More stories

  • in

    Ecological sensitivity and vulnerability of fishing fleet landings to climate change across regions

    Sumaila, U. R. & Tai, T. C. End overfishing and increase the resilience of the ocean to climate change. Front. Mar. Sci. 7, 1–8 (2020).Article 

    Google Scholar 
    Sumaila, U. R. et al. Benefits of the paris agreement to ocean life, economies, and people. Sci. Adv. 5, 1–10 (2019).Article 

    Google Scholar 
    Beaudreau, A. H. et al. Thirty years of change and the future of Alaskan fisheries: Shifts in fishing participation and diversification in response to environmental, regulatory and economic pressures. Fish Fish. 20, 601–619 (2019).
    Google Scholar 
    Finkbeiner, E. M. The role of diversification in dynamic small-scale fisheries: Lessons from Baja California Sur. Mexico. Glob. Environ. Chang. 32, 139–152 (2015).Article 

    Google Scholar 
    Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).Article 

    Google Scholar 
    IPCC. Climate Change 2007: Synthesis Report. Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change. (2007).Johnson, J. E. & Welch, D. J. Climate change implications for Torres Strait fisheries: Assessing vulnerability to inform adaptation. Clim. Change 135, 611–624 (2016).ADS 
    Article 

    Google Scholar 
    IPCC. Annex I: Glossary. in IPCC special report on the ocean and cryosphere in a changing climate e [H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)] 677–702 (Cambridge University Press, 2019). https://doi.org/10.1017/9781009157964.010Cheung, W. W. L., Watson, R., Morato, T., Pitcher, T. J. & Pauly, D. Intrinsic vulnerability in the global fish catch. Mar. Ecol. Prog. Ser. 333, 1–12 (2007).ADS 
    Article 

    Google Scholar 
    Pauly, D., Christensen, V., Dalsgaard, J., Froese, R. & Torres, F. Fishing down marine food webs. Science 80(279), 860 (1998).ADS 
    Article 

    Google Scholar 
    Lam, V. W. Y., Cheung, W. W. L., Reygondeau, G. & Rashid Sumaila, U. Projected change in global fisheries revenues under climate change. Sci. Rep. 6(6), 13 (2016).
    Google Scholar 
    Heck, N. et al. Fisheries at risk: Vulnerability of fisheries to climate change (Nat. Conserv. Tech. Rep, 2020).
    Google Scholar 
    Allison, E. H. et al. Vulnerability of national economies to the impacts of climate change on fisheries. Fish Fish. 10, 173–196 (2009).Article 

    Google Scholar 
    DuFour, M. R. et al. Portfolio theory as a management tool to guide conservation and restoration of multi-stock fish populations. Ecosphere 6(12), 1 (2015).Article 

    Google Scholar 
    Kasperski, S. & Holland, D. S. Income diversification and risk for fishermen. Proc. Natl. Acad. Sci. U. S. A. 110, 2076–2081 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bahri, T. et al. Adaptive management of fisheries in response to climate change. FAO Fisheries and Aquaculture Technical Paper 667, (FAO, 2021).Barker, M. J. & Schluessel, V. Managing global shark fisheries: Suggestions for prioritizing management strategies. Aquat. Conserv. Mar. Freshw. Ecosyst. 15, 325–347 (2005).Article 

    Google Scholar 
    Fletcher, W. J. F. & Fletcher, W. J. The application of qualitative risk assessment methodology to prioritize issues for fisheries management. ICES J. Mar. Sci. 62, 1576–1587 (2005).Article 

    Google Scholar 
    Cheung, W. W. L. The future of fishes and fisheries in the changing oceans. J. Fish Biol. 92, 790–803 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cinner, J. E. et al. Evaluating social and ecological vulnerability of coral reef fisheries to climate change. PLoS ONE 8(9), e74321 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Colburn, L. L. et al. Indicators of climate change and social vulnerability in fishing dependent communities along the Eastern and Gulf Coasts of the United States. Mar. Policy 74, 323–333 (2016).Article 

    Google Scholar 
    Pinnegar, J. K. et al. Assessing vulnerability and adaptive capacity of the fisheries sector in Dominica: Long-term climate change and catastrophic hurricanes. ICES J. Mar. Sci. 76, 1353–1367 (2019).
    Google Scholar 
    Aragão, G. M. et al. The importance of regional differences in vulnerability to climate change for demersal fisheries. ICES J. Mar. Sci. 1, 1–13 (2021).
    Google Scholar 
    Payne, M. R., Kudahl, M., Engelhard, G. H., Peck, M. A. & Pinnegar, J. K. Climate risk to European fisheries and coastal communities. Proc. Natl. Acad. Sci. U. S. A. 118, e2018086118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baptista, V., Silva, P. L., Relvas, P., Teodósio, M. A. & Leitão, F. Sea surface temperature variability along the Portuguese coast since 1950. Int. J. Climatol. 38, 1145–1160 (2018).Article 

    Google Scholar 
    Leitão, F. et al. (2019) A 60-year time series analyses of the upwelling along the Portuguese coast. Water 11(11), 1285 (2019).Article 

    Google Scholar 
    Leitão, F., Relvas, P., Cánovas, F., Baptista, V. & Teodósio, A. Northerly wind trends along the Portuguese marine coast since 1950. Theor. Appl. Climatol. 137(1), 19 (2018).
    Google Scholar 
    Bueno-Pardo, J. et al. Trends and drivers of marine fish landings in Portugal since its entrance in the European Union. ICES J. Mar. Sci. 77, 988–1001 (2020).Article 

    Google Scholar 
    Leitão, F., Maharaj, R. R., Vieira, V. M. N. C. S., Teodósio, A. & Cheung, W. W. L. The effect of regional sea surface temperature rise on fisheries along the Portuguese Iberian Atlantic coast. Aquat. Conserv. Mar. Freshw. Ecosyst. 28, 1351–1359 (2018).Article 

    Google Scholar 
    Leitão, F., Alms, V. & Erzini, K. A multi-model approach to evaluate the role of environmental variability and fishing pressure in sardine fisheries. J. Mar. Syst. 139, 128–138 (2014).Article 

    Google Scholar 
    Ullah, H., Leitão, F., Baptista, V. & Chícharo, L. An analysis of the impacts of climatic variability and hydrology on the coastal fisheries, Engraulis encrasicolus and Sepia officinalis, of Portugal. Ecohydrol. Hydrobiol. 12, 337–352 (2012).Article 

    Google Scholar 
    EUMOFA. The EU Fish Market – Highlights the EU in the world market supply consumption import-export landings in the EU aquaculture (2021) https://doi.org/10.2771/563899DGPM. Relatório de Monitorização da Estratégia Nacional para o Mar 2013–2020, Documento de Suporte às Políticas do Mar. (2020).Almeida, C., Karadzic, V. & Vaz, S. The seafood market in Portugal: Driving forces and consequences. Mar. Policy 61, 87–94 (2015).Article 

    Google Scholar 
    Pita, C. & Gaspar, M. (2020) Small-Scale Fisheries in Portugal: Current Situation, Challenges and Opportunities for the Future. In Small-Scale Fisheries in Europe: Status, Resilience and Governance. Springer, Cham 283–305https://doi.org/10.1007/978-3-030-37371-9_14Baeta, F., José Costa, M. & Cabral, H. Changes in the trophic level of Portuguese landings and fish market price variation in the last decades. Fish. Res. 97, 216–222 (2009).Article 

    Google Scholar 
    Leitão, F. Landing profiles of Portuguese fisheries: Assessing the state of stocks. Fish. Manag. Ecol. 22, 152–163 (2015).Article 

    Google Scholar 
    Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).Article 

    Google Scholar 
    Bueno-Pardo, J. et al. Climate change vulnerability assessment of the main marine commercial fish and invertebrates of Portugal. Sci. Rep. 11, 2958 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Szynaka, M. J., Erzini, K., Gonçalves, J. M. S. & Campos, A. Identifying métiers using landings profiles: An octopus-driven multi-gear coastal fleet. J. Mar. Sci. Eng. 9, 1022 (2021).Article 

    Google Scholar 
    Gamito, R., Teixeira, C. M., Costa, M. J. & Cabral, H. N. Climate-induced changes in fish landings of different fleet components of Portuguese fisheries. Reg. Environ. Chang. 13, 413–421 (2013).Article 

    Google Scholar 
    Leitão, F., Baptista, V., Zeller, D. & Erzini, K. Reconstructed catches and trends for mainland Portugal fisheries between 1938 and 2009: Implications for sustainability, domestic fish supply and imports. Fish. Res. 155, 33–50 (2014).Article 

    Google Scholar 
    Teixeira, C. M. et al. Trends in landings of fish species potentially affected by climate change in Portuguese fisheries. Reg. Environ. Chang. 14, 657–669 (2014).Article 

    Google Scholar 
    Wickham, H. ggplot2: Elegant graphics for data analysis (Springer-Verlag, 2016).MATH 
    Book 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 3–900051–07–0 (2020).Zuur, A. F., Fryer, R. J., Jolliffe, I. T., Dekker, R. & Beukema, J. J. Estimating common trends in multivariate time series using dynamic factor analysis. Environmetrics 14, 665–685 (2003).Article 

    Google Scholar 
    Zuur, A. F., Ieno, E. N. & Smith, G. M. (2007) Analysing Ecological Data. https://doi.org/10.1007/978-0-387-45972-1Anderson, M., Gorley, R. & Clarke, K. PERMANOVA for PRIMER: Guide to software and statistical methods. (PRIMER-E Ltd., 2008).Heppell, S. S., Heppell, S. a, Read, A. J. & Crowder, L. B. Effects of fishing on long-lived marine organisms. In Marine conservation biology: The science of maintaining the sea’s biodiversity (eds. Norse, E. & Crowder, L.) 211–231 (Island Press, 2005).Maynou, F. et al. Estimating trends of population decline in long-lived marine species in the Mediterranean sea based on fishers’ perceptions. PLoS ONE 6, e21818 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rolland, V., Barbraud, C. & Weimerskirch, H. Combined effects of fisheries and climate on a migratory long-lived marine predator. J. Appl. Ecol. 45, 4–13 (2008).Article 

    Google Scholar 
    Alves, L. M. F., Correia, J. P. S., Lemos, M. F. L., Novais, S. C. & Cabral, H. Assessment of trends in the Portuguese elasmobranch commercial landings over three decades (1986–2017). Fish. Res. 230, 105648 (2020).Article 

    Google Scholar 
    Correia, J. P., Morgado, F., Erzini, K. & Soares, A. M. V. M. Elasmobranch landings for the Portuguese commercial fishery from 1986 to 2009. Arquipel. Life Mar. Sci. 33, 81–109 (2016).
    Google Scholar 
    Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pinnegar, J. K. & Engelhard, G. H. The ‘shifting baseline’ phenomenon: A global perspective. Rev. Fish Biol. Fish. 18, 1–16 (2008).Article 

    Google Scholar 
    Moura, T. et al. Assessing spatio-temporal changes in marine communities along the Portuguese continental shelf and upper slope based on 25 years of bottom trawl surveys. Mar. Environ. Res. 160, 105044 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Martins, M. M., Skagen, D., Marques, V., Zwolinski, J. & Silva, A. Changes in the abundance and spatial distribution of the Atlantic chub mackerel (Scomber colias) in the pelagic ecosystem and fisheries off Portugal. Sci. Mar. 77, 551–563 (2013).Article 

    Google Scholar 
    Bordalo-Machado, P. & Figueiredo, I. The fishery for black scabbardfish (Aphanopus carbo Lowe, 1839) in the Portuguese continental slope. Rev. Fish Biol. Fish. 19, 49–67 (2009).Article 

    Google Scholar 
    Gordo, L. S. Black scabbardfish (Aphanopus carbo Lowe, 1839) in the southern Northeast Atlantic: Considerations on its fishery. Sci. Mar. 73, 11–16 (2009).Article 

    Google Scholar 
    Campos, A., Fonseca, P., Fonseca, T. & Parente, J. Definition of fleet components in the Portuguese bottom trawl fishery. Fish. Res. 83, 185–191 (2007).Article 

    Google Scholar 
    Bueno-Pardo, J. et al. Deep-sea crustacean trawling fisheries in Portugal: Quantification of effort and assessment of landings per unit effort using a Vessel Monitoring System (VMS). Sci. Rep. 7, 1–10 (2017).ADS 
    Article 

    Google Scholar 
    Gamito, R., Pita, C., Teixeira, C., Costa, M. J. & Cabral, H. N. Trends in landings and vulnerability to climate change in different fleet components in the Portuguese coast. Fish. Res. 181, 93–101 (2016).Article 

    Google Scholar 
    García-Seoane, E., Marques, V., Silva, A. & Angélico, M. M. Spatial and temporal variation in pelagic community of the western and southern Iberian Atlantic waters. Estuar. Coast. Shelf Sci. 221, 147–155 (2019).ADS 
    Article 

    Google Scholar 
    Vinagre, C., Duarte, F., Cabral, H. & Jose, M. Impact of climate warming upon the fish assemblages of the Portuguese coast under different scenarios. Reg. Environ. Change 11(4), 779. https://doi.org/10.1007/s10113-011-0215-z (2011).Article 

    Google Scholar 
    Goulart, P., Veiga, F. J. & Grilo, C. The evolution of fisheries in Portugal: A methodological reappraisal with insights from economics. Fish. Res. 199, 76–80 (2018).Article 

    Google Scholar 
    Pita, C., Pereira, J., Lourenço, S., Sonderblohm, C. & Pierce, G. J. (2015) The Traditional Small-Scale Octopus Fishery in Portugal: Framing Its Governability. 117–132. https://doi.org/10.1007/978-3-319-17034-3_7Pita, C. et al. Fisheries for common octopus in Europe: Socioeconomic importance and management. Fish. Res. 235, 105820 (2021).Article 

    Google Scholar 
    Moreno, A. et al. Essential habitats for pre-recruit Octopus vulgaris along the Portuguese coast. Fish. Res. 152, 74–85 (2014).ADS 
    Article 

    Google Scholar 
    Sbrana, M. et al. Spatiotemporal abundance pattern of deep-water rose shrimp, parapenaeus longirostris, and Norway lobster, nephrops norvegicus, in european mediterranean waters. Sci. Mar. 83, 71–80 (2019).Article 

    Google Scholar 
    Quattrocchi, F., Fiorentino, F., Lauria, V. & Garofalo, G. The increasing temperature as driving force for spatial distribution patterns of Parapenaeus longirostris (Lucas 1846) in the Strait of Sicily (Central Mediterranean Sea). J. Sea Res. 158, 101871 (2020).Article 

    Google Scholar 
    Colloca, F., Mastrantonio, G., Lasinio, G. J., Ligas, A. & Sartor, P. Parapenaeus longirostris (Lucas, 1846) an early warning indicator species of global warming in the central Mediterranean Sea. J. Mar. Syst. 138, 29–39 (2014).Article 

    Google Scholar 
    Woods, P. J. et al. (2021) A review of adaptation options in fisheries management to support resilience and transition under socio-ecological change. ICES J. Mar. Sci. fsab146Gonzalez-Mon, B. et al. Spatial diversification as a mechanism to adapt to environmental changes in small-scale fisheries. Environ. Sci. Policy 116, 246–257 (2021).Article 

    Google Scholar 
    Garza-Gil, M. D., Torralba-Cano, J. & Varela-Lafuente, M. M. Evaluating the economic effects of climate change on the European sardine fishery. Reg. Environ. Chang. 11, 87–95 (2011).Article 

    Google Scholar 
    Borges, M. F., Santos, A. M. P., Crato, N., Mendes, H. & Mota, B. Sardine regime shifts off Portugal: A time series analysis of catches and wind conditions. Sci. Mar. 67, 235–244 (2003).Article 

    Google Scholar 
    Garrido, S. et al. Temperature and food-mediated variability of European Atlantic sardine recruitment. Prog. Oceanogr. 159, 267–275 (2017).ADS 
    Article 

    Google Scholar 
    ICES. Report of the working group on southern horse mackerel, anchovy and sardine (WGHANSA). (2018).Szalaj, D. et al. Food-web dynamics in the Portuguese continental shelf ecosystem between 1986 and 2017: Unravelling drivers of sardine decline. Estuar. Coast. Shelf Sci. 251, 107259 (2021).Article 

    Google Scholar 
    Feijó, D. et al. Catch and yield trends of the Portuguese purse seine fishery (2006–2018). Front. Mar. Sci. https://doi.org/10.3389/conf.fmars.2019.08.00013 (2019).Article 

    Google Scholar 
    Schickele, A., Francour, P. & Raybaud, V. European cephalopods distribution under climate-change scenarios. Sci. Rep. 11, 3930 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Purcell, S. W., Crona, B. I., Lalavanua, W. & Eriksson, H. Distribution of economic returns in small-scale fisheries for international markets: A value-chain analysis. Mar. Policy 86, 9–16 (2017).Article 

    Google Scholar 
    Thiao, D., Leport, J., Ndiaye, B. & Mbaye, A. Need for adaptive solutions to food vulnerability induced by fish scarcity and unaffordability in Senegal. Aquat. Living Resour. 31, 25 (2018).Article 

    Google Scholar 
    Education, A. & Variability, H. Cardoso, C., Lourenço, H., Costa, S., Gonçalves, S. & Leonor Nunes, M. Survey Into the Seafood Consumption Preferences and Patterns in the Portuguese Population. J. Food Prod. Mark. 22, 421–435 (2016).Article 

    Google Scholar 
    Holsten, A. & Kropp, J. P. An integrated and transferable climate change vulnerability assessment for regional application. Nat. Hazards 64, 1977–1999 (2012).Article 

    Google Scholar 
    Umweltbundesamt guidelines for climate impact and vulnerability assessments recommendations of the interministerial working group on adaptation to climate change of the German federal government for our environment. More

  • in

    Benthic exometabolites and their ecological significance on threatened Caribbean coral reefs

    Benthic organism exudate collectionsExudate collections from benthic organisms were conducted on board the R/V Walton Smith in November 2018 in Lameshur Bay, St. John, U.S. Virgin Islands within the Virgin Islands National Park. In brief, we collected six species of benthic organisms (n = 6 specimens), incubated these organisms in separate containers for 8 h, and harvested the incubation water to characterize the composition of dissolved metabolites in their exudates. A description of the exudate collections is included below (additional details available in Supplementary Methods).Before each organism experiment, 58 l of surface (non-reef) seawater was collected ~1 mile offshore (18 17.127° N, 064 44.312° W, 31.6 m depth). Cells and particles were removed using peristaltic pressure through a 0.2 µm filter (47 mm, Omnipore, EMD Millipore Corporation, Billerica, MA, USA) using metabolomics-grade tubing and this filtrate (filtered seawater) was collected for the incubations. Additionally, two to three, 2 l filtrate subsets per experiment were acidified with concentrated hydrochloric acid (final concentration 1% volume/volume) and subjected to solid-phase-extraction (SPE) using a negative vacuum pressure of –3.7 to –5 100xkPA in Hg, to serve as controls. Before SPE, 6 ml, 1 gm Bond Elut PPL cartridges (Agilent, Santa Clara, CA, USA) were pre-conditioned with 6 ml of 100% HPLC-grade methanol.For the experiments, six species of benthic organisms were collected from reefs around Lameshur Bay by SCUBA divers. Experiments were completed on three stony corals (Porites astreoides, Siderastrea siderea, and Psuedodiploria strigosa), two octocorals (Plexaura homomalla and Gorgonia ventalina), and one encrusting alga (Ramicrusta textilis) (Table S1). P. astreoides, S. siderea, and R. textilis were held in a seawater table for 24 h (hrs) before the incubations and colonies from the other three species were held for 2-3 h due to timing constraints. Coral and algal fragments were generally small (2.5-5.0 cm in length).For each incubation, nine, acid-washed, 10 l polycarbonate bins (with lids) containing filtered seawater (4 l) were secured into an illuminated aquarium table (Prime HD, Aqua illumination, Bethlehem, PA, USA) (Photosynthetically Active Radiation = ~350–600 µmol quanta m−2 s−1). Air bubblers with sterilized Fluorinated Ethylene Propylene (FEP) tubing (890 Tubing, Nalgene, Thermo Scientific, Waltham, MA, USA) were used to inject air into each bin. Surface seawater was circulated through the aquarium table to maintain reef seawater temperature (29.5 °C). Six colonies/fragments of one species were randomly placed into 6 bins. The other 3 bins were reserved for control incubations containing filtered seawater only. A sensor (8 K HOBO/PAR loggers; Onset, Wareham, MA) monitored temperature and light conditions (data not shown). At the end of each 8 h experiment, colonies/fragments were wrapped in combusted aluminum foil and flash frozen in a charged dry shipper. The water in all incubations was re-filtered (as outlined above) and 2 l of each filtrate were acidified and subjected to SPE as described above. SPE cartridges were wrapped in combusted aluminum foil, placed in Whirl-Pak (Nasco, Madison, WI, USA) bags, and frozen at –20 °C.Metabolomics analyses and data processingAt the Woods Hole Oceanographic Institution (WHOI), metabolites were eluted from the thawed cartridges into combusted, borosilicate test tubes using 100% methanol (Optima grade) within 3 months of collection. The eluents were transferred into combusted amber 8 ml vials and nearly dried using a vacuum centrifuge. Samples were reconstituted in 200 µL of 95:5 (v/v) Milli-Q (MQ, Millipore Sigma, Burlington, MA, USA) water: acetonitrile with a deuterated standard mix added as an internal control (Table S2), vortexed, and prepared for targeted and untargeted metabolomics analyses in both positive and negative ion modes as described previously [16]. Samples prepared for untargeted analyses were further diluted (1:200) with the reconstitution solvent. A pooled sample (technical replicate) was made by combining aliquots from all samples and was injected repeatedly to assess instrument drift over the course of the run and for downstream sample processing. Samples prepared for targeted metabolomics were analyzed using an ultra-high performance liquid chromatography system (UHPLC; Accela Open Autosampler and Accela 1250 Pump, Thermo Scientific, Waltham, MA, USA) coupled to a heated electrospray ionization source (H-ESI) and a triple stage quadrupole mass spectrometer (TSQ Vantage, Thermo Scientific), operated in selected reaction monitoring (SRM) mode. Samples prepared for untargeted metabolomics were analyzed with a UHPLC system (Vanquish UHPLC, Thermo Scientific) coupled to an ultra-high resolution mass spectrometer (Orbitrap Fusion Lumos, Thermo Scientific). MS/MS spectra were collected in a data-dependent manner using higher energy collisional dissociation (HCD) with a normalized collision energy of 35% (detailed methods provided in [16]). A Waters Acquity HSS T3 column (2.1 × 100 mm, 1.8 μm) equipped with a Vanguard pre-column was used for chromatographic separation at 40 °C for targeted and untargeted analyses. Sample order was randomized and the pooled sample was analyzed after every six samples.For targeted metabolomics analysis, tandem MS/MS data files were converted into .mzML files using msconvert and processed with El-MAVEN [49]. Calibration curves for each compound (8 points each) were constructed based on the integrated peak areas using El-MAVEN. The concentrations of metabolites in the original samples were determined by dividing each concentration by the volume of the filtrate that passed through each PPL column. Finally, metabolite concentrations above the limits of detection and quantification were corrected for extraction efficiency using in-house values determined using standard protocols [50]. Statistical analyses of targeted metabolite concentrations were conducted using Welch’s independent t-tests and ANOVAs or Wilcoxon rank sum tests if data were not normally distributed (additional details in Supplementary Methods). We determined the mass of each colony and conducted Pearson correlations to investigate if colony size significantly correlated with concentrations of targeted metabolites, but no correlations were found.For the untargeted metabolomics analyses, raw files containing MS1 and MS/MS data were converted into .mzML files using msconvert and processed using XCMS [51]. Ion modes were analyzed separately. Before processing with XCMS, the R package AutoTuner [52] was used to find XCMS processing parameters appropriate for the data. In XCMS, the CentWave algorithm picked peaks using a gaussian fit. The specific parameters for peak picking for both ion modes were: noise = 10,000, peak-width = 3–15, ppm = 15, prefilter = c(2,168.600), integrate = 2, mzdiff = –0.005, snthresh = 10. Obiwarp was used to adjust retention times and this step was followed by correspondence analysis. For statistical analyses, including permutational PERMANOVA adonis tests and non-metric multidimensional scaling analysis (NMDS), MS1 features (defined as unique pairings of mass-to-charge (m/z) values with retention times) in both ion modes were culled following XCMS if they: (1) had >1 average fold change in the MQ blanks compared to the other samples, (2) occurred in less than 20% of samples (excluding pooled controls), and/or (3) were invariant (relative standard deviation of More

  • in

    Global distribution of soil fauna functional groups and their estimated litter consumption across biomes

    Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Fierer, N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro.2017.87 (2017).Article 
    PubMed 

    Google Scholar 
    Frouz, J. Effects of soil macro- and mesofauna on litter decomposition and soil organic matter stabilization. Geoderma 332, 161–172 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Hicks Pries, C. E., Castanha, C., Porras, R., Phillips, C. & Torn, M. S. Response to comment on “The whole-soil carbon flux in response to warming”. Science 359, 1420–1423 (2018).Article 

    Google Scholar 
    Lavelle, P. et al. Soil function in a changing world: The role of invertebrate ecosystem engineers. Eur. J. Soil Biol. 33, 159–193 (1997).CAS 

    Google Scholar 
    Frouz, J., Špaldoňová, A., Fričová, K. & Bartuška, M. The effect of earthworms (Lumbricus rubellus) and simulated tillage on soil organic carbon in a long-term microcosm experiment. Soil. Biol. Biochem. 78, 58–64 (2014).CAS 
    Article 

    Google Scholar 
    Lavelle, P., Blanchart, E., Martin, A., Martin, S. & Schaefer, R. A hierarchical model for decomposition in terrestrial ecosystems: Application to soils of the humid tropics. Assoc. Trop. Biol. 25, 130–150 (2016).
    Google Scholar 
    Lavelle, P. et al. Earthworms as a resource in tropical agroecosystems. Nat. Res. 34, 26–41 (1998).
    Google Scholar 
    Lavelle, P. Diversity of soil fauna and ecosystem function. Biol. Int. J. 33, 3–16 (1996).
    Google Scholar 
    Ruiz, N., Lavelle, P. & Jiménez, J. Soil macrofauna field manual. Recherche 113 (2008).Xiong, W. et al. Soil protist communities form a dynamic hub in the soil microbiome. ISME J. 12, 634–638 (2018).PubMed 
    Article 

    Google Scholar 
    Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A. & Cleveland, C. C. Global patterns in belowground communities. Ecol. Lett. 12, 1238–1249 (2009).PubMed 
    Article 

    Google Scholar 
    Nielsen, U. N. et al. Global-scale patterns of assemblage structure of soil nematodes in relation to climate and ecosystem properties. Glob. Ecol. Biogeogr. 23, 968–978 (2014).Article 

    Google Scholar 
    Špaldoňová, A. & Frouz, J. The role of Armadillidium vulgare (Isopoda: Oniscidea) in litter decomposition and soil organic matter stabilization. Appl. Soil. Ecol. https://doi.org/10.1016/j.apsoil.2014.04.012 (2014).Article 

    Google Scholar 
    McCay, T. S., Cardelus, C. L. & Neatrour, M. A. Rate of litter decay and litter macroinvertebrates in limed and unlimed forests of the Adirondack Mountains, USA. For. Ecol. Manag. 304, 254–260 (2013).Article 

    Google Scholar 
    Slade, E. M. & Riutta, T. Interacting effects of leaf litter species and macrofauna on decomposition in different litter environments. Basic Appl. Ecol. 13, 423–431 (2012).Article 

    Google Scholar 
    Joly, F.-X., Coq, S., Coulis, M., Nahmani, J. & Hättenschwiler, S. Litter conversion into detritivore faeces reshuffles the quality control over C and N dynamics during decomposition. Funct. Ecol. https://doi.org/10.1111/1365-2435.13178 (2018).Article 

    Google Scholar 
    Hättenschwiler, S. Isopod effects on decomposition of litter produced under elevated CO2, N deposition and different soil types Isopod effects on decomposition of litter produced under elevated CO2, N deposition and different soil types. Glob. Change Biol. https://doi.org/10.1046/j.1365-2486.2001.00402.x (2015).Article 

    Google Scholar 
    Wall, D. H. et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob. Change Biol. 14, 2661–2677 (2008).ADS 
    Article 

    Google Scholar 
    Brussaard, L., Pulleman, M. M., Ouédraogo, É., Mando, A. & Six, J. Soil fauna and soil function in the fabric of the food web. Pedobiologia (Jena) 50, 447–462 (2007).Article 

    Google Scholar 
    Frouz, J., Elhottová, D., Kuráž, V. & Šourková, M. Effects of soil macrofauna on other soil biota and soil formation in reclaimed and unreclaimed post mining sites: Results of a field microcosm experiment. Appl. Soil Ecol. 33, 308–320 (2006).Article 

    Google Scholar 
    García-Palacios, P., Maestre, F. T., Kattge, J. & Wall, D. H. Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes. Ecol. Lett. 16, 1045–1053 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Melguizo-Ruiz, N. et al. Field exclusion of large soil predators impacts lower trophic levels and decreases leaf-litter decomposition in dry forests. J. Anim. Ecol. 89, 334–346 (2020).PubMed 
    Article 

    Google Scholar 
    Lavelle, P. et al. Soil macroinvertebrate communities: A world-wide assessment. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13492 (2022).Article 

    Google Scholar 
    Coq, S. et al. Faeces traits as unifying predictors of detritivore effects on organic matter turnover. Geoderma 422, 115940 (2022).ADS 
    CAS 
    Article 

    Google Scholar 
    Lavelle, P. et al. Soil aggregation, ecosystem engineers and the C cycle. Act Oecol. 105, 103561 (2020).Article 

    Google Scholar 
    Filser, J. et al. Soil fauna: Key to new carbon models. Soil 2, 565–582 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Joly, F. X. et al. Detritivore conversion of litter into faeces accelerates organic matter turnover. Commun. Biol. 3, 1–9 (2020).MathSciNet 
    Article 

    Google Scholar 
    Frouz, J., Roubíčková, A., Heděnec, P. & Tajovský, K. Do soil fauna really hasten litter decomposition? A meta-analysis of enclosure studies. Eur. J. Soil Biol. 68, 18 (2015).CAS 
    Article 

    Google Scholar 
    Lavelle, P., Blanchart, E., Martin, A., Martin, S. & Spain, A. A hierarchical model for decomposition in terrestrial ecosystems: Application to soils of the humid tropics. Biotropica 25, 130–150 (1993).Article 

    Google Scholar 
    Crowther, T. W. & A’Bear, A. D. Impacts of grazing soil fauna on decomposer fungi are species-specific and density-dependent. Fungal Ecol. 5, 277–281 (2012).Article 

    Google Scholar 
    Decaëns, T. Macroecological patterns in soil communities. Glob. Ecol. Biogeogr. 19, 287–302 (2010).Article 

    Google Scholar 
    Tordoff, G. M., Boddy, L. & Jones, T. H. Species-specific impacts of collembola grazing on fungal foraging ecology. Soil. Biol. Biochem. 40, 434–442 (2008).CAS 
    Article 

    Google Scholar 
    Meysman, F. J. R., Middelburg, J. J. & Heip, C. H. R. Bioturbation: A fresh look at Darwin’s last idea. Trends Ecol. Evol. 21, 688–695 (2006).PubMed 
    Article 

    Google Scholar 
    Frouz, J. et al. Soil food web changes during spontaneous succession at post mining sites: A possible ecosystem engineering effect on food web organization? PLoS ONE 8, e79694 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Frouz, J., Moradi, J., Püschel, D. & Rydlová, J. Earthworms affect growth and competition between ectomycorrhizal and arbuscular mycorrhizal plants. Ecosphere 10, e02736 (2019).Article 

    Google Scholar 
    Marichal, R. et al. Soil macroinvertebrate communities and ecosystem services in deforested landscapes of Amazonia. Appl. Soil. Ecol. 83, 177–185 (2014).Article 

    Google Scholar 
    Prescott, C. E. & Vesterdal, L. Forest ecology and management decomposition and transformations along the continuum from litter to soil organic matter in forest soils. For. Ecol. Manag. 498, 119522 (2021).Article 

    Google Scholar 
    Kampichler, C. & Bruckner, A. The role of microarthropods in terrestrial decomposition: A meta-analysis of 40 years of litterbag studies. Biol. Rev. Camb. Philos. Soc. 84, 375–389 (2009).PubMed 
    Article 

    Google Scholar 
    Brennan, K. E. C., Christie, F. J. & York, A. Global climate change and litter decomposition: More frequent fire slows decomposition and increases the functional importance of invertebrates. Glob. Change. Biol. 15, 2958–2971 (2009).ADS 
    Article 

    Google Scholar 
    Birkhofer, K. et al. General relationships between abiotic soil properties and soil biota across spatial scales and different land-use types. PLoS ONE 7, e43292 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wu, T., Ayres, E., Bardgett, R. D., Wall, D. H. & Garey, J. R. Molecular study of worldwide distribution and diversity of soil animals. PNAS 108, 17720–17725 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    James, S. W. et al. Comment on Global distribution of earthworm diversity. Science 371, 4629 (2021).Article 

    Google Scholar 
    Cesarz, S. et al. Tree species diversity versus tree species identity: Driving forces in structuring forest food webs as indicated by soil nematodes. Soil. Biol. Biochem. 62, 36–45 (2013).CAS 
    Article 

    Google Scholar 
    Eppinga, M. B., Kaproth, M. A., Collins, A. R. & Molofsky, J. Litter feedbacks, evolutionary change and exotic plant invasion. J. Ecol. 99, 503–514 (2011).
    Google Scholar 
    Harrison, K. A., Bol, R. & Bardgett, R. D. Do plant species with different growth strategies vary in their ability to compete with soil microbes for chemical forms of nitrogen? Soil. Biol. Biochem. 40, 228–237 (2008).CAS 
    Article 

    Google Scholar 
    Wardle, D. A., Yeates, G. W., Barker, G. M. & Bonner, K. I. The influence of plant litter diversity on decomposer abundance and diversity. Soil Biol. Biochem. 38, 1052–1062 (2006).CAS 
    Article 

    Google Scholar 
    Zhang, D., Hui, D., Luo, Y. & Zhou, G. Rates of litter decomposition in terrestrial ecosystems: Global patterns and controlling factors. J. Plant Ecol. 1, 85–93 (2008).Article 

    Google Scholar 
    Preston, C. M. & Trofymow, J. A. Variability in litter quality and its relationship to litter decay in Canadian forests. Botany 78, 1269–1287 (2000).Article 

    Google Scholar 
    Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. PNAS 115, 6506–6511 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Phillips, H. R. P. et al. Global distribution of earthworm diversity. Science 366, 480–485 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Andersen, D. C. Below-ground herbivory in natural communities: A review emphasizing fossorial animals. Q. Rev. Biol. 62, 261–286 (1987).Article 

    Google Scholar 
    Cepáková, S. & Frouz, J. Changes in chemical composition of litter during decomposition: A review of published 13C NMR spectra. Plant Nutr. Soil Sci. 15, 805–815 (2015).
    Google Scholar 
    Pietsch, K. A. et al. Global relationship of wood and leaf litter decomposability: The role of functional traits within and across plant organs. Glob. Ecol. Biogeogr. 23, 1046–1057 (2014).Article 

    Google Scholar 
    Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).PubMed 
    Article 

    Google Scholar 
    Ponge, J.-F. Plant–soil feedbacks mediated by humus forms: A review. Soil. Biol. Biochem. 57, 1048–1060 (2013).CAS 
    Article 

    Google Scholar 
    Salmon, S., Mantel, J., Frizzera, L. & Zanella, A. Changes in humus forms and soil animal communities in two developmental phases of Norway spruce on an acidic substrate. For. Ecol. Manag. 237, 47–56 (2006).Article 

    Google Scholar 
    Desie, E. et al. Positive feedback loop between earthworms, humus form and soil pH reinforces earthworm abundance in European forests. Funct. Ecol. 34, 2598–2610 (2020).Article 

    Google Scholar 
    Samson, F. B. & Knopf, F. L. (eds) Organisms as Ecosystem Engineers BT—Ecosystem Management: Selected Readings 130–147 (Springer, 1996).
    Google Scholar 
    Araujo, P. I., Yahdjian, L. & Austin, A. T. Do soil organisms affect aboveground litter decomposition in the semiarid Patagonian steppe, Argentina? Oecologia 168, 221–230 (2012).ADS 
    PubMed 
    Article 

    Google Scholar 
    Frouz, J. et al. Soil biota in post-mining sites along a climatic gradient in the USA: Simple communities in shortgrass prairie recover faster than complex communities in tallgrass prairie and forest. Soil. Biol. Biochem. 67, 212–225 (2013).CAS 
    Article 

    Google Scholar 
    Hattenschwiler, S., Tiunov, A. V. & Scheu, S. Biodiversity and litter decomposition interrestrial ecosystems. Annu. Rev. Ecol. Evol. Syst. 36, 191–218 (2005).Article 

    Google Scholar 
    Deckmyn, G. et al. KEYLINK: Towards a more integrative soil representation for inclusion in ecosystem scale models I. Review and model concept. PeerJ 8, 1–69 (2020).Article 

    Google Scholar 
    Héry, M. et al. Effect of earthworms on the community structure of active methanotrophic bacteria in a landfill cover soil. SME J. 2, 92–104 (2008).
    Google Scholar 
    Roubickova, A., Mudrak, O. & Frouz, J. Effect of earthworm on growth of late succession plant species in postmining sites under laboratory and field conditions. Biol. Fert. Soils 45, 769–774 (2009).Article 

    Google Scholar 
    Bodine, M. C. & Ueckert, D. N. Effect litter in west of desert termites on herbage and in a shortgrass Texas. J. Range. Manag. 28, 353–358 (1975).Article 

    Google Scholar 
    Cebrian, J. Patterns in the fate of production in plant communities. Am. Nat. 154, 449–468 (1999).PubMed 
    Article 

    Google Scholar 
    Petersen, H. & Luxton, M. A comparative analysis of soil fauna populations and their role in decomposition processes. Oikos 39, 288 (1982).Article 

    Google Scholar 
    Gongalsky, K. B., Persson, T. & Pokarzhevskii, A. D. Effects of soil temperature and moisture on the feeding activity of soil animals as determined by the bait-lamina test. Appl. Soil Ecol. 39, 84–90 (2008).Article 

    Google Scholar 
    Simpson, J. E., Slade, E., Riutta, T. & Taylor, M. E. Factors affecting soil fauna feeding activity in a fragmented lowland temperate deciduous woodland. PLoS ONE 7, 0029616 (2012).ADS 
    Article 

    Google Scholar 
    Clarke, A. Is there a universal temperature dependence of metabolism? Funct. Ecol. 18, 252–256 (2004).Article 

    Google Scholar 
    Coq, S. & Ibanez, S. Soil fauna contribution to winter decomposition in subalpine grasslands. Soil Org. https://doi.org/10.25674/so91iss3pp107 (2019).Article 

    Google Scholar 
    Frouz, J., Špaldoňová, A., Lhotáková, Z. & Cajthaml, T. Major mechanisms contributing to the macrofauna-mediated slow down of litter decomposition. Soil. Biol. Biochem. 91, 23–31 (2015).CAS 
    Article 

    Google Scholar 
    Frouz, J., Šustr, V. & Kalčík, J. Energetic budget of three species of bibionid larvae. In Contributions to Soil Zoology in Central Europe I. ISB AS CR, České Budějovice, 15–18 (2005).Frouz, J., Jedlička, P., Šimáčková, H. & Lhotáková, Z. The life cycle, population dynamics, and contribution to litter decomposition of Penthetria holosericea (Diptera: Bibionidae) in an alder forest. Eur. J. Soil Biol. 71, 21–27 (2015).Article 

    Google Scholar 
    Brovkin, V. et al. Plant-driven variation in decomposition rates improves projections of global litter stock distribution. Biogeosciences 9, 565–576 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    Buis, G. M. et al. Controls of aboveground net primary production in mesic savanna grasslands: An inter-hemispheric comparison. Ecosystems 12, 982–995 (2009).CAS 
    Article 

    Google Scholar 
    O’Neill, D. W. & Abson, D. J. To settle or protect? A global analysis of net primary production in parks and urban areas. Ecol. Econ. 69, 319–327 (2009).Article 

    Google Scholar 
    Pan, S. et al. Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century. J. Geogr. Sci. 25, 1027–1044 (2015).Article 

    Google Scholar 
    Yanai, R. D. et al. Litterfall and litter chemistry change over time in an old-growth temperate forest, northeastern China. For. Ecol. Manag. 43, 279–287 (1999).
    Google Scholar 
    Shchelchkova, M., Davydov, S., Fyodorov-Davydov, D., Davydova, A. & Boeskorov, G. The characteristics of a relic steppe of Northeast Asia: Refuges of the Pleistocene Mammoth steppe (an example from the Lower Kolyma area). IOP Conf. Ser. Earth Environ. Sci. 438, 012025 (2020).Article 

    Google Scholar 
    Ayuke, F. O. et al. Soil fertility management: Impacts on soil macrofauna, soil aggregation and soil organic matter allocation. Appl. Soil Ecol. 48, 53–62 (2011).Article 

    Google Scholar 
    Blanchart, E. et al. Effect of direct seeding mulch-based systems on soil carbon storage and macrofauna in Central Brazil. Agric. Conspec. Sci. 72, 81–87 (2007).
    Google Scholar 
    Korboulewsky, N., Perez, G. & Chauvat, M. How tree diversity affects soil fauna diversity: A review. Soil Biol. Biochem. 94, 94–106 (2016).CAS 
    Article 

    Google Scholar 
    Frouz, J., Pizl, V., Cienciala, E. & Kalcik, J. Carbon storage in post-mining forest soil, the role of tree biomass and soil bioturbation. Biogeochemistry 94, 111–121 (2009).CAS 
    Article 

    Google Scholar 
    Milton, Y. & Kaspari, M. Bottom-up and top-down regulation of decomposition in a tropical forest. Oecologia 153, 163–172 (2007).ADS 
    PubMed 
    Article 

    Google Scholar 
    Öpik, M., Moora, M., Liira, J. & Zobel, M. Composition of root-colonizing arbuscular mycorrhizal fungal communities in different ecosystems around the globe. J. Ecol. 94, 778–790 (2006).Article 

    Google Scholar 
    Portela, M. B. et al. Do ecological corridors increase the abundance of soil fauna? Écoscience 27, 45–57 (2020).Article 

    Google Scholar 
    Prieto, I., Almagro, M., Bastida, F. & Querejeta, J. I. Altered leaf litter quality exacerbates the negative impact of climate change on decomposition. J. Ecol. 107, 2364–2382 (2019).CAS 
    Article 

    Google Scholar 
    Van der Putten, W. H. et al. Plant-soil feedbacks: The past, the present and future challenges. J. Ecol. 101, 265–276 (2013).Article 

    Google Scholar 
    Artz, R. et al. European atlas of soil. Biodiversity. https://doi.org/10.13140/RG.2.1.3178.2880 (2010).Article 

    Google Scholar 
    Orgiazzi, A. et al. Global Soil Biodiversity Atlas (European Soil Data Centre, 2016).
    Google Scholar 
    Peng, Y. et al. Litter quality, mycorrhizal association, and soil properties regulate effects of tree species on the soil fauna community. Geoderma 407, 115570 (2022).ADS 
    CAS 
    Article 

    Google Scholar 
    Bardgett, R. D. The Biology of Soil: A Community and Ecosystem Approach 255 (Oxford University Press, 2005).Book 

    Google Scholar 
    Jackson, R. B. et al. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411 (1996).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Jackson, R. B., Mooney, H. A. & Schulze, E.-D. A global budget for fine root biomass, surface area, and nutrient contents. PNAS 94, 7362–7366 (1997).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sanchez, G. PLS Path Modeling with R, 235 (2013).Holland, E. A. et al. A global database of litterfall mass and litter pool carbon and nutrients. 10.3334/ORNLDAAC/1244 (2014).Palpurina, S. et al. The type of nutrient limitation affects the plant species richness–productivity relationship: Evidence from dry grasslands across Eurasia. J. Ecol. 107, 1038–1050 (2019).CAS 
    Article 

    Google Scholar 
    Green, C. & Byrne, K. A. Biomass: Impact on carbon cycle and greenhouse gas emissions. In Encyclopedia of Energy (ed. Cleveland, C. J.) 223–236 (Elsevier, 2004).Chapter 

    Google Scholar 
    Liang, W. et al. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agric. For. Meteorol. 204, 22–36 (2015).ADS 
    Article 

    Google Scholar 
    Ise, T., Litton, C. M., Giardina, C. P. & Ito, A. Comparison of modeling approaches for carbon partitioning: Impact on estimates of global net primary production and equilibrium biomass of woody vegetation from MODIS GPP. J. Geo. Res. Biogeosci. 115, 1–11 (2010).
    Google Scholar 
    Ni, J. Net primary production, carbon storage and climate change in Chinese biomes. Nord. J. Bot. 20, 415–426 (2000).Article 

    Google Scholar 
    Jandl, R. et al. How strongly can forest management influence soil carbon sequestration? Geoderma 137, 253–268 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    Reeves, M. C., Moreno, A. L., Bagne, K. E. & Running, S. W. Estimating climate change effects on net primary production of rangelands in the United States. Clim. Change 126, 429–442 (2014).ADS 
    Article 

    Google Scholar 
    Cappai, C. et al. Small-scale spatial variation of soil organic matter pools generated by cork oak trees in Mediterranean agro-silvo-pastoral systems. Geoderma 304, 59–67 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Clark, D. A. et al. Net primary production in tropical forests: An evaluation and synthesis of existing field data. Ecol. Appl. 11, 371–384 (2001).Article 

    Google Scholar 
    Yanai, R. D., Arthur, M. A., Acker, M., Levine, C. R. & Park, B. B. Variation in mass and nutrient concentration of leaf litter across years and sites in a northern hardwood forest. Can. J. For. Res. 42, 1597–1610 (2012).CAS 
    Article 

    Google Scholar  More

  • in

    Enhanced dust emission following large wildfires due to vegetation disturbance

    Bowman, D. M. J. S. et al. Fire in the Earth system. Science 324, 481–484 (2009).Article 

    Google Scholar 
    Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058 (2017).Article 

    Google Scholar 
    Hamilton, D. S. et al. Earth, wind, fire, and pollution: aerosol nutrient sources and impacts on ocean biogeochemistry. Ann. Rev. Mar. Sci. 14, 303–330 (2022).Article 

    Google Scholar 
    Barkley, A. E. et al. African biomass burning is a substantial source of phosphorus deposition to the Amazon, tropical Atlantic Ocean, and Southern Ocean. Proc. Natl Acad. Sci. USA 116, 16216–16221 (2019).Article 

    Google Scholar 
    Schlosser, J. S. et al. Analysis of aerosol composition data for western United States wildfires between 2005 and 2015: dust emissions, chloride depletion, and most enhanced aerosol constituents. J. Geophys. Res. Atmos. 122, 8951–8966 (2017).Article 

    Google Scholar 
    Wagner, R., Schepanski, K. & Klose, M. The dust emission potential of agricultural-like fires—theoretical estimates from two conceptually different dust emission parameterizations. J. Geophys. Res. Atmos. 126, e2020JD034355 (2017).
    Google Scholar 
    Ichoku, C. et al. Biomass burning, land-cover change, and the hydrological cycle in northern sub-Saharan Africa. Environ. Res. Lett. 11, 095005 (2016).Article 

    Google Scholar 
    Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).Article 

    Google Scholar 
    Duniway, M. C. et al. Wind erosion and dust from US drylands: a review of causes, consequences, and solutions in a changing world. Ecosphere 10, e02650 (2019).Article 

    Google Scholar 
    Okin, G. S., Gillette, D. A. & Herrick, J. E. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. J. Arid. Environ. 65, 253–275 (2006).Article 

    Google Scholar 
    Raupach, M. R. Drag and drag partition on rough surfaces. Boundary Layer Meteorol. 60, 375–395 (1992).Article 

    Google Scholar 
    Webb, N. P. et al. Vegetation canopy gap size and height: critical indicators for wind erosion monitoring and management. Rangel. Ecol. Manag. 76, 78–83 (2021).Article 

    Google Scholar 
    Ellis, T. M., Bowman, D. M. J. S., Jain, P., Flannigan, M. D. & Williamson, G. J. Global increase in wildfire risk due to climate-driven declines in fuel moisture. Glob. Change Biol. 28, 1544–1559 (2022).Article 

    Google Scholar 
    Ravi, S. et al. Aeolian processes and the biosphere. Rev. Geophys. 49, RG3001 (2011).Article 

    Google Scholar 
    Wagenbrenner, N. S., Germino, M. J., Lamb, B. K., Robichaud, P. R. & Foltz, R. B. Wind erosion from a sagebrush steppe burned by wildfire: Measurements of PM10 and total horizontal sediment flux. Aeolian Res. 10, 25–36 (2013).Article 

    Google Scholar 
    Wagenbrenner, N. S. A large source of dust missing in Particulate Matter emission inventories? Wind erosion of post-fire landscapes. Elementa 5, 2 (2017).
    Google Scholar 
    Jeanneau, A. C., Ostendorf, B. & Herrmann, T. Relative spatial differences in sediment transport in fire-affected agricultural landscapes: a field study. Aeolian Res. 39, 13–22 (2019).Article 

    Google Scholar 
    Deb, P. et al. Causes of the widespread 2019–2020 Australian bushfire season. Earths Future 8, e2020EF001671 (2020).Article 

    Google Scholar 
    Nogrady, B. & Nicky, B. The climate link to Australia’s fires. Nature 577, 610–612 (2020).Yu, Y. & Ginoux, P. Assessing the contribution of the ENSO and MJO to Australian dust activity based on satellite- and ground-based observations. Atmos. Chem. Phys. 21, 8511–8530 (2021).Article 

    Google Scholar 
    Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. & Zhao, M. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev. Geophys. 50, RG3005 (2012).Article 

    Google Scholar 
    Yu, Y., Kalashnikova, O. V., Garay, M. J., Lee, H. & Notaro, M. Identification and characterization of dust source regions across North Africa and the Middle East using MISR satellite observations. Geophys. Res. Lett. 45, 6690–6701 (2018).Article 

    Google Scholar 
    Brianne, P., Rebecca, H. & David, L. The fate of biological soil crusts after fire: a meta-analysis. Glob. Ecol. Conserv. 24, e01380 (2020).Article 

    Google Scholar 
    Rodriguez-Caballero, E. et al. Global cycling and climate effects of aeolian dust controlled by biological soil crusts. Nat. Geosci. 15, 458–463 (2022).Article 

    Google Scholar 
    Goudie, A. S. & Middleton, N. J. Desert Dust in the Global System (Springer, 2006).Ginoux, P. Atmospheric chemistry: warming or cooling dust? Nat. Geosci. 10, 246–247 (2017).Article 

    Google Scholar 
    DeMott, P. J. et al. Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proc. Natl Acad. Sci. USA 107, 11217–11222 (2010).Article 

    Google Scholar 
    Yu, H. et al. The fertilizing role of African dust in the Amazon rainforest: a first multiyear assessment based on data from cloud–aerosol lidar and infrared Pathfinder satellite observations. Geophys. Res. Lett. 42, 1984–1991 (2015).Article 

    Google Scholar 
    Tang, W. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021).Article 

    Google Scholar 
    Sarangi, C. et al. Dust dominates high-altitude snow darkening and melt over high-mountain Asia. Nat. Clim. Change 10, 1045–1051 (2020).Article 

    Google Scholar 
    Cook, B. I. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earths Future 8, e2019EF001461 (2020).Article 

    Google Scholar 
    Zheng, B. et al. Increasing forest fire emissions despite the decline in global burned area. Sci. Adv. 7, eabh2646 (2021).Article 

    Google Scholar 
    Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).Article 

    Google Scholar 
    Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 1–17 (2021).Article 

    Google Scholar 
    Yu, Y. et al. Machine learning–based observation-constrained projections reveal elevated global socioeconomic risks from wildfire. Nat. Commun. 13, 1250 (2022).Article 

    Google Scholar 
    Pu, B. & Ginoux, P. How reliable are CMIP5 models in simulating dust optical depth? Atmos. Chem. Phys. 18, 12491–12510 (2018).Article 

    Google Scholar 
    Pu, B. & Ginoux, P. Climatic factors contributing to long-term variations in surface fine dust concentration in the United States. Atmos. Chem. Phys. 18, 4201–4215 (2018).Article 

    Google Scholar 
    Bodí, M. B. et al. Wildland fire ash: production, composition and eco-hydro-geomorphic effects. Earth Sci. Rev. 130, 103–127 (2014).Article 

    Google Scholar 
    NCAR Command Language v.6.6.2 (NCAR, 2019); https://doi.org/10.5065/D6WD3XH5Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).Article 

    Google Scholar 
    Ramo, R. et al. African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data. Proc. Natl Acad. Sci. USA 118, 1–7 (2021).Article 

    Google Scholar 
    Diner, D. J. et al. Multi-angle imaging spectroradiometer (MISR) instrument description and experiment overview. IEEE Trans. Geosci. Remote Sens. 36, 1072–1087 (1998).Article 

    Google Scholar 
    Pu, B. et al. Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0). Atmos. Chem. Phys. 20, 55–81 (2020).Article 

    Google Scholar 
    Sayer, A. M., Hsu, N. C., Bettenhausen, C. & Jeong, M. J. Validation and uncertainty estimates for MODIS collection 6 ‘Deep Blue’ aerosol data. J. Geophys. Res. Atmos. 118, 7864–7872 (2013).Article 

    Google Scholar 
    Hsu, N. C. et al. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. J. Geophys. Res. Atmos. 118, 9296–9315 (2013).Article 

    Google Scholar 
    Ginoux, P., Garbuzov, D. & Hsu, N. C. Identification of anthropogenic and natural dust sources using moderate resolution imaging spectroradiometer (MODIS) Deep Blue level 2 data. J. Geophys. Res. 115, D05204 (2010).Article 

    Google Scholar 
    Eck, T. F. et al. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. Atmos. 104, 31333–31349 (1999).Article 

    Google Scholar 
    Anderson, T. L. et al. Testing the MODIS satellite retrieval of aerosol fine-mode fraction. J. Geophys. Res. 110, 1–16 (2005).Article 

    Google Scholar 
    Baddock, M. C., Bullard, J. E. & Bryant, R. G. Dust source identification using MODIS: a comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sens. Environ. 113, 1511–1528 (2009).Article 

    Google Scholar 
    Baddock, M. C., Ginoux, P., Bullard, J. E. & Gill, T. E. Do MODIS-defined dust sources have a geomorphological signature? Geophys. Res. Lett. 43, 2606–2613 (2016).Article 

    Google Scholar 
    Pu, B. & Ginoux, P. Projection of American dustiness in the late 21st century due to climate change. Sci. Rep. 7, 5553 (2017).Article 

    Google Scholar 
    Pu, B., Ginoux, P., Kapnick, S. B. & Yang, X. Seasonal prediction potential for springtime dustiness in the United States. Geophys. Res. Lett. 46, 9163–9173 (2019).Article 

    Google Scholar 
    Garay, M. J. et al. Introducing the 4.4 km spatial resolution multi-angle imaging spectroradiometer (MISR) aerosol product. Atmos. Meas. Tech. 13, 593–628 (2020).Article 

    Google Scholar 
    Kalashnikova, O. V., Kahn, R., Sokolik, I. N. & Li, W.-H. Ability of multiangle remote sensing observations to identify and distinguish mineral dust types: optical models and retrievals of optically thick plumes. J. Geophys. Res. 110, D18S14 (2005).Article 

    Google Scholar 
    Yu, Y. et al. Assessing temporal and spatial variations in atmospheric dust over Saudi Arabia through satellite, radiometric, and station data. J. Geophys. Res. Atmos. 118, 13253–13264 (2013).Article 

    Google Scholar 
    Yu, Y., Notaro, M., Kalashnikova, O. V. & Garay, M. J. Climatology of summer Shamal wind in the Middle East. J. Geophys. Res. Atmos. 121, 289–305 (2016).Article 

    Google Scholar 
    Yu, Y. et al. Disproving the Bodélé depression as the primary source of dust fertilizing the Amazon rainforest. Geophys. Res. Lett. 47, e2020GL088020 (2020).Article 

    Google Scholar 
    Giles, D. M. et al. Advancements in the Aerosol Robotic Network (AERONET) version 3 database—automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmos. Meas. Tech. 12, 169–209 (2019).Article 

    Google Scholar 
    O’Neill, N. T., Eck, T. F., Smirnov, A., Holben, B. N. & Thulasiraman, S. Spectral discrimination of coarse and fine mode optical depth. J. Geophys. Res. Atmos. 108, 1–15 (2003).
    Google Scholar 
    Winker, D. M. et al. Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Ocean. Technol. 26, 2310–2323 (2009).Article 

    Google Scholar 
    Esselborn, M. et al. Spatial distribution and optical properties of Saharan dust observed by airborne high spectral resolution lidar during SAMUM 2006. Tellus B 61, 131–143 (2009).Article 

    Google Scholar 
    Kim, M. H. et al. The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. Atmos. Meas. Tech. 11, 6107–6135 (2018).Article 

    Google Scholar 
    Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS Vegetation Index User’s Guide (Collection 6) (Univ. Arizona, 2015).Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).Article 

    Google Scholar 
    Saleska, S. R. et al. Dry-season greening of Amazon forests. Nature 531, E4–E5 (2016).Article 

    Google Scholar 
    Remer, L. A., Kaufman, Y. J., Holben, B. N., Thompson, A. M. & McNamara, D. Biomass burning aerosol size distribution and modeled optical properties. J. Geophys. Res. Atmos. 103, 31879–31891 (1998).Article 

    Google Scholar 
    Tegen, I. & Lacis, A. A. Modeling of particle size distribution and its influence on the radiative properties of mineral dust aerosol. J. Geophys. Res. Atmos. 101, 19237–19244 (1996).Article 

    Google Scholar 
    Friedl, M. A. & Sulla-Menashe, D. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product 6 (USGS, 2018).Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. Hierarchical mapping of annual global land cover 2001 to present: the MODIS collection 6 land cover product. Remote Sens. Environ. 222, 183–194 (2019).Article 

    Google Scholar 
    Dorigo, W. et al. ESA CCI Soil Moisture for improved Earth system understanding: state-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).Article 

    Google Scholar 
    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 

    Google Scholar 
    Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A. & Dorigo, W. Homogenization of structural breaks in the global ESA CCI Soil Moisture multisatellite climate data record. IEEE Trans. Geosci. Remote Sens. 59, 2845–2862 (2021).Article 

    Google Scholar 
    Minola, L. et al. Near-surface mean and gust wind speeds in ERA5 across Sweden: towards an improved gust parametrization. Clim. Dyn. 55, 887–907 (2020).Article 

    Google Scholar 
    Molina, M. O., Gutiérrez, C. & Sánchez, E. Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset. Int. J. Climatol. 41, 4864–4878 (2021).Article 

    Google Scholar 
    Klose, M. et al. Mineral dust cycle in the Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) version 2.0. Geosci. Model Dev. 14, 6403–6444 (2021).Article 

    Google Scholar 
    Mondal, A., Kundu, S. & Mukhopadhyay, A. Rainfall trend analysis by Mann–Kendall test: a case study of north-eastern part of Cuttack District, Orissa. Int. J. Geol. Earth Environ. Sci. 2, 2277–208170 (2012).
    Google Scholar 
    Yu, Y. & Ginoux, P. Dust emission following large wildfires. figshare. 2022. https://doi.org/10.6084/m9.figshare.20648055.v2 More

  • in

    Interconnected marine habitats form a single continental-scale reef system in South America

    Roelfsema, C., Phinn, S., Jupiter, S., Comley, J. & Albert, S. Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis. Int. J. Remote Sens. 34, 6367–6388 (2013).Article 

    Google Scholar 
    Soares, M. O., Tavares, T. C. L. & Carneiro, P. Mesophotic ecosystems: Distribution, impacts and conservation in the South Atlantic. Divers. Distrib. 25(2), 255–268 (2019).
    Google Scholar 
    Leão, Z. M. A. N. et al. Brazilian coral reefs in a period of global change: A synthesis. Braz. J. Oceanogr. 64, 97–116 (2016).Article 

    Google Scholar 
    Leão, Z. M. A. N., Kikuchi, R. K. P. & Oliveira, M. D. M. The coral reef province of Brazil. World Seas: An Environmental Evaluation Volume I: Europe, the Americas and West Africa vol. 1 (Elsevier Ltd., 2018).Collette, B. B. & Rützler, K. Reef fishes over sponge bottoms off the mouth of the Amazon River. in Proceedings of Third International Coral Reef Symposium (ed. Taylor, D. L.) vol. 1 305–310 (Rosenstiel School of Marine and Atmospheric Science, 1977).Cordeiro, R. T. S., Neves, B. M., Rosa-Filho, J. S. & Pérez, C. D. Mesophotic coral ecosystems occur offshore and north of the Amazon River. Bull. Mar. Sci. 91, 491–510 (2015).Article 

    Google Scholar 
    Moura, R. L. et al. An extensive reef system at the Amazon River mouth. Sci. Adv. 2, e1501252 (2016).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Francini-Filho, R. B. et al. Perspectives on the Great Amazon Reef: Extension, biodiversity, and threats. Front Mar Sci 5, 1–5 (2018).ADS 
    Article 

    Google Scholar 
    de Mahiques, M. M. et al. Insights on the evolution of the living Great Amazon Reef System, equatorial West Atlantic. Sci. Rep. 9, 1–8 (2019).Article 

    Google Scholar 
    Vale, N. F. et al. Distribution, morphology and composition of mesophotic ‘reefs’ on the Amazon Continental Margin. Mar. Geol. 447, 106779 (2022).ADS 
    Article 

    Google Scholar 
    Moura, R. L. et al. Tropical rhodolith beds are a major and belittled reef fish habitat. Sci. Rep. 11, 1–10 (2021).Article 

    Google Scholar 
    Rocha, L. A. Patterns of distribution and processes of speciation in Brazilian reef fishes. J. Biogeogr. 30, 1161–1171 (2003).Article 

    Google Scholar 
    Floeter, S. R. et al. Atlantic reef fish biogeography and evolution. J. Biogeogr. 31, 22–47 (2008).
    Google Scholar 
    Vale, N. F. et al. Structure and composition of rhodoliths from the Amazon River mouth, Brazil. J. S. Am. Earth Sci. 84, 149–159 (2018).Article 

    Google Scholar 
    IMaRS/USF, IRD, UNEP/WCMC, The WorldFish Center & WRI. Global Coral Reefs composite dataset compiled from multiple sources for use in the Reefs at Risk Revisited project incorporating products from the Millennium Coral Reef Mapping Project. Preprint at (2011).Soares, M. O. et al. Challenges and perspectives for the Brazilian semi-arid coast under global environmental changes. Perspect. Ecol. Conserv. 19, 267–278 (2021).
    Google Scholar 
    Castro, C. B. & Pires, D. O. Brazilian coral reefs: What we already know and what is still missing. Bull. Mar. Sci. 69, 357–371 (2001).
    Google Scholar 
    Leão, Z., Kikuchi, R. & Testa, V. Corals and coral reefs of Brazil. in Latin American Coral Reefs (ed. Cortés, J.) 9–52 (Elsevier Science Inc., 2003). https://doi.org/10.1016/B978-044451388-5/50003-5.Laborel-Deguen, F., Castro, C. B., Nunes, F. D. & Pires, D. O. Recifes brasileiros: o legado de Laborel. (Museu Nacional, 2019).Carneiro, P. et al. Marine hardbottom environments in the beaches of Ceará state, equatorial coast of Brazil. Arquivos de Ciências do Mar 54, 120–153 (2021).Carneiro, P. B. M. et al. Structure, growth and CaCO3 production in a shallow rhodolith bed from a highly energetic siliciclastic-carbonate coast in the equatorial SW Atlantic Ocean. Mar. Environ. Res. 166, 105280 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Testa, V., Bosence, D. W. J. & Universita, C. Physical and biological controls on the formation of carbonate and siliciclastic bedforms on the north-east Brazilian shelf. Sedimentology 46, 279–301 (1999).ADS 
    Article 

    Google Scholar 
    Carneiro, P. & Morais, J. O. de. Carbonate sediment production in the equatorial continental shelf of South America: Quantifying Halimeda incrassata (Chlorophyta) contributions. J. S. Am. Earth Sci. 72, 1–6 (2016).Milliman, J. D. Role of Calcareous Algae in Atlantic Continental Margin Sedimentation. in Fossil algae: recent results and developments (ed. Flügel, E.) 232–247 (Springer, 1977). https://doi.org/10.1007/978-3-642-66516-5_26.Knoppers, B., Ekau, W. & Figueiredo, A. G. The coast and shelf of east and northeast Brazil and material transport. Geo-Mar. Lett. 19, 171–178 (1999).ADS 
    Article 

    Google Scholar 
    Vital, H. The north and northeast Brazilian tropical shelves. in Continental shelves of the world: their evolution during the lasta glacio-eustatic cycle (eds. Chiocci, F. L. & Chivas, A. R.) 35–46 (Geological Society, 2014).Soares, M. de O. et al. Brazilian marine animal forests: A new world to discover in the southwestern Atlantic. Mar. Anim. For. 1–38. https://doi.org/10.1007/978-3-319-17001-5_51-1 (2016).Soares, M. O. et al. Impacts of a changing environment on marginal coral reefs in the Tropical Southwestern Atlantic Ocean. Coast. Manag. 210, 105692 (2021).
    Google Scholar 
    Santos, C. L. A., Vital, H., Amaro, V. E. & de Kikuchi, R. K. P. Mapping of the submerged reefs in the coast of the Rio Grande do Norte, near Brazil: Macau to Maracajau. Revista Brasileira de Geofisica 25, 27–36 (2007).Article 

    Google Scholar 
    Neto, I. C., Córdoba, V. C. & Vital, H. Morfologia, microfaciologia e diagênese de beachrocks costa-afora adjacentes à costa norte do Rio Grande do Norte, brasil. Geociências 32, 471–490 (2013).
    Google Scholar 
    Gomes, M. P. et al. The investigation of a mixed carbonate-siliciclastic shelf, NE Brazil: Side-scan sonar imagery, underwater photography, and surface-sediment data. Ital. J. Geosci. 134, 9–22 (2015).Article 

    Google Scholar 
    Soares, M. O., Rossi, S., Martins, F. A. S. & Carneiro, P. The forgotten reefs: Benthic assemblage coverage on a sandstone reef (Tropical South-western Atlantic). J. Mar. Biol. Assoc. U.K. 97(8), 1585–1592. https://doi.org/10.1017/S0025315416000965 (2017).Article 

    Google Scholar 
    Morais, J. O., Ximenes Neto, A. R., Pessoa, P. R. S. & Souza, L. P. Morphological and sedimentary patterns of a semi-arid shelf, Northeast Brazil. Geo-Ma. Lett. 40, 835–842. https://doi.org/10.1007/s00367-019-00587-x (2019).Cordeiro, R. T., Neves, B. M., Kitahara, M. v., Arantes, R. C. & Perez, C. D. First assessment on Southwestern Atlantic equatorial deep-sea coral communities. Deep-Sea Res. Part I Oceanogr. Res. Papers 163, 103344 (2020).Freitas, J. E. P. & Lotufo, T. M. C. Reef fish assemblage and zoogeographic affinities of a scarcely known region of the western equatorial Atlantic. J. Mar. Biol. Assoc. U.K. 95, 623–633 (2015).Article 

    Google Scholar 
    Soares, M. O., Davis, M., Paiva, C. C. de & Carneiro, P. Mesophotic ecosystems: Coral and fish assemblages in a tropical marginal reef (northeastern Brazil). Mar. Biodivers. 1–6 (2016). https://doi.org/10.1007/s12526-016-0615-x.Carneiro, P. B. M., Sátiro, I., COE, C. M. & Mendonça, K. V. Valoração ambiental do Parque Estadual Marinho da Pedra da Risca do Meio, Ceará, Brasil. Arquivo de Ciências do Mar 50, 25–41 (2017).Gomes, M. P., Vital, H. & Droxler, A. W. Terraces, reefs, and valleys along the Brazil northeast outer shelf: Deglacial sea-level archives?. Geo-Mar. Lett. 40, 699–711. https://doi.org/10.1007/s00367-020-00666-4 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).PubMed 
    Article 

    Google Scholar 
    Raitsos, D. E. et al. Sensing coral reef connectivity pathways from space. Sci. Rep. 7, 1–10 (2017).CAS 
    Article 

    Google Scholar 
    Silveira, I. C. A., Miranda, L. B. & Brown, W. S. On the origins of the North Brazil Current. J. Geophys. Res. 99, 22501–22512 (1994).ADS 
    Article 

    Google Scholar 
    Dias, F. J. da S., Castro, B. M. & Lacerda, L. D. Tidal and low-frequency currents off the Jaguaribe River estuary (4° S, 37° 4′ W), northeastern Brazil. Ocean Dynamics 68, 967–985 (2018).Wellington, G. M. & Victor, B. C. Planktonic larval duration of one hundred species of Pacific and Atlantic damselfishes (Pomacentridae). Mar. Biol. 101, 557–567 (1989).Article 

    Google Scholar 
    Victor, B. C. Duration of the planktonic larval stage of one hundred species of Pacific and Atlantic wrasses (family Labridae). Mar. Biol. 90, 317–326 (1986).Article 

    Google Scholar 
    Endo, C. A. K., Gherardi, D. F. M., Pezzi, L. P. & Lima, L. N. Low connectivity compromises the conservation of reef fishes by marine protected areas in the tropical South Atlantic. Sci. Rep. 9, 1–11 (2019).Article 

    Google Scholar 
    Gomes, M. P. et al. Nature and condition of outer shelf habitats on the drowned Açu Reef, Northeast Brazil. in Seafloor Geomorphology as Benthic Habitat 571–585 (Elsevier, 2020). https://doi.org/10.1016/b978-0-12-814960-7.00034-8.Neto, I. C., Córdoba, V. C. & Vital, H. Petrografia de beachrock em zona costa afora adjacente ao litoral norte do Rio Grande do Norte Brasil. Quat. Environ. Geosci. 2, 12–18 (2010).
    Google Scholar 
    Gomes, M. P., Vital, H., Bezerra, F. H. R., de Castro, D. L. & Macedo, J. W. de P. The interplay between structural inheritance and morphology in the Equatorial Continental Shelf of Brazil. Mar. Geol. 355, 150–161 (2014).Rovira, D. P. T., Gomes, M. P. & Longo, G. O. Underwater valley at the continental shelf structures benthic and fish assemblages of biogenic reefs. Estuar. Coast. Shelf Sci. 224, 245–252 (2019).ADS 
    Article 

    Google Scholar 
    Tosetto, E. G., Bertrand, A., Neumann-Leitão, S. & Nogueira Júnior, M. The Amazon River plume, a barrier to animal dispersal in the Western Tropical Atlantic. Sci. Rep. 12, 537 (2022).ADS 
    Article 

    Google Scholar 
    Cord, I. et al. Brazilian marine biogeography: A multi-taxa approach for outlining sectorization. Mar. Biol. 169, 61 (2022).Article 

    Google Scholar 
    Moalic, Y. et al. Biogeography revisited with network theory: Retracing the history of hydrothermal vent communities. Syst. Biol. 61, 127 (2012).PubMed 
    Article 

    Google Scholar 
    López-Pérez, A. et al. The coral communities of the Islas Marias archipelago, Mexico: Structure and biogeographic relevance to the Eastern Pacific. Mar. Ecol. 37, 679–690 (2016).ADS 
    Article 

    Google Scholar 
    Cordeiro, C. A. M. M. et al. Conservation status of the southernmost reef of the Amazon Reef System: The Parcel de Manuel Luís. Coral Reefs 40, 165–185 (2021).Article 

    Google Scholar 
    Segal, B. & Castro, C. B. Coral community structure and sedimentation at different distances from the coast of the Abrolhos Bank Brazil. Braz. J. Oceanogr. 59, 119–129 (2011).Article 

    Google Scholar 
    Aued, A. W. et al. Large-scale patterns of benthic marine communities in the Brazilian Province. PLoS ONE 13, e0198452 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Soares, M. O. et al. Marginal Reefs in the Anthropocene: They Are Not Noah’s Ark. in Perspectives on the Marine Animal Forests of the World (eds. Rossi, S. & Bramanti, L.) 87–128 (Springer International Publishing, 2020). https://doi.org/10.1007/978-3-030-57054-5_4.Perry, C. T. & Larcombe, P. Marginal and non-reef-building coral environments. Coral Reefs 22, 427–432 (2003).Article 

    Google Scholar 
    Riegl, B. & Piller, W. E. Coral frameworks revisited – reefs and coral carpets in the northern Red Sea. Coral Reefs 18, 241–253 (1999).Article 

    Google Scholar 
    Rodríguez-Martínez, R. E., Jordán-Garza, A. G., Maldonado, M. A. & Blanchon, P. Controls on coral-ground development along the Northern Mesoamerican Reef Tract. PLoS ONE 6, e28461 (2011).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lotufo, T. M. et al. Sessile epifauna of Ceará´s shelf – high dominance of sponges. in 7th International Sponge Symposium – Biodiversity, Innovation, Sustainability 123–123 (Museu Nacional – UFRJ, 2006).Fonseca, V. P., Pennino, M. G., de Nóbrega, M. F., Oliveira, J. E. L. & de Figueiredo Mendes, L. Identifying fish diversity hot-spots in data-poor situations. Mar. Environ. Res. 129, 365–373 (2017).Olavo, G., Costa, P. A. S., Martins, A. S. & Ferreira, B. P. Shelf-edge reefs as priority areas for conservation of reef fish diversity in the tropical Atlantic. Aquat. Conserv. Mar. Freshwat. Ecosyst. 21, 199–209 (2011).Article 

    Google Scholar 
    Eduardo, L. N. et al. Identifying key habitat and spatial patterns of fish biodiversity in the tropical Brazilian continental shelf. Cont. Shelf Res. 166, 108–118 (2018).ADS 
    Article 

    Google Scholar 
    Carneiro, P. B. de M. et al. Structure, growth and CaCO3 production in a shallow rhodolith bed from a highly energetic siliciclastic-carbonate coast in the equatorial SW Atlantic Ocean. Mar. Environ. Res. 166, 105280 (2021).Costa, A. C. P., Garcia, T. M., Paiva, B. P., Ximenes Neto, A. R. & Soares, M. de O. Seagrass and rhodolith beds are important seascapes for the development of fish eggs and larvae in tropical coastal areas. Mar. Environ. Res. 161, 105064 (2020).Testa, V. & Bosence, D. W. J. Carbonate-siliciclastic sedimentation on a high-energy, ocean-facing, tropical ramp, NE Brazil. in Carbonate Ramps (eds. Wright, V. P. & Burchette, T. P.) 55–71 (The Geological Society, 1998).Ximenes Neto, A. R., de Morais, J. O. & Ciarlini, C. Modern and relict sedimentary systems of the semi-arid continental shelf in NE Brazil. J. S. Am. Earth Sci. 84, 56–68 (2018).CAS 
    Article 

    Google Scholar 
    Ximenes Neto, A. R., Morais, J. O. de, Paula, L. F. S. de & Pinheiro, L. de S. Transgressive deposits and morphological patterns in the equatorial Atlantic shallow shelf (Northeast Brazil). Region. Stud. Mar. Sci. 24, 212–224 (2018).Sponaugle, S., Lee, T., Kourafalou, V. & Pinkard, D. Florida Current frontal eddies and the settlement of coral reef fishes. Limnol. Oceanogr. 50, 1033–1048 (2005).ADS 
    Article 

    Google Scholar 
    Cruz, R. et al. Large-scale oceanic circulation and larval recruitment of the spiny lobster Panulirus argus (Latreille, 1804). Crustaceana 88, 298–323 (2015).Article 

    Google Scholar 
    Luiz, O. J. et al. Ecological traits influencing range expansion across large oceanic dispersal barriers: Insights from tropical Atlantic reef fishes. Proc. R. Soc. B Biol. Sci. 279, 1033–1040 (2012).Article 

    Google Scholar 
    Romero-Torres, M., Treml, E. A., Blanchon, P., Acosta, A. & Paz-García, D. A. The Eastern Tropical Pacific coral population connectivity and the role of the Eastern Pacific Barrier. Sci. Rep. 8, 1–13 (2018).CAS 
    Article 

    Google Scholar 
    Leal, C. v. et al. Integrative taxonomy of Amazon Reefs’ Arenosclera spp.: A new clade in the Haplosclerida (Demospongiae). Front. Mar. Sci. 4, 291 (2017).Peluso, L. et al. Contemporary and historical oceanographic processes explain genetic connectivity in a Southwestern Atlantic coral. Sci. Rep. 8, 1–12 (2018).CAS 
    Article 

    Google Scholar 
    Targino, A. K. G. & Gomes, P. B. Distribution of sea anemones in the Southwest Atlantic: Biogeographical patterns and environmental drivers. Mar. Biodivers. 50, 1–17 (2020).Article 

    Google Scholar 
    Barroso, C. X., Lotufo, T. M. da C. & Matthews-Cascon, H. Biogeography of Brazilian prosobranch gastropods and their Atlantic relationships. J. Biogeogr. 43, 2477–2488 (2016).Pinheiro, H. T. et al. South-western Atlantic reef fishes: Zoogeographical patterns and ecological drivers reveal a secondary biodiversity centre in the Atlantic Ocean. Divers. Distrib. 24, 951–965 (2018).Article 

    Google Scholar 
    Medeiros, A. P. M. et al. Deep reefs are not refugium for shallow-water fish communities in the southwestern Atlantic. Ecol. Evol. 11, 4413–4427 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sammon, J. W. A nonlinear mapping for data structure analysis. IEEE Trans. Comput. C–18, 401–409 (1969).Prim, R. C. Shortest connection networks and some generalizations. Bell Syst. Tech. J. 36, 1389–1401 (1957).ADS 
    Article 

    Google Scholar  More

  • in

    Contrasting life-history responses to climate variability in eastern and western North Pacific sardine populations

    All procedures accorded to administrative provision of animal welfare of the Fisheries Research Education Agency Japan. All statistical tests used in this study are two-sided.Otolith samplesFrom the western North Pacific, age-0 JP sardine were collected from samples taken during acoustic and sub-surface trawl surveys in the offshore Oyashio region conducted during 2006–2010 and 2014–2015. The surveys were conducted by Japan Fisheries Research and Education Agency every autumn since 2005 which aim to estimate the abundance of small pelagic species. The abundance of young-of-the-year sardine in the region in the season, approximately 10–15 cm in standard length (SL), is considered a proxy for the abundance of recruits of the Pacific stock and used to tune the cohort analysis in stock assessment4. As representatives of the young-of-the-year population in the region, 2–6 trawl stations each year that had relatively larger catch-per-unit-effort were selected (Supplementary Fig. 1), and 9–20 individuals were randomly selected from each station for otolith analyses (Supplementary Table 1). Age of fish was initially judged by SL (10–15 cm) and later confirmed by the counts of otolith daily increments.From the eastern North Pacific, archived otoliths of CA sardine captured in cruise surveys and in the pelagic fishery of the Southern California Bight during 1987, 1991–1998, and 2005–2007 were collected. Fish in the size range of 10–16 cm SL were regarded as age-1 individuals born in the previous year, following Takahashi and Checkley56. The number of individuals varied between year classes in the range of 4–20 (Supplementary Table 2).Otolith processing, microstructure and somatic growth analysisSagittal otoliths were cleaned to remove the attached tissue in freshwater and then air-dried. Otoliths of JP sardine were embedded in epoxy resin (Petropoxy 154, Burnham Petrographics LLC) on slide-glass, while those of CA were glued to slide-glass using enamel resin and then ground and polished with sandpaper to expose the core. For some otoliths of CA sardine, the polished surface was coated with additional resin to facilitate identification of the daily increment width. Using an otolith measurement system (RATOC System Engineering Co. Ltd.), the number and location of daily increments were examined along the axis in the postrostrum from the core. Although daily increments were clearly observed until the otolith edge for JP sardine, it was difficult to do this for CA sardine probably because they had experienced winter when otolith growth slowed down. Therefore, the rings were counted as far as possible for CA sardine, which typically resulted in more than 150 counts. The first daily increment was assumed to form after 3 days post hatch (dph) for JP and 8 dph for CA sardine following Takahashi et al.26 and Takahashi and Checkley56. The otolith radius at each age was calculated by adding all the increment widths up to that age. Standard lengths at each age were back-calculated assuming a linear relationship between otolith radius and standard length using the biological intercept method34 as follows:$${SL}_{n}=left({{SL}}_{{catch}}-{{SL}}_{{first}}right)times left({{OR}}_{n}-{{OR}}_{{first}}right)/left({OR}_{catch}-{{OR}}_{{first}}right)+{{SL}}_{{first}}$$
    (1)
    where SLn is the standard length at age n, SLcatch is the standard length at catch, SLfirst is the standard length at the age of first daily increment deposition fixed at 5.9 mm for JP sardine and 5.5 mm for CA sardine following the previous studies26,56, ORn is the otolith radius at age n, ORfirst is the otolith radius at the age of first daily increment deposition, and ORcatch is the otolith radius at catch. Based on rearing experiments of field collected eggs, Lasker57 showed the SL of CA sardine at 6–8 dph ranged between 3.8 to 6.5 mm, and Matsuoka and Mitani58 showed the total length at 2–4 dph ranged between 4.8 to 6.2 mm, corresponding to 4.7 to 6.1 mm in SL. To deal with these uncertainties regarding the size at the age of first daily increment deposition, we conducted Monte Carlo simulations (10,000 times) to estimate the uncertainties of back-calculated SL, assuming that the initial SLs fall between 3.8 to 6.5 mm for both sardines. Standard deviations of the temporal back-calculated SL at each age were presented as the uncertainty of each SLn estimation, which varied between 0.51 and 0.73 at the end of larval stage (JP: 45 dph, CA: 60 dph), between 0.34 and 0.64 at the end of early juvenile stage (JP: 75 dph, CA: 90 dph) and between 0.20 and 0.53 at the end of late juvenile stage (JP: 105 dph, CA: 120 dph). These values were significantly smaller than the variability of estimated SL among individuals assuming initial sizes of 5.9 and 5.5 mm for JP and CA sardine, respectively (standard deviations: 4.2, 8.1 and 8.3 in JP sardine and 5.5, 9.1 and 10.3 in CA sardine for the end of larval, early juvenile and late juvenile stages, respectively), suggesting that the back-calculated SL is robust to variations of initial size. Nevertheless, the biological intercept method assumes a constant linear relationship between fish and otolith size within individual59, which can vary depending on physiological or environmental conditions60,61. Therefore, to examine the relationships between temperature and growth, we used both otolith growth, which contains fewer assumptions, and back-calculated somatic growth as growth proxies. Since the use of the two proxies did not show remarkable differences in the relationships between temperature and growth (Supplementary Figs. 11, 12), we mainly used the back-calculated SL in the discussion, which has a more direct ecological implication.To more generally test whether growth trajectories are different between the western and eastern boundary current systems, otolith growth data of JP and CA sardines were compared with those of sardines in the east to south and west coasts of South Africa. The biological intercept method to back-calculate standard length could not be used in sardine from South Africa because the size at catch was large, some over 20 cm, and otolith radius and standard length were not linearly correlated for fish of this size. Therefore, the otolith radius and increment width were directly used as proxy for size and growth in this comparison, respectively. For visualisation (Fig. 2a), the means of year class mean otolith radii were estimated for JP and CA sardines. For CA sardine, otolith radii at ages were simply averaged within each year class. For JP sardine, to account for the variation in the number of individuals captured at the same station, otolith radii were first averaged within each station, and the station means were averaged within each year, weighted by catch-per-unit-effort. For South African sardine, data of otolith daily increment widths from hatch to 100 dph of 67 adults captured at six stations on the east to south coast ( >22oE), and 51 individuals captured at six stations on the west coast ( 0.05). Theoretically, the relationship between metabolism and temperature tends to show a linear trend after the metabolic rate is log-transformed79. Thus, we applied “identity (data without transformed)” and “log (data transformed)” links to evaluate if model shows a better linearity with data transformation. Based on AIC, however, the result showed Moto have a better linearity without data transformation (Supplementary Table 7). We, therefore, used “identity” links for the further model selection. Model selection base on AIC was performed for models including temperature, region (JP and CA sardines), life history stages (larvae, early juvenile and late juvenile) and interactions of these factors. The full model including all the interactions had the lowest AIC (Supplementary Table 7). As the diagnostic for the full model showed normality and homogeneity of residuals (Supplementary Fig. 9), we selected this model for interpretation. The CA sardine at the larval stage as the baseline, we found only JP sardine at early and late juvenile stages has relatively higher Moto values, and the temperature-dependent slope is significantly gentler in JP sardine at early and late juvenile stages (Supplementary Table 8).Next, the diversity of Moto across temperature range was assessed to estimate the optimal temperature in each stage. The relationship between the maximum metabolic rate and temperature is known to be parabolic, while that between the standard metabolic rate and temperature is logarithmic28,79. As the highest field metabolic rate would be constrained by maximum metabolic rate and the lowest field metabolic rate would be close to resting metabolic rate43, fish would have the most diverse metabolic performance at the optimal temperature with the widest aerobic scope. Thus, we modelled the highest and lowest Moto values in each 1 °C bin using a polynomial regression and a generalised linear model with Gaussian distribution and a log link for the 95th and 5th percentile values of each bin, respectively (Supplementary Fig. 10). The values of the bin that included less than four values were excluded from the regression analyses to reduce the uncertainty caused by under-sampled temperature bins. The gap between the two regression lines was considered as a proxy for the aerobic scope, and the temperature at which the gap reached the maximum was regarded as the optimal temperature.Statistical analyses for the relationships between temperature and growthTo understand how variation in ambient water temperature affects early life growth of sardines, we compared back-calculated standard length at around the end of the larval stage (hatch–35 mm; JP: 45 dph, CA: 60 dph), the end of the early juvenile stage (35–60 mm; JP: 75 dph, CA: 90 dph), and the end of the late juvenile stage (60–85 mm; JP: 105 dph, CA: 120 dph) and the mean seawater temperature from hatch to the ages. Median of each sampling batch were used as minimal data unit. Pearson’s r and p-values were first calculated for each comparison (Supplementary Table 9). As the relationship between mean temperature and standard length of JP at 75 dph seemed to be dome-shaped rather than linear, we introduced quadratic term of temperature and tested whether the term increased explanatory power using a linear model and stepwise model selection based on AIC. The model selection showed that the full model (Standard length ∼ Temperature2 + Temperature) was the best model, and the coefficients of the quadratic and linear terms were both significant (Supplementary Table 10). To account for these multiple tests, we corrected the p-values of the coefficients of the quadratic term in the linear model for JP sardine at 75 dph and of the Pearson’s r for the rest using the Benjamini-Hochberg procedure with α = 0.05, and selected the null hypotheses that could be rejected (Supplementary Table 9). To compare the temperature that allow maximisation of growth rate and optimal temperature derived from the analysis of Moto for each stage, median somatic growth rate and otolith increment width in each 1 °C bin was calculated together with its 3-window running mean (Supplementary Figs. 11, 12).Statistical analyses for the relationships between sea surface temperature and survival indexTo test whether habitat temperatures during the first 4 months after hatch affect the survival of sardines in the first year of life on a multidecadal scale, satellite-derived sea surface temperature (SST) since 1982 and survival of JP and CA sardines were compared. The log recruitment residuals from Ricker recruitment models (LNRR)13, representing early life survivals taking into account the effect of population density, were calculated based on the stock assessment data for JP and CA sardines as follows:$${LNR}{R}_{t}={ln}({R}_{t}/{S}_{t}) , – , (a+btimes {S}_{t})$$
    (6)
    where LNRRt is the LNRR at year t, Rt is the recruitment of year-class t, St is the spawning stock biomass in year t, and a and b are the coefficients of linear regression of ln(Rt/St) on St. Pearson’s r between the LNRR and the mean SST values from March to June for JP and from April to July for CA sardine was calculated for each grid points in the western and eastern boundaries of the North Pacific basin, derived from a SST product based on satellite and in situ observations80 (Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed (https://resources.marine.copernicus.eu/product-detail/SST_GLO_SST_L4_REP_OBSERVATIONS_010_011/INFORMATION), accessed on 11th August and 28th October 2021). The correlations were generally negative and positive in the western and eastern regions, respectively (Supplementary Fig 13a, b). In particular, mean SST values in the area where eggs, larvae and juveniles of JP or CA sardines are mainly found in the months26,39,49,56,78,81,82 (dotted areas in Supplementary Fig 13a, b) were compared with LNRR values to test the relationship between habitat temperature and survival in the early life stages (Supplementary Fig 13c). It should be noted that the mean SST values were not significantly correlated with otolith-derived year-class mean temperatures of JP and CA sardines during the larval to late juvenile stages (JP: r = 0.01, p = 0.98, n = 7, CA: r = 0.29, p = 0.38, n = 11), likely due to the short periods analysed, patchy distribution and inter annual variation in larval and juvenile dispersal and migration patterns. Nevertheless, the regions included areas where SST showed weak to significant (p  More

  • in

    Ecological factors are likely drivers of eye shape and colour pattern variations across anthropoid primates

    Kobayashi, H. & Kohshima, S. Unique morphology of the human eye. Nature 387(6635), 767–768 (1997).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Kobayashi, H. & Kohshima, S. Unique morphology of the human eye and its adaptive meaning: Comparative studies on external morphology of the primate eye. J. Hum. Evol. 40(5), 419–435 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mayhew, J. A. & Gómez, J. C. Gorillas with white sclera: A naturally occurring variation in a morphological trait linked to social cognitive functions. Am. J. Primatol. 77, 869–877 (2015).PubMed 
    Article 

    Google Scholar 
    Perea-García, J. O. Quantifying ocular morphologies in extant primates for reliable interspecific comparisons. J. Lang. Evol. 1(2), 151–158 (2016).Article 

    Google Scholar 
    Perea-García, J. O., Kret, M. E., Monteiro, A. & Hobaiter, C. Scleral pigmentation leads to conspicuous, not cryptic, eye morphology in chimpanzees. Proc. Natl. Acad. Sci. 116(39), 19248–19250 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Caspar, K., Biggemann, M., Geissmann, T. & Begall, S. Ocular pigmentation in humans, great apes, and gibbons is not suggestive of communicative functions. Sci. Rep. 11, 1–14 (2021).Article 

    Google Scholar 
    Mearing, A. S. & Koops, K. Quantifying gaze conspicuousness: Are humans distinct from chimpanzees and bonobos?. J. Hum. Evol. 157, 103043. https://doi.org/10.1016/J.JHEVOL.2021.103043 (2021).Article 
    PubMed 

    Google Scholar 
    Perea-García, J. O., Danel, D. P. & Monteiro, A. Diversity in primate external eye morphology: Previously undescribed traits and their potential adaptive value. Symmetry 13, 1270 (2021).ADS 
    Article 

    Google Scholar 
    Banks, M. S., Sprague, W. W., Schmoll, J., Parnell, J. A. & Love, G. D. Why do animal eyes have pupils of different shapes?. Sci. Adv. 1(7), e1500391 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Corfield, J. R. et al. Anatomical specializations for nocturnality in a critically endangered parrot, the kakapo (Strigops habroptilus). PLoS ONE 6(8), e22945 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lisney, T. J. et al. Ecomorphology of eye shape and retinal topography in waterfowl (Aves: Anseriformes: Anatidae) with different foraging modes. J. Comp. Physiol. A. 199(5), 385–402 (2013).Article 

    Google Scholar 
    Lisney, T. J., Iwaniuk, A. N., Bandet, M. V. & Wylie, D. R. Eye shape and retinal topography in owls (Aves: Strigiformes). Brain Behav. Evol. 79(4), 218–236 (2012).PubMed 
    Article 

    Google Scholar 
    Duke-Elder, S. S. The eye in evolution. In System of Ophthalmology (ed. Duke-Elder, S. S.) 453 (Henry Kimpton, 1985).
    Google Scholar 
    -Miller, D., & Sanghvi, S. (1990). Contrast sensitivity and glare testing in corneal disease. In Glare and Contrast Sensitivity for Clinicians (pp. 45–52). Springer.De Broff, B. M. & Pahk, P. J. The ability of periorbitally applied antiglare products to improve contrast sensitivity in conditions of sunlight exposure. Arch. Ophthalmol. 121(7), 997–1001 (2003).Article 

    Google Scholar 
    Caspar, K. R., Mader, L., Pallasdies, F., Lindenmeier, M. & Begall, S. Captive gibbons (Hylobatidae) use different referential cues in an object-choice task: Insights into lesser ape cognition and manual laterality. PeerJ 6, e5348 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kaplan, G. & Rogers, L. J. Patterns of gazing in orangutans (Pongo pygmaeus). Int. J. Primatol. 23(3), 501–526 (2002).Article 

    Google Scholar 
    Kamilar, J. M. & Bradley, B. J. Interspecific variation in primate coat colour supports Gloger’s rule. J. Biogeogr. 38(12), 2270–2277 (2011).Article 

    Google Scholar 
    Santana, S. E., Lynch Alfaro, J. & Alfaro, M. E. Adaptive evolution of facial colour patterns in Neotropical primates. Proc. R. Soc. B Biol. Sci. 279(1736), 2204–2211 (2012).Article 

    Google Scholar 
    Santana, S. E., Alfaro, J. L., Noonan, A. & Alfaro, M. E. Adaptive response to sociality and ecology drives the diversification of facial colour patterns in catarrhines. Nat. Commun. 4(1), 1–7 (2013).Article 

    Google Scholar 
    Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: Definitions, interpretations and evidence. Biol. Rev. 94(4), 1294–1316 (2019).PubMed 

    Google Scholar 
    Zhang, P. & Watanabe, K. Preliminary study on eye colour in Japanese macaques (Macaca fuscata) in their natural habitat. Primates 48(2), 122–129 (2007).PubMed 
    Article 

    Google Scholar 
    Bradley, B. J., Pedersen, A. & Mundy, N. I. Brief communication: blue eyes in lemurs and humans: Same phenotype, different genetic mechanism. Am. J. Phys. Anthropol. 139(2), 269–273 (2009).PubMed 
    Article 

    Google Scholar 
    Meyer, W. K., Zhang, S., Hayakawa, S., Imai, H. & Przeworski, M. The convergent evolution of blue iris pigmentation in primates took distinct molecular paths. Am. J. Phys. Anthropol. 151(3), 398–407 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Negro, J. J., Blázquez, M. C. & Galván, I. Intraspecific eye color variability in birds and mammals: A recent evolutionary event exclusive to humans and domestic animals. Front. Zool. 14(1), 1–6 (2017).Article 

    Google Scholar 
    van den Berg, T. J. T. P., IJspeert, J. K. & De Waard, P. W. T. Dependence of intraocular straylight on pigmentation and light transmission through the ocular wall. Vis. Res. 31(7–8), 1361–1367 (1991).PubMed 
    Article 

    Google Scholar 
    Mure, L. S. Intrinsically photosensitive retinal ganglion cells of the human retina. Front. Neurol. 12, 636330 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wald, L. (2018). Basics in solar radiation at earth surface. ffhal-01676634ff.Workman, L. Blue eyes keep away the winter blues: Is blue eye pigmentation an evolved feature to provide resilience to seasonal affective disorder. OA J. Behav. Sci. Psychol. 1(1), 180002 (2018).MathSciNet 

    Google Scholar 
    Smith, A. R. Color gamut transform pairs. ACM Siggraph Comput. Graph. 12(3), 12–19 (1978).CAS 
    Article 

    Google Scholar 
    Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. B: Biol. Sci. 368(1618), 20120341 (2013).Article 

    Google Scholar 
    Leutenegger, W. & Kelly, J. T. Relationship of sexual dimorphism in canine size and body size to social, behavioral, and ecological correlates in anthropoid primates. Primates 18(1), 117–136. https://doi.org/10.1007/bf02382954 (1977).Article 

    Google Scholar 
    Gómez, J. C. (1996). Ostensive behavior in great apes: The role of eye contact. Reaching into thought: The minds of the great apes, 131–151.Dovidio, J. F., & Ellyson, S. L. (1985). Pattern of visual dominance behavior in humans. In Power, Dominance, and Nonverbal Behavior (pp. 129–149). Springer.Nakatsukasa, M. Locomotor differentiation and different skeletal morphologies in mangabeys (Lophocebus and Cercocebus). Folia Primatol. 66(1–4), 15–24 (1996).CAS 
    Article 

    Google Scholar 
    Smith, R. J. & Jungers, W. L. Body mass in comparative primatology. J. Hum. Evol. 32(6), 523–559 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fioletov, V., Kerr, J. B. & Fergusson, A. The UV index: Definition, distribution and factors affecting it. Can. J. Public Health 101(4), I5–I9 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jablonski, N. G. & Chaplin, G. Human skin pigmentation as an adaptation to UV radiation. Proc. Natl. Acad. Sci. 107(Supplement 2), 8962–8968 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Do, M. T. H. & Yau, K. W. Intrinsically photosensitive retinal ganglion cells. Physiol. Rev. (2010).Pickard, G. E. & Sollars, P. J. Intrinsically photosensitive retinal ganglion cells. Rev. Physiol. Bioch. Pharmacol. 162, 59–90 (2012).Goel, N., Terman, M. & Terman, J. S. Depressive symptomatology differentiates subgroups of patients with seasonal affective disorder. Depress. Anxiety 15(1), 34–41 (2002).PubMed 
    Article 

    Google Scholar 
    Münch, M. et al. Blue-enriched morning light as a countermeasure to light at the wrong time: Effects on cognition, sleepiness, sleep, and circadian phase. Neuropsychobiology 74(4), 207–218 (2016).PubMed 
    Article 

    Google Scholar 
    Davidson, G. L., Thornton, A. & Clayton, N. S. Evolution of iris colour in relation to cavity nesting and parental care in passerine birds. Biol. Let. 13(1), 20160783 (2017).Article 

    Google Scholar 
    Volpato, G. L., Luchiari, A. C., Duarte, C. R. A., Barreto, R. E. & Ramanzini, G. C. Eye color as an indicator of social rank in the fish Nile tilapia. Braz. J. Med. Biol. Res. 36, 1659–1663 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fosbury, R. A. & Jeffery, G. Reindeer eyes seasonally adapt to ozone-blue Arctic twilight by tuning a photonic tapetum lucidum. Proc. R. Soc. B 289(1977), 20221002 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Allen, W. L., Stevens, M. & Higham, J. P. Character displacement of Cercopithecini primate visual signals. Nat. Commun. 5(1), 1–10 (2014).Article 

    Google Scholar 
    Frost, P. European hair and eye color: A case of frequency-dependent sexual selection?. Evol. Hum. Behav. 27(2), 85–103 (2006).Article 

    Google Scholar 
    Hart, D. (2000). Primates as prey: Ecological, morphological and behavioral relationships between primate species and their predators.Liebal, K., Waller, B. M., Slocombe, K. E. & Burrows, A. M. Primate communication: a multimodal approach. (Cambridge University Press, 2014).
    Google Scholar 
    Whitham, W., Schapiro, S. J., Troscianko, J. & Yorzinski, J. L. Chimpanzee (Pan troglodytes) gaze is conspicuous at ecologically-relevant distances. Sci. Rep. 12(1), 1–7 (2022).Article 

    Google Scholar 
    Kano, F., Kawaguchi, Y. & Hanling, Y. Experimental evidence that uniformly white sclera enhances the visibility of eye-gaze direction in humans and chimpanzees. Elife 11, e74086 (2022).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Emery, N. J. The eyes have it: The neuroethology, function and evolution of social gaze. Neurosci. Biobehav. Rev. 24, 581–604 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    -Bourjade, M. (2016). Social attention. Int. Encycl. Primatol. 1–2.Petersen, R. M., Dubuc, C. & Higham, J. P. Facial displays of dominance in non-human primates. In The facial displays of leaders (pp. 123–143) (Palgrave Macmillan, Cham, 2018).Laitly, A., Callaghan, C. T., Delhey, K. & Cornwell, W. K. Is color data from citizen science photographs reliable for biodiversity research?. Ecol. Evol. 11(9), 4071–4083 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chan, I. Z., Stevens, M. & Todd, P. A. PAT-GEOM: A software package for the analysis of animal patterns. Methods Ecol. Evol. 10(4), 591–600 (2019).Article 

    Google Scholar 
    Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125(1), 1–15 (1985).Article 

    Google Scholar 
    Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).Article 

    Google Scholar 
    Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Orme, D. et al. The caper package: Comparative analysis of phylogenetics and evolution in R. R Pack. Vers. 5(2), 1–36 (2013).
    Google Scholar 
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
    Book 

    Google Scholar 
    -Williamson, E. A., Maisels, F. G., Groves, C. P., Fruth, B. I., Humle, T., & Morton, F. B. (2013). Handbook of the Mammals of the World Volume 3: Primates. More

  • in

    Warming and predation risk only weakly shape size-mediated priority effects in a cannibalistic damselfly

    Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Angert, A. L., LaDeau, S. L. & Ostfeld, R. S. Climate change and species interactions: ways forward. Ann. N. Y. Acad. Sci. 1297, 1–7 (2013).ADS 
    PubMed 
    Article 

    Google Scholar 
    Yang, L. H. & Rudolf, V. H. W. Phenology, ontogeny and the effects of climate change on the timing of species interactions. Ecol. Lett. 13, 1–10 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kersting, D. K. et al. Experimental evidence of the synergistic effects of warming and invasive algae on a temperate reef-builder coral. Sci. Rep. 5, 18635 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhou, Y. et al. Warming reshaped the microbial hierarchical interactions. Glob. Chang. Biol. 27, 6331–6347 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Grainger, T. N., Rego, A. I. & Gilbert, B. Temperature-dependent species interactions shape priority effects and the persistence of unequal competitors. Am. Nat. 191, 197–209 (2018).PubMed 
    Article 

    Google Scholar 
    Ørsted, M., Schou, M. F. & Kristensen, T. N. Biotic and abiotic factors investigated in two Drosophila species: evidence of both negative and positive effects of interactions on performance. Sci. Rep. 7, 40132 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sniegula, S., Golab, M. J. & Johansson, F. Size-mediated priority and temperature effects on intra-cohort competition and cannibalism in a damselfly. J. Anim. Ecol. 88, 637–648 (2019).PubMed 
    Article 

    Google Scholar 
    Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Chang. Biol. 13, 1860–1872 (2007).ADS 
    Article 

    Google Scholar 
    Carter, S. K. & Rudolf, V. H. W. Shifts in phenological mean and synchrony interact to shape competitive outcomes. Ecology 100, e02826 (2019).PubMed 
    Article 

    Google Scholar 
    Rudolf, V. H. W. Nonlinear effects of phenological shifts link interannual variation to species interactions. J. Anim. Ecol. 87, 1395–1406 (2018).PubMed 
    Article 

    Google Scholar 
    Rasmussen, N. L., Allen, B. G. V. & Rudolf, V. H. W. Linking phenological shifts to species interactions through size-mediated priority effects. J. Anim. Ecol. 83, 1206–1215 (2014).PubMed 
    Article 

    Google Scholar 
    Bailey, L. D. & Pol, M. van de. Tackling extremes: challenges for ecological and evolutionary research on extreme climatic events. J. Anim. Ecol. 85, 85–96 (2016).Walker, R., Wilder, S. M. & González, A. L. Temperature dependency of predation: increased killing rates and prey mass consumption by predators with warming. Ecol. Evol. 10, 9696–9706 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schulte, P. M. The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment. J. Exp. Biol. 218, 1856–1866 (2015).PubMed 
    Article 

    Google Scholar 
    Anholt, B. R. Cannibalism and early instar survival in a larval damselfly. Oecologia 99, 60–65 (1994).ADS 
    PubMed 
    Article 

    Google Scholar 
    Johansson, F. & Crowley, P. H. Larval cannibalism and population dynamics of dragonflies. in Aquatic insects: challenges to populations (eds. Lancaster, J. & Briers, R. A.) 36–54 (CABI, 2008). doi:https://doi.org/10.1079/9781845933968.0036.Takashina, N. & Fiksen, Ø. Optimal reproductive phenology under size-dependent cannibalism. Ecol. Evol. 10, 4241–4250 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Crumrine, P. W. Body size, temperature, and seasonal differences in size structure influence the occurrence of cannibalism in larvae of the migratory dragonfly, Anax junius. Aquat. Ecol. 44, 761–770 (2010).Article 

    Google Scholar 
    Op de Beeck, L., Verheyen, J. & Stoks, R. Competition magnifies the impact of a pesticide in a warming world by reducing heat tolerance and increasing autotomy. Environ. Pollut. 233, 226–234 (2018).Enriquez-Urzelai, U., Nicieza, A. G., Montori, A., Llorente, G. A. & Urrutia, M. B. Physiology and acclimation potential are tuned with phenology in larvae of a prolonged breeder amphibian. Oikos 2022, e08566 (2022).Article 

    Google Scholar 
    Knight, C. M., Parris, M. J. & Gutzke, W. H. N. Influence of priority effects and pond location on invaded larval amphibian communities. Biol. Invasions 11, 1033–1044 (2009).Article 

    Google Scholar 
    Raczyński, M., Stoks, R., Johansson, F., Bartoń, K. & Sniegula, S. Phenological shifts in a warming world affect physiology and life history in a damselfly. Insects 13, 622 (2022).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Murillo-Rincón, A. P., Kolter, N. A., Laurila, A. & Orizaola, G. Intraspecific priority effects modify compensatory responses to changes in hatching phenology in an amphibian. J. Anim. Ecol. 86, 128–135 (2017).PubMed 
    Article 

    Google Scholar 
    Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

    Google Scholar 
    Jermacz, Ł. et al. Continuity of chronic predation risk determines changes in prey physiology. Sci. Rep. 10, 6972 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Raczyński, M., Stoks, R., Johansson, F. & Sniegula, S. Size-mediated priority effects are trait-dependent and consistent across latitudes in a damselfly. Oikos 130, 1535–1547 (2021).Article 

    Google Scholar 
    Peacor, S. D. & Werner, E. E. Predator effects on an assemblage of consumers through induced changes in consumer foraging behavior. Ecology 81, 1998–2010 (2000).Article 

    Google Scholar 
    Stoks, R., Block, M. D., Meutter, F. V. D. & Johansson, F. Predation cost of rapid growth: behavioural coupling and physiological decoupling. J. Anim. Ecol. 74, 708–715 (2005).Article 

    Google Scholar 
    Hermann, S. L. & Landis, D. A. Scaling up our understanding of non-consumptive effects in insect systems. Curr. Opin. Insect. Sci. 20, 54–60 (2017).PubMed 
    Article 

    Google Scholar 
    Sniegula, S., Nsanzimana, J. d’Amour & Johansson, F. Predation risk affects egg mortality and carry over effects in the larval stages in damselflies. Freshw. Biol. 64, 778–786 (2019).Preisser, E. L. & Orrock, J. L. The allometry of fear: interspecific relationships between body size and response to predation risk. Ecosphere 3, art77 (2012).Gehr, B. et al. Evidence for nonconsumptive effects from a large predator in an ungulate prey?. Behav. Ecol. 29, 724–735 (2018).Article 

    Google Scholar 
    Jiménez-Cortés, J. G., Serrano-Meneses, M. A. & Córdoba-Aguilar, A. The effects of food shortage during larval development on adult body size, body mass, physiology and developmental time in a tropical damselfly. J. Insect Physiol. 58, 318–326 (2012).PubMed 
    Article 

    Google Scholar 
    Weissburg, M., Smee, D. L., Ferner, M. C., Schmitz, A. E. O. J. & Bronstein, E. J. L. The sensory ecology of nonconsumptive predator effects. Am. Nat. 184, 141–157 (2014).PubMed 
    Article 

    Google Scholar 
    Zhang, D.-W., Xiao, Z.-J., Zeng, B.-P., Li, K. & Tang, Y.-L. Insect behavior and physiological adaptation mechanisms under starvation stress. Front. Physiol. 10, 163 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Arnett, H. A. & Kinnison, M. T. Predator-induced phenotypic plasticity of shape and behavior: parallel and unique patterns across sexes and species. Curr. Zool. 63, 369–378 (2017).PubMed 

    Google Scholar 
    Bell, A. M., Dingemanse, N. J., Hankison, S. J., Langenhof, M. B. W. & Rollins, K. Early exposure to nonlethal predation risk by size-selective predators increases somatic growth and decreases size at adulthood in threespined sticklebacks. J. Evol. Biol. 24, 943–953 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    De Block, M. & Stoks, R. Compensatory growth and oxidative stress in a damselfly. Proc. Royal Soc. B 275, 781–785 (2008).Article 

    Google Scholar 
    Lee, W.-S., Monaghan, P. & Metcalfe, N. B. The trade-off between growth rate and locomotor performance varies with perceived time until breeding. J. Exp. Biol. 213, 3289–3298 (2010).PubMed 
    Article 

    Google Scholar 
    Catalán, A. M. et al. Community-wide consequences of nonconsumptive predator effects on a foundation species. J. Anim. Ecol. 90, 1307–1316 (2021).PubMed 
    Article 

    Google Scholar 
    Preisser, E. L., Bolnick, D. I. & Benard, M. F. Scared to death? The effects of intimidation and consumption in predator-prey interactions. Ecology 86, 501–509 (2005).Article 

    Google Scholar 
    Gjoni, V., Basset, A. & Glazier, D. S. Temperature and predator cues interactively affect ontogenetic metabolic scaling of aquatic amphipods. Biol. Lett. 16, 20200267 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Miller, L. P., Matassa, C. M. & Trussell, G. C. Climate change enhances the negative effects of predation risk on an intermediate consumer. Glob. Chang. Biol. 20, 3834–3844 (2014).ADS 
    PubMed 
    Article 

    Google Scholar 
    Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
    Article 

    Google Scholar 
    Lancaster, L. T., Morrison, G. & Fitt, R. N. Life history trade-offs, the intensity of competition, and coexistence in novel and evolving communities under climate change. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 372, 20160046 (2017).Sniegula, S., Janssens, L. & Stoks, R. Integrating multiple stressors across life stages and latitudes: combined and delayed effects of an egg heat wave and larval pesticide exposure in a damselfly. Aquat. Toxicol. 186, 113–122 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stoks, R., Block, M. D., Slos, S., Doorslaer, W. V. & Rolff, J. Time constraints mediate predator-induced plasticity in immune function, condition, and life history. Ecology 87, 809–815 (2006).PubMed 
    Article 

    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).Article 

    Google Scholar 
    Pintanel, P., Tejedo, M., Salinas-Ivanenko, S., Jervis, P. & Merino-Viteri, A. Predators like it hot: thermal mismatch in a predator-prey system across an elevational tropical gradient. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13516 (2021).Article 
    PubMed 

    Google Scholar 
    Stoks, R., Swillen, I. & Block, M. D. Behaviour and physiology shape the growth accelerations associated with predation risk, high temperatures and southern latitudes in Ischnura damselfly larvae. J. Anim. Ecol. 81, 1034–1040 (2012).PubMed 
    Article 

    Google Scholar 
    Wang, Y.-J., Sentis, A., Tüzün, N. & Stoks, R. Thermal evolution ameliorates the long-term plastic effects of warming, temperature fluctuations and heat waves on predator–prey interaction strength. Funct. Ecol. 35, 1538–1549 (2021).Article 

    Google Scholar 
    Sniegula, S., Golab, M. J. & Johansson, F. Cannibalism and activity rate in larval damselflies increase along a latitudinal gradient as a consequence of time constraints. BMC Evol. Biol. 17, 167 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gyssels, F. & Stoks, R. Behavioral responses to fish kairomones and autotomy in a damselfly. J. Ethol. 24, 79–83 (2006).Article 

    Google Scholar 
    McPeek, M. A., Grace, M. & Richardson, J. M. L. Physiological and behavioral responses to predators shape the growth/predation risk trade-off in damselflies. Ecology 82, 1535–1545 (2001).Article 

    Google Scholar 
    Beermann, J., Boos, K., Gutow, L., Boersma, M. & Peralta, A. C. Combined effects of predator cues and competition define habitat choice and food consumption of amphipod mesograzers. Oecologia 186, 645–654 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Evol. Syst. 2, 369–404 (1971).Article 

    Google Scholar 
    Dijkstra, K., Schröter, A. & Lewington, R. Field Guide to the Dragonflies of Britain and Europe. Second edition. (Bloomsbury Publishing, 2020).Corbet, P. S., Suhling, F. & Soendgerath, D. Voltinism of Odonata: a review. Int. J. Odonatol. 9, 1–44 (2006).Article 

    Google Scholar 
    Zwick, P. & Corbet, P. S. Dragonflies: behaviour and ecology of Odonata. (Comstock Publishing Associates, 1999).Fontana-Bria, L., Selfa, J., Tur, C. & Frago, E. Early exposure to predation risk carries over metamorphosis in two distantly related freshwater insects. Ecol. Entomol. 42, 255–262 (2017).Article 

    Google Scholar 
    Sniegula, S., Raczyński, M., Golab, M. J. & Johansson, F. Effects of predator cues carry over from egg and larval stage to adult life-history traits in a damselfly. Freshw. Sci. 39, 804–811 (2020).Article 

    Google Scholar 
    Chivers, D. P., Wisenden, B. D. & Smith, R. J. F. Damselfly larvae learn to recognize predators from chemical cues in the predator’s diet. Anim. Behav. 52, 315–320 (1996).Article 

    Google Scholar 
    Mikolajczuk, P. Stwierdzenie wylotu drugiej generacji tężnicy małej Ischnura pumilio (Charpentier, 1825) i tężnicy wytwornej Ischnura elegans (Vander Linden, 1820) (Odonata: Coenagrionidae) w Polsce środkowo-wschodniej. Odonatrix 1, (2014).De Block, M., Pauwels, K., Van Den Broeck, M., De Meester, L. & Stoks, R. Local genetic adaptation generates latitude-specific effects of warming on predator-prey interactions. Glob. Chang. Biol. 19, 689–696 (2013).ADS 
    PubMed 
    Article 

    Google Scholar 
    IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2021).Buskirk, J. V., Krügel, A., Kunz, J., Miss, F. & Stamm, A. The rate of degradation of chemical cues indicating predation risk: an experiment and review. Ethology 120, 942–949 (2014).Article 

    Google Scholar 
    Hagler, J. R. & Jackson, C. G. Methods for marking insects: current techniques and future prospects. Annu. Rev. Entomol. 46, 511–543 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Crumrine, P. W. Size structure and substitutability in an odonate intraguild predation system. Oecologia 145, 132–139 (2005).ADS 
    PubMed 
    Article 

    Google Scholar 
    Strobbe, F. & Stoks, R. Life history reaction norms to time constraints in a damselfly: differential effects on size and mass. Biol. J. Linn. Soc. 83, 187–196 (2004).Article 

    Google Scholar 
    De Block, M., McPeek, M. A. & Stoks, R. Stronger compensatory growth in a permanent-pond Lestes damselfly relative to temporary-pond Lestes. Oikos 117, 245–254 (2008).Article 

    Google Scholar 
    Marsh, J. B. & Weinstein, D. B. Simple charring method for determination of lipids. J. Lipid Res. 7, 574–576 (1966).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stoks, R., Block, M. D. & McPeek, M. A. Physiological costs of compensatory growth in a damselfly. Ecology 87, 1566–1574 (2006).PubMed 
    Article 

    Google Scholar 
    R Development Core Team. R: The R Project for Statistical Computing. Vienna, Austria https://www.r-project.org/ (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Cyrus, A. Z., Swiggs, J., Santidrian Tomillo, P., Paladino, F. V. & Peters, W. S. Cannibalism causes size-dependent intraspecific predation pressure but does not trigger autotomy in the intertidal gastropod Agaronia propatula. J. Molluscan Stud. 81, 388–396 (2015).Jara, F. G. Trophic ontogenetic shifts of the dragonfly Rhionaeschna variegata: the role of larvae as predators and prey in Andean wetland communities. Ann. Limnol. 50, 173–184 (2014).Article 

    Google Scholar 
    Fréchette, M. & Lefaivre, D. On self-thinning in animals. Oikos 73, 425–428 (1995).Article 

    Google Scholar 
    Johansson, F., Stoks, R., Rowe, L. & De Block, M. Life history plasticity in a damselfly: effects of combined time and biotic constraints. Ecology 82, 1857–1869 (2001).Article 

    Google Scholar 
    Mikolajewski, D. J., Conrad, A. & Joop, G. Behaviour and body size: plasticity and genotypic diversity in larval Ischnura elegans as a response to predators (Odonata: Coenagrionidae). Int. J. Odonatol. 18, 31–44 (2015).Article 

    Google Scholar 
    Antoł, A. & Sniegula, S. Damselfly eggs alter their development rate in the presence of an invasive alien cue but not a native predator cue. Ecol. Evol. 11, 9361–9369 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hassall, C. & Thompson, D. J. The effects of environmental warming on Odonata: a review. Int. J. Odonatol. 11, 131–153 (2008).Article 

    Google Scholar 
    Debecker, S. & Stoks, R. Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes. Ecol. Monogr. 89, e01332 (2019).Article 

    Google Scholar 
    Anderson, T. L. & Semlitsch, R. D. Top predators and habitat complexity alter an intraguild predation module in pond communities. J. Anim. Ecol. 85, 548–558 (2016).PubMed 
    Article 

    Google Scholar 
    Norling, U. Growth, winter preparations and timing of emergence in temperate zone odonata: control by a succession of larval response patterns. Int. J. Odonatol. 24, 1–36 (2021).Article 

    Google Scholar 
    Abrams, P. A., Leimar, O., Nylin, S. & Wiklund, C. The effect of flexible growth rates on optimal sizes and development times in a seasonal environment. Am. Nat. 147, 381–395 (1996).Article 

    Google Scholar 
    Arendt, J. D. Adaptive intrinsic growth rates: an integration across taxa. Q. Rev. Biol. 72, 149–177 (1997).Article 

    Google Scholar 
    Bobrek, R. Odonate phenology recorded in a Central European location in an extremely warm season. Biologia 76, 2957–2964 (2021).Article 

    Google Scholar 
    Dmitriew, C. M. The evolution of growth trajectories: what limits growth rate?. Biol. Rev. 86, 97–116 (2011).PubMed 
    Article 

    Google Scholar 
    Śniegula, S., Johansson, F. & Nilsson-Örtman, V. Differentiation in developmental rate across geographic regions: a photoperiod driven latitude compensating mechanism?. Oikos 121, 1073–1082 (2012).Article 

    Google Scholar 
    Angell, C. S. et al. Development time mediates the effect of larval diet on ageing and mating success of male antler flies in the wild. Proc. R. Soc. B 287, 20201876 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Johansson, F., Watts, P. C., Sniegula, S. & Berger, D. Natural selection mediated by seasonal time constraints increases the alignment between evolvability and developmental plasticity. Evolution 75, 464–475 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nilsson-Örtman, V. & Rowe, L. The evolution of developmental thresholds and reaction norms for age and size at maturity. PNAS 118, (2021).Rohner, P. T. & Moczek, A. P. Evolutionary and plastic variation in larval growth and digestion reveal the complex underpinnings of size and age at maturation in dung beetles. Ecol. Evol. 11, 15098–15110 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rolff, J., Fellowes, M & Holloway, G. Insect Evolutionary Ecology: Proceedings of the Royal Entomological Society’s 22nd Symposium. (CABI Oxford University Press, 2006).Beukeboom, L. W. Size matters in insects: an introduction. Entomol. Exp. Appl. 166, 2–3 (2018).Article 

    Google Scholar 
    Honěk, A. Intraspecific variation in body size and fecundity in insects: a general relationship. Oikos 66, 483–492 (1993).Article 

    Google Scholar 
    Lee, W.-S., Monaghan, P. & Metcalfe, N. B. Experimental demonstration of the growth rate–lifespan trade-off. Proc. R. Soc. B 280, 20122370 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Burraco, P., Díaz-Paniagua, C. & Gomez-Mestre, I. Different effects of accelerated development and enhanced growth on oxidative stress and telomere shortening in amphibian larvae. Sci. Rep. 7, 7494 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dańko, M. J., Dańko, A., Golab, M. J., Stoks, R. & Sniegula, S. Latitudinal and age-specific patterns of larval mortality in the damselfly Lestes sponsa: Senescence before maturity?. Exp. Gerontol. 95, 107–115 (2017).PubMed 
    Article 

    Google Scholar 
    Kong, J. D., Hoffmann, A. A. & Kearney, M. R. Linking thermal adaptation and life-history theory explains latitudinal patterns of voltinism. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180547 (2019).Śniegula, S., Gołąb, M. J. & Johansson, F. Time constraint effects on phenology and life history synchrony in a damselfly along a latitudinal gradient. Oikos 125, 414–423 (2016).Article 

    Google Scholar 
    Popova, O. N. & Haritonov, AYu. Disclosure of biotopical groups in the population of the dragonfly Coenagrion armatum (Charpentier, 1840). Contemp. Probl. Ecol. 7, 175–181 (2014).Article 

    Google Scholar 
    Mikolajewski, D. J., De Block, M. & Stoks, R. The interplay of adult and larval time constraints shapes species differences in larval life history. Ecology 96, 1128–1138 (2015).PubMed 
    Article 

    Google Scholar 
    Wolf, J. B. & Wade, M. J. What are maternal effects (and what are they not)? Philos. Trans. R Soc. Lond. B Biol. Sci. 364, 1107–1115 (2009).Zehnder, C. B., Parris, M. A. & Hunter, M. D. Effects of maternal age and environment on offspring vital rates in the Oleander Aphid (Hemiptera: Aphididae). Environ. Entomol. 36, 910–917 (2007).PubMed 
    Article 

    Google Scholar 
    Hernández, C. M., van Daalen, S. F., Caswell, H., Neubert, M. G. & Gribble, K. E. A demographic and evolutionary analysis of maternal effect senescence. PNAS 117, 16431–16437 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shama, L. N. S., Campero-Paz, M., Wegner, K. M., De Block, M. & Stoks, R. Latitudinal and voltinism compensation shape thermal reaction norms for growth rate. Mol. Ecol. 20, 2929–2941 (2011).PubMed 
    Article 

    Google Scholar 
    Sniegula, S., Golab, M. J., Drobniak, S. M. & Johansson, F. Seasonal time constraints reduce genetic variation in life-history traits along a latitudinal gradient. J. Anim. Ecol. 85, 187–198 (2016).PubMed 
    Article 

    Google Scholar 
    De Block, M. & Stoks, R. Adaptive sex-specific life history plasticity to temperature and photoperiod in a damselfly. J. Evol. Biol. 16, 986–995 (2003).PubMed 
    Article 

    Google Scholar 
    Verberk, W. C. E. P. et al. Shrinking body sizes in response to warming: explanations for the temperature–size rule with special emphasis on the role of oxygen. Biol. Rev. 96, 247–268 (2021).PubMed 
    Article 

    Google Scholar 
    Sheriff, M. J., Peacor, S. D., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: a dearth of evidence. J. Anim. Ecol. 89, 1302–1316 (2020).PubMed 
    Article 

    Google Scholar 
    Wirsing, A. J., Heithaus, M. R., Brown, J. S., Kotler, B. P. & Schmitz, O. J. The context dependence of non-consumptive predator effects. Ecol. Lett 24, 113–129 (2021).PubMed 
    Article 

    Google Scholar 
    McCauley, S. J., Rowe, L. & Fortin, M.-J. The deadly effects of ‘nonlethal’ predators. Ecology 92, 2043–2048 (2011).PubMed 
    Article 

    Google Scholar 
    Palacios, M. del M. & McCormick, M. I. Positive indirect effects of top-predators on the behaviour and survival of juvenile fishes. Oikos 130, 219–230 (2021).Thaler, J. S., McArt, S. H. & Kaplan, I. Compensatory mechanisms for ameliorating the fundamental trade-off between predator avoidance and foraging. PNAS 109, 12075–12080 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Janssens, L., Van Dievel, M. & Stoks, R. Warming reinforces nonconsumptive predator effects on prey growth, physiology, and body stoichiometry. Ecology 96, 3270–3280 (2015).PubMed 
    Article 

    Google Scholar 
    Hawlena, D. & Schmitz, O. J. Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am. Nat. 176, 537–556 (2010).PubMed 
    Article 

    Google Scholar 
    Nation, J. L. Insect Physiology and Biochemistry. (CRC Press, 2011). doi:https://doi.org/10.1201/9781420061789.Rudolf, V. H. W. & Singh, M. Disentangling climate change effects on species interactions: effects of temperature, phenological shifts, and body size. Oecologia 173, 1043–1052 (2013).ADS 
    PubMed 
    Article 

    Google Scholar 
    Pfennig, D. W. Effect of predator-prey phylogenetic similarity on the fitness consequences of predation: a trade-off between nutrition and disease?. Am. Nat. 155, 335–345 (2000).PubMed 
    Article 

    Google Scholar 
    Lee, K. P., Simpson, S. J. & Wilson, K. Dietary protein-quality influences melanization and immune function in an insect. Funct. Ecol. 22, 1052–1061 (2008).Article 

    Google Scholar 
    Wu, Q., Patočka, J. & Kuča, K. Insect Antimicrobial Peptides, a Mini Review. Toxins (Basel) 10, 461 (2018).Bullard, B. et al. The molecular elasticity of the insect flight muscle proteins projectin and kettin. PNAS 103, 4451–4456 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mamat-Noorhidayah, Yazawa, K., Numata, K. & Norma-Rashid, Y. Morphological and mechanical properties of flexible resilin joints on damselfly wings (Rhinocypha spp.). PLoS One 13, e0193147 (2018).Muthukrishnan, S., Merzendorfer, H., Arakane, Y. & Kramer, K. J. 7 – Chitin Metabolism in Insects. in Insect Molecular Biology and Biochemistry (ed. Gilbert, L. I.) 193–235 (Academic Press, 2012). doi:https://doi.org/10.1016/B978-0-12-384747-8.10007-8.Van Dievel, M., Stoks, R. & Janssens, L. Beneficial effects of a heat wave: higher growth and immune components driven by a higher food intake. J. Exp. Biol. 220, 3908–3915 (2017).PubMed 

    Google Scholar  More