Potential consequences of climate change for primary production and fish production in large marine ecosystems – part III

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livello medio
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ARGOMENTO: PESCA
PERIODO: XXI SECOLO
AREA: DIDATTICA
parole chiave: riduzione delle risorse ittiche, pesca
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Potential consequences of climate change for primary production and fish production in large marine ecosystems – part III

Results
(a) Predicted effects of climate change on unexploited marine ecosystems
The predicted bottom-up effects of climate change, in the absence of fisheries exploitation, varied widely among EEZ (figure 1). In general, changes in fish production and biomass density mirrored the changes in primary production and phytoplankton density more strongly than changes in temperature. Although the greatest warming was predicted in the EEZ of China, South Korea and along the east coast of North America, only modest reductions in primary production and phytoplankton density (−6%), occurred in these areas, resulting in small changes in overall biomass density of fish (−6%). The largest predicted reductions in phytoplankton and zooplankton density occurred in EEZ within the Indo-Pacific (Palau, −60%), the Humboldt Current (Peru, Chile/Peru, −35%) and the Canary Current (Madeira) regions and caused similar magnitudes of change in the overall biomass of fish. At the other extreme, the largest increases in phytoplankton and zooplankton biomass density led to the largest increases in fish biomass density. Such was the case for EEZ of the Guinea Current (Ghana, Ivory coast, Togo) and the Nordic shelf seas (Jan-Mayen, Greenland), where overall fish biomass density increases exceeded 30 per cent. In some cases, the predicted effects of changes in phytoplankton biomass density on fish density were countered or enhanced by changes in the detritus and benthic detritivore pathway, in zooplankton, or by a very large increase in temperature.

Figure 1. Mean-predicted relative changes for 2050 under the SREASA1B scenario. Maps of change in (a) mixed-layer depth temperature; (b) near sea floor temperature (°C); and percentage changes in: (c) density of phytoplankton and (d) biomass density of detritus; (e) biomass density of pelagic predators and (f) biomass density of benthic detritivores.

In the absence of exploitation, the predictions of the dynamic size spectra model were closely correlated with those from the static size-based model (Spearman’s rank correlation, ρ = 0.88; electronic supplementary material, figure S1). Outliers occurred because the more simplistic static model did not account for a benthic detritus energy pathway that was predicted to account for a significant proportion of energy flux in some ecosystems.

(b) Top-down effects: validation of the model with fishing
When fishing mortality was added to the dynamic model for the period 1992–2001, modelled catches from 78 of the 107 EEZ were comparable to reported catches (figure 2a). Because true rates of community-wide fishing mortality and selectivity were not known, we assumed a fishing mortality rate of 0.8 yr−1 for all fished size classes, consistent with fishers heavily exploiting all fish that were present. The greatest discrepancies between predicted and reported catches were those for EEZ within the Indo-Pacific (Indonesia) and Northwest Pacific shelf sea (the Sea of Okhotsk, off Russia) regions, where predicted fisheries catches were more than 5 Mt greater than the mean reported catches. The largest deviations from reported catches were also associated with high interannual variability in both model- and data-based catch estimates, for example, for Peru and Chile EEZ. When catches and predictions were aggregated at the domain level across EEZ, the 50 per cent quantiles of predicted catches were strongly correlated with the 50 per cent quantiles of reported catches (figure 2b; Spearman’s rank was ρ = 0.8 for median catches at the domain level, compared with ρ = 0.63 for mean catches averaged over 1992–2001 at the EEZ level).

Figure 2. Comparison of model results with data. (a) Differences from mean observed values of fisheries landings averaged over the 1992–2001 from 11 large regional domains, grouped by 78 country EEZ. Grey lines indicate range of interannual variation in the observed fisheries landings over 1992–2001 data, whereas error bars show the range of interannual values predicted by the model. (b) Relationship between modelled catches (Mt per year) and the observed landed catches aggregated to the domain level. The points show the median across all EEZ within each domain and the grey lines show the extent from the 25th to the 75th percentiles. Solid line is 1 : 1 relationship. (c) Mean modelled relative growth rates over 1992–2001 across all EEZ (grey areas, mean for northeast Atlantic shown in central line) along with relative growth rates at 10% of asymptotic size estimated from empirical von Bertalanffy growth equations for a subset of the fish populations from the northeast Atlantic given in Pauly [45].

Modelled relative growth rates were also realistic and fell within the range of empirical growth rates of fish species from the North Sea (figure 2c). The highest relative growth rates occurred in warm and highly production regions (Bay of Bengal), whereas the slowest relative growth rates occurred in cold and lower production ecosystems (Nordic Shelf Seas), in line with expectations from food availability and temperature effects on growth.

(c) Top-down and bottom-up: relative effects of fishing and climate change
Ecosystems responded differently to the same fishing scenarios in the absence of climate change. At the EEZ level, the disruption to the size spectrum from fishing impacts was smaller when net primary production and mean relative growth rates of fish were higher (figure 3). There was a stronger relationship with the latter because realized fish growth rates integrated the effects of both temperature and food availability. Without fishing and only climate change, there was greater variation in the relationship between the disruption to the size spectrum and primary production owing to the mixed responses and multiple environmental drivers under the climate change scenario. The combined effects of both fishing and climate drivers depended on how heavily fished the community was. Under the low fishing mortality rate of 0.2 yr−1, climate effects dominated the deviation from the unexploited size spectrum, whereas fishing effects dominated when mortality rates were high, 0.8 yr−1.

Figure 3. Across-ecosystem effects of fishing and climate change. Deviation from the unexploited size spectrum versus mean net primary productivity (a,c) and mean relative growth rates of fish (b,d) when ecosystems are subjected to: fishing (black), climate change (grey) and climate and fishing (red). Each point represents an EEZ. Equilibrium results based on time-averaged environmental conditions under each scenario were used. Results shown for (a,b) low (0.2 yr−1) and (c,d) high fishing mortality rates (0.8 yr−1). Note the logarithmic scale markings on all of the axes.

Across-ecosystem effects of fishing and climate change. Deviation from the unexploited size spectrum versus mean net primary productivity (a,c) and mean relative growth rates of fish (b,d) when ecosystems are subjected to: fishing (black), climate change (grey) and climate and fishing (red). Each point represents an EEZ. Equilibrium results based on time-averaged environmental conditions under each scenario were used. Results shown for (a,b) low (0.2 yr−1) and (c,d) high fishing mortality rates (0.8 yr−1). Note the logarithmic scale markings on all of the axes.

Under heavy fishing pressure, reductions in the numerical density relative to the unexploited steady state were most pronounced at larger body sizes (figure 4) and in the domains where individual growth rates were slowest (e.g. Nordic seas, northwest Atlantic and northeast Atlantic shelf seas). Climate change increased or decreased numerical density and growth rates relative to the unexploited steady state across all sizes. The greatest increases occurred in the Nordic seas, northwest Atlantic, northeast Atlantic shelf seas and Gulf of Guinea and the greatest decreases were in the Humboldt, Canary and California current ecosystems. If increases in primary production occurred, the combined effects of climate and fishing relative to those in an unexploited ecosystem were less than fishing alone, but also resulted in stronger top-down cascading effects along the size spectrum. If reductions in primary production occurred, the response of fishing with climate change was magnified (figure 4).

Figure 4. Changes in community size structure from fishing and climate. Changes in density at size (a,c,e) and relative growth rates at size (b,d,f) relative to unexploited control size spectra for (a,b) fishing, (c,d) climate and fishing and (e,f) climate effects. Results shown are for heavy fishing mortality rates only (0.8 yr−1). Size spectra were averaged across each of the 11 large regional domains. The domains are ranked in the legend according to fish growth rates (from lowest in the Nordic seas to highest in Bay of Bengal).

The effect of heavy fisheries exploitation on the resilience of ecosystems to climate change was examined by comparing the coefficients of variation from the biomass density time-series for the most sensitive ‘large’ fish component of the size spectrum (e.g. across the 80 g–100 kg size range), generated from seasonal and interannual variability within each of the EEZ, scenario time slices and fishing mortalities. Overall, the variability in biomass density increased with higher fishing mortality and when the combined effects of fishing and climate reduced fish production (see the electronic supplementary material, figure S2).

Discussion
This study is a first, and necessarily simplified, attempt towards understanding the potential consequences of climate change on large marine ecosystems and their fisheries using a dynamical size-based food web approach. We achieve this by linking the trophic ecology of coupled size-structured communities with predicted changes in the physical and biogeochemical environment. The dynamic size-spectrum model predicts a mixture of positive and negative responses in fish biomass density and production that mirror the predicted changes in primary production more strongly than changes in temperature. The results corroborate empirical work by showing that potential marine fisheries production is primarily governed by available primary production [46–48].

Previous studies have predicted 30–70% average increases in potential fish production at high latitudes and decreases of up to 40 per cent in the tropics, based primarily on the effects of warming on species distributional ranges [21]. At a large geographical scale, the findings are broadly similar to those based on the species biogeography approach, even though the present projections are based on completely different mechanisms arising from food web processes widely held to govern empirical patterns of size spectra in the open ocean and shelf seas [24,49].

photo credit andrea mucedola – not part of the study

An advantage here is the inclusion of fishing effects, enabling the relative effects of climate change and fishing to be explored within and across size-structured ecosystems. Changes in primary production [47] and temperature [50] affected growth rates and fish production, altering the responses of ecosystems to fishing. Either low primary production or cold-water ecosystems conferred higher susceptibility to fishing effects, due to slow relative growth rates. Cold-water ecosystems with higher seasonal fluctuations have been previously described as less likely to sustain heavy exploitation [51]. Also in keeping with empirical studies of fish populations [52–54] and theoretical size-spectrum models [44,55], fishing effects caused ecosystems to become more variable through time, due to reductions in size structure and shifts towards smaller size and higher growth rates. For the same reason, heavily fished ecosystem states were less resilient to climate change compared with unexploited ecosystem states [56].

photo credit andrea mucedola – not part of the study

For 1992–2001, our models generated catches and growth rates that were broadly realistic when compared with reported catches and growth rates, keeping in mind that the true community-wide fishing mortality rates within these ecosystems are not well known and the landed catch data may be subject to misreporting and bias [57]. The size-based models with relatively limited parameter demands provided surprisingly good estimates of current catch from some of the EEZ, further emphasizing the dominant role of body size in accounting for patterns of predatory interaction and production in marine ecosystems. The weakness of the size-based perspective is that it does not provide predictions of catches from individual species and account for their responses to fishing, but this has to be considered in the context that long-term predictions of individual species dynamics; even when complex population models are developed, they can be unreliable [58]. For example, although total fish production in an ecosystem may be maintained, there may be significant and unpredictable switches in the species contributing. Because recruitment is not modelled at a population level and was held constant to facilitate cross-comparison, the resilience of the community to fishing is not recruitment limited; it does not consider the negative feedback that can result from the removal of highly fecund, large mature spawning fish. For this reason, the specific ecosystem responses associated with a given value of fishing mortality should not be taken in absolute terms and are presented for comparative purposes.

The approach also ignores energy inputs from sources of primary production other than phytoplankton (such as macroalgae, seagrasses and mangroves). Although these make a relatively small contribution globally, contributing to around 5.5 per cent of total marine primary production [59], they are locally and regionally important contributors to inshore fish production. If the underlying rules determining the links between primary production and fish production do not change markedly with a changing climate, then our capacity to predict future changes in fish production is largely predicated on our capacity to predict future changes in the primary production and the physical environment. We assumed a universal relationship between temperature and the activation energy of metabolism to predict temperature-dependent changes in feeding rates, which may underestimate the effects of warming. More complex species and size-specific empirical relationships with temperature and activation energies for different processes such as attack rates, handling times have been described in this issue [10,11].

photo credit andrea mucedola – not part of the study

Bearing in mind the earlier-mentioned strengths and limitations, the model results are of potential use for global-scale social–ecological analyses such as country-level metrics of vulnerability to climate change [2]. They may also be useful for establishing levels of threat and uncertainty in specific regions, if combined with other model predictions. For example, in the Indo-Pacific ecosystem, the EEZ surrounding the country of Palau experienced the greatest loss of primary production, potential fisheries production and an increase in susceptibility to overfishing. This region is located within the Indo-Pacific Biodiversity Triangle and has the highest richness of corals, molluscs, crustaceans, finfishes and chondrichthyans in the world. It has some of the highest catches of chondrichthyans, mainly through unregulated fisheries [60]. Our results are likely to underestimate climate impacts in these regions where there will be impacts on other sources of production. Furthermore, we considered only one possible forcing scenario taken from one global climate model, using a time slice approach. To fully quantify projected future changes in fish production, it is also necessary to consider the uncertainties associated with the forcing data used, the temporal and spatial scales, as well as the mechanisms included. A formal model ensemble approach (including alternative physical–biogeochemical and ecological models) combined with detailed empirical ground-truthing and retrospective analyses will improve our capabilities to gauge where the most important uncertainties lie. There is a clear need for much greater understanding of the effects of climate change within regional seas, at more localized scales than considered here, as well the role of human responses to change. Advancing these integrated areas of research alongside improving our mechanistic understanding of complex ecological communities across spatial scales will help to elucidate sustainability of fisheries for the larger human population and warmer oceans of the future [3,45,61].

Acknowledgments
This work was funded by UK Natural Environment Research Council’s Quantifying and Understanding the Earth System programme as part of the ‘QUEST-Fish’ project. The participation of J.L.B. at the SIZEMIC Workshop in Hamburg, Germany, was supported by the German Research Foundation (JA 1726/3-1), the DFG funded Cluster of Excellence (EXC177) at the University of Hamburg and the European Science Foundation SIZEMIC Network.

Footnotes
One contribution of 17 to a Theme Issue ‘Climate change in size -structured ecosystems’. This journal is © 2012 The Royal Society

in anteprima foto di @ Francesco Pacienza 

some photos are not part of the original study paper can be downloaded in PDF format for your convenience

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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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