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OIK-02486 Woodland, R. J., Warry, F. Y., Evrard, V

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OIK-02486 Woodland, R. J., Warry, F. Y., Evrard, V Oikos OIK-02486 Woodland, R. J., Warry, F. Y., Evrard, V., Clarke, R. H., Reich, P. and Cook, P. L. M. 2015. Niche-dependent trophic position distributions among primary, secondary and tertiary consumers. Oikos doi: 10.1111/oik.025486 Appendix 1. Supplemental methods describing the collection and preparation of stable isotope data used in the present study (includes tabulated information summaries of field surveys and taxa) Appendix 2.Table and plot of taxon-specific probability distribution fitting results Appendix 3. Plot of paired skewness, variance and range estimates calculated under constant and dynamic trophic fractionation models Appendix 4. Plots of trophic position and trophic position variance against consumer body size 1 Appendix 1 Supplemental methods describing the collection and preparation of stable isotope data used in the present study (includes tabulated information summaries of field surveys and taxa) Specimen collections were conducted during the ca four year interval spanning the austral autumn of 2010 to the autumn of 2013 in 34 estuaries along the coastline of southeastern Australia (Table A1). Taxa included a wide diversity of invertebrate and vertebrate consumers, all of which were considered potential groups of interest. Tissues sampled included white muscle and liver (fish), red blood cells and blood plasma (birds), and muscle and homogenized whole organisms (invertebrates; inorganic shell material of gastropods and bivalves were excluded). All tissues were oven-dried at 60°C until completely desiccated (minimum = 48 h), pulverized to a fine powder and analysed on an ANCA GSL2 elemental analyser interfaced to a Hydra 20-22 continuous-flow isotope ratio mass-spectrometer (Sercon Ltd., UK). Standards were Vienna Pee Dee Belemnite standard (RVPDB= 0.0111797) for C and atmospheric nitrogen (RAir = 0.0036765) for nitrogen with analytical precision: 13C = ± 0.1‰; 15N = ± 0.2‰ [SD]). Tissue-specific metabolic rates can result in dissimilar isotopic composition of different tissues within individuals (Sweeting et al. 2005), particularly for those individuals or cohorts that have recently altered their spatial or trophic ecology (Hobson 1999, Tieszen et al. 1983). To avoid potential errors arising from differences in tissue turnover rates, we categorized observations as derived from fast (i.e. liver, blood plasma) or slow (i.e. white muscle, red blood cells, whole body) turnover tissues. Due to low sample size (n = 269), fast turnover tissue samples were excluded from the analysis. The final combined dataset consisted of n = 4341 invertebrate and vertebrate specimens distributed amongst 65 unique taxonomic groups (57 identified to species level; Table A2). In addition, many fish species undergo large ontogenetic shifts in habitat and trophic ecology by the end of their first year of life. To account for the potential influence of ontogenetic effects within species, we assigned fish specimens to age-0 or age-1+ cohorts based on individual body size data collected during field surveys. Length thresholds for size-at-age determinations were based on information from the literature as well as evidence of modal length progression from the survey datasets. Of the 65 taxa identified, a subset of 47 taxa was further subdivided into age-0 and age-1+ age-classes. Invertebrates, birds and those fish for which individual length data were not available were aggregated at the species level. This resulted in a total of 90 groups classified at the species, species-age, or family level (Table A2). Beyond size-based age-classifications, we did not correct for size-dependent changes in δ15N (Vander Zanden et al. 2000) because size-related 2 variation in conspecific TP represent legitimate variability in the trophic ecology of these species before and after ontogenetic niche shifts. Prior to analysis, individuals were grouped by species, age, collection estuary, month and year of capture and site. Site identifiers were used to control for potential intra-estuary differences in trophic niche within species. Site identifiers were specific to each survey, and were based on one or more of the following: salinity regime (oligohaline, mesohaline, polyhaline), dominant habitat characteristics (bare sediment, seagrass, macroalgae), or geographical location within the estuary. This analysis primarily focused on δ15N composition of tissues; however, gradients in the isotopic composition of primary producers can yield intra-population enrichment–depletion patterns in δ15N that arise from differences in basal nutrition sources rather than changes in TP. To account for a potential interaction between nutrition sources and δ15N, we used ordinary least squares regression to test for a linear relationship between group δ13C (explanatory) and δ15N (response) values within each estuary. If the relationship was significant, the δ15N residuals of the regression were used instead of the observed δ15N values in subsequent analyses. Regression parameters for the calculation of dynamic estimates of trophic fractionation were calculated using the linear regression method described in Hussey et al. (2014). The absence of appropriate ancillary invertebrate δ15N data from which to calculate a trophic baseline forced us to discard consumers from several ecosystems. This led to a slightly reduced dataset for the dynamic fractionation calculations (Fig. 2). For most ecosystems, we directly applied the median estimates of slope = –0.27 and intercept = 5.92 identified by Hussey et al. from their meta-analysis of laboratory δ15N fractionation studies (Table A3). Due to highly enriched δ15N conditions in some of the ecosystems (n = 8), we were forced to substitute the upper 95% credible interval estimates of the slope and intercept posterior probability distributions from Hussey et al. (2014). Finally, in two extremely enriched ecosystems, we were obligated to increase the intercept value to an arbitrarily higher value (= 10.00‰) in order to derive sensible fractionation values from the regression model (Table A3). References Hobson, K. A. 1999. Tracing origins and migration of wildlife using stable isotopes: a review. – Oecologia 120: 314–326. Hussey, N. E. et al. 2014. Rescaling the trophic structure of marine food webs. – Ecol. Lett. 17: 239–250. Sweeting, C. J. et al. 2005. Variance in isotopic signatures as a descriptor of tissue turnover and degree of omnivory. – Funct. Ecol. 19: 777–784. Tieszen, L. L. et al. 1983. Fractionation and turnover of stable carbon isotopes in animal tissues: 3 implications for δ13C analysis of diet. – Oecologia 57: 32–37. Vander Zanden, M. J. et al. 2000. Within- and among-population variation in the trophic position of a pelagic predator, lake trout (Salvelinus namaycush). – Can. J. Fish. Aquat. Sci. 57: 725–731. 4 Table A1. Sample collection summary detailing the number of specimens collected from each south eastern Australian estuary and survey years. Estuary * 2010 2011 2012 2013 Aire River 45 80 63 Angelsea River 68 Avon River 17 Balcombe Creek 70 55 62 Bass River 46 58 135 62 Bennison River 22 Bunga Inlet 26 Bunyip River 5 21 15 Cardinia Creek 4 13 11 Chinamans Creek 41 Curdies River 48 106 109 Davis Creek 29 Eumeralla River 64 Fitzroy River 50 Franklin River 31 Gellibrand River 46 Gippslands Lakes 185 295 Hopkins River 49 Kennet River 40 Kororoit Creek 64 55 222 125 Little River 43 55 58 Maribyrnong River 7 19 13 Merricks Creek 43 27 30 Merriman River 78 Moyne River 93 72 Shipwreck Creek 29 Spring Creek 51 Tarra River 183 93 Thomposon River 53 Warringine Creek 13 24 22 Watsons Creek 13 27 35 Werribee River 49 53 200 111 Wingan River 168 89 Yarra River 50 50 53 * Collection gear differed slightly among estuaries; however, the primary gear types were relatively consistent with beach seines, fyke nets and multi-panel gill nets providing most of the specimens. Additional methods included hand extraction for some invertebrates, hook and line sampling for large fish species, and cannon netting for birds. 5 1 Table A2. Summary of taxonomic data for all consumers used in this study with literature-derived estimates of mean trophic position (TPLit) and 2 sample sizes for each of three different age-classes: undetermined (Und.), subadult-adult (Age-1+), young-of-the-year (YOY). All specimens 3 collected during multiple scientific surveys of aquatic consumers in southeastern Australian estuaries. Thermic group Phylum Family Taxon * TPLit Und. Age-1+ YOY Total Ecotherm Annelida Polycheata polychaetes 2.5 3 3 Arthropoda Amphipoda Gammarid amphipod 2 9 9 Atyidae Parataya sp. 2.8 33 33 Mollusca Gastropoda gastropod 2 5 5 Nematoda Nematoda parasitic liver nematode 3.5 8 8 Chordata Ambassidae Ambassis jacksoniensis 3.1 13 13 Anguillidae Anguilla australis 4.1 2 2 Arripidae Arripis truttaceus 3.5 119 119 4 50 50 Atherinidae Atherinosoma microstoma 3.2 251 69 320 Kestratherina brevirostris 3.2 3 3 Kestratherina esox 4.1 7 7 Leptatherina presbyteroides 3.2 5 5 Bovichtidae Pseudaphritis urvilli 3.3 58 43 101 Carangidae Pseudocaranx georgianus 3.4 2 2 Pseudocaranx wrighti 3.4 9 9 Clinidae Cristiceps australis 3.5 2 2 Clupeidae Hyperlophus vittatus 3.4 37 37 Sardinops sagax neopilchardus 2 15 15 Eleotridae Philypnodon grandiceps 3.8 2 291 107 400 Philypnodon macrostomus 3.3 9 9 Enoplosidae Enoplosus armatus 3.4 2 2 Galaxiidae Galaxias maculatus 3.1 92 71 163 6 Galaxias truttaceus 3.1 20 20 Gobiidae Acanthogobius flavimanus 3.3 5 5 Afurcagobius tamarensis 3.3 3 174 74 251 Arenigobius bifrenatus 3.3 7 42 53 102 Arenigobius frenatus 3.3
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