Deron Thomas C Adam
email: thomascadam@gmail.com

E Burkepile

Sally J Holbrook

Robert C Carpenter

Joachim Claudet

Charles Loiseau

Lauric Thiault

Andrew J Brooks

Libe Washburn

Russell J Schmitt

Deron E Burkepile

nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts

Keywords: coral reef, phase shift, macroalgae, nutrient pollution, nitrogen, sewage, topdown, bottom-up, Turbinaria ornata, stable isotopes, δ 15 N, herbivory

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Nutrient pollution is a major anthropogenic force altering coastal ecosystems worldwide [START_REF] Carpenter | Nonpoint pollution of surface waters with phosphorus and nitrogen[END_REF][START_REF] Howarth | Coastal nitrogen pollution: A review of sources and trends globally and regionally[END_REF]. Anthropogenic inputs of nitrogen and phosphorous can facilitate blooms of harmful algae, reduce water clarity, alter food webs, and deplete oxygen leading to dead zones [START_REF] Boesch | Challenges and opportunities for science in reducing nutrient over-enrichment of coastal ecosystems[END_REF], ultimately disrupting fisheries and harming human health [START_REF] Anderson | Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management[END_REF]. These adverse effects of excess nutrients on coastal ecosystems are likely to be exacerbated in coming decades by continued changes in land-use as well as altered precipitation regimes and ocean warming due to climate change [START_REF] Altieri | Climate change and dead zones[END_REF][START_REF] Gobler | Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans[END_REF][START_REF] Sinha | Eutrophication will increase during the 21st century as a result of precipitation changes[END_REF].

Excess nutrients can be especially harmful to oligotrophic systems such as coral reefs.

High levels of nutrient loading can lead to persistent blooms of algae that can dramatically alter the structure and function of reef ecosystems [START_REF] Naim | Seasonal responses of a fringing reef community to eutrophication (Reunion Island, Western Indian Ocean)[END_REF][START_REF] Loya | The coral reefs of Eilat-past, present and future: three decades of coral community structure studies[END_REF][START_REF] Lapointe | Macroalgal blooms on southeast Florida coral reefs: II. Cross-shelf discrimination of nitrogen sources indicates widespread assimilation of sewage nitrogen[END_REF].

Even moderate nutrient loading can favor algae and heterotrophic filter feeders that compete with corals and other reef building taxa [START_REF] Fabricius | Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis[END_REF], Rice et al. 2020), thereby impeding coral recovery following a disturbance [START_REF] Graham | Predicting climate-driven regime shifts versus rebound potential in coral reefs[END_REF][START_REF] Macneil | Water quality mediates resilience on the Great Barrier Reef[END_REF]. Nitrogen (N) loading can be particularly harmful to coral reefs, as excess N can negatively impact coral growth and survival in addition to fueling algal growth [START_REF] D'angelo | Impacts of nutrient enrichment on coral reefs: New perspectives and implications for coastal management and reef survival[END_REF], Shantz and Burkepile 2014[START_REF] Morris | Nutrient availability and metabolism affect the stability of coral-symbiodiniaceae symbioses[END_REF]. Thus, understanding the sources and impacts of anthropogenic N is critical for conservation and management of coral reef ecosystems.

In some cases, N pollution can be traced to a specific point source (e.g., sewage outfall) (Smith et al. 1981[START_REF] Dailer | Using δ15N values in algal tissue to map locations and potential sources of anthropogenic nutrient inputs on the island of Maui, Hawai'i, USA[END_REF]. However, most N input to coastal ecosystems is derived from diffuse nonpoint sources [START_REF] Carpenter | Nonpoint pollution of surface waters with phosphorus and nitrogen[END_REF]). On coral reefs, N from nonpoint sources tends to be delivered in surface water runoff during episodic rainfall events and via discharge of submarine groundwater. After reaching the ocean, N-rich water is then transported and mixed via local hydrodynamic features. Small-scale hydrodynamics also influence rates of nutrient uptake by modifying boundary-layer conditions of benthic organisms [START_REF] Carpenter | Measurements of primary productivity and nitrogenase activity of coral reef algae in a chamber incorporating oscillatory flow[END_REF]. Thus, the movement and ultimate fate of anthropogenically-derived N in nearshore ecosystems are complex and will depend on many interacting factors, including the location of N sources, the timing of rainfall, and the hydrodynamic environment [START_REF] Brocke | Organic matter degradation drives benthic cyanobacterial mat abundance on Caribbean coral reefs[END_REF].

A more complete understanding of the impacts of N pollution on nearshore ecosystems requires knowledge of the spatial and temporal patterns of enrichment and how the physical environment modulates these patterns. In many locations, water column nutrients are measured periodically in mandated water quality monitoring programs. These programs can provide valuable information on decadal-scale trends and large-scale spatial patterns of nutrient availability [START_REF] De'ath | Water quality as a regional driver of coral biodiversity and macroalgae on the Great Barrier Reef[END_REF][START_REF] Duprey | Reefs of tomorrow: eutrophication reduces coral biodiversity in an urbanized seascape[END_REF][START_REF] Lapointe | Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: a 3-decade study[END_REF]. However, the coarse temporal and spatial resolution of most water column monitoring programs precludes building a mechanistic understanding of the causes and consequences of nutrient enrichment in many coastal systems. This is particularly true on coral reefs, where researchers have used N content and isotopic signatures in benthic algae to resolve patterns of N enrichment at relatively small spatial scales [START_REF] Umezawa | Fine-scale mapping of land-derived nitrogen in coral reefs by δ15N in macroalgae[END_REF][START_REF] Lin | Macroalgal bioindicators (growth, tissue N, δ15N) detect nutrient enrichment from shrimp farm effluent entering Opunohu Bay, Moorea, French Polynesia[END_REF][START_REF] Dailer | Using δ15N values in algal tissue to map locations and potential sources of anthropogenic nutrient inputs on the island of Maui, Hawai'i, USA[END_REF]. However, these studies generally have not linked enrichment patterns to benthic community dynamics or ecosystem processes (but see [START_REF] Lapointe | Nutrient thresholds for bottom-up control of macroalgal blooms on coral reefs in Jamaica and Southeast Florida[END_REF]. As a result, there is widespread disagreement regarding the levels of nutrient enrichment that are harmful to coral reefs and how important nutrients are for impacting coral reef community dynamics [START_REF] Lapointe | Simultaneous top-down and bottom-up forces control macroalgal blooms on coral reefs (Reply to the comment by Hughes et al.)[END_REF][START_REF] Szmant | Nutrient enrichment on coral reefs: is it a major cause of coral reef decline?[END_REF][START_REF] D'angelo | Impacts of nutrient enrichment on coral reefs: New perspectives and implications for coastal management and reef survival[END_REF][START_REF] Arias-González | A coral-algal phase shift in Mesoamerica not driven by changes in herbivorous fish abundance[END_REF][START_REF] Bruno | Climate change, coral loss, and the curious case of the parrotfish paradigm: why don't marine protected areas improve reef resilience?[END_REF]).

Here we quantify the spatial and temporal patterns of N availability on coral reefs around the South Pacific island of Moorea, French Polynesia. The coral reefs around Moorea have undergone multiple cycles of disturbance and recovery over the past four decades. While the oligotrophic fore reef has shown remarkable resilience to disturbances [START_REF] Adjeroud | Recurrent disturbances, recovery trajectories, and resilience of coral assemblages on a South Central Pacific reef[END_REF], 2018, Holbrook et al. 2018[START_REF] Adjeroud | Recovery of coral assemblages despite acute and recurrent disturbances on a South Central Pacific reef[END_REF], the reefs within the lagoons have exhibited contrasting dynamics, with some reefs shifting from coral-to macroalgae-dominated communities [START_REF] Schmitt | Experimental support for alternative attractors on coral reefs[END_REF]. Nutrient input could be a major driver of algal proliferation within the lagoons, but to date, no studies have evaluated the spatial or temporal patterns of enrichment or how those patterns relate to benthic community dynamics. Here, we combine N content and stable isotopes (δ 15 N) in the tissue of the macroalga Turbinaria ornata, together with empirical models of nutrient discharge and time series data on the dynamics of benthic algae and coral to address the following questions: 1) What are the spatial patterns of N enrichment and how do these patterns vary across seasons with different levels of rainfall and wave forcing? 2)

To what extent are spatial patterns of N enrichment related to anthropogenic inputs? and 3) Are recent increases in macroalgae related to N enrichment?

Methods

Site Description

Moorea, French Polynesia (17 o 30′S, 149 o 50′W) is a small (~ 135 km 2 ) volcanic island located near the larger island of Tahiti in the central South Pacific. The ocean surrounding Moorea is oligotrophic, with levels of dissolved inorganic nitrogen (DIN) often at or below detection limits (nitrate plus nitrite in the top 25 m of the water column: 0.38 +/-0.84 uM; mean +/-SD) [START_REF] Alldredge | MCR LTER: Coral Reef: Water Column: Nearshore Water Profiles, CTD, Primary Production, and Chemistry ongoing since[END_REF]. The island is surrounded by a well-developed barrier reef located ~ 1 km offshore that is intersected by 12 reef passes that connect the shallow protected lagoons inside the barrier reef to the open ocean (Fig. 1a). Shoreward of the reef crest is a distinct back reef habitat that is less than 3 m deep and is dominated by patch reefs separated by sand, coral rubble, and reef pavement. Directly adjacent to land are shallow fringing reefs that in some locations are separated from shallow back reef habitats by a channel (> 10 m deep) that connects to a reef pass.

Tidal amplitudes are small (< 30 cm), and circulation within the lagoon is primarily driven by wave forcing across the reef crest, which creates flow into the lagoon and out of the reef passes [START_REF] Hench | Episodic circulation and exchange in a wave-driven coral reef and lagoon system[END_REF][START_REF] Leichter | Biological and physical interactions on a tropical island coral reef: Transport and retention processes on Moorea, French Polynesia[END_REF]. Wave forcing is largely driven by long period swells originating from storms thousands of kilometers away. As a result, wave exposure (and thus circulation patterns) varies seasonally and among sides of the island concurrent with seasonal differences in storm activity in the northern and southern hemispheres. The west and east shores receive the most wave energy, with peak wave energy occurring during winter in the southern hemisphere (hereafter 'austral winter') from May to October. In contrast, peak wave energy on the north shore occurs from November to April during winter in the northern hemisphere ('austral summer') [START_REF] Edmunds | Landscape-scale variation in coral recruitment in Moorea, French Polynesia[END_REF]). Moorea's climate is characterized by a warm, wet season from November to April and a cooler and drier season from May to October.

Due to the steep topography, the island's ~ 17,000 residents are concentrated along its 60 km coastline, with population centers located in several large valleys. Rapid population increases in recent decades have coincided with substantial land clearing for agriculture and urbanization [START_REF] Walker | Perceptions of environmental change in Moorea, French Polynesia: the importance of temporal, spatial, and scalar contexts[END_REF]. In contrast to ocean waters, DIN concentrations can be very high (> 100 uM) in groundwater and streams [START_REF] Knee | Assessment of submarine groundwater discharge (SGD) as a source of dissolved radium and nutrients to Moorea (French Polynesia) coastal waters[END_REF][START_REF] Haßler | Provenance of nutrients in submarine fresh groundwater discharge on Tahiti and Moorea, French Polynesia[END_REF]. Major sources of N to the lagoons include treated and untreated human sewage, animal waste from livestock, and fertilizer from agriculture and landscaping [START_REF] Haßler | Provenance of nutrients in submarine fresh groundwater discharge on Tahiti and Moorea, French Polynesia[END_REF].

What are the spatial patterns of N enrichment and how do these patterns vary across seasons with different levels of rainfall and wave forcing?

To quantify seasonal variation in nutrient delivery mechanisms, we obtained daily rainfall and wave data. Rainfall data were from a meteorological station in Faaa, Tahiti (Global Historical Climatology Network ID:FP000091938), ~ 15 km east of Moorea. We calculated a 30-day moving sum for October 2015 through August 2016, which corresponded with our high resolution N sampling, and plotted the LOESS smoothed values together with the monthly means during the different sampling intervals using the R package ggplot2 [START_REF] Wickham | ggplot2: Elegant Graphics for Data Analysis[END_REF].

Wave data have been collected continuously at three sites on the exposed fore reef of Moorea (one site on each of the island's three sides) since 2005 as part of the MCR LTER project (http://mcrlter.msi.ucsb.edu/cgi-bin/showDataset.cgi?docid=knb-lter-mcr.30; Washburn 2018). At each site, data from bottom-mounted Wave & Tide recorders (SBE 26plus; Sea-Bird Electronics, Inc., Bellevue, WA, USA) and bottom-mounted Acoustic Doppler Current Profilers (Sentinel ADCPs: Teledyne RD Instruments, Poway, CA, USA) were used to estimate significant wave height (Hs) and dominant wave period (Tp) at 2 h sampling intervals. Daily averages of Hs and Tp were then calculated and total wave power (P) per linear meter of reef was estimated using deep-water approximations as P = g 2 Hs 2 Tp/32 π where  is seawater density (1025 kg m -3 ) and g is the acceleration of gravity (9.81 ms -2 ).

We used ADCPs as the primary instrument to quantify patterns of wave forcing on the three shores of the island because ADCPs had fewer missing values compared to the SBE 26s during our study; to obtain a complete record we used data from the SBE 26s when ADCP data were missing. To ensure that data from the two instruments were comparable, we used the full time series of wave data in least squares linear regressions to estimate the site-specific log-log relationships between P as measured by the SBE 26s and ADCPs. The relationships were strong (r 2 ranging from 0.86 to 0.90), and missing ADCP data were gap filled with the predicted values based on these site-specific relationships. We then calculated daily mean wave power for each shore of the island during each 90-day period preceding our sampling efforts. We focused on a 90-day period assuming that N tissue content in the thalli of Turbinaria ornata (hereafter, 'Turbinaria'), the species of macroalgae we used to map N availability, reflects the nutrient environment during a period of up to three months prior to its collection. This estimate is based on the growth rates and longevity of Turbinaria thalli [START_REF] Davis | Associational refuge facilitates phase shifts to macroalgae in a coral reef ecosystem[END_REF], Schmitt et al. 2019).

However, because little is known about N storage and turnover in Turbinaria we also describe rainfall and wave conditions during the 45-day period prior to Turbinaria collection (Appendix S1: Figs. S1 andS2) since these two time scales (45 and 90 days) are likely to bracket the period of influence.

To map N availability in the lagoons of Moorea, we collected samples of Turbinaria at ~ 180 sites around the island during three different sampling periods, corresponding with different rainfall and wave regimes (January 2016, May 2016, and August 2016) [START_REF] Adam | MCR LTER: Coral Reef: Nitrogen tissue content in Turbinaria ornata: 2016 data in support of Adam et al[END_REF]. Sites were at least 0.5 km apart and were spaced to maximize coverage of the different reef habitats within the lagoons, including the fringing reefs, mid-lagoon/back reef, reef crest, reef passes, and bays (Fig. 1a). Like other macroalgae, Turbinaria responds to N pulses by storing surplus N [START_REF] Schaffelke | Short-term nutrient pulses as tools to assess responses of coral reef macroalgae to enhanced nutrient availability[END_REF]) and consequently N tissue content is believed to be an excellent time-integrated indicator of N availability [START_REF] Atkinson | C: N: P ratios of benthic marine plants[END_REF][START_REF] Fong | Nutrient concentration in tissue of the macroalga Enteromorpha as a function of nutrient history -An experimental evaluation using field microcosms[END_REF][START_REF] Shantz | Fish-derived nutrient hotspots shape coral reef benthic communities[END_REF]. Sampling was conducted over ~ 3 weeks during each of the three sampling periods; due to logistic constraints, some sites were not sampled in all three sampling periods (January n = 184, May n = 171, August n = 173). At each of the sites, we collected thalli from 10 different patches of Turbinaria across an area of ~ 500 m 2 . Samples were immediately placed on ice and transported to the laboratory. One blade from each of 10 thalli was sampled at 5 cm below the apical tip. Blades were scrubbed of epiphytes and rinsed with fresh water before being dried at 60° C to a constant weight and ground to a fine powder. Total N content was determined via elemental analysis using a CHN Carlo-Erba elemental analyzer (NA1500) at the University of Georgia, Center for Applied Isotope Studies.

In addition to analyzing N content of algal tissue, we also conducted stable isotope analyses to help determine the sources of N. The use of naturally occurring stable isotopes of N ( 15 N: 14 N, expressed as δ 15 N) is particularly useful for distinguishing between natural and sewage-derived N because natural sources generally have low signatures while sewage-derived N is high in 15 N (with δ 15 N values ranging from ~ 5% to 20%) (Risk et al. 2009[START_REF] Kendall | Tracing anthropogenic inputs of nitrogen to ecosystems[END_REF]). In the lagoons of Moorea, N likely comes from a mix of oceanic and terrestrial sources, the latter including synthetic and organic fertilizers, livestock, and human sewage. Because synthetic fertilizers tend to have δ 15 N signatures that are similar to or lower than natural sources (generally ranging from -4 to 4%) [START_REF] Dailer | Using δ15N values in algal tissue to map locations and potential sources of anthropogenic nutrient inputs on the island of Maui, Hawai'i, USA[END_REF], elevated δ 15 N values would indicate that human sewage or animal waste are important sources of N but would not rule out the importance of fertilizers or other sources. Isotopic analysis on dried and ground algal tissue was conducted using a Thermo Finnigan Delta-Plus Advantage isotope mass spectrometer with a Costech EAS elemental analyzer at the University of California, Santa Barbara, Marine Science Institute Analytical Laboratory. Spatial patterns of N enrichment (total N expressed as % of dry weight) were visualized using ordinary kriging with a spherical variogram model as implemented in the R package 'kriging' [START_REF] Olmedo | kriging: Ordinary Kriging. R package version 1[END_REF]. To test whether enrichment patterns were consistent across sample periods at the scale of individual sites we used linear correlation, with separate correlations conducted for each side of the island. Finally, we used ANOVA to test whether N levels varied among our three sample periods. Because of significant spatial autocorrelation among sites, we analyzed the mean % N on each of the island's three shores as a conservative test, using data only from the set of 155 sites that were surveyed in all three sample periods.

Visual inspection of N interpolations suggested that spatial patterns of enrichment were related to wave power (see Results). Therefore, we used the results of the correlation analysis described in the previous paragraph to test whether temporal consistency depended on variation in wave forcing. Specifically, we used Pearson's correlation to test whether the correlation coefficient describing the temporal correlation in N enrichment (% N) was related to differences in mean wave power (specific to each shore) across the different sample periods.

To what extent are spatial patterns of nitrogen enrichment related to anthropogenic inputs?

To test whether spatial patterns of N enrichment were associated with anthropogenic N inputs into the lagoons, we modeled the distribution of N from sewage and agriculture based on spatially explicit data from the urban planning office of French Polynesia and the Institut de la Statistique de la Polynésie Française (ISPF). We mapped the relative input of nutrients from agriculture in the lagoon using a three-step procedure. First, for each catchment area (n = 119), we calculated the total farmed area based on data available from the urban planning office of French Polynesia, and used it as an estimate of "agriculture intensity". Second, we estimated the maximum diffusion potential of each catchment (MDPw) using the following formula: MDPw = a * Aw + b where Aw is the size of the catchment w in square km and a and b are the slope (a= 0.0022) and intercept (b= 15.82) of the linear relationship, obtained from [START_REF] Adjeroud | Spatial patterns in biodiversity of a fringing reef community along Opunohu Bay, Moorea, French Polynesia[END_REF]. This approach is consistent with global [START_REF] Burgers | Size relationships of water discharge in rivers: scaling of discharge with catchment area, main-stem length and precipitation[END_REF]) and local [START_REF] Brown | Tracing the influence of landuse change on water quality and coral reefs using a Bayesian model[END_REF]) studies highlighting the positive correlation between Aw and nutrient discharge to the marine environment, and has been widely used in cumulative impact assessments [START_REF] Halpern | A global map of human impact on marine ecosystems[END_REF][START_REF] Ban | Cumulative impact mapping: Advances, relevance and limitations to marine management and conservation, using Canada's Pacific waters as a case study[END_REF][START_REF] Micheli | Cumulative human impacts on Mediterranean and Black Sea marine ecosystems: assessing current pressures and opportunities[END_REF]. Third, we applied a kernel density function at each river mouth that linearly decays "agriculture intensity" of the associated watershed basin from the river mouth (maximum value) to the MDPw (null value) to disperse these watershed-scale values onto Moorea's reefs. Then, values were re-scaled from 0 to 1 in order to represent the relative level of nutrient input from agriculture with the final layer having 5 m resolution. Kernel estimation was completed using the Heatmap plugin in QGIS v2.18.14 (QGIS Development Team 2016).

We mapped the relative sewage discharge by combining household density along the coast and the water treatment system used in each individual household (n = 8,614). Each treatment system was assigned a value based on its overall environmental impact (no treatment = 2; treatment with a sump = 1; and treatment with a plant = 0; data from ISPF) which was subsequently used as a weighting component of household density. We then extrapolated onto the reef using linear decay, assuming that pollution from sewage discharge spread linearly into the lagoon from the source. Kernel estimation was completed using the Heatmap plugin in QGIS v2.18.14 (QGIS Development Team 2016).

To test whether spatial patterns of N enrichment were related to anthropogenic inputs we used a two-step approach. First, we tested whether the isotopic signature (δ 15 N) of Turbinaria was related to the modeled distributions of N from agriculture and sewage. We found that δ 15 N signatures were well predicted by modeled sewage input, particularly during the rainy season, suggesting that δ 15 N is a good proxy for anthropogenic nutrients (see Results). Thus, to test whether the overall N enrichment patterns were driven by anthropogenic N input we tested for a relationship between δ 15 N and total N. Because we had no a priori expectation of the functional forms of the relationships between anthropogenic nutrient input, isotopic signature (δ 15 N), and total N, we used boosted regression tree (BRT) analyses; BRTs are a powerful machine learning approach to model fitting that use the data to characterize the relationships between variables (Elith et al. 2008, De'Ath and[START_REF] De'ath | Water quality as a regional driver of coral biodiversity and macroalgae on the Great Barrier Reef[END_REF]. BRT models were conducted using the 'dismo' package in R [START_REF] Hijmans | dismo: Species Distribution Modeling[END_REF]) and parameterized according to [START_REF] Elith | A working guide to boosted regression trees[END_REF]. Island shore was included in all models. In addition, to explore whether models had greater prediction power for some shores compared to others we also created separate models for each island shore.

To aid in data interpretation we show both the fitted relationships from the BRT analyses as well as correlation plots of the raw data.

Are recent increases in macroalgae related to nitrogen enrichment?

To track long-term variation in N availability to benthic organisms, we collected data on N content of Turbinaria tissue annually between 2007 and 2013 as part of the Moorea Coral Reef Long-Term Ecological Research (MCR LTER) program (http://mcrlter.msi.ucsb.edu/cgibin/showDataset.cgi?docid=knb-lter-mcr.20; Carpenter 2018). This time frame captures the period when macroalgae increased markedly at some locations in the lagoons. We sampled 10 Turbinaria from 18 locations on six cross-shore transects (corresponding to the six core MCR LTER sites), with samples taken on the fringing reef, back reef, and reef crest during the last week of May or first week of June. Total N content was determined via CHN analysis on a model CEC 440HA organic elemental analyzer at the University of California, Santa Barbara, Marine Science Institute Analytical Laboratory. To determine whether N enrichment varied consistently among sites, habitats, and years we used ANOVA and AICc to select among models with and without interaction coefficients [START_REF] Burnham | Model selection and multimodel inference: A practical information-theoretic approach[END_REF]. For this and other linear models, we inspected model residuals to ensure the assumptions of the tests were met.

To characterize the dynamics of macroalgae and coral, we collected data annually in three habitat types (fringing reef, back reef, and fore reef) at six sites (two on each of the three sides of the island) from 2006 to 2018 as part of the Moorea Coral Reef Long-Term Ecological Research (MCR LTER) program (http://mcrlter.msi.ucsb.edu/cgibin/showDataset.cgi?docid=knb-lter-mcr.8; Carpenter 2019). Here we focus on the back reef habitat, where macroalgae increased in abundance in some locations [START_REF] Schmitt | Experimental support for alternative attractors on coral reefs[END_REF]. At each site, the dominant space holders are characterized using point contacts at 50 fixed locations on five permanent 10 m long transects (n = 10, 0.25 m 2 quadrats per transect). Macroalgae are identified to species in situ with other benthic space holders categorized to functional group (e.g., turf algae, scleractinian coral, etc.). In addition to benthic space holders, annual data on the abundance and biomass of fishes are collected at the same sites via visual surveys on four 50 m long by 5 m wide fixed transects (http://mcrlter.msi.ucsb.edu/cgibin/showDataset.cgi?docid=knb-lter-mcr.6; Brooks 2018).

To determine whether the persistent increases in macroalgae that occurred on the back reef were related to spatial patterns of N enrichment and/or herbivore biomass we tested for an association between the change in the mean percent cover of macroalgae at each of our six longterm sites and 1) mean N enrichment, and 2) mean biomass of herbivorous fishes during the time when the coral-to-macroalgae phase shifts occurred (between 2007 and 2013). Ideally, we would use a model selection framework to disentangle the multiple factors that may be driving increases in macroalgae. Yet, with only six sites this was not possible. Instead we used two separate Spearman's rank correlations to independently test for an association between N and macroalgae and herbivore biomass and macroalgae. A positive correlation between N and macroalgae would indicate that reefs that have been consistently enriched in N were more likely to experience increases in macroalgae than less enriched reefs but would not rule out the importance of other factors. Similarly, a negative correlation between herbivore biomass and macroalgae would indicate that reefs with the highest biomass of herbivores were least likely to experience increases in macroalgae.

Results

What are the spatial patterns of N enrichment and how do these patterns vary across seasons with different levels of rainfall and wave forcing?

Rainfall varied among the three sample periods, reflecting typical seasonal patterns in Moorea. During 2016, mean monthly rainfall was more than 3-fold greater prior to the May sampling, compared to the January and August sampling (January = 121 mm, May = 372 mm, August = 110 mm) due to abundant rainfall during February, March, and April (Fig. 1b).

Wave power varied greatly among the three sides of the island and exhibited different seasonal patterns reflecting differences in exposure to waves generated from storms in the southern and northern hemispheres (Fig. 1c-e). Wave power was most variable on the north shore (among period CV = 70%), intermediate on the west shore (among period CV = 40%), and least variable on the east shore (among period CV = 28%). Mean daily wave power was consistently high on the north shore prior to the January and May sampling periods but dropped precipitously in May and remained low throughout the austral winter (Fig. 1c). The result was a seven-fold decline in wave power on the north shore in the three months leading up to the August sampling relative to the previous three months. Wave power was consistently high on the west shore, with wave power exceeding the north shore during all sampling periods (Fig. 1e).

Wave power was especially high on the west shore during the austral winter, with wave power ~ 2-fold greater in the three months leading up to the August sampling compared to the threemonth period preceding the January sampling (Fig. 1e). Mean wave power on the east shore was intermediate between the north and west shores. Similar to the west shore, wave power was greatest on the east shore during the austral winter and least during the austral summer (Fig. 1d).

Nitrogen content of Turbinaria tissue revealed evidence for enrichment hotspots on all three sides of the island (Fig. 2). Algae were enriched in N near bays and large watersheds and were also frequently enriched near reef passes. In contrast, algae tended to be lower in N near the reef crest and in the mid-lagoon (Fig. 2). Enrichment patterns varied through time and were related to seasonal changes in wave power and rainfall. Algae were more enriched in N in May following the rainy season compared to January and August, which followed drier periods (ANOVA, F2,6 = 18.4, P = 0.003; Post hoc Tukey tests comparing % N in May to January and August, P < 0.01 for both). The spatial extent of enrichment appeared to be related to the wave regime. During periods of low wave forcing (e.g., the north shore during August), enrichment signals were constrained to nearshore fringing reefs and reef passes compared to periods of high wave forcing (e.g., the west shore during August) when enrichment signals extended farther into the lagoons (Fig. 2).

Enrichment patterns were more consistent throughout the year on some sides of the island compared to others. Spatial patterns of enrichment were highly consistent on the east shore, where wave forcing was least variable among seasons (Appendix S1: Fig. S3). In contrast, enrichment patterns were much more variable on the north shore where wave forcing was highly variable (Appendix S1: Fig. S4). Spatial patterns of enrichment on the west shore were intermediate in consistency compared to the other two shores (Appendix S1: Fig. S5). Pairwise correlation of algal N content among sites during different seasons was strongest when wave power was similar, as indicated by a negative association between the among-period correlation coefficient and the difference in mean wave power during the 90-day period immediately preceding sampling (Pearson's correlation, r = -0.59, P = 0.09; Appendix S1: Fig. S6).

To what extent are spatial patterns of nitrogen enrichment related to anthropogenic inputs?

Isotopic signature (δ 15 N) of Turbinaria exhibited strong spatial patterns that varied considerably among sample periods (Fig. 3b, Appendix S1: Fig. S7). During the rainy season, when anthropogenic inputs of N are likely to be highest, kernel density estimates of human sewage derived from data on household sanitation predicted well δ 15 N values in Turbinaria (Fig. 3a-e) (BRT full model cross validation correlation = 0.51; relative importance of modeled sewage = 75%). The fitted relationship between modeled sewage and δ 15 N was positive and roughly linear across most of the range of the data (Appendix S1: Fig. S8). δ 15 N was also positively related to modeled nutrients from agriculture (Appendix S1: Fig. S8), though agriculture had little predictive power compared to sewage (BRT relative importance = 7%).

Kernel density estimates of human sewage were also correlated with δ 15 N during the January and August sampling (Appendix S1: Figs. S8, S9), though the correlations were less strong (January: full model cross validation correlation = 0.48; relative importance of modeled sewage = 59%; August: full model cross validation correlation = 0.38; relative importance of modeled sewage = 30%) and the fitted relationships were highly non-linear with δ 15 N initially decreasing with predicted sewage before increasing at higher levels (Appendix S1: Fig. S8).

Model performance also varied for different sides of the island. For example, during the rainy season, modeled sewage predicted as much as 73% of the variation in δ 15 N on the east shore of the island (BRT cross validation correlation = 0.73; relative importance of modeled sewage = 100%). In contrast, modeled sewage during the rainy season only predicted 24% of the variation in δ 15 N on the west shore (BRT cross validation correlation = 0.32; relative importance of modeled sewage = 100%) and 53% of the variation in δ 15 N on the north shore (BRT cross validation correlation = 0.55; relative importance of modeled sewage = 97%). Modeled nutrients from agriculture consistently had little predictive power (BRT relative importance < 10%).

Nitrogen tissue content was positively associated with δ 15 N during all sample periods, but the relationship was particularly strong (and roughly linear across most of the range of data) during the rainy season (Fig. 3f-h; Appendix S1: Figs. S10 andS11) (BRT full model cross validation correlation = 0.61; relative importance of δ 15 N = 89%). The association between δ 15 N and total N was strongest for the north and east shores of the island. For example, during the rainy season, δ 15 N predicted 75% and 81% of the variation in total N on these shores, respectively (BRT cross validation correlation north shore = 0.75, east shore = 0.81) while predicting a more modest 35% of the variation in total N on the west shore (BRT cross validation correlation = 0.35).

Are recent increases in macroalgae related to nitrogen enrichment?

Annual sampling of Turbinaria from three habitats at our six long-term sites revealed strong spatial patterns that were consistent through time (model without interactions outperformed all other models with a delta AICc > 13). Algal N content varied strongly among habitats (ANOVA, Habitat: F2,108 = 23.3, P < 0.001) and sites (ANOVA, Site: F5,108 = 3.2, P = 0.009 (Fig. 4). Algae were enriched in N on nearshore fringing reefs, and to a lesser extent the back reef, relative to the reef crest (Fig. 4a). These differences mirror long-term differences in water column DIN over the same time period (Appendix S1: Fig. S12). In addition to these differences among reef habitats, algae also tended to be more enriched in N at the north shore sites compared to sites on the east and west shores (Fig. 4b).

Beginning at or shortly after the onset of our benthic time series in 2007, corals began to decline and macroalgae began to increase on the back reef at permanent study sites LTER 1, LTER 2, and LTER 3 (Fig. 5). By 2013 at LTER 1 and LTER 2, and 2014 at LTER 3, the cover of macroalgae exceeded coral cover (Fig. 5). Since 2014, coral cover at these sites has continued to decline or stabilize at low (< 5%) levels while the cover of macroalgae has remained high. Species composition of algal assemblages varies somewhat among sites; dominant taxa include the brown algae Turbinaria ornata, Sargassum pacificum, and Dictyota bartayresiana as well as the red alga Amansia rhodantha (Fig. 5). Over the same time period, coral cover has remained high, and macroalgae cover low at permanent study sites LTER 4, LTER 5, and LTER 6, which experience lower nutrient conditions compared to LTER 1, LTER 2, and LTER 3 (Fig. 5).

Increases in macroalgae were strongly correlated with the long-term nutrient environment, with higher N associated with greater increases in macroalgae (Spearman correlation, rs = 0.94, P = 0.02; Fig. 6a). In contrast, there was no significant association between increases in macroalgae and the mean biomass of herbivorous fishes (Spearman correlation, rs = 0.20, P = 0.71; Fig. 6b).

Higher N was also associated with coral decline, although the correlation was only marginally significant (Spearman correlation, rs= -0.77, P = 0.10; Appendix S1: Fig. S13).

Discussion

Patterns of N enrichment in the lagoons of Moorea were related to several factors, including rainfall, wave power, and distance from anthropogenic nutrient sources, particularly sewage. High resolution maps of N enrichment revealed several patterns that were consistent among the three sample periods. During all sampling periods, algae had higher N on nearshore fringing reefs compared to the mid-lagoon and reef crest. In addition, algae were consistently enriched in N near large watersheds, where inputs of N from sewage and agriculture are high.

Our long-term data indicate that areas of the lagoon that are near shore and close to major watersheds have been consistently enriched in N for at least a decade. During this time, corals have decreased and macroalgae have increased at back reef locations with high N, while reefs with lower levels of N have maintained high cover of coral and low cover of macroalgae.

Importantly, these patchy phase shifts to macroalgae have occurred despite island-wide increases in the biomass of herbivorous fishes. Similar coral-to-macroalgae phase shifts have been observed on many reefs globally (e.g., [START_REF] Hughes | Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef[END_REF][START_REF] Graham | Predicting climate-driven regime shifts versus rebound potential in coral reefs[END_REF][START_REF] Bozec | Transient grazing and the dynamics of an unanticipated coral-algal phase shift[END_REF], and are often associated with reductions in the biomass of herbivorous fishes due to overfishing. Our observation that nutrient loading, and not herbivory, is strongly associated with phase shifts in the lagoons of Moorea emphasizes that bottom-up factors, such as nutrient pollution from sewage and agriculture, can also be important drivers of coral-to-macroalgae phase shifts on tropical reefs.

Influences of rainfall and wave-driven flow on seasonal patterns of nitrogen enrichment

Many of the enrichment patterns we observed in our high-resolution sampling were consistent among seasons, yet there were also differences that were related to seasonal differences in wave forcing and rainfall. For example, overall levels of N in Turbinaria tissue were higher in May following the wet summer period compared to January and August which followed periods with less rainfall. In addition, spatial patterns of enrichment were related to wave forcing, which varied asynchronously around the island due to seasonal differences in wave exposure. During periods of low wave energy, enrichment signals were constrained to nearshore fringing reefs and embayments, while during periods of higher wave energy enrichment signals extended farther into the lagoons. The precise mechanisms driving these spatial patterns are unknown but are likely to be related to circulation since waves are the primary driver of circulation in the lagoons [START_REF] Hench | Episodic circulation and exchange in a wave-driven coral reef and lagoon system[END_REF][START_REF] Leichter | Biological and physical interactions on a tropical island coral reef: Transport and retention processes on Moorea, French Polynesia[END_REF].

Irrespective of the precise mechanism, our finding that rainfall and waves can interact to determine spatial patterns of nutrient enrichment in a lagoonal coral reef system has important implications. For example, on the north shore of Moorea where the wave climate is highly seasonal, the rainy season corresponds with a period of high wave energy. Because increased rainfall results in N enriched conditions in the lagoons and waves appear to be important for delivering N to the reef crest, the coincidence of the rainy season with a period of large waves could result in nutrient pulses to wide reaches of the lagoon that otherwise might be restricted to the fringing reefs when waves are smaller. Given that waves drive circulation patterns on many tropical oceanic islands [START_REF] Hench | Episodic circulation and exchange in a wave-driven coral reef and lagoon system[END_REF], seasonality in wave-driven circulation could be an important mechanism driving patterns of nutrient enrichment on many coral reefs.

Anthropogenic nitrogen sources

We observed the highest levels of N enrichment near areas with dense human populations and known nutrient sources, particularly sewage. In addition, isotopic signatures of N in areas of high enrichment were consistent with terrigenous rather than oceanic sources of N [START_REF] Lin | Macroalgal bioindicators (growth, tissue N, δ15N) detect nutrient enrichment from shrimp farm effluent entering Opunohu Bay, Moorea, French Polynesia[END_REF][START_REF] Dailer | Using δ15N values in algal tissue to map locations and potential sources of anthropogenic nutrient inputs on the island of Maui, Hawai'i, USA[END_REF][START_REF] Page | Stable isotopes reveal trophic relationships and diet of consumers in temperate kelp forest and coral reef ecosystems[END_REF]). While it was not possible for us to quantify the relative contributions of all the possible N sources to the lagoons, the strong association between the predicted spatial distribution of human sewage and the δ 15 N signature in Turbinaria tissue strongly suggests that algae are incorporating sewage-derived N. In addition, the fact that the δ 15 N signature predicted well total N tissue content suggests that anthropogenic sources of N (including sewage) are driving spatial patterns of N availability across the lagoons.

In contrast to sewage, our model of N enrichment from agricultural fertilizer was not a good predictor of the isotopic signature or total N content in Turbinaria tissue. The lack of an isotopic signature is unsurprising, given that δ 15 N values of fertilizers are often similar to natural sources [START_REF] Kendall | Tracing anthropogenic inputs of nitrogen to ecosystems[END_REF]. Our ability to detect an effect of fertilizer on N tissue content also may have been limited by the assumptions we made when modeling fertilizer input to the lagoons. A key assumption of our predictive model was that N from fertilizer was delivered to the lagoons in surface water runoff via one of several dozen streams around the island. In contrast, we modeled sewage assuming diffusion from the source based on the assumption that sewage contamination would primarily reach the lagoons via groundwater. While little is known about the extent of submarine groundwater discharge in Moorea or the relative importance of groundwater versus surface water runoff for delivering different types of nutrients, recent studies in Moorea suggest that septic waste and animal manure in groundwater are likely an important source of N to the lagoons [START_REF] Knee | Assessment of submarine groundwater discharge (SGD) as a source of dissolved radium and nutrients to Moorea (French Polynesia) coastal waters[END_REF][START_REF] Haßler | Provenance of nutrients in submarine fresh groundwater discharge on Tahiti and Moorea, French Polynesia[END_REF]. Given the potential importance of anthropogenic N input for shaping the structure and function of coral reef ecosystems, future work is needed to better characterize the sources and delivery pathways of anthropogenic N to the lagoons.

The role of nitrogen enrichment in driving coral-to-algae phase shifts

Patterns of N enrichment have important implications for understanding the dynamics of benthic algae, which can strongly influence overall ecosystem health. Nutrient enrichment can allow for the proliferation of algae that are physiologically unable to grow under nutrient poor conditions. In addition, excess nutrients can also facilitate algae indirectly through the loss of top-down control if adding nutrients causes algal production to outpace herbivory [START_REF] Scheffer | Pulse-driven loss of top-down control: The critical-rate hypothesis[END_REF][START_REF] Arias-González | A coral-algal phase shift in Mesoamerica not driven by changes in herbivorous fish abundance[END_REF], Briggs et al. 2018). In this study, we found that several species of macroalgae have recently increased in abundance on back reef habitats of Moorea, but only where levels of N are relatively high. Benthic algal blooms have been noted on some nearshore fringing reefs in Moorea in the past [START_REF] Payri | Zonation and seasonal variation of the commonest algae on Tiahura Reef (Moorea Island, French Polynesia)[END_REF][START_REF] Adjeroud | Spatial patterns in biodiversity of a fringing reef community along Opunohu Bay, Moorea, French Polynesia[END_REF][START_REF] Gattuso | Primary production, calcification, and air-sea CO2 fluxes of a macroalgal-dominated coral reef community (Moorea, French Polynesia)[END_REF], and it had been hypothesized that those blooms could be related to waste water discharge [START_REF] Wolanski | Modeling the fate of pollutants in the Tiahura Lagoon, Moorea, French Polynesia[END_REF][START_REF] Gattuso | Primary production, calcification, and air-sea CO2 fluxes of a macroalgal-dominated coral reef community (Moorea, French Polynesia)[END_REF]. Our results support this hypothesis and also suggest that the impacts of nutrients may extend beyond nearshore fringing reefs to back reef habitats farther from shore.

Several factors in addition to nutrient enrichment could be contributing to the increases in macroalgae in the lagoons of Moorea. Phase shifts to macroalgae frequently occur following large coral-killing disturbances that liberate space for the growth of benthic algae, particularly in systems with low levels of herbivory due to overfishing [START_REF] Done | Regional and local variability in recovery of shallow coral communities: Moorea, French Polynesia and central Great Barrier Reef[END_REF][START_REF] Hughes | Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef[END_REF][START_REF] Graham | Predicting climate-driven regime shifts versus rebound potential in coral reefs[END_REF]. The reefs in Moorea experienced two large coral-killing disturbances in the late 2000's, including an outbreak of corallivorous crown-of-thorns seastars (Acanthaster planci; COTS) and a large cyclone that together reduced the cover of living coral to near zero on the fore reef [START_REF] Adam | Herbivory, connectivity, and ecosystem resilience: response of a coral reef to a large-scale perturbation[END_REF][START_REF] Olmedo | kriging: Ordinary Kriging. R package version 1[END_REF][START_REF] Kayal | Predator crown-ofthorns starfish (Acanthaster planci) outbreak, mass mortality of corals, and cascading effects on reef fish and benthic communities[END_REF]. While reefs in the lagoons were much less impacted by these disturbances, some reefs have experienced a period of protracted coral decline that appears unrelated to these recent disturbances [START_REF] Han | Response of herbivore functional groups to sequential perturbations in Moorea, French Polynesia[END_REF], Schmitt et al. 2019, this study). Coral decline on both the fore reef and within the lagoons coincided with an increase in the biomass of herbivorous fishes [START_REF] Adam | Herbivory, connectivity, and ecosystem resilience: response of a coral reef to a large-scale perturbation[END_REF][START_REF] Olmedo | kriging: Ordinary Kriging. R package version 1[END_REF][START_REF] Lamy | Understanding the spatiotemporal response of coral reef fish communities to natural disturbances: Insights from beta-diversity decomposition[END_REF][START_REF] Han | Response of herbivore functional groups to sequential perturbations in Moorea, French Polynesia[END_REF][START_REF] Dubois | Recovery Debts Can Be Revealed by Ecosystem Network-Based Approaches[END_REF]). Yet, unlike the fore reef, where herbivores controlled the proliferation of algae and corals have rapidly recovered [START_REF] Holbrook | Coral reef resilience, tipping points and the strength of herbivory[END_REF][START_REF] Holbrook | Recruitment drives spatial variation in recovery rates of resilient coral reefs[END_REF], many reefs within the lagoons have become dominated by persistent algal assemblages, despite the presence of a diverse and abundant herbivore assemblage [START_REF] Han | Response of herbivore functional groups to sequential perturbations in Moorea, French Polynesia[END_REF][START_REF] Edmunds | Why more comparative approaches are required in time-series analyses of coral reef ecosystems[END_REF]). Higher nutrient loading in the lagoons is one possible explanation for why many reefs within the lagoons have become dominated by macroalgae while macroalgae have remained uncommon on the fore reef despite increases in herbivore biomass in both habitats. Likewise, differences in nutrient enrichment among lagoons may help explain why reefs in some lagoons have undergone persistent phase shifts to macroalgae, while others remain coral dominated despite similar increases in the biomass of herbivorous fishes.

Patterns of N loading may have also influenced the heterogeneous decline in corals across the lagoons. In addition to the positive correlation between long-term N enrichment and increase in macroalgae, N enrichment was also related to the loss of coral cover. Part of the impact of N on corals could be mediated by the rise in macroalgae, as algae can outcompete juvenile and adult corals (Bulleri et al. 2013, Brown and[START_REF] Brown | Water flow influences the mechanisms and outcomes of interactions between massive Porites and coral reef algae[END_REF] and inhibit coral recruitment [START_REF] Kuffner | Inhibition of coral recruitment by macroalgae and cyanobacteria[END_REF][START_REF] Mumby | High resilience masks underlying sensitivity to algal phase shifts of Pacific coral reefs[END_REF][START_REF] Bulleri | Erect macroalgae influences epilithic bacterial assemblages and reduce coral recruitment[END_REF]. But, excess nutrients also can directly negatively impact corals by increasing the prevalence and severity of coral disease [START_REF] Bruno | Nutrient enrichment can increase the severity of coral diseases[END_REF][START_REF] Vega Thurber | Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching[END_REF] and by exacerbating coral bleaching and mortality during thermal stress events [START_REF] Wiedenmann | Nutrient enrichment can increase the susceptibility of reef corals to bleaching[END_REF][START_REF] Zaneveld | Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales[END_REF][START_REF] Burkepile | Nitrogen identity drives differential impacts of nutrients on coral bleaching and mortality[END_REF][START_REF] Donovan | Nitrogen pollution interacts with heat stress to increase coral bleaching across the seascape[END_REF]. Human sewage may be particularly harmful to corals as it both delivers excess nutrients as well as pathogenic microbes that cause coral disease and mortality [START_REF] Patterson | The etiology of white pox, a lethal disease of the Caribbean elkhorn cora, Acropora palmata[END_REF][START_REF] Sutherland | Human sewage identified as likely source of white pox disease of the threatened Caribbean elkhorn coral, Acropora palmata[END_REF]. Nutrients from sewage and other land based sources of pollution are also commonly associated with toxins, organic carbon, and sediments, all of which have direct detrimental effects on corals (Fabricius 2005, Wear and[START_REF] Wear | Sewage pollution: Mitigation is key for coral reef stewardship[END_REF].

Conclusion

Spatial patterns of N availability in the lagoons of Moorea, a small oceanic island in the South Pacific, are shaped by terrigenous inputs even though lagoons are continuously flushed with oligotrophic ocean water. Long-term data indicate that reefs experiencing the highest levels of N enrichment have undergone persistent phase shifts to a macroalgae-dominated state despite the presence of a diverse and abundant herbivore assemblage. These results indicate that anthropogenic nutrient pollution is likely an important driver of reef degradation in Moorea.

Persistent phase shifts from coral to macroalgae are a problem on many reefs worldwide and are often associated with declines in the abundance of herbivorous fishes. Our results emphasize that bottom-up factors can also play an important role in driving these algal phase shifts. Future work is necessary to better characterize the sources and impacts of anthropogenic nutrients on Moorea, but sewage from septic systems appears to be an important contributor. Impacts of nonpoint sources of N are difficult to isolate yet are likely widespread in tropical countries with coral

Figure 1 .

 1 Figure 1. (a) Map of Moorea showing the locations of the six MCR LTER sites and the sampling

Figure 2 .

 2 Figure 2. Spatial patterns of nitrogen enrichment (% N) in tissue from the macroalga Turbinaria

Figure 3 .

 3 Figure 3. (a) Modeled N enrichment based on locations of septic systems and sources of untreated sewage. (b) Empirical patterns of δ 15 N in Turbinaria ornata tissue during the rainy season. Association between modeled N from sewage and δ 15 N for the (c) north, (d) east, and (e) west shores of Moorea during the rainy season. Association between δ 15 N and nitrogen content

Figure 4 .

 4 Figure 4. Mean (+/-SE) N content of Turbinaria ornata tissue in (a) three habitats (fringing

Figure 5 .

 5 Figure 5. Area plot showing the percent cover of coral and algae at the six MCR LTER back reef

Figure 6 .

 6 Figure 6. Correlation between the change in the percent cover of macroalgae between 2007 and

  Figure 2

  Figure 3

Acknowledgements

National Science Foundation grants OCE-1619697 to SJH, DEB, and RJS, OCE-1547952 to DEB, and OCE-1236905 and OCE-1637396 for the Moorea Coral Reef LTER to RJS, SJH, RCC, and P Edmunds, as well as an Agence Nationale de la Recherche grant ANR-14-CE03-0001-01 to JC supported this research. We thank A. Shantz, K. Munsterman, K. Speare, M. Rice, A. Duran, K. Seydel, J. Verstaen, R. Honeycutt, D. Cook, M. Ladd, V. Moriarty, C. Gottschalk, J. Curtis, and G. Paradis for field and laboratory assistance, and A. Thompson, M. Donovan and J. Lecky for assistance with maps. The digital terrain model used in Figs. 1, 2, and 3 was provided courtesy of Matthias Troyer and was funded through ETH Zurich and the Hacettepe University Scientific Research Projects Coordination Unit as part of the Moorea IDEA Project. Research was completed under permits issued by the Territorial Government of French Polynesia (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2015-2017).

Authorship Statement

TCA, DEB, SJH, RJS and RCC designed the study and secured funding; TCA, DEB, AJB, and RCC performed field work; JC, CL, and LT created empirical models of nutrient discharge; LW oversaw collection and processing of wave data; TCA conducted analyses and wrote the initial manuscript draft. All authors contributed to manuscript revisions.

Adam, T. C., A. J. Brooks, S. J. Holbrook, R. J. Schmitt, L. Washburn, and G. Bernardi. 2014.


Damien Alvarez De Toledo

Laurent D'orazio
email: laurent.dorazio@irisa.fr

Frederic Andres
email: andres@nii.ac.jp

Maria C A Leite
email: mcleite@usf.edu

Cooking related Carbon Footprint Evaluation and Optimisation

Keywords: Carbon Footprint, Recipe, Food, Health, Climate Change

Carbon Footprint of foods has been a major concern for the past few years. Food production is responsible for a quarter of all GHG (Green House Gas) emissions. Many food's Carbon Footprint calculators can be found online but most of them give individual results per ingredient and do not offer a perspective of the whole recipe's Carbon Footprint. Many factors have to be taken into account for this calculation as the origin of the food, the location of the cooker, but also the way to cook and to assemble ingredients. In this paper, we present the CROPPER (CaRbon fOotprint reciPe oPtimizER) that improves an input recipe by updating its ingredients (origin, type) and its cooking procedures to reduce its Carbon Footprint while keeping it savory.

Introduction

The Earth's atmosphere is warming faster than it probably ever has. In some cases weather patterns, climates and natural environments are changing quicker than wildlife or people can adapt. GHG as carbon dioxide, nitrous oxide and methane form emissions have been recognized as partly responsible for the accelerated rate of the climate change [START_REF]Climate Change Indicators: Greenhouse Gases[END_REF].Several reports including standards [START_REF] Clark | Multiple health and environmental impacts of foods[END_REF][START_REF]Folk and Knife: Food & CO2, What are the carbon emissions of different foods?[END_REF][START_REF]How to assess the carbon footprint of goods and services[END_REF] pointed out the relationship between food consumption and climate change patterns. Also, food production has been recognized to contribute greatly for the anthropogenic environmental disturbances [START_REF] Poore | Reducing food's environmental impacts through producers and consumers[END_REF]. On the other hand, the choice of our ingredients and ways of cooking has a direct influence on the GHG emissions we produce as citizens (e.g., [START_REF] Sandström | The role of trade in the greenhouse gas footprints of EU diets[END_REF][START_REF] Weber | Food-Miles and the Relative Climate Impacts of Food Choices in the United States[END_REF]). Thus, it is important to increase our awareness on both how our food can contribute towards the problem of climate change, and how climate change threatens the supply of ingredients we take for D. Alvarez de Toledo has been a research student at NII during the research period that led to the production of this paper.

granted. In particular, a conscious approach to the choice of ingredients is vital to reduce GHC and, consequently, our personal environmental impact.The concept of food Carbon Footprint is being increasingly studied by researchers around the world. It has not yet reached, however, a high awareness amongst consumers. The general public as well as specialised chefs need a concise and simple method to evaluate their cooking GHG emissions and fulfilling this awareness. Most information around food's carbon footprint is detailed for separate ingredients and does not take into account the impact that their combination can have as a recipe [3,[START_REF]Climate Change Indicators: Greenhouse Gases[END_REF][START_REF] Röös | Analysing the Carbon Footprint of Food[END_REF]. Our research, part of the CRWB project [START_REF] Andres | The CRWB RSbench: Towards a Cooking Recipe Benchmark Initiative[END_REF] aims to overcome this boundary and to assist reducing the individual's Carbon Footprint when cooking a specific recipe, by submitting the recipe to a Carbon footprint optimizer algorithm, the CROPPER.

This paper is structured as follows. Section 2 reviews the state of art. Section 3 describes the Low Carbon Footprint recipe optimiser and our implementation choices. Section 4 introduces the preliminary results. Finally, Section 5 concludes and introduces the future works.

State of the art

The concept of Carbon Footprint (CF) appeared around 2006, but the climate impact of products has been calculated for decades as part of full Life cycle assessment(LCA) [START_REF] Röös | Analysing the Carbon Footprint of Food[END_REF]. Numerous papers about the emitted CF of individual and specific ingredients (e.g., [START_REF] Röös | Analysing the Carbon Footprint of Food[END_REF][START_REF] Speck | Creating Sustainable Meals Supported by the NAHGAST Online Tool -Approach and Effects on GHG Emissions and Use of Natural Resources[END_REF] and references therein), but also Danish diets [START_REF] Bruno | The carbon footprint of Danish diets[END_REF] and foods life-cycle in Finnish households [START_REF] Kauppinen | Carbon footprint of food-related activities in Finnish households[END_REF] are accessible nowaday. However, the computation of CF is still a challenging endeavor. Availability of online CF calculators is quite recent and they focus on single specific ingredients only [3,[START_REF]Climate Change Indicators: Greenhouse Gases[END_REF]. Some other calculators try to offer an opportunity for the user to optimize a recipe's environmental impact by swapping ingredients, but they lack an interesting variety of ingredients in their database and are designed for outof-home catering sector [START_REF] Speck | Creating Sustainable Meals Supported by the NAHGAST Online Tool -Approach and Effects on GHG Emissions and Use of Natural Resources[END_REF]. They are not yet available for private households and their design is in a single language only, not extended to English, which limit their usage by general public worldwide. Furthermore, the available online calculator do not incorporate the CF and budget optimizer feature that is been built into CROPPER. Previous works can be considered as stepping stones for an upcoming system that can evaluate the CF of a recipe in a complete manner, the CROPPER, that "crops" the Carbon Footprint (CF) of an input recipe to make it more environmentally friendly. This system will contain a multilingual service and database, including most European and Asian languages.

CROPPER Model

CROPPER theoretical Approach

The following theoretical approach aims to reduce the Carbon Footprint (named CF) of an input recipe by considering a Desired Carbon Footprint (named DCF) and a Money Threshold, both entered by the user. We assume the ingredient similarity between the input and output recipe for this research. When the ingredient is swapped, it can only be replaced by an ingredient from the exact same kind i.e. a banana by another banana (different origin, organic instead of conventional, etc.).

We define the output recipe CF, the CF estimated for the output recipe, as follows:

otpt recpe CF = nb ngredents =1 CF(ngredent ), otpt recpe CF ≤ DCF
where nb Ingredients and CF(ingredient i) denotes, respectively, the number of ingredients and the price of the ith ingredient in the recipe.

If DCF cannot be attained through ingredient swapping, then the closest output recipe CF to DCF will be returned jointly to the updated recipe. The price of the processed recipe (recipePrice) must respect the condition of being inferior or equal to the user's budget (Money Threshold) as follows :

recpePrce = nb ngredents =1 Prce(ngredent  ), recpePrce ≤ Money Threshod,
where Money Threshold is the budget of the User and Price(ingredient i) is the price of the ith ingredient of the recipe. The CaRbon fOotprint reciPe oPtimizER (CROPPER) ecosystem of this study is represented in (Fig. 1a). The user selects a recipe and a desired CF value as an input in addition to a money threshold, which corresponds to the user's budget. Two conditions must be met for the recipe to be updated (ingredients swapping): 1) The user must provide the required amount of money for swapping.

2) The recipe will be updated when its current CF is higher than the desired one, for a given budget. The final goal of this research is to develop a service that interacts with two knowledge bases FoodPriceBazaar5 and CFKB. The former (Prices and Origins KB in Fig. 1a) contains information related to a product's shop, price and origin. The latter (CF KB in Fig. 1a) provides data on each ingredient's CF according to their origin. Both of the knowledge bases will be design so that, as opposed to the existing one, each ingredient will have its own semantic signification. Thus, making them more suitable for use compared to raw data included in a conventional database.

CROPPER pseudo-code implementation

The key parts of the CROPPER algorithms are (1) the evaluation of the actual Carbon Footprint (CF) and the cost of the recipe CF-Evaluation() in Algorithm 1, and (2) the CF reduction accomplished through the use of Ingredients-Swapping() according to the user's requirements in Algorithm 2. By default the first ingredients retrieved by Information-Retrieval() algorithm, which is not the focus of this article and, therefore, its description it is not included, are the ones that are geographically closest to the cooker's location (the distance is given in km). The implicit assumption is that these ingredients have a smaller CF. This may not always be true as it has been shown in some recent studies [START_REF] Weber | Food-Miles and the Relative Climate Impacts of Food Choices in the United States[END_REF]. However, the United Nations recommend to 'Eat local' aiming to reduce food related CF. As this is a first developed system that can evaluate and optimise the CF of a recipe, we made this assumption as a starting point. Its relaxation is seen as a future study. Observe that if DCF cannot be attained by the algorithm Ingredients Swapping, the condition Old Recipe == New Recipe must be met for the algorithm to stop. The number of ingredients in the database (either the Dummy Database or the future KB) is finite and the choice of the ingredients in the algorithm is always oriented towards a"better" one (lower yet closest CF compared to the previous ingredient). We can thus infer that it is not possible to go back to choosing an ingredient with a higher CF and New Recipe is a loop variant. When New Recipe cannot change anymore (no "better" ingredients left in the database) redundancy is met with Old Recipe. The algorithm then stops.

Algorithm 1 CF Evaluation

The Knowledge Bases FoodPriceBazaar and CFKB are yet not available. Hence, the study was conducted on a dummy database created by retrieving information from distinct sources for ingredients CF [START_REF]Chart: The Carbon Footprint of the Food Supply Chain[END_REF][START_REF]Do you know the carbon footprint of these common foods[END_REF][START_REF]Émissions indirectes -autres, Restauration[END_REF]14] and prices 6,7 .

Implementation and Results

Our use-case was coded in Python, whilst the dummy database presents three different origins for each ingredient e.g. Sour Cream from France, USA or Argentina. The code implementation and the dummy database are available on bitbucket 8 . A study was performed to determine whether it is possible to develop an algorithm that can swipe ingredients in a given recipe aiming to reduce its CF with a pre-specified budget (in USD). The Use-Case used in this study was the dish: "Coffee flavoured carrot cream, beet chips" (see Fig. 1b). After launching our three sub-algorithms altogether, the default recipe is firstly determined with the cooker's location accordingly. We then obtained an update of our recipe with a lower CF (1.65 kgCO 2 to 1.55 kgCO 2 ) yet a higher price (6.15 USD to 6.67 USD). The results of our first implementation remain substantially simplistic in the sense that the choice of variety for each ingredient is not very broad in our dummy database (three for each). More significant results will be obtained when the connection with our two Knowledge Bases (Prices and CF) is materialized.

Conclusion and Future Work

Climate change is happening at high speed and has many origins, and our diet is one of them. Developing models to reduce our environmental impact when cooking/eating is thus a valuable goal. In this study, we present a Carbon Footprint (CF) recipe optimiser which has a number of benefits over existing calculators. Current CF calculators for foods focus on one ingredient at a time and thus lack the assembling feature for getting the environmental impact of a whole recipe.

The findings in this paper can be used as a base for a better recipe updater that takes into account ingredients pairing and also the proper connectivity to the relevant knowledge bases. The latter will provide a large scope of ingredients variety. Future features are its scalability and bulking, allowing the simultaneous feeding of many recipes as an input, which will yield more diversified results.

Our current model and preliminary tests show that it is possible to compute a new recipe given a certain money budget and a desired CF. Thus, it satisfy our query for a better CF for an input recipe. This novel study has the advantage of proposing a clear design of a recipe CF optimiser, easily understood and visualized through the pseudo-code and model exposed. Although the process is limited to summations for now, it offers the capability of processing a whole recipe at once, and not forcing the user to enter each ingredient with its respective volume. The three main limitations of our approach is the transport CF of ingredients, find eco-friendly cooking procedures, and the access to the target knowledgebases have not yet fully implemented. Additionaly, to improve the quality of the optimised recipes, the Ingredient pairing feature will be included.

Fig. 1 :

 1 Fig. 1: (a) CROPPER Ecosystem schematic representation. (b) Use-case result tracing.

https://bit.ly/3cF7hP7

Walmart, www.walmart.com. Last accessed 25/4/2020

Climate change food calculator: What's your diet's carbon footprint?, www.bbc.com/news/science-environment-46459714. Last accessed 23/4/2020

https://bit.ly/2zbpQNe

Acknowledgements

We would like to express our deepest appreciation to the National Institute of Informatics for the ongoing research support.