Ecology

Longhurst, A., Sathyendranath, S., Platt, T. & Caverhill, C. An estimate of global primary production in the ocean from satellite radiometer data. J. Plankton Res. 17, 1245–1271 (1995).
Google Scholar 
Karl, D. M. et al. Seasonal and interannual variability in primary production and particle flux at station ALOHA. Deep Res. Part II Top. Stud. Oceanogr. 43, 539–568 (1996).CAS 

Google Scholar 
Yang, B., Emerson, S. R. & Quay, P. D. The subtropical ocean’s biological carbon pump determined from O2 and DIC/DI13C tracers. Geophys. Res. Lett. 46, 5361–5368 (2019).
Google Scholar 
Nowicki, M., DeVries, T. & Siegel, D. A. Quantifying the carbon export and sequestration pathways of the ocean’s biological carbon pump. Glob. Biogeochem. Cycles 36, 1–22 (2022).
Google Scholar 
Chávez, F. P., Messié, M. & Pennington, J. T. Marine primary production in relation to climate variability and change. Annu. Rev. Mar. Sci. 3, 227–260 (2011).
Google Scholar 
Polovina, J. J., Howell, E. A. & Abecassis, M. Ocean’s least productive waters are expanding. Geophys. Res. Lett. 35, 2–6 (2008).
Google Scholar 
Irwin, A. J. & Oliver, M. J. Are ocean deserts getting larger? Geophys. Res. Lett. 36, 1–5 (2009).
Google Scholar 
Signorini, S. R., Franz, B. A. & McClain, C. R. Chlorophyll variability in the oligotrophic gyres: Mechanisms, seasonality and trends. Front. Mar. Sci. 2, 1–11 (2015).
Google Scholar 
Sarmiento, J. L., Hughes, T. M. C., Stouffer, R. J. & Manabe, S. Simulated response of the ocean carbon cycle to anthropogenic climate warming. Nature 393, 245–249 (1998).CAS 

Google Scholar 
Bopp, L. et al. Potential impact of climate change on marine export production. Glob. Biogeochem. Cycles 15, 81–99 (2001).CAS 

Google Scholar 
Taucher, J. & Oschlies, A. Can we predict the direction of marine primary production change under global warming? Geophys. Res. Lett. 38, 1–6 (2011).
Google Scholar 
Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020).CAS 

Google Scholar 
Behrenfeld, M. Uncertain future for ocean algae. Nat. Clim. Chang. 1, 33–34 (2011).CAS 

Google Scholar 
Flombaum, P. & Martiny, A. C. Diverse but uncertain responses of picophytoplankton lineages to future climate change. Limnol. Oceanogr. 66, 4171–4181 (2021).
Google Scholar 
Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 10, 1063–1085 (1972).
Google Scholar 
Falkowski, P. G. & Oliver, M. J. Mix and max: how climate selects phytoplankton. Nature. Rev. Microbiol. 5, 813–819 (2007).CAS 

Google Scholar 
van de Waal, D. B. & Litchman, E. Multiple global change stressor effects on phytoplankton nutrient acquisition in a future ocean. Philos. Trans. R. Soc. B Biol. Sci. 375, 1–8 (2020).
Google Scholar 
Kremer, C. T., Thomas, M. K. & Litchman, E. Temperature- and size-scaling of phytoplankton population growth rates: reconciling the Eppley curve and the metabolic theory of ecology. Limnol. Oceanogr. 62, 1658–1670 (2017).
Google Scholar 
Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. Chang. Biol. 21, 1025–1040 (2015).PubMed 

Google Scholar 
Marañón, E., Lorenzo, M. P., Cermeño, P. & Mouriño-Carballido, B. Nutrient limitation suppresses the temperature dependence of phytoplankton metabolic rates. ISME J. 12, 1836–1845 (2018).PubMed 
PubMed Central 

Google Scholar 
Skau, L. F., Andersen, T., Thrane, J.-E. & Hessen, D. O. Growth, stoichiometry and cell size; temperature and nutrient responses in haptophytes. PeerJ 5, e3743 (2017).PubMed 
PubMed Central 

Google Scholar 
Fernández‐González, C. et al. Effects of temperature and nutrient supply on resource allocation, photosynthetic strategy and metabolic rates of Synechococcus sp. J. Phycol. 56, 818–829 (2020).PubMed 

Google Scholar 
O’Connor, M. I., Piehler, M. F., Leech, D. M., Anton, A. & Bruno, J. F. Warming and resource availability shift food web structure and metabolism. PLoS Biol. 7, e1000178 (2009).PubMed 
PubMed Central 

Google Scholar 
Liu, K., Suzuki, K., Chen, B. & Liu, H. Are temperature sensitivities of Prochlorococcus and Synechococcus impacted by nutrient availability in the subtropical northwest Pacific? Limnol. Oceanogr. 66, 639–651 (2020).
Google Scholar 
Hayashida, H., Matear, R. J. & Strutton, P. G. Background nutrient concentration determines phytoplankton bloom response to marine heatwaves. Glob. Chang. Biol. 26, 4800–4811 (2020).PubMed 

Google Scholar 
Davey, M. et al. Nutrient limitation of picophytoplankton photosynthesis and growth in the tropical North Atlantic. Limnol. Oceanogr. 53, 1722–1733 (2008).CAS 

Google Scholar 
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 

Google Scholar 
Browning, T. J. et al. Nutrient co-limitation at the boundary of an oceanic gyre. Nature 551, 242–246 (2017).CAS 
PubMed 

Google Scholar 
Ustick, L. J. et al. Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation. Science 372, 287–291 (2021).CAS 
PubMed 

Google Scholar 
Zubkov, M. V., Sleigh, M. A., Tarran, G. A., Burkill, P. H. & Leakey, R. J. G. Picoplanktonic community structure on an Atlantic transect from 50°N to 50°S. Deep Res. Part I Oceanogr. Res. Pap. 45, 1339–1355 (1998).
Google Scholar 
Marañón, E., Behrenfeld, M. J., González, N., Mouriño, B. & Zubkov, M. V. High variability of primary production in oligotrophic waters of the Atlantic Ocean: Uncoupling from phytoplankton biomass and size structure. Mar. Ecol. Prog. Ser. 257, 1–11 (2003).
Google Scholar 
Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).
Google Scholar 
Worden, A. Z., Nolan, J. K. & Palenik, B. Assessing the dynamics and ecology of marine picophytoplankton: the importance of the eukaryotic component. Limnol. Oceanogr. 49, 168–179 (2004).CAS 

Google Scholar 
Visintini, N., Martiny, A. C. & Flombaum, P. Prochlorococcus, Synechococcus, and picoeukaryotic phytoplankton abundances in the global ocean. Limnol. Oceanogr. Lett. 6, 207–215 (2021).
Google Scholar 
Chen, B., Liu, H., Huang, B. & Wang, J. Temperature effects on the growth rate of marine picoplankton. Mar. Ecol. Prog. Ser. 505, 37–47 (2014).
Google Scholar 
Stawiarski, B., Buitenhuis, E. T. & Le Quéré, C. The physiological response of picophytoplankton to temperature and its model representation. Front. Mar. Sci. 3, 1–13 (2016).
Google Scholar 
Marañón, E. et al. Unimodal size scaling of phytoplankton growth and the size dependence of nutrient uptake and use. Ecol. Lett. 16, 371–379 (2013).PubMed 

Google Scholar 
Duhamel, S., Kim, E., Sprung, B. & Anderson, O. R. Small pigmented eukaryotes play a major role in carbon cycling in the P-depleted western subtropical North Atlantic, which may be supported by mixotrophy. Limnol. Oceanogr. 64, 2424–2440 (2019).CAS 

Google Scholar 
Berthelot, H. et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 13, 651–662 (2019).CAS 
PubMed 

Google Scholar 
Berthelot, H., Duhamel, S., L’Helguen, S., Maguer, J. F. & Cassar, N. Inorganic and organic carbon and nitrogen uptake strategies of picoplankton groups in the northwestern Atlantic Ocean. Limnol. Oceanogr. 66, 3682–3696 (2021).CAS 

Google Scholar 
Marañón, E. et al. Degree of oligotrophy controls the response of microbial plankton to Saharan dust. Limnol. Oceanogr. 55, 2339–2352 (2010).
Google Scholar 
Mouriño-Carballido, B. et al. Nutrient supply controls picoplankton community structure during three contrasting seasons in the northwestern Mediterranean Sea. Mar. Ecol. Prog. Ser. 543, 1–19 (2016).
Google Scholar 
Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012).CAS 
PubMed 

Google Scholar 
Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).
Google Scholar 
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).PubMed 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).CAS 
PubMed 

Google Scholar 
Babin, S. M., Carton, J. A., Dickey, T. D. & Wiggert, J. D. Satellite evidence of hurricane-induced phytoplankton blooms in an oceanic desert. J. Geophys. Res. Oceans 109, 1–21 (2004).
Google Scholar 
Walker, N. D., Leben, R. R. & Balasubramanian, S. Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico. Geophys. Res. Lett. 32, 1–5 (2005).
Google Scholar 
Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob. Chang. Biol. 24, 2239–2261 (2018).PubMed 

Google Scholar 
Mills, M. M., Ridame, C., Davey, M., La Roche, J. & Geider, R. J. Iron and phosphorus co-limit nitrogen fixation in the eastern tropical North Atlantic. Nature 429, 292–294 (2004).CAS 
PubMed 

Google Scholar 
Marañón, E. Phytoplankton growth rates in the Atlantic subtropical gyres. Limnol. Oceanogr. 50, 299–310 (2005).
Google Scholar 
Halsey, K. H. & Jones, B. M. Phytoplankton strategies for photosynthetic energy allocation. Annu. Rev. Mar. Sci. 7, 265–297 (2015).
Google Scholar 
Quevedo, M. & Anadón, R. Protist control of phytoplankton growth in the subtropical north-east Atlantic. Mar. Ecol. Prog. Ser. 221, 29–38 (2001).
Google Scholar 
Schmoker, C., Hernández-León, S. & Calbet, A. Microzooplankton grazing in the oceans: Impacts, data variability, knowledge gaps and future directions. J. Plankton Res. 35, 691–706 (2013).
Google Scholar 
Landry, M. R. & Hassett, R. P. Estimating the grazing impact of marine micro-zooplankton. Mar. Biol. 67, 283–288 (1982).
Google Scholar 
Kiørboe, T. Turbulence, phytoplankton cell size, and the structure of pelagic food webs. Adv. Mar. Biol. 29, 1–72 (1993).
Google Scholar 
Cermeño, P. et al. Marine primary productivity is driven by a selection effect. Front. Mar. Sci. 3, 1–10 (2016).Browning, T. J. et al. Nutrient co-limitation in the subtropical Northwest Pacific. Limnol. Oceanogr. Lett. 7, 52–61 (2022).
Google Scholar 
Klausmeier, C. A., Litchman, E. & Levin, S. A. Phytoplankton growth and stoichiometry under multiple nutrient limitation. Limnol. Oceanogr. 49, 1463–1470 (2004).
Google Scholar 
Behrenfeld, M. J. & Milligan, A. J. Photophysiological expressions of iron stress in phytoplankton. Annu. Rev. Mar. Sci. 5, 217–246 (2013).
Google Scholar 
Geider, R. J. Light and temperature dependence of the carnon to chlorophyll a ratio in microalgae and cyanobacteria: implications for physiology and growth of phytoplankton. N. Phytol. 106, 1–34 (1987).CAS 

Google Scholar 
Maxwell, D. P., Laudenbach, D. E. & Huner, N. P. Redox regulation of light-harvesting complex II and cab mRNA abundance in Dunaliella salina. Plant Physiol. 109, 787–795 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ye, H. J., Sui, Y., Tang, D. L. & Afanasyev, Y. D. A subsurface chlorophyll a bloom induced by typhoon in the South China Sea. J. Mar. Syst. 128, 138–145 (2013).
Google Scholar 
Zhang, H., He, H., Zhang, W. Z. & Tian, D. Upper ocean response to tropical cyclones: a review. Geosci. Lett. 8, 1–12 (2021).
Google Scholar 
Lin, I. et al. New evidence for enhanced ocean primary production triggered by tropical cyclone. Geophys. Res. Lett. 30, 1–4 (2003).Chai, F. et al. A limited effect of sub-tropical typhoons on phytoplankton dynamics. Biogeosciences 18, 849–859 (2021).
Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).CAS 
PubMed 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
PubMed 

Google Scholar 
Somero, G. N. Adaptation of enzymes to temperature: Searching for basic ‘strategies’. Comp. Biochem. Physiol.—B Biochem. Mol. Biol. 139, 321–333 (2004).PubMed 

Google Scholar 
Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol. Oceanogr. 52, 886–895 (2007).
Google Scholar 
Harvey, B. P., Marshall, K. E., Harley, C. D. G. & Russell, B. D. Predicting responses to marine heatwaves using functional traits. Trends Ecol. Evol. 37, 20–29 (2022).PubMed 

Google Scholar 
Staehr, P. A. & Birkeland, M. J. Temperature acclimation of growth, photosynthesis and respiration in two mesophilic phytoplankton species. Phycologia 45, 648–656 (2006).
Google Scholar 
Morán, X. A. G., Calvo-Díaz, A., Arandia-Gorostidi, N. & Huete-Stauffer, T. M. Temperature sensitivities of microbial plankton net growth rates are seasonally coherent and linked to nutrient availability. Environ. Microbiol. 20, 3798–3810 (2018).PubMed 

Google Scholar 
Courboulès, J. et al. Effects of experimental warming on small phytoplankton, bacteria and viruses in autumn in the Mediterranean coastal Thau Lagoon. Aquat. Ecol. 55, 647–666 (2021).
Google Scholar 
López-Sandoval, D. C., Duarte, C. M. & Agustí, S. Nutrient and temperature constraints on primary production and net phytoplankton growth in a tropical ecosystem. Limnol. Oceanogr. 66, 2923–2935 (2021).
Google Scholar 
Landry, M. R., Selph, K. E., Hood, R. R., Davies, C. H. & Beckley, L. E. Low temperature sensitivity of picophytoplankton P:B ratios and growth rates across a natural 10 °C temperature gradient in the oligotrophic Indian Ocean. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10224 (2021)Martiny, A. C. et al. Strong latitudinal patterns in the elemental ratios of marine plankton and organic matter. Nat. Geosci. 6, 279–283 (2013).CAS 

Google Scholar 
Fernández-González, C. & Marañón, E. Effect of temperature on the unimodal size scaling of phytoplankton growth. Sci. Rep. 11, 1–9 (2021).
Google Scholar 
Marañón, E. et al. Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments. Mar. Ecol. Prog. Ser. 216, 43–56 (2001).
Google Scholar 
Tarran, G. A., Heywood, J. L. & Zubkov, M. V. Latitudinal changes in the standing stocks of nano- and picoeukaryotic phytoplankton in the Atlantic Ocean. Deep Res. Part II Top. Stud. Oceanogr. 53, 1516–1529 (2006).
Google Scholar 
Hillebrand, H. et al. Cell size as driver and sentinel of phytoplankton community structure and functioning. Funct. Ecol. 1–18 https://doi.org/10.1111/1365-2435.13986 (2021).Partensky, F. & Garczarek, L. Prochlorococcus: advantages and limits of minimalism. Annu. Rev. Mar. Sci. 2, 305–331 (2010).
Google Scholar 
Landry, M. R. et al. Biological response to iron fertilization in the eastern equatorial Pacific (IronEx II). I. Microplankton community abundances and biomass. Mar. Ecol. Prog. Ser. 201, 27–42 (2000).CAS 

Google Scholar 
Morel, A. et al. Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sens. Environ. 111, 69–88 (2007).
Google Scholar 
Fofonoff, N. P. & Millard, R. C. Algorithms for computation of fundamental properties of seawater. UNESCO Tech. Pap. Mar. Sci. 44, 1–53 (1983).
Google Scholar 
Becker, S. et al. GO-SHIP repeat hydrography nutrient manual: the precise and accurate determination of dissolved inorganic nutrients in seawater, using continuous flow analysis methods. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.581790 (2020).Marañón, E. et al. Resource supply overrides temperature as a controlling factor of marine phytoplankton growth. PLoS ONE 9, 20–23 (2014).
Google Scholar 
Schuback, N. et al. Single-turnover variable chlorophyll fluorescence as a tool for assessing phytoplankton photosynthesis and primary productivity: opportunities, caveats and recommendations. Front. Mar. Sci. 8, 1–24 (2021).Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).PubMed 
PubMed Central 

Google Scholar  More

Fauna, culture and chronology datasetsA geo-referenced dataset of chronometric dates covering the late MIS 3 (55–30 kyr cal bp) was compiled from the literature (dataset 1). The dataset included 363 radiocarbon, thermoluminescence, optically stimulated luminescence and uranium series dates obtained from 62 archaeological sites and seven palaeontological sites. These chronological determinations were obtained from ten palaeontological levels and 138 archaeological levels. The archaeological levels were culturally attributed to the Mousterian (n = 75), Châtelperronian (n = 6) and Aurignacian (n = 57) technocomplexes. A number of issues can potentially hamper the chronological assessment of Palaeolithic technocomplexes from radiocarbon dates, such as pretreatment protocols that do not remove sufficient contaminants or the quality of the bone collagen extracted. Moreover, discrepancies in cultural attributions or stratigraphic inconsistencies are commonly detected in Palaeolithic archaeology. Information regarding the quality of date determinations and cultural attribution or stratigraphic issues is provided in the Supplementary Information.Our dataset also included the presence of herbivore species recovered from each archaeo-palaeontological site (hereafter referred to as local faunal assemblages (LFAs)), their body masses and their chronology. The mean body mass of both sexes, for each species, was obtained from the PHYLACINE database53 and used in the macroecological modelling approach described below (see ‘Carrying capacity of herbivores’). For visual representation purposes, the herbivore species were grouped into four weight categories: small (500 kg). The chronology of the occurrence of each herbivore species was assumed to be the same as the dated archaeo-palaeontological layer where the species remains were recovered. Thus, to estimate the chronological range of each species in each region, all radiocarbon determinations were calibrated with the IntCal20 calibration curve54 and OxCAL4.2 software55. The BAMs were run to compute the upper and lower chronological boundaries at a CI of 95.4% of each LFA (see ‘Chronological assessment’ for more details). One of the purposes of the current study was to estimate the potential fluctuations in herbivore biomass during the stadial and interstadial periods of the late MIS 3. Accordingly, the time spans of the LFAs were classified into the discrete GS and GI phases provided by Rasmussen et al.51.Geographic settingsThe Iberian Peninsula locates at the southwestern edge of Europe (Fig. 1). It constitutes a large geographic area that exhibits a remarkable diversity of ecosystems, climates and landscapes. Both now and in the past, altitudinal, latitudinal and oceanic gradients affected the conformation of two biogeographical macroregions with different flora and fauna species pools: the Eurosiberian and Mediterranean regions13,46. In the north, along the Pyrenees and Cantabrian strip, the Eurosiberian region is characterized by oceanic influence and mild temperatures in the present day, whereas the Mediterranean region features drier summers and milder winters (Fig. 1). Between the Eurosiberian and Mediterranean regions, there is a transitional area termed Submediterranean or Supramediterranean. Lastly, the Mediterranean region is divided into two distinctive bioclimatic belts: (1) the Thermomediterranean region, located at lower latitudes, with high evapotranspiration rates and affected by its proximity to the coast; and (2) the Mesomediterranean region, with lower temperatures and wetter conditions (Fig. 1).Previous studies have shown that zoocoenosis and phytocenosis differed between these macroregions in the Pleistocene13,46. However, flora and fauna distributions changed during the stadial–interstadial cycles in the Iberian Peninsula, which suggests potential alterations in the boundaries of these biogeographical regions. The modelling approach used in this study to estimate the biomass of primary consumers is dependent on the reconstructed NPP and the herbivore guild structure in each biogeographical region. To test the suitability of the present-day biogeographical demarcations of the Iberian Peninsula during MIS 3, we assessed whether the temporal trends of NPP and the composition of each herbivore palaeocommunity differed between these biogeographical regions during the MUPT.Chouakria and Nagabhusan56 proposed a dissimilarity index to compare time series data by taking into consideration the proximity of values and the temporal correlation of the time series:$${rm{CORT}}(S_1,S_2) = frac{{mathop {sum}nolimits_{i = 1}^{p – 1} {left( {u_{left( {i + 1} right)} – u_i} right)} (v_{(i + 1)} – v_i)}}{{sqrt {mathop {sum}nolimits_{i = 1}^{p – 1} {(u_{(i + 1)} – u_i)^2} } sqrt {mathop {sum}nolimits_{i = 1}^{p – 1} {(v_{(i + 1)} – v)^2} } }}$$
(1)
where S1 and S2 are the time series of data, u and v represent the values of S1 and S2, respectively, and p is the length of values of each time series. CORT(S1, S2) belongs to the interval (−1,1). The value CORT(S1, S2) = 1 indicates that in any observed period (ti, ti+1), the values of the sequence S1 and those of S2 increase or decrease at the same rate, whereas CORT = −1 indicates that when S1 increases, S2 decreases or vice versa. Lastly, CORT(S1, S2) = 0 indicates that the observed trends in S1 are independent of those observed in S2. To complement this approach by considering not only the temporal correlation between each pair of time series but also the proximity between the raw values, these authors proposed an adaptive tuning function defined as follows:$$d{rm{CORT}}left( {S_1,S_2} right) = fleft({{rm{CORT}}left( {S_1,S_2} right)} right)times dleft( {S_1,S_2} right)$$
(2)
where$$fleft( x right) = frac{2}{{1 + exp left( {k,x} right)}},k ge 0$$
(3)
In this study, k was 2, meaning that the behaviour contribution was 76% and the contribution of the proximity between values was 24%57. Hence, f(x) modulates a conventional pairwise raw data distance (d(S1,S2)) according to the observed temporal correlation56. Consequently, dCORT adjusts the degree of similarity between each pair of observations according to the temporal correlation and the proximity between values. This function was used to compare the reconstructed NPP between biogeographical regions during MIS 3 in the Iberian Peninsula. However, two different biogeographical regions could have experienced similar evolutionary trends in their NPP, even though their biota composition was different. Therefore, this analysis was complemented with a JSI to assess whether the reconstructed herbivore species composition in each palaeocommunity differed among biogeographical regions during the late MIS 3. The JSI was based on presence–absence data and was calculated as follows:$${rm{JSI}} = frac{c}{{(a + b + c)}}$$
(4)
where c is the number of shared species in both regions and a and b are the numbers of species that were only present in one of the biogeographical regions. Therefore, the higher the value the more similar the palaeocommunities of both regions were.Chronological assessmentPivotal to any hypothesis of Neanderthal replacement patterns by AMHs is the chronology of that population turnover. To this end, we used three different approaches to provide greater confidence in the results: BAMs, the OLE model and SPD of archaeological assemblages. As detailed below, each of these approaches provides complementary information about the MUPT.First, we built a set of BAMs for the Mousterian, Châtelperronian and Aurignacian technocomplexes in each region during the MIS 3. As stated above, we compiled the available radiocarbon dates for Iberia between 55 and 30 kyr cal bp. However, not all dates or levels were included in the Bayesian chronology models. Radiocarbon determinations obtained from shell remains were incorporated in the dataset (dataset 1); however, the local variation of the reservoir age was unknown from 55 to 30 kyr bp. Because of uncertainties related to marine reservoir offsets, all BAMs that incorporated dates from marine shells were run twice: including and excluding these dates. All of the archaeological levels with cultural attribution issues or stratigraphic inconsistencies were excluded. The Supplementary Note provides a detailed description of the sites, levels and dates excluded and their justification. All BAMs were built for each technocomplex using the OxCAL4.2 software55 and IntCal20 calibration curve54.Bayesian chronology models were built for each archaeological and palaeontological level. Then, the dates associated with each technocomplex were grouped within a single phase to determine each culture’s regional appearance or disappearance. Our interest was not focused on the chronological duration of the Mousterian, Châtelperronian and Aurignacian cultures, but on the probability distribution function of the temporal boundaries of these cultures in each region. Thus, this chronological assessment aims to provide an updated chronological frame for Neanderthal replacement by AMHs in Iberia. For this reason, we did not differentiate between proto- and early Aurignacian cultures, since both are attributed to AMHs.In each BAM, we inserted into the same sequence the radiocarbon dates associated with a given technocomplex within a start and end boundary to bracket each culture, which allowed us to determine the probability distribution function for the beginning and end moment of each cultural phase6. The resolution of all models was set at 20 years. We used a t-type outlier model with an initial 5% probability for each determination, but when more than one radiocarbon date was obtained from the same bone remain, we used an s-type outlier model and the combine function. The thermoluminescence dating likelihoods were included in the models, together with their associated 1σ uncertainty ranges. When dates with low agreement ( More

The impacts of climate change on host–parasite dynamics are particularly complex to predict, as they involve an interplay of both physiological and behavioural factors, from both host and parasite. For example, while warming may increase parasite developmental rates and thus increase transmission, excessive heat may instead exceed thermal limits, leading to higher parasite mortality. Transmission also relates to both the distribution and abundance of host species, which may also shift under changing climates. More

Barnett, T. P. et al. Penetration of human-induced warming into the world’s oceans. Science 309, 284–287 (2005).CAS 
Article 

Google Scholar 
Levitus, S. et al. Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophys. Res. Lett. 36, L07608 (2009).
Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).Article 

Google Scholar 
García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).Article 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).CAS 
Article 

Google Scholar 
Hughes, N. F. & Grand, T. C. Physiological ecology meets the ideal-free distribution: predicting the distribution of size-structured fish populations across temperature gradients. Environ. Biol. Fishes 59, 285–298 (2000).Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).CAS 
Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B 278, 1823–1830 (2011).Article 

Google Scholar 
Waldock, C., Stuart‐Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).Article 

Google Scholar 
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).Article 

Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 
Article 

Google Scholar 
Beaugrand, G., Edwards, M., Raybaud, V., Goberville, E. & Kirby, R. R. Future vulnerability of marine biodiversity compared with contemporary and past changes. Nat. Clim. Change 5, 695–701 (2015).Article 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
Article 

Google Scholar 
Levin, L. A. & Le Bris, N. The deep ocean under climate change. Science 350, 766–768 (2015).CAS 
Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).Article 

Google Scholar 
Lotterhos, K. E., Láruson, Á. J. & Jiang, L.-Q. Novel and disappearing climates in the global surface ocean from 1800 to 2100. Sci. Rep. 11, 15535 (2021).CAS 
Article 

Google Scholar 
Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).CAS 
Article 

Google Scholar 
Henson, S. A. et al. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun. 8, 14682 (2017).Article 

Google Scholar 
Séférian, R. et al. Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: role of Earth system processes in present‐day and future climate. J. Adv. Model. Earth Syst. 11, 4182–4227 (2019).Article 

Google Scholar 
Gidden, M. J. et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12, 1443–1475 (2019).CAS 
Article 

Google Scholar 
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article 

Google Scholar 
Beszczynska-Möller, A., Fahrbach, E., Schauer, U. & Hansen, E. Variability in Atlantic water temperature and transport at the entrance to the Arctic Ocean, 1997–2010. ICES J. Mar. Sci. 69, 852–863 (2012).Article 

Google Scholar 
Sutton, T. T. Vertical ecology of the pelagic ocean: classical patterns and new perspectives. J. Fish. Biol. 83, 1508–1527 (2013).CAS 
Article 

Google Scholar 
Richter, I. Climate model biases in the eastern tropical oceans: causes, impacts and ways forward. WIREs Clim. Change 6, 345–358 (2015).Article 

Google Scholar 
Pozo Buil, M. et al. A dynamically downscaled ensemble of future projections for the California Current System. Front. Mar. Sci. 8, 612874 (2021).Article 

Google Scholar 
Leonard, M. et al. A compound event framework for understanding extreme impacts. WIREs Clim. Change 5, 113–128 (2014).Article 

Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).CAS 
Article 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).Article 

Google Scholar 
Cheng, L., Abraham, J., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming? Science 363, 128–129 (2019).CAS 
Article 

Google Scholar 
Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).Article 

Google Scholar 
Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).CAS 
Article 

Google Scholar 
Filbee-Dexter, K. et al. Marine heatwaves and the collapse of marginal North Atlantic kelp forests. Sci. Rep. 10, 13388 (2020).CAS 
Article 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
CAS 

Google Scholar 
Silvy, Y., Guilyardi, E., Sallée, J.-B. & Durack, P. J. Human-induced changes to the global ocean water masses and their time of emergence. Nat. Clim. Change 10, 1030–1036 (2020).CAS 
Article 

Google Scholar 
Cheng, L., Zheng, F. & Zhu, J. Distinctive ocean interior changes during the recent warming slowdown. Sci. Rep. 5, 14346 (2015).CAS 
Article 

Google Scholar 
Brito-Morales, I. et al. Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nat. Clim. Change 10, 576–581 (2020).CAS 
Article 

Google Scholar 
Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 650 (2018).Article 
CAS 

Google Scholar 
Oliver, E. C. J. et al. Marine Heatwaves. Ann. Rev. Mar. Sci. 13, 313–342 (2021).Article 

Google Scholar 
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).CAS 
Article 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).CAS 
Article 

Google Scholar 
Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B280, 20121890 (2013).Article 

Google Scholar 
Hastings, R. A. et al. Climate change drives poleward increases and equatorward declines in marine species. Curr. Biol. 30, 1572–1577.e2 (2020).CAS 
Article 

Google Scholar 
Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evol. 4, 109–114 (2020).Article 

Google Scholar 
Dulvy, N. K. et al. Climate change and deepening of the North Sea fish assemblage: a biotic indicator of warming seas. J. Appl. Ecol. 45, 1029–1039 (2008).Article 

Google Scholar 
Thatje, S. Climate warming affects the depth distribution of marine ectotherms. Mar. Ecol. Prog. Ser. 660, 233–240 (2021).Article 

Google Scholar 
Manuel, S. A., Coates, K. A., Kenworthy, W. J. & Fourqurean, J. W. Tropical species at the northern limit of their range: composition and distribution in Bermuda’s benthic habitats in relation to depth and light availability. Mar. Environ. Res. 89, 63–75 (2013).CAS 
Article 

Google Scholar 
Peck, L. S., Webb, K. E. & Bailey, D. M. Extreme sensitivity of biological function to temperature in Antarctic marine species. Funct. Ecol. 18, 625–630 (2004).Article 

Google Scholar 
Peck, L. S., Morley, S. A., Richard, J. & Clark, M. S. Acclimation and thermal tolerance in Antarctic marine ectotherms. J. Exp. Biol. 217, 16–22 (2014).Article 

Google Scholar 
Walsh, J. E. Climate of the Arctic marine environment. Ecol. Appl. 18, S3–S22 (2008).Article 

Google Scholar 
Storch, D., Menzel, L., Frickenhaus, S. & Pörtner, H.-O. Climate sensitivity across marine domains of life: limits to evolutionary adaptation shape species interactions. Glob. Change Biol. 20, 3059–3067 (2014).Article 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).Article 

Google Scholar 
Pörtner, H. O., Peck, L. & Somero, G. Thermal limits and adaptation in marine Antarctic ectotherms: an integrative view. Philos. Trans. R. Soc. B 362, 2233–2258 (2007).Article 
CAS 

Google Scholar 
Qu, Y.-F. & Wiens, J. J. Higher temperatures lower rates of physiological and niche evolution. Proc. R. Soc. B 287, 20200823 (2020).Article 

Google Scholar 
Cohen, D.M., Inada, T., Iwamoto, T. and Scialabba, N. FAO Species Catalogue, Vol. 10. Gadiform Fishes of the World (Order Gadiformes) (FAO, 1990).Strand, E. & Huse, G. Vertical migration in adult Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 64, 1747–1760 (2007).Article 

Google Scholar 
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Article 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).CAS 
Article 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 

Google Scholar 
Cheung, W. W. L. & Frölicher, T. L. Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep. 10, 6678 (2020).CAS 
Article 

Google Scholar 
Brierley, A. S. & Kingsford, M. J. Impacts of climate change on marine organisms and ecosystems. Curr. Biol. 19, R602–R614 (2009).CAS 
Article 

Google Scholar 
Bijma, J., Pörtner, H.-O., Yesson, C. & Rogers, A. D. Climate change and the oceans—what does the future hold? Mar. Pollut. Bull. 74, 495–505 (2013).CAS 
Article 

Google Scholar 
Jackson, J. B. C. et al. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).CAS 
Article 

Google Scholar 
Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science 371, eaba4658 (2021).CAS 
Article 

Google Scholar 
Rochman, C. M. & Hoellein, T. The global odyssey of plastic pollution. Science 368, 1184–1185 (2020).CAS 
Article 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).CAS 
Article 

Google Scholar 
Madec, G. et al. NEMO ocean engine. Zenodo https://www.earth-prints.org/handle/2122/13309 (2017).Mathiot, P., Jenkins, A., Harris, C. & Madec, G. Explicit representation and parametrised impacts of under ice shelf seas in the z∗- coordinate ocean model NEMO 3.6. Geosci. Model Dev. 10, 2849–2874 (2017).Article 

Google Scholar 
Dai, A. & Bloecker, C. E. Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Clim. Dyn. 52, 289–306 (2019).Article 

Google Scholar 
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).Article 

Google Scholar 
Middag, R. et al. Intercomparison of dissolved trace elements at the Bermuda Atlantic Time Series station. Mar. Chem. 177, 476–489 (2015).CAS 
Article 

Google Scholar 
Welch, B. L. The generalization of Student’s’ problem when several different population variances are involved. Biometrika 34, 28 (1947).CAS 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 

Google Scholar 
Janzen, D. H. Why mountain passes are higher in the Tropics. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
Article 

Google Scholar 
Sandblom, E. et al. Physiological constraints to climate warming in fish follow principles of plastic floors and concrete ceilings. Nat. Commun. 7, 11447 (2016).CAS 
Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).CAS 
Article 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
Article 

Google Scholar  More

Agriculture soils are a source of nitrous oxide and account for 60% of total emissions. It is well established that nitrogen addition via fertilizers drives nitrous oxide emissions during crop growing season. However, little is known about the role of melting snow and thawing surface soil layers during the spring. Limited knowledge of this phenomenon reduces our ability to develop accurate nitrous oxide emissions inventories required under the UN Framework Convention on Climate Change (UNFCCC). More

Nearly two years into the United Nations Decade of Ocean Science, research, including some featured in this month’s issue, shows that there is still a wealth of scientific secrets to uncover in the ocean depths.
In many ways, considering the ocean as a single unit is overly broad. The global ocean covers 71% of the planet’s surface, reaches down to depths of over 10 kilometres, includes about 1.35 billion cubic kilometres of water and houses an approximated 2.2 million eukaryotic species. There are distinct regions, with distinct physical properties, and, in turn, there are distinct species. Yet, the world’s oceans do have a level of physical and thematic connectivity.
Credit: Daria Zaseda / DigitalVision Vectors / GettyPhysically, a large part of the connection is related to the presence of large rotating ocean currents that transfer heat across latitudes and contribute to ocean mixing (thermohaline circulation). Some of these currents are warming at alarming rates — up to three times faster than the rest of the ocean, leading to questions about the underlying mechanisms of the warming and expectations for change.Focusing on western boundary currents (WBCs) in the Southern Hemisphere, in an Article in this issue of Nature Climate Change, Li and colleagues answer a long-debated question on the mechanisms of change, showing that temperature-gradient-related instabilities, rather than flow-speed-related instabilities are behind the shifts. In another Article, focusing on the global future changes of eddies (including eddy-rich WBCs), Beech and colleagues report the development of a flexible method that maximizes local model resolution while minimizing computational costs, to reveal the long-term geographical specificities and nonlinear temperature increases expected to 2100 (see also the News and Views article by Yang on these papers).A recent paper1 has demonstrated the important role of large ocean currents in defining plankton biogeography and dynamics, and WBC warming has previously been linked to impacts such as fishery collapses. The tight link between physical processes and biological responses is an underscoring theme of climate change ecology, but is perhaps more apparent in the open ocean, where physical processes can be easily (if imperfectly) linked to primary productivity using remotely sensed phytoplankton pigment absorption, and where life is generally less impacted by geographical, political or disturbance-based boundaries compared with land and freshwater systems. These aspects may facilitate modelling of current and future communities, while also allowing broader assumptions to be made about biological movement and connectivity.Despite these benefits, understanding ocean change comes with its own difficulties. Biological sampling, while easy enough in the surface waters, becomes increasingly difficult at depth. Although future habitats for various organisms have been projected on the basis of their thermal limits in the ocean, these predictions often still rely on temperatures at the surface of the sea. Addressing this, Santana-Falcón and colleagues report in an Article the global mapping of ocean temperature changes to depths of 1,000 metres, and reveal the complex depth-dependent changes in thermal upper and lower bounds that marine organisms will soon be subjected to. In another Article, Ariza and colleagues neatly address the issue of directly monitoring deep-ocean change by compiling a large database of sound-based observations, and subsequently classifying the ocean’s ‘echobiomes’, defined as sound-scattering communities with comparable structural and functional properties (see also the accompanying News and Views article by Hazen). Sound-based methods are also increasingly being used on land2, and represent an exciting tool for monitoring change, particularly in hard-to-reach places such as deep forests, high mountaintops or underground. While the sound reflection method used in the study by Ariza and colleagues has limits in its ability to identify organisms at the individual or species levels, it does provide a community-level focus on change, which remains much needed in the field of global change ecology.At the other end of the spatial spectrum, research by Lee and colleagues reported in an Article also in this issue dives deep into the DNA of a keystone ocean organism (a copepod), to understand the mechanisms that may allow longer-term adaptation to warming and pH stress. The work reveals remarkable adaptation over just a few short generations, which is linked to epigenetic changes. As climate change impacts continue to escalate, the ability of organisms to invoke both shorter- and longer-term adaptations has become an increasingly relevant area of research. Epigenetics has previously been reported as a quick-response method to cope with environmental stress, and may be particularly relevant in defining the adaptation of short-lived animals such as insects and the resilience of the communities they uphold.The five research pieces linked to the oceans in this issue reveal just some of the diversity of topics, methods and scales relevant to understanding global change. Also increasingly relevant are works on ocean conservation3 and on the social and economic impacts of ocean change4,5. Like climate change science, the topic of ocean change is less of a field, and more of a cross-disciplinary theme. More

MaterialsWe collected from the literature and available databases a dataset of radiocarbon dates from Europe (West of 60°E and North of 37°N) either obtained from remains of the four analyzed species or from archaeological layers where they have been observed. However, we only considered observations dated between 7500 and 47,000 cal BP: their scarcity before this period may bias the GAMs, and after it, domesticated cattle, pigs and (later) horses arrived in Europe, making it difficult to differentiate them from their wild forms.We excluded any record fitting one or more of the following conditions: unreliable; not in accord with the expected chronology of their archaeological layer; without a reported standard error; available only as terminus ante/post quem.All dates were calibrated with OxCal5 version 4.4 using the IntCal20 curve51, and we further excluded any record for which calibration resulted in an error, resulting in the number of points presented in Table 1 as “Original dataset” (available at the link https://doi.org/10.6084/m9.figshare.20510364).Table 1 Number of observations for each species.Full size tableSDMs based on GAMs need presence/background data, not frequencies; moreover, multiple observations (i.e., presence in different archaeological layers) from the same site and time slice are likely to introduce stronger sample biases linked to chrono-geographically differential sampling efforts. For this reason, we collapsed our observations by keeping only one point per grid cell per time slice for each species, leaving the number of observations reported in Table 1 as “Collapsed datasets”, used for all the analyses presented in this work.To perform all analyses, we used the R package pastclim v. 1.042 to couple each observation from the collapsed datasets to paleoclimatic reconstructions published in8 by setting dataset = “Beyer2020”. These are based on the Hadley CM3 model, include 14 different bioclimatic variables at a spatial resolution of 0.5°, and are available for the whole world every 1000 years until 22 kya and every 2000 years before that date (referred to in the manuscript as “time slices”). Specifically, each observation was associated with the relevant bioclimatic reconstruction based on its average age and spatial coordinates.As already mentioned, the four species analyzed show different preferences regarding temperature, habitat, and altitude. Therefore, for the Species Distribution Modelling, we choose five environmental variables that should be able to capture such differences: two measures of temperature (BIO5, maximum temperature of the warmest month, and BIO6, minimum temperature of the coldest month); two variables to help capture habitat differentiation (BIO12, total annual precipitation, and Net Primary Productivity, NPP), and one measure of topography (rugosity42).High collinearity can be problematic in SDMs; we confirmed that all our variables had a correlation below 0.7, a threshold commonly adopted for this kind of analysis52,53.Whilst the GAMs predicted all time points; we visualized our results by creating an average estimate for the following periods: pre-LGM (from the beginning of the time range analyzed, i.e., 47 kya to 27 kya), LGM (from 27 to 18 kya), Late Glacial (from 18 to 11.7 kya), Holocene (from 11.7 kya to the end of the time range analyzed, i.e., 7.5 kya).MethodsWe generated 25 sets of background points for each species to adequately represent the existing climatic space in our SDMs. Each set was generated by sampling, for each observation, 50 random locations matched by time. This resulted in n = 25 datasets (“repetitions”) of background points and presences (observations) for each species, which we used to repeat our analyses to account for the stochastic sampling of the background. For each dataset, we used GAMs to fit two possible models: a “constant niche” model, which included only the environmental variables as covariates, and a “changing niche” model, that also included interactions of each environmental variable with time (fitted as tensor products).In GAMs, the effect of a given continuous predictor on the response variable (in our case, the logit transformed probability of a presence) is represented by a smooth function; this smooth function can be linear or non-linear and can become highly complex in shape depending on the number of knots selected by the GAM fitting algorithm. The interaction between two covariates is modelled by tensor products54; this approach is equivalent to an interaction term in a linear model but with the added complexity of the smooth function. In our models, we confine tensor products to the interaction between an environmental variable and time; a simple way to think about such a tensor product is that it allows the smooth representation of the relationship between the variable and the probability of a presence to change progressively over time.GAMs were fitted using the mgcv package in R54 using thin plate regression splines (TPNR; bs = “tp”, default in mgcv) for environmental variables and their tensor products with time in the “niche changing” models. The GAM algorithm automatically selects the complexity of the smooth most appropriate to the data that are being fitted; as GAM can have issues with overfitting, we added an additional penalty against overly complex smooths (gamma = 1.4) and used Restricted Maximum Likelihood (REML = TRUE), as recommended by54. It is possible that even with these settings, the complexity of the smooth is not sufficient; we used mgcv::gam.check() to check this, and increased the basis dimension of the smooth, k, to make sure that k-1 was larger than the estimated degrees of freedom (edf). We found the best maximum thresholds for k to be 16 for bio06 and 10 for all other variables.We checked for non-linear correlation among variables using the mgcv::collinearity function and checked the values of estimated concurvity. All estimates were below the threshold of 0.8 in all models, runs and variables except for a few instances for time (Supplementary Figs. 5–8). We consider this not to be worrying: this is most likely a result of sample bias, and GAM is known to be robust to correlation/concurvity55,56.We verified the model assumptions by inspecting the residuals using the R package DHARMa57. Standard tests for deviations from the expected distribution and dispersion were non-significant for all repetitions for all species, as were the tests for outliers. Furthermore, we tested for spatial autocorrelation among residuals by computing Moran’s I; all tests were either non-significant or, when significance was detected, the estimate of Moran’s I was very close to zero, revealing a trivial deviation from the assumptions which should not impact the results (Supplementary Tables 1–4).We performed model choice (Supplementary Tables 5–8) by comparing the constant- and changing-niche models for each combination of species and repetition using the Akaike Information Criterion (AIC). AIC strongly supported the changing-niche model in all species and repetitions, an inference supported by the higher Nagelkerke R2 and expected deviance for those models than for the constant-niche ones (Supplementary Tables 5–8).The model fit for each of the changing niche GAMs was evaluated with the Boyce Continuous Index25,26, designed to be used with presence-only data58,59. We set a threshold of Pearson’s correlation coefficient  >  0.8 to define acceptable models25 (Supplementary Table 9).The relative importance of each environmental variable was quantified for all the models above the BCI threshold of 0.8 in two different ways. Firstly, we computed the total deviance explained by each variable by simply fitting a GAM with only that variable. We then estimated the unique deviance explained by each variable by comparing the full model with one for which that variable was excluded (i.e., we computed the explained deviance lost by dropping that predictor). The difference between the two values represents the deviance explained by a variable which can also be accounted for by other variables (i.e., the deviance in common with other variables).To achieve more robust predictions60, we averaged in two different ensembles the repetitions for the changing niche GAMs with BCI  > 0.8: by mean and median. This step is intended to reduce the weight of models that are highly sensitive to the random sampling of the background60. Then, for each species, we selected the ensemble (either based on mean or median) with the higher BCI as the most supported and used it to perform all further analyses.The effect of different variables through time was visualized by plotting the interactions of the GAMs. For each model with a BCI  > 0.8, we used the R package gratia27 to generate a surface with time as the x-axis, the environmental variable as the y-axis, and the effect size as the z-axis (visualized as colour shades). We then plotted the mean surface for each species, which captures the signal consistent across all randomized background sets.To visualize the prediction for each species, we then transformed the predicted probabilities of occurrence from the ensemble into binary presence/absences by using the threshold needed to get a minimum predicted area encompassing 99% of our presences (function ecospat.mpa() from the ecospat R package61). The binary predictions were then visualized using the mean over the time steps within each major climatic period.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Levin, L. A. et al. The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems 4, 430–451 (2001).CAS 

Google Scholar 
Wagner, G. M. & Sallema-Mtui, R. in Estuaries: A Lifeline of Ecosystem Services in the Western Indian Ocean Estuaries of the World (eds S. Diop, P. Scheren, & J. Machiwa) 183–207 (2016).Brown, C. J. et al. The assessment of fishery status depends on fish habitats. Fish Fish. 20, 1–14 (2019).CAS 

Google Scholar 
De La Morinière, E. C., Pollux, B., Nagelkerken, I. & Van der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).ADS 

Google Scholar 
Branton, M. & Richardson, J. S. Assessing the value of the umbrella-species concept for conservation planning with meta-analysis. Conserv. Biol. 25, 9–20 (2011).PubMed 

Google Scholar 
Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).PubMed 

Google Scholar 
Zainal Abidin, D. H. et al. DNA-based taxonomy of a mangrove-associated community of fishes in Southeast Asia. Sci. Rep. 11, 1–15. https://doi.org/10.1038/s41598-021-97324-1 (2021).CAS 
Article 

Google Scholar 
Gauthier, G. et al. Long-term monitoring at multiple trophic levels suggests heterogeneity in responses to climate change in the Canadian Arctic tundra. Philos. Trans. Roy. Soc. B Biol. Sci. 368, 20120482 (2013).
Google Scholar 
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25, 929–942 (2016).CAS 
PubMed 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Chong, V. C., Lee, P. K. & Lau, C. M. Diversity, extinction risk and conservation of Malaysian fishes. J. Fish Biol. 76, 2009–2066. https://doi.org/10.1111/j.1095-8649.2010.02685.x (2010).CAS 
Article 
PubMed 

Google Scholar 
Zainal Abidin, D. H. et al. Ichthyofauna of Sungai Merbok Mangrove Forest Reserve, northwest Peninsular Malaysia, and its adjacent marine waters. Check List 17, 601–631. https://doi.org/10.15560/17.2.601 (2021).Article 

Google Scholar 
Ong, J. et al. in Hutan paya laut Merbok, Kedah: Pengurusan hutan, persekitaran fizikal dan kepelbagaian flora. Vol. 23 Siri kepelbagaian biologi hutan (ed Ku Aman KA Abd Rahim AR, Abu Hassan MN, Abdullah M, Nor Hazliza MB, Latiff A) 21–33 (Jabatan Perhutanan Semenanjung Malaysia, 2015).Hookham, B., Shau-Hwai, A. T., Dayrat, B. & Hintz, W. A baseline measure of tree and gastropod biodiversity in replanted and natural mangrove stands in Malaysia: Langkawi Island and Sungai Merbok. Trop. Life Sci. Res. 25, 1 (2014).PubMed 
PubMed Central 

Google Scholar 
Jamaluddin, J. A. F. et al. DNA barcoding of shrimps from a mangrove biodiversity hotspot. Mitochondrial DNA Part A 30, 618–625. https://doi.org/10.1080/24701394.2019.1597073 (2019).CAS 
Article 

Google Scholar 
Mansor, M., Mohammad-Zafrizal, M., Nur-Fadhilah, M., Khairun, Y. & Wan-Maznah, W. Temporal and spatial variations in fish assemblage structures in relation to the physicochemical parameters of the Merbok estuary, Kedah. J. Nat. Sci. Res. 2, 110–127 (2012).
Google Scholar 
Alshari, N. F. M. A. H. et al. Metabarcoding of Fish Larvae in the Merbok River reveals species diversity and distribution along its mangrove environment. Zool. Stud. 60, 60–76. https://doi.org/10.6620/ZS.2021 (2021).Article 

Google Scholar 
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 1–9 (2016).
Google Scholar 
Hupało, K. et al. An urban Blitz with a twist: Rapid biodiversity assessment using aquatic environmental DNA. Environ. DNA 3, 200–213 (2020).
Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

Google Scholar 
Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).CAS 
PubMed 

Google Scholar 
Ahn, H. et al. Evaluation of fish biodiversity in estuaries using environmental DNA metabarcoding. PLoS ONE 15, e0231127 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Polanco, F. A. et al. Detecting aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53, 1606–1619 (2021).
Google Scholar 
Zhang, H., Yoshizawa, S., Iwasaki, W. & Xian, W. Seasonal fish assemblage structure using environmental DNA in the Yangtze Estuary and its adjacent waters. Front. Mar. Sci. 6, 515. https://doi.org/10.3389/fmars.2019.00515 (2019).Article 

Google Scholar 
Stat, M. et al. Ecosystem biomonitoring with eDNA: Metabarcoding across the tree of life in a tropical marine environment. Sci. Rep. 7, 1–11 (2017).ADS 
CAS 

Google Scholar 
West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27, 1942–1957 (2021).
Google Scholar 
Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).
Google Scholar 
Seymour, M. et al. Environmental DNA provides higher resolution assessment of riverine biodiversity and ecosystem function via spatio-temporal nestedness and turnover partitioning. Commun. Biol. 4, 1–12 (2021).
Google Scholar 
Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2021).PubMed 

Google Scholar 
Fujii, K. et al. Environmental DNA metabarcoding for fish community analysis in backwater lakes: A comparison of capture methods. PLoS ONE 14, e0210357 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshwat. Ecosyst. 29, 1785–1800 (2019).
Google Scholar 
Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Klymus, K. E., Marshall, N. T. & Stepien, C. A. Environmental DNA (eDNA) metabarcoding assays to detect invasive invertebrate species in the Great Lakes. PLoS ONE 12, 24. https://doi.org/10.1371/journal.pone.0177643 (2017).CAS 
Article 

Google Scholar 
Wilson, C. et al. Tracking ghosts: Combined electrofishing and environmental DNA surveillance efforts for Asian carps in Ontario waters of Lake Erie. Manag. Biol. Invasion 5, 225–231. https://doi.org/10.3391/mbi.2014.5.3.05 (2014).Article 

Google Scholar 
Alexander, J. B. et al. Development of a multi-assay approach for monitoring coral diversity using eDNA metabarcoding. Coral Reefs 39, 159–171. https://doi.org/10.1007/s00338-019-01875-9 (2020).Article 

Google Scholar 
Port, J. A. et al. Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Mol. Ecol. 25, 527–541. https://doi.org/10.1111/mec.13481 (2016).CAS 
Article 
PubMed 

Google Scholar 
Fritts, A. K. et al. Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps. Biol. Invasions 21, 1143–1153 (2019).
Google Scholar 
Maruyama, A., Nakamura, K., Yamanaka, H., Kondoh, M. & Minamoto, T. The release rate of environmental DNA from juvenile and adult fish. PLoS ONE 9, e114639 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Amberg, J. J., Merkes, C. M., Stott, W., Rees, C. B. & Erickson, R. A. Environmental DNA as a tool to help inform zebra mussel, Dreissena polymorpha, management in inland lakes. Manag. Biol. Invasion 10, 96 (2019).
Google Scholar 
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812. https://doi.org/10.1093/bioinformatics/btu393 (2014).CAS 
Article 
PubMed 

Google Scholar 
Zainal Abidin, D. H. & Noor Adelyna, M. A. Environmental DNA (eDNA) Metabarcoding as a Sustainable Tool of Coastal Biodiversity Assessment in Universities as Living Labs for Sustainable Development 211–225 (Springer, 2020).Sard, N. M. et al. Comparison of fish detections, community diversity, and relative abundance using environmental DNA metabarcoding and traditional gears. Environ. DNA 1, 368–384 (2019).
Google Scholar 
Hoffman, J. C., Kelly, J. R., Trebitz, A. S., Peterson, G. S. & West, C. W. Effort and potential efficiencies for aquatic non-native species early detection. Can. J. Fish. Aquat. Sci. 68, 2064–2079 (2011).
Google Scholar 
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 1–12 (2017).
Google Scholar 
Whitfield, A. K. Fish species in estuaries—From partial association to complete dependency. J. Fish Biol. 97, 1262–1264 (2020).PubMed 

Google Scholar 
Carpenter, K. & Niem, V. The living marine resources of the Western Central Pacific. Volume 5. Bony Fishes Part 3 (Menidae to Pomacentridae). Vol. 5, 2791–3380 (Food and Agriculture Organization of the United Nations, 2001).Carpenter, K. E. & Niem, V. FAO species identification guide for fishery purposes. The Living Marine Resources of the Western Central Pacific. Volume 6. Bony Fishes Part 4 (Labridae to Latimeriidae), Estuarine Crocodiles, Sea Turtles, Sea Snakes and Marine Mammals. Vol. 6, 3381–4218 (Food and Agriculture Organization of the United Nations, 2001).Carpenter, K. E. & Niem, V. H. The living marine resources of the Western Central Pacific: Batoid fishes, chimaera and bony fishes part 1 (Elopidae to Linophrynidae). Vol. 3, 1397–2068 (Food and Agriculture Organization of the United Nations, 1999).Carpenter, K. E. & Niem, V. H. The living marine resources of the Western Central Pacific. Volume 4. Bony Fishes Part 2 (Mugilidae to Carangidae). Vol. 4, 2069–2790 (Food and Agriculture Organization of the United Nations, 1999).Benson, D. A. et al. GenBank. Nucleic Acids Res. 46, D41–D47 (2018).CAS 
PubMed 

Google Scholar 
Pentinsaari, M., Ratnasingham, S., Miller, S. E. & Hebert, P. D. N. BOLD and GenBank revisited—Do identification errors arise in the lab or in the sequence libraries?. PLoS ONE 15, e0231814–e0231814. https://doi.org/10.1371/journal.pone.0231814 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ardura, A., Planes, S. & Garcia-Vazquez, E. Applications of DNA barcoding to fish landings: Authentication and diversity assessment. Zookeys 365, 49–65. https://doi.org/10.3897/zookeys.365.6409 (2013).Article 

Google Scholar 
ZainalAbidin, D. H. et al. Population genetics of the black scar oyster, Crassostrea iredalei: Repercussion of anthropogenic interference. Mitochondrial DNA Part A 27, 647–658 (2016).CAS 

Google Scholar 
Kelly, R. P. et al. Genetic and manual survey methods yield different and complementary views of an ecosystem. Front. Mar. Sci. 3, 283 (2017).
Google Scholar 
Ratnasingham, S. & Hebert, P. D. BOLD: The barcode of life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17. https://doi.org/10.1007/s10592-015-0775-4 (2016).CAS 
Article 

Google Scholar 
Vasconcelos, R. P. et al. Global patterns and predictors of fish species richness in estuaries. J. Anim. Ecol. 84, 1331–1341 (2015).PubMed 

Google Scholar 
Shah, A. S. R. M., Hashim, Z. H. & Sah, S. A. M. Freshwater fishes of Gunung Jerai, Kedah Darul Aman: A preliminary study. Trop. Life Sci. Res. 20, 59 (2009).PubMed 
PubMed Central 

Google Scholar 
Md. Zain, K. et al. Fish diversity along streams in Ulu Muda Forest Reserve, Kedah, Peninsular Malaysia. Malayan Nat. J. 73, 349–361 (2021).
Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
PubMed 

Google Scholar 
Wang, S. et al. Methodology of fish eDNA and its applications in ecology and environment. Sci. Total Environ. 755, 142622. https://doi.org/10.1016/j.scitotenv.2020.142622 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

Google Scholar 
Southeast Asian Fisheries Development Centre (SEAFDEC). Status and trends of sharks fisheries in South East Asia in Malaysia Shark Fisheries (Fisheries and Resources Monitoring System (FIRMS), Rome, 2004).Zhang, S., Zhao, J. & Yao, M. A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods Ecol. Evol. 11, 1609–1625 (2020).ADS 

Google Scholar 
Doi, H. et al. Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshw. Biol. 62, 30–39 (2017).CAS 

Google Scholar 
Hayami, K. et al. Effects of sampling seasons and locations on fish environmental DNA metabarcoding in dam reservoirs. Ecol. Evol. 10, 5354–5367 (2020).PubMed 
PubMed Central 

Google Scholar 
Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Morey, K. C., Bartley, T. J. & Hanner, R. H. Validating environmental DNA metabarcoding for marine fishes in diverse ecosystems using a public aquarium. Environ. DNA 2, 330–342 (2020).
Google Scholar 
Shaw, J. L. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).
Google Scholar 
Siegenthaler, A. et al. Metabarcoding of shrimp stomach content: Harnessing a natural sampler for fish biodiversity monitoring. Mol. Ecol. Resour. 19, 206–220. https://doi.org/10.1111/1755-0998.12956 (2019).CAS 
Article 
PubMed 

Google Scholar 
Stoeckle, M. Y., Das Mishu, M. & Charlop-Powers, Z. Improved environmental DNA reference library detects overlooked marine fishes in New Jersey, United States. Front. Mar. Sci. 7, 226 (2020).
Google Scholar 
Collins, R. A. et al. Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods Ecol. Evol. 10, 1985–2001 (2019).
Google Scholar 
Hebert, P. D., Ratnasingham, S. & De Waard, J. R. Barcoding animal life: Cytochrome c oxidase subunit 1 divergences among closely related species. Proc. Roy. Soc. Lond. Ser. B Biol. Sci. 270, S96–S99 (2003).CAS 

Google Scholar 
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. Roy. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 

Google Scholar 
Mariani, S., Baillie, C., Colosimo, G. & Riesgo, A. Sponges as natural environmental DNA samplers. Curr. Biol. 29, R401–R402 (2019).CAS 
PubMed 

Google Scholar 
Bylemans, J., Gleeson, D. M., Duncan, R. P., Hardy, C. M. & Furlan, E. M. A performance evaluation of targeted eDNA and eDNA metabarcoding analyses for freshwater fishes. Environ. DNA 1, 402–414 (2019).
Google Scholar 
Chin, A. T. et al. Beta diversity changes in estuarine fish communities due to environmental change. Mar. Ecol. Prog. Ser. 603, 161–173 (2018).ADS 

Google Scholar 
Sloterdijk, H. et al. Composition and structure of the larval fish community related to environmental parameters in a tropical estuary impacted by climate change. Estuar. Coast. Shelf Sci. 197, 10–26 (2017).ADS 

Google Scholar 
Malaysian Meteorological Department. Tinjauan Cuaca bagi Tempoh November 2017 hingga April 2018. National Climate Centre: Ministry of Science, Technology and Innovation. Retrieved on February 1st, 2018, from https://www.met.gov.my/iklim/ramalanbermusim/ (2017).Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 

Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
PubMed 

Google Scholar 
Illumina. 16S Metagenomic Sequencing Library Preparation. https://support.illumina.com/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf 1–28 (2013).Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. (Babraham Bioinformatics (Babraham Institute, 2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).CAS 
PubMed 

Google Scholar 
Andruszkiewicz, E. A. et al. Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS ONE 12, e0176343 (2017).PubMed 
PubMed Central 

Google Scholar 
Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
Google Scholar 
Fricke, R., Eschmeyer, W. N. & Van der Laan, R. Eschmeyer’s Catalog of Fishes: Genera, species, references. http://www.calacademy.org/scientists/catalog-of-fishes-family-group-names/ (2021).Ebert, D. A. & Fowler, S. Sharks of the World (Princeton University Press, 2013).
Google Scholar 
R Core Team. RStudio: integrated development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com42, 14 (2015).McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’. Commun. Ecol. Pack. 2, 1–295 (2013).
Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar  More