs, through visual inspection of residual vs. fitted, square root residual vs. fitted, normal quantile-quantile and residual vs. leverage plots. The significance level was set at α = 0.05.

RESULTS

C, N and P dynamics in the water

Dissolved C, N and P concentrations in the water, measured just after collection, amounted to 3800 μg C l -1 (DOC), 215 μg N l -1 (TDN) and 10 μg P l -1 (TDP) (Fig. 2). However, after the first nutrient addition on Day 0, the CNP concentrations in the water in creased to approximately 5000 μg C l -1 , 500 μg N l -1 and 140 μg P l -1 in both types of CN additions. Even though there was no DIN addition to the DON-supplemented microcosms, DIN soon accumulated due to rapid ammonium regeneration (Fig. 2A,B). Hence, DIN was available during the whole experiment in both the CN-associated and CN-dissociated incubations. Nitrate was predominant where C and N were supplied separately (Fig. 2C), while ammonium was the prevailing inorganic N form where DON was added (Fig. 2B). The DON concentrations showed a strong decrease from Day 1 to Day 3 in the DON-supplemented microcosms (Fig. 2D), indicating significant DON consumption. In contrast, DON accumulated from the start to Day 7 in the DIN/DOC-associated microcosms (Fig. 2D), showing that DON was internally generated in this system.

The 2 types of CN additions induced large differences in the timing of C uptake. The major DOC reduction occurred already at Day 1 in the DONsupplemented microcosms, while the uptake mainly occurred after Day 3 in the DIN/DOC-associated microcosms. However, after Day 5, the DOC concentrations were relatively similar in both types of additions (Fig. 2F).

Seston stoichiometry response

At the end of the experiment, the C:N ratio in the size fraction 0.3-0.7 μm was slightly higher in the DIN/ DOC treatment than in the DON microcosms (p = 0.00997, Table S1 in the Sup plement at www.int-res. com/articles/suppl/a088p187_supp.pdf, Fig. 3A). No other stoichiometric CNP differences in seston were found between treatments (Table S1, Fig. 3A,B,C).

Trophic dynamics within

the food web 3.3.1. Response of basal trophic levels (bacteria and phytoplankton) to DON and DIN/DOC additions Heterotrophic bacteria and pico phytoplankton constituted the lowest trophic levels in the system. The 2 groups showed significant temporal trends. Initially, the heterotrophic bacteria did not respond to any of the CN additions, but after Day 1, their abundance increased. The bacterial abundance increased rapidly until Day 5 and dropped dramatically to its original level after Day 7. An increase occurred on Day 9, with higher values in the DON treatment (Fig. 4A). Both picoeukaryotes and picocyanobacteria showed larger increases in the DON addition than in the DIN/DOC-associated microcosms during the first 5 d, followed by a marked decrease where the abundance of picoeukaryotes and picocyanobacteria dropped to 10 times their initial abundance (Fig. 4B,C). The AU:BA biomass ratios of different size classes showed variations in relation to CN addition and time of the experiment (Table S2). The overall AU:BA ratios were consistently higher in the DON addition than in the DIN/DOC-associated microcosms on both Days 5 and 9. However, the largest and significant difference on Day 5 was observed for the picophytoplankton fraction, while on Day 9, the difference appeared to be largest for the AU<10:BA ratio (Fig. 5A).

Response of higher trophic levels (mixotrophs and heterotrophs) to DON and DIN/DOC additions

Since the basal trophic levels responded differently to DON and DIN/DOC additions, we expected concomitant alterations of the grazers, e.g. mixotrophic and heterotrophic grazers. The ratio of grazer biomass to basal food resource showed similar responses for all higher trophic levels (MX, HT<10, HT>10); however, the ratios were higher in the DON addition than in the DIN/DOC-associated micro-cosms (Table S3, Fig. 5B). The largest difference between treatments was observed for the MX:Basal and HT<10:Basal ratios on Day 5 and the HT>10: Basal ratio on Day 9 (Fig. 5B).

Whole food web response to DON and DIN/DOC additions

To synthesize the effect of the 2 different types of CN additions, we summarized the biomass structure of the whole food web (Fig. 6). In the original water, picophytoplankton and heterotrophic bacteria equally dominated the food web, while the biomass of larger phytoplankton and smaller heterotrophic grazers was low. In the middle of the experiment (Day 5), bacteria had doubled their C biomass in both the DON addition and DIN/DOC-associated microcosms. However, in the system re ceiving DON, there was significantly higher biomass of pico phytoplankton, mixotrophs <10 μm and larger phytoplankton (AU>10), while grazers such as rotifers (zooplankton >10 μm, ZP>10) and ciliates (HT>10) started to produce more biomass in the middle of the experiment (Day 5). At the end of the experiment (Day 9), the food web structure diverged even more between the different types of additions. In the <10 μm fraction, heterotrophic bacteria gained more biomass in the DON than in the DIN/DOC addition. The same observation applies to the other components within this size fraction. To sum up, DON addition yielded higher C biomass in the whole food web compared to DIN/DOC addition (Fig. 6).

DISCUSSION

To the best of our knowledge, this study is the first to try to go beyond nutrient enrichment to consider the food web effect of organic C associated with different forms of N. By using the 2 most contrasted possible treatments (100% associated C and N vs. 100% dissociated C and N), we increase the possibility to capture any potential effect of this elemental association. Of course, the study has a few limitations. For instance, (1) it was a short-term experiment, so the results will possibly represent temporary effects; (2) the continuous addition of DON or DIN/DOC probably prevents competition to strongly affect the basis of the food web; (3) C and N were added in similar amounts in all treatments, thus preventing us by design to test for an interactive effect between the amounts of nutrient addition and C and N association; and (4) the remineralization of the DON addition led to different DIN species in both treatments. In natural systems, nitrate is often the predominant form of N available in the water [START_REF] Korth | Nitrate source identification in the Baltic Sea using its isotopic ratios in combination with a Bayesian isotope mixing model[END_REF]. Further, phytoplankton have a higher affinity for nitrate than do bacteria; therefore, using nitrate in the DIN addition allows testing the most contrasting sides of the bacteria-phytoplankton interaction.

Despite such limitations, the results of the study showed that changes in the food web between the treatments were rapid and significant. The repeated addition of C and N had marked positive effects on the growth of a nutrient-limited microplanktonic food web [START_REF] Gruner | A cross-system synthesis of consumer and nutrient resource control on producer biomass[END_REF]) associated with delayed topdown responses that led to a subsequent biomass decrease in lower trophic levels. In addition to these effects of nutrient addition, the degree of association between C and N added further impacts on the productivity and efficiency of the microbial food web. The DON addition promoted the entire microplankton food web, and at the end of the experiment, the biomass of the whole food web was much higher than that in the DIN/DOC treatment. All trophic levels had been influenced by the addition of the associated or dissociated C and N, which could possibly, through altering the bacteria-phytoplankton inter - Cases where the ratio is higher in one of the treatments than in another indicate a greater coupling. (B) Food web transfer efficiency from basal food web resources to higher trophic levels: mixotroph and heterotroph biomass to basal food resource biomass (sum of picophytoplankton and bacterial biomass) ratios (mean ± SE) in different size fractions at Days 5 and 9. MX:Basal = mixotroph to basal food resource biomass ratio; HT (<10 μm) :Basal = heterotrophs <10 μm to basal food resource biomass ratio; HT (>10 μm) :Basal = heterotrophs >10 μm to basal food resource biomass ratio. Cases where the ratio is larger in one of the treatments than in another indicate a higher food web transfer efficiency. All ratios were ln transformed prior to plotting. Abbreviations as in Fig. 1 action, switch between increased coupling or increased competition. More importantly, our study suggests that trophic interactions within microplankton food webs vary depending on whether the C and N elements are supplied associated in 1 molecule or dissociated in 2 different molecules, highlighting the importance of the chemical form of N inputs to the short-term dynamics of microplankton food webs.

Although provided with the same total amount of C and N, almost all food web components, including bacteria, autotrophs, mixotrophs and heterotrophs, showed a marked increase in biomass when N and organic C were supplied associated in a single molecule (DON addition) compared to the addition where N and organic C were supplied in dissociated molecules (DIN/DOC addition). There are several lines of arguments converging to suggest that this positive effect results from a relative shift of the bacteriaphytoplankton interaction from competition to increased coupling with C and N in association: (1) ammonium concentrations were substantially higher when supplying DON, showing that large amounts of ammonium were internally regenerated from DON in the microcosms, to support further phytoplankton growth, while in the DIN/DOC addition, phytoplankton and bacteria mainly competed for nitrate; (2) in the DON addition, bacteria had a C:N ratio that was slightly lower than that in the DIN/DOC addition, suggesting that bacteria were relatively C limited in this treatment; (3) bacterial and autotrophic biomasses were higher when adding DON, and the AU:BA biomass ratio also was higher. All these results suggest that bacteria synergistically benefitted the phytoplankton by utilizing DON and recycling N as ammonium. The effect of DON became, to some extent, the effect of ammonium. This important result may lead one to hypothesize that when N is added as ammonium instead of nitrate in the DIN/DOC treatment, the differences between the 2 treatments might have been much less. Our choice of nitrate in the DIN/DOC treatment is based on the importance of this nutrient as a source of N in coastal environments worldwide and singularly in the Baltic Sea [START_REF] Korth | Nitrate source identification in the Baltic Sea using its isotopic ratios in combination with a Bayesian isotope mixing model[END_REF]. By using nitrate as the source of inorganic N, our experimental design helped uncover the recycling of organic N in a highly bio available form as one of the mechanisms by which different degrees of CN association may affect coastal food webs. To uncover other mechanisms, it will be important to test other experimental designs, where, for example, DIN would be supplied either as ammonium or nitrate or as combinations of both nutrients. The commensalism interaction between bacteria and phytoplankton promoted by the recycling of ammonium was then transferred to mixotrophic and heterotrophic grazers, since the higher overall biomass of mixotrophic and heterotrophic grazers and higher grazer to prey biomass ratios were found in the DON addition.

Effect of different associations of C and N on stoichiometry

Although the stoichiometric composition was different between size fractions, the only difference due to C and N association was found for the size fraction 0.3-0.7 μm, i.e. the bacterial fraction. A previous study showed that the bacterial C:N ratio can be more flexible at the community level even though bacteria at the strain level can maintain a certain degree of homeostasis [START_REF] Makino | Are bacteria more like plants or animals? Growth rate and resource dependence of bacterial C: N: P stoichiometry[END_REF]. In particular, bacterial strains with lower C:N ratios are more competitive under C limitation (Godwin & Cotner 2014). In our study, the supplied DON had a lower C:N ratio (= 5) than the bacterial fraction (~9 at Day 0 and ~5.5 at Day 9), suggesting excessive N uptake by bacteria when utilizing DON. An alternative mechanism for the lower C:N ratio might be physiological. In effect, classical experiments on the growth of bacteria under dual C and N nutrient limitation (e.g. Egli 1991) found that bacteria had lower C:N ratios under an excess of available N (as was likely the case in our DON-addition treatment in which ammonium accumulated). Irrespective of the mechanism, the observed change in stoichiometry was modest and not reflected in the C:P ratio. Thus, the question of its biological relevance remains open.

At the end of the experiment, there was no difference in the stoichiometric composition of the 2 other fractions, 0.7-10 and 10-50 μm, even though the taxonomic compositions of these 2 size fractions were largely different: the 0.7-10 μm fraction contained small mixotrophs, pico-and nanophytoplankton and nanoflagellates, and the 10-50 μm fraction contained mostly ciliates, rotifers and large phytoplankton (but to a lesser extent). Ciliates and rotifers are known to be stoichiometrically homeostatic [START_REF] Golz | Stoichiometric regulation in micro-and mesozooplankton[END_REF] and could be responsible for the similar stoichiometry of the 10-50 μm fraction between treatments.

Smaller unicellular plankton are known to be more stoichiometrically flexible [START_REF] Golz | Stoichiometric regulation in micro-and mesozooplankton[END_REF], except mixotrophs, which can better balance their assimilation of elements because they combine phototrophy and phagotrophy at the same time [START_REF] Mitra | The role of mixotrophic protists in the biological carbon pump[END_REF][START_REF] Ward | Marine mixotrophy increases trophic transfer efficiency, mean organism size, and vertical carbon flux[END_REF]. Mixotrophs have intermediate stoichiometric homeostasis, more variable than strict heterotrophs but less variable than flexible phytoplankton [START_REF] Katechakis | Mixotrophic versus photoautotrophic specialist algae as food for zooplankton: the light: nutrient hypothesis might not hold for mixotrophs[END_REF], Chrzanowski et al. 2010). Since mixotrophs dominated the 0.7-10 μm fraction in biomass, they may offer an explanation as to the relative stoichiometric stability of this size fraction between treatments. However, given the high stoichiometric flexibility observed within and between species that make functional groups, depending on environmental conditions and relative growth rate (see, e.g., Finkel et al. 2010, Godwin & Cotner 2017), only a detailed stoichiometric analysis at the species level could explain with certainty the patterns in stoichiometry observed in our experiment.

Effect of different associations of C and N on bacteria-phytoplankton interaction

The molecular association between C and N yielded higher biomass of bacteria. Besides a substantial increase in the overall biomass at the end of the experiment in the DON addition, the average biomass of the autotrophs was more than twice as large as that in the DIN/DOC addition. These outcomes may result from the addition of C and N in associated form, which triggered early mineralization of the supplied DON releasing high amounts of ammonium. Due to the additional energy cost needed to reduce nitrate for assimilation, phytoplankton prefer to use ammonium above nitrate (Glibert et al. 2016). Hence, the increased availability of a better-quality N resource in the DON treatment could promote higher trophic transfer efficiency through the food web and might explain why the total autotroph biomass was significantly higher in the DON addition. The remarkable increases in both bacterial and phytoplankton biomass confirmed that enriching DON for the microbial food web could promote commensalism between bac teria and phytoplankton.

Different size classes of phytoplankton reacted differently to C and N association and thus interacted with bacteria differently. The smaller phytoplankton responded rapidly to the addition of resources to the point where all microcosms tended towards picophytoplankton dominance in the middle of the experiment, probably due to strong bottom-up control and the quasi-absence of consumers. However, the picophytoplankton response also showed an earlier differential response to the additions, with higher biomass in the DON addition from the very first days, while the other phytoplankton size classes showed differences between treatments only near the end of the experiment (Fig. S1). An earlier study has shown that picophytoplankton are major users of regenerated ammonium (Chang et al. 1995), supporting their early response to accumulated ammonium concentration in the DON addition. It is also possible that the picophytoplankton may have used some of the DON directly, as a source of N. Previous studies showed that various groups of phytoplankton can take up DON using pinocytosis or cell surface enzymes (Berman & Bronk 2003, Bronk et al. 2007). However, such mechanisms are not specific to picophytoplankton; for instance, pinocytosis is also common in dinoflagellates and especially for ingesting dissolved macromolecules and thus cannot fully explain their early response.

Overall, our results are indicative of a strong coupling between bacteria and picophytoplankton when supplying C and N associated as DON, through the regeneration of ammonium by bacteria and a shift in food web transfer pathways. However, the response of the phytoplankton diverged between different size fractions, likely depending on the initial abundance and growth characteristics but also on different degrees of top-down control from higher trophic levels, as all the trophic levels of the food web showed significant responses to the treatment.

Effect of different associations of C and N on whole food web dynamics

Besides phytoplankton and bacteria, the heterotrophic components of the food web also benefitted from the association between C and N; mixotrophs, nanoflagellates (HT<10 μm), ciliates (HT>10 μm) and larger zooplankton (ZP>10 μm) all reached higher biomass in the DON treatment both in absolute terms and relative to the biomass of their resources. These increases at the higher trophic levels indicate that the benefits at lower levels from the commensalistic interaction between phytoplankton and bacteria were transferred up the food web. Hence, the whole microplankton food web benefitted from the DON addition. The higher overall biomass of the food web in the DON addition may partly result from the higher C content of the different size fraction of the food web (Fig. S2), suggesting that a large percent-age of C supplied as a dissolved resource is locked in biomass and thus available for consumption by higher trophic levels.

Higher trophic levels exerted feedbacks on the bacteria-phytoplankton dynamics through top-down control. At the end of the experiment, the increase in heterotrophic grazers co-occurred with a collapse in the picophytoplankton biomass. The picophytoplankton biomass was reduced to levels below those at the start of the experiment, and autotrophs were dominated by cells between 3 and 10 μm. The de cline of picophytoplankton occurred despite continuous enrichment of resources in the microcosms but followed an increase in the abundance of consumers, suggesting that the picophytoplankton were significantly grazed at the end of the experiment in both treatments, while the 3-10 μm autotrophs were re leased from grazing pressure and became dominant. According to previous studies, in both freshwater and marine systems, picophytoplankton in the size range of 0.3 to 3 μm are the most important food source for microzooplankton [START_REF] Šimek | Ciliate grazing on picoplankton in a eutrophic reservoir during the summer phytoplankton maximum: a study at the species and community level[END_REF][START_REF] Zingel | Ciliates are the dominant grazers on pico-and nanoplankton in a shallow, naturally highly eutrophic lake[END_REF], Grinienė et al. 2016). The most abundant consumers in our experiment were ciliates and rotifers larger than 10 μm (Fig. S1), which graze on both bacteria and picophytoplankton but with a strong preference for the latter. Our data on stoichiometry composition also show that the size fraction 0.7-10 μm (picophytoplankton) had a lower C:N ratio than the size fractions 0.3-0.7 μm (bacteria) and 10-50 μm (microplankton) in both treatments. Hence, picophytoplankton might represent a higher-quality food for ciliates and rotifers than do bacteria. Thus, differential consumer top-down control on the various basal trophic levels based on stoichiometric preferences may be another important factor controlling food web structure in response to C and N association.

CONCLUSIONS

Although utilizing different forms of N by the aquatic food web has been extensively studied, the concept of linking N to C upon different degrees of association for bacteria-phytoplankton interaction is novel. Our results show that the degree of association between N and C is an important factor to consider in the study of microplankton food web dynamics, at least when subjected to short-term perturbation in the form of nutrient additions. The association or dissociation of C and N inputs has important effects on all trophic levels, most probably through its effect on bacteria-phytoplankton interactions, which switch from commensalism to competition. Indeed, the entire microbial food web benefitted when C and N were associated in a single DON molecule. The important role of DON in promoting the productivity and efficiency of the microbial food web calls for the inclusion of this factor in the study of the effects of inputs of allochthonous dissolved organic matter in coastal food webs. In the case of the northern Baltic ecosystem, terrestrial runoff and freshwater inputs are predicted to increase in the future as a consequence of climate change (Andersson et al. 2015), increasing N loads. Since terrestrial runoffs in northerly regions often bring organic C and N compounds in an associated form, coastal zones may be more affected by DON than other ecosystems in the near future. We thus recommend to evaluate and expand the results of this pioneer experiment with further experimentation. A more comprehensive gradient of coupling between C and N could be explored. Association between C and other nutrients, such as P and S, could be addressed, depending on the ecological system investigated. The use of isotopes as a tracer of elemental circulation among the various trophic levels, and of relevant molecular approaches for the monitoring of potential changes in the taxonomic composition of the various trophic levels, would be valuable extensions to our experimental approach.

Fig. 1 .

 1 Fig. 1. Conceptual CN uptake models of the food web in the experiment. DON: dissolved organic N; DIN: dissolved inorganic N; DOC: dissolved organic C

Fig. 2 .

 2 Fig. 2. Nutrient concentration (mean ± SE) of (A) dissolved inorganic N (DIN), (B) ammonium (NH 4 + ), (C) nitrate + nitrite (NO 2 -+ NO 3 -), (D) dissolved organic N (DON), (E) phosphate (PO 4 3-) and (F) dissolved organic C (DOC)

Fig. 3 .

 3 Fig. 3. Molar C:N:P ratios of different size fractions (mean ± SE) at the end of the experiment: (A) C:N ratio, (B) C:P ratio and (C) N:P ratio. All ratios were ln transformed. *Significant at 5% level. Abbreviations as in Fig. 1

Fig. 5 .

 5 Fig. 5. (A) Ratio of autotroph to bacterial biomass (mean ± SE) in different size fractions at Days 5 and 9. Pico:BA = picophytoplankton to bacteria biomass ratio; AU (<10 μm) :BA = autotrophs <10 μm to bacteria biomass ratio; AU (>10 μm) :BA = autotrophs >10 μm to bacteria biomass ratio; AU:BA = ratio of the biomass of all autotrophs to the biomass of bacteria.Cases where the ratio is higher in one of the treatments than in another indicate a greater coupling. (B) Food web transfer efficiency from basal food web resources to higher trophic levels: mixotroph and heterotroph biomass to basal food resource biomass (sum of picophytoplankton and bacterial biomass) ratios (mean ± SE) in different size fractions at Days 5 and 9. MX:Basal = mixotroph to basal food resource biomass ratio; HT (<10 μm) :Basal = heterotrophs <10 μm to basal food resource biomass ratio; HT (>10 μm) :Basal = heterotrophs >10 μm to basal food resource biomass ratio. Cases where the ratio is larger in one of the treatments than in another indicate a higher food web transfer efficiency. All ratios were ln transformed prior to plotting. Abbreviations as in Fig.1

Fig. 6 .

 6 Fig. 6. Food web structure in the original water, and dissolved inorganic N/dissolved organic C (DIN/DOC) and dissolved organic N (DON) treatments at Days 5 and 9. The average C biomass of different functional groups is presented within ellipses (mg C m -3 ), and their size is proportional to the biomass. The grey area represents autotrophs, and the white area represents heterotrophs. BA: bacteria; Pico: picophytoplankton; AU: autotrophs; MX: mixotrophs; HT: heterotrophs; ZP: microzooplankton

Table 1 .

 1 Nutrient additions to different treatments.

	Treatment	DIN	DOC	DON	DIP	Total C	Total N	Total P
		(NaNO 3 )	(C 5 H 6 O 5 )	(C 5 H 9 NO 4 )	(NaH 2 PO 4 )	addition (μM)	addition (μM)	addition (μM)
	DIN/DOC	+	+		+	500	100	20
	DON			+	+	500	100	20

DIN: dissolved inorganic N; DOC: dissolved organic C; DON: dissolved organic N; DIP: dissolved inorganic P; +: nutrient addition

Acknowledgements. This project was supported by funds from Umeå Marine Sciences Center to J.G. and by the Swedish marine strategic research environment EcoChange (the Swedish Research Council Formas) to A.A. We thank Jonas Forsberg for phytoplankton analysis; Joanna Paczkowska for helping in the lab; and Ann-Kristin Bergström, Sebastian Diehl and Göran Englund for reviewing the manuscript. We are grateful to the staff at the Umeå Marine Sciences Center for their expert assistance, in particular Henrik Larsson for chemical analyses and experimental setup. M.C. acknowledges financial support from the Knut and Alice Wallenberg Foundation via the project 'Climate change induced regime shifts in northern lake ecosystems.' LITERATURE CITED Andersen T, Hessen DO (1991) Carbon, nitrogen, and phosphorus content of freshwater zooplankton. Limnol Oceanogr 36: 807-814 Andersson A, Hajdu S, Haecky P, Kuparinen J, Wikner J (1996) Succession and growth limitation of phytoplankton in the Gulf of Bothnia (Baltic Sea). Mar Biol 126: 791-801 Andersson A, Meier HEM, Ripszam M, Rowe O and others (2015) Projected future climate change and Baltic Sea ecosystem management. Ambio 44: 345-356 Balode M, Purina I, Béchemin C, Maestrini SY (1998) Effects of nutrient enrichment on the growth rates and community structure of summer phytoplankton from the Gulf of Riga, Baltic Sea. J Plankton Res 20: 2251-2272 Berman T, Bronk DA (2003) Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat Microb Ecol 31: 279-305 Bratbak G, Thingstad TF (1985) Phytoplankton-bacteria interactions: an apparent paradox? Analysis of a model system with both competition and commensalism. Mar Ecol Prog Ser 25: 23-30 Bronk DA, See JH, Bradley P, Killberg L and others (2007) DON as a source of bioavailable nitrogen for phytoplankton. Biogeosciences 4: 283-296 Chang FH, Bradford-Grieve JM, Vincent WF, Woods PH (1995) Nitrogen uptake by the summer size-fractionated phytoplankton assemblages in the Westland, New Zealand, upwelling system. N Z J Mar Freshw Res 29: 147-161 Cherif M, Loreau M (2007) Stoichiometric constraints on resource use, competitive interactions, and elemental cycling in microbial decomposers. Am Nat 169: 709-724 Chrzanowski TH, Lukomski NC, Grover JP (2010) Element stoichiometry of a mixotrophic protist grown under varying resource conditions. J Eukaryot Microbiol 57: 322-327 Cotner JB, Hall EK, Scott JT, Heldal M (2010) Freshwater bacteria are stoichiometrically flexible with a nutrient composition similar to seston. Front Microbiol 1: 132 Danger M, Leflaive J, Oumarou C, Ten-Hage L, Lacroix G (2007) Control of phytoplankton-bacteria interactions by stoichiometric constraints. Oikos 116: 1079-1086 Daufresne T, Loreau M (2001) Ecological stoichiometry, primary producer-decomposer interactions, and ecosystem persistence. Ecology 82: 3069-3082 Degerman R, Lefébure R, Byström P, Båmstedt U, Larsson S, Andersson A (2018) Food web interactions determine energy transfer efficiency and top consumer responses to inputs of dissolved organic carbon. Hydrobiologia 805: 131-146 Egli T (1991) On multiple-nutrient-limited growth of microorganisms, with special reference to dual limitation by carbon and nitrogen substrates. Ant Leeuwenhoek 60: 225-234 Finkel ZV, Beardall J, Flynn KJ, Quigg A, Rees TAV, Raven JA (2010) Phytoplankton in a changing world: cell size and elemental stoichiometry. J Plankton Res 32: 119-137 Fonte ES, Amado AM, Meirelles-Pereira F, Esteves FA, Rosado AS, Farjalla VF (2013) The combination of different carbon sources enhances bacterial growth efficiency in aquatic ecosystems. Microb Ecol 66: 871-878 Glasby TM, Underwood AJ (1996) Sampling to differentiate between pulse and press perturbations. Environ Monit Assess 42: 241-252 Glibert PM, Wilkerson FP, Dugdale RC, Raven JA and others (2016) Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions. Limnol Oceanogr 61: 165-197 Godwin CM, Cotner JB (2014) Carbon: phosphorus homeostasis of aquatic bacterial assemblages is mediated by shifts in assemblage composition. Aquat Microb Ecol 73: 245-258 Godwin CM, Cotner JB (2017) What intrinsic and extrinsic factors explain the stoichiometric diversity of aquatic heterotrophic bacteria? ISME J 12: 598-609 Goldman JC, Dennett MR (1991) Ammonium regeneration and carbon utilization by marine bacteria grown on mixed substrates. Mar Biol 109: 369-378


Sirine Maalej

Zoubeir Lafhaj

Jean Yim

Pascal Yim

Colin Noort

Prediction of HVAC System Parameters Using Deep Learning

Heating, ventilation, and air conditioning (HVAC) systems consume between 10 -20% of developed countries' energy annually. Up to 30% of this energy is often wasted due to mismanagement or improper control strategies. In order to overcome this issue and optimize energy consumption, this paper proposes a predictive modeling technique to effectively forecast HVAC system parameters using machine and deep learning models. A case study of an air handling unit (AHU) at the British Columbia Institute of Technology (BCIT, Canada) is used to test and confirm the results of this research. Five models were applied to predict the supply air temperature. Each model was compared with actual supply air temperature and its accuracy was explored. The results reveal that all investigated models were successful in predicting the supply air temperature, and that the combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) models has obtained the highest accuracy.

Introduction Global Energy Consumption

Over the last few decades, the world has seen a substantial increase in its energy consumption. The International Energy Agency has estimated that over a period of two decades primary energy use has grown by 49%, and CO2 emissions have increased by 43%. This translates to a global average annual increase of about 2% and 1.8% respectively [START_REF] Perez-Lombard | A Review on Building Energy Consumption Information[END_REF]. Discussions regarding this increased energy consumption have brought to light connections between global warming, climate change, increased pollution, and the growth in global energy consumption. With worldwide population growth expected to increase in coming years, and worldwide energy demands expected to continue to grow, reducing energy consumption worldwide is a topic deserving of further inquiry and analysis.

The building sector sits among the top worldwide energy consumers, surpassed only by the food, and transportation industries. The operation of heating, ventilation, and air conditioning (HVAC) systems accounts for about 50% of the energy consumed in the building sector worldwide [START_REF] Moran | Analysis of Parallel Process In HVAC Systems Using Deep Autoencoders[END_REF]. This translates to between 10 -20% of the total energy consumption in developed countries [START_REF] Perez-Lombard | A Review on Building Energy Consumption Information[END_REF][START_REF] Ramesh | Life Cycle Energy Analysis of Buildings: An Overview[END_REF][START_REF] Verbert | Combining Knowledge and Historical Data for System-Level Fault Diagnosis of HVAC Systems[END_REF] Additionally, degradation and mismanagement of these HVAC systems, and improper control strategies can often lead to 30% of this energy being wasted [START_REF] Verbert | Combining Knowledge and Historical Data for System-Level Fault Diagnosis of HVAC Systems[END_REF]. Improved control of HVAC systems, through data-driven modeling and prediction methods, has the potential to significantly reduce the overall energy consumption of these systems.

Existing Control & Prediction Methods

Most modern building management systems monitor and record HVAC system data (temperature, pressure, humidity, etc.) using software and sensors connected to various system components (dampers, fans, cooling, heating coils, etc.). Over time, this leads to a substantial amount of historical HVAC system data being available. This data can be used to develop data-driven models using predictive modeling techniques. The models would make the simulation and optimization of HVAC systems possible and contribute to the energy reduction in buildings.

Various data-driven approaches have been explored and established as effective at overcoming the shortcomings of analytical methods. For example, [START_REF] Fan | A Short-Term Building Cooling Load Prediction Method Using Deep Learning Algorithms[END_REF] showed that these data-driven methods are effective at cooling load predictions in buildings, while [START_REF] Korolija | Regression Models for Predicting UK Office Building Energy Consumption from Heating and Cooling Demands[END_REF] showed them to be effective in energy consumption modeling. Many of these data-driven methods rely on artificial intelligence (AI) and machine learning (ML) to do most of the data processing involved. AI has proven to be proficient in modeling complex systems such as HVAC systems, due to its information processing and real-time decision-making capabilities [START_REF] Manic | Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting[END_REF]. Additionally, the heterogeneous nature of an HVAC system's datasets makes AI a perfect tool for data-driven HVAC system modeling applications. AI and ML have already been shown to be capable of predicting future HVAC system states by identifying patterns and learning from past system datasets.

Although many studies in the literature reviewed proved good results in indoor air temperature predictions using classical machine learning models, few works investigated the deep learning prediction performance. In recent years, deep learning (DL) techniques have emerged as subsets of ML and AI. Where ML achieves AI through established algorithms and training, DL is differentiated by its use of artificial neural networks (ANN). DL has been used in a wide variety of analytical tasks, such as image classification, and speech recognition [START_REF] Fan | Deep learning-based feature engineering methods for improved building[END_REF]. Because of DL's more complex architecture, it can describe more accurately heterogeneous and non-linear relationships seen in datasets. As such, DL has the potential of being more suited for applications in data-driven HVAC system predictive modeling.

This paper looks at a specific application case of an Air Handling Unit (AHU) in an HVAC system at the British Columbia Institute of Technology. Artificial intelligence, in combination with various machine and deep learning models, is used to develop predictive models of the preheated air serving 7 rooms in this AHU. This paper will first break down the specific application case, provide a brief background on AI and deep learning, discuss the methodology employed in this specific application case, and finally outline the results.

Methodology

Predictive modeling has been widely accepted as an effective tool by both academic researchers and building professionals. Predictive modeling is a mathematical process that aims to predict future events based on patterns present in past historical datasets. Predictive algorithms present an opportunity for the building sector to reduce failures, maintenance costs and energy consumption while improving the comfort of the occupants. The larger the dataset, the more accurate the prediction will be. Although this data could theoretically be analyzed manually by a human operator, this would be incredibly inefficient. A much more effective and efficient tool which can be used to analyze this data is artificial intelligence, specifically machine learning and deep learning.

Artificial intelligence and machine learning encompass a wide range of algorithms and techniques [START_REF] Manic | Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting[END_REF]. Machine learning algorithms such as Linear Regression, Logistic Regression, and Dimensionality Reduction Algorithms are well established and have been effectively used for predicting future events from past datasets [START_REF] Manic | Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting[END_REF].

Implementing a predictive approach presents several challenges, such as the availability of valuable data and the accuracy of predictive algorithms. In this paper, learning algorithms belonging to classical ML and DL are compared. The main purpose of the study is to identify an algorithm that accurately predicts the supply air temperature of an AHU unit and that can be used to build a digital twin for energy optimization in future work.

This study focuses on predicting the supply air temperature that allows the adjustment of the primary delivery temperature in an HVAC system. It is a critical parameter in controlling the AHU equipment and the Variable Air Volumes (VAVs) in the served zones.

Predicting the supply air temperature is a time series problem. Therefore, the selected models in this study are chosen based on their proven efficacy in time series scenarios.

Machine Learning

Tree-based models are built by recursively partitioning the considered observations following some criteria. The criteria are created by comparing all possible splits in the data and choosing the one that offers the highest mean squared-error (MSE) reduction in Variance of child nodes.

Ensemble methods (e.g. Bagging and Boosting) combine several decision trees predictors to produce better performance and build stronger predictive models. The bagging technique uses average predictions from different trees constructed by creating several data subsets. While Boosting is an iterative technique that fits consecutive trees constructed on random samples. At every step simple models are fit to the data and data is analyzed for errors. The effectiveness of models based on ensemble methods (such as Random Forest, XGBoost, and Extra Tree) in forecasting time series data sets and HVAC systems, in particular, was examined by a few studies [START_REF] Alawadi | A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings[END_REF][START_REF] Ampomah | Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement[END_REF][START_REF] Manivannan | Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics[END_REF].

In this study, we selected one bagging model and one boosting model: Extra Tree and XGBoost. The Extra Tree is very similar to Random Forest except that the decision rule during tree construction is randomly selected. Alawadi et al. [START_REF] Alawadi | A comparison of machine learning algorithms for forecasting indoor temperature in smart buildings[END_REF] found that the Extra Tree model is the most accurate and performant model for HVAC temperature prediction. Thus, the Extra Tree regressor was selected as the bagging model in this study. XGBoost is built based on parallel tree boosting and is designed for high efficiency. The model has been widely used for supervised learning problems including HVAC analysis [START_REF] Chakraborty | Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold[END_REF][START_REF] Divina | A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings[END_REF].

Deep Learning

Deep learning is a powerful tool and is quickly gaining popularity, especially in the field of data analytics. In recent years, Deep Learning methodologies outperformed traditional learning models. It has taken predictive modeling one step further and continues to revolutionize numerous fields through its strength in predictive modeling applications. Common applications of deep learning include image recognition, speech recognition, and computer vision applications [START_REF] Manic | Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting[END_REF]. Deep learning refers to a collection of machine learning algorithms that take a deeper approach to discovering knowledge [START_REF] Fan | A Short-Term Building Cooling Load Prediction Method Using Deep Learning Algorithms[END_REF]. It transforms the input data multiple times in either a linear or non-linear manner before deriving an output. Conversely, most conventional machine learning algorithms tend only to translate the input data once or twice before producing an output, resulting in a shallower model incapable of modeling some more complex systems.

In this section, the adopted deep learning models for this study are briefly presented: Convolutional Neural Networks (CNN), and Long Short Term Memory (LSTM). These algorithms are selected in this paper because they are relevant to the considered application and the specific case study discussed later.

Long Short-Term Memory (LSTM) Long short-term memory (LSTM) networks are a type of Recurrent Neural Network (RNN) capable of learning order dependence in sequence prediction problems. Unlike standard feedforward neural networks, LSTM has feedback connections. LSTMs are typically used in generative models, especially in natural language processing tasks. However, LSTM networks are very well suited to classifying, processing, and making predictions based on time series data (Sülo, Keskin, Dogan, & Brown) & (Xu, Chen, Wang, Guo, & Yuan, 2019). These models can capture features and remember them over time and therefore they were considered in this investigation.

Convolutional Neural Networks (CNN)

The convolutional neural network (CNN) is a type of artificial neural network which has so far been most used for analyzing images. Although image analysis has been the most common application for CNN's, they can be used for any type of classification problem (DeepLizard, 2017). CNN's have been designed to learn spatial hierarchies of features automatically and adaptively, from low-to-highlevel patterns [START_REF] Yamashita | Convolutional Neural Networks: An Overview and Application in Radiology[END_REF]. A CNN is typically composed of three types of layers: convolution, pooling, and fully connected layer. The convolution and pooling layers perform the feature extraction, while the fully connected layer maps the extracted features into a final output, such as a classification [START_REF] Yamashita | Convolutional Neural Networks: An Overview and Application in Radiology[END_REF]. CNN has been proven effective in time series problems [START_REF] Krizhevsky | ImageNet Classification with Deep Convolutional Neural Networks[END_REF] and is thus considered in this study.

Case Study -BCIT AHU-07

To reduce the ecological footprint of the British Columbia Institute of Technology's Burnaby campus, various initiatives have been put in place. Among these initiatives is a larger goal of reducing the greenhouse gas emissions at BCIT by 33% by the year 2023 (British Columbia Institute of Technology, 2021). The purpose of this case study is to contribute in a meaningful way to meeting this goal.

HVAC System Structure and Components

The British Columbia Institute of Technology, British Columbia, Canada (BCIT) is a post-secondary institute with a total of five campuses and 62 buildings. The HVAC system being looked at is owned and operated by BCIT -The Burnaby Campus. In this study, a single Air Handling Unit (AHU-07) located on the fourth floor of the North-East 1 (NE01) building of BCIT is analyzed. The building mainly consists of offices, classrooms, washrooms, and mechanical rooms, with a gross floor area of 20,076.88 m 2 . The building zones temperature is controlled by a forced-air heating system. A total of 10 AHUs serve the four floors. AHU-07 serves 7 zones on the fourth floor: 6 classrooms and 1 office.

Figure 1 shows the schematic diagram of AHU-07 with the monitored variables. Outdoor air is mixed with air returning from the seven served zones. The composition of the mixture is controlled by three dampers regulating the percentages of air exhausted from the system, entering the system, and recirculating in the system. The mixed air is cooled or heated when passing through the cooling and heating coils. The heating coil uses hot water and is connected to the boiler. The amount of water flowing through the heating coil is controlled by a valve. Pressure in the AHU system is maintained by the return and supply fans. A description of the monitored variables and the role they play in the overall system is provided in Table 1. These variables are the input variables for the predictive models as will be detailed later. Detailed technical information of the monitored equipment can be found through ASHRAE & Consulting-Specifying Engineer (2019). The study being conducted was designed to investigate the impacts of AHU parameters on the supply air temperature at temperature

Data Collection

Over the last few years, the facilities division at BCIT has established a modern and sophisticated building management system (BMS). The BMS interface and dedicated archival equipment have enabled the precise monitoring and control of the HVAC systems at BCIT. The data from the various system sensors is recorded, archived and can be extracted. Minimal campus occupancy is being observed in the years 2020 and 2021 due to the COVID-19 pandemic. Thus, data from these two years was not considered to be useful for this predictive modeling application. Therefore, the data used for this study is from the period of January 2016 -December 2019. This represents a total of four years and about 360,000 data points per system component. Each row of this data corresponds directly to the state of the system, and each row is separated by a 15-minute time interval. Using ML and DL data mining techniques, patterns can be found, and system trends and correlations can be derived from this data. Through closer inspection of these trends, it is possible to eliminate some redundant system components when developing models of the system.

Application

The application framework of the prediction models to the BCIT case study is composed of three steps: Data collection and preparation, model development, and performance evaluation. The data collection step has been discussed above. The performance evaluation is detailed in the next section. The data preparation and algorithm development are discussed and detailed below.

Data Preparation

Data preparation refers to the process of constructing relevant features and data sets as model inputs by cleaning and transforming the raw data. In the presented case study, all equipment and sensor data were extracted from the Building Management System (BMS). In total there were 19 features and this number was reduced to 12 features that are meaningful to the Supply Air Temperature (SAT) prediction (see table 1). Return Air Temperature and Return fan speed were dropped due to high correlations with other features.

The considered case study is a campus where the HVAC system is working according to a schedule. The latter is based on operational rules that vary depending on the day of the week (weekday/weekend) and the hour of the day in addition to Holidays. The SAT was affected by this operation schedule. Figure 2 illustrates the variation of the SAT across the hours of the day for the four considered years. A variance in SAT across the 24h can be observed.

The figure shows a constant SAT between 11:00 pm and 4:00 am. That is when the HVAC system is off. The system is programmed for an Optimal Start at 4:00 am during weekdays and that extends over two hours.

Figure 2 The change in Supply Air Temperature per hour

In an attempt to capture the time dependency in the model, the parameters that represent the seasonality including the month of the year, the hour of the day, and the day of the week (weekday/weekend) are also utilized as inputs. Table 2 summarizes the schedule-related variables defined as input features for the prediction models. The data was split into three subsets to ensure the accuracy of the models. For all five architectures, the first three years were used as the training data and the last year was used as testing data. A validation set was needed to ensure that the models are unbiased and to select the best iteration of the models based on the mean squared error (MSE). Historical HVAC features are known to impact the SAT of the AHU system. Hence a sliding window algorithm was employed for the sampling process to efficiently train the considered models.

The first training sample of the input variable "x" is constructed as a matrix of dimension 𝑊𝑊 × 𝐹𝐹 , where 𝑊𝑊 is the window length (W=n*15 min, if n=4 then W=1h) and 𝐹𝐹 is the number of features (26 features). Then the window slides by 1 step (15 min) and the readings from the time step i+1 make the second sample and so on (see figure 3). The length of this sliding window determines the influence of past events in the prediction (e.g. 15 min, 30 min, 1h, etc.).

Figure 3 Sliding windows sampling process Considering 15 min step, there is an overlap between the sliding windows, and a reading from a single time step belongs to multiple windows. If a total of N rows of data was considered, the final training dataset shape is a matrix [N, n, 26]. For each sample (window of length W), the model predicts the supply air temperature for T+i (variable "y") where i is the horizon response in the feature. Each model was developed to effectively predict the temperature of supply air at two different forecast responses: T + 1 and T+2 (15 and 30-min in the future). A number of 128 consecutive samples generated by the sliding windows technique are placed in a group referred to as the batch. The learning takes place one batch at a time. A model is always trained only on the current batch.

Predictive Models Development

To develop and select the most effective predictive model, various algorithms including ExtraTree, XGboost, LSTM, and CNN are employed. These algorithms were implemented on an open-source Python™-based dedicated machine learning package. Python 3.9 programming language and the Keras deep learning framework were used. To ensure that the optimal model is selected, we evaluated the following five models: • Model1 -Extra Tree: Used classical Extra Tree Machine learning model. This model was selected as the bagging method-based tree model.

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Model2 -XGBoost: Used XGBoost model to predict the Supply Air Temperature as the boosting methodbased tree model.

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Model3-LSTM: Used an LSTM layer and a dense layer to build the model. This model has shown better performances over other RNN models tested in this application. It allows better control of which features will be useful for the predictions and avoids the vanishing gradient problem when larger sequences will be considered to build a digital twin of the system.

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Model4 -CNN: Used two CONV1D filters and 1 dense layer • Model5 -CNN+LSTM: Considering that CNN can learn local features and can reduce the number of parameters and that LSTM can capture variation in long/short term dependencies, a hybrid method based on LSTM and CNN is used to benefit from the potentials of both models. In this proposed model the LSTM layer supports sequential dependencies in the input data, while the CNN layers inform the model by extracting the effective features using multiple filters. Two CNN layers and one LSTM layer are used for building the model. The loss function is MSE, the optimizer is Stochastic Gradient Descent (SGD), and the metrics is MAE.

Results and Discussion

In this section, predictions obtained by implementing the models' framework developed above on the BCIT AHU system are discussed. The purpose of this investigation is first, to assess which technique is more suitable in predicting HVAC parameters and second, to select the optimal model settings to predict the Supply Air Temperature. The selected prediction model will be used to adjust the equipment setting of an AHU in real-time without human involvement and preset rules. The equipment response time is generally very short and a 15-min to 30-min ahead forecast should be sufficient. Since the size of the time delay of input variables (sliding window) and the forecast horizon (T+1, T+2, etc.) have a significant impact on the prediction model accuracy, different combinations of the two parameters are set to analyze their influence. The lookback time delay was parameterized and sliding windows of time-steps 1, 2, 4, 6, and 8 (8 windows = 2 hours) were considered. The models were used to predict the SAT for 15 and 30 minutes ahead forecast. A simple regression algorithm was also tested. The model assumes that the SAT at T+1 and T+2 is equal to the temperature at T for the 15 and 30 minutes ahead forecast. Figures 4 &5 show the performance of the different architectures on the supply air temperature test data. They show a snapshot of the prediction result of 1-year from January to December 2019 with 15-and 30-minute ahead forecast and 1-h lookback window. It is noticed that the overall prediction performance of all models is comparable.

Overall, all models are able to predict the 15min-ahead and the 30min-ahead Supply Air Temperature within ±0.5°C and ±0.8°C error respectively. Although the considered ML and DL models are able to predict the SAT with an acceptable error, the authors examined the achieved accuracy differences. The model sensitivity is important because this investigation is part of a bigger study aiming to build a digital twin of a complex HVAC system with all served zones and with extended future time predictions.

𝑅𝑅𝑀𝑀𝑅𝑅𝑀𝑀 = � ∑ (𝑦𝑦 𝚤𝚤 �-𝑦𝑦 𝑖𝑖 ) 2 𝑛𝑛 𝑖𝑖=1 𝑛𝑛 (2)
where 𝑦𝑦 𝑖𝑖 and 𝑦𝑦 𝚤𝚤 � are the actual and predicted values, and n is the total number of observations. MAE and RMSE are commonly used on time series predictions [START_REF] Galicia | Multi-step forecasting for big data time series based on ensemble learning[END_REF][START_REF] Fan | Deep learning-based feature engineering methods for improved building[END_REF]. They provide the user a straightforward way to quantify the prediction error because the forecast error is expressed in the same units of the variables to be predicted. MAE measures the average magnitude of the errors in the prediction set. RMSE gives a relatively high weight to the large errors since errors are squared before they are averaged. The lower value of MAE and RMSE implies higher accuracy of the considered model. MAE and RMSE obtained on the validation and test sets for different historical sliding windows are detailed in Appendix A1 and A2. For 15-min ahead prediction, the lowest errors were observed for a sliding window of 1h. the lookback window size does not have a significant impact on the prediction except for when the lookback window is set to 15-min. A 15-min lookback window shows less good predictions for all the studied models. For the 30-min ahead prediction, a lookback window of 1.5h resulted in the optimal temperature forecast across models. The simple regression model, ML and DL models' best performances at 1h and 1.5h lookback windows for 15-min and 30-min ahead predictions, respectively, are shown in Tables 3 &4. The simple regression model has the lowest accuracy compared to all other models. This proves the need for more elaborate ML and DL models that learn time dependency and how the SAT evolves over time. The MAE for the five ML and DL models' predictions is less than 0.6 ℃ for 15-min ahead prediction and less than 0.8°C for 30-min ahead prediction. Hence, the approach, data, and models considered for this investigation are proven to provide a reliable prediction of the supply air temperature, considering that the sensors' overall uncertainty is around 0.3 °C. The CNN+LSTM relatively outperforms the other models on test data with an improved prediction of 13% to 29% and 15% to 24% on 15-min and 30-min ahead forecast targets respectively. Looking at the errors' values, it is noticed that RMSE errors are higher than MAE errors for both the validation and test sets for all the studied models. This refers to the existence of large errors values in the data set. A closer look at the large errors showed that the models are less accurate in predicting sudden changes in the predicted temperature. This will be investigated in future work. The results show that 15-min and 30-min ahead Supply Air Temperature of an AHU system can be accurately predicted using a designed deep learning model. Hence the study provides the framework to build a predictive data-based model -a digital twin that would include multiple AHU systems and their served rooms in a given building. The digital twin model would be used to simulate and generate the optimal settings of the HVAC systems that would imply a reduction in energy consumption and an improvement of the occupant comfort level. This will be addressed in future work.

Figure 1

 1 Figure 1 Schematic Diagram of AHU System

Figure 4 -

 4 Figure 4 -15min ahead and 1-h sliding window predicted temperature for the five Models

Table 1 AHU7

 1 Parameters description

	Variable label	Description	Unit
	AHU7_RESET	Cooling Coil Valve	% open
	AHU7_HCV	Hot Water Valve	% open
	AHU7_HC_SWT Hot Water Supply	°C
		Temperature
	AHU7_HC_RWT Hot Water Return	°C
		Temperature
	AHU7_MAD_FB Dampers position	% open
	AHU7_HC_SAT	Supply Air Temperature	°C
	AHU7_MAT	Mixed Air Temperature	°C
	AHU7_RAT	Return Air Temperature	°C
	AHU7_SF_SPD	Supply Fan Speed	% max
			speed
	AHU7_EF_SPD	Return Fan Speed	% max
			speed
	AHU7_OAT	Outside Air Temperature	°C
	AHU7_P1_S	Hot Water Pump	A

Table 2

 2 Schedule related features

	Input variable	Description	N°	of
			Inputs
	Month of the year Month of the recorded data 12
	Day of week	Weekday vs Weekend	2
	Hour of the day	Day time, evening, night	3

Table 3 -

 3 15 min Prediction Error [°C] with a sliding window of 4 (1h)When designing smart buildings, energy optimization and user comfort are key performance indicators. Hence, it is important to develop accurate and reliable HVAC systems that optimize energy and improve comfort. The primary purpose of this study was to propose a reliable data-driven model that represents AHU systems. Five algorithms were implemented, over a real data set, to predict the supply air temperature at a BCIT building.

	Model		Validation Set	Test Set
			RMSE	MAE	RMSE	MAE
	Simple Regression		-	-	1.1043	0.5131
	ExtraTree		0.6020	0.3084	0.8187	0.4677
	Xgboost		0.5838	0.3189	0.8789	0.4895
	LSTM		0.6763	0.4113	0.9340	0.6028
	CNN		0.6688	0.3686	0.8177	0.4715
	CNN LSTM	+	0.5685	0.3119	0.7287	0.4058

Acknowledgment

This work was supported by the Seed Fund and the Institute Research Fund granted by the British Columbia Institute of Technology.

Appendix