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03 November 2021

A whale of an appetite revealed by analysis of prey consumption

Reaching a deeper understanding of the ocean ecosystems that maintain whales might aid conservation efforts. Measurements of the animals’ krill intake indicate that previous figures were substantial underestimates.

Victor Smetacek

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Victor Smetacek

Victor Smetacek is at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany.

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Baleen whales are the largest known animals that have ever lived. They feed on centimetre-sized prey by filtering seawater through plates of frayed, bristle-like combs, termed baleen, that are fixed to their upper jaws. Previous estimates of the food requirements of whale populations indicate the animals’ enormous food demand1. In the Southern Ocean near Antarctica, before the whaling era, the krill biomass consumed by whales alone is estimated to have been 190 million tonnes annually1, an amount substantially greater than the entire annual world fish catch in modern times2. Intense fishing by humans has decimated ocean fish stocks in a few decades. By contrast, whale feeding seems to be sustainable, as evidenced by hallmarks of the animals’ evolution, such as their long lifespan and high degree of specialization geared to the consumption of just one prey — krill.

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Nature 599, 33-34 (2021)
doi: https://doi.org/10.1038/d41586-021-02951-3

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03 November 2021

A seagrass harbours a nitrogen-fixing bacterial partner

How underwater seagrasses obtain the nitrogen they need has been unclear. Evidence has now emerged of a partnership with a bacterium that might be analogous to the system used by many land plants to gain nitrogen.

Douglas G. Capone

 ORCID: http://orcid.org/0000-0002-3968-736X

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Douglas G. Capone

Douglas G. Capone is in the Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA.

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Seagrass meadows are a prominent feature of many shallow coastal areas of the temperate through to the tropical ocean. Seagrasses provide a crucial habitat for invertebrates and juvenile fish, stabilize sediments and buffer the shoreline against erosion1. Moreover, they contribute directly and positively to the ‘blue economy’ of the oceans through their long-term storage of carbon2. Lush and highly productive seagrass beds often thrive in nutrient-deficient waters, and attempts to solve the enigma of how they accomplish this feat have driven considerable research over the years. Writing in Nature, Mohr et al.3 provide crucial evidence indicating that the success of a seagrass called Posidonia oceanica (Fig. 1), which proliferates throughout the warm waters of the Mediterranean Sea (and elsewhere), might be attributed to the development of a highly integrated partnership with a bacterium. This system is reminiscent of those found in some terrestrial plants.

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Etymology‘Candidatus Celerinatantimonas neptuna’ (nep.tu’na L. fem. n.), pertaining to Neptunus (L. masc. n. Neptune), the Roman god of the seas and the Neptune grass, Posidonia oceanica.SamplingA P. oceanica meadow at 8 m water depth and nearby sandy sediments in Fetovaia Bay, Elba, Italy13 were sampled between June 2014 and September 2019; individual sampling months and years are indicated in the sections below and/or in the figures and tables. In May 2017, a P. oceanica meadow at the island of Pianosa, Italy was also sampled. All of the samples were obtained via SCUBA diving.Complete plants of P. oceanica were carefully separated from the meadow by hand and stored in seawater-filled containers until arrival at the shore-based laboratory. Sediment for use in the laboratory-based aquaria was scooped into containers from nearby sandy patches. Seawater was pumped through a hose (placed at about 0.5 m above the P. oceanica meadow) into several 50 l barrels onboard the boat and was later used in the laboratory for the aquarium and the incubation experiments.The sediment within the seagrass meadow was sampled with stainless steel core tubes (length, 50 cm), which were drilled into the sediment by divers, and the cores were briefly stored at 22 °C (ambient temperature, September 2019) in a seawater-filled barrel until further processing at the shore-based laboratory.Porewater nutrient samples were obtained using stainless steel lances41 at intervals of around 10 cm. Water column nutrient samples were obtained from above the seagrass meadow at the start or end of sampling. Nutrient samples were collected in 15 ml or 50 ml centrifuge tubes and were stored in a cooler box until further processing.Nutrient measurementsWater column nutrients were measured during several sampling campaigns as indicated in Extended Data Table 1a. Ammonium (NH4+) concentrations were measured fluorometrically42 in the nearby shore-based laboratory, and the remaining water was frozen (−20 °C) for later analyses of nitrate (NO3−), nitrite (NO2−), phosphate (PO43−) and silicate (SiO44−) using an autoanalyser (QuAAtro, Seal Analytical). Porewater samples were obtained in June 2019 and were processed the same as the water column nutrient samples with the exception that ammonium was not measured on site but at the home laboratory at the same time as the other nutrients. Dissolved inorganic nitrogen (ammonium plus NOx−) concentrations in the porewater were averaged for the upper 20 cm (Extended Data Table 1b).Net primary production measurements using the EC methodNet carbon dioxide (CO2) fluxes were calculated on the basis of oxygen (O2) fluxes determined using the aquatic eddy covariance (EC) method. In this non-invasive approach, turbulence-induced transport is resolved using high-frequency current meters combined with fast O2 microsensors. Under the assumption of stationarity, the instantaneous turbulent flux contributions are calculated by correlating vertical current fluctuations to oxygen fluctuations. Our EC system was equipped with an acoustic Doppler velocimeter (ADV, Nortek) and ultra-fast responding optode microsensors with a tip diameter of 430 µm (t90  More

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Data setsMonthly climate data of maximum and minimum temperature, dewpoint temperature, and precipitation at a 1/24th degree horizontal resolution from 1950 to 2020 was acquired from the Parameterized Regression on Independent Slopes Model (PRISM)44. Monthly surface downward shortwave radiation and 10-m wind speeds at a 0.25-degree horizontal resolution were acquired from ERA-545 for the same period and bilinearly interpolated to the PRISM grid. Monthly data for the same variables from a single ensemble member from each of 30 climate models participating in the Sixth Coupled Model Intercomparison Project (CMIP6) were acquired from the historical climate experiment for 1950–2014 and from the SSP2-45 experiment for 2015–2050 and interpolated to a common 1.0-degree horizontal resolution grid (Supplementary Table 4).Following Abatzoglou and Williams, we calculated three proxies of aridity using monthly climate data: mean vapor pressure deficit (VPD), Penman-Monteith reference evapotranspiration (ETo), and climatic water deficit (CWD46, defined as ETo minus actual evapotranspiration3). We modified ETo to account for potential reduced stomatal conductance due to increasing atmospheric carbon dioxide, which reduces surface resistance to evapotranspiration. We made this modification following the method of Yang et al.47. Importantly, the effect of CO2 on surface resistance at the scale of the western US is highly uncertain and this method derives the strength of this effect from earth system models. Each index was calculated as follows. At each grid cell, we calculated mean Mar–Sep VPD, the sum of Mar–Sep ETo, and Jan-Dec CWD; each of these time series was standardized to the 1991–2020 baseline using z-score transformations to create a fuel aridity index f for each grid cell. The regionally averaged fuel aridity index F was calculated by first taking the average of f over grid cells that have a majority of land classified as forest or woodland in the LANDFIRE environmental site potential product48. We then re-standardized F relative to the 1991–2020 reference period and applied equidistant quantile mapping49 to each model. The latter ensures that the distributions of modeled Z match those of observed Z for the 1991–2020 period while preserving changes in Z from this reference period. Herein we used CWD for F because it presents a more balanced view of precipitation and atmospheric demand than VPD or ETo alone, exhibits strong links to the forest-fire area over the observational record, and has more conservative increases in fire under future climate (Supplementary Fig. 2). The variance explained in forest-fire area when defining F as VPD, ETo, and detrended CWD is presented in Supplementary Table 1. We note that our approach does not explicitly incorporate daily meteorology such as the number of dry days or critical fire-weather patterns10 beyond that already included in F.Burned area data from wildland fires were acquired from Monitoring Trends in Burn Severity (MTBS) during 1984–201850 and from the version 6 MODIS burned area dataset during 2001–202051. The forested burned area was aggregated by lands classified as forest or woodland48. MTBS includes primarily fires ≥404 ha that comprises >95% of burned area in the region52. We further excluded areas in the unburned-to-low burn severity class53 as well as fires classified as prescribed burns in MTBS. Further, we did not include forested area treated by prescribed fire as a contemporary area for prescribed fire is more than an order of magnitude less than that of forest-fire area41. Forest-fire area estimates for 2019–2020 were obtained using adjusted burned areas from MODIS based on a linear model that relates MODIS and to the MTBS forest-fire area time series during the overlapping 2001–2018 period26.Experimental designWe focus on macroscale climate–fire models operating at the scale of the entire western US forested area. While there is value in spatially refined models, efforts to parameterize empirical relationships at localized scales can be limited by the stochastic nature of ignitions and fire weather—particularly in locations with long fire return intervals with zero-inflated distributions of annual burned area. Strong interannual relationships between fuel aridity and strain on national fire suppression resources shared across the region highlight the implicit value in considering larger spatial scales54. The macroscale approach is further justified because the leading mode of variability in fuel aridity across forested land is a commonly signed regionwide pattern that is strongly correlated (r2 = 0.79) to the logarithm of forest-fire area (Supplementary Fig. 3).Static modelFollowing previous empirical models of annual forest-fire area3,25, we first consider a static model of western US annual forest-fire area (FFA) based on F (fuel aridity) of the form:$${{{{{rm{log }}}}}}left({{{{{{mathrm{FFA}}}}}}}(t)right)={alpha }_{{{{{{mathrm{s}}}}}}}+{beta }_{{{{{{mathrm{s}}}}}}}Fleft(tright)+{{{{{rm{varepsilon }}}}}},$$
(1)
where t is the year, αs and βs, are regression coefficients, and ε represents an error term. We use annual CWD for F as it accounts for precipitation and atmospheric demand, exhibits strong interannual relationships with FFA, and provide more conservative estimates of projected changes in aridity and thus area burned than other aridity metrics such as VPD3,7,12. The error term ε is drawn from the population of the log-residual of observed minus modeled FFA. This error term represents variability not captured in the FFA–F relationship (e.g., extreme fire-weather conditions, human ignitions) that is important for the full distribution of FFA.Dynamic modelsThe contemporary climate–fire relationship in Eq. 1 should persist with increased F until increased burned area and severity cause fuel limitations15. Fire-fuel feedbacks that alter the climate–fire relationship primarily occur through temporary reduction of fine fuels; such feedbacks can reduce the burning potential for approximately three decades post-fire38,55. Further, longer-lived reductions in the forest-fire area can occur when forests do not recover from fire and instead transition to non-forest vegetation that can still carry fire. However, constraints on the area burned imposed by fire-fuel feedbacks are weakened by concurrent drought, which allows the fire to propagate across sparser fuels, and can markedly shorten the window of reduced burning18.We incorporate these effects through a term L, which represents the fraction of contemporary forested land that is incapable of carrying fire in a predominately forested environment in a given year, in a dynamic model of the form:$${log }left(frac{{{{{{{mathrm{FFA}}}}}}}}{1-Lleft(tright)}right)={alpha }_{{{{{{mathrm{d}}}}}}}+{beta }_{{{{{{mathrm{d}}}}}}}Fleft(tright)+{{{{{rm{varepsilon }}}}}},$$
(2)
where the response of log(FFA) to fuel aridity reduces as a function of L. We present various potential forms and strengths of fire-fuel feedbacks in L that are guided by the ecological literature and account for post-fire tree regeneration failure, fuel limitations imposed by recent fire history, and waning of fuel limitations during drought18,22,23,24. L is influenced by semi-permanent limitations due to failure of post-fire forest regeneration (Lrf), and temporary limitations due to recent fire history (Lf):$$Lleft(tright)={L}_{{{{{{{mathrm{rf}}}}}}}}left(tright)+{L}_{{{{{{mathrm{f}}}}}}}(t).$$
(3)
Importantly, L is poorly constrained and likely varies in geographically and temporally complex ways18,34. For example, L can differ for a fixed fraction of recently burned forest. A relatively small L implies weak feedbacks allowing forests to more easily reburn. A relatively large L implies strong feedbacks, for example, where heterogeneous fire effects create patch mosaics that constrain fire spread even though there is ample fuel. Finally, the age threshold for L may decrease with continued climate change, with some indications that recent fires burned through forests , 2end{array}right.,$$
(4)
where μ is set at 0.1 (Eq. 4 is plotted in Supplementary Fig. 4a). Hence, the fraction of forested land that is semi-permanently ineligible to carry forest fire because previously burned forest did not regenerate as forest (Lrf) is the cumulative sum of the product of annual FFA and ρ since 1984:$${L}_{{{{{{{mathrm{rf}}}}}}}}left(tright)=mathop{sum }limits_{i=1984}^{t}frac{rho left(tright){{{{{{mathrm{FFA}}}}}}}(t)}{T},$$
(5)
where T refers to the contemporary area of forested land48. Note that Eq. 4 and μ can be modified to account for the diversity of species-specific responses at local-to-regional scales given the acknowledgement that some species are more resilient than others and local plant water stress alters regeneration probabilities58,59. Overall, Lrf as parameterized here resulted in values approaching Lrf ~0.01 by 2050, suggesting that the inability of trees to regenerate post-fire is a minor contributor to fire-fuel feedbacks through mid-century. Modifications to the parameters in Eq. 4 resulted in only minor differences in projected FFA (Supplementary Table 3).Temporary fire-fuel feedbacks L
f
Most studies in forested environments show strong fire-fuel feedbacks in the first 5–10 years post-fire55,60. This temporary fire-fuel feedback, which we refer to here as Lf, tends to wane after 10 years60, with the longevity τ of the fire-fuel feedbacks varying geographically, from as short as ~15 years in warmer sites in the southwest to over ~30 years in cold mesic systems in the northern Rockies18. Herein, we use a baseline τ = 30 years, which results in a conservative estimate of future area burned.We consider two forms for how Lf incorporates information on annual fire histories over the previous τ years: a constant feedback and a fading feedback. These forms of Lf are defined below in Eqs. 6 and 7 and plotted in Supplementary Fig. 4c.In the case of the constant feedback, the effect of burned area on Lf remains constant over the τ years following fire. At the scale of the whole western US forested area, the constant form, therefore, assumes that the transient limitation is simply proportional to the total FFA over the preceding τ years:$${L}_{{{{{{mathrm{f}}}}}}}left(tright)=gamma mathop{sum }limits_{i=-tau }^{-1}frac{{{{{{{mathrm{FFA}}}}}}}(i)}{T}.$$
(6)
In Eq. 6, parameter γ represents the strength of the feedback, described in more depth below.The fading feedback form of Lf more heavily weights the contribution from recent FFA compared to older FFA. At the scale of the whole western US forested area, this form applies constant weight to FFA in the five most recent years given strong fire-fuel feedbacks of recent fires, and increasingly reduces the contributions from prior years based on a sinusoid function:$${L}_{{{{{{mathrm{f}}}}}}}left(tright)=gamma frac{mathop{sum }nolimits_{i=-5}^{-1}{{{{{{mathrm{FFA}}}}}}}left(iright)+mathop{sum }nolimits_{i=-tau }^{-6}{{{{{{mathrm{FFA}}}}}}}left(iright)ast left[1-{cos }frac{pi left(-i-5right)}{tau -5}right]/2}{T}.$$
(7)
Given the uncertainty in the efficacy of the fire-fuel feedback, we present results using both the constant and fading formulations for the temporary fire-fuel feedbacks.We additionally considered three different fuel-limitation strengths γ in Eqs. 6 and 7 to account for direct and indirect potential effects of past fires: γ = 0.5, referred to as weak; γ = 1, referred to as moderate; and γ = 1.5, referred to as strong. For the weak (γ = 0.5) fuel-limitation case using the constant feedback model, the fractional forested area ineligible to burn is only half of the total area burned in the past 30 years, indicating that half of recent burned areas can reburn. For the strong-constant fuel-limitation case, the forested area ineligible to burn post-fire exceeds the total recent burned area by 50%. An example of a strong fuel limitation is a burn mosaic with reduced connectivity that constrains the ability of subsequent fire spread into the adjacent forest that did not burn in the previous τ years. We considered higher values of γ, but these yielded degraded cross-validation skills when modeling the historical period (Supplementary Table 2).Longevity of fire-fuel feedbacks during droughtFinally, some temporary fuel limitations can be overcome during extreme fire-weather conditions and during periods of drought. For example, while reduced fuel loads in a post-fire landscape serve as an effective barrier for fire propagation under moderate fuel aridity, the fire spread probability increases with increasing F34. Studies have found that the longevity of fire-fuel feedbacks was a third shorter during periods of extreme drought than in periods without drought stress18,34. For example, there is evidence of short-interval (95% of the iterations had bias CE  > 0, >95% of the iterations had r  > 0, and the inner 95% of the simulations included a bias of 0.Supplementary Table 2 shows that the static model and many of the dynamic models have significant cross-validated skills. However, skill decreased in the dynamic models as the feedback strength increases. While the weak dynamic feedback models had similar cross-validation skill as the static model, dynamic models with very strong feedbacks (γ ≥ 2) had sizeable underpredictions in FFA by up to 46% for the validation period. Hence, we excluded such parameters from the further analysis given that such results were incongruent with the observational record.Three statistical metrics of annual variability of FFA were calculated for both static and dynamic models. First, we used generalized extreme value theory to estimate recurrence intervals for FFA greater than equal to that of the 2020 fire season. Second, we calculated the interquartile range (IQR) in modeled FFA to examine changing interannual variability. Lastly, we examined the percent of years with modeled FFA below the 1991–2020 observed median as a measure of quiescent fire years. Calculations were performed separately for each climate model for 1991–2020 and 2021–2050. More

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