Structure and functioning of two pelagic communities in ... - Springer Link

Hydrobiologia (2013) 717:85–108 DOI 10.1007/s10750-013-1576-8

PRIMARY RESEARCH PAPER

Structure and functioning of two pelagic communities in the North Chilean Patagonian coastal system Hećtor J. Pave´s • Humberto E. Gonza´lez Villy Christensen

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Received: 7 July 2012 / Revised: 15 May 2013 / Accepted: 25 May 2013 / Published online: 19 June 2013 Ó Springer Science+Business Media Dordrecht 2013

Abstract The size composition of primary producers is important for how energy is channeled through a food web and on to the higher trophic levels and eventually to fisheries. To evaluate this, we studied the productive patterns for large (micro) versus small (nano) phytoplankton in two south marine Patagonian ecosystems: The Inner Sea of Chiloe—ISCh and, Moraleda Channel—MCh. We built Ecopath models

Handling editor: Mariana Meerhoff

Electronic supplementary material The online version of this article (doi:10.1007/s10750-013-1576-8) contains supplementary material, which is available to authorized users. H. J. Pave´s H. E. Gonza´lez (&) Instituto de Ciencias Marinas y Limnolo´gicas, Facultad de Ciencias, Universidad Austral de Chile, Valdivia, Chile e-mail: [email protected] H. J. Pave´s Fisheries Centre, University of British Columbia, Vancouver, BC, Canada H. E. Gonza´lez COPAS Sur Austral (PFB-31/2007) and COPAS, Center of Oceanography, Universidad de Concepcioń, Concepcioń and Patagonian Ecosystem Research Center (CIEP), Coyhaique, Chile V. Christensen Fisheries Centre, University of British Columbia, Vancouver, BC, Canada

(EwE), and evaluated the hypothesis that the overall primary productivity—rather than the ratio of large to small primary producers—constitutes an adequate proxy for predicting the amount of secondary and tertiary production and biomass (up to the fisheries). The EwE model included four small-scale fisheries and 36 functional groups. The functioning of both ecosystems was similar but the ecosystem parameters (biomass, energy transfer efficiencies from primary producers, secondary, and tertiary production) were twice as much in the basin with more microphytoplankton biomass. Overall, the hypothesis was rejected, albeit it was possible to highlight the importance of the quality and size spectrum of plankton on the structure of marine ecosystem, and to demonstrate the key role of the microbial loop over traditional food web in the functioning of the carbon biological pump in Patagonia ecosystems. Keywords Ecopath Microbial loop Traditional food web Patagonian coastal system

Introduction Comparisons of the planktonic community structure and ecosystem properties have shown considerable biomass differences between different pelagic systems, irrespective of primary productivity (PP) rates (Penã et al., 1990; Smith et al., 2001; Pave´s & Gonza´lez, 2008; Pave´s et al., unpublished data). Such

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differences in PP may affect secondary production, which, due to bottom-up effects on the food web, may then impact tertiary production including fisheries, e.g., changes registered under El Ninõ conditions in the Pacific Ocean (Iriarte & Gonza´lez, 2004; Escribano et al., 2004; Mattern et al., 2004; Oliveira et al., 2006; Arcos et al., 2001, 2004). Another important feature is when the microphytoplankton/nanophytoplankton biomass ratio is[1, the levels of biomass are higher than when this ratio is\1. These characteristics hold true on different temporal (winter: nanophdominated versus spring: microph-dominated) and spatial scales (coastal: microph-dominated versus oceanic: nanoph-dominated) (Penã et al., 1990; Iriarte & Gonza´lez, 2004; Gonza´lez et al., 2010, 2011). Thus, the environments with high biomass levels for primary producers are also characterized by high levels of secondary and tertiary production. In order to understand the relationship described above, the different parts of the pelagic community, considering all groups from bacterioplankton to mammals and fisheries, must be described within a single model. Computational tools such as Ecopath with Ecosim (EwE; Christensen & Pauly, 1992; Pauly et al., 2000; Christensen et al., 2008; Christensen & Walters, 2011) can be used to analyze and simulate trophic subwebs and to determine the amounts of biomass and fluxes of matter for the different trophic levels. Herein, we use EwE to study the trophic relationships and energy flows among different parts of the trophic web (EwE v 6.0; Christensen et al., 2000, 2008; Pauly et al., 2000; Christensen & Walters, 2004), impacted with different levels of fishery pressure in the Chilean North Patagonian pelagic ecosystem. This study aims: (1) To determine the ecosystem properties, biomass levels, and energy flows of the pelagic community in the studied systems, and to evaluate the relationship between PP and the producer’ biomasses; and (2) To determine differences in the functioning of these pelagic food webs between the ecosystems under evaluation. We tested the hypothesis that PP measured in different marine systems constitutes an adequate proxy for predicting the amount of secondary and tertiary production (up to the fisheries level) in coastal marine systems, fjords, and channels. In addition, irrespective of the PP, when the energy flows through the classical food web (i.e., large-sized phytoplankton and mesoplankton), differences in the ecosystem functioning and higher level in

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Hydrobiologia (2013) 717:85–108

secondary and tertiary productivity/biomass and fishery activities can be observed than when the energy flows through the microbial food web (channeling through nano- and picoplankton).

Materials and methods Models were built for two semi-enclosed basins of the Chilean Patagonia, Inner Sea of Chiloe´ (ISCh) and Moraleda Channel (MCh) (Fig. 1). Topographically, these two areas are different: ISCh is considered to be an inner sea because of its wide connection with the ocean and great basin size, whereas MCh is a channel– fjord system (Silva & Palma, 2008) with a more restricted connection to the ocean. The ISCh form a basin *11,000 km2 and with an average depth of 250 m and receives freshwater from different rivers (Davila et al., 2002). The ISCh basin is connected to the Pacific Ocean through two channels: Chacao (*2 km wide) and Corcovado (*30 km wide). The latter offers the most important area for the exchange of water masses with the ocean (Palma & Silva, 2004). Thus, a great part of the ISCh is influenced by Sub Antarctic Water (SAAW) and Sub Antarctic Modified Water (SAMW), salty water masses that contribute macronutrients from the ocean (Palma & Silva, 2004). On the other hand, MCh, is a basin of *4,100 km2 and has an average depth of 230 m with a smaller water exchange with the ocean than the ISCh. The MCh basin is connected to the ocean by a series of 11 transversal channels (*2 km wide) and the Corcovado Gulf (*19 km wide) at the northern end of the basin. Freshwater input enters to the MCh from different fjords, estuaries, and glaciers (Davila et al., 2002). The shallowest portion of the MCh is located close to the Minenea Constriction-Sill, a natural barrier that prevents the entrance of nutrient-rich SAAW below 150 m, and the dominance of Estuarine Water (EW), which is rich in silicic acid and has lower salinity and nutrient concentrations than the oceanic water (Palma & Silva, 2004). These topographical differences have been linked to their biomass differences: ISCh has more biomass of primary and secondary producers than MCh (Pave´s et al., unpublished data), however, the PP at the ISCh (786 mg C m-2 d-1) is slightly lower than at the MCh (947 mg C m-2 d-1) (annual averages obtained for the same area considered in this research; Gonza´lez et al., 2010, 2011).


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Fig. 1 Study area in the Chilean Patagonian fjords and channels region, showing the two contrasted basins: Inner Sea of Chiloe´ (ISCh, 41–43°S) (upper panel) and Moraleda Channel (MCh, 43–46°S) (bottom panel). The black circles show the sampling sites for organisms from the microbial and traditional food webs

For modeling, we used information from the two most contrasting seasons (winter and spring), thereby covering the seasonal variability in the annual averages. The 36 functional groups used were those with enough data available for the studied area. Emphasis was placed on the role of the microbial (i.e., picoplankton, nanoplankton, microplankton) and traditional food webs (i.e., mesoplankton, megaplankton, nekton, marine mammals, seabirds) and the more important fisheries activities in each basin. The microbial loop and mesoplankton data for ISCh and MCh, were collected during 2006–2007. The megaplankton was collected during 2005–2007, the nekton

during 2005–2008, the seabirds during 2008, the marine mammals during 1998 (sea lion) and 2009 (cetacean). And the fisheries activities information and their resources were obtained for the 2003–2009 period (Annex 1, 2, 3). The groups considered were: (1) Otariidae (Otaria flavescens, Arctophoca australis gracilis); (2) Aves (Thalassarche melanophrys, Puffinus griseus, Pelecanus thagus, Spheniscus humboldti, Spheniscus magellanicus, Phalacrocorax atriceps, Phalacrocorax magellanicus, Phalacrocorax gaimardi); (3) Orcinus orca; (4) Mysticeti (Balaenoptera musculus, Balaenoptera acutorostrata, Megaptera novaeangliae,

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Balaenoptera physalus, Eubalaena australis); (5) Delphinidae (Cephalorhynchus eutropia, Lagenorhynchus australis, Tursiops truncatus); (6) Gempylidae (Thyrsites atun); (7) Sciaenidae (Cilus gilberti); (8) Atherinopsidae (Odontesthes regia); (9) Ophidiiformes (Genypterus chilensis, Genypterus blacodes, Genypterus maculatus); (10) Gadiformes adult (Merluccius australis, Micromesistius australis, Macruronus magellanicus); (11) Gadiformes juvenilelarvae; (12) Carangidae (Trachurus murphyi); (13) Clupeiformes adult-juvenile (Sprattus fuegensis, Strangomera bentincki, Engraulis ringens); (14) Clupeiformes larvae; (15) Ichthyoplankton; (16) Scyphomedusae (Chrysaora plocamia); (17) Hydromedusae (Soltmtndetla hitentacittata, Ciytia simplex, Amphogona apicata, Bougainvillia macloviana); (18) Ctenophora (Pleurobrachia bachei); (19) Appendicularians (Oikopleura dioica, Oikopleura longicauda); (20) Siphonophora (Lensia conoidea, Muggiaea atlantica, Sphaeronectes gracilis); (21) Salpida (Salpa fusiforme, Thalia democratica); (22) Decapoda larvae (Neotrypaea uncinata, Sergestes articus, Munida subrugosa); (23) Euphausiacea (Euphausia vallentini, Nematoscelis megalop); (24) Chaetognatha (Sagitta tasmanica, Sagitta marri, Eukrohnia hamata, Sagitta gazellae); (25) Cladocera (Podon leuckarti, Evadne nordmanni, Pseudevadne tergestina); (26) Copepoda calanoida (Calanus australis, Calanoides patagoniensis, Drepanopus forcepatus, and copepods [ 800 lm); (27) Copepoda cyclopoida (Oithona similis, and copepods \ 800 lm); (28) Copepoda nauplii; (29) Ciliophora (ciliates); (30) Microphytoplankton (diatoms, autotrophic flagellates); (31) Microflagellates (heterotrophic dinoflagellates); (32) Heterotrophic nanoflagellates (HNF); (33) Autotrophic nanoflagellates/nanophytoplankton (ANF); (34) Bacteria (picoplankton); (35) Dissolved organic matter (DOM—detritus); and (36) Detritus—particulate organic carbon (POC) (Annex 1). Ecopath with Ecosim provides mathematical models at the static and dynamic ecosystem-levels (EwE; Christensen et al., 2008; Pauly et al., 2000; Christensen & Walters, 2011). EwE produces trophic ecosystem models that are mass- and energy-balanced, for a nonsteady-state, and allow the input of a variable number of functional groups and fishing fleets. All of these are connected through physiological and ecological linear equations that describe the ecosystem features (Ecopath module) or predict variations in the biomass of

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their components, either along temporal (Ecosim module) or temporal-spatial (Ecospace module) dimensions (Christensen et al., 2000, Christensen & Walters, 2004; Pauly et al., 2000, Walters et al. 1999). The EwE software uses a system of concurrent linear equations based on ecological assumptions, connecting the different functional groups and fisheries in a multi-taxa model. The model assumes mass balance within the system over the study period (in this case, averaged over 1 year) such that the production of any prey is equal to the biomass consumed by the predators plus any other use of production within or exported from the system, and was expressed by the equation: Bi ðP=BÞ EEi ¼ R Bj ðQ=BÞj DCij þ Yi þ Exi þ BAi

ð1Þ

where Bi and Bj are the biomass of prey and predator, respectively; (P/B)i is the production/biomass ratio; EEi is the ecotrophic efficiency that corresponds to the fraction of the production consumed (or caught) within the system with the remaining fraction going to detritus; (Q/B)j is the food consumed per unit biomass of predator j; DCij is the proportion of prey i in the diet of predator j; Yi is the fish catch rate; and Exi is other export (e.g., net migration); BAi is the biomass accumulation rate for i (Christensen et al., 2000). The input data for these models were B, DC, P/B, and Q/B, using the B, P, Q, and DC data available for the study area and from the literature (for references and periods considered see below, and Annex 1). The B values were expressed as mg C m-2; P and Q values as mg C m-2 year-1; and P/B and Q/B as year-1. The daily Q/B and P/B ratios for the plankton groups were multiplied by 365 to obtain annual rates (Annex 1). Data on fish catch rates (Y) were annual and expressed as mg C m-2 year-1. Migrations (E) were not considered in this study, i.e., they were assumed to be zero. We assumed that our study system was in a steady state (sensu Christensen et al., 2000) and that its populations were in equilibrium (sensu Allen, 1971; in Christensen et al., 2000). This implies that the biomass accumulation (BA, mg C m-2 year-1) rates were assumed to be zero. For biomass data of microbial loop and zooplankton species, we pooled the information from the springs and winters of 2006 (cruise CIMAR 12-Inner Sea of Chiloe), and 2007 (cruise CIMAR 13-Moraleda


Channel) to obtain an annual mean. All cruises were conducted on board the AGOR ‘‘Vidal Gorma´z’’ of the Chilean Navy. For biological data of zooplankton species, we used information from the literature (see Annex 1). For biomass and biological data of the traditional food web parts, we pooled the seasonal information from the fisheries reports of SERNAPesca (www.sernapesca.cl) and Fondo de Investigacioń Pesquera (www.fip.cl), as well as other published information on seabirds and marine mammals (see Annex 1). The Clupeiformes and Gadiformes biomass ([80% of total regional landings) were obtained from stock assessments in each study area (see Annex 1). Other fish resources (e.g., Gempylidae, Sciaenidae, Atherinopsidae, Ophidiiformes, Carangidae) for which little information was available and that represented low biomass landings (\20%) were included only as a first evaluation of their potential ecological (trophic) impact in the studied sub-food webs. We estimated the diet composition for each group using different approaches, but all were expressed as frequency of weight (WF%): (a) from the frequency of occurrence or number of the prey in the guts multiplied by carbon weight (e.g., Clupeiformes, Chaetognatha, Euphausiacea, Ctenophora); (b) from experiments on ingestion rates conducted in the studied areas (for Copepoda, Appendicularia, Salpida); and (c) from other carbon models (Flagellata, Ciliophora, see Annex 1, 2). Predation on Copepoda cyclopoida, Copepoda calanoida, and Cladocera was estimated according to bibliographic information. However, in those cases in which the diet information did not differentiate between these crustacean classes (for half of the copepod predators), predation on copepods and cladocerans was estimated according to the in situ availability of each one, assuming no predator selectivity (e.g., Salpida, see Vargas & Madin, 2004) (Annex 2). This constituted an oversimplification of the model that should be improved by including new empirical data on diet composition, abundance, and selectivity from the studied areas. Although mechanical selection of large over small prey might be possible in small pelagic fishes and euphausiids, for purposes of simplification (and because very little information is available on this issue), we did not consider this possibility. The contributions of DOM and detritus from each functional group were obtained from published information (see Annex 2).

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The artisanal (small-scaled fisheries) landings of the most important pelagic fishes (Clupeiformes purse-seine on Clupeiformes species, gillnet fishery on Sciaenidae and Atherinopsidae, mackerel purseseine on Carangidae, line hand fishery on Gempylidae) and some deep-sea species (Long line fishery on Ophidiiformes and Gadiformes) in the study area were obtained from the ‘‘Anuario Estadistico de Pesca’’, National Agency of Fisheries (Servicio Nacional de Pesca; www.sernapesca.cl). This fishing fleet is formed by vessels among 6–15 m length and until 80 ton of storage capacity. The data input were an average of 5 years (2003, 2006, 2007, 2008, 2009) (Annex 3). Fish discard biomasses were obtained from stock evaluation and discard reports (see Annex 1, 3). Biomasses of top predator discards were obtained from publications or reports about operational interactions between marine mammals, seabirds, and fisheries realized in or near the studied areas (see Annex 1). The fate of the discarded fishes was assumed to be 50 % of detritus (POC), 49.9 % of export of pelagic system and 0.1 % of DOM. The EwE model required a balance between the input and output of energy for all functional groups. This balance was expressed by means of the linear equation described by Winberg (1956; see Christensen et al., 2000), which assumes that the material consumed by one organism (Q) corresponded to the sum required by the individual for somatic and gonadic growth (P), metabolic costs (R), and the production of organic waste or non-assimilated food (UF) (Christensen et al., 2000). To determine the trophic level of each functional group considered, we applied the theoretical formulation of fractional trophic levels by Odum and Heald (1975). Trophic level one (TL1) corresponded to primary producers and detritus. Consumers were located in trophic level 1? the weighted average of the prey’s trophic level (Christensen et al., 2000). For both models and when required, we adjusted the original data of B, P/B, and Q/B until obtaining mass- and energy-balanced models. When the respiration (R) of a functional group was negative, we increased the ingestion rate values until reaching a value of R giving about 0.5 R/Q (Christensen et al., 2000). If the EE of a functional group was [1, modifications in the B, P/B, Q/B, and DC were made using bibliographic information about life histories of the species (e.g., average life span, age at first sexual

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maturity, stock evaluation, behavior data; see Annex 1). The modifications in P/B and Q/B were done until the P/Q ratio was 0.001 to 0.100 for marine mammals and seabirds, 0.100 to 0.300 for fish groups, 0.200 to 0.400 for macro and mesoplankton groups, 0.300 to 0.500 for nano- and microplankton groups, and [0.500 for the picoplankton group (bacteria). To make these changes, we sought to adjust values from the literature so as to be physiologically acceptable for each species (Christensen et al., 2000). In normal cases, P/Q values ranged from 0.05 to 0.3, i.e., the consumption of most groups was about three to ten times higher than their production (Christensen et al., 2000). Exceptions were top predators, e.g., marine mammals, which could have lower P/Q values, and small fast-growing fish larvae, nauplii, or bacteria, which could have higher P/Q values (Christensen et al., 2000). The B of the poorly represented zooplankton groups in the samples were increased between two and 300 times depending on the group, with the respective extremes being Copepoda and Salpida. This increase was based on the fact that vertical hauls with a zooplankton net usually underestimate the abundance of small-sized zooplankton and gelatinous plankton groups like Cladocera, Salpida, Copepoda cyclopoida, Chaetognatha, Siphonophore, Scyphomedusae, and Ctenophora (Gallienne & Robins, 2001; Giesecke & Gonza´lez, 2004; Stehle et al., 2007; Antacli et al., 2010; Riccardi, 2010). In addition, these modifications were made in order to obtain EEs \1 for each group, and thus, reach the criteria of mass balance (Christensen et al., 2000). We obtained a table of pedigree data that could be used to evaluate the precision of the information (Annex 4). Also, we obtained output to describe the matter and energy flows and the ecological relationships between all the functional groups evaluated in the ISCh and MCh basins. The following additional estimates were made in order to describe the functioning of the trophic food web in each ecosystem: (1) Trophic overlap index (TOI): The index values range from 0 to 1, a value of 0 indicate that the two species do not share resources, 1 indicates complete overlap, and intermediate values show partial overlap in resource utilization (Christensen et al., 2000). (2) Mixed trophic impacts: This analysis allowed a description of the direct (i.e., predator–prey) and indirect (e.g., cascade effect) trophic relationships

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among the different groups of the system (Christensen et al., 2000). A positive effect of a prey on a predator would indicate that an increment in the prey biomass may result in an increment in the predator biomass. In line with this, a negative effect of a predator on a prey would indicate that an increment in the predator biomass may result in a decline in the prey biomass. Thus, the effect depended on the biomasses of both predator and prey (Christensen et al., 2000). The estimation of these impacts was based on the differences between the average values of all trophic impact generated by all functional groups for each ecosystem. These values exclude the impact generated by those groups that did not impact the functional group under analysis (i.e. those that were not prey nor predator). (3) Keystoneness index (KS): Keystone species are defined as those having a relatively low biomass and a disproportionate structuring role in the food web. This parameter was estimated from the network mixed trophic impact analysis described above. This parameter was related to the biomass of the same species (function KS, from relation between biomass proportion and overall effect on food web) (Libralato et al., 2006). For the characterization of the ecosystem size were used; (1) Flow to the detritus: For each group, the flow to the detritus consisted of what was egested (i.e., percentage of non-assimilated food relative to food consumption) and those elements of the group that died due to old age, diseases, etc. (i.e., sources of ‘other mortality’, expressed by 1 - EE). The flow to detritus was expressed as mg C m-2 year-1 and should be positive for all groups. (2) Summary of parameters of the analyzed system: Based on Odum (1969) and applying EwE, it was possible to estimate parameters that allowed an analysis of the size of a system (Christensen, 1995). The size of any ecosystem was characterized for the sum of all consumption, exports, respiration flows, flows to detritus, total system throughput, total primary production, net system production, biomass, and total catches (Christensen, 1995). (3) The energy transfer efficiencies (ETE): Based on the flows and biomasses, the transfer efficiencies between successive discrete trophic levels were calculated as the ratio between the sum of the exports from a given trophic level, plus the flow that is transferred from one trophic level to the next, and the trophic level throughout (Christensen et al., 2000). The discrete trophic levels were estimated sensu Lindeman (1942) in Ecopath with Ecosim.


Results Ecosystem Functioning The application of EwE allowed us to characterize the trophic energy flows and amount of biomass in the pelagic realm, identifying in both ecosystems under observation four important pathways through the micro-, meso-, and macro-plankton (both chitinous and gelatinous zooplankton), nekton (fishes), and meganekton (marine mammals) in both ecosystems (Fig. 2). These important pathways started in microphytoplankton (diatoms), nanophytoplankton (autotrophic nanoflagellates, ANF), DOM groups linked to picoplankton (bacteria), and the detritus (POC) (Fig. 2). The first pathway started from microphytoplankton, which is grazed down by micro- (copepod nauplii, ciliates), meso- (copepods), and macrozooplankton (euphausiids, decapod larvae). The second started from nanophytoplankton (ANF), which is heavily grazed down by micro- (microflagellates, copepod nauplii, ciliates) and mesozooplankton (appendicularia, salpida, copepods) and receives less pressure from macrozooplankton (euphausiids, decapoda larvae). The third pathway started from DOM, which flows through bacteria and nano- (heterotrophic nanoflagellates, HNF) to micro- (microflagellates, ciliates) and mesoplankton (Cladocera). All these pathways linked micro- and mesozooplankton to fishes and top predators. Among plankton groups, all pathways occupied the Copepoda calanoida, Euphausiacea, and Decapoda larvae as linking pathways toward fishes and marine mammals (mainly baleen whales). Among fish groups, the Clupeiformes act as connectors between the plankton and larger nekton, and Gadiformes act as a link toward top predators such as marine mammals (Fig. 2). In addition, in both food webs, the jellyfish groups (Siphonophore, Ctenophora, Hydromedusae, Scyphomedusae) consumed the same preys as fish, a situation that under limiting resource condition could result in competition (e.g., Euphausiacea, Cop. calanoida, Decapoda larvae; Fig. 2). Niches overlap analysis The above-mentioned pathways allowed us to determine the characteristic overlap index between the different functional groups of the sub-trophic webs in

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the North Patagonian System. In the ISCh, Clupeiformes larvae and Ichthyoplankton, Ichthyoplankton and Siphonophora, Ichthyoplankton and Ctenophora, and Clupeiformes larvae and Ctenophora showed a high overlap of the index of predators and preys, with their niche overlap value being close to 1 ([0.70) (Fig. 3). In the MCh, the niche overlap index was[0.7 for these same groups, but niche overlaps were also registered between Gadiformes juvenile-larvae and Siphonophora, Gadiformes juvenile-larvae and Clupeiformes adult-juvenile, Copepoda cyclopoida and Cladocera, and Gadiformes adults and Clupeiformes adult-juvenile (Fig. 3). Mixed trophic impact analysis In general in both models, Euphausiacea positively affected the Mysticeti (baleen whale) (Fig. 4E) and Scyphomedusae negatively impacted the larvae fish group, its food item (Fig. 4F). In addition, a negative effect of one group on itself (higher intra- than intergroup competition for resources or cannibalistic behavior) is observed for Gadiformes (Fig. 4I), Otariidae (Fig. 4J), Aves (Fig. 4K), and O. orca (Fig. 4L). Indirect effects can be detected for microphytoplankton (Fig. 4C), which had a negative effect on the biomasses of HNF and ANF but a positive effect on the microflagellates because the latter group predates moderately on microphytoplankton but strongly on nanoflagellates (both HNF and ANF; Fig. 4). The bacteria in these ecosystems had a positive impact on their predators, the microbial loop species, and a negative impact on their unique food source, the DOM reservoir; however, bacteria had a very strong intraspecific effect (Fig. 4A). The ANF positively impacted the zooplankton group and, in turn, higher trophic levels (i.e., fishes and top predators), and even produced an increase in fishery activities (Fig. 4B). Microphytoplankton produced the same effect as ANF on these groups, but this effect was 57–64 % stronger on the fishes, marine mammals, and fisheries than ANF (Fig. 4C). Thus, microphytoplankton would had a stronger impact on ecosystem productivity and showed more efficient trophic flows than ANF. A similar situation was determined with Copepoda calanoida and Euphausiacea: the positive impact of Euphausiacea on higher trophic levels (including fisheries) exceeds that of the copepods by a 65–91 % (Fig. 4D, E).

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Fig. 2 Trophic pathways between groups of the traditional and microbial pelagic food webs that represent the annual average community flows of the two basins in the Chilean Patagonian coastal system: A Inner Sea of Chiloe´ (41–438S), and B Moraleda Channel (43–468S). The thickness of the lines is proportional to the magnitude of the energy flow between functional groups. Only flows[0.5% are depicted in the figure. The size of the nodes are comparable between the different functional groups of the same model. Otar = Otariidae; Aves; O. orca = Orcinus orca; Mys = Mysticeti; Delph = Delphinidae; Gemp = Gempylidae; Scian = Sciaenidae; Athe = Atherinopsidae; Ophi = Ophidiiformes; Gad (A) = Gadiformes adult; Gad (J-L) = Gadiformes

juvenile-larva; Caran = Carangidae; Clup (A) = Clupeiformes adult-juvenile; Clup (J-A) = Clupeiformes larva; Ichthyo (L) = Ichthyoplankton; Scypho = Scyphomedusae; Hydro = Hydromedusae; Cteno = Ctenophora; Appe = Appendicularians; Sipho = Siphonophora; Salp = Salpida; Deca (L) = Decapoda larvae; Euph = Euphausiacea; Chaeto = Chaetognatha; Clado = Cladocera; C. cal = Copepoda calanoida; C. cyclo = Copepoda cyclopoida; C. nauplii = Copepoda nauplii; Cilio = Ciliophora; Mphyto = Microphytoplankton; Mflage = Microflagellates; HNF = Heterotrophic nanoflagellates; ANF = Autotrophic nanoflagellates; Bacteria; DOM = Dissolved organic matter (detritus); Detritus = particulate organic carbon (POC)

On the other hand, the jellyfish groups (Scyphomedusae, Siphonophora) had a negative impact mainly on the fish larvae group (Clupeiformes larvae and Ichthyoplankton), potential competitors (other jellies), and jellyfish prey (e.g., Decapoda Larvae, Cladocera, Copepoda), but they did not show intraspecific impacts (Fig. 4F, G). The Clupeiformes and Gadiformes had a positive impact on all top predator species

(seabirds, marine mammals) and the fisheries (Fig. 4H, I). However, both these species showed a strong intraspecific impact. In Gadiformes, this intraspecific control-effect was twofold higher than in Clupeiformes (Fig. 4H, I). Finally, all top predators (e.g., Aves, Otariidae, and O. orca) produced a negative impact on their different fish prey groups, potential competitors (e.g., seabirds on carnivorous

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1,0 14:15

29:31

0,9

27:28

19:26 10:13

Prey overlap index

14:15

10:13

0,8

11:14 12:13

0,7

Flow to the detritus and biomass

15:20

15:20 14:20 14:18 25:27 14:18 25:27 11:13 15:18 25:27 11:15 15:18 10:13

11:18 18:20 23:26

0,6

18:20

10:11

1:5

23:26 28:29

0,5

19:22 11:13

10:11

11:20

0,4

19:23

18:22 19:23 17:21

0,3 0,4

Ecosystem size

29:31

0,5

0,6

0,7

0,8

0,9

1,0

Predator overlap index

Fig. 3 Trophic overlap level among the groups of the pelagic sub-web of the Inner Sea of Chiloe (ISCh) and the Moraleda Channel (MCh). Only groups with an overlap index higher than 0.4 are shown (0 = without overlap, 1 = total overlap). The functional groups are numbered as in Table 1, where numbers in bold and cursive correspond to ISCh and MCh, respectively

fishes, O. orca on Mysticeti whale), and fisheries catches. But these entire top predator groups had an important intraspecific effect (Fig. 4J–L). Despite of this general trend described in the trophic impact, it was possible to indeed observe difference and similarities between models. For example, changes in bacteria and ANF biomasses produced the same (or similar) impact on both the ISCh and MCh food webs. But, in the others high trophic level groups this trophic impact is higher in the ISCh’s predators than preys, or higher in MCh’s preys than predators (microphytoplankton, Copepoda calanoida, Euphausiacea). The jellyfish trophic impact was stronger for the ISCh’s (*20–90 %) than MCh food webs. On the other hand, change in the biomass of Clupeiformes and Gadiformes, affected mainly the MCh than the ISCh fisheries activities (*15–100 %). In addition, changes in top predators’ biomass would affect mainly their prey and the fisheries, being more intense this effect in the MCh than in the ISCh (*50 %). Keystoneness index In the ISCh and MCh ecosystems, the Scyphomedusae, Hydromedusae, Copepoda calanoida, O. orca and Ichthyoplankton were estimated to be the groups with high keystoneness indexes (Fig. 5).

Other aspects that showed us some differences between these two ecosystems are related to the amount of matter that flows from the different functional groups to the detritus. In ISCh and MCh, the total planktonic and nektonic organisms produced *699 and 376 g C m-2 year-1 of detritus, respectively, of which 63 and 59 % were in the form of DOM and the rest was POC (Fig. 6). The microbial loop (ciliates, microflagellates, nanoflagellates, bacteria) produced 31 (ISCh) and 23 % (MCh) of this detritus, mainly as dissolve excretion (DOM) products. In these systems, the microphytoplankton produced 65 and 70 % of the detritus, i.e., *234 and 137 g POC m-2 year-1 as phytodetritus and *216 and 126 DOM g C m-2 year-1 as exudates, respectively (Fig. 6). In the ISCh and MCh, the average annual detritus (POC, DOC) production by the chitinous and gelatinous zooplankton was 3–4 and 1–2 %, respectively. Thus, chitinous zooplankton produced 8 and 6 POC g C m-2 year-1 and 11 and 9 DOM g C m-2 year-1 in these two systems. On the other hand, gelatinous zooplankton produced between *7 and 5 g POC m-2 year-1 and * 2 g DOM m-2 year-1 in the MCh and ISCh, respectively (Fig. 6). The fish groups and top predators produced only *1 and *0.006 % of the detritus in the both ecosystems (mainly POC detritus), i.e., the fishes produced *6 and 4 POC g C m-2 year-1 and the top predators produced *39 and 45 POC mg C m-2 year-1 in ISCh and MCh, respectively (Fig. 6). This means that microphytoplankton contributed 90 % of the total POC, the micro- and mesoplankton groups added 7 %, and the nekton (fish, top predators) contributed 3 % in these ecosystems. Microplankton also produced 49 and 56 % of the DOM in ISCh and MCh, and the pico- and nanoplankton were responsible for 46 and 38 % of this total DOM. In the MCh, the biomass was greater than in the ISCh by 11–19 times for top predators (cetaceans), 1.5 for Gadiformes, 1.4 for Hydromedusae, and 2.6 for Copepod nauplii. However, in the ISCh, the total biomass is *1.5 times more than in the MCh (13.5 vs. 8.9 g C m-2; Tables 1, 2). This difference was mainly related to the greater biomass of microplankton

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Hydrobiologia (2013) 717:85–108 0,4

A)

0,2 0 1

3

5

7

9

11

13

15

17

19

-0,2

21

23

25

27

29

31

33

35 CFs LFs LhFs

,

-0,4

Bacteria ISCh

-0,6

Bacteria MCh

Mixed trophic impact index

-0,8

C)

0,3

0,3 0,25 0,2 0,15 0,1 0,05 0 -0,05 -0,1 -0,15 -0,2

0,4

0,1

0,2

0 5

7

9

11

13

15

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35 CFs LFs LhFs

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15

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13

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19

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35 CFs LFs LhFs

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35 CFs LFs LhFs

21

23

25

27

29

31

33

35 CFs LFs LhFs

ANF ISCh ANF MCh

D)

, 1

3

5

7

9

11

-0,2

Mphyto ISCh

-0,2

Cop.cal ISCh

-0,4

Mphyto MCh

Cop.cal MCh

-0,3

-0,6

E)

0,6 0,5 0,4 0,3 0,2

,

0,1 0 -0,1

5

0

, 3

, 1

0,6

0,2

1

B)

1

3

5

7

9

11

13

15

17

19

21

23

25

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29

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33

35 CFs LFs LhFs

-0,2

Euph ISCh

-0,3

Euph MCh

2 1,5 1 0,5 0 -0,5 -1 -1,5 -2 -2,5 -3

F)

1

3

5

7

9

11

,

Scypho ISCh Scypho MCh

Fig. 4 Mixed trophic impact index for the functional groups of Inner Sea of Chiloe (ISCh) and Moraleda Channel (MCh). Positive and negative impacts are represented by bars above and below the baseline, respectively. The height of the bars indicates relative impact, comparable among groups. The impacted functional groups are numbered as in Table 1. The impacting groups are named in each graph’s legend. Black bars indicate the mixed trophic impact index of the different species

belonging to ISCh and white bars to MCh. In A is presented the MTI of Bacteria, B for ANF, C for Microphytoplankton, D Copepoda Calanoida, E Euphausiacea, F Scyphomedusae, G Siphonophora, H Clupeiformes adult-juvenile, I Gadiformes adult, J Otariidae, K Aves, and L for Orcinus orca. The fisheries are, CFs = Clupeiformes Purse-seine; GFs = Gillnet fishery; LFs = Long line fishery; MFs = Mackerel Purse-siene; LhFs = Line hand fishery

(2.3 times more), bacteria (2.0), nanoflagellates (2.0), Copepoda (1.7), and secondarily to Carangidae (3.5), Ichthyoplankton (1.9), Clupeiformes (1.7), Sciaenidae (100 %), and Atherinopsidae (1.7) observed in the ISCh ecosystem (Table 1). When we compare the biomasses of microphytoplankton, nanophytoplankton (ANF), and picoplankton (bacteria) in the ISCh and MCh, we obtained ratios of 7.8:1.0:3.6 and 5.7:1.0:3.2, respectively. This suggests a higher dominance of the microphytoplankton (mainly diatoms) in the ISCh than in the MCh. In addition, in the ISCh ecosystem, the biomasses of the

diatoms, autotrophic nanoflagellates, and bacteria constituted 7.4, 0.9, and 3.4 % of the total biomass, unlike the MCh, where the percentages were lower (4.6, 0.8, and 2.6 %, respectively) (Table 1).

123

Ecotrophic efficiency, energy flows, system throughput, and energy transfer efficiencies Over 57 and 66 % of the functional groups had an EE [ 0.9 in MCh and ISCh, respectively. Here, bacteria consumed only DOM and HNF consumed only bacteria, whereas microflagellates were

Hydrobiologia (2013) 717:85–108 0,2 0,15 0,1 0,05 0 -0,05 -0,1 -0,15 -0,2 -0,25 -0,3 -0,35

G)

H)

1 0,8 0,6 0,4

1

3

5

7

9

11

13

15

17

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29

31

33

35 CFs LFs LhFs

0,2

,

,

0 1

Sipho ISCh

3

5

7

9

11

13

15

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25

27

I)

0,6 0,4 0,2 ,

0 1

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35 CFs LFs LhFs

-0,2

Gad (A) ISCh

-0,4

Gad (A) MCh

-0,6 0,2

31

33

35 CFs LFs LhFs

Clup (J-A) ISCh Clup (J-A) MCh

-0,6 0,2 0,1 0 -0,1 -0,2 -0,3 -0,4 -0,5 -0,6 -0,7 -0,8

J) 1

3

5

7

9

11

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33

35 CFs LFs LhFs

,

Otaria ISCh Otaria MCh

0,6

K)

29

-0,2 -0,4

Sipho MCh

0,8

Mixed trophic impact index

95

L)

0,4

0,1

0,2

0 1

3

5

7

9

11

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35 CFs LFs LhFs

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0

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,

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35 CFs LFs LhFs

,

-0,4

-0,3 -0,4

3

Aves ISCh Aves MCh

-0,6

-0,6 -0,8 -1

O.orca ISCh O.orca MCh

Fig. 4 continued

consumed by zooplankton, zooplankton by fishes, and these two latter groups were consumed by marine mammals, seabirds, fishes, and fisheries. Two groups of top predators had higher EEs: Aves, mainly due to cannibalistic behavior and fisheries interaction, and Delphinidae, due to predation and fisheries interaction (Table 1, Annex 3). The different biomasses found in the pelagic communities of these two ecosystems also produced different carbon flows, consumption, exports, respiration, and total system throughput (Table 2). For all these parameters, ISCh showed from 1.7 to 1.9 times more flows than MCh, e.g., total system throughput for ISCh is 2.697 versus 1.432 gC m-2 year-1 for MCh (Table 2). In the same way, all productions in the ISCh were between 1.2 and 5.2 times higher than in MCh (i.e., net system production, all production, secondary,

and tertiary production) but only the PP was *3 times higher in MCh than ISCh (Table 2). The energy transfer efficiencies (ETE) of MCh and ISCh were similar (32 vs. 30 %). However, the energy available from primary producers in ISCh (26 %) was slightly higher than in MCh (25 %). The relationship between detritus (D) and biomass of primary producers (PP), (D = DOM ? POC)/PP was 1.7 times higher in ISCh than MCh (i.e., MChD/PP = 35.8, ISChD/PP = 59.9). Moreover, the amount of energy available at the last trophic level was greater in ISCh (9.3 %) than in MCh ecosystem (5.5 %). Fisheries and fishing mortality rates The total catch was ca. 20 times higher in ISCh than in MCh (Table 2). Moreover in the ISCh ecosystem, only

123

96


Keystone index

1

17

0.5 3

0

0

-0.5

26 26

0.2

16

17 16 3 15

0.4

0.6

0.8

1

15

-1

ISCh MCh

-1.5 -2

Relative total impact

Fig. 5 Keystoneness for the functional groups of the pelagic communities in the Inner Sea of Chiloe (ISCh) and Moraleda Channel (MCh), sensu Libralato et al. (2006). For each functional group, the keystoneness index (‘‘y’’ axis) is reported against overall effect (‘‘x’’ axis). Overall effects are relative to the maximum effect measured in each trophic web. Thus, for the ‘‘x’’ axis, the scale is between 0 and 1. The keystone functional groups are those that have value close to or greater than zero, and they are numbered in the graph. The functional groups are numbered as in Table 1, where numbers in bold and cursive correspond to ISCh and MCh, respectively

two functional groups have fishing mortality rates higher than predation mortality rates (i.e., Otariidae by incidental catch or bycatch, Gempylidae by direct catch) (Table 3). However, when we compare fishing with other natural mortalities (life history, senescence, disease), fishing mortality exceeded 90 % of the natural mortalities in eight groups (Aves and Delphinidae by incidental catch or bycatch, Scianidae, Gempylidae, Atherinopsidae, Ophidiiformes, Gadiformes, and Carangidae by direct catch; Table 3). In three of these groups, mortalities from fishing activities comprise [30 % of total mortality. This situation differs

from that observed in MCh, where only one groups had fishing mortality rates higher than predation mortality rates (Otariidae by incidental catch or bycatch), five groups had fishing mortality higher than other natural mortalities (Delphinidae, Gempylidae, Atherinopsidae, Ophidiiformes, Carangidae), and one group had a fishing mortality [10 % of total mortalities (Otariidae) (Table 3). The total catches of all fish resources were 194 and 10 mg C m-2 year-1 in ISCh and MCh, respectively, and the most important species were Clupeiformes, Carangidae, and Gadiformes in ISCh, and Gadiformes and Clupeiformes species (caught by artisanal fisheries) in MCh (Fig. 8). The bycatch levels for ISCh and MCh were 0.7 and 0.3 mg C m-2 year-1, respectively, with the most important species being Otariidae and Aves (Fig. 7). According to the mixed trophic impact analysis, any increase in fisheries activities would affect both commercial and non-commercial species (Fig. 8). In these fisheries, greater Clupeiformes catches would produce a negative effect, reducing Clupeiformes, Gempylidae, and Otariidae biomasses and triggering competition with Clupeiformes and hand-line fisheries (Fig. 8A). Higher gillnet fisheries catches would negatively affect the Otariidae and Delphinidae biomasses; the effect would be felt more strongly in ISCh than MCh (Fig. 8B). Increased purse-seine catches of mackerel would negatively affect Gempylidae and Carangidae biomasses, producing competition with hand-line and mackerel purse-seine fisheries

Flows to detritus (mgC m-2 year -1)

1000000.00000 100000.00000 10000.00000 1000.00000 100.00000 10.00000 1.00000 0.10000 0.01000 0.00100

ISCh (POC)

MCh (POC)

0.00010

ISCh (DOM)

MCh (DOM)

0.00001 1

2

3

4

5

6

7

8

9 10 12 13 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Fig. 6 Carbon flow (mgC m-2 y-1) to detritus from the different functional groups of the Northern Patagonia pelagic system: Moraleda Channel (MCh) and Inner Sea of Chiloe

123

(ISCh). The functional groups are numbered as in Table 1. Note log scale in the abscise

Delphinidae

Gempylidae Sciaenidae

Atherinopsidae

Ophidiiformes

Gadiformes (A)

Gadifomes (J-L)

Carangidae

Clupeiformes (J-A)

Clupeiformes (L)

Ichthyoplankton (L)

Scyphomedusae

Hydromedusae

Ctenophora

Appendicularia

Siphonophore

Salpida

Decapoda (L) Euphausiacea

Chaetognatha

Cladocera

Copepoda calanoida

Copepoda cyclopoida

5

6 7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22 23

24

25

26

27

Ciliophora

Mysticeti

4

29

Orcinus orca

3

Copepoda nauplii (L)

Aves

2

28

Otariidae

1

2.71

3.04

3.12

2.64

3.36

3.72

3.08 3.18

3.41

3.96

2.50

3.93

3.91

4.26

3.78

3.83

4.18

4.65

4.39

4.65

5.09

3.83

5.32 5.20

5.24

4.51

5.76

5.23

5.91

2.71

3.04

3.12

2.63

3.36

3.72

3.07 3.17

3.41

3.95

2.50

3.92

3.90

4.26

3.78

3.83

4.08

4.63

3.91

4.56

5.02

3.83

5.17 –

5.10

4.49

5.60

5.14

5.54

55.75

33.36

61.85

1197.67

4.47

17.76

893.85 1556.64

3.62

28.50

9.25

3.54

23.75

85.28

2071.69

146.44

2466.22

588.43

353.85

1311.05

266.62

60.95

11.00 359.76

0.34

0.29

0.01

15.33

22.19

38.77

86.63

29.43

727.00

2.92

11.76

648.35 949.38

3.85

17.94

6.61

2.28

34.06

53.68

1096.56

86.72

1422.51

168.53

528.59

1829.35

204.11

36.89

11.41 –

4.71

3.07

0.18

11.27

17.27

MCh

ISCh

ISCh

MCh

Biomass (mgC/m2)

Trophic level

533.98

46.03

42.94

28.84

52.40

11.73

5.53 5.37

39.35

60.83

158.51

57.51

32.24

41.06

2.29

1.65

0.70

0.09

0.28

0.12

0.11

0.56

0.09 0.11

0.08

0.04

0.06

0.10

0.09

ISCh

533.98

46.03

42.94

28.84

52.40

11.73

5.53 5.37

39.35

60.83

158.51

57.51

32.24

41.06

2.29

1.73

0.70

0.09

0.31

0.12

0.11

0.56

0.09 –

0.08

0.04

0.06

0.10

0.09

MCh

Production/biomass (/year)

1167.20

131.37

122.18

81.92

149.58

50.76

12.34 17.58

131.12

187.96

523.49

250.31

101.42

116.25

8.62

7.73

2.85

0.42

0.74

0.44

0.53

2.19

0.47 0.40

11.16

10.10

11.58

7.29

6.32

ISCh

1167.20

131.37

122.18

81.92

149.58

50.76

12.15 17.58

131.12

187.96

523.49

231.47

101.42

113.88

8.62

7.77

2.85

0.42

0.73

0.43

0.53

2.19

0.47 –

11.16

9.32

11.58

7.29

6.15

MCh

Consumption/biomass (/year)

0.457

0.350

0.351

0.352

0.350

0.457

0.350

0.351

0.352

0.350

0.231

0.305

0.305 0.231

0.455

0.300

0.324

0.303

0.248

0.318

0.361

0.265

0.222

0.244

0.218

0.433

0.275

0.212

0.255

0.198 –

0.007

0.005

0.005

0.014

0.014

MCh

0.448

0.300

0.324

0.303

0.230

0.318

0.353

0.265

0.214

0.244

0.218

0.381

0.265

0.212

0.255

0.198 0.269

0.007

0.004

0.005

0.014

0.014

ISCh

Production/ Consumption

0.992

0.995

0.945

0.996

0.999

0.997

0.997

0.999

0.998

0.126

0.999

0.999

0.659

0.008

0.995

0.992

0.303

0.993

0.975

0.991

0.995

0.991

0.991 0.998

0.992

0.083

0.000

0.994

0.211

ISCh

0.999

0.321

0.991

0.995

0.991

0.997

0.997

0.999

0.996

0.124

0.996

0.991

0.311

0.019

0.998

0.991

0.255

0.995

0.997

0.694

0.993

0.996

0.995 –

0.994

0.154

0.000

0.860

0.179

MCh

Ecotrophic efficiency

Table 1 Data output on biomass (B), P/B ratio, Q/B ratio, P/Q ratio, EE for the different groups that represent the pelagic subweb of Inner Sea of Chiloe´ (ISCh) and Moraleda Channel (MCh)

Hydrobiologia (2013) 717:85–108 97

123

0.683

0.933 0.636

–

–

0.636

–

–

–

1174.98

0.009

0.268 0.445 1597.53

– 4055.08

– – Detritus 36

1.00

1.00

5750.58

1248.98

–

747.11

–

–

– 24330.48

–

794.57 225.503 453.32

34503.45 1.00

2.00

DOM 35

1.00

Bacteria 34

2.00

– 710.83 710.83 70.85 127.09 1.00 1.00 ANF 33

Similar to other ecosystem models that consider both microbial and traditional food webs (Pave´s & Gonza´lez, 2008), the application of EwE allowed us to characterize the trophic energy flows and amount of biomass in the pelagic realm. In addition, with EwE it was possible to determine the existence of a tight link between the microbial loop and the traditional food web; a condition also mentioned in previous studies (Sherr & Sherr, 1988; Pave´s & Gonza´lez, 2008; Reynolds, 2008). Niche overlap index (TOI), prey and predator overlap

Bold numbers denote the data estimated by the EwE models

0.457

0.009

0.389

0.837

0.887 0.457 1441.35

1597.53 710.83 710.83

1441.35 658.82 658.82

93.66

27.56 43.90

206.38 3.00

2.76

HNF 32

3.00

Microflagellates 31

2.76

Microphytoplankton 30

1.00

1.00

997.37

– 793.61 541.24 403.30

MCh

0.996

0.180 0.165 –

0.995

MCh ISCh ISCh MCh ISCh MCh ISCh MCh ISCh MCh ISCh

Consumption/biomass (/year) Biomass (mgC/m2) Trophic level Table 1 continued

123

(Fig. 8C). The long-line fishery would produce a largely negative effect on the Otariidae, but positive in Scianidae, Ophidiiformes, Carangidae. Unfortunately, very little data was available on benthic-demersal communities and, therefore, the real effect of long-line fisheries was underestimated in this ecosystem by our models, which focus on pelagic ecosystem (Fig. 8D). Increased hand-line fisheries catch would had negative effect on Gempylidae group as well as on same fishery activity by intraspecific effect (Fig. 8E).

Discussion

Production/biomass (/year)

0.445

Ecotrophic efficiency Production/ Consumption

0.886

Hydrobiologia (2013) 717:85–108 0.914

98

In both ecosystems, the TOI values were highest for fish larvae and jellyfish; species become competitors under conditions of limiting prey resources (i.e., zooplankton). Swarms of either filter-feeding (0.6–5.0 salps l-1, Everett et al., 2011) or carnivorous jellyfishes (Purcell & Sturdevant, 2001; Arai, 2005) are able to ‘‘clean’’ the water of potential food particles reducing the food for other species (e.g., fishes). As similar salp abundances have been found in the ISCh (Giesecke, unpublished data) negative effect were observed on the fishes, thus a strong competition between fish larvae and jellyfishes cannot be ruled out. Moreover, the jellyfish groups have higher P/B ratios (Palomares & Pauly, 2009; Table 1, Annex 1) and fewer predators than fishes (Fig. 2), such that the former respond more quickly to environmental changes with an accelerated rate of population growth. Thus, jellyfish could be more likely to benefit from the competition (Arai, 2005). Jellyfish could also affect the abundance of fish larvae and eggs (Purcell, 1981; Larson, 1987; Carr & Pitt, 2008) and, thus, the

Hydrobiologia (2013) 717:85–108 Table 2 Summary of the statistical data for Inner Sea of Chiloe (ISCh) and Moraleda Channel (MCh) for the pelagic sub-web off the North Patagonian coastal ecosystem

99

Parameter

ISCh

Sum of all consumption

1225.92

MCh

Difference (times)

Unit

633.02

1.9

gC m-2 year-1

Sum of all exports

128.351

75.56

1.7

gC m-2 year-1

Sum of all respiratory flows Sum of all flows into detritus

515.41 826.92

271.64 451.52

1.9 1.8

gC m-2 year-1 gC m-2 year-1

2696.59

1431.74

1.9

gC m-2 year-1

Primary production

119.26

370.43

0.3

gC m-2 year-1

Bacteria production

360.19

168.48

2.1

gC m-2 year-1

Secondary and tertiary production

783.67

150.43

5.2

gC m-2 year-1

Sum of all production

1263.13

689.34

1.8

gC m-2 year-1

Net system production

114.75

98.79

1.2

gC m-2 year-1

13.48

8.85

1.5

gC m-2

0.19

0.01

Total system throughput

Total biomass (excluding detritus) Total catches

19

gC m-2 year-1

Table 3 Natural and fishing mortality rates on mainly artisanal fishing groups and discarded species in Inner Sea of Chiloe (ISCh, bold values) and Moraleda Channel (MCh) for the pelagic sub-food web of the North Patagonian coastal ecosystem Group name

Ecosystem

P/B or Z (/year)

Fishing mort. rate (/year)

?Predation mort. rate (/year)

?Other mort. rate (/year)

Fishing mort./ total mort.

Proportion natural mort.

Otariidae

ISCh

0.088

0.018

0.000

0.069

0.209

0.791

MCh

0.088

0.011

0.005

0.072

0.125

0.875

ISCh

0.099

0.018

0.080

0.001

0.184

0.816

Aves Delphinidae Gempylidae

MCh

0.099

0.005

0.080

0.014

0.050

0.950

ISCh

0.083

0.026

0.056

0.001

0.317

0.683

MCh

0.083

0.004

0.079

0.000

0.046

0.954

ISCh

0.092

0.068

0.023

0.001

0.741

0.259

MCh

0.092

0.002

0.090

0.000

0.018

0.982

ISCh

0.107

0.002

0.105

0.000

0.020

0.980

MCh

–

–

–

–

–

–

Atherinopsidae

ISCh MCh

0.558 0.558

0.030 0.014

0.523 0.542

0.005 0.002

0.054 0.025

0.946 0.975

Ophidiiformes

ISCh

0.112

0.006

0.105

0.001

0.055

0.945

MCh

0.112

0.002

0.109

0.001

0.020

0.980

ISCh

0.117

0.008

0.108

0.001

0.068

0.932

Sciaenidae

Gadiformes (A) Carangidae Clupeiformes (A)

MCh

0.117

0.004

0.077

0.036

0.036

0.964

ISCh

0.092

0.035

0.057

0.001

0.376

0.624

MCh

0.092

0.001

0.090

0.000

0.012

0.988

ISCh

0.695

0.064

0.147

0.484

0.092

0.908

MCh

0.695

0.001

0.176

0.518

0.001

0.999

recruitment of fish populations (Lynam et al., 2005). These situations have been observed during overfishing and strong eutrophication events, when the fisheries decreased dramatically or collapsed, in turn,

producing negative effects over the whole trophic web (Cury & Shannon, 2004; Arai, 2005; Frank et al., 2005; Daskalov et al., 2007). For example, in the Black sea, the overfishing of marine mammals and

123

100

Fig. 7 Catches of artisanal fishing resources (catch) and incidental catches (bycatch) of marine mammals and seabirds in the Inner Sea of Chiloe (ISCh) and Moraleda Channel (MCh)

small pelagic fishes has made the system more vulnerable to jellyfish proliferation, resulting in a less diverse and low-fish biomass system (Daskalov et al., 2007). In the North Benguela Current System, the trophic structure has shifted, due to overfishing, to a system dominated by medusae, small pelagic gobiids, mesopelagic fish, and horse mackerel (Cury & Shannon, 2004; Moloney, 2010). Mixed trophic impact analysis At the bottom of the food web built, the finding that bacteria have a very strong intra-functional group effect that would regulate their biomass and avoid fast and sustained growth might help understand the stable size observed in the different marine systems. Also, this effect might be caused by multiple factors not assessed in this model (e.g., chemical signals, DOM quality, bacteria cell size) that could regulate both inter- and intraspecific competition (Keller & Surette, 2006; Baquero & Lemonnier, 2009). Thus, in addition to these intra-group regulation processes, several external factors that enhance bacterial mortality (e.g., viral attack, bacterivorous nanoflagellates) or growth rate mechanisms (low requirements, efficient resource uptake) seem to be comparable in magnitude, resulting in low bacterial biomass variability (Gonza´lez et al., 2011). The stronger effect of microphytoplankton than ANF on the food web (by 2–4 times stronger) estimated

123


here has been reported also in other studies (Iriarte & Gonza´lez, 2004; Gonza´lez et al., 2010, 2011). These authors have determined that picoplankton dominate in periods of low productivity (winter) and El Ninõ events, whereas microphytoplankton are predominant during productive periods (spring) or La Ninã events, linking large-sized phytoplankton ([20 lm) with higher levels of biomass in the pelagic systems (Iriarte & Gonza´lez, 2004), similar to that described for equatorial marine systems (Penã et al., 1990). Moreover, the higher impact of Euphausiacea than Copepoda calanoida on higher trophic levels exceeds (by 4 and up to 150 times) the impact estimated here, suggesting a more important role of Euphausiacea on ecosystem productivity and energy flows, as expected considering that krill are reported to be the preferred food item of several fish species along the Humboldt Current System (HCS) (Antezana, 2010). Such relations have been observed in several disparate systems along the Chilean coast, including upwelling areas along the HCS where they have been reported as key species in the top–down control on primary producers, carbon fluxes to the sediment, and principal prey for upper trophic level such as fishes (Gonza´lez et al., 2009; Antezana, 2010). In addition euphausiids seems to be a keystone component in the transfer of phytoplankton (and small-size plankton in general) to a wide variety of top consumers in several environments such as Patagonian fjords (Sańchez et al., 2011) and Antarctic Ecosystem (CornejoDonoso & Antezana, 2008). The mixed trophic impact analyses permitted us determinate that the Clupeiformes and Gadiformes had a positive impact on higher trophic level and the fisheries, but the intraspecific control-effect in Gadiformes is twofold higher than in Clupeiformes. This situation could be related with the different cannibalism level in these specie: where up to 60 % of the Gadiformes diet was reported to be juveniles of the same species (Lillo et al. 2005; Co´rdova et al. 2006; Saavedra et al. 2007), and in contrast, Clupeiformes prey on larval fish but apparently not on juveniles (Espinoza & Bertrand, 2008; Prokopchuk, 2009). Similar to Clupeiformes and Gadiformes, the top predators (e.g., Aves, Otariidae, and O. orca) all would have an important intra-functional group effect that may determine their population trend and avoids uncontrolled population growth. Within this top predator group, several cases indicate that reinforced


A)

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35 CFs LFs LhFs

0,1

0,002

0,05

0,001 0

0 -0,05

-0,001

,

-0,002

-0,1 Clupeiformes Purse-seine fishery ISCh

-0,15

-0,003

Clupeiformes Purse-seine fishery MCh

-0,004

-0,2

B)

0,23

0,6

0,18

0,4

0,13 0,08

Mixed trophic impact index (ISCh)

0,2

0,03

0

,

-0,02 -0,07

-0,2 Gillnet fishery ISCh

-0,4

C)

-0,12

Gillnet fishery MCh

-0,17

-0,6

-0,22

0,05

0,002 0

0 -0,05

-0,002

-0,1

-0,004 -0,006

-0,15 ,

-0,2

-0,008 -0,01

-0,25 Mackerel Purse-seine fishery ISCh

-0,3

-0,012

Mackerel Purse-seine fishery MCh

-0,35

-0,014 -0,016

-0,4

D)

Mixed trophic impact index (MCh)

Fig. 8 Mixed trophic impact (MTI) index for the different fisheries groups of the Inner Sea of Chiloe (ISCh) and Moraleda Channel (MCh). Positive and negative impacts are represented by dots (black and white) above and below the baseline, respectively. The height of the lines indicates relative impact, comparable among groups. In A is presented the MTI of Clupeiformes Purse-seine, B for Gillnet fishery, C for Mackerel Purse-seine, D Long line fishery, and E for Line hand fishery. Functional groups are numbered as in Table 1

101

0,3

0,6

0,2

0,4

0,1

0,2

0

0

,

-0,2

-0,1 Long line fishery ISCh

-0,2

E)

-0,4

Long line fishery MCh

-0,3

-0,6

0,02

0,00018

0

-0,00002

-0,02

-0,00022

-0,04 -0,00042 -0,06

,

-0,00062

-0,08 Line hand fishery ISCh

-0,1

-0,00082

Line hand fishery MCh

-0,00102

-0,12 1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35 CFs LFs LhFs

123

102

intraspecific competition and overfishing likely contribute to a strong dietary shift (e.g., Southern sea lion—Otaria flavescens, Drago et al., 2009), or impacting the seabird community structure (Ballance et al., 1997). In addition, the effects of intraspecific competition in these top predators resulted in different levels of aggressive behavior displayed under conditions of limited resources or high population density (e.g., sea lions and fur seals, Cassini, 2000; dolphins, Samuels and Gifford, 1997; odontocete cetaceans, MacLeod, 1998; baleen whales, Baker & Herman, 1984). Moreover, we found more differences than similarities in the effect that each functional group would produce in these two modelled pelagic ecosystems. Only bacteria would have the same effect in both ecosystems through the whole food web. In this case, even though the bacteria biomass was twice as higher in the ISCh than MCh, its effect was the same, and, therefore its role (function) would not change in these environments. Different situation was registered in the other groups, where the effect of each one of them was different according to the preys, predators, and for fisheries. All these differences and/or similarities of the predators and preys trophic impact index, may change dramatically depending on the specific characteristics of the ecosystem under observation, and thus will be a mistake to generalize the role of these functional groups in the different environment. Keystoneness of functional groups In the ISCh and MCh ecosystems, the same groups showed the highest keystoneness indexes (jellyfish, copepods, Orca, fish larvae). This situation implies that any change in the abundance of these groups, especially the jellyfish (which have the highest keystoneness value), could result in strong modifications of the ecosystem structure and functioning. Similar situations have been observed in the South Chilean fjords, where an increment in the jellyfish abundance produced strong mortalities in salmon farms (Giesecke, pers. comm., 2010) as well as in other fish species (Cury & Shannon, 2004; Arai, 2005; Frank et al., 2005; Daskalov et al., 2007). Moreover, this situation had been observed in some ecosystem models, particularly in 3 out of 16 ecosystem models analyzed by Pauly et al. (2009) and covering different habitats (from semi-enclosed waters to large areas of

123


the open ocean) and levels of exploitation (from heavily fished to targeting mainly top predators). In our study, the smallest semi-enclosed ecosystem shows the highest jellyfish keystoneness, similar to the modeled results in past studies (i.e., Lancaster Sound, Chesapeake Bay; Pauly et al., 2009). Flow to the detritus and biomass With this ecosystem approach it was possible to highlight the importance of the microbial loop over traditional food web for DOM and POC production. Thus, the microbial loop produced 13 and 30 times more POC than zooplankton and nekton groups (fish, top predators) respectively, and 24 and 96-thousand times more DOM than these groups. This suggests that the microbial loop could be the key ecosystem component to the functioning of the biological carbon pump (Fig. 6). For other hand, the huge amount of detritus matter that is produced but not consumed by the high trophic levels, could exit these ecosystems as material/energy input for demersal, coastal, or oceanic ecosystems, typically as marine-snow resources that are utilized by other species, (e.g., the mesopelagic community, Wilson et al., 2008; migrating plankton, Lampitt et al., 1993) or simply by organic matter sinking to the sediment through the biological pump (De La Rocha & Passow, 2007; Honjo et al., 2008; Gonza´lez et al., 2009). This major contribution to the detritus flows by the microbial loop could be related with the higher P/B they have and, therefore, more somatic production than the other functional group in these models (Table 1). Moreover, in the ISCh it produces 46 % more detritus than MCh, difference, which is associated to the P/B ration and biomass of each functional group in both ecosystems. However, the biomass, because the P/B is very similar between the models, would be the more important responsible of this difference. In all groups and in the whole ecosystem the biomass in ISCh is almost double than MCh, for example, the microbial loop at the ISCh had two times more biomass and more production than the MCh (Fig. 6; Table 1). The double amount of biomass in the ISCh than in the MCh could be related with the fact that in the ISCh the phytoplankton was dominated by microphytoplankton while in the MCh predominated nano- and picophytoplankton. At the ISCh we found a micro- to nano-phytoplankton ratio[1, where the microphytoplankton group represent more than


7 % of the total biomass in the ecosystem, a fraction that is 2.8 % higher than in MCh (Table 1). Ecotrophic efficiency, energy flows, system throughput, and energy transfer efficiencies More than 50 % of the functional groups have an EE [0.9 in the ecosystems compared, and 19 of them had a similar value in both ecosystems (see Table 1), and other 4, have a high EE value either in ISCh (Aves, DOM) or in MCh (Microflagellates). These aspects that show us differences in the functioning in the ecosystems might be related mainly with the biomass value of each functional group and the consumption rate of their predator, because this is the only one parameter with difference between ISCh and MCh. In addition, the great amount of the functional groups with EE [ 0.9 means that all production and biomass is widely used in both ecosystems and, therefore, these ecosystems would have a little surplus production. We must, however, also consider the existence of other groups, not considered herein due to a lack of information, that might generate additional impacts on these species (e.g., other species of fish, crustaceans, cephalopods, Chondrichthyes, etc.). Overall, the ecosystem production was utilized almost totally and the incorporation of a new agent in these ecosystems would imply that another existing group is concerned, their biomass decreased or removed (if it is a weak competitor), changing the ecosystem equilibrium state (Cury & Shannon, 2004; Arai, 2005; Frank et al., 2005; Daskalov et al., 2007). The different biomasses found in the pelagic communities of these two ecosystems also produce different carbon flows, consumption, exports, respiration, total system throughput, and production. These parameters were about two times higher in ISCh than MCh. For example, the difference in productivity of any high trophic level between these ecosystems should be related to differences in the productivity of the lower trophic levels (phytoplankton and zooplankton) and may help explain the greater differences in potential fisheries productivity of these ecosystems, where the total catch is ca. 20 times higher in ISCh than in MCh. This situation is probably related to the type and amount of biological resources available in the environments, which results from multivariate parameters and conditions such as topographic characteristics (Palma & Silva, 2004; Pave´s et al.,

103

submitted), physical–chemical water characteristics (Palma & Silva, 2004; Iriarte et al., 2007), marine productivity (Iriarte et al., 2007), life histories of marine fauna (nursery site, Bustos et al., 2007, 2008, 2011), and marine biodiversity (Cassis et al., 2002; Hucke-Gaete et al., 2004; Palma & Silva, 2004; Haüssermann, 2006; Reyes-Arriagada et al., 2007; Pave´s & Schlatter, 2008; Zamorano-Abramson et al., 2010) within each basin. In relation to the energy transfer efficiencies (ETE) the value in MCh and ISCh were similar (32 vs. 30 %), and relatively high, probably because others models did not include the microbial loop due to the shadow effect produced by microbial species (see Christensen et al., 2008). Our results show that an important part of the energy flows through the bacteria group, which had an ETE between 52 and 54 %, thus, increasing the average ecosystem ETE up to levels of 32 %, however, the origin of this energy is from different sources. The high ETE in MCh comes from detritus, whereas in ISCh, this comes from primary producers. This difference could also explain the greater amount of energy available for the top trophic level of the ISCh (9.3 %) vrsus the MCh ecosystem (5.5 %). This relatively low level of energy in MCh could be due to the relationship between the quantity and quality of the food resources consumed. Thus, consumers that feed mainly in ecosystems with a high quantity but low quality of resources (i.e., MCh is rich in carbondominated detritus) obtain less energy than consumers that obtain energy from phytoplankton (i.e., ISCh is rich in high quality phosphorus-dominated detritus). This relation has also been observed in other ecosystems (e.g., lakes: Hessen, 2008; Mu¨ller-Solger et al., 2002, estuaries: Heinle et al., 1977), ingestion rate experiments (Paffenhofer et al., 1995; Mayzaud et al., 1998), and plankton food web models (Perhar & Arhonditsis, 2009). All these approaches showed that the highest zooplankton growth rates and egg productions were obtained in conditions where the diets are based on natural assemblages of phytoplankton (live food) rather than on a mixture of detritus (POC). In addition, this should have implications for a more efficient energy transfer to higher trophic levels. Fisheries and fishing mortality rates In the ISCh and MCh ecosystems, nine different functional groups have fishing mortality rates higher

123

104

than the predation mortality rates (see Table 3). Moreover, all functional groups under fisheries effect (by catch ? capture) present higher mortalities in the ISCh than the MCh ecosystems. Thus, in these ecosystems under analysis, the levels of fisheries pressure result in different effect on the fish resources. This effect depends both on the biomass of each functional group under direct or indirect (by-catches) catches, as well as on the fishing effort of each fishery in any of the specific ecosystems. The ecosystem view allows us to determine that fisheries activities affect both commercially important and unimportant resources, the latter through bycatch, with the non-commercial resources flowing directly to the detritus. Thus, according to the mixed trophic impact analysis, an increase in fisheries activities would affect both commercial and non-commercial species, as well as other fisheries activities. The fisheries activities that produce more negative effect on the catches of other fisheries are the Clupeiformes (affect three fisheries), and mackerel fisheries (affect two fisheries). However, this effect is 10–20 fold higher between the fisheries activities in the ISCh compared to the MCh ecosystem. These negative effects on the marine biota and on the other fisheries activities could be related to the un-selective catch of the purse-seine fisheries (Clupeiformes, Carangidae), as well as to the difference in the landing volumes registered in these two ecosystems (see Annex 3). Also, the mixed impact trophic data suggests that some increase in the long line fisheries effort could affect mainly the ISCh ecosystem, while the MCh ecosystem could be more affected by a catch effort increase in the purse-seine fisheries and gillnet fisheries. Given the situation described for both pelagic marine communities at both study areas, an increase in the fishing effort—considering the high EE and keystoneness of all fish groups, the seabird and marine mammal by-catches, the cascade effects on commercial resources and other species, and intraspecific competition—could generate strong alterations in these ecosystems, such as the decline of fisheries resources and/or an increase of the gelatinous zooplankton group. This shift has been reported frequently and in disparate environments showing us the need to increase our understanding of the marine system at the ecosystem level, in order to develop an adequate management policy on our marine resources at the same time to enhance the ecosystem sustainability.

123


Conclusions Overall, the functioning of the two ecosystems studied here showed that the energy that flows from detritus to top predators is channeled through the same functional groups. Some ecosystem parameters such as total biomass flowing to detritus, fisheries catches, ETE (from primary producers), secondary, and tertiary production were twice as high in the basin with the greater connection to the oceanic water and more macronutrients supply (ISCh) than in the channel and fjord area, which is protected from the oceanic water effect and with has lower nutrient supply (MCh). Two major lines of evidence seem to explain these differences: 1.

2.

The size spectrum of plankton biomass: The higher importance of microphytoplankton and euphausiids at the ISCh, compared with nanoflagellates and copepods at the MCh, suggest that larger preys may generate a more effective effect in the trophic transfer toward top predators. Thus, the higher zooplankton, fish biomass, top predator, and catches in ISCh compared to MCh may be partially a consequence of the larger size of the phyto- and zoo-plankton, than the PP per se. The quality of the food resources consumed in the lower trophic level: Consumers in systems dominated by nitrogen and phosphorous-rich phytoplankton obtain more energy than the consumers that feed mainly in ecosystems with a high quantity but low quality of carbon-dominated detritus. This affects the energy available from one to the next trophic level. Again, the PP per se seems not to be adequate proxy for predicting the amount of secondary and tertiary production (up to the fisheries level) in coastal marine systems.

Herein, we presented a general idea of the behavior and trophic status of the two systems assayed. This understanding could be improved through access to more data generated by oceanographic surveys, stock assessments, dietary ecology, and evaluations of bycatch, amongst others. These models represent a first step in helping us understand the structure and functioning of the pelagic Patagonian ecosystems, and to establish how fisheries activities affect them. A strong local research line on autoecology, population dynamics, and community studies should be encouraged in all taxa, as this would allow the generation of


more local models. This, in turn can lead to the design of appropriate management policies for natural resources from an ecosystem approach—an initiative which has not previously been attempted for Patagonian marine ecosystems. Acknowledgments We thank our many colleagues who provided the data, information, and constructive input that allowed us to construct the trophic models for the southern coastal system of Chile: Dr. Leonardo Castro and Maria Ines Munõz (Universidad de Concepcioń); Dr. Giovanni Daneri (CIEP); and Dr. Edwin Niklisheck, Dr. Ricardo Giesecke, Cecilia Torres (M.S.), Eduardo Menschel, Nicola´s Sańchez, and Marıá Jose´ Calderoń (Universidad Austral de Chile). The authors thank the suggestions and comments of two anonymous reviewers that substantially improved the original version of the MS. The authors are indebted to all persons who have been working on the development of the Ecopath approach since the early 1980s, especially Carl Walters from Fisheries Centre (University of British Columbia, Vancouver, Canada). Principal author (HP) acknowledges the assistance provided by Jeroen Steenbeek, Shawn Booth, and Andre´s Cisneros during his postdoctorate research at the Fisheries Centre (UBC). This study was funded by the CIMAR-Fjords Program (grants 9, 12, and 13); FONDAPCOPAS No. 15010007 Etapa II; Programa Financiamiento Basal PFB-31/2007; and FONDECYT No. 1080187, and by the office of Research and Development (Universidad Austral de Chile) (DIDS-2010-45). HJP was supported by BECASCHILE Postdoctorate Program 2010, the Fisheries Centre of University of British Columbia, Vancouver, Canada, and the Postdoctorate Program 2011—Fondecyt No. 3120100 during the conduction of this research. VC acknowledges support from the Nippon Foundation—UBC Nereus Program and NSERC.

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