Global Change Biology Global Change Biology (2014), doi: 10.1111/gcb.12726

Temperature tracking by North Sea benthic invertebrates in response to climate change J A N G . H I D D I N K 1 , M I C H A E L T . B U R R O W S 2 and J O R G E G A R C IA M O L I N O S 2 1 School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey LL59 5AB, UK, 2Department of Ecology, Scottish Association for Marine Science, Marine Institute, Oban, Argyll, UK

Abstract Climate change is a major threat to biodiversity and distributions shifts are one of the most significant threats to global warming, but the extent to which these shifts keep pace with a changing climate is yet uncertain. Understanding the factors governing range shifts is crucial for conservation management to anticipate patterns of biodiversity distribution under future anthropogenic climate change. Soft-sediment invertebrates are a key faunal group because of their role in marine biogeochemistry and as a food source for commercial fish species. However, little information exists on their response to climate change. Here, we evaluate changes in the distribution of 65 North Sea benthic invertebrate species between 1986 and 2000 by examining their geographic, bathymetric and thermal niche shifts and test whether species are tracking their thermal niche as defined by minimum, mean or maximum sea bottom (SBT) and surface (SST) temperatures. Temperatures increased in the whole North Sea with many benthic invertebrates showing north-westerly range shifts (leading/trailing edges as well as distribution centroids) and deepening. Nevertheless, distribution shifts for most species (3.8–7.3 km yr1 interquantile range) lagged behind shifts in both SBT and SST (mean 8.1 km yr1), resulting in many species experiencing increasing temperatures. The velocity of climate change (VoCC) of mean SST accurately predicted both the direction and magnitude of distribution centroid shifts, while maximum SST did the same for contraction of the trailing edge. The VoCC of SBT was not a good predictor of range shifts. No good predictor of expansions of the leading edge was found. Our results show that invertebrates need to shift at different rates and directions to track the climate velocities of different temperature measures, and are therefore lagging behind most temperature measures. If these species cannot withstand a change in thermal habitat, this could ultimately lead to a drop in benthic biodiversity. Keywords: benthic invertebrate, benthos, distribution shifts, North Sea, sea bottom temperature, sea surface temperature, velocity of climate change Received 25 February 2014; revised version received 12 August 2014 and accepted 12 August 2014

Introduction The long-term persistence of species in the face of climate change depends on the ability of populations to keep pace with moving climates or adapt to changes in situ (Burrows et al., 2011). In particular, shifts in the distributional ranges of populations and communities have been frequently observed in response to these changes (e.g. Parmesan & Yohe, 2003; Helmuth et al., 2006; Dulvy et al., 2008), but only a few studies have examined whether these shifts allow species to keep pace with climate change (e.g. Hiddink et al., 2012; La Sorte & Jetz, 2012; Pinsky et al., 2013). Furthermore, as most marine ectotherms are thermal range conformers, they tend to occupy fully their thermal niche and are therefore more responsive to warming than their terresCorrespondence: Jan G. Hiddink, tel. + 44 1248 382 864, fax + 44 1248 716 367, e-mail: [email protected]

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trial counterparts (Sunday et al., 2012). For marine species responding to climate change, rates of distribution shifts are, on average, consistent with those required to track mean ocean surface temperature changes (Poloczanska et al., 2013). Nevertheless, similar analyses on the tracking of minimum and maximum temperature by species responding to climate change are lacking (Warren & Chick, 2013), yet extremes rather than mean temperatures may be the primary drivers of distribution shifts (Sunday et al., 2012). Improving our current understanding of the factors governing range shifts is crucial for climate change conservation because, by changing the identity of biological communities, they are expected to alter importantly ecosystem function and structure under future anthropogenic climate change (Dawson et al., 2011). The climate variability hypothesis proposes that species’ latitudinal ranges reflect their thermal tolerance as

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2 J . G . H I D D I N K et al. defined by the highest summer and coldest winter temperatures within their ranges, whereby species would retreat from areas that become too warm and expand into areas that were previously too cold under climate change (Sunday et al., 2012). Therefore, assuming that species ranges are limited by maximum and minimum temperatures at their equatorial trailing edge and poleward leading edge, respectively, predictions from changes in temperature extremes might reflect more accurately observed shifts at range edges whether mean temperatures might be a better predictor of overall range centroid shifts. Under the expectation of pole-ward shift responses to a warming climate, shifts in temperatures and distributions are often expressed simplistically along the latitudinal axis (e.g. Chen et al., 2011). While this procedure might be acceptable for regions with a predominantly north–south temperature gradient, it can otherwise lead to severe underestimations of the fingerprint of climate change (VanDerWal et al., 2013). Local differences in climate velocities, defining the rate and direction of isotherm shift through space (Loarie et al., 2009), are emerging as a consistent predictor of the directions and rates of shift in marine species (Pinsky et al., 2013). Taking into account both the rate and directionality of change in thermal conditions is necessary to fully characterize the shift of species tracking their thermal niche (Burrows et al., 2014). Such is the situation in the North Sea where mean annual and maximum temperatures increase along a northwest to southeast gradient, while minimum temperature decreases (Otto et al., 1990). This results in the southeast of the North Sea having the largest annual temperature range of over 10 °C while parts of the northern North Sea experience an annual range of less than 2 °C. The complex oceanography of the North Sea, generating effectively opposite directions of movement for those species tracking maximum and minimum temperatures, is particularly suitable for studying and disentangling the effect of different temperature variables on the distribution of species. If species are not shifting at the trailing edge of their distribution (warm boundary) at the rate dictated by climate change, they will build-up an ‘extinction’ or ‘climatic’ debt (Jackson & Sax, 2010; Devictor et al., 2012; Hylander & Ehrlen, 2013). This will be the case, for example, where adults are long lived or reproduction continues at a rate slightly below that required for persistence. It can be assumed that shifts at the trailing edge are conditioned by its sensitivity to increasing temperatures (as no shift is expected if conditions are still within the suitable range of the organism), while failure to shift results in the build-up of an extinction debt. Alternatively, species could adapt physiologically

or through micro-evolution. Few marine studies have recorded range contractions at the trailing edge of species distribution ranges defining their warm, equatorial boundary (Lima et al., 2007; Wethey & Woodin, 2008), suggesting that many marine species may be building up an extinction debt. On the other hand, shifts at the leading edge (the cold, polar boundary) are related to the ability of a species to disperse into areas that were previously too cold, and are likely to relate to the dispersal and settling capability of the species. A failure to colonize newly available habitat at the leading edge will result in a ‘colonization lag’ (Jackson & Sax, 2010). By evaluating the temperatures experienced by a species in a period of climatic warming, we can obtain an insight into its ability to track their thermal niche and the build-up of extinction debts and immigration lags. The North Sea is one of the fastest warming continental shelf seas (Burrows et al., 2011) and has been experiencing changes in the distribution and composition of fish species. For example, fish species richness has increased, with small southerly species increasing and large northerly species decreasing in abundance and range (Hiddink & ter Hofstede, 2008; Simpson et al., 2011). The ranges of many fish species have also moved northwards and towards deeper water (Perry et al., 2005; Dulvy et al., 2008). Because most species of fish are mobile and migratory, they are likely to adapt quickly to changing temperature patterns. However, most species of benthic invertebrates are less likely to be able to keep pace with rapid climate shifts as they cannot move large distances as adults despite having pelagic eggs and larvae (but see Hiddink et al., 2012). Existing studies on the effect of climate change on range shifts on marine benthic invertebrates have largely focused on intertidal and rocky shore species (e.g. Helmuth et al., 2006; Keith et al., 2011), but no evidence of range shifts for off-shore soft-sediment invertebrates yet exists. Soft-sediment invertebrates are important because many are foundation species or ecosystem engineers with an important role in marine biogeochemistry, and constitute an important food source for commercial fish species, mobilizing organic carbon back to the pelagic realm (Snelgrove, 1999). Here, we (1) analyse distribution shifts in the geographic (the centroid of the distribution as well as the leading and trailing edges) and depth ranges of 65 benthic invertebrates occurring in the North Sea between 1986 and 2000, (2) evaluate if observed distribution shifts match those corresponding to their thermal niches, using the velocity of climate change (VoCC) as a predictor, and (3) estimate whether these species are effectively maintaining their thermal niches over time or accumulating extinction debts or immigration lags. To do so, we examine thermal niches of the species in

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

TEMPERATURE TRACKING BY NORTH SEA BENTHOS 3 terms of minimum, mean and maximum sea bottom temperature (SBT) and sea surface temperature (SST). SBT is the temperature experienced by the adults of these organisms, while SST is the temperature at which most primary production will occur, and to which the larvae of the species may be exposed. Specifically, we test the hypotheses that (H1) benthic invertebrates in the North Sea are shifting their distribution in response to ocean warming induced by climate change, (H2) the direction and magnitude of observed range shifts can be predicted from that of temperature change (i.e. the VoCC), (H3) shifts in the distribution of species occurring at leading/trailing edges are better determined by changes in minimum and maximum temperatures, while those corresponding to the overall distribution are better described by changes in mean temperature, and (H4) species are tracking their thermal niche without extinction debts or immigration lags. The outcome of this study has important implications for conservation. Because changes in different temperature parameters induced by climate change have different directions and magnitudes, it is important to understand which parameters are best tracked by organisms if we are to predict the effect of climate change on patterns of marine biodiversity. If species fail to keep pace with climate change, or shift in the wrong direction, and cannot withstand the resulting change in thermal conditions, this could ultimately lead to a drop in benthic biodiversity.

2012. We transferred the irregularly spaced data into 2° latitude 9 2° longitude cells by assigning each CTD cast to a cell (337  117 and 331  119 stations per cell for SST and SBT, respectively; mean  1SD). Given that not enough CTD observations were available in each year for each cell, the temperature cycles in 1986 and 2000 were estimated using a linear model fitted to the 1950–2012 series of the form: Ti ¼ a þ ½a sin ð2p ðXi þ bÞ=12Þ þ fðXi Þ þ ei ; where ei  Nð0; r2 Þ ð1Þ where Ti is the temperature in a cell for the ith month in the series (Xi). Parameters a, a, and b define respectively the intercept, the amplitude of the temperature annual cycle and the month in which the temperature is maximal. The sinusoidal part of the model accounts for the seasonal component of the temperature in cell i and the smoothing curve f(Xi) for the long-term trend using a spline smoother over the 62 year period to ensure that the difference in temperature between 1986 and 2000 represents the long-term change. ei represents the error term: independent and normally distributed random residuals with variance, r2. Fitted models were validated by visual examination of the model residuals. Minimum and maximum temperatures were defined as the temperatures in the coldest and hottest months respectively. Maps of predicted minimum, mean and maximum temperature in 1986 and 2000 were finally downscaled from the 2° to 0.2° resolution using bilinear interpolation to smooth the resulting maps. Results were not sensitive to the interpolation methods or downscaling factor. Figure S1 illustrates the long-term temperature variations in the North Sea. It shows that 1986 was one of the coldest years since the 1960s, while 2000 was typical for the postmillennial warm years.

Materials and methods

Velocity and direction of climate change

Outline

The estimated 0.2° resolution maps for the minimum, mean and maximum sea bottom and surface temperatures in 1986 and 2000 were used to calculate corresponding maps of the VoCC giving the speed and direction with which organisms need to move to stay at the same temperature. Loarie et al. (2009) developed a method to calculate the velocity of temperature change (km yr1), derived locally as the ratio between the rate of temperature increase (°C yr1) and the spatial temperature gradient (°C km1), with the direction of shifts given by the direction of spatial gradient (Burrows et al., 2011). This index represents the instantaneous local velocity needed by a species to maintain constant temperatures. As the spatial gradients and temporal changes in temperature are different for minimum, mean and maximum bottom and surface temperature, the direction and magnitude of the VoCC will also be different for each temperature parameter.

Benthic invertebrate distribution data were obtained from the North Sea Benthos Project for all stations across the North Sea sampled in 1986 and 2000 (Rees et al., 2007). Geographic and depth range shifts were quantified for the common species, and compared to the expected shifts based on the SBT and SST observed temperature changes in the North Sea. We then examined if the observed shifts match those of their thermal niches predicted using the VoCC over the same period, and if species were eventually trailing, tracking or overcompensating their thermal niches, with regard to minimum, mean and/ or maximum temperature.

Sea bottom and surface temperature Sea bottom (records in the bottom 25% of the water column at each sampling station) and surface temperatures (5 cm) providing comparable results. For example, Beukema (1974) found a good correlation between the number of invertebrates between grabs and corers. About 86% of all benthic invertebrate individuals are found in the top 5 cm of the sediment in the North Sea (Dauwe et al., 1998), and the species that were included in our analysis (Table S1) are all species that occur in this surficial sediment, with the exception of the mud shrimp Callianassa subterannea. Rees et al. (2007) compared the community composition in the two surveys at colocated stations, and found that the distributions of ranked densities were nearly identical but that the species richness was slightly but consistently lower in 2000. They concluded these broad comparisons of data structure provide reassuring evidence of the integrity of the dataset, and that it provides a sound basis for an evaluation of the status of North Sea benthic assemblages in 2000, and of any changes since 1986, while recognizing that sampling and analytical influences must also be accounted for in ecological interpretations. We standardized both datasets (1986 and 2000) to balance the spatial distribution of the sampling stations by only selecting stations that were sampled in both years for further analysis. Two sampled locations were defined as the ‘same’ station in 1986 and 2000 when they were within 60 km of each other and when no other stations were closer to either of the pair. In total, 143 stations were selected for analysis with 90% of stations being within 16 km of each other; the density of stations in the southern North Sea being higher than in the northern North Sea (see Fig. S2 for a map of the sampling stations). On average, stations in 2000 were positioned 3.4 km south and 1.5 km east of the stations in 1986. This bias is comparatively smaller and in the opposite direction to most of the reported range shifts (see Results), and therefore cannot have caused the observed changes.

Sixty five benthos species that were found on at least 10 stations in both years were selected for analyses (42  22 occupied stations; mean  1SD). This approach limited the analysis to common and widespread species where stochastic effects (i.e. false absences) are likely to be smaller. We expect stochasticity to increase variation, but it should not introduce biases. Data with a higher temporal resolution are not available to explore the relative importance of stochasticity. Nevertheless, it is likely that some species were not recorded at stations where they are present because of the relatively small size of the sampling gears ( 0.10 and maximum W = 0.2, P > 0.05) and correlating significantly to the magnitude of shift (Fig. S9; Table S3, mean R2 = 0.08, F1,63= 5.72, P = 0.020 and maximum R2 = 0.09, F1,63= 6.06, P = 0.017). No significant differences in shift direction were found between observed and predicted COGE shifts using the VoCC of mean and maximum SST (Fig. 6), but the relationship with the magnitude of the shift for the leading edge expansion was not significant (Fig. S9). Velocities based on

Fig. 2 Observed magnitude of shifts of the centre of gravity of the distribution of benthic invertebrates. The comparable temperature shift rates are given in Figs S5 and S6.

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Fig. 1 Frequencies of observed direction of changes of the distribution of benthic invertebrates. (a) The centre of gravity of the leading edge expansion (COGE), (b) the centre of gravity of the overall distribution (COGD) and (c) the centre of gravity of the trailing edge contraction (COGC). © 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

TEMPERATURE TRACKING BY NORTH SEA BENTHOS 7 (a)

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Fig. 3 Examples of changes in the distribution of the polychaetes (a) Amphictene auricoma and (b) Levinsenia gracilis. Blue points are 1986 only presence records, red points are 2000 only presence records, and purple points are locations where the species was present in both years. The blue and red crosses indicate the centre of the distribution ‘COGD’ in 1986 and 2000 and are connected by a thick black arrow. ‘COGC’ indicates the centre of gravity for the contraction of the trailing edge (i.e. blue points), ‘COGE’ indicates the centre of gravity for the expansion of the leading edge (i.e. red points). The observed direction and relative magnitude of the leading edge expansion and trailing edge contraction relative to the 1986 COGD (see Methods) are indicated with a red (expansion) and a green (contraction) arrow. The thin black arrows give the isotherm trajectories followed by the mean SBT at the stations were the species was present in 1986 until 2000.

SBT parameters failed to predict accurately distribution shifts with the sole exception of the mean SBT for the COGD shifts (Fig. S8; Table S4, W = 0.1, P > 0.10), though the relationship between observed and predicted magnitudes of shift was nonsignificant R2 = 0.04, (F1,63 = 2.91, P = 0.093). Overall, the best predictor of the overall distribution shift (COGD) was mean SST, while maximum SST best predicted the shift of the trailing edge (COGC). Even so, the magnitude of these shifts predicted by the VoCC seems to have underestimated the magnitude of the observed COG shifts (Fig. S9C, D). None of the three temperature parameters for neither SST nor SBT predicted accurately both the direction and the magnitude of the COGE shifts at the leading edge of the distribution.

Thermal niche tracking A large percentage of species (>32%) effectively did not shift their geographical distribution (i.e. the temperature at the stations occupied in 2000 was within 0.25 °C of the temperature that the species would have experienced if they had not moved since 1986; green symbols in Fig. 7). Many of these species were therefore experiencing a SBT (33–77% of species; depending on the

temperature parameter) and SST (63–78% of species) in 2000 that was higher than in 1986 (Table S5, categories ‘static and lagging’ and ‘shifting against temperature’). However, not all species (SBT: 11.0  11.4, SST: 9.3  3.8) needed to move to track their temperature niche (i.e. green points within the grey lines in Fig. 7). Among the three temperature parameters, a higher percentage of species tracked mean SBT (57%, ‘static and tracking’ and ‘shifting and tracking’) than either minimum or maximum temperatures. In contrast, more species (24%) tracked maximum SST than the minimum or mean (Table S5). Some species overcompensated (SBT: 24.7  7.8, SST: 15.3  4.7), and ended up at lower temperatures in 2000 than in 1986 (Fig. 7, blue symbols below the grey lines), while other species (SBT: 11.3  8.5, SST: 7.0  8.2) moved in the opposite direction from that required to track temperature shifts and ended up in warmer waters than those they would have experienced if they had remained stationary, in particular for minimum temperature (Fig. 7, red symbols).

Discussion Our results give strong evidence in support of our first hypothesis, that species of benthic invertebrates have

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

8 J . G . H I D D I N K et al. (a)

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Fig. 4 Shifts in the (a, d) minimum, (b, e) mean and (c, d) maximum depth at which species were found between 1986 and 2000. A negative change means an increase in the depth at which a species was found. The 1 : 1 line indicates no change between years. Points above the line indicate shallowing, while points below the line indicate deepening of the distribution.

shifted their distribution in response to the increasing trend in minimum, mean and maximum SST and SBT across the North Sea between 1986 and 2000. Northwesterly range shifts and deepening were recorded for many species over this time period. Similar depth and distribution shifts have been observed for North Sea fish (Perry et al., 2005; Dulvy et al., 2008). While depth shifts were more pronounced at the deep than the shallow end of the depth range, distribution shifts were greater at the leading edge (cold boundary) of the distribution of species, implying that the extinction debts are likely to be larger than immigration lags (Jackson & Sax, 2010). Species could not physically track all temperature parameters simultaneously because the shifts

in parameters had divergent directions due to the oceanography of the North Sea. Previous studies have generally only examined tracking of a single temperature parameter, e.g. minimum winter temperature (La Sorte & Jetz, 2012) or mean annual temperature (Bertrand et al., 2011). Our results show that the choice of temperature parameter is likely to affect the perceived extent to which species can track their temperature niche. Without prior information on what climate parameters are limiting the distribution of a species under climate change, it is hard to justify choosing one parameter above the others. Our second (H2) and third (H3) hypotheses were partly supported: that the direction and magnitude of

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

TEMPERATURE TRACKING BY NORTH SEA BENTHOS 9 (a)

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Fig. 5 The direction of shifts in distribution predicted using the velocity of sea bottom temperatures. The predicted direction of changes in the distribution of the centre of gravity for (a–c) the expansion of the leading edge, (d–f) the overall distribution and (g–i) the contraction of the trailing edge of benthic invertebrates species using the velocity of climate change for minimum (a, d, g), mean (b, e, h) and maximum (c, f, i) sea bottom temperature. The numbers in the top right hand corner of each diagram give the mean  SD difference between the observed and predicted direction of the shift (bold indicates nonsignificant angular differences and therefore a match between predicted and observed shifts). Observed angles are given in Fig. 1.

range shifts can be predicted from the velocity and direction of isotherm movements; and that mean temperatures are better predictors of the overall distribution shift, with minimum and maximum temperatures better predicting leading and trailing edge shifts. Contrary to expectations, the expansion of the leading edge was better predicted by the velocity of mean and maximum SST rather than minimum temperatures. The north-west expansion of the leading edge experienced by many species cannot be explained by an increase in minimum temperatures to above the lower thermal tolerance limit, because the minimum temperature increases with latitude in the North Sea. As species are not expanding into areas that were previously too cold, the climate variability hypothesis that proposes that thermal tolerance at a global scale is related to latitudinal range (Sunday et al., 2012), seems not to apply at finer spatial scales where the relationship among temperature parameters is more complex. This observation does not disprove the climate variability hypothesis for

minimum temperatures, but instead indicates that other factors than increases in minimum temperature can explain the north-westerly expansion of the distribution of benthic invertebrates. It is possible that species range shifts respond to shifting temperatures in idiosyncratic ways, and that the shift in mean temperatures will best fit the average of the species shifts (Pinsky et al., 2013). This does not imply that the mean temperature is the most important determinant of distributions but just indicates that the average temperature is most likely to correlate with most measures of temperature. It is also likely that different thermal windows are important for each species. For example, northern barnacles are particularly sensitive to spring temperatures at the southern end of their range (Poloczanska et al., 2008) and this is neither the minimum nor the maximum temperature. As such, it may be worth repeating the analysis by examining VoCC for all 12 months individually in future studies. These results add to the mounting evidence on the ability of the

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

10 J . G . H I D D I N K et al. (a)

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Fig. 6 The direction of shifts in distribution predicted using the velocity of sea surface temperatures. The predicted direction of changes in the distribution of the centre of gravity for (a–c) the expansion of the leading edge, (d–f) the overall distribution and (g–i) the contraction of the trailing edge of benthic invertebrates species using the velocity of climate change for minimum (a, d, g), mean (b, e, h) and maximum (c, f, i) sea surface temperature. The numbers in the top right hand corner of each diagram gives the mean  SD difference between the observed and predicted direction of the shift (bold indicates nonsignificant angular differences and therefore a match between predicted and observed shifts). Observed angles are given in Fig. 1.

VoCC to predict the magnitude and direction of shifts in marine species (Pinsky et al., 2013; Poloczanska et al., 2013), and introduces the potential usefulness of considering extremes as well as the mean for improving the prediction of distribution shifts. Strong evidence in support for our last hypothesis (H4), that species will track their original thermal niche with no extinction debts or immigration lags, was only found for mean SBT. Even for this temperature parameter, we found that many species did not shift their ranges fast enough to keep pace with climate change. Depending on the temperature parameter examined, 25–78% of species were static and lagging behind temperature changes between 1986 and 2000, even though all three temperature parameters changed substantially (mean increase of 0.31 and 0.41 °C for mean SBT and SST respectively) in almost the whole North Sea over this period. It is therefore likely that many species built up extinction debts because of a failure to track temperatures, i.e. a large fraction of species are not tracking and end up warmer than before (Jackson & Sax, 2010).

These results seem to contradict those of the VoCC analysis, where mean SBT only succeeded in predicting the direction but not the magnitude of the geographic shifts, yet mean and maximum SST predicted well both the direction and magnitude of geographic shifts. This may be related to the underestimation of the magnitude of the observed shifts of species by about 2 km yr1 using the SST VoCC (Fig. S9). Immigration lags related to failure to track minimum temperature are also likely to develop as hardly any species are shifting in the right direction to track this parameter. Basic physiological principles dictate that environmental temperature affects the scope for activity, energy use, growth and other physiological processes for ectothermic organisms. Ongoing temperature changes may yet be within the thermal tolerance of these species, and their distribution therefore limited by other factors. Several studies have also shown that species can adapt quickly to the locally changing conditions through phenotypic plasticity and adaptive microevolution (Parmesan, 2006; Bellard et al., 2012), which would make them less

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

T E M P E R A T U R E T R A C K I N G B Y N O R T H S E A B E N T H O S 11 (a)

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Fig. 7 The temperature experienced by species across stations within their range in 1986 and 2000 for the (a, d) minimum (10% quantile of minimum temperatures), (b, e) mean (of mean temperatures) and (c, f) maximum (90% quantile of maximum temperatures) of sea bottom (a–c) and surface (d–f) temperature. Species that experienced the same temperature (0.25 °C) in 1986 and 2000 were defined as tracking their 1986 temperature niche, as indicated by the area within the thin grey lines; the thick grey line indicates the 1 : 1 line of equal temperatures. Species that did not change their distribution between 1986 and 2000 are indicated by green points, these species will generally experience higher temperatures. Blue points indicate species that have shifted their geographic distribution towards cooler areas. Red points indicate species that have shifted their geographic distribution towards warmer areas and are therefore shifting their distribution in the opposite direction of what would be adaptive. Figure S10 provides an aid to the interpretation of this figure.

sensitive to temperature changes. However, species with higher thermal tolerance are often more susceptible to climate change because of a trade-off between thermal tolerance and acclamatory capacity (Stillman, 2003; Krenek et al., 2013). Species that may adapt well to short-term temperature increases may therefore not cope well with ongoing climate change. As 14 years between 1986 and 2000 is longer than the life span of most species studied here, the only obvious exception being the ocean quahog Arctica islandica (Witbaard & Bergman, 2003), adaptive microevolution may also have played a role. A final explanation could be that these species are building up an extinction debt where populations are thriving, either because adults are long lived or because population mortality is only slightly

higher than new recruitment, but will not be viable in the long term (Jackson & Sax, 2010; Sax et al., 2013). Apart from climate change, the most important factor influencing the abundance, biomass and species richness of benthic invertebrates in the North Sea is bottom trawling. Trawling could result in changes in the distribution of benthos if the distribution of the trawling effort changes, and could explain our results if the relative trawling effort increased in shallow and south-easterly areas of the North Sea between 1986 and 2000. Nevertheless, there is no evidence that such a divergence in the spatial allocation of fishing effort occurred in this period (Jennings et al., 1999). On the other hand, primary production is generally assumed to be limiting the carrying capacity of the ecosystem for benthic

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

12 J . G . H I D D I N K et al. invertebrates, and changes in primary production can therefore also result in changes in the distribution of the benthos (Hiddink et al., 2006). The two main drivers for changes in primary production are likely to be climate change and eutrophication. The effects of changes in primary production induced by climate change cannot be separated from those of temperature changes in this study, but no clear trends in primary production with climate change have been recorded for the North Sea (Beaugrand & Reid, 2003). Reductions in eutrophication could led to reduced primary production in coastal areas, and subsequently to the observed relative decrease in benthos in the southern North Sea, but cannot explain the observed expansions of species ranges into the northern North Sea. Finally, the abundance of major benthic predators, commercial fish species, decreased over the study period across the North Sea, releasing the benthos from predation pressure (Heath, 2005). There is however no reason to assume that this would have led to a change in the spatial distribution of benthic species. In conclusion, although geographical and depth shifts were observed for many benthic invertebrates in the North Sea at the same time as ongoing changes in sea surface and bottom temperatures, only a minority of species did actually track effectively their thermal niches over time for most temperature measures. The exception was mean bottom temperature which was tracked without extinction debts or immigration lags by 57% of species. The distribution of most benthic species was lagging behind both bottom and surface temperature and many species were therefore experiencing higher temperatures in 2000 than in 1986. In contrast, the only predictors of the direction and magnitude of shifts of the distribution of benthos was the VoCC of mean surface temperature, while maximum surface temperature predicted accurately the expansion of the leading edge. Benthic invertebrates that are lagging behind climate change will run out of suitable thermal habitat in the long run, and part of the population is likely to be already occupying unsuitable thermal habitat at the moment. It is hard to envisage conservation strategies that could negate such effects beyond minimizing the effect of other disturbances and therefore ensure maximum reproductive potential, which would result in enhanced production and therefore dispersal of eggs and larvae.

Acknowledgements We thank all involved in NSBS 1986 and NSBP 2000 for carrying out the surveys and making the data available. M.T.B. and J.G.M. were supported by the UK Natural Environment Research Council grant NE/J024082/1.

References Agostinelli C, Lund U (2013) R package ‘circular’: Circular Statistics (version 0.4-7). Available at: http://r-forge.r-project.org/projects/circular/ (accessed 20 February 2014). Beaugrand G (2004) The North Sea regime shift: evidence, causes, mechanisms and consequences. Progress in Oceanography, 60, 245–262. Beaugrand G, Reid PC (2003) Long-term changes in phytoplankton, zooplankton and salmon related to climate. Global Change Biology, 9, 1–17. Becker JJ, Sandwell DT, Smith WHF et al. (2009) Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy, 32, 355–371. Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecology Letters, 15, 365–377. Bertrand R, Lenoir J, Piedallu C et al. (2011) Changes in plant community composition lag behind climate warming in lowland forests. Nature, 479, 517–520. Beukema JJ (1974) The efficiency of the Van Veen grab compared with the Reineck box sampler. Journal du conseil, 35, 319–327. Burrows MT, Schoeman DS, Buckley LB et al. (2011) The pace of shifting climate in marine and terrestrial ecosystems. Science, 334, 652–655. Burrows MT, Schoeman DS, Richardson AJ et al. (2014) Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507, 492–495. Chen I-C, Hill JK, Ohlem€ uller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science, 333, 1024–1026. Dauwe B, Herman PMJ, Heip CHR (1998) Community structure and bioturbation potential of macrofauna at four North Sea stations with contrasting food supply. Marine Ecology Progress Series, 173, 67–83. Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity conservation in a changing climate. Science, 332, 53. Devictor V, van Swaay C, Brereton T et al. (2012) Differences in the climatic debts of birds and butterflies at a continental scale. Nature Climate Change, 2, 121–124. Dulvy NK, Rogers SI, Jennings S, Stelzenmuller V, Dye SR, Skjoldal HR (2008) Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. Journal of Applied Ecology, 45, 1029–1039. Heath MR (2005) Changes in the structure and function of the North Sea fish foodweb, 1973–2000, and the impacts of fishing and climate. ICES Journal of Marine Science, 62, 847–868. Helmuth B, Mieszkowska N, Moore P, Hawkins SJ (2006) Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annual Review of Ecology, Evolution and Systematics, 37, 373–404. Hiddink JG, ter Hofstede R (2008) Climate induced increases in species richness of marine fishes. Global Change Biology, 14, 453–460. Hiddink JG, Jennings S, Kaiser MJ, Queir os AM, Duplisea DE, Piet GJ (2006) Cumulative impacts of seabed trawl disturbance on benthic biomass, production and species richness in different habitats. Canadian Journal of Fisheries and Aquatic Sciences, 63, 721–736. Hiddink JG, Lasram FBR, Cantrill J, Davies AJ (2012) Keeping pace with climate change: what can we learn from the spread of Lessepsian migrants? Global Change Biology, 18, 2161–2172. Hylander K, Ehrlen J (2013) The mechanisms causing extinction debts. Trends in Ecology and Evolution, 28, 341–346. Jackson ST, Sax DF (2010) Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends in Ecology and Evolution, 25, 153–160. Jennings S, Alvsvag J, Cotter AJR et al. (1999) Fishing effects in northeast Atlantic shelf seas: patterns in fishing effort, diversity and community structure. III. International trawling effort in the North Sea: an analysis of spatial and temporal trends. Fisheries Research, 40, 125–134. Keith SA, Herbert RJH, Norton PA, Hawkins SJ, Newton AC (2011) Individualistic species limitations of climate induced range expansions generated by meso scale dispersal barriers. Diversity and Distributions, 17, 275–286. Krenek S, Petzoldt T, Berendonk TU (2013) Coping with temperature at the warm edge-patterns of thermal adaptation in the microbial eukaryote Paramecium caudatum. PLoS ONE, 7, e30598. Kroencke I, Reiss H, Eggleton JD et al. (2011) Changes in North Sea macrofauna communities and species distribution between 1986 and 2000. Estuarine, Coastal and Shelf Science, 94, 1–15. La Sorte FA, Jetz W (2012) Tracking of climatic niche boundaries under recent climate change. Journal of Animal Ecology, 81, 914–925. Lima FP, Ribeiro PA, Queiroz N, Hawkins SJ, Santos AM (2007) Do distributional shifts of northern and southern species of algae match the warming pattern? Global Change Biology, 13, 2592–2604.

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

T E M P E R A T U R E T R A C K I N G B Y N O R T H S E A B E N T H O S 13 Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD (2009) The velocity of climate change. Nature, 462, 1052–1055.

Witbaard R, Bergman MJN (2003) The distribution and population structure of the bivalve Arctica islandica L. in the North Sea: what possible factors are involved?

Otto L, Zimmerman JTF, Furnes GK, Mork M, Saetre R, Becker G (1990) Review of the physical oceanography of the North-Sea. Netherlands Journal of Sea Research, 26, 161–238. Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution and Systematics, 37, 637–69. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37–42.

Journal of Sea Research, 50, 11–25. deYoung B, Beaugrand G, Harris R, Perry RI, Scheffer M, Werner F (2008) Regime shifts in marine ecosystems: detection, prediction and management. Trends in Ecology and Evolution, 23, 402–409.

Perry AL, Low PJ, Ellis JR, Reynolds JD (2005) Climate change and distribution shifts in marine fishes. Science, 308, 1912–1915. Pinsky ML, Worm B, Fogarty MJ, Sarmiento JL, Levin SA (2013) Marine taxa track local climate velocities. Science, 341, 1239–1242. Poloczanska ES, Hawkins SJ, Southward AJ, Burrows MT (2008) Modeling the response of populations of competing species to climate change. Ecology, 89,

Supporting Information

3138–3149. Poloczanska ES, Brown CJ, Sydeman WJ et al. (2013) Global imprint of climate change on marine life. Nature Climate Change, 3, 919–925. Rayner NA, Parker DE, Horton EB et al. (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research Atmospheres, 108, 4407. Rees HL, Eggleton JD, Rachor E, Vanden Berghe E (2007) Structure and dynamics of

Figure S1. Long-term temperature trends in the North Sea. Figure S2. Map of sampled stations. Figure S3. Maps of SBT and SST in 1986 and 2000. Figure S4. Maps of the velocity and direction of climate change. Figure S5. The velocity and direction of climate change in SBT. Figure S6. The velocity and direction of climate change in SST. Figure S7. The velocity of climate change estimated using the HadISST dataset. Figure S8. Observed and predicted shifts the distribution for SBT. Figure S9. Observed and predicted shifts the distribution for SST. Figure S10. A schematic to help the interpretation of Fig. 7. Table S1. Species included in the analysis. Table S2. VoCC (km yr1) for sea bottom and surface temperature in the North Sea. Table S3. Statistics comparing observed and predicted shifts based on VoCC of SST. Table S4. Statistics comparing observed and predicted shifts based on VoCC of SBT. Table S5. The percentage of species that are responding to climate change.

the North Sea benthos. In: ICES Cooperative Research Report, p. 265. Riddle MJ (1989) Bite profiles of some benthic grab samplers. Estuarine, Coastal and Shelf Science, 29, 285–292. Sax DF, Early R, Bellemare J (2013) Niche syndromes, species extinction risks, and management under climate change. Trends in Ecology and Evolution, 28, 517–523. Simpson SD, Jennings S, Johnson MP, Blanchard JL, Schon P-J, Sims DW, Genner MJ (2011) Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Current Biology, 21, 1565–1570. Snelgrove PVR (1999) Getting to the bottom of marine biodiversity: sedimentary habitats: ocean bottoms are the most widespread habitat on earth and support high biodiversity and key ecosystem services. BioScience, 49, 129–138. Stillman JH (2003) Acclimation capacity underlies susceptibility to climate change. Science, 301, 65–65. Sunday JM, Bates AE, Dulvy NK (2012) Thermal tolerance and the global redistribution of animals. Nature Climate Change, 2, 686–690. VanDerWal J, Murphy HT, Kutt AS, Perkins GC, Bateman BL, Perry JJ, Reside AE (2013) Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nature Climate Change, 3, 239–243. Warren RJ, Chick L (2013) Upward ant distribution shift corresponds with minimum, not maximum, temperature tolerance. Global Change Biology, 19, 2082–2088. Wethey DS, Woodin SA (2008) Ecological hindcasting of biogeographic responses to climate change in the European intertidal zone. Hydrobiologia, 606, 139–151.

Additional Supporting Information may be found in the online version of this article:

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12726

Temperature tracking by North Sea benthic invertebrates in response to climate change.

Climate change is a major threat to biodiversity and distributions shifts are one of the most significant threats to global warming, but the extent to...
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