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Biol. Rev. (2014), pp. 000–000. doi: 10.1111/brv.12169

Temperature impacts on deep-sea biodiversity Moriaki Yasuhara1,∗ and Roberto Danovaro2,3 1 School

of Biological Sciences, Swire Institute of Marine Science, and Department of Earth Sciences, The University of Hong Kong, Hong Kong, China 2 Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche, 60131 Ancona, Italy 3 Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy

ABSTRACT Temperature is considered to be a fundamental factor controlling biodiversity in marine ecosystems, but precisely what role temperature plays in modulating diversity is still not clear. The deep ocean, lacking light and in situ photosynthetic primary production, is an ideal model system to test the effects of temperature changes on biodiversity. Here we synthesize current knowledge on temperature–diversity relationships in the deep sea. Our results from both present and past deep-sea assemblages suggest that, when a wide range of deep-sea bottom-water temperatures is considered, a unimodal relationship exists between temperature and diversity (that may be right skewed). It is possible that temperature is important only when at relatively high and low levels but does not play a major role in the intermediate temperature range. Possible mechanisms explaining the temperature–biodiversity relationship include the physiological-tolerance hypothesis, the metabolic hypothesis, island biogeography theory, or some combination of these. The possible unimodal relationship discussed here may allow us to identify tipping points at which on-going global change and deep-water warming may increase or decrease deep-sea biodiversity. Predicted changes in deep-sea temperatures due to human-induced climate change may have more adverse consequences than expected considering the sensitivity of deep-sea ecosystems to temperature changes. Key words: deep-sea, temperature, diversity, global warming, climate change. CONTENTS I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II. Potential Controlling Factors of Deep-sea Biodiversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III. Evidence for an Effect of Temperature on Deep-sea Biodiversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Temporal patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Spatial patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV. Temperature–Diversity Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (1) Positive relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (2) Negative relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . (3) A unified unimodal relationship? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Potential Mechanisms of Temperature–Diversity Relationship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI. Rates of Temperature Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII. Sensitivity to Temperature Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII. Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XI. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XII. Supporting Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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* Address for correspondence (Tel: +852-2299-0317; E-mail: [email protected]; [email protected]). Biological Reviews (2014) 000–000 © 2014 Cambridge Philosophical Society

Moriaki Yasuhara and Roberto Danovaro

2 I. INTRODUCTION Correlation between temperature and marine diversity is one of the most pervasive ecological phenomena not only in the present day (Tittensor et al., 2010) but also throughout the last 3 million years (Yasuhara et al., 2012b), and many ecological and evolutionary hypotheses have been proposed to explain the underlying mechanism for this correlation. However, available large-scale diversity patterns in relation to temperature are still limited to several taxa with sufficient records due to the vastness and inaccessibility of the deep sea, as well as costs associated with technologies needed for deep-sea exploration. Moreover, the different hypotheses proposed so far are still largely controversial (Willig, Kaufman & Stevens, 2003; Currie et al., 2004; Danovaro, Dell’Anno & Pusceddu, 2004; Yasuhara et al., 2009, 2012a,b; Brown & Thatje, 2014), thus further testing and investigations are needed. Since present Intergovernmental Panel on Climate Change (IPCC) scenarios indicate that the temperature of most oceanic regions will change rapidly in coming decades, a better understanding of the potential responses to these changes is one of the main priorities in current ecological research. Palaeoceanographic and oceanographic studies indicate that deep-sea bottom-water temperature can change over various time scales. For example, during the Cenozoic, the deep sea cooled by more than 10∘ C over the last 60 million years (Lear, Elderfield & Wilson, 2000). During the Late Quaternary glacial/interglacial cycles, deep-sea temperature was ∼4∘ C cooler in glacials than in interglacials (Dwyer et al., 1995; Sosdian & Rosenthal, 2009). Even on millennial and centennial time scales, palaeoceanographic records indicate 1–2∘ C deep-sea temperature changes (Farmer et al., 2011; Cronin et al., 2012). Furthermore, recent oceanographic studies showed dynamic temperature changes even over periods as short as decades or a few years. For example, rapid deep-water warming (∼0.1∘ C per decade) over the last ∼50 years is known in the western Mediterranean (Bethoux et al., 1990). A recent study in this region also reported rapid drops in temperature (∼0.3∘ C cooling within a few years) linked to climate-driven episodic events (Danovaro et al., 2004). In the Labrador Sea, deep-water temperature has shown dynamic decadal variation for the last ∼60 years at rates of change up to ∼0.5∘ C per decade (van Aken, de Jong & Yashayaev, 2011). Abrupt changes in deep-water temperature also have been observed in relation to dense shelf water cascading events, which are able to influence physical and biological processes down to bathyal depths (Canals et al., 2006). Deep-sea temperature shows substantial differences, especially among oceans, depths, and water masses (Fig. 1). For example, deep-sea temperature is ∼1∘ C

warmer in the North Atlantic Ocean compared to the North Pacific Ocean at abyssal depths and much warmer at bathyal depths. Some marginal seas such as the Mediterranean, the Red and the Sulu seas have extremely high deep-sea temperature (from ∼13∘ C for the Mediterranean to >20∘ C for the Red Sea at 2000 m depth). Some deep waters at high latitudes are very cold, with temperatures close to −2∘ C (e.g. Antarctic Bottom Water). Two deep-water masses in the Atlantic Ocean show distinct temperatures: colder Antarctic Bottom Water and warmer North Atlantic Deep Water. Temperature typically decreases with increasing water depth, except for some very warm intermediate water masses (e.g. Mediterranean Overflow Water in the Atlantic Ocean). Even though the above-mentioned deep-sea temperature changes and differences in space and time are not subtle, bottom-water temperature has been rather neglected as a possible controlling factor of deep-sea diversity because of its relative stability in space and time compared to shallow-marine systems. However, there is increasing evidence for significant temperature–diversity relationships in the deep sea (Danovaro et al., 2004; Yasuhara et al., 2009, 2014; O’Hara & Tittensor, 2010). Moreover, it is likely that deep-sea organisms are sensitive even to small temperature changes because they live under temperature conditions with much less daily and seasonal variation compared to shallow-marine organisms, although taxa that originated in shallow-marine environments and then penetrated into the deep sea (Raupach et al., 2009) may have stronger tolerance to temperature changes compared to deep-sea taxa originating at depth. In this review, we explore and analyse the relationships between deep-sea benthic alpha (local) diversity and temperature reported from the world’s oceans; compare present spatial and past temporal deep-sea temperature–biodiversity patterns; and show that the deep-sea temperature–diversity relationship is positive at low temperatures (∼10–15∘ C). When considered over a sufficiently broad temperature range, the temperature–diversity relationship appears to be unimodal, although temperature may be important only at relatively high (>∼10–15∘ C) and low (105

b c

j

k

NA 0

5

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Temperature (°C) Fig. 2. Summary of known deep-sea temperature–diversity relationships. Time scales and temperature ranges in studies investigating this relationship are shown. Orange line: significant positive temperature–diversity relationship. Blue line: significant negative temperature–diversity relationship. Black line: no significant relationship with temperature. a, Corliss et al. (2009) on benthic foraminifera (see text and Tables 1 and 2 for our re-analysis); b, O’Hara & Tittensor (2010) on ophiuroids; c, McClain et al. (2012) on gastropods and bivalves; d, Tittensor et al. (2011) on gastropods and bivalves; e, Yasuhara et al. (2012c) on benthic foraminifera and ostracodes; f, Danovaro et al. (2004) on nematodes; g, Cronin et al. (1999) on benthic ostracodes; h, Hunt et al. (2005) on benthic foraminifera; i, Yasuhara et al. (2009) on benthic ostracodes; j, Yasuhara et al. (2012a) on benthic foraminifera; k, Cronin & Raymo (1997) on benthic ostracodes. NA, no data available.

showed the same relationship over a millennial time scale. Subsequently, a decadal biological monitoring study showed a significant temperature–diversity relationship and identified temperature as a primary driver of deep-sea diversity (Danovaro et al., 2004). Because temperature may covary with POC flux (an indicator of food availability), Hunt, Cronin & Roy (2005) and Yasuhara et al. (2009) used multiple regression modelling considering both temperature and POC flux, and showed that temperature but not POC flux was a significant predictor of deep-sea biodiversity in their palaeoecological records of benthic ostracodes and foraminifera. Since most of these studies were conducted in the North Atlantic Ocean, Yasuhara et al. (2012a) investigated this pattern in the Pacific Ocean, finding contrasting results between taxa. Foraminiferal diversity showed a weak relationship with temperature, but ostracode diversity showed a significant unimodal relationship with POC flux. Thus, the dominant driver may be taxon or region dependent. A recent North Atlantic deep-sea palaeoecological study (Yasuhara et al., 2014) found a significant temperature–diversity relationship even over multi-decadal and centennial time scales. These results together suggest the presence of consistent temperature–diversity relationships over time scales ranging from 101 to 104 years. These temporal patterns are derived mostly from palaeoecological time series, and as such may lack information for environmental parameters that do not have reliable geological proxy records. However, most

important environmental parameters (temperature, POC flux, and seasonality of surface productivity) usually considered in present-day deep-sea biological studies (Tittensor et al., 2011; McClain et al., 2012) are available as palaeo-proxies (Hunt et al., 2005; Yasuhara et al., 2009, 2012a). Thus palaeoecological time-series studies are reasonably comparable to present-day deep-sea biological studies. (2) Spatial patterns Until recently, all studies on temperature–diversity relationships used time-series data from either living or fossil faunas; there was no work based on a present-day, large-geographical-scale spatial dataset. Tittensor et al. (2011) first attempted to test the species–energy hypothesis using regression models and a large present-day faunal dataset from the North Atlantic Ocean. Their results showed that POC flux was a more important factor than temperature for mollusc diversity. McClain et al. (2012) confirmed their findings using a larger dataset covering the whole Atlantic Ocean. However, O’Hara & Tittensor (2010) used a similar modelling framework to demonstrate that temperature was the strongest correlate of diversity for ophiuroids from southwestern Pacific seamounts. In the Arctic Ocean, neither temperature nor POC flux showed a significant relationship with diversity (Yasuhara et al., 2012c). To date, no temperature–diversity relationships have been reported from the deep North Atlantic Ocean

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Species diversity E(S51)

Species diversity E(S51)

Temperature impacts on deep-sea biodiversity

30 25 20 15 10 5 –1

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Temperature (°C) Fig. 3. Examples of deep-sea temperature–diversity relationships. (A) Positive relationship from North Atlantic palaeo-time-series dataset (Yasuhara et al., 2009). (B) Negative relationship from Mediterranean Sea biological time-series dataset (Danovaro et al., 2004). (C) Unified unimodal relationship from present-day Atlantic Ocean and Mediterranean Sea spatial dataset (Danovaro et al., 2009; Bongiorni et al., 2010; Bianchelli et al., 2013; Pusceddu et al., 2013; R. Danovaro, unpublished data; see online Table S2) (we used the data from 1000 to 4000 m water depth only: see online Fig. S1). Species diversity shown as E(S51 ), species richness rarefied to 51 individuals.

based on present-day spatial data. Corliss et al.’s (2009) benthic foraminifera study showed a significant correlation between species diversity and seasonality of productivity, but not surface productivity. Because they did not consider bottom-water temperature, here we reanalyse their data [foraminiferal diversity E(S200 ) (expected species richness rarefied to 200 individuals; calculated from census data) and environmental data for bottom-water temperature, POC flux (calculated from surface productivity data; see online supporting information Appendix S1 for details), and seasonality of surface productivity (from Sun et al., 2006; Corliss et al., 2009); see online Table S1] using regression models and model-averaged parameter estimates (see online Appendix S1 for the full methods). This re-analysis showed a significant effect of temperature (Tables 1 and 2). All of the best five models consistently indicated that bottom-water temperature is a significant predictor of

species diversity, and model-averaging results showed that the coefficient for the temperature term is significantly different from zero. Present-day spatial faunal data show weaker support for a temperature–diversity relationship compared to temporal faunal data, although spatial (i.e. macroecological) and temporal (i.e. time-series and palaeo-) faunal data available to test the temperature–diversity relationship are still limited (Fig. 2). This discrepancy despite the ‘space-for-time’ substitution (Blois et al., 2013) could arise because (i) the present-day spatial pattern is a ‘snapshot’, i.e. a composite of not only ecological-time-scale consequences (including temperature control of coexistence of species), but also evolutionary-time-scale consequences (including speciation and extinction), and differences between ecological and evolutionary responses could obscure the relationship; (ii) rates of temperature change

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Table 1. Best five regression models of present-day North Atlantic deep-sea foraminiferal species diversity E(S200 ) as a function of temperature, POC flux, and seasonality of surface productivity. Several other models are shown for comparison. Geographic region term is included in all models (see online Appendix S1 for full modelling methods) Model

T Coef.

P Coef.

P2 Coef.

SP Coef.

Foraminiferal E(S200 ) models with the Lutz et al. (2007) POC flux as P 1 6.880 — — — 2 7.694 — — −21.887 3 7.950 −4.619 — — 4 7.142 −1.706 19.471 — 5 6.500 6.371 22.440 −33.610 SP — — — 16.817 P — 13.573 — — — 15.019 26.459 — P2 + P Null — — — — Foraminiferal E(S200 ) models with the Pace et al. (1987) POC flux as P 1 6.880 — — — 2 7.694 — — −21.887 3 6.759 0.208 — — 4 6.222 0.126 4.480 — 5 6.955 1.627 — −27.406 SP — — — 16.817 P — 5.575 — — — 4.929 5.692 — P2 + P Null — — — —

r2

AICc

AW

0.56 0.57 0.57 0.60 0.62 0.36 0.43 0.50 0.35

250.6 252.4 253.2 253.2 254.8 265.8 260.9 259.2 263.6

0.451 0.187 0.127 0.124 0.056 0.000 0.003 0.006 0.001

0.56 0.57 0.56 0.59 0.58 0.36 0.45 0.50 0.35

250.6 252.4 253.6 254.0 255.2 265.8 259.6 258.9 263.6

0.489 0.202 0.112 0.092 0.050 0.000 0.006 0.008 0.001

T, temperature; SP, seasonality of surface productivity; P, particulate organic carbon (POC) flux; P2 , quadratic term of POC flux. The table shows the coefficient for each term (T Coef., P Coef., P2 Coef., SP Coef.), r2 , the Akaike information criterion corrected for small sample size (AICc ), and the Akaike weight (AW). Bold denotes significance at P < 0.05.

that can not be examined using spatial data may be important; (iii) present-day spatial studies tend to cover very broad POC flux ranges (Tittensor et al., 2011; McClain et al., 2012) that may overwhelm temperature effects; and (iv) the temperature–diversity relationship is consistently significant at a relatively low temperature range (∼0–5∘ C), regardless of the use of palaeo or present-day data (Fig. 2). In addition, the dominant driver may be different at different time and spatial scales (Gambi et al., 2014), for example a temperature effect may be stronger over longer time scales but a POC-flux effect may be stronger over shorter time scales. However this is less plausible because the time-series studies shown in Fig. 2 indicate consistency of the temperature effect among different time scales. Significant temperature effects are known even over short, decadal–centennial time scales (Danovaro et al., 2004; Yasuhara et al., 2014). Many environmental and biological factors covary with water depth. But the fact that the temperature– diversity relationship is supported more strongly in time-series studies than in present-day spatial studies strongly implies that temperature, rather than other factors covarying with water depth, has a primary effect on diversity because time-series studies do not involve water-depth change. Glacial–interglacial sea-level changes of ∼120 m (Yokoyama et al., 2000) have an almost negligible effect in palaeoecological time-series studies in sites >1000–2000 m water depths.

IV. TEMPERATURE–DIVERSITY RELATIONSHIP As discussed above, evidence is accumulating for a relationship between temperature and diversity. However, studies have identified two apparently opposite relationships, i.e. positive and negative. (1) Positive relationships Most temperature–diversity relationships reported previously are positive, meaning that species diversity increases with increasing temperature (Fig. 2). All fossil evidence shows a positive temperature–diversity relationship (Cronin & Raymo, 1997; Cronin et al., 1999; Hunt et al., 2005; Yasuhara & Cronin, 2008; Yasuhara et al., 2012b, 2014). Our reanalysis of present-day North Atlantic deep-sea foraminiferal diversity also shows this positive relationship (Tables 1 and 2). (2) Negative relationships Negative temperature–diversity relationships are known from nematode time series records from the deep Mediterranean Sea (Danovaro et al., 2004) and from present-day ophiuroid data from southwestern Pacific seamounts (O’Hara & Tittensor, 2010) (Fig. 2). (3) A unified unimodal relationship? The negative relationships reported previously were from relatively warm deep seas including the warm

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Table 2. Model-averaged parameter estimates and confidence intervals of present-day North Atlantic deep-sea foraminiferal species diversity E(S200 ) Term

RI

Coefficient

Lower CI

Upper CI

Foraminiferal E(S200 ) models with the Lutz et al. (2007) POC flux as P R-SPA 1 −4.13 −13.73 R-TEA 1 −1.18 −8.14 R-TWA 1 6.98 1.67 T 0.98 7.21 3.01 P 0.36 −0.28 −20.75 SP 0.29 −24.94 −75.59 0.19 20.91 −4.06 P2 Foraminiferal E(S200 ) models with the Pace et al. (1987) POC flux as P R-SPA 1 −4.20 −13.53 R-TEA 1 −1.33 −8.30 R-TWA 1 7.15 1.91 T 0.98 6.96 2.98 P 0.31 0.87 −5.27 SP 0.29 −23.28 −69.00 0.14 4.53 −1.49 P2

5.48 5.79 12.29 11.42 20.20 25.70 45.87 5.13 5.63 12.40 10.94 7.00 22.44 10.55

T, temperature; SP, seasonality of surface productivity; P, particulate organic carbon (POC) flux; P2 , quadratic term of POC flux; R, geographic regions (SPA, subpolar North Atlantic; TEA, tropical eastern North Atlantic; TWA, tropical western North Atlantic); RI, relative importance; CI, confidence interval. Bold denotes CIs that exclude zero.

marginal sea of the Mediterranean (Fig. 3B) and relatively shallow (and thus warmer) seamounts. By contrast, the positive relationships are typically reported from colder deep-sea systems (∼10–15∘ C, and low (positive relationship), i.e. 10∘ C, for which enzymatic activities are known to be generally favoured; most enzymatic systems typically decrease their performance only at

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temperatures higher than 30∘ C (Childress, 1995; Cavicchioli et al., 2002). Furthermore, it has been suggested that the metabolic theory of ecology is poor predictor for diversity-level phenomena (Hawkins et al., 2007). Island biogeography theory proposes that richness depends upon an equilibrium between colonization and extinction rates (MacArthur & Wilson, 1967; Boucher-Lalonde et al., 2014). Deep-sea alpha diversity may be controlled by immigration from a regional species pool (that is much larger than local diversity) and local extinction rate, and both may be modulated by temperature (Boucher-Lalonde et al., 2014), although the underlying mechanisms are largely unknown. The importance of regional processes themselves on alpha diversity (known as regional enrichment) is becoming increasingly well understood in various ecosystems including deep sea (Ricklefs, 1987; Rex & Etter, 2010). In fact, the size of the regional species pool (i.e. regional diversity) controls deep-sea alpha diversity (Stuart & Rex, 1994; Rex & Etter, 2010). However regional enrichment cannot explain the temperature–diversity relationship in the time-series records reviewed herein, because the size of the regional species pool would not change over ecological time scales, given no speciation and extinction. Thus, immigration and local extinction (i.e. island biogeography theory) may be more important than the size of the regional species pool itself (i.e. regional enrichment), at least in terms of the temperature–diversity relationship. The last possibility is that the ascending and descending arms of the curve may be explained by different combinations of the physiological tolerance hypothesis (Currie et al., 2004), metabolic hypothesis (Allen et al., 2002), or island biogeography theory (MacArthur & Wilson, 1967; Boucher-Lalonde et al., 2014). A positive relationship at low temperatures and a negative relationship at high temperatures may not necessarily be caused by a single mechanism.

VI. RATES OF TEMPERATURE CHANGE We know little about the impact of rates of temperature change on deep-sea biodiversity. Among the biological and palaeoecological time-series studies reviewed herein, the shortest (i.e. months to years) time-scale study (Danovaro et al., 2004) is the only one to show a negative temperature–diversity relationship with all other time-series studies showing a positive relationship, suggesting that the rate of temperature change may be significant in deep-sea biodiversity. The extent to which the negative relationship observed by Danovaro et al. (2004) was due to the rapid rate of temperature change rather than due to the higher temperature itself is not clear, but it may be hypothesized that this response depends, at least partially, on the body size and generation time of the investigated taxa. Smaller

organisms have a higher surface area relative to body volume and thus may be more sensitive to rapid temperature change (although this may be less relevant to poikilotherms), and shorter generation times may allow more rapid responses or adaptation. Small species such as nematodes, that typically display generation times between a few days and several months (Heip, Vincx & Vranken, 1985), will react strongly and rapidly to temperature changes but also may adapt to such changes through generations. Conversely large-biomass taxa with much longer generation times (often >5–10 years; such as molluscs, deep-sea solitary corals, brachiopods, and echinoderms; Gage & Tyler, 1991) will be more resistant to the effects of rapid changes, but then may be unable to adapt over short time scales. We argue that rates of temperature change in the deep sea are potentially important in selecting winner and loser species in the deep sea. Understanding this aspect could become one of the future priorities for a better comprehension of the effects of temperature change on deep-sea biodiversity. However, we do not know the temperature tolerance of most (if not almost all) deep-sea species, and it remains the case that temperature itself could be the most important controlling factor of deep-sea species diversity. In fact, our findings could be simply explained by the unimodal temperature–diversity relationship without considering rates of temperature change.

VII. SENSITIVITY TO TEMPERATURE CHANGES Besides the importance of the direction of the relationship (positive versus negative) between temperature and deep-sea biodiversity, another crucial factor is the sensitivity of different taxa to changes in temperature. Deep-sea taxa are likely to be more sensitive to temperature changes than coastal taxa in general, because deep-sea temperature is relatively stable, especially over daily and seasonal time scales, compared to shallow-marine environments. In addition, benthic components of the biota are likely to be more sensitive to temperature changes than pelagic components (especially piezotolerant organisms and those migrating within the water column) for the same reason (i.e. bottom-water temperature is relatively stable compared to surface-water temperature). Thermal tolerance of deep-sea species will clearly be higher for species showing a wide bathymetric distribution, such as some cold-water corals belonging to the genera Dendrophyllia and Desmophyllum, whose distribution spans 8 to >4000 m water depth (Naumann, Orejas & Ferrier-Pages, 2013). Information on deep-sea taxa is extremely limited but a temporal analysis conducted in the deep Mediterranean Sea indicates that minor temperature shifts in the order of 0.1∘ C or less are sufficient to cause significant changes in biodiversity and community structure of deep-sea nematode assemblages

Biological Reviews (2014) 000–000 © 2014 Cambridge Philosophical Society

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10 (Danovaro et al., 2004). Nematodes are the numerically dominant component of marine sediments accounting for more than 90% of the abundance of all benthic organisms in the deep sea (Lambshead, 2004; Danovaro et al., 2009). Since nematodes are direct developers (i.e. lack a planktonic larval stage), they do not experience variations in temperature through their short life (often in the order of weeks), meaning that they are an example of a stenothermic deep-sea organism.

VIII. FUTURE DIRECTIONS To better understand the temperature–diversity relationship in the deep sea, we need to focus future studies in six main directions. (1) We need more information on short (101 – 102 year) time scales using either biological time-series data or exceptionally highly resolved microfossil records, because these time scales are directly relevant to on-going global warming as recent climate projections show deep-water warming by 2100 (Mora et al., 2013). The available biological and palaeontological evidence from these time scales is still very limited (Cannariato, Kennett & Behl, 1999; Danovaro et al., 2004; Wollenburg, Mackensen & Kuhnt, 2007; Yasuhara et al., 2008, 2014). (2) We need more large-spatial-scale data on deep-sea biodiversity, covering wide geographic and temperature ranges, to determine which model best fits the data. It may be important to use a dataset from an intermediate water depth range (e.g. focusing on the bathymetric range 1000–4000 m) as applied in many large-geographical-scale studies (Culver & Buzas, 2000; Rex, Stuart & Coyne, 2000; Lambshead et al., 2002; Corliss et al., 2009), because, for example, shallower uppermost bathyal sites have unusually high POC fluxes compared to ordinary deep-sea sites that may overwhelm the effects of temperature and make it undetectable. Very deep sites (>5000 m) below lysocline or carbonate compensation depth are under the influence of corrosive bottom waters that result in unusually severe conditions for organisms with calcareous shells or parts. Such studies will help to understand the role of temperature in food-limited deep-sea ecosystems. In fact, our nematode dataset shows a much noisier temperature–diversity relationship (r 2 = 0.094, P < 0.001) when we include all data (i.e. not only the data from 1000 to 4000 m but also the data from 4000 m water depth) than for the 1000–4000 m depth data only (r 2 = 0.300, P < 0.001; see online Fig. S1). If sufficient large-spatial-scale data were available, it would be possible to use statistical modelling not only for the entire dataset but also for low-temperature and high-temperature ranges separately. Model 1 would be supported if all of these approaches show a significant

temperature–diversity relationship (i.e. unimodal for the entire dataset, positive for the low-temperature range, and negative for the high-temperature range) and if the slopes of the temperature–diversity relationships in the low- and high-temperature ranges were similar. Model 2 will be supported if the slope of the temperature–diversity relationship over the high-temperature range is significantly steeper than that in the low-temperature range. Model 3 will be supported if the entire dataset shows a much weaker temperature–diversity relationship than for separately constructed relationships in low- and high-temperature ranges. (3) We need comprehensive data from other oceans than the North Atlantic. The majority of the data reviewed herein are from the North Atlantic Ocean; additional data from other oceans are needed to test our models of the temperature–diversity relationship rigorously. For example, the recent discovery of unexpectedly high Southern Ocean deep-sea biodiversity (Brandt et al., 2007) strongly suggests that more research is needed especially in the southern hemisphere, where deep-sea biology data are limited (Rex & Etter, 2010). We also need more data from warm deep oceans, such as the Mediterranean, the Red and the Sulu seas. (4) The underlying mechanisms explaining the temperature–diversity models are still uncertain, although several hypotheses are available: the physiological tolerance hypothesis, metabolic hypothesis, and island biogeography theory. Future research should test these hypotheses by inter-region comparisons or mesocosm experiments. ‘Top-down’ hypotheses (i.e. metabolic hypothesis and island biogeography theory) predict that the environment (temperature in this case) imposes top-down limits on species diversity, regardless of species identity (Boucher-Lalonde et al., 2014). Thus, temperature–diversity relationships will be the same globally. By contrast, in ‘bottom-up’ hypotheses (i.e. the physiological tolerance hypothesis), the environment (temperature in this case) controls species diversity bottom-up by constraining individual species’ physiological tolerance ranges, and so temperature–diversity relationships will differ among oceanic regions with different species. Future research could differentiate between these two hypothesis groups by comparing temperature–diversity relationships among different oceans (e.g. Atlantic, Pacific, Arctic, and Southern Oceans). If the ‘shapes’ of temperature–diversity relationships (e.g. slope of ascending and descending arms and position of peak) are similar or the same among different oceans then ‘top-down’ hypotheses are plausible, and vice versa. Mesocosm or in situ experiments using nematodes, foraminifera or even larger organisms may be one avenue to test these hypotheses. (5) We need to increase our understanding of physiological responses and other ecological characteristics of individual deep-sea species, which are largely unknown

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for most deep-sea species, including their tolerance to changing temperature conditions as well as their metabolic responses (e.g. Brooke et al., 2013; Brown & Thatje, 2014; Naumann, Orejas & Ferrier-Pages, 2014). (6) Finally, longer (i.e. >105 year) time-scale dynamics are needed to understand the evolutionary dynamics of deep-sea biodiversity. Such studies are essential to establish the role of evolutionary dynamics in shaping global biodiversity patterns in the deep sea. Even though evolutionary dynamics have been investigated in shallow-marine fossil records (Jablonski et al., 2006), deep-sea studies investigating biodiversity dynamics over evolutionary time scales are very limited (e.g. Thomas & Gooday, 1996; Yamaguchi & Norris, 2012). Further research is needed to take advantage of the rich microfossil records of benthic ostracodes and foraminifera in deep-sea sediment cores, perhaps complemented by ancient DNA approaches now developing rapidly (Lejzerowicz et al., 2013), to estimate biodiversity in the deep past and to elucidate whether deep-sea evolutionary dynamics could be driven by temperature, perhaps through changes in metabolic rate and resulting speciation rate (Allen et al., 2002; Brown & Thatje, 2014).

IX. CONCLUSIONS (1) Despite notable technological advances in the last two decades, the deep sea is still a remote environment difficult to access, and thus biological and palaeoecological data are limited. We compiled all published spatial (i.e. present-day) and time-series (both biological and palaeoecological) data on deep-sea temperature–diversity relationships, and used the integration of different temperature–diversity patterns covering a wide range of temperatures to create three possible models: a unimodal (hump-shaped) model, a ‘right-skewed’ unimodal model, and a model in which temperature is important only at relatively high (negatively) and low (positively) temperatures. (2) There are several hypotheses (physiological tolerance hypothesis, metabolic hypothesis, island biogeography theory, or combinations of these) to explain these three models. The metabolic hypothesis may be less plausible and the physiological tolerance hypothesis may best explain the observed patterns discussed herein. However, further research is needed to differentiate between these models. (3) These results provide important baseline information for the present and future management of deep-sea ecosystems in this rapidly warming Anthropocene era.

X. ACKNOWLEDGEMENTS We thank C. L. Wei for help with POC flux calculation, G. Hunt, M. A. Rex, and an anonymous reviewer

for valuable comments, the Editor W. Foster, and the Assistant Editor A. Cooper. R.D. was supported by the National Flagship Programme RITMARE (Ricerca Italiana in Mare) and the European Union project MIDAS (Managing Impacts of Deep-seA reSource exploitation.

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XII. SUPPORTING INFORMATION Additional supporting information may be found in the online version of this article. Fig. S1. Comparison of temperature–diversity relationships between (A) 1000–4000 m dataset and (B) whole dataset (including 4000 m data) of present-day North Atlantic Ocean and Mediterranean Sea nematode species diversity (Danovaro et al., 2009; Bongiorni et al., 2010; Bianchelli et al., 2013; Pusceddu et al., 2013; R. Danovaro, unpublished data; see online Table S2). Quadratic regression lines are shown. The whole dataset (B; r 2 = 0.094, P < 0.001) is much noisier than the 1000–4000 m dataset (A; r 2 = 0.300, P < 0.001). Table S1. North Atlantic deep-sea foraminiferal dataset used for regression modelling in the present study. Faunal and environmental data from Sun et al. (2006) and Corliss et al. (2009). See Appendix S1 for Lutz et al.’s (2007) and Pace et al.’s (1987) particulate organic carbon (POC) flux calculation. TEA, tropical eastern North Atlantic; TWA, tropical western North Atlantic; MA, middle North Atlantic; SPA, subpolar North Atlantic. Table S2. Present-day North Atlantic Ocean and Mediterranean Sea nematode species diversity dataset used to construct Fig. 3C and Fig. S1. Data from Danovaro et al. (2009), Bongiorni et al. (2010), Bianchelli et al. (2013), Pusceddu et al. (2013), and R. Danovaro (unpublished data). Appendix S1. Methods for regression modelling using the North Atlantic deep-sea foraminiferal dataset.

(Received 9 July 2014; revised 19 November 2014; accepted 21 November 2014 )

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