Marine Ecology Progress Series 542:25

The following supplements accompany the article

Nitrogen stable isotope values of large-bodied consumers reflect urbanization of coastal catchments

F. Y. Warry*, Paul Reich, Ryan J. Woodland, James R. Thomson, Ralph Mac Nally, Perran L. M. Cook

*Corresponding author: [email protected]

Marine Ecology Progress Series 542: 25–37 (2016)

Supplement 1. Table S1: Estuary and catchment characteristics for study estuaries and the percentage of the catchment area covered by urbanization and intensive agriculture land uses. ID numbers correspond to the numbers in Figure 1 in the main paper. Catchment Estuary Estuary Intensive ID Estuary Area Area Length Urbanization Agriculture (km2) (km2) (km) (%) (%) 1 Franklin R. 1416.39 42.54 12.88 0.24 52.97 2 Eumeralla R. 913.88 102.12 8.10 0.15 73.58 3 Hopkins R. 8229.26 143.30 9.60 0.41 63.26 4 Curdies Inlet 950.51 338.34 17.19 0.36 64.78 5 Gellibrand R. 1154.47 29.27 8.75 0.04 21.39 6 Aire R. 271.73 86.00 8.72 0.00 10.92 7 Kennett R. 20.37 1.51 0.73 0.57 0.00 8 Anglesea R. 126.65 11.16 2.62 1.36 0.75 9 Spring Ck. 51.07 4.10 2.06 3.08 56.13 10 Thompson Ck. 296.40 23.28 5.45 1.19 61.88 11 Little R. 492.16 10.21 3.64 0.06 38.82 12 Werribee R. 1444.50 42.10 8.34 2.65 38.34 13 Kororoit Ck. 296.36 6.64 3.50 24.13 35.31 14 Yarra R. 5466.52 319.08 22.64 13.31 32.26 15 Balcombe Ck. 102.39 6.24 2.12 8.81 40.70 16 Merricks Ck. 47.90 7.07 2.56 2.78 72.22 17 Warrengine Ck. 19.98 4.69 1.15 1.02 53.38 18 Watsons Ck. 53.40 6.69 3.50 15.40 26.16 19 Cardinia Ck. 406.48 11.91 5.58 4.33 39.67 20 Bunyip R. 1142.58 14.98 4.37 1.89 50.41 21 Bass R. 292.89 20.84 8.46 0.23 73.16 22 Bennison Ck. 22.24 30.03 1.40 0.22 92.00 23 Franklin R. 132.28 61.84 5.41 0.36 60.01 24 Chinaman Ck. 42.49 2.90 1.39 0.00 0.00 25 Tarra R. 277.91 154.75 19.54 0.20 41.61 26 Merriman Ck. 519.33 13.43 2.22 0.13 32.50 27 Avon R. 2042.01 25.00 5.00 0.14 21.44 28 Bunga Inlet 18.46 14.28 2.01 2.13 31.82 29 Wingan Inlet 446.76 135.50 6.34 0.00 0.64 30 Shipwreck Ck. 28.43 2.63 1.08 0.00 0.00 31 Davis Ck. 3.89 2.50 1.05 6.59 0.00

Supplement 2. Contribution of treated wastewater to nitrogen loads in five estuaries

Methods

Geoscience Australia’s National Wastewater Treatment Plant Database

(http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_74625, August 2015) was used to identify wastewater treatment plants that discharge to surface waters in the catchments studied. Five of the estuaries studied had wastewater treatment plants in their catchments that discharge tertiary treated effluent to surface waters. These were the Fitzroy

River, Hopkins River, Curdies River, Yarra River and Bunyip River. In all cases the discharge points where located in freshwater reaches of the catchment, spatially removed from the estuaries and often on tributaries to the river.

Wastewater discharge licenses issued by the Victorian Environmental Protection

Authority (EPA) were consulted to identify the limits for daily flow discharge (megalitres d-1) and total nitrogen concentrations in discharge effluent (mg l-1) permitted for the treatment plants within these five catchments. These limit values were used to calculate the maximum percentage of nitrogen loading to the estuary that could be comprised of treated wastewater effluent. Actual discharge volumes were available for treatment plants in the Yarra River catchment from the Australian Bureau of Meteorology’s National Water Account Program

(http://www.bom.gov.au/water/nwa/index.shtml, August 2015).

Four of these estuaries have monthly nutrient monitoring programs and hydrological gauges positioned just upstream of the estuary that measure daily flow volume, so that annual nitrogen loads to the estuaries could be calculated. These data were used to calculate total nitrogen loads for the 2010/2011, 2011/2012 and 2012/2013 hydrological years. Annual loads of total nitrogen entering these estuaries were derived using long-term flow and nutrient concentration monitoring data (Victorian Water Monitoring Information System, October

2014, http://data.water.vic.gov.au/monitoring.htm). The frequency of nutrient sampling

2 ranged from approximately biweekly (N = 23) to quarterly (N = 3 - 4) with an average of N =

12 samples per river per year (i.e., monthly sampling). Data were assigned to a 1 June–31

May hydrologic year rather than a calendar year to reflect the annual flow–nutrient cycle responsible for primary production dynamics in Victorian estuaries during the austral summer

(Cook & Holland 2012). Annual loads (Mg yr-1) of TN were calculated with the flow regime- stratified Kendall’s ratio estimator (Tan et al. 2005) using the Generator for Uncertainty

Measures and Load Estimates using Alternative Formulae program (GUMLEAF version 0.1 alpha; Tan et al. 2005).

The Fitzroy River does not have a nutrient monitoring program, so nitrogen loads calculated for the Bunyip River (which has a similar sized catchment, subject to similar land use), were substituted for the Fitzroy River (See Table S1 in Supplement 1).

Results The maximum contribution of nitrogen from treated wastewater discharged from wastewater treatment plants in a catchment to total nitrogen loads entering the estuary was ≤ 11% for the

Yarra River and ≤ 6% the Fitzroy, Hopkins, Curdies and Bunyip Rivers (Table S3).

Therefore, inputs from wastewater treatment plants in these catchments were not expected to overly influence nitrogen pools in the downstream estuaries and these estuaries were included in the modelling of relationships between catchment land use and δ15N.

Table S2: The contribution of total nitrogen (TN) from tertiary treated wastewater to total nitrogen loads entering estuaries with treatment plants within their catchments that discharge to surface waters.

Contribution of Total TN Wastewater treated load to TN load to wastewater to estuary catchment total TN load Estuary Hydrological Year (tons.yr-1) (tons.yr-1) (%) Bunyip R. 2010 618.86 7.30 1.18 Bunyip R. 2011 465.13 7.30 1.57 Bunyip R. 2012 257.78 7.30 2.83 Curdies R. 2010 682.13 0.37 0.05 Curdies R. 2011 262.40 0.37 0.14 Fitzroy R. 2010 618.86* 9.13 1.47 Fitzroy R. 2011 465.13* 9.13 1.96 Hopkins R. 2010 2417.48 32.81 1.36 Hopkins R. 2011 513.38 32.81 6.39 Yarra R. 2010 1012.38 86.00 8.49 Yarra R. 2011 706.19 81.00 11.47 Yarra R. 2012 593.38 36.00 6.07 * The Fitzroy River does not have a nutrient monitoring program, so nitrogen loads calculated for the Bunyip River (which has a similar sized catchment, subject to similar land use), were substituted for the Fitzroy River

Literature cited Cook PLM, Holland DP (2012) Long term nutrient loads and chlorophyll dynamics in a large temperate Australian lagoon system affected by recurring blooms of cyanobacteria. Biogeochemistry 107:261-274 Tan, KS, Fox D, Etchells T. (2005) Generator for uncertainty measures and load estimates using alternative formulae. Australian Centre for Environmetrics, Univ. of Melbourne.

Supplement 3. Linear mixed modelling using detailed agricultural land use variables

Methods

Model selection analyses were run treating the three agricultural land uses that comprise the

‘intensive agriculture’ predictor variable individually. These land use types were: 1. modified pasture, 2. intensive animal production, 3. intensive plant production. These land use data were obtained from the National Environmental Stream Attributes database v1.1 (Stein et al.

2014) and Bureau of Rural Sciences’ 2005/06 Land Use of Australia V4 maps

(www.agriculture.gov.au/abares/aclump). The aggregated ‘intensive agriculture’ variable was also included in the suite of candidate models assessed. Fifty-six candidate models were assessed for each response variable, using the statistical approached outlined in the manuscript.

Results

The best models identified from this suite of 56 candidate moles through the model selection process were identical to those identified in the original analyses that only considered an aggregated intensive agriculture predictor (see Table S4 and Table 1 of the main article). No models containing agricultural predictors were identified as the best models. Low values of adjusted variable weights (Wvar) indicated no statistical association between responses and the aggregate or individual (i.e. modified pasture, intensive animal production, intensive plant production) agricultural predictor variables (Table S5). Models containing interaction terms between catchment urbanization and agricultural variables also performed poorly and the adjusted variable weights (Wvar) for these interaction terms were very low (Table S5). This indicates that the noise in relationships between responses and urbanization could not be systematically explained by variation in agricultural land uses.

Table S3: Results from model selection analysis relating the response of ln-transformed δ15N of biota to catchment land use (urbanization and intensive agriculture), tidal exchange, season and selected interaction terms. Model probability weights based on AICc values (AICw; probability of each variant given the other variants) provided for each model variant. Prior odds (OPrior), Posterior odds (OPost) and odds ratio (OR; posterior odds/prior odds) for each variable is shown at bottom of table.

Model Variant Construction Model Variant AICw Seas* Seas* Seas* Seas* Seas* Seas* Tide* Tide* Tide* Tide* Tide* Urb* Urb* Urb* Urb* del Seasa Tideb Agc Urbd Paste Animf Plantg Tide Ag Urb Past Anim Plant Ag Urb Past Anim Plant Ag Past Anim Plant NOxh GFAi PAj Zosk ABl AFm P 1 0.330 0.433 0.824 0.003 0.166 0.076 0. 2 yes 0.048 0.315 0.076 0.868 0.149 0.029 0. 3 yes 0.413 0.034 0.006 <0.001 <0.001 <0.001 0. 4 yes 0.002 0.013 0.013 <0.001 <0.001 <0.001 0. 5 yes 0.019 0.032 0.011 <0.001 0.340 0.463 0. 6 yes 0.001 0.007 0.013 <0.001 <0.001 <0.001 0. 7 yes 0.019 0.038 0.031 <0.001 0.002 0.001 0. 8 yes 0.013 0.019 0.016 <0.001 0.003 0.001 0. 9 yes yes 0.057 0.008 0.001 0.002 <0.001 <0.001 <0 10 yes yes <0.001 0.008 0.001 0.003 <0.001 <0.001 <0 11 yes yes 0.003 0.028 0.001 0.047 0.274 0.361 0. 12 yes yes <0.001 0.005 0.001 0.003 <0.001 <0.001 <0 13 yes yes 0.003 0.026 0.003 0.027 0.001 <0.001 0. 14 yes yes 0.002 0.010 0.001 0.035 0.003 <0.001 0. 15 yes 0.025 <0.001 <0.001 <0.001 <0.001 <0.001 <0 16 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 17 yes <0.001 <0.001 <0.001 0.001 <0.001 0.004 <0 18 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 19 yes <0.001 0.001 <0.001 0.001 <0.001 <0.001 <0 20 yes <0.001 <0.001 <0.001 0.001 <0.001 <0.001 <0 21 yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0 22 yes yes 0.014 0.001 <0.001 <0.001 <0.001 <0.001 <0 23 yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0 24 yes yes 0.014 0.002 <0.001 <0.001 <0.001 <0.001 <0 25 yes yes 0.011 0.001 <0.001 <0.001 <0.001 <0.001 <0 26 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 27 yes 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 28 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 29 yes 0.006 <0.001 <0.001 <0.001 <0.001 <0.001 <0 30 yes 0.005 <0.001 <0.001 <0.001 <0.001 <0.001 <0 31 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 32 yes yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0 33 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 34 yes yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0 35 yes yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0 36 yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 6

Model Variant Construction Model Variant AICw Seas* Seas* Seas* Seas* Seas* Seas* Tide* Tide* Tide* Tide* Tide* Urb* Urb* Urb* Urb* del Seasa Tideb Agc Urbd Paste Animf Plantg Tide Ag Urb Past Anim Plant Ag Urb Past Anim Plant Ag Past Anim Plant NOxh GFAi PAj Zosk ABl AFm P 37 yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 38 yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 39 yes yes 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 40 yes yes 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 41 yes yes <0.001 0.003 <0.001 <0.001 <0.001 0.001 <0 42 yes yes <0.001 0.003 <0.001 <0.001 0.001 0.001 <0 43 yes yes 0.001 0.001 <0.001 <0.001 0.011 0.021 0. 44 yes yes 0.001 0.001 <0.001 <0.001 0.023 0.015 0. 45 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 46 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 47 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 48 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 49 yes yes yes <0.001 0.003 <0.001 <0.001 <0.001 <0.001 <0 50 yes yes yes <0.001 0.003 <0.001 <0.001 0.001 0.001 <0 51 yes yes yes <0.001 0.001 <0.001 0.005 0.007 0.016 <0 52 yes yes yes <0.001 0.001 <0.001 0.003 0.016 0.010 <0 53 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 54 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 55 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 56 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0 ior 0.600 0.400 0.143 0.400 0.143 0.143 0.143 0.018 0.018 0.018 0.018 0.018 0.018 0.037 0.037 0.037 0.037 0.037 0.018 0.018 0.018 0.018 a season

b st 0.137 1.077 0.004 0.042 0.004 0.040 0.030 0.026 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.002 <0.001 0.006 0.006 <0.001 <0.001 <0.001 <0.001 tidal exchange c 0.229 2.693 0.031 0.105 0.025 0.280 0.207 1.423 <0.001 0.002 <0.001 0.011 0.012 0.001 0.042 0.001 0.172 0.161 <0.001 <0.001 <0.001 <0.001 percentage of catchment used for intensive agriculture - st 0.692 0.050 0.028 0.085 0.019 0.074 0.032 <0.001 <0.001 <0.001 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 aggregate of Modified Pasture, Intensive Animal Prod and Intensive Pla d 1.153 0.125 0.199 0.212 0.131 0.518 0.225 0.002 0.001 0.024 0.001 0.044 0.026 <0.001 <0.001 <0.001 <0.001 <0.001 0.004 <0.001 0.001 <0.001 percentage of the catchment urbanized e st 0.092 0.007 0.015 0.013 0.014 0.036 0.018 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 percentage of the catchment that is modified pasture f 0.153 0.018 0.105 0.033 0.099 0.254 0.124 <0.001 <0.001 0.002 <0.001 0.006 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 <0.001 percentage of the catchment used for intensive animal production st 145.822 0.003 0.003 0.059 0.003 0.033 0.040 <0.001 <0.001 0.001 <0.001 0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 g percentage of the catchment used for intensive plant production 243.037 0.007 0.024 0.147 0.022 0.229 0.279 <0.001 0.001 0.062 0.001 0.040 0.044 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 * interaction terms st 0.826 0.001 0.001 2.069 0.002 0.022 0.047 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 h inorganic nitrogen 1.376 0.003 0.008 5.172 0.013 0.154 0.332 <0.001 <0.001 0.026 <0.001 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.003 i green fillamentous algae st 0.716 0.001 0.001 7.982 0.002 0.039 0.026 <0.001 <0.001 0.004 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 j Phragmites australis 1.193 0.002 0.009 19.954 0.015 0.271 0.184 <0.001 <0.001 0.210 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.002 0.001 k Zostera spp. st 0.028 0.001 0.009 0.040 0.008 0.028 0.028 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 lAcanthopagrus butcheri 0.047 0.002 0.064 0.099 0.056 0.196 0.194 <0.001 0.001 <0.001 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 0.001 m Aldrichetta forsteri n st 0.058 0.007 0.005 0.783 0.006 0.015 0.009 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 <0.001 <0.001 <0.001 Philypnodon grandiceps 0.096 0.017 0.038 1.957 0.040 0.104 0.066 <0.001 <0.001 0.007 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.074 0.004 0.001 <0.001 o Pseudogobius Sp. 9

Table S4: Relative importance of each predictor variable used in linear mixed models, in explaining patterns of ln-transformed δ15N responses. Adjusted variable weights (Wvar; probability that a given variable is included in the best model variant) for each variable are shown. See Table S3 for AICw values for each model variant and prior and posterior odds and odds ratios for explanatory variables. Bold font denotes Wvar values indicating predictors that have a statistical association with the response variable. Model Terms Seas* Seas* Seas* Seas* Seas* Seas* Tide* Tide* Tide* Tide* Tide* Urb* Urb* Urb* Urb* Response Seasa Tideb Agc Urbd Paste Animf Plantg Tide Ag Urb Past Anim Plant Ag Urb Past Anim Plant Ag Past Anim Plant Inorganic Nitrogen 0.19 0.73 0.03 0.09 0.02 0.22 0.17 0.59 <0.01 <0.01 <0.01 0.01 0.01 <0.01 0.04 <0.01 0.15 0.14 <0.01 <0.01 <0.01 <0.01 Plants Green fillamentous algae 0.54 0.11 0.17 0.17 0.12 0.34 0.18 <0.01 <0.01 0.02 <0.01 0.04 0.02 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Phragmites australis 0.13 0.02 0.09 0.03 0.09 0.20 0.11 <0.01 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Zostera spp. 1.00 0.01 0.02 0.13 0.02 0.19 0.22 <0.01 <0.01 0.06 <0.01 0.04 0.04 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Fish Acanthopagrus butcheri 0.58 <0.01 0.01 0.84 0.01 0.13 0.25 <0.01 <0.01 0.03 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Aldrichetta forsteri 0.54 <0.01 0.01 0.95 0.01 0.21 0.16 <0.01 <0.01 0.17 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Philypnodon grandiceps 0.04 <0.01 0.06 0.09 0.05 0.16 0.16 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Pseudogobius sp. 9 0.09 0.02 0.04 0.66 0.04 0.09 0.06 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.07 <0.01 <0.01 <0.01 a season b tidal exchange c percentage of catchment used for intensive agriculture - aggregate of Modified Pasture, Intensive Animal Production and Intensive Plant Production d percentage of the catchment urbanized e percentage of the catchment that is modified pasture f percentage of the catchment used for intensive animal production g percentage of the catchment used for intensive plant production * interaction terms

Supplement 4. Table S5: Results from model selection analysis relating the response of ln-transformed δ15N of biota to catchment land use (urbanization and intensive agriculture), tidal exchange, season and selected interaction terms. Model probability weights based on AICc values (AICw; probability of each variant given the other variants) provided for each model variant. Prior odds (OPrior), Posterior odds (OPost) and odds ratio (OR; posterior odds/prior odds) for each variable is shown at bottom of table.

Model Variant Construction Model Variant AICw Model Seasa Tideb Agc Urbd Seas*Tide Seas*Ag Seas*Urb Tide*Ag Tide*Urb NOxe GFAf PAg Zosh ABi AFj PGk P.Sp9l 1 0.360 0.492 0.883 0.004 0.178 0.082 0.926 0.527 2 yes 0.052 0.358 0.081 0.938 0.160 0.031 0.026 0.027 3 yes 0.451 0.039 0.007 <0.001 <0.001 <0.001 0.001 0.003 4 yes 0.002 0.015 0.014 <0.001 <0.001 <0.001 0.009 0.002 5 yes 0.021 0.037 0.012 <0.001 0.365 0.495 0.036 0.407 6 yes yes 0.063 0.009 0.001 0.002 <0.001 <0.001 <0.001 <0.001 7 yes yes <0.001 0.009 0.001 0.004 <0.001 <0.001 <0.001 <0.001 8 yes yes 0.003 0.032 0.001 0.051 0.294 0.386 0.001 0.027 9 yes 0.028 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 10 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 11 yes <0.001 <0.001 <0.001 0.001 0.001 0.004 <0.001 <0.001 12 yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 13 yes yes 0.015 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.004 14 yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 15 yes 0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 16 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 17 yes yes yes 0.002 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 18 yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 19 yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 20 yes yes <0.001 0.004 <0.001 <0.001 <0.001 0.001 <0.001 0.003 21 yes yes yes <0.001 0.003 <0.001 <0.001 <0.001 0.001 <0.001 <0.001 22 yes yes yes <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

Oprior 0.692 0.467 0.467 0.467 0.048 0.048 0.048 0.100 0.100

NOx Opost 0.137 1.138 0.005 0.043 0.028 <0.001 <0.001 <0.001 0.002 a season OR 0.198 2.438 0.010 0.092 0.594 <0.001 0.001 0.001 0.017 b tidal exchange GFA Opost 0.699 0.053 0.033 0.084 <0.001 <0.001 <0.001 <0.001 <0.001 c percentage of catchment used for intensive agriculture OR 1.010 0.113 0.070 0.180 0.001 0.001 0.010 <0.001 <0.001 d percentage of the catchment urbanized * interaction PA Opost 0.092 0.007 0.016 0.013 <0.001 <0.001 <0.001 <0.001 <0.001 terms OR 0.133 0.016 0.034 0.029 <0.001 <0.001 0.001 <0.001 <0.001 e inorganic nitrogen Zos Opost 184.249 0.003 0.004 0.054 <0.001 <0.001 0.001 <0.001 <0.001 f green fillamentous algae OR 266.137 0.006 0.008 0.116 <0.001 0.001 0.026 <0.001 <0.001 g Phragmites australis AB Opost 0.058 0.007 0.006 0.789 <0.001 <0.001 <0.001 <0.001 <0.001 h Zostera spp. 9

OR 0.083 0.015 0.012 1.691 <0.001 <0.001 0.003 <0.001 <0.001 iAcanthopagrus butcheri AF Opost 0.716 0.001 0.001 7.578 <0.001 <0.001 0.004 <0.001 <0.001 j Aldrichetta forsteri OR 1.035 0.002 0.003 16.238 <0.001 <0.001 0.086 <0.001 <0.001 k Philypnodon grandiceps PG Opost 0.028 0.001 0.010 0.039 <0.001 <0.001 <0.001 <0.001 <0.001 l Pseudogobius Sp. 9 OR 0.041 0.002 0.021 0.084 <0.001 0.001 <0.001 <0.001 <0.001 P.Sp9 Opost 0.058 0.007 0.006 0.789 <0.001 <0.001 <0.001 <0.001 <0.001 OR 0.083 0.015 0.012 1.691 <0.001 <0.001 0.003 <0.001 <0.001

Supplement 5. Correlations among δ15N values of fish species.

Methods

Pearson’s correlations were performed to examine relationships among the δ15N values of different fish species collected from each estuary on each sampling occasion.

Results

The δ15N values of different fish species were well correlated in all cases (R2 > 0.6) and

highly correlated (R2 > 0.8) in most cases (Figure S1).

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Figure S1: Correlations among δ15N values of fish species; R2 values are presented.