Changes of macrofauna stable isotope compositions in a very inconstant seagrass detritic habitat: actual diet modification or baseline shift?

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1 MSW 15 4th Mediterranean Seagrass Workshop 20/05/ Oristano Changes of macrofauna stable isotope compositions in a very inconstant seagrass detritic habitat: actual diet modification or baseline shift? François REMY, Thibaud Mascart, Patrick Dauby, Sylvie Gobert, Gilles Lepoint *Contact:

2 Seagrass detritus

3 What is «seagrass detritic habitat»? Posidonia oceanica seagrass P.oceanica dead leaves Macro-algae Macro-fauna P.oceanica living leaves µ «Dead leaves litter» François Remy François Remy % exported

4 Predator species Omnivore species Primary consumers/ detritic feeders M A C R O F A U N A Dead seagrass leaves Living seagrass leaves Algae Epiphytes D E T R I T U S

5 Exported litter patches are inconstant places! STORM 31/10/12 16:00h 01/11/12-10:00h Mean thickness: 53,4 cm Cover : 100% Mean thickness: 1,8 cm Cover : < 5%

6 Exported litter patches are inconstant places! HARBOR-site Day-level variations Seasonal variations Potential effect on food availability/composition!!

7 Stable isotopes

8 Why use N and C stable isotopes? Main rule in isotopic ecology : You are what you eat, plus (or minus) a few permill (DeNiro & Epstein 1976)

9 Why use N and C stable isotopes? < 13 C/ 12 C = food marker 15 N/ 14 N = trophic level/ food marker δ 15 N ( ) >> Food source composition consumer composition δ 13 C ( )

10 Aim? Aim of the study : Determine if the vagile macrofauna community experiences spatio-temporal changes of its isotopic composition Determine whether these variations are due to real diet modifications, or only due to isotopic baseline shifts of sources.

11 Sampling techniques Sampling in August 11, November 11, March 12 and June 12 2 sites (7-10m depth) Food sources + macrofauna

12 Results : the global community Food sources : Red algae Dead litter Living Posidonia Epiphytes + Photo. algae

13 Results : the global community Amphipods Molluscs Leptostraceans Decapods Isopods Fishes Polychetes

14 Results : the global community Amphipods Molluscs Leptostraceans Decapods Isopods Fishes Polychetes

15 Increasing trophic level Results : the global community Amphipods Molluscs Leptostraceans Decapods Isopods Fishes Polychetes

16 Results : zoom on the most abundant species SUMMER AUTUMN WINTER SPRING Spatio-temporal composition variation

17 Results : basal sources variations HARBOR-site SUMMER AUTUMN WINTER SPRING Litter Living leaves Epiphytes Photophilous algae Red algae

18 For more information SIBER «Isotopic niches» Ellipses metrics

19 δ 15 d15n N Increasing trophic level Results : SIBER run 5 DOMINANT SPECIES Gammarella fucicola Gammarus aequicauda Athanas nitescens Palaemon xiphias Melita hergensis δ 13 C d13c δ 13 C Interspecific niches variations

20 δ 15 d15n N Results : SIBER run δ 15 d15n N Gammarus aequicauda Primary consumers Summer Harbor Summer Oscelluccia Autumn Harbor Autumn Oscelluccia Winter Harbor Winter Oscelluccia Spring Harbor Spring Oscelluccia Gammarella fucicola d13c δ 13 C d13c δ 13 C Spatio-temporal intraspecific level niche variations

21 d15n δ 15 N Results : SIBER run δ 15 N d15n Also true for omnivores and predators title Athanas nitescens Summer Harbor Summer Oscelluccia Autumn Harbor Autumn Oscelluccia Winter Harbor Winter Oscelluccia Spring Harbor Spring Oscelluccia Palaemon xiphias d13c δ C δ 13 C d13c

22 Okay BUT But are these differences reflecting a diet change, or only a food sources baseline shift?

23 δ 15 N δ 15 N δ 15 N C Highly simplified example S1 S2 δ 13 C Diet shift C Baseline shift C S1 S2 S1 S2 δ 13 C δ 13 C

24 Mixing model Source 1 Source 2 Consumer Mixing equation δ m = faδ a + fbδ b faδ a + fbδ b = 1 The model gives the proportions of each food source

25 Mixing model Source 1 Source 5 Source 4 Source 6 Consumer Source 2 Source 3 Mixing equation δ m = faδ a + fbδ b + fbδ c + fbδ d + fbδ e + fbδ f faδ a + fbδ b + fbδ c + fbδ d + fbδ e + fbδ f = 1

26 SIAR Bayesian Mixing Model Parnell et al. 2010

27 Proportion of food source Proportion Proportion of food source Proportion d15n d13c SIAR mixing model run Gammarus aequicauda Gammarus aequicauda, Proportions by group: summer, 1 HARBOR Gammarus aequicauda, Proportions by autumn, group: 1 HARBOR Epiphytes + Photo Algae 0.78 Dead Litter 0.86 Dead Litter Epiphytes + Photo Algae 0.20 Red Algae Red Algae 0.02 L RA EA Source L RA EA Source Drastic changes even if the model takes baseline variations into account Real diet change independently of food sources isotopic composition!

28 Proportion of food source Proportion Proportion Proportion of food source d15n SIAR mixing model run title Changes also for omnivorous species d13c Athanas nitescens, Proportions summer, by group: 1HARBOR Athanas nitescens, Proportions autumn, by group: 1HARBOR 0.42* 0.50* 0.38* 0.28* 0.26* 0.08* 0.04* 0.03* GA GFMH LITTER EPIALG Food Sources GA GFMH LITTER EPIALG Food Sources * Median proportion values

29 Proportion Proportion of food source Proportion of food source Proportion d15n SIAR mixing model run Palaemon xiphias Changes at the top of the food web d13c Palaemon xiphias, Proportions summer, by group: 1 HARBOR Palaemon xiphias, Proportions autumn, by group: 1 HARBOR 0.49* 0.29* 0.21* 0.24* 0.24* 0.07* 0.04* 0.11* 0.01* 0.15* GA GFMH POOL COP FISHES Food Sources GA GFMH POOL COP FISHES Source Food Sources * Median proportion values

30 Take home message SIBER and mixing models like SIAR are powerful tools for trophic ecology to identify isotopic niches spatio-temporal variations and explain these modifications. BUT Need to work at a more precise taxonomic level Sampling all potential food sources Various trophic preferences Litter signal can sometimes be detected at the top of the trophic chain Importance of dead leaves

31 Acknowledgment The authors warmly thank the STARESO field station staff for their support during the sampling campaign. The first author acknowledges a PhD F.R.I.A. grant (Fund for Research Training in industry and in agriculture) of the Belgian National Fund for Scientific Research (FRS-FNRS). This study was conducted within the frame of FRS-FNRS research project FRFC (University of Liège).

32 Thank you for your attention!