Food-web Implications for Pelagic Top Predators: from Guts and Isotopes to Models

Transcription

1 Food-web Implications for Pelagic Top Predators: from Guts and Isotopes to Models Robert J. Olson Inter-American Tropical Tuna Commission La Jolla, California Photo compliments of Dr. Frederic Menard, IRD, France

2 Food webs and Ecosystem-based Fisheries Science Ecosystem Ecology : multispecies approaches to management, reduction of bycatch, including environmental factors in stock assessment models. Ecosystem: a geographically specified system of organisms, including humans, the environment, and the processes that control its dynamics (NOAA 2005). The time has come for community ecology to replace population ecology as the fundamental ecological science underlying fisheries (Mangel and Levin 2005). Communities are assemblages of species. Interactions makes the community more than the sum of its parts. Communities interact via the food web. NOAA New priorities for the 21st century: NOAA's strategic plan. NOAA, Washington, D.C. Mangel, M., and P.S. Levin Regime, phase and paradigm shifts: making community ecology the basic science for fisheries. Phil. Trans. R. Soc. B, 360 (1453):

3 Why study food webs? Trophic structure represented in food webs is thought to be the central organizing concept in ecology (Martinez 1995). Knowledge of pelagic food webs is still rudimentary, in many aspects. Better food-web models are needed (preferably, spatially-explicit). Review an assortment of information about foodweb research in eastern Pacific, and (less-so) on modeling efforts.

4 Eight ecosystem characteristics NMFS Ecosystem Principles Advisory Panel: 1. The ability to predict ecosystem behavior is limited 2. Ecosystems have thresholds and limits which, when exceeded, can effect major ecosystem restructuring 3. Once thresholds and limits have been exceeded, changes can be irreversible 4. Diversity is important to ecosystem functioning 5. Multiple scales interact within and among ecosystems 6. Components of ecosystems are linked 7. Ecosystem boundaries are open 8. Ecosystems change over time

5 Components of ecosystems are linked How do we determine what the important components and linkages are? Critical food-web connections. Keystone species Dietary specialists Models can help The tools for food-web research: Diet studies (stomach-contents analysis) Stable isotope analysis Compound specific stable isotope analysis (amino-acids) Fatty acid analysis

6 Stomach-contents analysis (species identification) (and monitoring) V. Allain, SPC F. Galvan, CICIMAR IATTC, Manta, Ecuador

7 Diet data for eastern Pacific predators ( 92-94) 100% 80% Percent weight 60% 40% 20% 0% Dolphins Tunas Dorado, Wahoo, R. Runner Billfishes Sharks Colleagues: Felipe Galván-M, CICIMAR, La Paz, BCS, Mexico Julio Martínez, Cumaná, Venezuela

8 Diet data formulated food web (ETP) Trophic level Nivel trófico Olson, R.J., and G.M. Watters A model of the pelagic ecosystem in the eastern tropical Pacific Ocean. Inter-American Tropical Tuna Commission, Bulletin 22 (3):

9 Yellowfin tuna stomach-contents (1990s, 2000s) Set Locations 1990s 2000s

10 Feeding Ecology of Surface Migrating Myctophid Fishes in the eastern Tropical Pacific Joel Van Noord, Univ. of San Diego Jessica Redfern et al., NMFS SWFSC

11 Trophic position: stable isotopes δ 15 N = [( 15 N/ 14 N) / R std 1] x 1000 δ 15 N predator δ 15 N prey ( ) = Isotopic fractionation the light 14 N isotope is excreted more than the heavy 15 N isotope, leaving the animal enriched by 3 in δ 15 N relative to its food source.

12 Trophic position: stable isotopes, stomach contents δ 15 N ( ) Yellowfin tuna (5-deg areas) Yellowfin tuna (outside 5-deg areas) Mesozooplankton (5-deg areas) Mesozoopl. (outside 5-deg areas) CSIA samples Latitude (degrees) Mean TP = 4.5 PFRP, B. Popp, B. Graham, C. Hannides, F. Galván, G. López, B. Fry

13 Yellowfin trophic position (TP) YFT δ 15 N = Copepods δ 15 N = 6-12 Gladis Lopez-I., CICIMAR, Mexico Δ YFT-COP = TP , spanning ~ 1 trophic level B. Popp, UH B. Graham, UH F. Galvan-Magana, CICIMAR C. Lennert-Cody, IATTC PFRP

14 Popp, B.N., B.S. Graham, R.J. Olson, C.C.S. Hannides, M.J. Lott, G.A. López-Ibarra, F. Galván-Magaña, and B. Fry Insight into the trophic ecology of yellowfin tuna, Thunnus albacares, from compound-specific nitrogen isotope analysis of proteinaceous amino acids. In Dawson, T.E., and R.T.W. Siegwolf (eds.), Stable Isotopes as Indicators of Ecological Change. Elsevier-Academic Press, Terrestrial Ecology Series, San Diego: δ 15 N of Amino Acids Yellowfin tuna eastern tropical Pacific Bulk white muscle ( Source AA) ( Trophic AA) TL 4.5

15 E-W shift in trophic position in ETP TP shift 1

16 Lipids as Dietary Tracers Traditional techniques problematic, e.g. gut content analysis Prey species have unique lipid / fatty acid compositions Many fatty acids readily transferred from prey to predator with minimal modification Constituent fatty acids therefore represent, to some extent, a temporal integration of diet Can be quantitative and allows temporal integration (cf gut content analysis) Signature fatty acids: combinations of fatty acids preserved as they pass up the food chain Complements other approaches * Jock Young, CSIRO

17 Eight ecosystem characteristics NMFS Ecosystem Principles Advisory Panel: 1. The ability to predict ecosystem behavior is limited 2. Ecosystems have thresholds and limits which, when exceeded, can effect major ecosystem restructuring 3. Once thresholds and limits have been exceeded, changes can be irreversible 4. Diversity is important to ecosystem functioning 5. Multiple scales interact within and among ecosystems 6. Components of ecosystems are linked 7. Ecosystem boundaries are open 8. Ecosystems change over time

18 Can models predict ecosystem behavior? Nature is seldom linear, and often unpredictable (Francis et al. 2007). Ecosystem resilience depends on stability domain of existing food web: how broad is it, how resistant is it to change, how close is it to reorganizing? (Francis et al. 2007) Models are required. How should components of the food web be represented in models? Can models highlight key areas for field/lab studies? Francis, R.C., M.A. Hixon, M.E. Clarke, S.A. Murawski, and S. Ralston Ten commandments for ecosystem-based fisheries scientists. Fisheries, 32 (5):

19 Taxonomy in models Trophic level Nivel trófico (Olson, R.J., and G.M. Watters A model of the pelagic ecosystem in the eastern tropical Pacific Ocean. Inter-American Tropical Tuna Commission, Bulletin 22 (3): )

20 Functional groups in models Epi Epipelagic Epi-Meso Meso Epi- Bathy Meso Bathy Meso-Bathy Bathy

21 Qualitative analysis of Pacific Ocean predators 20 N Food webs composed of 200+ taxa Trophic level South-Western Pacific Ocean Central-Eastern Pacific Ocean Aggregated food webs composed of 24 nodes with similar predator prey relationships

22 Can models highlight research needs? Sensitivity analysis of ETP Ecopath model Large marlins Large Marlins Small sharks Small Sharks Small marlins Small Marlins Toothed whales Toothed Whales Large bigeye Large Bigeye Tuna Spotted dolphins Spotted Dolphins Large sharks Large Sharks Large wahoo Large Wahoo Large swordfish Large Swordfish Large sailfish Large Sailfish Pursuit birds Pursuit Birds Small mahimahi Small Mahimahi Small sailfish Small Sailfish Large mahimahi Large Mahimahi Large yellowfin Large Yellowfin Tuna Mesopelagic Mesopelagic dolphins Dolphins Small wahoo Small Wahoo Small Bigeye bigeye Tuna Small yellowfin Small Yellowfin Tuna Skipjack Tuna Cephalopods Cephalopods Small swordfish Small Swordfish Misc. piscivores Misc. Piscivores Bluefin Bluefin tuna Tuna Auxis spp. Auxis spp. Baleen whales Baleen Whales Rays Rays Grazing Grazing birds Birds Sea turtles Sea Turtles Crabs Misc. mesopelagic fishes Misc. Mesopelagic Fishes Flyingfishes Misc. epipelagic fishes Misc. Epipelagic Fishes Secondary Consumers Secondary consumers Primary consumers Primary Consumers Producers (5.3) (5.2) (5.1) (5.1) (5.1) (4.9) (4.8) (4.8) (4.8) (4.8) (4.8) (4.7) (4.7) (4.7) (4.6) (4.5) (4.5) (4.1) (3.9) (3.9) (3.8) (3.6) (3.6) (3.6) (1.0) (5.2) (5.5) (5.4) (4.7) (2.0) (5.4) (3.3) (3.0) (5.4) (4.1) Cephalopods Auxis spp. (4.6) Index of Sensitivity

23 Eight ecosystem characteristics NMFS Ecosystem Principles Advisory Panel: 1. The ability to predict ecosystem behavior is limited 2. Ecosystems have thresholds and limits which, when exceeded, can effect major ecosystem restructuring 3. Once thresholds and limits have been exceeded, changes can be irreversible 4. Diversity is important to ecosystem functioning 5. Multiple scales interact within and among ecosystems 6. Components of ecosystems are linked 7. Ecosystem boundaries are open 8. Ecosystems change over time

24 Ecosystems change over time Jumbo (Humboldt) squid range expansion Are tunas effective biological samplers of the middle trophic levels*? Indicator species in stomach contents? Squid consumption by tunas has increased over time Decadal changes in yellowfin tuna diet composition * Generalist predators (opportunistic), high energy requirements, food limited, range widely, prey size-predator size ranges widely

25 Pelagic ommastrephid squids (e.g. Dosidicus gigas): Ecosystem indicators? Olson, R.J., M.H. Román-Verdesoto, and G.L. Macías-Pita Bycatch of jumbo squid Dosidicus gigas in the tuna purse-seine fishery of the eastern Pacific Ocean and predatory behaviour during capture. Fish. Res. 79(1-2):

26 Percent frequency of cephalopods in the stomach contents of yellowfin tuna in the eastern Pacific Ocean 100 Percent frequency of occurrence Unidentified Octopus Squid All cephalopods PFRP, F. Galvan, N. Bocanegra, V. Alatorre, J. Martinez, F. Alverson Hunsicker, Essington, Olson, Duffy. Manuscript in prep. Evidence of increased cephalopod production in a large marine ecosystem.

27 Decadal variation in yellowfin tuna diet composition Classification tree analysis Nonparametric: relationships between variables that may include: nonlinearity, high order interactions, lack of balance, missing values Combinations of explanatory variables used to explain variation of a response variable (prey groups % weight), by repeatedly splitting the data into groups that are as homogenous as possible Each possible value for each explanatory variable is considered as a potential candidate split The candidate split which provides the largest decrease in impurity, or minimizes the misclassification rate, is chosen to split the data into two subgroups Procedure is repeated with each subgroup until no significant decrease in impurity is possible, resulting in a terminal node (leaf). 10-fold cross-validation used to prune trees (1-SE Rule) The proportions in each category are represented in each leaf Classification tree model constructed using the Diet library of R written by Petra Kuhnert, CSIRO

28 Classification tree analysis Response variable (18 dominant prey groups) Cephalopods Argonauta spp. Dosidicus gigas Sthenoteuthis oualaniensis Crustaceans Pleuroncodes planipes Portunidae family Other Crustaceans Fishes Cetengraulis mysticetus Engraulis mordax Phosichthyidae family Myctophidae family Exocoetus spp. Other Exocoetids Oxyporhamphus micropterus Carangidae family Auxis spp. Scomber japonicus Cubiceps spp. Lactoria diaphana Explanatory variables Year Quarter of year Purse-seine set time of day Latitude Longitude SST Yellowfin size Yellowfin sex Yellowfin stomach fullness Purse-seine set type

29 Yellowfin tuna stomach sample locations Set Locations 1990s 2000s

30 Classification Tree Analysis: Variable Importance Rankings Proportion Relative Importance Predictor variable Lat = latitude, Lon = longitude, SST = Sea Surface Temperature, YR = year, SA = set type, Qtr = quarter, Time = set time of day, FL = yellowfin fork length, Full = yellowfin stomach fullness

31 b-dg: c-so: e-pp: g-cm: h-em: i-phos: m-om: n-car: o-aux: p-sj: q-cub: r-ld: Dosidicus gigas Sthenoteuthis oualaniensis Pleuroncodes planipes Cetengraulis mysticetus Engraulis mordax Phosichthyidae family Oxyporhamphus micripterus Carangidae family Auxis spp. Scomber japonicus Cubiceps spp. Lactoria diaphana squids crustacean N of 17.3 N fishes R 2 = 0.45

32 Yellowfin tuna stomach sample locations North of Latitude 17.3 N Set Locations 1990s 2000s

33 Yellowfin tuna diet composition at first split 35% 30% north of latitude 17.3N south of latitude 17.3N Mean %Weight (± 2SE) 25% 20% 15% 10% 5% 0%

34 b-dg: c-so: e-pp: g-cm: h-em: i-phos: m-om: n-car: o-aux: p-sj: q-cub: r-ld: Dosidicus gigas Sthenoteuthis oualaniensis Pleuroncodes planipes Cetengraulis mysticetus Engraulis mordax Phosichthyidae family Oxyporhamphus micripterus Carangidae family Auxis spp. Scomber japonicus Cubiceps spp. Lactoria diaphana squids crustacean fishes South of 4.8 S R 2 = 0.45

35 Yellowfin tuna stomach sample locations North of Latitude 17.3 N Set Locations 1990s 2000s South of Latitude 4.8 S

36 Yellowfin tuna diet composition 70% mean %W (± 2SE) 60% 50% 40% 30% 20% Jumbo squid Anchoveta North of Latitude 4.8 S South of Latitude 4.8 S 10% 0%

37 Yellowfin tuna stomach sample locations South of Latitude 17.3 N North of Latitude 4.8 S Set Locations 1990s 2000s

38 b-dg: c-so: e-pp: g-cm: h-em: i-phos: m-om: n-car: o-aux: p-sj: q-cub: r-ld: Dosidicus gigas Sthenoteuthis oualaniensis Pleuroncodes planipes Cetengraulis mysticetus Engraulis mordax Phosichthyidae family Oxyporhamphus micripterus Carangidae family Auxis spp. Scomber japonicus Cubiceps spp. Lactoria diaphana squids crustacean fishes R 2 = 0.45

39 Yellowfin tuna diet composition at year split 45% Yellowfin tuna diet composition at time split (south of latitude 17.3 N, north of latitude 6.1 S, SST Mean %Weight (± 2SE) 40% 35% 30% 25% 20% 15% 10% diet composition diet composition Jumbo squid Mesopel. fishes Auxis spp. Epipel. fishes 5% 0%

40 Summary Research on pelagic food webs is progressing; should be encouraged. Chemical tracer methods are providing insight (SIA, AA-CSIA, fatty acids). Stomach contents analyses are still necessary (monitoring indicator prey species). Better ecosystem (food web) models are needed (spatially-explicit). Identify critical food-web connections. Food-web models should depict taxonomic ecosystem components (indicator species). Epipelagic ecosystems appear to change over time. Recommendation: low-level, well-designed, continuous stomach sampling of tunas, biological samplers, to monitor changes.

41 Acknowledgements Pelagic Fisheries Research Program and John Sibert, Univ. Hawaii NOAA Fisheries STAR Project, Lisa Ballance, G. Watters, and many others IATTC observers and staff in Ecuador and Mexico Brian Popp, B. Graham, N. Wallsgrove, E. Gier, J. Tanimoto, T. Rust, A. Carter, Isotope Biogeochemistry Laboratory, Univ. Hawaii Felipe Galván-Magaña, G. López-Ibarra, N. Bocanegra-Castillo, V. Alatorre-Ramírez, CICIMAR, La Paz Mexico Tim Essington, M. Hunsicker, Univ. Washington C. Lennert-Cody, M. Maunder, L. Duffy, M. Román-Verdesoto, C. Patnode, G.L. Macías-Pita, IATTC B. Fry, Louisiana State Univ., Baton Rouge V. Allain, Secretariat of the Pacific Community, New Caledonia Jock Young, Jeff Dambacher, CSIRO Jim Kitchell, Univ. Wisconsin