More stories

  • in

    Climate-driven convergent evolution in riparian ecosystems on sky islands

    Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).Article 
    ADS 

    Google Scholar 
    Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant Environ. Int. 1, 166–180 (2020).Article 

    Google Scholar 
    Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292, 673–679 (2001).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hendry, A. P. Eco-Evolutionary Dynamics (Princeton University Press, 2017).Book 

    Google Scholar 
    Anstett, D. N., Branch, H. A. & Angert, A. L. Regional differences in rapid evolution during severe drought. Evol. Lett. 5, 130–142 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grainger, T. N., Rudman, S. M., Schmidt, P. & Levine, J. M. Competitive history shapes rapid evolution in a seasonal climate. PNAS 118, e2015772118 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bokhorst, S., Bjerke, J. W., Street, L. E., Callaghan, T. V. & Phoenix, G. K. Impacts of multiple extreme winter warming events on sub-Arctic heathland: Phenology, reproduction, growth, and CO2 flux responses. Glob. Change Biol. 17, 2817–2830 (2011).Article 
    ADS 

    Google Scholar 
    Anderson, J. T., Perera, N., Chowdhury, B. & Mitchell-Olds, T. Microgeographic patterns of genetic divergence and adaptation across environmental gradients in Boechera stricta (Brassicaceae). Am. Nat. 186, S60–S73 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wooliver, R., Tittes, S. B. & Sheth, S. N. A resurrection study reveals limited evolution of thermal performance in response to recent climate change across the geographic range of the scarlet monkeyflower. Evolution 74, 1699–1710 (2020).Article 
    PubMed 

    Google Scholar 
    McCormack, J. E., Huang, H. & Knowles, L. L. Sky Islands. in Encyclopedia of Islands (eds. Gillespie, R. G. & Clague, D. A.) 839–843 (2009).Knowles, J. F., Scott, R. L., Minor, R. L. & Barron-Gafford, G. A. Ecosystem carbon and water cycling from a sky island montane forest. Agric. For. Meteorol. 281, 107835 (2020).Article 
    ADS 

    Google Scholar 
    Heald, W. Sky Islands (Van Nostrand, 1967).
    Google Scholar 
    DeBano, L. H. et al. Biodiversity and management of the Madrean Archipelago: The Sky Islands of southwestern United States and northwestern Mexico: 1994 September 19–23; Tucson, AZ. Gen Tech Rep RM-GTR-264. Fort Collins, CO: US Dep Agric For Serv, Rocky Mt For Range Exp Stn. 669 p. (1995).Pérez-Alquicira, J. et al. The role of historical factors and natural selection in the evolution of breeding systems of Oxalis alpina in the Sonoran desert ‘Sky Islands’. J. Evol. Biol. 23, 2163–2175 (2010).Article 
    PubMed 

    Google Scholar 
    Wiens, J. J. et al. Climate change, extinction, and Sky Island biogeography in a montane lizard. Mol. Ecol. 28, 2610–2624 (2019).Article 
    PubMed 

    Google Scholar 
    Pielou, E. C. After the Ice Age. The return of Life to Glaciated North America (The University of Chicago Press, 1991).Book 

    Google Scholar 
    Hosner, P. A., Nyári, Á. S. & Moyle, R. G. Water barriers and intra-island isolation contribute to diversification in the insular Aethopyga sunbirds (Aves: Nectariniidae). J. Biogeogr. 40, 1094–1106 (2013).Article 

    Google Scholar 
    Favé, M.-J. et al. Past climate change on Sky Islands drives novelty in a core developmental gene network and its phenotype. Bmc Evol. Biol. 15, 183 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yanahan, A. D. & Moore, W. Impacts of 21st-century climate change on montane habitat in the Madrean Sky Island Archipelago. Divers. Distrib. 25, 1625–1638 (2019).Article 

    Google Scholar 
    Oline, D. K., Mitton, J. B. & Grant, M. C. Population and subspecific genetic differentiation in the Foxtail Pine (Pinus balfouriana). Evolution 54, 1813–1819 (2000).CAS 
    PubMed 

    Google Scholar 
    Barrowclough, G. F., Groth, J. G., Mertz, L. A. & Gutiérrez, R. J. Genetic structure of Mexican spotted owl (Strix Occidentalis Lucida) populations in a fragmented landscape. Auk 123, 1090–1102 (2006).
    Google Scholar 
    Atwood, T. C. et al. Modeling connectivity of black bears in a desert sky island archipelago. Biol. Conserv. 144, 2851–2862 (2011).Article 

    Google Scholar 
    Halbritter, D. A., Storer, C. G., Kawahara, A. Y. & Daniels, J. C. Phylogeography and population genetics of pine butterflies: Sky islands increase genetic divergence. Ecol. Evol. 9, 13389–13401 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    DeChaine, E. G. & Martin, A. P. Marked genetic divergence among sky island populations of Sedum lanceolatum (Crassulaceae) in the Rocky Mountains. Am. J. Bot. 92, 477–486 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Baker, A. J. Islands in the sky: The impact of Pleistocene climate cycles on biodiversity. J. Biol. 7, 32 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Robin, V. V., Sinha, A. & Ramakrishnan, U. Ancient geographical gaps and paleo-climate shape the phylogeography of an endemic bird in the sky islands of southern India. PLoS ONE 5, e13321 (2010).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Manthey, J. D. & Moyle, R. G. Isolation by environment in White-breasted Nuthatches (Sitta carolinensis) of the Madrean Archipelago sky islands: A landscape genomics approach. Mol. Ecol. 24, 3628–3638 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vásquez, D. L. A., Balslev, H., Hansen, M. M., Sklenář, P. & Romoleroux, K. Low genetic variation and high differentiation across sky island populations of Lupinus alopecuroides (Fabaceae) in the northern Andes. Alpine Bot. 126, 135–142 (2016).Article 

    Google Scholar 
    Mairal, M. et al. Geographic barriers and Pleistocene climate change shaped patterns of genetic variation in the Eastern Afromontane biodiversity hotspot. Sci. Rep. 7, 45749 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kidane, Y. O., Steinbauer, M. J. & Beierkuhnlein, C. Dead end for endemic plant species? A biodiversity hotspot under pressure. Glob. Ecol. Conserv. 19, e00670 (2019).Article 

    Google Scholar 
    Williamson, J. L. et al. Ecology, not distance, explains community composition in parasites of sky-island Audubon’s Warblers. Int. J. Parasitol. 49, 437–448 (2019).Article 
    PubMed 

    Google Scholar 
    Knowles, L. L. & Richards, C. L. Importance of genetic drift during Pleistocene divergence as revealed by analyses of genomic variation. Mol. Ecol. 14, 4023–4032 (2005).Article 
    PubMed 

    Google Scholar 
    Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).Article 
    PubMed 

    Google Scholar 
    Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host-associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).Article 

    Google Scholar 
    Kooyers, N. J., Greenlee, A. B., Colicchio, J. M., Oh, M. & Blackman, B. K. Replicate altitudinal clines reveal that evolutionary flexibility underlies adaptation to drought stress in annual Mimulus guttatus. New Phytol. 206, 152–165 (2015).Article 
    PubMed 

    Google Scholar 
    Price, E. A. C. & Marshall, C. Clonal plants and environmental heterogeneity—An introduction to the proceedings. Plant Ecol. 141, 3–7 (1999).Article 

    Google Scholar 
    Matsuo, A. et al. Female and male fitness consequences of clonal growth in a dwarf bamboo population with a high degree of clonal intermingling. Ann. Bot. Lond. 114, 1035–1041 (2014).Article 
    CAS 

    Google Scholar 
    Barrett, S. C. H. Influences of clonality on plant sexual reproduction. PNAS 112, 8859–8866 (2015).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bittebiere, A.-K., Benot, M.-L. & Mony, C. Clonality as a key but overlooked driver of biotic interactions in plants. Persp. Plant Ecol. Evol. Syst. 43, 125510 (2020).Article 

    Google Scholar 
    King, D. & Roughgarden, J. Multiple switches between vegetative and reproductive growth in annual plants. Theor. Popul. Biol. 21, 194–204 (1982).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    LaDeau, S. L. & Clark, J. S. Rising CO2 levels and the fecundity of forest trees. Science 292, 95–98 (2001).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Qiu, T. et al. Is there tree senescence? The fecundity evidence. PNAS 118, e2106130118 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oddou-Muratorio, S. et al. Crown defoliation decreases reproduction and wood growth in a marginal European beech population. Ann. Bot. Lond. 128, 193–204 (2021).Article 

    Google Scholar 
    Knops, J. M. H., Koenig, W. D. & Carmen, W. J. Negative correlation does not imply a tradeoff between growth and reproduction in California oaks. PNAS 104, 16982–16985 (2007).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nakamura, I. et al. Phenotypic and genetic differences in a perennial herb across a natural gradient of CO2 concentration. Oecologia 165, 809–818 (2011).Article 
    ADS 
    PubMed 

    Google Scholar 
    Robinson, E. A., Ryan, G. D. & Newman, J. A. A meta-analytical review of the effects of elevated CO2 on plant–arthropod interactions highlights the importance of interacting environmental and biological variables. New Phytol. 194, 321–336 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chen, X. Spatiotemporal Processes of Plant Phenology, Simulation and Prediction (Springer, 2017).Book 

    Google Scholar 
    Bradshaw, H. D. & Stettler, R. F. Molecular genetics of growth and development in Populus. IV. Mapping QTLs with large effects on growth, form, and phenology traits in a forest tree. Genetics 139, 963–973 (1995).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rae, A. M. et al. QTL for yield in bioenergy Populus: Identifying G×E interactions from growth at three contrasting sites. Tree Genet. Genom. 4, 97–112 (2008).Article 

    Google Scholar 
    Rae, A. M., Street, N. R., Robinson, K. M., Harris, N. & Taylor, G. Five QTL hotspots for yield in short rotation coppice bioenergy poplar: The poplar biomass loci. Bmc Plant Biol. 9, 23 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allwright, M. R. et al. Biomass traits and candidate genes for bioenergy revealed through association genetics in coppiced European Populus nigra (L.). Biotechnol. Biofuels 9, 195 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Badmi, R. et al. A new calmodulin-binding protein expresses in the context of secondary cell wall biosynthesis and impacts biomass properties in Populus. Front. Plant Sci. 9, 1669 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dai, A. Drought under global warming: a review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).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. (2021).Hughes, L., Hughes, L. & Hughes, L. Biological consequences of global warming: Is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

    Google Scholar 
    Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fuller, A. et al. Physiological mechanisms in coping with climate change. Physiol. Biochem. Zool. 83, 713–720 (2010).Article 
    PubMed 

    Google Scholar 
    Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: Lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).Article 
    ADS 

    Google Scholar 
    Zavaleta, E. et al. Ecosystem responses to community disassembly. Ann. NY. Acad. Sci. 1162, 311–333 (2009).Article 
    ADS 
    PubMed 

    Google Scholar 
    Bertel, C. et al. Natural selection drives parallel divergence in the mountain plant Heliosperma pusillum s.l. Oikos 127, 1355–1367 (2018).Article 

    Google Scholar 
    Knotek, A. et al. Parallel alpine differentiation in Arabidopsis arenosa. Front. Plant Sci. 11, 561526 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tusiime, F. M. et al. Afro-alpine flagships revisited: Parallel adaptation, intermountain admixture and shallow genetic structuring in the giant senecios (Dendrosenecio). PLoS ONE 15, e0228979 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cooke, J. E. K. & Rood, S. B. Trees of the people: The growing science of poplars in Canada and worldwide. Botany 85, 1103–1110 (2007).
    Google Scholar 
    Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Braatne, J. H., Rood, S. B. & Heilman, P. E. Life history, ecology, and conservation of riparian cottonwoods in North America. 57–86 (1996).Schweitzer, J. A., Martinsen, G. D. & Whitham, T. G. Cottonwood hybrids gain fitness traits of both parents: A mechanism for their long-term persistence?. Am. J. Bot. 89, 981–990 (2002).Article 
    PubMed 

    Google Scholar 
    Moore, W. et al. Introduction to the Arizona Sky Island Arthropod Project (ASAP): Systematics, biogeography, ecology, and population genetics of arthropods of the Madrean Sky Islands. Proc. RMRS 2013, 144–168 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).Article 

    Google Scholar 
    Tuskan, G. A. et al. Characterization of microsatellites revealed by genomic sequencing of Populus trichocarpa. Can. J. For. Res. 34, 85–93 (2004).Article 
    CAS 

    Google Scholar 
    Tuskan, G. A. et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 313, 1596–1604 (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Peakall, R. & Ssmouse, P. E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

    Google Scholar 
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. Arxiv https://doi.org/10.48550/arxiv.1406.5823 (2014).Article 

    Google Scholar 
    Schielzeth, H. & Nakagawa, S. Nested by design: Model fitting and interpretation in a mixed model era. Methods Ecol. Evol. 4, 14–24 (2013).Article 

    Google Scholar 
    Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fox, J. et al. Package ‘car’: Companion to Applied Regression. R package version 3.0–10 (2020). More

  • in

    Faunal engineering stimulates landscape-scale accretion in southeastern US salt marshes

    Regional contextTo understand the variation in salt marsh geomorphology and mussel coverage across the South Atlantic Bight (SAB), we assessed density and areal coverage of1 tidal creekheads and2 mussel aggregations with a combination of published data and new field surveys across the region. First, to assess creekhead density, we selected 10 sites ranging from Cape Romain (SC) to Amelia Island (FL). Given that all of our experiments were conducted on Sapelo Island and the surrounding marsh islands, we selected three sites on Sapelo Island for comparison with four sites to the north and three to the south61. At each site, we scored the total number of tidal creekheads in a 1 km2 contiguous marsh area using Google Earth. Assuming each tidal creekhead constitutes approximately 0.0025 km2, we calculate the creekhead areal coverage to be:$$Creekhead,Areal,Coverage,(%)=frac{(0.0025k{m}^{2})times {{{{{rm{Creekhead}}}}}},{{{{{rm{Density}}}}}}(#k{m}^{-2})}{{{{{{rm{Marsh}}}}}},{{{{{rm{Creekshed}}}}}},{{{{{rm{Area}}}}}},(1k{m}^{2})}times 100%$$
    (1)
    Differences across northern, Sapelo Island, and southern sites were assessed with a one-way ANOVA with location as the main factor.To next test the hypothesis that creekhead mussel coverage is similar at sites across the SAB, we conducted surveys of mussel aggregations at 12 sites across the region from Edisto Beach (SC) to Amelia Island (FL). Previous work14 has shown that mussel aggregations decrease in size and density with increasing distance from the tidal creekhead, so we focused our measurements at three distances from one tidal creekhead onto the marsh platform: 0 m, 20 m, and 40 m. We note that at all sites, mussel aggregations extended >40 m from the tidal creekhead. Sites were again distributed across the region, and included 3 sites to the north, 3 sites to the south, and 6 sites on Sapelo and its back barrier marsh islands. At each site, we selected one representative creek 100–175 m in length and ensured that the tidal creekhead did not overlap spatially with a tidal creekhead of an adjacent creek. At each distance from creekhead, we established one 50 m x 1 m transect. Walking the transect line, we scored each mussel aggregation, counting the total number of mussels and measuring the mound dimensions (L x W x H). We then calculated the areal coverage of mussels within each transect (50 m2) and took the mean value across the three distances as the measure for the site. All data was collected between May and August in 2016 and 2017. Differences across northern, Sapelo Island, and southern sites were assessed with a one-way ANOVA with location as the main factor. Finally, we calculated creekshed mussel areal coverage in the three sub-regions, as the product of the percent of creekshed occupied by creekheads (sub-region mean, %) and the proportion of creekhead area occupied by mussels at each site.Landscape assays of sediment deposition over seasons and tidal phasesTo quantify the relative rates of sediment deposition across marsh landscapes, we deployed 9-cm diameter filter papers (Whatman Quantitative Filter Paper, Grade 42 Circles, Ashless, 90 mm; 57) at 13 location types across 3 sites. Locations included: 1) outer marsh levee (‘outer levee’), 2) marsh platform 10 m inland from outer marsh levee (‘outer levee-adjacent’), 3) inner tidal creek levee (‘inner levee’), 4) marsh platform 10 m inland from inner tidal creek levee (‘inner levee-adjacent’), 5) non-mussel marsh platform ( >50 m from mussel creekhead), 6,7) ridge/runnel area at tidal creekhead (‘ridge’ and ‘runnel’), 8,9) mussel aggregations and adjacent non-mussel marsh areas at the tidal creekhead (‘0 m ON mussel mound’ and ‘0 m OFF mound’), 10,11) mussel aggregations and adjacent marsh areas 10 m onto marsh platform from tidal creekhead (‘10 m ON mussel mound’ and ‘10 m OFF mound’), and 12,13) mussel aggregations and adjacent marsh areas 20 m onto marsh platform from tidal creekhead (‘20 m ON mussel mound’ and ‘20 m OFF mound’). At each location type, we used 15 replicate filters, spaced 1–2 m apart. Each pre-weighed and labeled filter paper was deployed attached to a Polystyrene Petri Dish (100 × 15 mm) using 1.5 mm steel wire. After 24 h in the field, all filters were harvested, dried in an oven at 60 °C, and reweighed. Filter papers were deployed at four tides: Summer Spring (August 2017, +2.5 m), Summer Neap (August 2017, +2.1 m), Winter Spring (February 2018, +2.5 m), and Winter Neap (February 2018, +2.0 m).To quantify the total and percent inorganic and organic material that was deposited on the marsh surface over a 24 h period, we deployed 8 replicate 4.7 cm diameter filter papers (Whatman Glass Microfiber Filter Paper, Grade GF/F Circles, 47 mm) across five marsh locations at one site. Locations included: 1) outer marsh levee (‘outer levee’), 2) marsh platform 10 m inland from outer marsh levee (‘outer levee-adjacent’), 3–4) mussel aggregations and adjacent non-mussel marsh areas at the tidal creekhead (‘0 m ON mussel mound’ and ‘0 m OFF mound’), and 5) non-mussel marsh platform. Prior to deployment, filter papers were combusted in a 450 °C furnace for 4 h and stored in aluminum foil packets. Packeted filter papers were then labeled and pre-weighed. Once in the field, filter papers were removed from their packet with forceps, placed on a petri dish inserted into the marsh sediment during a Summer Spring low tide (+2.5 m), and secured with 1.5 mm steel wire.After 24 h, the filter papers were collected with forceps and inserted back in their corresponding packet. Upon transport back to the lab, the packeted filter papers were dried in a 60 °C oven until constant mass was obtained and re-weighed. The change in weight between pre- and post-deployment was used to calculate total dry weight. Packeted filter papers were combusted again in a 450 °C furnace for 4 h and re-weighed. The total dry weight and the weight lost from the second combustion were then used to calculate total inorganic and organic dry weight and percent organic material for each filter paper.To calculate the organic and inorganic material in persistent in marsh sediment layers, 5-cm cores were collected from the sediment layer using a 60 mL syringe with a 2.5 cm diameter. Cores were taken at same five location types: levee crest, levee-adjacent, on-mound, mound-adjacent, and non-mussel marsh platforms. Eight cores, 1–2 m apart, were collected from each location and placed into pre-weighed foil packets. Cores were dried at 60 °C in an oven until constant mass was obtained, weighed, and combusted in a 450 °C furnace for 4 h. The cores were then reweighed, and the weight loss after combustion was used to calculate the percent organic (and inorganic) material.The mass of both organic and inorganic material deposited on each mussel aggregation filter was far greater (0.11 g and 0.50 g, organic and inorganic sediment, respectively, here and below) than that deposited on levee crests (0.02 g and 0.06 g), levee-adjacent (0.04 g and 0.15 g), and non-mussel marsh platforms (0.04 g and 0.19 g; F4,38 = 9.5; p  0.20), with all locations exhibiting 13–14% organic content (Fig. S3).Field experiment 1: fate of mussel biodepositsTo assess the distribution of sediment supplemented by mussels via local biodeposition and, in turn, their contribution to sediment supply across the broader marsh landscape, we measured the transport of previously settled biodeposits as well as those actively deposited over one tidal cycle. For each process, we selected 6 mussel mounds in two marsh zones where mussels commonly aggregate: 1) the creekhead and 2) 20 meters away from the creekhead on the marsh platform. All focal mounds were at least 5 meters apart to avoid mixing of biodeposits. We addressed the transport of previously settled biodeposits by first removing 2 cm of each mound’s biodeposit layer, homogenizing it with fluorescent chalk (Irwin Straight-Line Fluorescent Orange Marking Chalk) at a 2:1 ratio (biodepost:chalk), and evenly distributing the mixture back on the mounds. We then revisited the mounds at night after one tide had flooded over the mounds (max tidal height +2.2 m) and traced the distribution of fluorescent material through black light detection. We measured the maximum distance fluorescent material traveled in each direction to quantify transport of previously settled biodeposits across the marsh landscape.To account for the distribution of biodeposits ejected by actively filter-feeding mussels, we collected 10 mussels from each mound, transported them back to University of Georgia Marine Institute’s wet lab, depurated them in saltwater (Instant Ocean, 28 ppt) for 24 h, and allowed them to feed on a mixture of seawater and fluorescent chalk for 2 h. We then rinsed the mussels to remove any loose fluorescent material from their shells before transplanting them back into the focal mounds at low tide. We then revisited the mounds at night after one tide had flooded over the mounds and traced the distribution of fluorescent material through black light detection. We measured the maximum distance fluorescent material traveled in each direction to quantify transport of actively ejected biodeposits across the marsh landscape.Field experiment 2: local scale depositional effects of mussels and cordgrassThe second experimental study was conducted at Airport Marsh on Sapelo Island, Georgia, USA. At this site, the experiment was deployed at two zones: the marsh platform >85 m from the nearest tidal creek (31°25’25.3“N 81°17’29.8“W) and the creekhead, where the tidal creek enters onto the marsh platform and tidal water first floods the marsh (31°25’28.1“N 81°17’30.2“W). Within each zone, we deployed seven experimental treatments (n = 5 replicates per treatment per zone) in which we varied mussel (M) presence and density, as well as cordgrass (C) presence. The full set of seven treatments included: 1) no-mussel, no-cordgrass controls (0 M, 0 C); 2) cordgrass-only controls (0 M, C + ); 3) 1-mussel (1 M, 0 C) blocks; 4) small mussel aggregations (20 M, 0 C); 5) intermediate size mussel aggregations (50 M, 0 C); 6) intermediate size mussel aggregations plus cordgrass (50 M, C + ); and 7) large mussel aggregations (80 M, 0 C; Fig. S5).In July 2017, we harvested 70 blocks of marsh peat (50 cm x 50 cm x 20 cm) from the experimental site using flat-edge shovels. We selected 30 blocks of standardized cordgrass density (48.9 ± 9.0 g dry biomass per block; mean ± SD) from non-mussel areas, 10 blocks containing small mussel aggregations (~20 mussels), 20 blocks of intermediate-size mussel aggregations (~50 mussels), and 10 blocks of large mussel aggregations (~80 mussels). All marsh blocks were transported back to the lab where they were washed completely clean of all surface sediment. With the exception of 10 non-mussel blocks and 10 intermediate-size mussel aggregation blocks, all cordgrass was clipped to the marsh surface. For the 1-mussel treatments, we harvested 10 mussels (6–8 cm in length) from the experimental site and individually inserted them in the center of the marsh block so that they were 40–50% below the marsh surface.After cleaning and cordgrass removal, all blocks were cut to new dimensions (36 cm x 36 cm x 16 cm) and placed within plastic-encased bins of the same dimensions. Bins containing marsh blocks were then centrally placed and fitted within an additional larger bin (61 cm x 61 cm x 8 cm), with the top of each box flush to the same height. The outside bin was filled with 64, 5 cm diameter PVC poles and 32, 2.5 cm diameter PVC poles (both 8 cm in height) so that all bin edges were held upright and PVC was rigidly filling all space within the outer box (Fig. S4). PVC poles were oriented in this way to capture all deposited sediment and minimize resuspension by substantially decreasing the fetch within the catchment bins. These sediment catchment units were then transported back to the experimental site where recipient holes were dug to the exact dimensions, so that the top of the marsh block (along with the top of each PVC pole) was exactly flush with the marsh surface sediment. We stapled 1-cm hardware cloth mesh (66 cm x 66 cm, with central 36 cm x 36 cm cutout) above PVC and flush to the marsh surface to allow invertebrate access to and from mussel aggregations and to limit the amount of disturbance to and resuspension of the settled material. Finally, to minimize mussel mortality in the absence of cordgrass, we built shades using 2 layers of 5-cm Aquamesh, attached these shades to four bamboo stakes, and inserted them above each plot at a height of ~1 m. The experiment ran for one month, from July 18 to August 18, 2017.After one month in the field, all experimental units and their contents were returned to the lab, rinsed into recipient aluminum tins, dried, and weighed. The contents of the central bins and sediment on plant tissue were dislodged and collected using spatulas, scraper tools, and a Waterpik Flosser device. After all sediment was collected, each mussel was removed from the aggregation, measured for length, and weighed for biomass. Finally, from treatments containing vegetation, all aboveground cordgrass biomass was harvested, dried, and weighed (Fig. S6).Delft3D ModelTo evaluate the contribution of mussel mounds to marsh accretion, we performed numerical simulations using the Delft3D-FLOW model63,64. We first modified the source code by adding a bivalve module (Delft3D-BIVALVES) to simulate sediment filtration and deposition processes that lead to mussel mound formations. In building this module, we assumed that mussels remove sediments from the water column because of filtration, and expel them as very cohesive pseudofeces, which are attached to the mounds, increasing their elevation. These processes are simulated by adding, in the computational cells containing the mussel mounds, a depositional term due to mussel filtration that reads:$${triangle z}_{{FILT}}={rho }_{{MM}}cdot {f}_{{MM}}cdot {C}_{{sed}}cdot {dt}cdot {{rho }_{{sed},{dry}}}^{-1},$$
    (2)
    where ({rho }_{{MM}}) is the density of mussels in the mounds [mussel m−2], set equal to 177 mussel m−214, and ({f}_{{MM}}) is the volume of water filtered by each mussel per unit of time [m3 s−1 mussel−1], set equal to 0.115 m3 s−1 mussel−1. ({C}_{{sed}}) is the sediment concentration in the water column above each mussel mound [kg m−3], ({dt}) is the simulation time step [s], set equal to 0.6 s, and ({rho }_{{sed},{dry}}) is the dry density of the sediments [kg m−3], set equal to 800 kg m−373. The volume of sediments correspondent to the mussel filtration depositional term obtained from Eq 2. is removed from the lower computational layer of the water column above the mussel aggregation by adding the following sink term in the advection-diffusion equation:$${SINK}={rho }_{{MM}}cdot {f}_{{MM}}cdot {C}_{{sed}}cdot {A}_{{cell}},$$
    (3)
    where ({A}_{{cell}}) is the area of the computational cell [m2]. Numerically, the term is implemented implicitly to prevent the appearance of negative concentrations. For settling velocity, we used a value of 0.1 mm s−1. This value provides the best fit of the Total Suspended Sediment (TSS) concentration we surveyed in a creek, on the adjacent Little Sapelo Island, with an error of 0.022 ± 0.025 kg m−3 (Fig. S8, MAE + RMSE). The fit was obtained by using the exponential decay formulation that reads:$${C}_{s}={C}_{s0}{e}^{-{tcdot w}_{s}/h},$$
    (4)
    where ({w}_{s}) is the settling velocity in [m s−1], (h) is the slow depth in [m], ({C}_{s0}) is the initial sediment concentration in [kg m−3], and (t) is the time in [s]. We set ({C}_{s0}) equal to 0.10 g m−3, which approximates the average value measured during flood tide, at the same location and tidal cycle. In addition, we set h equal to 0.30 m, which is the local mean annual high tide, calculated for 2018. To assess the sensitivity of the results to settling velocity, we ran a simulation in which we increased settling velocity by 50% (i.e., settling velocity equal to 0.15 mm s−1), and extra deposition due to mussel mounds varied by only approximately 6.5% of the original value.We next established a rectangular model domain to describe our study area in a simplified fashion (Fig. 5a). Within the model domain, the marsh platform is connected to the main channel by a tidal creek. The domain extends for 50 m and 207 m in the long-shore and landward directions, respectively. It is discretized using a rectangular grid constituted of 50 cm × 50 cm cells at the creek head and 50 cm × 100 cm cells elsewhere. In our model domain, mussel aggregations occupy only the creekhead, which is the 50 m × 50 m area between the creek and the upper part of the domain. We assign that each mussel mound has an area of 0.25 m2, corresponding to a mound diameter of ~0.5 m. At our resolution, a mound occupies a single cell. A sensitivity analysis using cells of 0.25 m and 0.125 m showed negligible changes in the results. The main channel occupies the lower 20 m of the domain, and its depth goes from 0 m AMSL at the marsh edge to −6 m at the seaward boundary. The tidal creek is located in the middle of the marsh platform and stops 50 m from the landward boundary of the domain. It is 2 m wide, and its depth goes from 0.79 m AMSL at the creek head to −1 m where it connects to the main channel. The marsh system consists of four subareas: (i) the levees (0.94 m AMSL), which are 5 m wide cordons separating the marsh platform from the channel and the creek (except at the creek head) and are vegetated by tall-form cordgrass; (ii) the levee adjacent areas (0.79 m AMSL), which are 10 m wide and vegetated by intermediate size cordgrass, (iii) mussel aggregations, which occupy a set proportion of the creek head (0, 10, or 20%), are vegetated by short-form cordgrass, and form a regular array (0.79 m AMSL, a newly formed mound); and (iv) the marsh platform, all remaining area consisting of short-form cordgrass and located at a uniform elevation of 0.79 m AMSL (Table S2).We used the Delft3D “trachytopes” functionality to impose vegetation resistance on flow propagating through the model domain. At every time step, a Chézy friction coefficient ((C)) is calculated for the vegetation, using a formulation developed by83. The formula is based on the unvegetated bed roughness (({C}_{b})), the drag coefficient (({C}_{D})), the vegetation height (({h}_{v})), and the vegetation density ((n)), expressed as the number of stems per square meter ((m)) times the stem diameter (({D}_{S})). In our model, only cells with an elevation higher than 0 m above MSL are vegetated. We considered four vegetation zones, as described above (Table S2; see details for collection of cordgrass and mussel parameters below). For each vegetation type, we used the same ({C}_{b}) and ({C}_{D}), equal to 45 m1/2s−1 and 1.65, respectively84. The vegetation properties, for each class, are based on local surveys and are reported in Table S1. For each of the three mussel scenarios analyzed, we considered two vegetation distributions. The first one sticks with the description of the vegetation zones we report above. In the second scenario, the vegetation is absent from the entire domain.To compute the sediment deposition in our numerical model, we simulated deposition from October 6th to October 22nd, 2018. This period contains the most representative spring and neap tides of the year and was obtained using the following procedure. First, we reconstructed the astronomic signal for 2018 using the tidal components of the NOAA station “Daymark #156, Head of Mud River, GA” # 8674975”, which is the closest to our study area. We then calculated the tidal ranges in 2018 using consecutive low and high tide levels extrapolated from the astronomic tidal signal. Next, we classified the tidal ranges using the 25th and 75th quantiles of their distribution (i.e., Q25 and Q75): ranges lower than the 25th quartile were neap tides and ranges greater than the 75th were spring tides. The 2018 astronomic tide was then divided into periods containing a spring and a consecutive neap tide. For each period, we identified the tidal ranges associated with spring and neap tides by using Q25 and Q75. Finally, for each period, we calculated the average tidal range for neap and spring tide, the difference between these average values and the yearly average, and the sum of these two differences. The period with the lowest value of this sum contains the most representative spring and neap tides of 2018. For this date range, we then ran our model under six scenarios: mussel cover at 0, 10, and 20%, but with and without vegetation present. We report both sediment deposition and annual accretion in the five location types (i.e., levee crest, levee-adjacent, mussel aggregation, aggregation-adjacent, and non-mussel marsh platform) at local (1 m2), creekhead (2500 m2) and entire domain scales (10,350-m2).Field experiment 3: creekshed mussel manipulationTo assess the effects of mussel presence and population size on marsh accretion at the creekshed scale, we first selected a marsh creekshed with three adjacent tidal creeks of similar length, structure, associated mussel populations, and marsh platform characteristics (i.e., size, elevation, and cordgrass characteristics). For each of the three tidal creeks, we first delineated a 50 m by 50 m creekhead area, oriented perpendicular to the direction of the tidal creek entry into the marsh, and located with midpoint of the front edge positioned at the point of tidal creek entry into the marsh. We then delineated a larger creekshed area associated with each creek of ≥10,000 m2 within which we would deploy our experimental treatments. To quantify initial mussel and cordgrass cover, we set up three 50 m2 transects (50 m long, 1 m wide) within the creekhead area, located at 0 m, 20 m, and 40 m distance from the tidal creek point of entry (and oriented perpendicular to the direction of flow). Within each transect, we counted each mussel aggregation, scoring each individual mussel as well as the length, width, and height from marsh platform of each mussel aggregation structure.For a subset of 20 mussel aggregations randomly selected within each transect (3 transects per creek, 180 aggregations total), we scored the total number of cordgrass tillers on each aggregation. For a subset of 5 randomly selected tillers on each aggregation, we measured both length and width. To assess the differences in cordgrass characteristics between mussel aggregations and aggregation-adjacent areas, we also measured cordgrass stem density, height, and diameter in non-aggregation areas (1 m2) located 1m away from each mussel aggregation.After all initial data was collected, we removed and transplanted approximately 200,000 mussels from one tidal creekhead to another. To do so, we initially flagged approximately 4000 mussel aggregations within the creekshed area of the “Removal” creek, encompassing both the 2500 m2 creekhead area as well as the surrounding ≥10,000 m2 creekshed extent. Mussel individuals were removed by hand over the course of 16 weeks, with all field personnel taking care to leave all pseudofeces in place and cordgrass intact. Field crews were split between the mussel removal and mussel addition creek, such that mussels were re-transplanted within 24 h of removal to minimize mortality. Due to logistical and permitting constraints, it was not feasible to replicate the treatments across multiple sites; instead, the three plots occupied a single contiguous creekshed (Fig. 6a, b).To assess changes in marsh elevation, we first quantified initial creekhead elevation (mean m AMSL in 2500-ft2 area perpendicular to point of entry) using two metrics: 1) Real Time Kinematic (RTK) elevation datapoints (Trimble R6 GNSS System) distributed across the creekshed; and 2) measurements of mussel mound heights throughout each transect at set distances from the point of water entry. For the RTK datapoints, we collected 86 total points across the creekshed in June 2017. Elevation datapoints were randomly selected in each 2500 m2 creekhead zone (minimum of 20 points per creekhead; Fig. S7). However, given the low number of RTK points across a large area, we additionally utilized mussel mound height calculations to provide a second estimate of initial elevation across the creekshed. Mussel aggregations and other bivalves, such as oysters, exhibit a height ceiling of growth, above which survivorship and growth are hypothesized to decrease. Previous work on Sapelo Island marshes reported the height ceiling to be +0.84 ± 0.004 m AMSL (mean ± SE). Therefore, assuming mature mussel aggregations (i.e., with tops at the aforementioned height ceiling), then mussel aggregation height (i.e., the distance between the marsh platform and the topmost point of the mussel aggregation mounded structure) will inform our knowledge of the marsh platform elevation by the following equation: Marsh Elevation (m AMSL) = Mussel Height Ceiling (+0.84 m AMSL) – Mussel Aggregation Height (m AMSL). For each distance from creekhead from which we conducted a 50 m2 transect (0, 20, and 40 m), we estimated mean platform elevation using each of the measured mussel aggregation heights. We then took the mean value of marsh elevation across the three distances (0 m, 20 m, and 40 m) as a measure of creekhead elevation in 2017 for each of our experimental creeks ( >60 mounds per creekhead; 250 total).To assess elevation three years after treatment deployment, we compared creekhead elevation using a 2020 Digital Elevation Model (DEM) of the creekshed. To build the DEM, we flew a DJI Matrice 600 Pro drone carrying a custom build Lidar payload in August 2020. The payload consisted of a Velodyne Puck Lite VLP16, paired with a Novatel Stim300 Inertial Measurement Unit. The point clouds from the drone were orthorectified from GPS data continuously measured on the drone (see the procedure described in 85,86). To remove the vegetation and any other surface perturbations (i.e., from digital surface model to digital elevation model), we used the CloudCompare software (https://github.com/cloudcompare/cloudcompare). The cloth Simulation Filter (CSF; 87) was applied twice to the dataset, which successfully removed the vegetation data. The point cloud of the marsh surface was then exported to ArcGIS 10.7 where the DEM was generated by raster interpolation. Once completed, the mean elevation within each 2500 m2 creekhead location was calculated using the Zonal Statistics tool in ArcGIS 10.7.Statistical analysesTo quantify the effects of season, tidal phase, and location type on short-term deposition, we first square root transformed short-term sediment deposition (i.e., filter paper results) to meet the assumptions of parametric statistics. We then conducted a three-way fully factorial ANOVA, with main effects season, tidal phase, and location type. Post-hoc analyses were conducted with Tukey HSD test, with Bonferroni-corrected p-values (STATA v 15.1). We further analyzed the effects of site, season, tide, and marsh location on short-term sediment deposition using regression tree analysis (rpart, R version 3.1.0). Over-fitted trees were pruned using k fold cross-validation. To next assess the effects of marsh location type on total organic material deposited over 24 h (surface) and percent organic material (surface and to 5 cm depth), we ran three separate one-way ANOVAs. Post-hoc analyses were again conducted with Tukey HSD tests, with Bonferroni-corrected p-values (STATA v 15.1). For Experiment 1, we assessed the fate of mussel biodeposits, both previously settled and newly ejected, with a one-way ANOVA with location (creekhead versus platform) as the main effect. Finally, for Experiment 2, to assess whether cordgrass and mussel aggregations significantly affected sediment deposition over the one-month experimental deployment, we used multiple regression analysis with cordgrass biomass and mussel biomass as predictor variables for sediment biomass collected in each zone (STATA v 15.1; Table S1).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

  • in

    Combating the unseen enemy of yam

    Bjornlund, V., Bjornlund, H. & Van Rooyen, A. F. Int. J. Water Resour. Dev. 36 (Suppl. 1), S20–S53 (2020).World Population Prospects: the 2017 Revision (United Nations Department of Economic and Social Affairs Population Division, 2017).Affokpon, A. et al. In 69th International Symposium on Crop Protection (2017).Adesiyan, S. O. & Odihirin, R. A. Nematologica 24, 132–134a (1978).Article 

    Google Scholar 
    Gao, Q. K. Chinese Vegetables 5, 24–25 (1992).
    Google Scholar 
    Pirzada, T. et al. Nat. Food https://doi.org/10.1038/s43016-023-00695-z (2023).Article 

    Google Scholar 
    Hague, N. G. M. Nematodes, The Unseen Enemy: A Guide to Nematode Damage (Du Pont, 1980).Zasada, I. A. et al. Annu. Rev. Phytopathol. 48, 311–328 (2010).Article 
    CAS 

    Google Scholar 
    Ochola, J. et al. Nat. Sustain. 5, 425–433 (2022).Article 

    Google Scholar 
    Pirzada, T. et al. ACS Sustain. Chem. Eng. 8, 6590–6600 (2020).Article 
    CAS 

    Google Scholar 
    Cao, J. et al. Cellulose 23, 673–687 (2016).Article 
    CAS 

    Google Scholar  More

  • in

    Effects of moisture and density-dependent interactions on tropical tree diversity

    Gentry, A. H. Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann. Missouri Bot. Gard. 75, 1–34 (1988).Article 

    Google Scholar 
    Givnish, T. J. On the causes of gradients in tropical tree diversity. J. Ecol. 87, 193–210 (1999).Article 

    Google Scholar 
    Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).Article 

    Google Scholar 
    Connell, J. H. in Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G. R.) 298–312 (PUDOC, 1971).Esquivel-Muelbert, A. et al. Seasonal drought limits tree species across the Neotropics. Ecography 40, 618–629 (2017).Article 

    Google Scholar 
    Gillett, J. B. Pest pressure, an underestimated factor in evolution. Syst. Assoc. Publ. 4, 37–46 (1962).
    Google Scholar 
    Engelbrecht, B. M. J. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Condit, R., Engelbrecht, B. M. J., Pino, D., Pérez, R. & Turner, B. L. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proc. Natl Acad. Sci. USA 110, 5064–5068 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).Article 

    Google Scholar 
    Harrison, S., Spasojevic, M. J. & Li, D. Climate and plant community diversity in space and time. Proc. Natl Acad. Sci. USA 117, 4464–4470 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. Responses of plant–pathogen interactions to precipitation: Implications for tropical tree richness in a changing world. J. Ecol. 108, 1800–1809 (2020).Article 

    Google Scholar 
    Mangan, S. A. et al. Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gripenberg, S. et al. Testing for enemy-mediated density-dependence in the mortality of seedlings: field experiments with five Neotropical tree species. Oikos 123, 185–193 (2014).Article 

    Google Scholar 
    Bagchi, R. et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506, 85–88 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fricke, E. C., Tewksbury, J. J. & Rogers, H. S. Multiple natural enemies cause distance-dependent mortality at the seed-to-seedling transition. Ecol. Lett. 17, 593–598 (2014).Article 
    PubMed 

    Google Scholar 
    Augspurger, C. K. & Kelly, C. K. Pathogen mortality of tropical tree seedlings: experimental studies of the effects of dispersal distance, seedling density, and light conditions. Oecologia 61, 211–217 (1984).Article 
    ADS 
    PubMed 

    Google Scholar 
    Chen, L. et al. Differential soil fungus accumulation and density dependence of trees in a subtropical forest. Science 366, 124–128 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Eck, J. L., Stump, S. M., Delavaux, C. S., Mangan, S. A. & Comita, L. S. Evidence of within-species specialization by soil microbes and the implications for plant community diversity. Proc. Natl Acad. Sci. USA 116, 7371–7376 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kishimoto-Yamada, K. & Itioka, T. How much have we learned about seasonality in tropical insect abundance since Wolda (1988)? Entomol. Sci. 18, 407–419 (2015).Article 

    Google Scholar 
    Huberty, A. F. & Denno, R. F. Plant water stress and its consequences for herbivorous insects: a new synthesis. Ecology 85, 1383–1398 (2004).Article 

    Google Scholar 
    Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rodríguez-Castañeda, G. The world and its shades of green: a meta-analysis on trophic cascades across temperature and precipitation gradients. Glob. Ecol. Biogeogr. 22, 118–130 (2013).Article 

    Google Scholar 
    Janzen, D. H. & Schoener, T. W. Differences in insect abundance and diversity between wetter and drier sites during a tropical dry season. Ecology 49, 96–110 (1968).Article 

    Google Scholar 
    Sturrock, R. N. et al. Climate change and forest diseases. Plant Pathol 60, 133–149 (2011).Article 

    Google Scholar 
    Desprez-Loustau, M.-L., Marçais, B., Nageleisen, L.-M., Piou, D. & Vannini, A. Interactive effects of drought and pathogens in forest trees. Ann. For. Sci. 63, 597–612 (2006).Article 

    Google Scholar 
    Swinfield, T., Lewis, O. T., Bagchi, R. & Freckleton, R. P. Consequences of changing rainfall for fungal pathogen-induced mortality in tropical tree seedlings. Ecol. Evol. 2, 1408–1413 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jactel, H. et al. Drought effects on damage by forest insects and pathogens: a meta-analysis. Glob. Chang. Biol. 18, 267–276 (2012).Article 
    ADS 

    Google Scholar 
    Maharjan, S. K. et al. Plant functional traits and the distribution of West African rain forest trees along the rainfall gradient. Biotropica 43, 552–561 (2011).Article 

    Google Scholar 
    Klironomos, J. N. Feedback with soil biota contributes to plant rarity and invasiveness in communities. Nature 417, 67–70 (2002).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Petermann, J. S., Fergus, A. J. F., Turnbull, L. A. & Schmid, B. Janzen–Connell effects are widespread and strong enough to maintain diversity in grasslands. Ecology 89, 2399–2406 (2008).Article 
    PubMed 

    Google Scholar 
    Chesson, P. Updates on mechanisms of maintenance of species diversity. J. Ecol. 106, 1773–1794 (2018).Article 

    Google Scholar 
    Barabás, G., Michalska-Smith, M. J. & Allesina, S. The effect of intra- and interspecific competition on coexistence in multispecies communities. Am. Nat. 188, E1–E12 (2016).Article 
    PubMed 

    Google Scholar 
    Lebrija-Trejos, E., Wright, S. J., Hernández, A. & Reich, P. B. Does relatedness matter? Phylogenetic density-dependent survival of seedlings in a tropical forest. Ecology 95, 940–951 (2014).Article 
    PubMed 

    Google Scholar 
    Lebrija-Trejos, E., Reich, P. B., Hernández, A. & Wright, S. J. Species with greater seed mass are more tolerant of conspecific neighbours: a key driver of early survival and future abundances in a tropical forest. Ecol. Lett. 19, 1071–1080 (2016).Article 
    PubMed 

    Google Scholar 
    Green, P. T., Harms, K. E. & Connell, J. H. Nonrandom, diversifying processes are disproportionately strong in the smallest size classes of a tropical forest. Proc. Natl Acad. Sci. USA 111, 18649–18654 (2014).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Comita, L. S. et al. Testing predictions of the Janzen–Connell hypothesis: a meta-analysis of experimental evidence for distance- and density-dependent seed and seedling survival. J. Ecol. 102, 845–856 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moles, A. T. & Westoby, M. What do seedlings die from and what are the implications for evolution of seed size? Oikos 106, 193–199 (2004).Article 

    Google Scholar 
    Paine, C. E. T., Harms, K. E., Schnitzer, S. A. & Carson, W. P. Weak competition among tropical tree seedlings: implications for species coexistence. Biotropica 40, 432–440 (2008).Article 

    Google Scholar 
    Weissflog, A., Markesteijn, L., Lewis, O. T., Comita, L. S. & Engelbrecht, B. M. J. Contrasting patterns of insect herbivory and predation pressure across a tropical rainfall gradient. Biotropica 50, 302–311 (2018).Article 

    Google Scholar 
    Brenes-Arguedas, T., Coley, P. D. & Kursar, T. A. Pests vs. drought as determinants of plant distribution along a tropical rainfall gradient. Ecology 90, 1751–1761 (2009).Article 
    PubMed 

    Google Scholar 
    Gaviria, J. & Engelbrecht, B. M. J. Effects of drought, pest pressure and light availability on seedling establishment and growth: their role for distribution of tree species across a tropical rainfall gradient. PLoS ONE 10, e0143955 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spear, E. R., Coley, P. D. & Kursar, T. A. Do pathogens limit the distributions of tropical trees across a rainfall gradient? J. Ecol. 103, 165–174 (2015).Article 

    Google Scholar 
    Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Chang. Biol. 22, 2329–2352 (2016).Article 
    ADS 
    PubMed 

    Google Scholar 
    Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).Article 
    CAS 

    Google Scholar 
    Lebrija-Trejos, E., Pérez-García, E. A., Meave, J. A., Poorter, L. & Bongers, F. Environmental changes during secondary succession in a tropical dry forest in Mexico. J. Trop. Ecol. 27, 477–489 (2011).Article 

    Google Scholar 
    Krishnadas, M. & Comita, L. S. Edge effects on seedling diversity are mediated by impacts of fungi and insects on seedling recruitment but not survival. Front. Glob. Chang. 2, 76 (2019).Article 

    Google Scholar 
    Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).Article 
    PubMed 

    Google Scholar 
    Uriarte, M., Muscarella, R. & Zimmerman, J. K. Environmental heterogeneity and biotic interactions mediate climate impacts on tropical forest regeneration. Glob. Chang. Biol. 24, e692–e704 (2018).Article 
    ADS 
    PubMed 

    Google Scholar 
    Bachelot, B., Kobe, R. K. & Vriesendorp, C. Negative density-dependent mortality varies over time in a wet tropical forest, advantaging rare species, common species, or no species. Oecologia 179, 853–861 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Zhu, Y. et al. Density‐dependent survival varies with species life‐history strategy in a tropical forest. Ecol. Lett. 21, 506–515 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wright, S. J., Calderón, O., Hernandéz, A. & Muller-Landau, H. C. Annual and spatial variation in seedfall and seedling recruitment in a neotropical forest. Ecology 86, 848–860 (2005).Article 

    Google Scholar 
    Condit, R. Tropical Forest Census Plots https://doi.org/10.1007/978-3-662-03664-8 (Springer, 1998).Kupers, S. J., Wirth, C., Engelbrecht, B. M. J. & Rüger, N. Dry season soil water potential maps of a 50 hectare tropical forest plot on Barro Colorado Island, Panama. Sci. Data 6, 63 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Garwood, N. C. in The Ecology of a Tropical Forest: Seasonal Rhythms and Long-term Changes (eds Leigh, E. G., Rand, A. S. & Windsor, D. M.) 173–185 (Smithsonian Institution Press, 1982).Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference https://doi.org/10.1007/b97636 (Springer, 2004).Muller-Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).Article 
    PubMed 

    Google Scholar 
    Detto, M., Visser, M. D., Wright, S. J. & Pacala, S. W. Bias in the detection of negative density dependence in plant communities. Ecol. Lett. 22, 1923–1939 (2019).Article 
    PubMed 

    Google Scholar 
    Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).Article 
    PubMed 

    Google Scholar 
    Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

    Google Scholar 
    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 
    Bates, D. et al. Package ‘lme4’ Reference Manual https://cran.r-project.org/web/packages/lme4/lme4.pdf (2021).Wilkinson, G. N. & Rogers, C. E. Symbolic description of factorial models for analysis of variance. Appl. Stat. 22, 392 (1973).Article 

    Google Scholar 
    Afshartous, D. & Preston, R. A. Key results of interaction models with centering. J. Stat. Educ. https://doi.org/10.1080/10691898.2011.11889620 (2011).Cohen, J. Statistical Power Analysis for the Behavioral Sciences https://doi.org/10.1016/C2013-0-10517-X (Elsevier, 1977).Steiger, J. H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (2016).Pinheiro, J. et al. nlme: Linear and Nonlinear Mixed Effects Models https://CRAN.R-project.org/package=nlme (2020).Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-level/Mixed) Regression Models https://CRAN.R-project.org/package=DHARMa (2021).Lebrija-Trejos, E., Wright, S. J. & Hernández, A. Moisture, Density-dependent Interactions, and Tropical Tree Diversity https://figshare.com/s/a4d2dbb2a73b3eb09f9f (2022).Kupers, S. J., Wirth, C., Engelbrecht, B. M. J. & Rüger, N. Dry Season Soil Water Potential Maps of a 50 Hectare Tropical Forest Plot on Barro Colorado Island, Panama https://doi.org/10.6084/m9.figshare.7611005.v1 (2019).Paton, S. Barro Colorado Island, Lutz Catchment, Soil Moisture, Manual https://doi.org/10.25573/data.10042517.v1 (2019). More

  • in

    Genetic monitoring on the world’s first MSC eco-labeled common octopus (O. vulgaris) fishery in western Asturias, Spain

    FAO. El estado mundial de la pesca y la acuicultura 2020 (FAO, 2020).
    Google Scholar 
    Jackson, J. B. C. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Scheffer, M., Carpenter, S. & de Young, B. Cascading effects of overfishing marine systems. Trends Ecol. Evol. 20, 579–581 (2005).Article 
    PubMed 

    Google Scholar 
    Coll, M., Libralato, S., Tudela, S., Palomera, I. & Pranovi, F. Ecosystem overfishing in the ocean. PLoS ONE 3, e3881 (2008).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Peterson, M. S. & Lowe, M. R. Implications of cumulative impacts to estuarine and marine habitat quality for fish and invertebrate resources. Rev. Fish. Sci. 17, 505–523 (2009).Article 

    Google Scholar 
    Claudet, J. & Fraschetti, S. Human-driven impacts on marine habitats: A regional meta-analysis in the Mediterranean Sea. Biol. Cons. 143, 2195–2206 (2010).Article 

    Google Scholar 
    Smith, V. H., Tilman, G. D. & Nekola, J. C. Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100, 179–196 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Derraik, J. G. B. The pollution of the marine environment by plastic debris: A review. Mar. Pollut. Bull. 44, 842–852 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Doney, S. C. et al. Climate change impacts on marine ecosystems. Ann. Rev. Mar. Sci. 4, 11–37 (2012).Article 
    PubMed 

    Google Scholar 
    Molnar, J. L., Gamboa, R. L., Revenga, C. & Spalding, M. D. Assessing the global threat of invasive species to marine biodiversity. Front. Ecol. Environ. 6, 485–492 (2008).Article 

    Google Scholar 
    Wojnarowska, M., Sołtysik, M. & Prusak, A. Impact of eco-labelling on the implementation of sustainable production and consumption. Environ. Impact Assess. Rev. 86, 106505 (2021).Article 

    Google Scholar 
    Yan, H. F. et al. Overfishing and habitat loss drive range contraction of iconic marine fishes to near extinction. Sci. Adv. 7, 6026 (2021).Article 
    ADS 

    Google Scholar 
    Bastardie, F. et al. Spatial planning for fisheries in the Northern Adriatic: Working toward viable and sustainable fishing. Ecosphere 8, e01696 (2017).Article 

    Google Scholar 
    Arkema, K. K. et al. Integrating fisheries management into sustainable development planning. Ecol. Soc. 24, 0201 (2019).Article 

    Google Scholar 
    Aguión, A. et al. Establishing a governance threshold in small-scale fisheries to achieve sustainability. Ambio. https://doi.org/10.1007/s13280-021-01606-x (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gudmundsson, E. & Wessells, C. R. Ecolabeling seafood for sustainable production: Implications for fisheries management. Mar. Resour. Econ. 15, 97–113 (2000).Article 

    Google Scholar 
    FAO. Guidelines for the Ecolabelling of Fish and Fishery Products from Marine Capture Fisheries. Revision 1 (FAO, 2009).
    Google Scholar 
    Hilborn, R. & Ovando, D. Reflections on the success of traditional fisheries management. ICES J. Mar. Sci. 71, 1040–1046 (2014).Article 

    Google Scholar 
    Casey, J., Jardim, E. & Martinsohn, J. T. H. The role of genetics in fisheries management under the E.U. common fisheries policy. J. Fish Biol. 89, 2755–2767 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    MSC. MSC Fisheries Standard v2.01. https://www.msc.org/docs/default-source/default-document-library/for-business/program-documents/fisheries-program-documents/msc-fisheries-standard-v2-01.pdf?sfvrsn=8ecb3272_9 (2018).Costello, C. et al. Status and solutions for the world’s unassessed fisheries. Science 338, 517–520 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. PNAS 117, 2218–2224 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Worm, B. & Branch, T. A. The future of fish. Trends Ecol. Evol. 27, 594–599 (2012).Article 
    PubMed 

    Google Scholar 
    Palomares, M. L. D. et al. Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins. Estuar. Coast. Shelf Sci. 243, 106896 (2020).Article 

    Google Scholar 
    Ihssen, P. E. et al. Stock identification: Materials and methods. Can. J. Fish. Aquat. Sci. 38, 1838–1855 (1981).Article 

    Google Scholar 
    Carvalho, G. R. & Hauser, L. Molecular genetics and the stock concept in fisheries. In Molecular Genetics in Fisheries (eds Carvalho, G. R. & Pitcher, T. J.) 55–79 (Springer, 1995).Chapter 

    Google Scholar 
    Worm, B. et al. Rebuilding global fisheries. Science 325, 578–585 (2009).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gough, C. L. A., Dewar, K. M., Godley, B. J., Zafindranosy, E. & Broderick, A. C. Evidence of overfishing in small-scale fisheries in Madagascar. Front. Mar. Sci. 7, 317 (2020).Article 

    Google Scholar 
    Widjaja, S. et al. Illegal, Unreported and Unregulated Fishing and Associated Drivers 60 (2020).Walters, C. & Martell, S. J. D. Stock assessment needs for sustainable fisheries management. Bull. Mar. Sci. 70, 629–638 (2002).
    Google Scholar 
    Moreira, A. A., Tomás, A. R. G. & Hilsdorf, A. W. S. Evidence for genetic differentiation of Octopus vulgaris (Mollusca, Cephalopoda) fishery populations from the southern coast of Brazil as revealed by microsatellites. J. Exp. Mar. Biol. Ecol. 407, 34–40 (2011).Article 

    Google Scholar 
    Allendorf, F. W., Ryman, N. & Utter, F. M. Genetics and fishery management. In Population Genetics and Fishery Management 1–19 (1987).Oosthuizen, A., Jiwaji, M. & Shaw, P. Genetic analysis of the Octopus vulgaris population on the coast of South Africa. S. Afr. J. Sci. 100, 603–607 (2004).CAS 

    Google Scholar 
    Botsford, L. W., Castilla, J. C. & Peterson, C. H. The management of fisheries and marine ecosystems. Science 277, 509–515 (1997).Article 
    CAS 

    Google Scholar 
    Hilborn, R., Orensanz, J. M. & Parma, A. M. Institutions, incentives and the future of fisheries. Philos. Trans. R. Soc. B Biol. Sci. 360, 47. https://doi.org/10.1098/rstb.2004.1569 (2005).Article 

    Google Scholar 
    Ovenden, J. R., Berry, O., Welch, D. J., Buckworth, R. C. & Dichmont, C. M. Ocean’s eleven: A critical evaluation of the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish Fish. 16, 125–159 (2015).Article 

    Google Scholar 
    Aguirre-Sarabia, I. et al. Evidence of stock connectivity, hybridization, and misidentification in white anglerfish supports the need of a genetics-informed fisheries management framework. Evol. Appl. 14, 2221 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grover, A. & Sharma, P. C. Development and use of molecular markers: Past and present. Crit. Rev. Biotechnol. 36, 290 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valenzuela-Quiñonez, F. How fisheries management can benefit from genomics? Brief. Funct. Genom. 15, 352–357 (2016).Article 

    Google Scholar 
    Khoufi, W., Jabeur, C. & Bakhrouf, A. Stock assessment of the common octopus (Octopus vulgaris) in Monastir; the Mid-eastern Coast of Tunisia. Int. J. Mar. Sci. 2, 1 (2012).
    Google Scholar 
    Pita, C. et al. Fisheries for common octopus in Europe: Socioeconomic importance and management. Fish. Res. 235, 105820 (2021).Article 

    Google Scholar 
    Melis, R. et al. Genetic population structure and phylogeny of the common octopus Octopus vulgaris Cuvier, 1797 in the western Mediterranean Sea through nuclear and mitochondrial markers. Hydrobiologia 807, 277–296 (2018).Article 
    CAS 

    Google Scholar 
    De Luca, D., Catanese, G., Procaccini, G. & Fiorito, G. Octopus vulgaris (Cuvier, 1797) in the Mediterranean Sea: Genetic diversity and population structure. PLoS ONE 11, e0149496 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernández-Rueda, P. & García-Flórez, L. Octopus vulgaris (Mollusca: Cephalopoda) fishery management assessment in Asturias (north-west Spain). Fish. Res. 83, 351–354 (2007).Article 

    Google Scholar 
    Gobierno del Principado de Asturias. BOPA núm. 233 de 03-XII-2021, Vol. 233 (2021).Roa-Ureta, R. H. et al. Estimation of the spawning stock and recruitment relationship of Octopus vulgaris in Asturias (Bay of Biscay) with generalized depletion models: Implications for the applicability of MSY. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab113 (2021).Article 

    Google Scholar 
    González, A. F., Macho, G., de Novoa, J. & García, M. Western Asturias Octopus Traps Fishery of Artisanal Cofradías 181 (2015).Sánchez, J. L. F., Fernández Polanco, J. M. & Llorente García, I. Evidence of price premium for MSC-certified products at fishers’ level: The case of the artisanal fleet of common octopus from Asturias (Spain). Mar. Policy 119, 104098 (2020).Article 

    Google Scholar 
    Murphy, J. M., Balguerías, E., Key, L. N. & Boyle, P. R. Microsatellite DNA markers discriminate between two Octopus vulgaris (Cephalopoda: Octopoda) fisheries along the northwest African coast. Bull. Mar. Sci. 71, 545–553 (2002).
    Google Scholar 
    Cabranes, C., Fernandez-Rueda, P. & Martínez, J. L. Genetic structure of Octopus vulgaris around the Iberian Peninsula and Canary Islands as indicated by microsatellite DNA variation. ICES J. Mar. Sci. 65, 12–16 (2008).Article 

    Google Scholar 
    Quinteiro, J., Rodríguez-Castro, J., Rey-Méndez, M. & González-Henríquez, N. Phylogeography of the insular populations of common octopus, Octopus vulgaris Cuvier, 1797, in the Atlantic Macaronesia. PLoS ONE 15, e0230294 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Greatorex, E. C. et al. Microsatellite markers for investigating population structure in Octopus vulgaris (Mollusca: Cephalopoda). Mol. Ecol. 9, 641–642 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    De Luca, D., Catanese, G., Fiorito, G. & Procaccini, G. A new set of pure microsatellite loci in the common octopus Octopus vulgaris Cuvier, 1797 for multiplex PCR assay and their cross-amplification in O. maya Voss & Solís Ramírez, 1966. Conserv. Genet. Resour. 7, 299–301 (2015).Article 

    Google Scholar 
    Zuo, Z., Zheng, X., Liu, C. & Li, Q. Development and characterization of 17 polymorphic microsatellite loci in Octopus vulgaris Cuvier, 1797. Conserv. Genet. Resour. 4, 367–369 (2012).Article 

    Google Scholar 
    Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358 (1984).CAS 
    PubMed 

    Google Scholar 
    Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nei, M. & Takezaki, N. Estimation of Genetic Distances and Phylogenetic Trees from DNA Analysis 8 (1983).Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Waples, R. S. Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J. Hered. 89, 438–450 (1998).Article 

    Google Scholar 
    Taboada, F. G. & Anadón, R. Patterns of change in sea surface temperature in the North Atlantic during the last three decades: Beyond mean trends. Clim. Change 115, 419–431 (2012).Article 
    ADS 

    Google Scholar 
    Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sinclair, M. & Valdimarsson, G. Responsible Fisheries in the Marine Ecosystem (CABI, 2003).Book 

    Google Scholar 
    Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39 (2014).Article 
    PubMed 

    Google Scholar 
    Bradbury, I. R., Laurel, B., Snelgrove, P. V. R., Bentzen, P. & Campana, S. E. Global patterns in marine dispersal estimates: The influence of geography, taxonomic category and life history. Proc. R. Soc. B Biol. Sci. 275, 1803–1809 (2008).Article 

    Google Scholar 
    Waples, R. S. Testing for Hardy-Weinberg proportions: Have we lost the plot? J. Hered. 106, 1–19 (2015).Article 
    PubMed 

    Google Scholar 
    Casu, M. et al. Genetic structure of Octopus vulgaris (Mollusca, Cephalopoda) from the Mediterranean Sea as revealed by a microsatellite locus. Ital. J. Zool. 69, 295–300 (2002).Article 

    Google Scholar 
    Fadhlaoui-Zid, K. et al. Genetic structure of Octopus vulgaris (Cephalopoda, Octopodidae) in the central Mediterranean Sea inferred from the mitochondrial COIII gene. C.R. Biol. 335, 625–636 (2012).Article 
    PubMed 

    Google Scholar 
    Queiroga, H. et al. Oceanographic and behavioural processes affecting invertebrate larval dispersal and supply in the western Iberia upwelling ecosystem. Prog. Oceanogr. 74, 174–191 (2007).Article 
    ADS 

    Google Scholar 
    Mereu, M. et al. Mark–recapture investigation on Octopus vulgaris specimens in an area of the central western Mediterranean Sea. J. Mar. Biol. Assoc. U.K. 95, 131–138 (2015).Article 
    ADS 

    Google Scholar 
    Mereu, M. et al. Movement estimation of Octopus vulgaris Cuvier, 1797 from mark recapture experiment. J. Exp. Mar. Biol. Ecol. 470, 64–69 (2015).Article 

    Google Scholar 
    Roura, Á. et al. Life strategies of cephalopod paralarvae in a coastal upwelling system (NW Iberian Peninsula): Insights from zooplankton community and spatio-temporal analyses. Fish. Oceanogr. 25, 241–258 (2016).Article 

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

    Google Scholar 
    Chédia, J., Widien, K. & Amina, B. Role of sea surface temperature and rainfall in determining the stock and fishery of the common octopus (Octopus vulgaris, Mollusca, Cephalopoda) in Tunisia. Mar. Ecol. 31, 431–438 (2010).Article 
    ADS 

    Google Scholar 
    Otero, J. et al. Bottom-up control of common octopus Octopus vulgaris in the Galician upwelling system, northeast Atlantic Ocean. Mar. Ecol. Prog. Ser. 362, 181–192 (2008).Article 
    ADS 

    Google Scholar 
    Hedgecock, D. & Pudovkin, A. I. A. I. Sweepstakes reproductive success in highly fecund marine fish and shellfish: A review and commentary. Bull. Mar. Sci. 87, 971–1002 (2011).Article 

    Google Scholar 
    Kalinowski, S. T. & Waples, R. S. Relationship of effective to census size in fluctuating populations. Conserv. Biol. 16, 129–136 (2002).Article 
    PubMed 

    Google Scholar 
    Sonderblohm, C. P., Pereira, J. & Erzini, K. Environmental and fishery-driven dynamics of the common octopus (Octopus vulgaris) based on time-series analyses from leeward Algarve, southern Portugal. ICES J. Mar. Sci. 71, 2231–2241 (2014).Article 

    Google Scholar 
    Sonderblohm, C. P. et al. Participatory assessment of management measures for Octopus vulgaris pot and trap fishery from southern Portugal. Mar. Policy 75, 133–142 (2017).Article 

    Google Scholar 
    Arkhipkin, A. I. et al. Stock assessment and management of cephalopods: Advances and challenges for short-lived fishery resources. ICES J. Mar. Sci. 78, 714–730 (2021).Article 

    Google Scholar 
    Franklin, I. R. Evolutionary change in small populations. In Conservation Biology: An Evolutionary-Ecological Perspective (eds Soulé, M. E. & Wilcox, B. A.) 395 (Sinauer Associates, 1980).
    Google Scholar 
    Slatkin, M. Rare alleles as indicators of gene flow. Evolution 39, 53–65 (1985).Article 
    PubMed 

    Google Scholar 
    Holleley, C. E. & Geerts, P. G. Multiplex manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46, 511–517 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

    Google Scholar 
    Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Paradis, E. Pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Goudet, J. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    Adamack, A. T. & Gruber, B. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).Article 

    Google Scholar 
    Goudet, J. FSTAT (Version 1.2): A computer program to calculate F-STATISTICS. J. Hered. 86, 485–486 (1995).Article 

    Google Scholar 
    Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223 (1989).Article 
    PubMed 

    Google Scholar 
    Piry, S., Luikart, G. & Cornuet, J. M. M. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).Article 

    Google Scholar 
    Luikart, G., Allendorf, F. W., Cornuet, J.-M.M. & Sherwin, W. B. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. https://doi.org/10.1093/jhered/89.3.238 (1998).Article 
    PubMed 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Besnier, F. & Glover, K. A. ParallelStructure: A R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8, e70651 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gilbert, K. J. et al. Recommendations for utilizing and reporting population genetic analyses: The reproducibility of genetic clustering using the program structure. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2012.05754.x (2012).Article 
    PubMed 

    Google Scholar 
    Earl, D. A. & VonHoldt, B. M. Structure harvester: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

    Google Scholar 
    Takezaki, N., Nei, M. & Tamura, K. POPTREEW: Web version of POPTREE for constructing population trees from allele frequency data and computing some other quantities. Mol. Biol. Evol. 31, 1622–1624 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

    Google Scholar 
    Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).Article 
    PubMed 

    Google Scholar 
    Cavalli-Sforza, L. L. & Edwards, A. W. F. Phylogenetic analysis. Models and estimation procedures. Am. J. Hum. Genet. 19, 233–257 (1967).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Waples, R. S. A generalized approach for estimating effective population size from temporal changes in allele frequency. Genetics 121, 379–391 (1989).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katsanevakis, S. & Verriopoulos, G. Seasonal population dynamics of Octopus vulgaris in the eastern Mediterranean. ICES J. Mar. Sci. 63, 151–160 (2006).Article 

    Google Scholar 
    Jereb, P. et al. Cephalopod Biology and Fisheries in Europe: II Species Accounts 360 (ICES, 2015).
    Google Scholar  More

  • in

    Origination of the modern-style diversity gradient 15 million years ago

    Fine, P. V. Ecological and evolutionary drivers of geographic variation in species diversity. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).Article 

    Google Scholar 
    Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).Article 
    PubMed 

    Google Scholar 
    Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).Article 
    PubMed 

    Google Scholar 
    Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).Article 

    Google Scholar 
    Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).Article 
    PubMed 

    Google Scholar 
    Crame, J. A. Taxonomic diversity gradients through geological time. Divers Distrib. 7, 175–189 (2011).
    Google Scholar 
    Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).Article 
    PubMed 

    Google Scholar 
    Powell, M. G. Latitudinal diversity gradients for brachiopod genera during late Palaeozoic time: links between climate, biogeography and evolutionary rates. Glob. Ecol. Biogeogr. 16, 519–528 (2007).Article 

    Google Scholar 
    Powell, M. G., Beresford, V. P. & Colaianne, B. A. The latitudinal position of peak marine diversity in living and fossil biotas. J. Biogeogr. 39, 1687–1694 (2012).Article 

    Google Scholar 
    Hillebrand, H. Strength, slope and variability of marine latitudinal gradients. Mar. Ecol. Prog. Ser. 273, 251–267 (2004).Article 
    ADS 

    Google Scholar 
    Beaugrand, G., Rombouts, I. & Kirby, R. R. Towards an understanding of the pattern of biodiversity in the oceans. Glob. Ecol. Biogeogr. 22, 440–449 (2013).Article 

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

    Google Scholar 
    Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).Article 

    Google Scholar 
    Saupe, E. E. et al. Spatio-temporal climate change contributes to latitudinal diversity gradients. Nat. Ecol. Evol. 3, 1419–1429 (2019).Article 
    PubMed 

    Google Scholar 
    Stehli, F. G., Douglas, R. G. & Newell, N. D. Generation and maintenance of gradients in taxonomic diversity. Science 164, 947–949 (1969).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 4000, 749–752 (1999).Article 
    ADS 

    Google Scholar 
    Klopfer, P. H. Environmental determinants of faunal diversity. Am. Nat. 93, 337–342 (1959).Article 

    Google Scholar 
    Haffer, J. & Prance, G. T. Climatic forcing of evolution in Amazonia during the Cenozoic: on the refuge theory of biotic differentiation. Amazoniana 16, 579–607 (2001).
    Google Scholar 
    Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).
    Google Scholar 
    Williams, C. B. Patterns in the Balance of Nature (Academic Press, 1964).Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).Article 

    Google Scholar 
    Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).Article 

    Google Scholar 
    Currie, D. J. Energy and large-scale patterns of animal and plant species richness. Am. Nat. 137, 27–49 (1991).Article 

    Google Scholar 
    Connell, J. H. & Orias, E. The ecological regulation of species diversity. Am. Nat. 98, 399–414 (1964).Article 

    Google Scholar 
    Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge Univ. Press, 1995).Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Phil. Trans. R. Soc. B 371, 20150224 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).Article 
    PubMed 

    Google Scholar 
    Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biol. Rev. 92, 199–215 (2017).Article 
    PubMed 

    Google Scholar 
    Fenton, I. S. et al. Triton, a new species-level database of Cenozoic planktonic foraminiferal occurrences. Sci. Data 8, 160 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science 375, 25–26 (2022).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yasuhara, M. et al. Time machine biology cross-timescale integration of ecology, evolution, and oceanography. Oceanography 33, 16–28 (2020).Article 

    Google Scholar 
    Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Al-Sabouni, N., Kucera, M. & Schmidt, D. N. Vertical niche separation control of diversity and size disparity in planktonic foraminifera. Mar. Micropaleontol. 63, 75–90 (2007).Article 
    ADS 

    Google Scholar 
    Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Lear, C. H., Elderfield, H. & Wilson, P. A. Cenozoic deep-sea temperatures and global ice volumes from Mg/Ca in benthic foraminiferal calcite. Science 287, 269–272 (2000).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Weiner, A., Aurahs, R., Kurasawa, A., Kitazato, H. & Kučera, M. Vertical niche partitioning between cryptic sibling species of a cosmopolitan marine planktonic protist. Mol. Ecol. 21, 4063–4073 (2012).Article 
    PubMed 

    Google Scholar 
    Schneider, E. & Kennett, J. P. Segregation and speciation in the Neogene planktonic foraminiferal clade Globoconella. Paleobiology 25, 383–395 (1999).Article 

    Google Scholar 
    Raja, N. B. & Kiessling, W. Out of the extratropics: the evolution of the latitudinal diversity gradient of Cenozoic marine plankton. Proc. Biol. Sci. 288, 20210545 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).Article 
    PubMed 

    Google Scholar 
    Irigoien, X., Huisman, J. & Harris, R. P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–886 (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer-Verlag, 2017).Ruddimann, W. F. Recent planktonic foraminifera: dominance and diversity in North Atlantic surface sediments. Science 164, 1164–1167 (1969).Article 
    ADS 

    Google Scholar 
    Bé, A. W. H. & Tolderlund, D. S. in Micropaleontology of Marine Bottom Sediments (eds Funnell, B. M. & Riedel, W. K.) 105–149 (Cambridge Univ. Press, 1971).Sibert, E., Norris, R., Cuevas, J. & Graves, L. Eighty-five million years of Pacific Ocean gyre ecosystem structure: long-term stability marked by punctuated change. Proc. Biol. Sci. 283, 20160189 (2016).PubMed 
    PubMed Central 

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

    Google Scholar 
    Worm, B. & Tittensor, D. P. A Theory of Global Biodiversity (Princeton Univ. Press, 2018).Boscolo-Galazzo, F. et al. Temperature controls carbon cycling and biological evolution in the ocean twilight zone. Science 371, 1148–1152 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Boscolo-Galazzo, F. et al. Late Neogene evolution of modern deep-dwelling plankton. Biogeosciences 19, 743–762 (2022).Article 
    ADS 

    Google Scholar 
    Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
    PubMed 

    Google Scholar 
    Matthews, K. J. et al. Global plate boundary evolution and kinematics since the late Paleozoic. Glob. Planet. Change 146, 226–250 (2016).Article 
    ADS 

    Google Scholar 
    Gyldenfeldt, A.-B. V., Carstens, J. & Meincke, J. Estimation of the catchment area of a sediment trap by means of current meters and foraminiferal tests. Deep Sea Res. Part II 47, 1701–1717 (2000).Article 
    ADS 

    Google Scholar 
    Qiu, Z., Doglioli, A. M. & Carlotti, F. Using a Lagrangian model to estimate source regions of particles in sediment traps. Sci. China Earth Sci. 57, 2447–2456 (2014).Article 
    ADS 

    Google Scholar 
    Siegel, D. A. & Deuser, W. G. Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. Deep Sea Res. Part I 44, 1519–1541 (1997).Article 

    Google Scholar 
    Waniek, J., Koeve, W. & Prien, R. D. Trajectories of sinking particles and the catchment areas above sediment traps in the Northeast Atlantic. J. Mar. Res. 58, 983–1006 (2000).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2019).Alroy, J. The fossil record of North American mammals: evidence for a Paleocene evolutionary radiation. Syst. Biol. 48, 107–118 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marcot, J. D. The fossil record and macroevolutionary history of North American ungulate mammals: standardizing variation in intensity and geography of sampling. Paleobiology 40, 238–255 (2014).Article 

    Google Scholar 
    Gaston, K. J., Williams, P. H., Eggleton, P. & Humphries, C. J. Large scale patterns of biodiversity: spatial variation in family richness. Proc. R. Soc. Lond. B 260, 149–154 (1995).Article 
    ADS 

    Google Scholar 
    Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Cox, P. M. et al. The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim. Dyn. 15, 183–203 (1999).Article 

    Google Scholar 
    Sagoo, N., Valdes, P., Flecker, R. & Gregoire, L. J. The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions? Phil. Trans. R. Soc. A 371, 20130123 (2013).Article 
    ADS 
    PubMed 

    Google Scholar 
    Kiehl, J. T. & Shields, C. A. Sensitivity of the Palaeocene–Eocene thermal maximum climate to cloud properties. Phil. Trans. R. Soc. A 371, 20130093 (2013).Article 
    ADS 
    PubMed 

    Google Scholar 
    Cox, M. D. A Primitive Equation, 3-Dimensional Model of the Ocean. GFDL Ocean Group Technical Report No. 1 (GFDL Princeton Univ., 1984).Collins, M., Tett, S. F. B. & Cooper, C. The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim. Dyn. 17, 61–81 (2001).Article 

    Google Scholar 
    Farnsworth, A. et al. Climate sensitivity on geological timescales controlled by nonlinear feedbacks and ocean circulation. Geophys. Res. Lett. 46, 9880–9889 (2019).Article 
    ADS 

    Google Scholar 
    Valdes, P. J., Scotese, C. R. & Lunt, D. J. Deep ocean temperatures through time. Clim. Past 17, 1483–1506 (2021).Article 

    Google Scholar 
    Farnsworth, A. et al. Past East Asian monsoon evolution controlled by paleogeography, not CO2. Sci. Adv. 5, eaax1697 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jones, L. A., Mannion, P. D., Farnsworth, A., Bragg, F. & Lunt, D. J. Climatic and tectonic drivers shaped the tropical distribution of coral reefs. Nat. Commun. 13, 3120 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Scotese, C. R. & Wright, N. PALEOMAP paleodigital elevation models (PaleoDEMS) for the Phanerozoic. Zenodo https://doi.org/10.5281/zenodo.5460860 (2018).Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).Article 
    ADS 
    CAS 

    Google Scholar 
    Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).Article 
    CAS 

    Google Scholar 
    Bahcall, J. N., Pinsonneault, M. H. & Basu, S. Solar models: current epoch and time dependences, neutrinos, and helioseismological properties. Astrophys. J. 555, 990–1012 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).Article 
    ADS 

    Google Scholar 
    Kraus, E. B. & Turner, J. S. A one-dimensional model of the seasonal thermocline II. The general theory and its consequences. Tellus 19, 98–105 (1967).ADS 

    Google Scholar 
    Foreman, S. J. The Ocean Model Report. Unified Model Documentaiton Paper Number 40 (The Met Office, 2005).HH: Statistical Analysis and Data Display: Heiberger and Holland. R package version 3.1-47 (2022).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    Bivand, R., Millo, G. & Piras, G. A review of software for spatial econometrics in R. Mathematics 9, 1276 (2021).Article 

    Google Scholar 
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    Cooper, N. & Purvis, A. Body size evolution in mammals: complexity in tempo and mode. Am. Nat. 175, 727–738 (2010).Article 
    PubMed 

    Google Scholar 
    geosphere: Spherical Trigonometry. R package version 1.5-14 (2021).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Wade, B. S., Pearson, P. N., Berggren, W. A. & Pälike, H. Review and revision of Cenozoic tropical planktonic foraminiferal biostratigraphy and calibration to the geomagnetic polarity and astronomical time scale. Earth Sci. Rev. 104, 111–142 (2011).Article 
    ADS 

    Google Scholar  More

  • in

    Late Cenozoic cooling restructured global marine plankton communities

    Jonkers, L., Hillebrand, H. & Kucera, M. Global change drives modern plankton communities away from the pre-industrial state. Nature 570, 372–375 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA 113, 2964–2969 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beaugrand, G., Reid, P. C., Ibanez, F., Lindley, J. A. & Edwards, M. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692–1694 (2002).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Cheung, W. W., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Herbert-Read, J. E. et al. A global horizon scan of issues impacting marine and coastal biodiversity conservation. Nat. Ecol. Evol. 6, 1262–1270 (2022).Article 
    PubMed 

    Google Scholar 
    Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science 375, 25–26 (2022).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fenton, I. S. et al. Triton, a new species-level database of Cenozoic planktonic foraminiferal occurrences. Sci. Data 8, 160 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strack, A., Jonkers, L., Rillo, M. C., Hillebrand, H. & Kucera, M. Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age. Nat. Ecol. Evol. 6, 1871–1880 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Mokany, K. & Ferrier, S. Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling. Divers. Distrib. 17, 374–380 (2011).Article 

    Google Scholar 
    Pörtner, H.-O. et al. eds IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2022).Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).Article 
    PubMed 

    Google Scholar 
    Schumm, M. et al. Common latitudinal gradients in functional richness and functional evenness across marine and terrestrial systems. Proc. R. Soc. B 286, 20190745 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 400, 749–753 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Worm, B., Lotze, H. K. & Myers, R. A. Predator diversity hotspots in the blue ocean. Proc. Natl Acad. Sci. USA 100, 9884–9888 (2003).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Fenton, I. S., Pearson, P. N., Dunkley Jones, T. & Purvis, A. Environmental predictors of diversity in recent planktonic foraminifera as recorded in marine sediments. PLoS ONE 11, e0165522 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chaudhary, C., Saeedi, H. & Costello, M. J. Bimodality of latitudinal gradients in marine species richness. Trends Ecol. Evol. 31, 670–676 (2016).Article 
    PubMed 

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

    Google Scholar 
    Rillo, M. C., Miller, C. G., Kučera, M. & Ezard, T. H. G. Intraspecific size variation in planktonic foraminifera cannot be consistently predicted by the environment. Ecol. Evol. 10, 11579–11590 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thomas, E. Descent into the icehouse. Geology 36, 191–192 (2008).Article 
    ADS 

    Google Scholar 
    Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Phil. Trans. R. Soc. B 371, 20150224 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Crame, J. A. Early Cenozoic evolution of the latitudinal diversity gradient. Earth Sci. Rev. 202, 103090 (2020).Article 

    Google Scholar 
    Yasuhara, M. et al. Time machine biology. Oceanography 33, 16–28 (2020).Article 

    Google Scholar 
    Alegret, L., Arreguín-Rodríguez, G. J., Trasviña-Moreno, C. A. & Thomas, E. Turnover and stability in the deep sea: benthic foraminifera as tracers of Paleogene global change. Global Planet. Change 196, 103372 (2021).Article 

    Google Scholar 
    Gaskell, D. E. et al. The latitudinal temperature gradient and its climate dependence as inferred from foraminiferal δ18O over the past 95 million years. Proc. Natl Acad. Sci. USA 119, e2111332119 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mannion, P. D., Upchurch, P., Benson, R. B. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).Article 
    PubMed 

    Google Scholar 
    Raja, N. B. & Kiessling, W. Out of the extratropics: the evolution of the latitudinal diversity gradient of Cenozoic marine plankton. Proc. R. Soc. B 288, 20210545 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Herbert, T. D. et al. Late Miocene global cooling and the rise of modern ecosystems. Nat. Geosci. 9, 843–847 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Steinthorsdottir, M. et al. The Miocene: the future of the past. Paleoceanogr. Paleoclimatology 36, e2020PA004037 (2021).Article 

    Google Scholar 
    Brown, R. M., Chalk, T. B., Crocker, A. J., Wilson, P. A. & Foster, G. L. Late Miocene cooling coupled to carbon dioxide with Pleistocene-like climate sensitivity. Nat. Geosci. 15, 664–670 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Guillermic, M., Misra, S., Eagle, R. & Tripati, A. Atmospheric CO2 estimates for the Miocene to Pleistocene based on foraminiferal δ11B at Ocean Drilling Program Sites 806 and 807 in the Western Equatorial Pacific. Clim. Past 18, 183–207 (2022).Article 

    Google Scholar 
    Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).Article 
    PubMed 

    Google Scholar 
    Ezard, T. H. G., Aze, T., Pearson, P. N. & Purvis, A. Interplay between changing climate and species’ ecology drives macroevolutionary dynamics. Science 332, 349–351 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Peters, S. E., Kelly, D. C. & Fraass, A. J. Oceanographic controls on the diversity and extinction of planktonic foraminifera. Nature 493, 398–401 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Woodhouse, A. et al. Adaptive ecological niche migration does not negate extinction susceptibility. Sci. Rep. 11, 15411 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biol. Rev. 92, 199–215 (2017).Article 
    PubMed 

    Google Scholar 
    Bindoff, N. L. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (IPCC, Cambridge Univ. Press, 2019).Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
    PubMed 

    Google Scholar 
    Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. 94, 16–36 (2019).Article 
    PubMed 

    Google Scholar 
    Rojas, A., Calatayud, J., Kowalewski, M., Neuman, M. & Rosvall, M. A multiscale view of the Phanerozoic fossil record reveals the three major biotic transitions. Commun. Biol. 4, 309 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Swain, A., Devereux, M. & Fagan, W. F. Deciphering trophic interactions in a mid-Cambrian assemblage. iScience 24, 102271 (2021).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shaw, J. O. et al. Disentangling ecological and taphonomic signals in ancient food webs. Paleobiology 47, 385–401 (2021).Article 

    Google Scholar 
    Swain, A., Maccracken, S., Fagan, W. & Labandeira, C. Understanding the ecology of host plant–insect herbivore interactions in the fossil record through bipartite networks. Paleobiology 48, 239–260 (2022).Article 

    Google Scholar 
    Poisot, T., Canard, E., Mouquet, N. & Hochberg, M. E. A comparative study of ecological specialization estimators. Methods Ecol. Evol. 3, 537–544 (2012).Article 

    Google Scholar 
    Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Boscolo-Galazzo, F. and Crichton, K.A. et al. Temperature controls carbon cycling and biological evolution in the ocean twilight zone. Science 371, 1148–1152 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Boscolo-Galazzo, F. et al. Late Neogene evolution of modern deep-dwelling plankton. Biogeosciences 19, 743–762 (2022).Article 
    ADS 

    Google Scholar 
    Keller, G. in The Miocene Ocean: Paleoceanography and Biogeography Vol. 163, 177–196 (Geological Society of America, 1985).Holbourn, A. E. et al. Late Miocene climate cooling and intensification of southeast Asian winter monsoon. Nat. Commun. 9, 1584 (2018).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Willeit, M., Ganopolski, A., Calov, R., Robinson, A. & Maslin, M. The role of CO2 decline for the onset of Northern Hemisphere glaciation. Quat. Sci. Rev. 119, 22–34 (2015).Article 
    ADS 

    Google Scholar 
    Hayashi, T. et al. Latest Pliocene Northern Hemisphere glaciation amplified by intensified Atlantic meridional overturning circulation. Commun. Earth Environ. 1, 25–10 (2020).Article 
    ADS 

    Google Scholar 
    Lam, A. R., Crundwell, M. P., Leckie, R. M., Albanese, J. & Uzel, J. P. Diachroneity rules the mid-latitudes: a test case using late Neogene planktic foraminifera across the Western Pacific. Geosciences 12, 190 (2022).Article 
    ADS 

    Google Scholar 
    Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Rillo, M. C. et al. On the mismatch in the strength of competition among fossil and modern species of planktonic Foraminifera. Global Ecol. Biogeogr. 28, 1866–1878 (2019).Article 

    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).Article 
    ADS 

    Google Scholar 
    Monllor-Hurtado, A., Pennino, M. G. & Sanchez-Lizaso, J. L. Shift in tuna catches due to ocean warming. PLoS ONE 12, e0178196 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).Article 
    PubMed 

    Google Scholar 
    Mora, C. et al. Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century. PLoS Biol. 11, e1001682 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Renaudie, J., Lazarus, D.B. & Diver, P. NSB (Neptune Sandbox Berlin): an expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy. Palaeontol. Electron. 23, p.a11 (2020).
    Google Scholar 
    Pearson, P. N. in Atlas of Oligocene Planktonic Foraminifera (eds Wade, B. S. et al) 415–428 (Cushman Foundation of Foraminiferal Research, 2018).Liow, L. H., Skaug, H. J., Ergon, T. & Schweder, T. Global occurrence trajectories of microfossils: environmental volatility and the rise and fall of individual species. Paleobiology 36, 224–252 (2010).Article 

    Google Scholar 
    Lazarus, D., Weinkauf, M. & Diver, P. Pacman profiling: a simple procedure to identify stratigraphic outliers in high-density deep-sea microfossil data. Paleobiology 38, 144–161 (2012).Article 

    Google Scholar 
    Woodhouse, A. et al. Paleoecology and evolutionary response of planktonic foraminifera to the Plio-Pleistocene intensification of Northern Hemisphere glaciations. Preprint at EGUsphere https://doi.org/10.5194/egusphere-2022-844 (2022).Woodhouse, A. et al. Paleoecology and evolutionary response of planktonic foraminifera to the mid-Pliocene Warm Period and Plio-Pleistocene bipolar ice sheet expansion. Biogeosciences 20, 121–139 (2023).Article 
    ADS 

    Google Scholar 
    Dormann, C. F., Fründ, J., Blüthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. Op. Ecol. J. 2, 7–24 (2009).Article 

    Google Scholar 
    Swain, A. et al. Sampling bias and the robustness of ecological metrics for plant-damage-type association networks. Ecology https://doi.org/10.1002/ecy.3922 (2022).Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).Article 
    PubMed 

    Google Scholar 
    Vaughan, I. P. et al. econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods Ecol. Evol. 9, 728–733 (2018).Article 
    MathSciNet 

    Google Scholar  More

  • in

    Tropical biodiversity linked to polar climate

    Wallace, A. R. Tropical Nature and Other Essays (Macmillan, 1878).
    Google Scholar 
    von Humboldt, A. Ansichten der Natur: mit wissenschaftlichen Erläuterungen (Cotta, 1808).
    Google Scholar 
    Brown, J. H. J. Biogeogr. 41, 8–22 (2014).Article 
    PubMed 

    Google Scholar 
    Fenton, I. S., Aze, T., Farnsworth, A., Valdes, P. & Saupe, E. E. Nature https://doi.org/10.1038/s41586-023-05712-6 (2023).Article 

    Google Scholar 
    Woodhouse, A., Swain, A., Fagan, W. F., Fraass, A. J. & Lowery, C. M. Nature https://doi.org/10.1038/s41586-023-05694-5 (2023).Article 

    Google Scholar 
    Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Biol. Rev. 92, 199–215 (2017).Article 
    PubMed 

    Google Scholar 
    Yasuhara, M. et al. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
    PubMed 

    Google Scholar 
    Song, H. et al. Proc. Natl Acad. Sci. USA 117, 17578–17583 (2020).Article 
    PubMed 

    Google Scholar 
    Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Science 362, eaat1327 (2018).Article 
    PubMed 

    Google Scholar 
    Janzen, D. H. Am. Nat. 101, 233–249 (1967).Article 

    Google Scholar 
    Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M. & Donohoe, A. Front. Earth Sci. 9, 710036 (2021).Article 

    Google Scholar 
    Penn, J. L. & Deutsch, C. Science 376, 524–526 (2022).Article 
    PubMed 

    Google Scholar  More