More stories

  • 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

    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

    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

    Rainfall affects interactions between plant neighbours

    Lebrija-Trejos, E., Hernández, A. & Wright, S. J. Nature https://doi.org/10.1038/s41586-023-05717-1 (2023).Article 

    Google Scholar 
    Chesson, P. J. Ecol. 106, 1773–1794 (2018).Article 

    Google Scholar 
    Barabás, G., D’Andrea, R. & Stump, S. M. Ecol. Monogr. 88, 277–303 (2018).Article 

    Google Scholar 
    Broekman, M. J. E. et al. Ecol. Lett. 22, 1957–1975 (2019).Article 
    PubMed 

    Google Scholar 
    Freckleton, R. P. & Lewis, O. T. Proc. R. Soc. B 273, 2909–2916 (2006).Article 
    PubMed 

    Google Scholar 
    Bagchi, R. et al. Nature 506, 85–88 (2014).Article 
    PubMed 

    Google Scholar 
    Chen, L. et al. Science 366, 124–128 (2019).Article 
    PubMed 

    Google Scholar 
    Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. J. Ecol. 108, 1800–1809 (2020).Article 

    Google Scholar 
    Song, X. & Corlett, R. T. Oikos 2022, e08509 (2022).Article 

    Google Scholar 
    Engelbrecht, B. M. J. et al. Nature 447, 80–82 (2007).Article 
    PubMed 

    Google Scholar 
    Krishnadas, M. & Stump, S. M. J. Ecol. 109, 2137–2151 (2021).Article 

    Google Scholar 
    Van Dyke, M. N., Levine, J. M. & Kraft, N. J. B. Nature 611, 507–511 (2022).Article 
    PubMed 

    Google Scholar  More

  • in

    Seasonal variation in the lipid content of Fraser River Chinook Salmon (Oncorhynchus tshawytscha) and its implications for Southern Resident Killer Whale (Orcinus orca) prey quality

    Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215 (1994).Article 

    Google Scholar 
    Fisheries and Oceans Canada. National recovery strategy for northern and southern resident killer whales (Orcinus orca) in Canada [proposed]. vol. Species at (2018).National Marine Fisheries Service. Recovery Plan for Southern Resident Killer Whales (Orcinus orca). (2008).Barrett-Lennard, L. G. & Ellis, G. M. Population Structure and Genetic Variability in Northeastern Pacific Killer Whales: Towards an Assessment of Population Viability. DFO Can. Sci. Advis. Secr. Res. Deocument 2001/065 65 (2001).DFO. Evaluation of the scientific evidence to inform the probability of effectiveness of mitigation measures in reducing shipping-related noise levels received by southern resident killer whales. CSAS Science Advisory Report vol. 2017/041 (2017).Ross, P. S., Ellis, G. M., Ikonomou, M. G. & Addison, R. F. High PCB concentrations in free-ranging Pacific Killer Whales, Orcinus orca: Effects of age, sex and dietary preference. Mar. Pollut. Bull. 40, 504–515 (2000).Article 
    CAS 

    Google Scholar 
    Ward, E. J., Holmes, E. E. & Balcomb, K. C. Quantifying the effects of prey abundance on killer whale reproduction. J. Appl. Ecol. 46, 632–640 (2009).Article 

    Google Scholar 
    Ford, J. K. B., Ellis, G. M., Olesiuk, P. F. & Balcomb, K. C. Linking killer whale survival and prey abundance: Food limitation in the oceans’ apex predator ?. Biol. Lett. 6, 139–142 (2010).Article 
    PubMed 

    Google Scholar 
    Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).Article 

    Google Scholar 
    Ford, J. K. B., Ellis, G. M. & Olesiuk, P. F. Linking Prey and Population Dynamics Did Food Limitation Cause Recent Declines of RKW in BC, vol. 3848 (2005).O’Neill, S. M., Ylitalo, G. M. & West, J. E. Energy content of Pacific salmon as prey of northern and southern resident killer whales. Endanger. Species Res. 25, 265–281 (2014).Article 

    Google Scholar 
    Ford, J. K. B. & Ellis, G. M. Selective foraging by fish-eating killer whales Orcinus orca in British Columbia. Mar. Ecol. Prog. Ser. 316, 185–199 (2006).Article 
    ADS 

    Google Scholar 
    Jeffrey, K. M., Côté, I. M., Irvine, J. R. & Reynolds, J. D. Changes in body size of Canadian Pacific salmon over six decades. Can. J. Fish. Aquat. Sci. 74, 191–201 (2017).Article 

    Google Scholar 
    Ohlberger, J., Schindler, D. E., Ward, E. J., Walsworth, T. E. & Essington, T. E. Resurgence of an apex marine predator and the decline in prey body size. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1910930116 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ohlberger, J., Ward, E. J., Schindler, D. E. & Lewis, B. Demographic changes in Chinook salmon across the Northeast Pacific Ocean. Fish Fish. 19, 533–546 (2018).Article 

    Google Scholar 
    Bigler, B. S., Welch, D. W. & Helle, J. H. A review of size trends among North Pacific salmon (Oncorhynchus spp.). Can. J. Fish. Aquat. Sci. 53, 455–465 (2011).Article 

    Google Scholar 
    Hanson, M. B. et al. Species and stock identification of prey consumed by endangered southern resident killer whales in their summer range. Endanger. Species Res. 11, 69–82 (2010).Article 
    ADS 

    Google Scholar 
    Losee, J. P., Kendall, N. W. & Dufault, A. Changing salmon: An analysis of body mass, abundance, survival, and productivity trends across 45 years in Puget Sound. Fish Fish. 20, 934–951 (2019).Article 

    Google Scholar 
    Riddell, B. et al. Assessment of Status and Factors for Decline of Southern BC Chinook Salmon: Independent Panel’s Report (2013).DFO. Integrated Biological Status of Southern British Columbia Chinook Salmon (Oncorhynchus tshawytscha) Under the Wild Salmon Policy. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2016/042, 15 (2016).
    Google Scholar 
    COSEWIC. COSEWIC assessment and status report on the Chinook Salmon Oncorhynchus tshawytscha, Designatable Units in Southern British Columbia, in Canada. (2019).Pacific Salmon Commission Joint Chinook Technical Committee. Annual Report of Catch and Escapement for 2021. Tcchinook (13)-01 (2021).Quinn, T. P. Behavior and ecology of Pacific Salmon and trout. Fish Fish. 7, 75–76 (2004).
    Google Scholar 
    Brett, J. R. Energetics. In Phsyiological Ecology of Pacific Salmon (eds Groot, C. et al.) 1–68 (UBC Press, 1995).
    Google Scholar 
    Chamberlain, M. W. & Parken, C. Utilizing the Albion test fishery as an in-season predictor of run size of the Fraser River spring and summer age 52 Chinook. DFO Can. Sci. Advis. Sec. Res. Doc. 2012, 42 (2012).
    Google Scholar 
    Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Syst. 2, 369–404 (1971).Article 

    Google Scholar 
    Williams, R. et al. Competing conservation objectives for predators and prey: Estimating Killer Whale prey requirements for Chinook Salmon. PLoS ONE 6, e26738 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Courtney, K. R., Falke, J. A., Cox, M. K. & Nichols, J. Energetic status of Alaskan Chinook Salmon: Interpopulation comparisons and predictive modeling using bioelectrical impedance analysis. North Am. J. Fish. Manag. https://doi.org/10.1002/nafm.10398 (2019).Article 

    Google Scholar 
    Pothoven, S. A. et al. Reliability of bioelectrical impedance analysis for estimating whole-fish energy density and percent lipids. Trans. Am. Fish. Soc. 137, 1519–1529 (2008).Article 

    Google Scholar 
    Crossin, G. T. & Hinch, S. G. A Nonlethal, rapid method for assessing the somatic energy content of migrating adult pacific salmon. Trans. Am. Fish. Soc. 134, 184–191 (2005).Article 

    Google Scholar 
    Colt, J. & Shearer, K. D. Evaluation of the Use of the Torry Fish Fatmeter to Non-Lethally Estimate Lipid in Adult Salmon (2001).Hanson, K. C., Ostrand, K. G., Gannam, A. L. & Ostrand, S. L. Comparison and validation of nonlethal techniques for estimating condition in Juvenile Salmonids. Trans. Am. Fish. Soc. 139, 1733–1741 (2010).Article 

    Google Scholar 
    Naughton, G., Caudill, C. & Clabough, T. Migration Behavior and Spawning Success of Spring Chinook Salmon in Fall Creek and the North Fork Middle Fork Willamette River: Relationship Among Fate, Fish Condition, and Environmental Factors, 2011. (2012).Folch, J., Lees, M. & Sloane Stanley, G. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226, 497–509 (1957).Article 
    CAS 
    PubMed 

    Google Scholar 
    Post, J. R. & Parkinson, E. A. Energy allocation strategy in young fish: Allometry and survival. Ecology 82, 1040–1051 (2001).Article 

    Google Scholar 
    Arrington, D. A., Davidson, B. K., Winemiller, K. O. & Layman, C. A. Influence of life history and seasonal hydrology on lipid storage in three neotropical fish species. J. Fish Biol. 68, 1347–1361 (2006).Article 
    CAS 

    Google Scholar 
    Holty, B. L. & Ciruna, K. A. Conservation units for Pacific Salmon under the Wild Salmon Policy. DFO Can. Sci. Advis. Sec. Res. Doc 2007/070, 350 (2007).
    Google Scholar 
    PSC. Catch and Escapement of Chinook Under Pacific Salmon Commission Jurisdiction, 2001 (PSC, 2002).
    Google Scholar 
    Waples, R. S., Teel, D. J., Myers, J. M. & Marshall, A. R. Life-history divergence in Chinook Salmon: Historic contingency and parallel evolution. Evolution 58, 386–403 (2004).PubMed 

    Google Scholar 
    Beacham, T. D. et al. Pacific rim population structure of chinook salmon as determined from microsatellite analysis. Trans. Am. Fish. Soc. 135, 1604–1621 (2006).Article 
    CAS 

    Google Scholar 
    Crossin, G. T. et al. Energetics and morphology of sockeye salmon: Effects of upriver migratory distance and elevation. J. Fish Biol. 65, 788–810 (2004).Article 

    Google Scholar 
    MacDonald, B. In-Season Forecasting of Fraser Chinook Salmon Using Genetic Stock Identification of Test Fishery Data By (2016).Parken, C. K., Candy, J. R., Irvine, J. R. & Beacham, T. D. Genetic and coded wire tag results combine to allow more-precise management of a complex Chinook salmon aggregate. North Am. J. Fish. Manag. 28, 328–340 (2008).Article 

    Google Scholar 
    Mann, R., Peery, C., Pinson, A. & Anderson, C. Energy use, migration times, and spawning success of adult spring–summer Chinook salmon returning to spawning areas in the South Fork Salmon River in Central Idaho: 2002–2007. Technical report 2009–4 http://www.cnr.uidaho.edu/uiferl/pdfreports/SFS_Tech_Report_2009-4_Final.pdf (2009).Hearsey, J. W. & Kinziger, A. P. Diversity in sympatric chinook salmon runs: Timing, relative fat content and maturation. Environ. Biol. Fishes 98, 413–423 (2015).Article 

    Google Scholar 
    Arimitsu, M. L. et al. Heatwave-induced synchrony within forage fish portfolio disrupts energy flow to top pelagic predators. Glob. Chang. Biol. 27, 1859–1878 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lloret-Lloret, E. et al. Small pelagic fish fitness relates to local environmental conditions and trophic variables. Prog. Oceanogr. 202, 102745 (2022).Article 

    Google Scholar 
    Mesa, M. G. & Magie, C. D. Evaluation of energy expenditure in adult spring Chinook salmon migrating upstream in the Columbia River Basin: An assessment based on sequential proximate analysis. River Res. Appl. 22, 1085–1095 (2006).Article 

    Google Scholar 
    Crossin, G. T., Hinch, S. G., Farrell, A. P., Higgs, D. A. & Healey, M. C. Somatic energy of sockeye salmon Oncorhynchus nerka at the onset of upriver migration: A comparison among ocean climate regimes. Fish. Oceanogr. 13, 345–349 (2004).Article 

    Google Scholar 
    Roni, P. & Quinn, T. P. Geographic variation in size and age of North American Chinook salmon. North Am. J. Fish. Manag. 15, 325–345 (1995).Article 

    Google Scholar 
    Hendry, A. P., Berg, O. K., Quinn, T. P. & Condition, T. P. Condition dependence and adaptation-by-time: Breeding date, life history, and energy allocation within a population of salmon. Oikos 85, 499–514 (1999).Article 

    Google Scholar 
    Hanson, M. B. et al. Endangered predators and endangered prey: Seasonal diet of Southern Resident killer whales. PLoS ONE 16, e0247031 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weitkamp, L. A. Marine distributions of Chinook Salmon from the West Coast of North America determined by coded wire tag recoveries. Trans. Am. Fish. Soc. 139, 147–170 (2010).Article 

    Google Scholar 
    Shields, M. W., Lindell, J. & Woodruff, J. Declining spring usage of core habitat by endangered fish-eating killer whales reflects decreased availability of their primary prey. Pac. Conserv. Biol. 24, 189–193 (2018).Article 

    Google Scholar 
    Brown, G. S. et al. Pre-COSEWIC review of southern British Columbia Chinook Salmon (Oncorhynchus tshawytscha) conservation units Part I: Background. Can. Sci. Advis. Sec. Res. Doc. 2019/11, 67 (2019).
    Google Scholar 
    NOAA Fisheries West Coast & Washington Department of Fish and Wildlife. Southern Resident Killer Whale Priority Chinook Stocks Report. https://www.westcoast.fisheries.noaa.gov/publications/protected_species/marine_mammals/killer_whales/recovery/srkw_priority_chinook_stocks_conceptual_model_report___list_22june2018.pdf (2018).Chalifour, L. et al. Chinook salmon exhibit long-term rearing and early marine growth in the fraser river, british columbia, a large urban estuary. Can. J. Fish. Aquat. Sci. 78, 539–550 (2021).Article 
    CAS 

    Google Scholar 
    Lamperth, J. S., Quinn, T. P. & Zimmerman, M. S. Levels of stored energy but not marine foraging patterns differentiate seasonal ecotypes of wild and hatchery steelhead (Oncorhynchus mykiss) returning to the Kalama river, Washington. Can. J. Fish. Aquat. Sci. 74, 157–167 (2017).Article 
    CAS 

    Google Scholar 
    Von Biela, V. R. et al. Extreme reduction in nutritional value of a key forage fish during the pacific marine heatwave of 2014–2016. Mar. Ecol. Prog. Ser. 613, 171–182 (2019).Article 
    ADS 

    Google Scholar 
    Healey, M. C. Life history of Chinook Salmon (Oncorhynchus tshawytscha). In Pacific Salmon Life Histories (eds Groot, C. & Margolis, L.) 313–393 (University of British Columbia Press, 1991).
    Google Scholar 
    Freshwater, C. et al. An integrated model of seasonal changes in stock composition and abundance with an application to Chinook salmon. PeerJ 9, 1–27 (2021).Article 

    Google Scholar 
    Couture, F., Oldford, G., Christensen, V., Barrett-lennard, L. & Walters, C. Requirements and availability of prey for northeastern pacific southern resident killer whales. PLoS ONE 17, e0270523 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    DFO. Government of Canada Takes Action to Address Fraser River Chinook Decline (DFO, 2019).
    Google Scholar 
    Brown, R. F. & Musgrave, M. M. Preliminary Catalogue of Salmon Steams and Escapements of Misson-Harrison Sub District. Fisheries and Marine Service Data Report No. 133 (1979).Manzon, C. I. & Marshall, D. E. Catalogue of salmon streams and spawning escapements of Cariboo subdistrict. Can. Data Rep. Fish. Aquat. Sci. 211, 51 (1980).
    Google Scholar 
    Marshall, D. E. & Manzon, C. I. Catalogue of Salmon Streams and Spawning Escapements of the Prince George Subdistrict (Department of Fisheries and Oceans Fisheries and Marine Services Data Report N0o. 79, 1980).
    Google Scholar 
    Olmsted, W., Whelen, M. & Stewart, R. 1980 Investigations of fall-spawning chinook salmon (Oncorhynchus tshawytscha), Quesnel, blackwater (west road) and cottonwood river drainages, B.C. 34, 131–134 (1981).Brown, R. F., Musgrave, M. M. & Marshall, D. E. Catalogue of salmon streams and spawning escapements for Kamloops sub-district. Fish. Mar. Serv. Data Rep. 151, 226 (1979).
    Google Scholar 
    DFO. Information Document to Assist Development of a Fraser Chinook Management Plan 56 (DFO, 2006).
    Google Scholar 
    Kosakoski, G. T. & Hamilton, R. E. Water Requirements for the Fisheries Resource of the Nicola River, B.C. Can. Manuscr. Rep. Fish. Aquat. Sci. 140 (1982). More

  • in

    Two odorant receptors regulate 1-octen-3-ol induced oviposition behavior in the oriental fruit fly

    Insect rearingWT B. dorsalis were collected from Haikou, Hainan province, China, in 2008. They were maintained at the Key Laboratory of Entomology and Pest Control Engineering in Chongqing at 27 ± 1 °C, 70 ± 5% relative humidity, with a 14-h photoperiod. Adult flies were reared on an artificial diet containing honey, sugar, yeast powder, and vitamin C. Newly hatched larvae were transferred to an artificial diet containing corn and wheat germ flour, yeast powder, agar, sugar, sorbic acid, linoleic acid, and filter paper.Behavioral assaysDouble trap lure assays were set up to compare the olfactory preferences of gravid and virgin females in a 20 × 20 × 20 cm transparent cage with evenly distributed holes (diameter = 1.5 mm) on the side walls. The traps were refitted from inverted 50-mL centrifuge tubes and were placed along the diagonal of the cage. The top of each trap was pierced with a 1-mL pipette tip, which was shortened to ensure flies could access the trap from the pipette. For the olfactory preference assay with mango, one trap was loaded with 60 mg mango flesh and the other trap with 20 μL MO in the cap of a 200-μL PCR tube. For the olfactory preference assay with 1-octen-3-ol (≥98%, sigma, USA), one trap was loaded with 20 μL 10% (v/v) 1-octen-3-ol diluted in MO, and the other with 20 μL MO. A cotton ball soaked in water was placed at the center of the cage to provide water for the flies. Groups of 30 female flies were introduced into the cage for each experiment, and each experiment was repeated to provide eight biological replicates. All experiments commenced at 10 am to ensure circadian consistency. The number of flies in each trap was counted every 2 h for 24 h. We compared the preferences of 3-day-old immature females, 15-day-old virgin females, and 15-day-old mated females. The olfactory preference index was calculated using the following formula41: (number of flies in mango/odorant trap – number of flies in control trap)/total number of flies.Oviposition behavior was monitored in a 10 × 10 × 10 cm transparent cage with evenly distributed holes on the side walls as above. A 9-cm Petri dish filled with 1% agar was served as an oviposition substrate, and the mango flesh, 10% (v/v) 1-octen-3-ol or MO were added at opposite edges of the dish. We tested the preference of flies for different substrates: (1) ~60 mg of mango flesh on one edge and 20 μL of MO on the other; (2) 20 μL of 1-octen-3-ol on one edge and 20 μL of MO on the other; (3) ~60 mg mango flesh on one edge and 20 μL of 1-octen-3-ol on the other; and (4) ~60 mg mango flesh plus 20 μL 1-octen-3-ol on one side and ~60 mg of mango flesh plus 20 μL MO on the other. The agar disc was covered in a pierced plastic wrap to mimic fruit skin, encouraging flies extend their ovipositor into the plastic wrap to lay eggs. The agar disc was placed at the center of the cage, and we introduced eight 15-day-old gravid females. Two Sony FDR-AX40 cameras recorded the behavior of the flies for 24 h, one fixed above the cage to record the tracks and the other placed in front of the cage to record the oviposition behavior. Based on the results from double traps luring assays, a 3 h duration (6–9 h) of the videos was selected to analyze the tracks and spent time of all flies in observed area (the surface of Petri dish). The videos were analyzed using EthoVision XT v16 (Noldus Information Technology) to determine the total time of all flies spent on each side in seconds and the total distance of movement in centimeters, and the tracks were visualized in the form of heat maps17. The number of eggs laid by the eight flies in each experiment was counted under a CNOPTEC stereomicroscope, and each experimental group comprised 7–16 replicates.Annotation of B. dorsalis OR genesD. melanogaster amino acid sequences downloaded from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) were used as BLASTP queries against the B. dorsalis amino acid database with an identity cut-off of 30%. The candidate OR genes were compared with deep transcriptome data from B. dorsalis antennae42, maxillary palps and proboscis, and other tissues.Cloning of candidate B. dorsalis OR genesHigh-fidelity PrimerSTAR Max DNA polymerase (TaKaRa, Dalian, China) was used to amplify the full open reading frame of BdorOR genes by nested PCR using primers (Supplementary Table 2) designed according to B. dorsalis genome data. Each 25-μL reaction comprised 12.5 μL 2 × PrimerSTAR Max Premix (TaKaRa), 10.5 μL ultrapure water, 1 μL of each primer (10 μM), and 1 μL of the cDNA template. An initial denaturation step at 98 °C for 2 min was followed by 35 cycles of 10 s at 98 °C, 15 s at 55 °C and 90 s at 72 °C, and a final extension step of 10 min at 72 °C. Purified PCR products were transferred to the vector pGEM-T Easy (Promega, Madison, WI) for sequencing (BGI, Beijing, China).Transcriptional profilingTotal RNA was extracted from (i) male and female antennae, maxillary palps, head cuticle (without antenna, maxillary palps, and proboscis), proboscis, legs, wings and ovipositors, and (ii) from the heads of 15-day-old virgin and mated females using TRIzol reagent (Invitrogen, Carlsbad, CA). Genomic DNA was eliminated with RNase-free DNase I (Promega) and first-strand cDNA was synthesized from 1 µg total RNA using the PrimeScript RT reagent kit (TaKaRa). Standard curves were used to evaluate primer efficiency (Supplementary Table 3) with fivefold serial dilutions of cDNA. Quantitative real-time PCR (qRT-PCR) was carried out using a CFX Connect Real-Time System (Bio-Rad, Hercules, CA) in a total reaction volume of 10 µL containing 5 μL SYBR Supermix (Novoprotein, Shanghai, China), 3.9 μL nuclease-free water, 0.5 μL cDNA (~200 ng/μL) and 0.3 μL of the forward and reverse primers (10 μM). We used α-tubulin (GenBank: GU269902) and ribosomal protein S3 (GenBank: XM_011212815) as internal reference genes. Four biological replicates were prepared for each experiment. Relative expression levels were determined using the 2−∆∆Ct method43, and data were analyzed using SPSS v20.0 (IBM).Two-electrode voltage clamp electrophysiological recordingsVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pT7Ts for expression in oocytes. The plasmids were linearized for the synthesis of cRNAs using the mMESSAGE mMACHINE T7 Kit (Invitrogen, Lithuania). The purified cRNA was diluted to 2 µg/µL and ~60 ng cRNA was injected into X. laevis oocytes. The oocytes were pre-treated with 1.5 mg/mL collagenase I (GIBCO, Carlsbad, CA) in washing buffer (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, pH 7.6) for 30–40 min at room temperature before injection. After incubation for 2 days at 18 °C in Ringer’s solution (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, 0.8 mM CaCl2), the oocytes were exposed to different concentrations of 1-octen-3-ol diluted in Ringer’s solution from a 1 M stock in DMSO. Odorant-induced whole-cell inward currents were recorded from injected oocytes using a two-electrode voltage clamp and an OC-725C amplifier (Warner Instruments, Hamden, CT) at a holding potential of –80 mV. The signal was processed using a low-pass filter at 50 Hz and digitized at 1 kHz. Oocytes injected with nuclease-free water served as a negative control. Data were acquired using a Digidata 1550 A device (Warner Instruments, Hamden, CT) and analyzed using pCLAMP10.5 software (Axon Instruments Inc., Union City, CA).Calcium imaging assayVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pcDNA3.1(+) along with an mCherry tag that confers red fluorescence to confirm transfection. High-quality plasmid DNA was prepared using the Qiagen plasmid MIDIprep kit (QIAgen, Düsseldorf, Germany). The B. dorsalis OR and BdorOrco plasmids were co-transfected into HEK 293 cell using TransIT-LT1 transfection reagent (Mirus Bio LLC, Japan) in 96-well plates. The fluorescent dye Fluo-4 AM (Invitrogen) was prepared as a 1 mM stock in DMSO and diluted to 2.5 μM in Hanks’ balanced salt solution (HBSS, Invitrogen, Lithuania) to serve as a calcium indicator. The cell culture medium was removed 24–30 h after transfection and cells were rinsed three times with HBSS before adding Fluo 4-AM and incubating the cells for 1 h in the dark. After three rinses in HBSS, 99 μL of fresh HBSS was added to each well before testing in the dark with 1 μL of diluted 1-octen-3-ol. Fluorescent images were acquired on a laser scanning confocal microscope (Zeiss, Germany). Fluo 4-AM was excited at 488 nm and mCherry at 555 nm. The relative change in fluorescence (ΔF/F0) was used to represent the change in Ca2+, where F0 is the baseline fluorescence and ΔF is the difference between the peak fluorescence induced by 1-octen-3-ol stimulation and the baseline. The healthy and successfully transfected cells (red when excited at 555 nm) were used for analysis. The final concentration of 10−4 M was initially used to screen corresponding ORs, and then to determine the response of screened ORs to stimulation with different concentrations of 1-octen-3-ol. Each concentration of 1-octen-3-ol was tested in triplicate. Concentration–response curves were prepared using GraphPad Prism v8.0 (GraphPad Software).Genome editingThe exon sequences of BdorOR7a-6 and BdorOR13a were predicted using the high-quality B. dorsalis genome assembly. Each gRNA sequence was 20 nucleotides in length plus NGG as the protospacer adjacent motif (PAM). The potential for off-target mutations was evaluated by using CasOT to screen the B. dorsalis genome sequence. Each gRNA was synthesized using the GeneArt Precision gRNA Synthesis Kit (Invitrogen) and purified using the GeneArt gRNA Clean-up Kit (Invitrogen). Embryos were microinjected as previously described20. Purified gRNA and Cas9 protein from the GeneArt Platinum Cas9 Nuclease Kit (Invitrogen) were mixed and diluted to final concentrations of 600 and 500 ng/µL, respectively. Fresh eggs (laid within 20 min) were collected and exposed to 1% sodium hypochlorite for 90 s to soften the chorion. The eggs were fixed on glass slides and injected with the mix of gRNA and Cas9 protein at the posterior pole using an IM-300 device (Narishige, Tokyo, Japan) and needles prepared using a Model P-97 micropipette puller (Sutter Instrument Co, Novato, CA). Eggs were injected with nuclease-free water as a negative control. Injection was completed within 2 h. The injected embryos were cultured in a 27 °C incubator and mortality was recorded during subsequent development.G0 mutants were screened as previously described20. G0 adult survivors were individually backcrossed to WT flies (single pair) to collect G1 offspring. Genomic DNA was extracted from G0 individuals after oviposition using the DNeasy Blood & Tissue Kit (Qiagen). The region surrounding each gRNA target was amplified by PCR using the extracted DNA as a template, the specific primers listed in Supplementary Table 2, and 2 × Taq PCR MasterMix (Biomed, Beijing, China). PCR products were analyzed by capillary electrophoresis using the QIAxcel DNA High Resolution Kit (Qiagen). PCR products differing from the WT alleles were purified and transferred to the vector pGEM-T Easy for sequencing. To confirm the mutation was inherited, genomic DNA was also extracted from one mesothoracic leg of G1 flies using InstaGene Matrix (Bio-Rad, Hercules, CA) and was analyzed as above. To avoid potential off-target mutations, heterozygous G1 mutants were backcrossed to WT flies more than 10 generations before self-crossing to generate homozygous mutant flies.Electroantennogram (EAG) recordingThe antennal responses of 15-day-old B. dorsalis adults to 1-octen-3-ol were determined by EAG recording (Syntech, the Netherlands) as previously reported20. Briefly, antennae were fixed to two electrodes using Spectra 360 electrode gel (Parker, Fairfield, NJ, USA). The signal response was amplified using an IDAC4 device and collected using EAG-2000 software (Syntech). Before each experiment, 1-octen-3-ol and other three volatiles (ethyl tiglate, ethyl acetate, ethyl butyrate) were diluted to 10%, 1% and 0.1% (v/v) with MO to serve as the electrophysiological stimulus, and MO was used as a negative control. A constant air flow (100 mL/min) was produced using a controller (Syntech) to stimulate the antenna. We then placed 10 µL of each dilution or MO onto a piece of filter paper (5 × 1 cm), and the negative control (MO) was applied before and after the diluted odorants to calibrate the response signal. The EAG responses at each concentration were recorded for 15–20 antennae, and each concentration was recorded twice. Each test lasted 1 s, and the interval between tests was 30 s. EAG response data from WT and mutant flies for the diluted odorants were analyzed using Student’s t test with SPSS v20.0.Molecular docking and site-directed mutagenesisThe three dimensional-structures of BdorOR7a-6 and BdorOR13a were modeled using AlphaFold 2.044. The quality and rationality of each protein structure was evaluated online using a PROCHECK Ramachandran plot in SAVES 6.0 (https://saves.mbi.ucla.edu/). AutoDock Vina 1.1.2 was used for docking analysis, and the receptor protein structure and ligand molecular structure were pre-treated using AutoDock 4.2.6. The docking parameters were set according to the protein structure and active sites, and the optimal docking model was selected based on affinity (kcal/mol). Docking models were imported into Pymol and Discovery Studio 2016 Client for analysis and image processing. Based on the molecular docking data, three residues (Asn86 in OR7a-6, Asp320, and Lys323 in OR13a) were replaced with alanine by site-directed mutagenesis45 using the primers listed in Supplementary Table 2. Calcium imaging assays and molecular docking of mutated proteins were then carried out as described above.Statistics reproducibilityAll of the olfactory preference assays, oviposition bioassays, expression profiles analysis, EAG recording assays were analyzed using Student’s t-test (*p  More

  • in

    The degree of urbanisation reduces wild bee and butterfly diversity and alters the patterns of flower-visitation in urban dry grasslands

    Sánchez-Bayo, F. & Wyckhuys, K. A. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27. https://doi.org/10.1016/j.biocon.2019.01.020 (2019).Article 

    Google Scholar 
    van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420. https://doi.org/10.1126/science.aax9931 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480. https://doi.org/10.1146/annurev-ento-011019-025151 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Goulson, D. The insect apocalypse, and why it matters. Curr. Biol. 29, R967–R971. https://doi.org/10.1016/j.cub.2019.06.069 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426. https://doi.org/10.1016/j.biocon.2020.108426 (2020).Article 

    Google Scholar 
    Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353. https://doi.org/10.1016/j.tree.2010.01.007 (2010).Article 
    PubMed 

    Google Scholar 
    Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957. https://doi.org/10.1126/science.1255957 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ollerton, J. Pollinator diversity: Distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376. https://doi.org/10.1146/annurev-ecolsys-110316-022919 (2017).Article 

    Google Scholar 
    Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721 (2007).Article 
    PubMed 

    Google Scholar 
    Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals?. Oikos 120, 321–326. https://doi.org/10.1111/j.1600-0706.2010.18644.x (2011).Article 

    Google Scholar 
    Ollerton, J., Erenler, H., Edwards, M. & Crockett, R. Pollinator declines. Extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science 346, 1360–1362. https://doi.org/10.1126/science.1257259 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Wenzel, A., Grass, I., Belavadi, V. V. & Tscharntke, T. How urbanization is driving pollinator diversity and pollination—A systematic review. Biol. Conserv. 241, 108321. https://doi.org/10.1016/j.biocon.2019.108321 (2020).Article 

    Google Scholar 
    Senapathi, D., Goddard, M. A., Kunin, W. E. & Baldock, K. C. R. Landscape impacts on pollinator communities in temperate systems: Evidence and knowledge gaps. Funct. Ecol. 31, 26–37. https://doi.org/10.1111/1365-2435.12809 (2017).Article 

    Google Scholar 
    Fenoglio, M. S., Rossetti, M. R. & Videla, M. Negative effects of urbanization on terrestrial arthropod communities: A meta-analysis. Glob. Ecol. Biogeogr. 29, 1412–1429. https://doi.org/10.1111/geb.13107 (2020).Article 

    Google Scholar 
    Ives, C. D. et al. Cities are hotspots for threatened species. Glob. Ecol. Biogeogr. 25, 117–126. https://doi.org/10.1111/geb.12404 (2016).Article 

    Google Scholar 
    Soanes, K. & Lentini, P. E. When cities are the last chance for saving species. Front. Ecol. Evol. 17, 225–231. https://doi.org/10.1002/fee.2032 (2019).Article 

    Google Scholar 
    Lynch, L. et al. Changes in land use and land cover along an urban-rural gradient influence floral resource availability. Curr. Landsc. Ecol. Rep. 6, 46–70. https://doi.org/10.1007/s40823-021-00064-1 (2021).Article 

    Google Scholar 
    Hall, D. M. et al. The city as a refuge for insect pollinators. Conserv. Biol. 31, 24–29. https://doi.org/10.1111/cobi.12840 (2017).Article 
    PubMed 

    Google Scholar 
    Buchholz, S. & Egerer, M. H. Functional ecology of wild bees in cities: Towards a better understanding of trait-urbanization relationships. Biodivers. Conserv. 29, 2779–2801. https://doi.org/10.1007/s10531-020-02003-8 (2020).Article 

    Google Scholar 
    Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 576. https://doi.org/10.1038/s41467-020-14496-6 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Khalifa, S. A. M. et al. Overview of bee pollination and its economic value for crop production. Insects https://doi.org/10.3390/insects12080688 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Doyle, T. et al. Pollination by hoverflies in the Anthropocene. Proc. Biol. Sci. 287, 20200508. https://doi.org/10.1098/rspb.2020.0508 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl. Acad. Sci. USA. 113, 146–151. https://doi.org/10.1073/pnas.1517092112 (2016).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Persson, A. S., Ekroos, J., Olsson, P. & Smith, H. G. Wild bees and hoverflies respond differently to urbanisation, human population density and urban form. Landsc. Urban Plan. 204, 103901. https://doi.org/10.1016/j.landurbplan.2020.103901 (2020).Article 

    Google Scholar 
    Gathof, A. K., Grossmann, A. J., Herrmann, J. & Buchholz, S. Who can pass the urban filter? A multi-taxon approach to disentangle pollinator trait-environmental relationships. Oecologia 199, 165–179. https://doi.org/10.1007/s00442-022-05174-z (2022).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baldock, K. C. R. et al. Where is the UK’s pollinator biodiversity? The importance of urban areas for flower-visiting insects. Proc. Biol. Sci. 282, 20142849. https://doi.org/10.1098/rspb.2014.2849 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ramírez-Restrepo, L. & MacGregor-Fors, I. Butterflies in the city: A review of urban diurnal Lepidoptera. Urban Ecosyst. 20, 171–182. https://doi.org/10.1007/s11252-016-0579-4 (2017).Article 

    Google Scholar 
    Kuussaari, M. et al. Butterfly species’ responses to urbanization: Differing effects of human population density and built-up area. Urban Ecosyst. 24, 515–527. https://doi.org/10.1007/s11252-020-01055-6 (2020).Article 

    Google Scholar 
    Theodorou, P. The effects of urbanisation on ecological interactions. Curr. Opin. Insect. Sci. 52, 100922. https://doi.org/10.1016/j.cois.2022.100922 (2022).Article 
    PubMed 

    Google Scholar 
    Martins, K. T., Gonzalez, A. & Lechowicz, M. J. Patterns of pollinator turnover and increasing diversity associated with urban habitats. Urban Ecosyst. 20, 1359–1371. https://doi.org/10.1007/s11252-017-0688-8 (2017).Article 

    Google Scholar 
    Theodorou, P. et al. The structure of flower visitor networks in relation to pollination across an agricultural to urban gradient. Funct. Ecol. 31, 838–847. https://doi.org/10.1111/1365-2435.12803 (2017).Article 

    Google Scholar 
    Geslin, B., Gauzens, B., Thébault, E. & Dajoz, I. Plant pollinator networks along a gradient of urbanisation. PLoS ONE 8, e63421. https://doi.org/10.1371/journal.pone.0063421 (2013).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Udy, K. L., Reininghaus, H., Scherber, C. & Tscharntke, T. Plant–pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes. Ecosphere https://doi.org/10.1002/ecs2.3020 (2020).Article 

    Google Scholar 
    Jędrzejewska-Szmek, K. & Zych, M. Flower-visitor and pollen transport networks in a large city: Structure and properties. Arthropod. Plant Interact. 7, 503–516. https://doi.org/10.1007/s11829-013-9274-z (2013).Article 

    Google Scholar 
    von der Lippe, M., Buchholz, S., Hiller, A., Seitz, B. & Kowarik, I. CityScapeLab Berlin: A research platform for untangling urbanization effects on biodiversity. Sustainability 12, 2565. https://doi.org/10.3390/su12062565 (2020).Article 

    Google Scholar 
    Dylewski, Ł, Maćkowiak, Ł & Banaszak-Cibicka, W. Are all urban green spaces a favourable habitat for pollinator communities? Bees, butterflies and hoverflies in different urban green areas. Ecol. Entomol. 44, 678–689. https://doi.org/10.1111/een.12744 (2019).Article 

    Google Scholar 
    Grossmann, A. J., Herrmann, J., Buchholz, S. & Gathof, A. K. Dry grassland within the urban matrix acts as favourable habitat for different pollinators including endangered species. Insect Conserv. Divers. https://doi.org/10.1111/icad.12607 (2022).Article 

    Google Scholar 
    Settele, J., Steiner, R., Feldmann, R. & Hermann, G. Schmetterlinge. Die Tagfalter Deutschlands: 720 Farbfotos. 3rd ed. (2015).Amiet, F. Hymenoptera Apidae, 1. Teil. Allgemeiner Teil, Gattungsschlüssel – Die Gattungen Apis, Bombus und Psithyrus (Centre Suisse de Cartographie de la Faune, 1996).
    Google Scholar 
    Amiet, F., Müller, A. & Neumeyer, R. Apidae 2. Colletes, Dufourea, Hylaeus, Nomia, Nomioides, Rhophitoides, Rophites, Sphecodes, Systropha (Fauna Helvetica, 1999).
    Google Scholar 
    Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 3. Halictus, Lasioglossum (Centre Suisse de Cartographie de la Faune, 2001).
    Google Scholar 
    Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 4. Anthidium, Chelostoma, Coelioxys, Dioxys, Heriades, Lithurgus, Megachile, Osmia, Stelis (Centre Suisse de Cartographie de la Faune, 2004).
    Google Scholar 
    Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 5. Ammobates, Ammobatoides, Anthophora, Biastes, Ceratina, Dasypoda, Epeoloides, Epeolus, Eucera, Macropis, Melecta, Melitta, Nomada, Pasites, Tetralonia, Thyreus, Xylocopa (Centre Suisse de Cartographie de la Faune, 2007).
    Google Scholar 
    Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 6. Andrena, Melliturga, Panurginus, Panurgus (Centre Suisse de Cartographie de la Faune, 2010).
    Google Scholar 
    Gokcezade, J. F., Gereben-Krenn, B.-A., Neumayer, J. & Krenn, H. W. Feldbestimmungsschlüssel für die Hummeln Österreichs, Deutschlands und der Schweiz (Hymenoptera, Apidae). Linzer biologische Beiträge 47, 5–42 (2015).
    Google Scholar 
    Bartsch, H. Tvåvingar: Blomflugor. Diptera: Syrphidae: Syrphinae: denna volym omfattar samtliga nordiska arter (ArtDatabanken Sveriges lantbruksuniversitet, 2009).
    Google Scholar 
    Bartsch, H. Tvåvingar: Blomflugor. Diptera: Syrphidae: Eristalinae & Microdontinae: denna volym omfattar samtliga nordiska arter (ArtDatabanken Sveriges lantbruksuniversitet, 2009).
    Google Scholar 
    Bot, S. & van de Meutter, F. Veldgids zweefvliegen (KNNV Uitgeverij, 2019).
    Google Scholar 
    Jäger, E. J. Rothmaler-Exkursionsflora von Deutschland. Gefäßpflanzen: Grundband 20th edn. (Springer Spektrum, 2011).
    Google Scholar 
    Senate Department for Urban Development and Housing. Berlin Environmental Atlas. 06.01 Actual Use of Built-up Areas/06.02 Inventory of Green and Open Spaces 2010 (2011).Holland, J. D., Bert, D. G. & Fahrig, L. Determining the spatial scale of species’ response to habitat. Bioscience 54, 227. https://doi.org/10.1641/0006-3568(2004)054[0227:DTSSOS]2.0.CO;2 (2004).Article 

    Google Scholar 
    Senate Department for Urban Development and Housing. Berlin Environmental Atlas. 05.08 Biotope Types (2014).Hanski, I. A practical model of metapopulation dynamics. J. Anim. Ecol. 63, 151. https://doi.org/10.2307/5591 (1994).Article 

    Google Scholar 
    Hanski, I. Habitat connectivity, habitat continuity, and metapopulations in dynamic landscapes. Oikos 87, 209. https://doi.org/10.2307/3546736 (1999).Article 

    Google Scholar 
    Senate Department for Urban Development and Housing. Berlin Environmental Atlas. 06.10 Building and Vegetation Heights (2014).Saura, S. & Torné, J. Conefor Sensinode 2.2: A software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Model. Softw. 24, 135–139. https://doi.org/10.1016/j.envsoft.2008.05.005 (2009).Article 

    Google Scholar 
    Saure, C. Rote Liste und Gesamtartenliste der Bienen und Wespen (Hymenoptera part.) von Berlin mit Angaben zu den Ameisen. In Rote Listen der gefährdeten Pflanzen und Tiere von Berlin.Speight, M. C. D. Species Accounts of European Syrphidae (Diptera) (Syrph the Net Publications, 2014).
    Google Scholar 
    Middleton-Welling, J. et al. A new comprehensive trait database of European and Maghreb butterflies, Papilionoidea. Sci. Data 7, 351. https://doi.org/10.1038/s41597-020-00697-7 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dormann, C. F., Fründ, J., Blüthgen, N. & Gruber, B. Indices, graphs and null models: Analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24. https://doi.org/10.2174/1874213000902010007 (2009).Article 

    Google Scholar 
    Kaiser-Bunbury, C. N. & Blüthgen, N. Integrating network ecology with applied conservation: A synthesis and guide to implementation. AoB Plants https://doi.org/10.1093/aobpla/plv076 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Almeida-Neto, M., Guimarães, P., Guimarães, P. R., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: Reconciling concept and measurement. Oikos 117, 1227–1239. https://doi.org/10.1111/J.0030-1299.2008.16644.X (2008).Article 

    Google Scholar 
    Dormann, C. F. & Strauss, R. A method for detecting modules in quantitative bipartite networks. Methods Ecol. Evol. 5, 90–98. https://doi.org/10.1111/2041-210X.12139 (2014).Article 

    Google Scholar 
    Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9. https://doi.org/10.1186/1472-6785-6-9 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Patefield, W. M. Algorithm AS 159: An efficient method of generating random R × C tables with given row and column totals. J. Appl. Stat. 30, 91. https://doi.org/10.2307/2346669 (1981).Article 
    MATH 

    Google Scholar 
    Stein, K. et al. Plant–pollinator networks in Savannas of Burkina Faso, West Africa. Diversity 13, 1. https://doi.org/10.3390/d13010001 (2021).Article 
    ADS 

    Google Scholar 
    Escobedo-Kenefic, N. et al. Disentangling the effects of local resources, landscape heterogeneity and climatic seasonality on bee diversity and plant–pollinator networks in tropical highlands. Oecologia 194, 333–344. https://doi.org/10.1007/s00442-020-04715-8 (2020).Article 
    ADS 
    PubMed 

    Google Scholar 
    Renaud, E., Baudry, E. & Bessa-Gomes, C. Influence of taxonomic resolution on mutualistic network properties. Ecol. Evol. 10, 3248–3259. https://doi.org/10.1002/ece3.6060 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ropars, L., Dajoz, I., Fontaine, C., Muratet, A. & Geslin, B. Wild pollinator activity negatively related to honey bee colony densities in urban context. PLoS ONE 14, e0222316. https://doi.org/10.1371/journal.pone.0222316 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Egerer, M. & Kowarik, I. Confronting the modern gordian knot of urban beekeeping. Trends Ecol. Evol. 35, 956–959. https://doi.org/10.1016/j.tree.2020.07.012 (2020).Article 
    PubMed 

    Google Scholar 
    Zuur, A. F., Ieono, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).Book 
    MATH 

    Google Scholar 
    Bartón, K. MuMIn. multi-model inference, R package version 1.42.1 (2018).Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290. https://doi.org/10.1093/bioinformatics/btg412 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wood, T. J., Kaplan, I. & Szendrei, Z. Wild bee pollen diets reveal patterns of seasonal foraging resources for honey bees. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00210 (2018).Article 

    Google Scholar 
    Proske, A., Lokatis, S. & Rolff, J. Impact of mowing frequency on arthropod abundance and diversity in urban habitats: A meta-analysis. Urban For Urban Green 76, 127714. https://doi.org/10.1016/j.ufug.2022.127714 (2022).Article 

    Google Scholar 
    Bates, A. J. et al. Changing bee and hoverfly pollinator assemblages along an urban-rural gradient. PLoS ONE 6, e23459. https://doi.org/10.1371/journal.pone.0023459 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Geslin, B. et al. The proportion of impervious surfaces at the landscape scale structures wild bee assemblages in a densely populated region. Ecol. Evol. 6, 6599–6615. https://doi.org/10.1002/ece3.2374 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Birdshire, K. R., Carper, A. L. & Briles, C. E. Bee community response to local and landscape factors along an urban-rural gradient. Urban Ecosyst. 23, 689–702. https://doi.org/10.1007/s11252-020-00956-w (2020).Article 

    Google Scholar 
    Goddard, M. A., Benton, T. G. & Dougill, A. J. Beyond the garden fence: Landscape ecology of cities. Trends Ecol. Evol. 25, 202–203. https://doi.org/10.1016/j.tree.2009.12.007 (2010).Article 

    Google Scholar 
    Theodorou, P. et al. Bumble bee colony health and performance vary widely across the urban ecosystem. J. Anim. Ecol. 91, 2135–2148. https://doi.org/10.1111/1365-2656.13797 (2022).Article 
    PubMed 

    Google Scholar 
    Potts, S. G., Vulliamy, B., Dafni, A., Ne’eman, G. & Willmer, P. Linking bees and flowers: How do floral communities structure pollinator communities?. Ecology 84, 2628–2642. https://doi.org/10.1890/02-0136 (2003).Article 

    Google Scholar 
    Ebeling, A., Klein, A.-M., Schumacher, J., Weisser, W. W. & Tscharntke, T. How does plant richness affect pollinator richness and temporal stability of flower visits?. Oikos 117, 1808–1815. https://doi.org/10.1111/j.1600-0706.2008.16819.x (2008).Article 

    Google Scholar 
    Theodorou, P. et al. Urban fragmentation leads to lower floral diversity, with knock-on impacts on bee biodiversity. Sci. Rep. 10, 21756. https://doi.org/10.1038/s41598-020-78736-x (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Potts, S. G. et al. Role of nesting resources in organising diverse bee communities in a Mediterranean landscape. Ecol. Entomol. 30, 78–85. https://doi.org/10.1111/j.0307-6946.2005.00662.x (2005).Article 

    Google Scholar 
    Fründ, J., Linsenmair, K. E. & Blüthgen, N. Pollinator diversity and specialization in relation to flower diversity. Oikos 119, 1581–1590. https://doi.org/10.1111/j.1600-0706.2010.18450.x (2010).Article 

    Google Scholar 
    Fornoff, F. et al. Functional flower traits and their diversity drive pollinator visitation. Oikos 126, 1020–1030. https://doi.org/10.1111/oik.03869 (2017).Article 
    CAS 

    Google Scholar 
    Hofmann, M. M. & Renner, S. S. One-year-old flower strips already support a quarter of a city’s bee species. J. Hymenopt. Res. 75, 87–95. https://doi.org/10.3897/jhr.75.47507 (2020).Article 

    Google Scholar 
    Verboven, H. A., Uyttenbroeck, R., Brys, R. & Hermy, M. Different responses of bees and hoverflies to land use in an urban–rural gradient show the importance of the nature of the rural land use. Landsc. Urban Plan. 126, 31–41. https://doi.org/10.1016/j.landurbplan.2014.02.017 (2014).Article 

    Google Scholar 
    Luder, K., Knop, E. & Menz, M. H. M. Contrasting responses in community structure and phenology of migratory and non-migratory pollinators to urbanization. Divers. Distrib. 24, 919–927. https://doi.org/10.1111/ddi.12735 (2018).Article 

    Google Scholar 
    Merckx, T. & van Dyck, H. Urbanization-driven homogenization is more pronounced and happens at wider spatial scales in nocturnal and mobile flying insects. Glob. Ecol. Biogeogr. 28, 1440–1455. https://doi.org/10.1111/geb.12969 (2019).Article 

    Google Scholar 
    Tzortzakaki, O., Kati, V., Panitsa, M., Tzanatos, E. & Giokas, S. Butterfly diversity along the urbanization gradient in a densely-built Mediterranean city: Land cover is more decisive than resources in structuring communities. Landsc. Urban Plan. 183, 79–87. https://doi.org/10.1016/j.landurbplan.2018.11.007 (2019).Article 

    Google Scholar 
    Krauss, J., Steffan-Dewenter, I. & Tscharntke, T. How does landscape context contribute to effects of habitat fragmentation on diversity and population density of butterflies?. J. Biogeogr. 30, 889–900. https://doi.org/10.1046/j.1365-2699.2003.00878.x (2003).Article 

    Google Scholar 
    Cozzi, G., Müller, C. B. & Krauss, J. How do local habitat management and landscape structure at different spatial scales affect fritillary butterfly distribution on fragmented wetlands?. Landsc. Ecol. 23, 269–283. https://doi.org/10.1007/s10980-007-9178-3 (2008).Article 

    Google Scholar 
    He, M. et al. Effects of landscape and local factors on the diversity of flower-visitor groups under an urbanization gradient, a case study in Wuhan, China. Diversity 14, 208. https://doi.org/10.3390/d14030208 (2022).Article 

    Google Scholar 
    Buchholz, S., Gathof, A. K., Grossmann, A. J., Kowarik, I. & Fischer, L. K. Wild bees in urban grasslands: Urbanisation, functional diversity and species traits. Landsc. Urban Plan. 196, 103731. https://doi.org/10.1016/j.landurbplan.2019.103731 (2020).Article 

    Google Scholar 
    Chapman, R. E. & Bourke, A. F. G. The influence of sociality on the conservation biology of social insects. Ecol. Lett. 4, 650–662. https://doi.org/10.1046/j.1461-0248.2001.00253.x (2001).Article 

    Google Scholar 
    Gaertner, M. et al. Non-native species in urban environments: Patterns, processes, impacts and challenges. Biol. Invasions 19, 3461–3469. https://doi.org/10.1007/s10530-017-1598-7 (2017).Article 

    Google Scholar 
    Kowarik, I. On the role of alien species in urban flora and vegetation. In Urban Ecology. An International Perspective on the Interaction Between Humans and Nature (ed. Marzluff, J. M.) 321–338 (2008).Lorenz, S. & Stark, K. Saving the honeybees in Berlin? A case study of the urban beekeeping boom. Environ. Sociol. 1, 116–126. https://doi.org/10.1080/23251042.2015.1008383 (2015).Article 

    Google Scholar 
    Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. USA 104, 19891–19896. https://doi.org/10.1073/pnas.0706375104 (2007).Article 
    ADS 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 
    Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856. https://doi.org/10.1126/science.1188321 (2010).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Dormann, C. F., Fründ, J. & Schaefer, H. M. Identifying causes of patterns in ecological networks: Opportunities and limitations. Annu. Rev. Ecol. Evol. Syst. 48, 559–584. https://doi.org/10.1146/annurev-ecolsys-110316-022928 (2017).Article 

    Google Scholar 
    Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279. https://doi.org/10.1016/j.biocon.2009.12.004 (2010).Article 

    Google Scholar 
    Grilli, J., Rogers, T. & Allesina, S. Modularity and stability in ecological communities. Nat. Commun. 7, 12031. https://doi.org/10.1038/ncomms12031 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant–pollinator and host-parasitoid networks. Nat. Ecol. Evol 2, 1408–1417. https://doi.org/10.1038/s41559-018-0631-2 (2018).Article 
    PubMed 

    Google Scholar 
    Kaiser-Bunbury, C. N. et al. Ecosystem restoration strengthens pollination network resilience and function. Nature 542, 223–227. https://doi.org/10.1038/nature21071 (2017).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bommarco, R. et al. Dispersal capacity and diet breadth modify the response of wild bees to habitat loss. Proc. Biol. Sci. 277, 2075–2082. https://doi.org/10.1098/rspb.2009.2221 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alarcón, R., Waser, N. M. & Ollerton, J. Year-to-year variation in the topology of a plant–pollinator interaction network. Oikos 117, 1796–1807. https://doi.org/10.1111/j.0030-1299.2008.16987.x (2008).Article 

    Google Scholar 
    Dupont, Y. L., Padrón, B., Olesen, J. M. & Petanidou, T. Spatio-temporal variation in the structure of pollination networks. Oikos 118, 1261–1269. https://doi.org/10.1111/j.1600-0706.2009.17594.x (2009).Article 

    Google Scholar 
    Santamaría, S. et al. Landscape effects on pollination networks in Mediterranean gypsum islands. Plant Biol. 20(Suppl 1), 184–194. https://doi.org/10.1111/plb.12602 (2018).Article 
    PubMed 

    Google Scholar  More

  • in

    Balancing the bloom

    Algal blooms that form because of phytoplankton proliferation have key roles in marine ecology and carbon fixation. When the blooms die, most of the fixed carbon is transferred to higher trophic levels, and a small fraction sinks into the deep sea. Viral infection is one of the causes of bloom termination, but its effect on the fate and flow of carbon in the ocean is unknown. In this study, Vincent et al. perform a mesocosm experiment to analyse the bloom dynamics of the coccolithophore microalga Emiliania huxleyi and the impact of viral infection on surrounding bacterial communities and the carbon cycle. The authors observed that viral infection was not only the main cause of phytoplankton mortality, but it also shaped the composition of free-living bacterial and eukaryotic species in the blooms. On viral infection of E. huxleyi, the authors found a comparable biomass of eukaryotic and bacterial heterotrophic recyclers, as well as increased organic and inorganic carbon release that contributed to carbon sinking into the deep ocean. Altogether, these results highlight the impact of viruses on the microbial communities of blooms and the consequences on carbon cycling. More