Ecology

Moore, J., Pope, J., Woods, M. & Ellis, A. 2018 Aerial Survey Results: California (USDA, 2018).Stephens, S. L. et al. Drought, tree mortality, and wildfire in forests adapted to frequent fire. Bioscience 68, 77–88 (2018).Article 

Google Scholar 
Li, S. & Banerjee, T. Spatial and temporal pattern of wildfires in California from 2000 to 2019. Sci. Rep. 11, 8779 (2021).Article 

Google Scholar 
Wang, D. et al. Economic footprint of California wildfires in 2018. Nat. Sustain. 4, 252–260 (2020).Article 

Google Scholar 
Asner, G. P. et al. Progressive forest canopy water loss during the 2012–2015 California drought. Proc. Natl Acad. Sci. USA 113, E249–E255 (2016).
Google Scholar 
Brodrick, P. G., Anderegg, L. D. L. & Asner, G. P. Forest drought resistance at large geographic scales. Geophys. Res. Lett. 46, 2752–2760 (2019).Article 

Google Scholar 
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).Article 

Google Scholar 
Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).Article 

Google Scholar 
Paz-Kagan, T. et al. What mediates tree mortality during drought in the southern Sierra Nevada? Ecol. Appl. 27, 2443–2457 (2017).Article 

Google Scholar 
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is Tree Drought Mortality so Hard to Predict? Trends Ecol. Evol. 36, 520–532.(2021).Goodfellow, B. W. et al. The chemical, mechanical, and hydrological evolution of weathering granitoid. J. Geophys. Res. Earth Surf. 121, 1410–1435 (2016).Article 

Google Scholar 
Shen, X., Arson, C., Ferrier, K. L., West, N. & Dai, S. Mineral weathering and bedrock weakening: modeling microscale bedrock damage under biotite weathering. J. Geophys. Res. Earth Surf. 124, 2623–2646 (2019).Article 

Google Scholar 
McLaughlin, B. C. et al. Weather underground: subsurface hydrologic processes mediate tree vulnerability to extreme climatic drought. Glob. Change Biol. 26, 3091–3107 (2020).Article 

Google Scholar 
Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).Article 

Google Scholar 
Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).Article 

Google Scholar 
Tague, C. & Peng, H. The sensitivity of forest water use to the timing of precipitation and snowmelt recharge in the California Sierra: implications for a warming climate. J. Geophys. Res. Biogeosci. 118, 875–887 (2013).Article 

Google Scholar 
Hahm, W. J., Riebe, C. S., Lukens, C. E. & Araki, S. Bedrock composition regulates mountain ecosystems and landscape evolution. Proc. Natl Acad. Sci. USA 111, 3338–3343 (2014).Article 

Google Scholar 
Uhlig, D., Schuessler, J. A., Bouchez, J., Dixon, J. L. & von Blanckenburg, F. Quantifying nutrient uptake as driver of rock weathering in forest ecosystems by magnesium stable isotopes. Biogeosciences 14, 3111–3128 (2017).Article 

Google Scholar 
Stone, E. C. Dew as an ecological factor: II. The effect of artificial dew on the survival of Pinus ponderosa and associated species. Ecology 38, 414–422 (1957).Article 

Google Scholar 
Wald, J. A., Graham, R. C. & Schoeneberger, P. J. Distribution and properties of soft weathered bedrock at ≤1 m depth in the contiguous United States. Earth Surf. Process. Landf. 38, 614–626 (2013).Article 

Google Scholar 
Klos, P. Z. et al. Subsurface plant-accessible water in mountain ecosystems with a Mediterranean climate. WIREs Water 5, e1277 (2018).Article 

Google Scholar 
Dawson, T. E., Hahm, W. J. & Crutchfield-Peters, K. Digging deeper: what the critical zone perspective adds to the study of plant ecophysiology. N. Phytol. 226, 666–671 (2020).Article 

Google Scholar 
Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).Article 

Google Scholar 
Holbrook, W. S. et al. Links between physical and chemical weathering inferred from a 65-m-deep borehole through Earth’s critical zone. Sci. Rep. 9, 4495 (2019).Article 

Google Scholar 
Krone, L. V. et al. Deep weathering in the semi-arid Coastal Cordillera, Chile. Sci. Rep. 11, 13057 (2021).Article 

Google Scholar 
Callahan, R. P. et al. Subsurface weathering revealed in hillslope‐integrated porosity distributions. Geophys. Res. Lett. 47, e2020GL088322 (2020).Holbrook, W. S. et al. Geophysical constraints on deep weathering and water storage potential in the Southern Sierra Critical Zone Observatory. Earth Surf. Process. Landf. 39, 366–380 (2014).Article 

Google Scholar 
Hayes, J. L., Riebe, C. S., Holbrook, W. S., Flinchum, B. A. & Hartsough, P. C. Porosity production in weathered rock: where volumetric strain dominates over chemical mass loss. Sci. Adv. 5, eaao0834 (2019).Article 

Google Scholar 
Riebe, C. S. et al. Anisovolumetric weathering in granitic saprolite controlled by climate and erosion rate. Geology 49, 551–555 (2021).Article 

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 

Google Scholar 
Vitousek, P. M., Porder, S. & Houlton, B. Z. Terrestrial phosphorus limitation: mechanisms, implications, and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).Article 

Google Scholar 
Bateman, P. C., Dodge, F. C. W. & Bruggman, P. E. Major Oxide Analyses, CPIW Norms, Modes, and Bulk Specific Gravities of Plutonic Rocks from the Mariposa 1° × 2° Sheet, Central Sierra Nevada, California Open-File Report 84–162 (USGS, 1984).Amundson, R., Richter, D. D., Humphreys, G. S., Jobbagy, E. G. & Gaillardet, J. Coupling between biota and earth materials in the critical zone. Elements 3, 327–332 (2007).Article 

Google Scholar 
Tune, A. K., Druhan, J. L., Wang, J., Bennett, P. C. & Rempe, D. M. Carbon dioxide production in bedrock beneath soils substantially contributes to forest carbon cycling. J. Geophys. Res. Biogeosci. 125, e2020JG005795 (2020).Gabet, E. J. & Mudd, S. M. Bedrock erosion by root fracture and tree throw: a coupled biogeomorphic model to explore the humped soil production function and the persistence of hillslope soils. J. Geophys. Res. 115, F04005 (2010).Bateman, P. C. Plutonism in the Central Part of the Sierra Nevada Batholith, California (USGS, 1992); http://pubs.er.usgs.gov/publication/pp1483Callahan, R. P. et al. Arrested development: erosional equilibrium in the southern Sierra Nevada, California, maintained by feedbacks between channel incision and hillslope sediment production. GSA Bull. 131, 1179–1202 (2019).Article 

Google Scholar 
Flinchum, B. A. et al. Estimating the water holding capacity of the critical zone using near-surface geophysics. Hydrol. Process. 32, 3308–3326 (2018).Article 

Google Scholar 
St. Clair, J. Geophysical Investigations of Underplating at the Middle American Trench, Weathering in the Critical Zone, and Snow Water Equivalent in Seasonal Snow. PhD thesis, Univ. Wyoming (2015).Dvorkin, J. & Nur, A. Elasticity of high‐porosity sandstones: theory for two North Sea data sets. Geophysics 61, 1363–1370 (1996).Article 

Google Scholar 
Gu, X. et al. Seismic refraction tracks porosity generation and possible CO2 production at depth under a headwater catchment. Proc. Natl Acad. Sci. USA 117, 18991–18997 (2020).Article 

Google Scholar 
Pasquet, S., Holbrook, W. S., Carr, B. J. & Sims, K. W. W. Geophysical imaging of shallow degassing in a Yellowstone hydrothermal system. Geophys. Res. Lett. 43, 12,027–12,035 (2016).Article 

Google Scholar 
Dahlgren, R. A., Boettinger, J. L., Huntington, G. L. & Amundson, R. G. Soil development along an elevational transect in the western Sierra Nevada, California. Geoderma 78, 207–236 (1997).Article 

Google Scholar 
Stone, E. L. & Kalisz, P. J. On the maximum extent of tree roots. For. Ecol. Manage. 46, 59–102 (1991).Article 

Google Scholar 
Carlson, T. N. & Ripley, D. A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62, 241–252 (1997).Article 

Google Scholar 
Goulden, M. L. et al. Evapotranspiration along an elevation gradient in California’s Sierra Nevada. J. Geophys. Res. 117, G03028 (2012).Ma, Q. et al. Wildfire controls on evapotranspiration in California’s Sierra Nevada. J. Hydrol. 590, 125364 (2020).Article 

Google Scholar 
Roche, J. W., Goulden, M. L. & Bales, R. C. Estimating evapotranspiration change due to forest treatment and fire at the basin scale in the Sierra Nevada, California. Ecohydrology 11, e1978 (2018).Bales, R. C. et al. Mechanisms controlling the impact of multi-year drought on mountain hydrology. Sci. Rep. 8, 690 (2018).Article 

Google Scholar 
Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).Article 

Google Scholar 
Su, Y. et al. Emerging stress and relative resiliency of giant sequoia groves experiencing multiyear dry periods in a warming climate. J. Geophys. Res. Biogeosci. 122, 3063–3075 (2017).Article 

Google Scholar 
Moore, J., McAfee, L. & Iaccarino, J. 2016 Aerial Survey Results: California (USDA, 2017).Budyko, M. I., Miller, D. H. & Miller, D. H. Climate and Life (Academic Press, 1974).Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1, 96–99 (1985).Article 

Google Scholar 
PRISM Climate Group PRISM Climate Data (Oregon State Univ., 2019).Bales, R. et al. Spatially distributed water-balance and meteorological data from the rain–snow transition, southern Sierra Nevada, California. Earth Syst. Sci. Data 10, 1795–1805 (2018).Article 

Google Scholar 
Callahan, R. P. Supplement for “Forest vulnerability to drought controlled by bedrock composition”. Hydroshare https://doi.org/10.4211/hs.edbb6ebfbc744186b5800932cd00b507 (2022).Earth Resources Observation and Science (EROS) Center USGS EROS Archive—Aerial Phorography—National Agriculture Imagery Program (NAIP) (USGS, 2017); https://doi.org/10.5066/F7QN651G More

Bedrock composition can play a critical role in determining the structure and water demand of forests, influencing their vulnerability to drought. The properties of bedrock can help explain within-region patterns of tree mortality in the 2011–2017 California drought.Montane forests are iconic natural resources that provide habitat, carbon sequestration, regulation of water, and, for many cultures, profound meaning. A warming climate and prolonged droughts threaten these forests, as shown by the 2011–2017 drought in California, USA, which killed over 140 million trees. However, the vulnerability of forests to climate-driven risks is not evenly distributed across these landscapes. In the 2011–2017 drought, some contiguous forested areas (or forest stands) suffered more than 70% mortality while forests in other locations experienced few or no losses1. Understanding these spatial patterns is critical for the projection of future risks and for targeted forest management. Writing in Nature Geoscience, Callahan and colleagues look beneath the surface at the composition of bedrock and find a link to these patterns of drought mortality in the California Sierra2. More

Izdebski, A. et al. Palaeoecological data indicates land-use changes across Europe linked to spatial heterogeneity in mortality during the Black Death pandemic. Nat. Ecol. Evol. 6, 297–306 (2022).CAS 
Article 

Google Scholar 
Benedictow, O. J. The Complete History of the Black Death (The Boydell Press, 2021).Palermo, L. Mercati del Grano a Roma tra Medioevo e Rinascimento. Il Mercato Distrettuale del Grano in Età Comunale (Istituto Nazionale di Studi Romani, 1990).Cortonesi, A. I cereali nell’Italia del tardo medioevo. Note sugli aspetti qualitativi del consumo. Riv. Stor. Agricol. 37, 3–30 (1997).
Google Scholar 
Nanni, P. in The Crisis of the 14th Century. Teleconnections Between Environmental and Societal Change? (eds Bauch M. & Schenk G. J.) 169–189 (De Gruyter, 2020).Lagerås, P. Environment, Society and the Black Death: An Interdisciplinary Approach to the Late-Medieval Crisis in Sweden (Oxbow Books, 2016).Roosen, J. & Curtis, D. The ‘light touch’ of the Black Death in the southern Netherlands: an urban trick? Econ. Hist. Rev. 72, 32–56 (2019).Article 

Google Scholar 
Preiser-Kapeller, J. Der Lange Sommer und die Kleine Eiszeit: Klima, Pandemien und der Wandel der Alten Welt 500–1500 n. Chr. (Mandelbaum, 2021).Sadori, L. The Lateglacial and Holocene vegetation and climate history of Lago di Mezzano (central Italy). Quat. Sci. Rev. 202, 30–44 (2018).Article 

Google Scholar 
Cortonesi, A. Ruralia. Economie e Paesaggi del Medioevo Italiano (Il Calamo, 1995).Cortonesi, A. L’olivo nell’Italia medievale. Reti Medievali Riv. 6, 1–29 (2005).
Google Scholar 
Mensing, S. A. et al. Historical ecology reveals landscape transformation coincident with cultural development in central Italy since the Roman Period. Sci. Rep. 8, 2138 (2018).Article 

Google Scholar 
Cortonesi, A. in Il Paesaggio Agrario Italiano Medievale: Storia e Didattica, 113–120 (Istituto Alcide Cervi, 2011). More

Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jackson, R. B. et al. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 
Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change 2021: The Physical Science Basis. (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in press).Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).Wood, T. E. et al. in Ecosystem Consequences of Soil Warming: Microbes, Vegetation, Fauna and Soil Biogeochemistry (ed. Mohan, J.) Ch. 14 (Academic Press, 2019).Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).CAS 
PubMed 
Article 

Google Scholar 
van Gestel, N. et al. Predicting soil carbon loss with warming. Nature 554, E4–E5 (2018).PubMed 
Article 
CAS 

Google Scholar 
Melillo, J. M. et al. Long-term pattern and magnitude of soil carbon feedback to the climate system in a warming world. Science 358, 101–104 (2017).CAS 
PubMed 
Article 

Google Scholar 
Romero-Olivares, A. L., Allison, S. D. & Treseder, K. K. Soil microbes and their response to experimental warming over time: a meta-analysis of field studies. Soil Biol. Biochem. 107, 32–40 (2017).CAS 
Article 

Google Scholar 
Anderson-Teixeira, K. J., Wang, M. M. H., McGarvey, J. C. & LeBauer, D. S. Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db). Glob. Change Biol. 22, 1690–1709 (2016).Article 

Google Scholar 
Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kimball, B. A. et al. Infrared heater system for warming tropical forest understory plants and soils. Ecol. Evol. 8, 1932–1944 (2018).DeAngelis, K. M. et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00104 (2015)Bååth, E. Temperature sensitivity of soil microbial activity modeled by the square root equation as a unifying model to differentiate between direct temperature effects and microbial community adaptation. Glob. Change Biol. 24, 2850–2861 (2018).Article 

Google Scholar 
Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).CAS 
Article 

Google Scholar 
Ratkowsky, D. A., Olley, J., Mcmeekin, T. A. & Ball, A. Relationship between temperature and growth-rate of bacterial cultures. J. Bacteriol. 149, 1–5 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rinnan, R., Rousk, J., Yergeau, E., Kowalchuk, G. A. & Bååth, E. Temperature adaptation of soil bacterial communities along an Antarctic climate gradient: predicting responses to climate warming. Glob. Change Biol. 15, 2615–2625 (2009).Article 

Google Scholar 
Nottingham, A. T., Bååth, E., Reischke, S., Salinas, N. & Meir, P. Adaptation of soil microbial growth to temperature: using a tropical elevation gradient to predict future changes. Glob. Change Biol. https://doi.org/10.1111/gcb.14502 (2019).Li, J. Q., Bååth, E., Pei, J. M., Fang, C. M. & Nie, M. Temperature adaptation of soil microbial respiration in alpine, boreal and tropical soils: an application of the square root (Ratkowsky) model. Glob. Change Biol. 27, 1281–1292 (2021).CAS 
Article 

Google Scholar 
Rousk, J., Frey, S. D. & Bååth, E. Temperature adaptation of bacterial communities in experimentally warmed forest soils. Glob. Change Biol. 18, 3252–3258 (2012).Article 

Google Scholar 
Nottingham, A. T. et al. Annual to decadal temperature adaptation of the soil bacterial community after translocation across an elevation gradient in the Andes. Soil Biol. Biochem. 158, 108217 (2021).CAS 
Article 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).PubMed 
Article 

Google Scholar 
Donhauser, J., Niklaus, P. A., Rousk, J., Larose, C. & Frey, B. Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils. Soil Biol. Biochem. 148, 107873 (2020).CAS 
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).Pold, G., Melillo, J. M. & DeAngelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.00480 (2015).Zhou, J. Z. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7, 12083 (2016).Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).Wu, L. et al. Reduction of microbial diversity in grassland soil is driven by long-term climate warming. Nat. Microbiol. 7, 1054–1062 (2022).Oliverio, A. M., Bradford, M. A. & Fierer, N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob. Change Biol. 23, 2117–2129 (2017).Article 

Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).CAS 
PubMed 
Article 

Google Scholar 
Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Env. Resour. 43, 193–218 (2018).Article 

Google Scholar 
Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. https://doi.org/10.3389/Fmicb.2013.00333 (2013).Pietikäinen, J., Pettersson, M. & Bååth, E. Comparison of temperature effects on soil respiration and bacterial and fungal growth rates. FEMS Microbiol. Ecol. 52, 49–58 (2005).PubMed 
Article 
CAS 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed 
Article 

Google Scholar 
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
Article 

Google Scholar 
LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).CAS 
PubMed 
Article 

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

Google Scholar 
Lapebie, P., Lombard, V., Drula, E., Terrapon, N. & Henrissat, B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat. Commun. https://doi.org/10.1038/s41467-019-10068-5 (2019).Makhalanyane, T. P. et al. Microbial ecology of hot desert edaphic systems. FEMS Microbiol. Rev. 39, 203–221 (2015).CAS 
PubMed 
Article 

Google Scholar 
Aydogan, E. L., Moser, G., Muller, C., Kampfer, P. & Glaeser, S. P. Long-term warming shifts the composition of bacterial communities in the phyllosphere of Galium album in a permanent grassland field-experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.00144 (2018).Hu, D. Y., Zang, Y., Mao, Y. J. & Gao, B. L. Identification of molecular markers that are specific to the class thermoleophilia. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01185 (2019).Mohan, J. E. et al. Mycorrhizal fungi mediation of terrestrial ecosystem responses to global change: mini-review. Fungal Ecol. 10, 3–19 (2014).Article 

Google Scholar 
Manzoni, S., Taylor, P., Richter, A., Porporato, A. & Agren, G. I. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79–91 (2012).CAS 
PubMed 
Article 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).CAS 
Article 

Google Scholar 
Reed, S. C. et al. Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance. Adv. Ecol. Res. 62, 225–252 (2020).Article 

Google Scholar 
Nottingham, A. T., Turner, B. L., Stott, A. W. & Tanner, E. V. J. Nitrogen and phosphorus constrain labile and stable carbon turnover in lowland tropical forest soils. Soil Biol. Biochem. 80, 26–33 (2015).CAS 
Article 

Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Kemmitt, S. J. et al. Mineralization of native soil organic matter is not regulated by the size, activity or composition of the soil microbial biomass—a new perspective. Soil Biol. Biochem. 40, 61–73 (2008).CAS 
Article 

Google Scholar 
Nannipieri, P., Trasar-Cepeda, C. & Dick, R. P. Soil enzyme activity: a brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fert. Soils 54, 11–19 (2018).CAS 
Article 

Google Scholar 
Wallenstein, M., Allison, S., Ernakovich, J., Steinweg, J. M. & Sinsabaugh, R. in Soil Enzymology. Soil Biology Vol. 22 (eds Shukla, G. & Varma, A.) Ch. 13 (Springer, 2011).Zhou, X. Y., Chen, L., Xu, J. M. & Brookes, P. C. Soil biochemical properties and bacteria community in a repeatedly fumigated-incubated soil. Biol. Fert. Soils 56, 619–631 (2020).CAS 
Article 

Google Scholar 
Sanchez-Julia, M. & Turner, B. L. Abiotic contribution to phenol oxidase activity across a manganese gradient in tropical forest soils. Biogeochemistry https://doi.org/10.1007/s10533-021-00764-0 (2021).Razavi, B. S., Liu, S. B. & Kuzyakov, Y. Hot experience for cold-adapted microorganisms: temperature sensitivity of soil enzymes. Soil Biol. Biochem. 105, 236–243 (2017).CAS 
Article 

Google Scholar 
Pinney, M. M. et al. Parallel molecular mechanisms for enzyme temperature adaptation. Science 371, eaay2784 (2021).CAS 
PubMed 
Article 

Google Scholar 
Fanin, N. et al. Soil enzymes in response to climate warming: mechanisms and feedbacks. Funct. Ecol. https://doi.org/10.1111/1365-2435.14027 (2022).Hall, S. J. & Silver, W. L. Iron oxidation stimulates organic matter decomposition in humid tropical forest soils. Glob. Change Biol. 19, 2804–2813 (2013).Article 

Google Scholar 
Freeman, C., Ostle, N. & Kang, H. An enzymic ‘latch’ on a global carbon store. Nature 409, 149 (2001).CAS 
PubMed 
Article 

Google Scholar 
Sarmiento, C. et al. Soilborne fungi have host affinity and host-specific effects on seed germination and survival in a lowland tropical forest. Proc. Natl Acad. Sci. USA 114, 11458–11463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Condit, R., Perez, R., Lao, S., Aguilar, S. & Hubbell, S. P. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. For. Ecosyst. https://doi.org/10.1186/s40663-017-0103-1 (2017).Woodring, W. P. Geology of Barro Colorado Island. Smithson. Misc. Collect. 135, 1–39 (1958).
Google Scholar 
Sanchez, P. A. & Logan, T. J. Myths and science about the chemistry and fertility of soils in the tropics. SSSA Spec. Publ. 29, 35–46 (1992).CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).CAS 
Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
Jenkinson, D. S., Brookes, P. C. & Powlson, D. S. Measuring soil microbial biomass. Soil Biol. Biochem. 36, 5–7 (2004).CAS 
Article 

Google Scholar 
Kouno, K., Tuchiya, Y. & Ando, T. Measurement of soil microbial biomass phosphorus by an anion-exchange membrane method. Soil Biol. Biochem. 27, 1353–1357 (1995).CAS 
Article 

Google Scholar 
Tabatabai, M. A. in Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties (ed. Page, A.L.) 778–833 (SSSA, 1994).Marx, M. C., Wood, M. & Jarvis, S. C. A microplate fluorimetric assay for the study of enzyme diversity in soils. Soil Biol. Biochem. 33, 1633–1640 (2001).CAS 
Article 

Google Scholar 
Price, N. & Stevens, L. Fundamentals of Enzymology: Cell and Molecular Biology of Catalytic Proteins (Oxford Univ. Press, 1999).Hagerty, S. B., Allison, S. D. & Schimel, J. P. Evaluating soil microbial carbon use efficiency explicitly as a function of cellular processes: implications for measurements and models. Biogeochemistry 140, 269–283 (2018).CAS 
Article 

Google Scholar 
Frey, S. D., Lee, J., Melillo, J. M. & Six, J. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, 395–398 (2013).CAS 
Article 

Google Scholar 
Spohn, M. et al. Soil microbial carbon use efficiency and biomass turnover in a long-term fertilization experiment in a temperate grassland. Soil Biol. Biochem. 97, 168–175 (2016).CAS 
Article 

Google Scholar 
Sinsabaugh, R. L. et al. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).Article 

Google Scholar 
Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).CAS 
Article 

Google Scholar 
Bååth, E., Pettersson, M. & Söderberg, K. H. Adaptation of a rapid and economical microcentrifugation method to measure thymidine and leucine incorporation by soil bacteria. Soil Biol. Biochem. 33, 1571–1574 (2001).Article 

Google Scholar 
Bárcenas-Moreno, G., Gomez-Brandon, M., Rousk, J. & Bååth, E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob. Change Biol. 15, 2950–2957 (2009).Article 

Google Scholar 
Smirnova, E., Huzurbazar, S. & Jafari, F. PERFect: PERmutation Filtering test for microbiome data. Biostatistics 20, 615–631 (2019).PubMed 
Article 

Google Scholar 
Alberdi, A. & Gilbert, M. T. P. hilldiv: an R package for the integral analysis of diversity based on Hill numbers. Preprint at bioRxiv https://doi.org/10.1101/545665 (2019).Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community ecology package, R Package version 2 https://cran.r-project.org/web/packages/vegan/ (2018).Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. https://doi.org/10.1186/gb-2011-12-6-r60 (2011).Roesch, L. F. W. et al. PIME: a package for discovery of novel differences among microbial communities. Mol. Ecol. Resour. 20, 415–428 (2020).CAS 
PubMed 
Article 

Google Scholar 
Roberts, D.W. labdsv: Ordination and multivariate analysis for ecology. R package version 2.0-1 https://cran.r-project.org/web/packages/labdsv/ (2019).Cao, Y. et al. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics 38, 4027–4029 (2022).Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. Peerj 3, e1319 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Peterson, R. A. & Cavanaugh, J. E. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J. Appl. Stat. 47, 2312–2327 (2020).PubMed 
Article 

Google Scholar  More

l-cry mutants show higher spawning synchrony than wild-type animals under non-natural light conditionsIn order to test for a functional involvement of L-Cry in monthly oscillator function, we generated two l-cry mutant alleles (Δ34 and Δ11bp) (Fig. 1a) using TALENs28. In parallel, we generated a monoclonal antibody against Platynereis L-Cry. By testing mutant versus wildtype worms with the anti-L-Cry antibody in Western blots (Fig. 1b) and immunohistochemistry (Fig. 1e–j), we verified the absence of L-Cry protein in mutants. Furthermore, we confirmed that the staining of the antibody in wildtype worms (Fig. 1e–h) matches the regions where l-cry mRNA is expressed (Fig. 1d). These tests confirmed that the engineered l-cry mutations result in loss-of-function alleles. In turn, they validate the specificity of the raised anti-L-Cry antibody.Fig. 1: l-cry–/– mutants are loss-of-function alleles.a Overview of the l-cry genomic locus for wt and mutants. Both mutant alleles result in an early frameshift and premature stop codons. The Δ34 allele has an additional 9 bp deletion in exon 3. b Western Blots of P. dumerilii heads probed with anti-L-Cry antibody. In the context of further investigations such Western blots of mutant versus wild types have been performed more than 10 times with highly consistent results. Also see further analyses in this manuscript and ref. 36. c overview of P. dumerilii. d whole mount in situ hybridization against l-cry mRNA on worm head. ae, anterior eye; pe, posterior eye. e–j Immunohistochemistry of premature wild-type (e–h) and mutant (i, j) worm heads sampled at zt19/20 using anti-L-Cry antibody (green) and Hoechst staining (magenta), dorsal views, anterior up. e, f: z-stack images (maximal projections of 50 layers, 1.28 µm each) in the area highlighted by the rectangle in (d), whereas (g–j) are single layer images of the area highlighted by the white rectangles in (e, f). In the context of further investigations such stainings of mutant versus wild types have been performed more than 10 times with highly consistent results. Also see further analyses in this manuscript and ref. 36.Full size imageWe next assessed the circalunar maturation timing of wild types and l-cry mutant populations in conventional culture conditions, i.e. worms grown under typical indoor room lighting (named here artificial sun- and moonlight, Supplementary Fig. 1b).We expected either no phenotype (if L-Cry was not involved in circalunar clock entrainment) or a decreased spawning precision (if L-Cry was functioning as moonlight receptor in circalunar clock entrainment). Instead we observed an increased precision of the entrained worm population:We analyzed the maturation data using two statistical approaches, linear and circular statistics. We used the classical linear plots5 and statistics to compare the monthly spawning data distribution (Fig. 2a–c, i). This revealed a clear difference between mutant animals, which exhibited a stronger spawning peak at the beginning of the NM phase, compared to their wildtype and heterozygous counterparts (Fig. 2a–c, Kolmogorov–Smirnov test on overall data distribution, Fig. 2i).Fig. 2: L-Cry shields the circalunar clock from light that is not naturalistic moonlight.a–d, j Spawning of l-cry +/+ (a), l-cry +/– (Δ34) (b) and l-cry −/−(Δ34/ Δ34) (c) animals over the lunar month in the lab with 8 nights of artificial moonlight (a–c), under natural conditions in the sea (d, replotted from ref. 34,50,) and in the lab using naturalistic sun- and moonlight (j, 8 nights moonlight). e–h, k Data as in (a–d, j) as circular plot. 360° correspond to 30 days of the lunar month. The arrow represents the mean vector, characterized by the direction angle µ and r (length of µ). r indicates phase coherence (measure of population synchrony). p-values inside the plots: result of Rayleigh Tests. Significance indicates non-random distribution of data points. The inner circle represents the Rayleigh critical value (p = 0.05). i–l Results of two-sided multisample statistics on spawning data shown in (a–h, j, k). The phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageWe then analyzed the same data using circular statistics (as the monthly cycle is repeating, see details in Methods section), which allowed us to describe the data with the mean vector (defined by the direction angle µ and its length r, shown as arrows in Fig. 2e–g). The phase coherence r (ranging from 0 to 1) serves as a measure for synchrony of the population data. As expected for entrained populations, all genotypes distributed their spawning across a lunar month significantly different from random (Fig. 2e–g, p values in circles, Rayleigh’s Uniformity test29). In line with the observed higher spawning peak of the l-cry−/− mutants in the linear plots, the circular analysis revealed a significant difference in spawning distribution (Mardia–Watson-Wheeler test, for details see Methods section) and higher spawning synchrony of mutants (r = 0.614) than in wild types and heterozygotes (r = 0.295 and r = 0.222) (Fig. 2i). The specificity of this phenotype of higher spawning precision for l-cry homozygous mutants was confirmed by analyses on trans-heterozygous l-cry (Δ34/Δ11) mutants (Supplementary Fig. 2), and by the fact that such a phenotype is not detectable in any other light receptor mutant available in Platynereis (r-opsin130: Supplementary Fig. 3a, b, e, f, i; c-opsin131: Supplementary Fig. 3c, d, g, h, i, Go-opsin: refs. 32, 33).The higher spawning synchrony of l-cry mutants under artificial light mimics the spawning precision of wild-type at its natural habitatThis increased spawning precision of l-cry mutants under artificial (but conventional indoor) laboratory light conditions let us wonder about the actual population synchrony of the worms under truly natural conditions. The lunar spawning synchrony of P. dumerilii at the Bay of Naples (the origin of our lab culture) has been worked on for more than 100 y. This allowed us to re-investigate very detailed spawning data records from the worms’ natural habitat published prior to environmental/light pollution. For better accessibility and comparability we combined all months and replotted the data published in 192934 (Fig. 2d, h, I; see details in Methods section; r = 0.631). This analysis revealed that the higher spawning synchrony in l-cry–/– worms mimics the actual spawning synchrony of P. dumerillii populations in their natural habitat34 (compare Fig. 2c, g with 2d, h.)Given that recent, non-inbred isolates from the same habitat as our lab inbred strains (which is the same habitat as the data collected in ref. 34) exhibit a broad spawning distribution under standard worm culture light conditions (which includes the bright artificial moonlight)35, we hypothesized that the difference in spawning synchrony between wildtype laboratory cultures and populations in their natural habitat is caused by the rather bright nocturnal light stimulus typically used for the standard laboratory culture (Supplementary Fig. 1a vs. b).Lunar spawning precision of wild-type animals depends on naturalistic moonlight conditionsWe next tested the resulting prediction that naturalistic moonlight should increase the spawning precision of the wildtype population, using naturalistic sun- and moonlight devices we specifically designed based on light measurements at the natural habitat of P. dumerilii31 (Supplementary Fig. 1a, c). We assessed the impact of the naturalistic sun- and moonlight (Supplementary Fig. 1a, c) on wildtype animals, maintaining the temporal aspects of the lab light regime (i.e. 8 nights of “full moon”). Indeed, merely adjusting the light intensity to naturalistic conditions increased the precision and phase coherence of population-wide reproduction: After several months under naturalistic sun- and moonlight, wildtype worms spawned with a major peak highly comparable to the wildtype precision reported at its natural habitat (Fig. 2d, h vs. j, k), and also exhibited an increased population synchrony (r = 0.398 compared to r = 0.295 under standard worm room light conditions). This increased similarity to the spawning distribution at the natural habitat (“Sea”) is confirmed by statistical analyses (Fig. 2l): The phase difference (angle between the two mean vectors) is only one day (corresponding to 12°). In contrast, the spawning distribution of wild types under standard worm room light versus naturalistic light conditions is highly significantly different in linear and circular statistical tests and has a phase difference of 7.7 days (Fig. 2l).These findings show that it is the naturalistic light that is critical for a highly precise entrainment of the monthly clock of wild-type worms. Given that l-cry–/– animals reach this high precision with the artificial light (i.e. standard lab light) implies that in wildtype L-Cry blocks artificial, but not naturalistic full-moonlight from efficiently synchronizing the circalunar clock. This block is removed in l-cry–/– animals, leading to a better synchronization of the l-cry–/– population. This finding suggests that L-Cry’s major role could be that of a gatekeeper controlling which ambient light is interpreted as full-moonlight stimulus for circalunar clock entrainment.
l-cry functions as a light signal gatekeeper for circalunar clock entrainmentA prediction of this hypothesis is that mutants should entrain better to an artificial full-moonlight stimulus provided out-of-phase than their wild type counterparts (in which L-Cry should block the “wrong” moonlight at least partially from re-entraining the circalunar oscillator).We thus compared the spawning rhythms of l-cry+/+ and l-cry–/– worms under a re-entrainment paradigm, where we provided our bright artificial culture full-moonlight at the time of the subjective new moon phase (Fig. 3a). In order to compare the spawning data distribution relative to the initial full moon (FM) stimulus, as well as to the new full moon stimulus (i.e. new FM), we used two nomenclatures for the months: months with numbers are analyzed relative to the initial nocturnal light stimulus (i.e. FM), whereas months with letters are analyzed relative to the new (phase-shifted) nocturnal light stimulus (i.e. new FM, Fig. 3a). When the nocturnal light stimulus is omitted (to test for the oscillator function) we then refer to ‘free-running FM’ (FR-FM) or ‘new free-running FM’ (new FR-FM), respectively (Fig. 3a). Using these definitions, the efficiency of circalunar clock re-entrainment will be reflected in the similarity of spawning data distributions between month 1 and month D, i.e. the more similar the distribution, the more the population has shifted to the new phase.Fig. 3: l-cry−/− mutants entrain the circalunar clock faster than wt to a high-intensity artificial moonlight stimulus.a Nocturnal moonlight exposure protocol of lunar phase shift (entrained by 8 nights, phased shifted by 6 nights of artificial culture moon, light green). b, c Number of mature animals (percent per month, rolling mean with a window of 3 days) of l-cry wild-type (b) and homozygous mutant (c) animals. p-values indicate results of Kolomogorov–Smirnov tests. Dark blue arrowheads- old FM phase: wt show a spawning minimum, indicative that the worms are not properly phase shifted. Mutants spawn in high numbers, but don’t spawn at the old NM indicated by light blue arrowhead. Also compare to initial FM and NM in months 1,2. d, e Circular plots of the data shown in (b) and (c). Each circle represents one lunar month. Each dot represents one mature worm. The arrow represents the mean vector characterized by the direction angle µ and r. r (length of µ) indicates phase coherence (measure of population synchrony). The inner circle represents the Rayleigh critical value (p = 0.05). f, g Results of two-sided multisample statistics of data in (d, e). Phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageWhen using the artificial nocturnal light conditions, the re-entrainment of l-cry–/– animals was both faster and more complete than for their wildtype relatives, as predicted from our gate keeper hypothesis. This is evident from the linear data analysis and Kolmogorov–Smirnov tests when comparing the month before the entrainment (month 1) with two months that should be shifted after the entrainment (months C,D, Fig. 3b, c, f, g).Most notably, while l-cry−/− worms were fully shifted in month D (Fig. 3c: compare boxes and see complete lack of spawning at the light blue arrowhead indicating the old NM/new FR-FM phase versus massive spawning at new NM phase around dark blue arrowhead), wildtype animals were still mostly spawning according to the initial lunar phase (Fig. 3b: compare boxes and see spawning at the light blue arrowhead versus almost lack of spawning at dark blue arrowhead). The faster re-entrainment of l-cry–/–, compared to l-cry+/+ animals is also confirmed by the Mardia–Watson-Wheeler test (see Methods section for details). For l-cry+/+ animals, the comparisons of the spawning distributions before and after re-entrainment show a 1000-fold (months 1 versus C) and tenfold (months 1 versus D) higher statistical significance difference than the corresponding comparisons for l-cry−/− worms (Fig. 3f, g). Consistently, the phase differences in days calculated from the angle between the two mean vectors from the circular analysis is smaller in the mutants than in the wild types when comparing the phase of the month before the entrainment (month 1) with two months after the entrainment (months C, D) (Fig. 3d–g). The fact that there are still differences in the mutant population before and after entrainment is likely due to the fact that even the mutants are not fully re-entrained. However, they have shifted more robustly in response to an artificial nocturnal light stimulus than the wild types. This provides further evidence that in wildtype worms L-Cry indeed blocks the “wrong” light from entering into the circalunar clock and thus functions as a light gatekeeper.L-Cry functions mainly as light interpreter, while its contribution as direct moonlight entraining photoreceptor is (at best) minorWe next tested to which extent L-Cry is itself a sensor for the re-entrainment signal under naturalistic light conditions. Based on the finding that l-cry−/− worms can still re-entrain the circalunar oscillator (see above), it is clear that even if L-Cry also directly contributed to the entrainment, it cannot be the only moonlight receptor mediating entrainment. With the experiments below, we aimed to test if L-Cry has any role as an entraining photoreceptor to the monthly oscillator.Thus, we tested how the circalunar clock is shifted in response to a re-entrainment with naturalistic moonlight in Platynereis wt versus l-cry−/− worms. For this, animals initially raised and entrained under standard worm room light conditions of artificial sun- and moonlight (Supplementary Fig. 1b, e) were challenged by a deviating FM stimulus of 8 nights of naturalistic moonlight (Fig. 4a, Supplementary Fig. 1c, e). This re-entraining stimulus was repeated for three consecutive months (Fig. 4a).Fig. 4: l-cry has a minor contribution as entraining photoreceptor to circalunar clock entrainment.a Nocturnal moonlight exposure protocol of lunar phase shift with 8 nights of naturalistic moonlight (dark green). Number of mature animals (percent per month, rolling mean with a window of 3 days) of l-cry wild-type (b) and mutant (c) animals. p-values: Kolomogorov–Smirnov tests. Black arrowheads indicate spawning-free intervals of the wildtype, which shifted to the position of the new FM (under free-running conditions: FR-FM). d, e Data as in (b, c) plotted as circular data. 360° correspond to 30 days of the lunar month. The arrow represents the mean vector characterized by the direction angle µ and r. r (length of µ) indicates phase coherence (measure of population synchrony). p values are results of Rayleigh Tests: Significance indicates non-random distribution of data points. The inner circle represents the Rayleigh critical value (p = 0.05). f, g Results of two-sided multisample statistics on spawning data shown in (a–e). Phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageThe resulting spawning distribution was analyzed for the efficacy of the naturalistic moonlight to phase-shift the circalunar oscillator. In order to test if the animals had shifted their spawning to the new phase, we again compared the spawning pattern before the exposure to the new full moon stimulus (months with numbers: data distribution analyzed relative to the initial/old FM, see Fig. 4a for an overview) to the spawning pattern after the exposure to the new full moon stimulus (months with letters: data distribution analyzed relative to the new FM, Fig. 4a). The more similar the data distributions of month 1 is to the months C, D, the more the population was shifted to the new phase.The first re-entraining full moon stimulus (Fig. 4b, first dark green box) is given in the middle of the main spawning period. The nocturnal light itself does not cause immediate effects on the number of spawning worms (Fig. 4b, see also Fig. 2b, c), but the repeated exposure resulted in a noticeable shift of the spawning distribution indicating a phase shift of the monthly oscillator in wildtype. Already at the third re-entraining full moon stimulus, wildtype animals exhibited a completely shifted spawning pattern (Fig. 4b, d-d″, month 1, 2 vs. month C). This is supported by statistical analyses: When comparing the months 1 and 2 (relative to the old FM before the shift) to the month C (relative to the new FM after the shift), both the Kolmogorov–Smirnov test (Fig. 4b: gray rectangles, 4f) and the Mardia–Watson–Wheeler test of the same data were non-significant (Fig. 4f), indicative of the population shifting to the new phase. Consistently, the direction angle (µ) of the mean vectors before and after the shift was highly similar, resulting in a phase difference of only 0.2 days between months 1 and C and 0.5 days between month 2 and month C (Fig. 4f, for details see methods). The month under circalunar free-running conditions (month D) supports this observation, albeit with lower statistical support (Fig. 4b, d″, f).Of note, wild-type worms would eventually reach the high spawning precision found under naturalistic moonlight only after several more months based on independent experiments (Fig. 2j, k).When we analyzed the spawning distribution of l-cry mutants in the same way as the wild types, we found that the data distribution exhibited significant differences in the linear Kolmogorov–Smirnov test when comparing months 1 and 2 before the shift to the months C and D after the shift (Fig. 4c: gray rectangles, Fig. 4g); as well as in the phase distribution in the circular analyses when comparing the months before the shift (months 1 and 2) with the last months of the shift (months C,D) (Fig. 4e, e′ versus e″, e‴, g). The populations also exhibited a noticeable phase difference of ≥3.5 days (Fig. 4g).Based on the statistical significant difference in the re-entrainment of l-cry–/–, but not wild-type populations under a naturalistic sun- and moonlight regime, we conclude that L-Cry also likely contributes to circalunar entrainment as a photoreceptor. However, as these differences are rather minor, compared to the much stronger differences seen under artificial light regime, we conclude that its major role is the light gatekeeping function.In an independent study that focused on the impact of moonlight on daily timing, we identified r-Opsin1 as a lunar light receptor that mediates moonlight effects on the worms’ ~24 h clock36. We tested if r-opsin1 is similarly important for mediating the moonlight effects on the monthly oscillator of the worm, analyzed here. This is not the case. r-opsin1–/– animals re-entrain as well as wildtype worms under naturalistic light conditions (Supplementary Fig. 4). This adds to and is also consistent with our above observation that the spawning distribution is un-altered between r-opsin1–/– and wildtype animals under artificial light conditions (Supplementary Fig. 3a, b, e, f). This finding also further enforces the notion that monthly and daily oscillators use distinct mechanisms, but both require L-Cry as light interpreter.L-Cry discriminates between naturalistic sun- and moonlight by forming differently photoreduced statesGiven that the phenotype of l-cry–/– animals suggests a role of L-Cry as light gatekeeper, i.e. only allowing the ‘right’ light to most efficiently impact on the circalunar oscillator, we next investigated how this could function on the biochemical and cell biological level.While we have previously shown that Pdu-L-Cry is degraded upon light exposure in S2 cell culture15, it has remained unclear if L-Cry has the spectral properties and sensitivity to sense moonlight and whether this would differ from sunlight sensation. To test this, we purified full length L-Cry from insect cells (Supplementary Fig. 5a–c). Multi-angle light scattering (SEC-MALS) analyses of purified dark-state L-Cry revealed a molar mass of about 130 kDa, consistent with the formation of an L-Cry homodimer (theoretical molar mass of L-Cry monomer is 65.6 kDa) (Fig. 5a). Furthermore, purified L-Cry binds Flavin Adenine Dinucleotide (FAD) as its chromophore (Supplementary Fig. 5d, e). We then used UV/Vis absorption spectroscopy to analyze the FAD photoreaction of purified L-Cry in presence of 1 mM TCEP to prevent protein oxidation. The absorption spectrum of dark-state L-Cry showed maxima at 450 nm and 475 nm, consistent with the presence of oxidized FAD (Supplementary Fig. 5f, black line). As basic starting point to analyze its photocycle, L-Cry was photoreduced using a LED (PerkinElmer ACULED Dyo) with a blue-light dominated spectrum and spectral peak at 450 nm (Supplementary Fig. 1d, d′, henceforth referred to as “blue-light”) for 110 s37. The light-activated spectrum showed that blue-light irradiation of L-Cry leads to the complete conversion of FADox into an anionic FAD radical (FADo-) with characteristic FADo- absorption maxima at 370 nm and 404 nm and reduced absorbance at 450 nm (Supplementary Fig. 5f, blue spectrum, black arrows). In darkness, L-Cry reverted back to the dark-state with time constants of 2 min (18 °C), 4 min (6 °C) and 4.7 min (ice) (Supplementary Fig. 5g–k).Fig. 5: L-Cry forms differently photoreduced sunlight- and moonlight states.a Multi-Angle Light Scattering (MALS) analyses of dark-state L-Cry fractionated by size exclusion chromatography (SEC). Black dashed line: normalized UV absorbance, solid line: normalized scattering signal. The molar mass of about 130 kDa derived from MALS (mass signal shown in red) corresponds to an L-Cry homodimer. b Absorption spectrum of L-Cry in darkness (black) and after sunlight exposure (orange). Additional timepoints: Supplementary Fig. 6a. c Dark recovery of L-Cry after 20 min sunlight on ice. Absorbance at 450 nm in Supplementary Fig. 6b. d, e Absorption spectra of L-Cry after exposure to naturalistic moonlight for different durations. f Full spectra of dark recovery after 6 h moonlight. Absorbance at 450 nm: Supplementary Fig. 6d. g Absorption spectrum of L-Cry after 6 h of moonlight followed by 20 min of sunlight. h Absorption spectrum of L-Cry after 20 min sunlight followed by moonlight first results in dark-state recovery. Absorbance at 450 nm: Supplementary Fig. 6e. i Absorption spectrum of L-Cry after 20 min sunlight followed by 4 h and 6 h moonlight builds up the moonlight state. j Model of L-Cry responses to sunlight (orange), moonlight (green) and darkness (black). Only transitions between stably accumulating states are shown. Absorbances in (b–i) were normalized when a shift in the baseline occurred between different measurements of the same measurement set, which is then indicated on the Y-axis as “normalized absorbance”.Full size imageWe then investigated the response of L-Cry to ecologically relevant light, i.e. sun- and moonlight using naturalistic sun- and moonlight devices that we designed based on light measurements at the natural habitat of P. dumerilii31 (Supplementary Fig. 1a, c, e). Upon naturalistic sunlight illumination, FAD was photoreduced to FADo-, but with slower kinetics than under the stronger blue-light source, likely due to the intensity differences between the two lights (Supplementary Fig. 1c–e).While blue-light illumination led to a complete photoreduction within 110 s (Supplementary Fig. 5f), sunlight induced photoreduction to FADo- was completed after 5–20 min (Fig. 5b) and did not further increase upon continued illumation for up to 2 h (Supplementary Fig. 6a). Dark recovery kinetics had time constants of 3.2 min (18 °C) and 5 min (ice) (Fig. 1c, Supplementary Fig. 6b, c).As the absorbance spectrum of L-Cry overlaps with that of moonlight at the Platynereis natural habitat (Supplementary Fig. 1a), L-Cry has the principle spectral prerequisite to sense moonlight. However, the most striking characteristic of moonlight is its very low intensity (5.8 × 1010 photons/cm2/s at −5m, Supplementary Fig. 1a–e). To test if Pdu-L-Cry is sensitive enough for moonlight, we illuminated purified L-Cry with our custom-built naturalistic moonlight, closely resembling full-moonlight intensity and spectrum at the Platynereis natural habitat (Supplementary Fig. 1a, c, e). Naturalistic moonlight exposure up to 2.75 h did not markedly photoreduce FAD, notably there was no difference between 1 h and 2.75 h (Fig. 5d). However, further continuous naturalistic moonlight illumination of 4 h and longer resulted in significant changes (Fig. 5d), whereby the spectrum transitioned towards the light activated state of FADo- (note peak changes at 404 nm and at 450 nm). This photoreduction progressed further until 6 h naturalistic moonlight exposure (Fig. 5d). No additional photoreduction could be observed after 9 h and 12 h of naturalistic moonlight exposure (Fig. 5e), indicating a distinct state induced by naturalistic moonlight that reaches its maximum after ~6 h, when about half of the L-Cry molecules are photoreduced. This time of ~6 h is remarkably consistent with classical work showing that a minimum of ~6 h of continuous nocturnal light is important for circalunar clock entrainment, irrespective of the preceding photoperiod5. The dark recovery of L-Cry after 6 h moonlight exposure occurred with a time constant of 6.7 min at 18 °C (Fig. 5f, Supplementary Fig. 6d). Given that both sunlight and moonlight cause FAD photoreduction, but with different kinetics and different final FADo- product/FADox educt ratios, we wondered how purified L-Cry would react to transitions between naturalistic sun- and moonlight (i.e. during “sunrise” and “sunset”).Mimicking the sunrise scenario, L-Cry was first illuminated with naturalistic moonlight for 6 h followed by 20 min of sunlight exposure. This resulted in an immediate enrichment of the FADo- state (Fig. 5g). Hence, naturalistic sunlight immediately photoreduces remaining oxidized flavin molecules, that are characteristic of moonlight activated L-Cry, to FADo-, to reach a distinct fully reduced sunlight state.In contrast, when we next mimicked the day-night transition (“sunset”) by first photoreducing with naturalistic sunlight (or strong blue-light) and subsequently exposed L-Cry to moonlight, L-Cry first returned to its full dark-state within about 30 min (naturalistic sunlight: τ = 7 min (ice), Fig. 5h, Supplementary Fig. 6e; blue-light: τ = 9 min (ice), Supplementary Fig. 6f–h), despite the continuous naturalistic moonlight illumination. Prolonged moonlight illumination then led to the conversion of dark-state L-Cry to the moonlight state (Fig. 5i, Supplementary Fig. 6f). Hence, fully photoreduced sunlight-state L-Cry first has to return to the dark-state before accumulating the moonlight state characterized by the stable presence of the partial FADo- product/FADox educt. In contrast to sunlight-state L-Cry, moonlight-state L-Cry does not return to the oxidized (dark) state under naturalistic moonlight (Fig. 5e), i.e. moonlight maintains the moonlight state, but not the sunlight state. We note, that a partially photoreduced L-Cry state may be formed transiently during dark-state recovery of the sunlight state under moonlight. However, this transiently occurring partially photoreduced L-Cry state would differ from the “true” moonlight state (e.g. by an allosteric change) preventing its accumulation (see discussion and Supplementary Fig. 6i).Given that L-Cry forms a homodimer and moonlight photoreduces about half of the FAD molecules, we propose that the moonlight state corresponds to a half-reduced FADo- FADox dimer, where FAD is only photoreduced in one L-Cry monomer, whereas in the sunlight state both monomers are photoreduced (FADo- FADo-) (Fig. 5j). This implies that the quantum yield for FADox to FADo- photoreduction differs between the two L-Cry monomers. One monomer (referred to as “A” in Fig. 5j) acts as “very low intensity light sensor” with a high quantum yield ΦA. Hence, the very low photon number provided after 6 h of moonlight illumination is sufficient to photoreduce its flavin co-factor, resulting in the partially photoreduced FADo- FADox moonlight state (Fig. 5j).For direct comparison, our naturalistic moonlight’s emission (in the main absorbance range of L-Cry: 330 nm–510 nm) is 5.4 × 1010 photons/cm2/s (Supplementary Fig. 1e), which accumulates to ~1.2 × 1015 photons/cm2 in the 6 h required to reach the half-reduced moonlight state (Fig. 5d, e). For naturalistic sunlight, emitting ~7.5 × 1014 photons/cm2/s (330–510 nm), at least 5 min of sunlight illumination (i.e. > ~1.8 × 1017 photons/cm2) are required to photoreduce the flavin in both L-Cry monomers in order to reach the fully photoreduced FADo- FADo- sunlight state (Fig. 5b, j). Thus, the second L-Cry monomer (monomer “B” in Fig. 5j) has a significantly lower quantum yield ΦB for FAD photoreduction (ΦB  More

Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Change Biol. 18, 566–584 (2012).Article 

Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).CAS 
Article 

Google Scholar 
Piao, S. L. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 
Article 

Google Scholar 
Penuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).CAS 
Article 

Google Scholar 
Garonna, I. et al. Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011). Glob. Change Biol. 20, 3457–3470 (2014).Article 

Google Scholar 
Piao, S. L. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).CAS 
Article 

Google Scholar 
Zhao, Y. et al. ABA receptor PYL9 promotes drought resistance and leaf senescence. Proc. Natl Acad. Sci. USA 113, 1949–1954 (2016).CAS 
Article 

Google Scholar 
Keskitalo, J., Bergquist, G., Gardestrom, P. & Jansson, S. A cellular timetable of autumn senescence. Plant Physiol. 139, 1635–1648 (2005).CAS 
Article 

Google Scholar 
Liu, Q. et al. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Glob. Change Biol. 22, 3702–3711 (2016).Article 

Google Scholar 
Wu, C. Y. et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nat. Clim. Change 8, 1092–1096 (2018).CAS 
Article 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).CAS 
Article 

Google Scholar 
Zhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).CAS 
Article 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).Article 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).CAS 
Article 

Google Scholar 
Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).Article 

Google Scholar 
Liu, L. B. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 4892 (2020).CAS 
Article 

Google Scholar 
Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

Google Scholar 
Piao, S. L. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Change 7, 359–363 (2017).CAS 
Article 

Google Scholar 
Fu, Y. S. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 
Article 

Google Scholar 
Seastedt, T. R. & Knapp, A. K. Consequences of nonequilibrium resource availability across multiple time scales: the transient maxima hypothesis. Am. Nat. 141, 621–633 (1993).CAS 
Article 

Google Scholar 
Korner, C. Paradigm shift in plant growth control. Curr. Opin. Plant Biol. 25, 107–114 (2015).CAS 
Article 

Google Scholar 
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).CAS 
Article 

Google Scholar 
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought. New Phytol. 178, 719–739 (2008).Article 

Google Scholar 
Nolan, R. H. et al. Differences in osmotic adjustment, foliar abscisic acid dynamics, and stomatal regulation between an isohydric and anisohydric woody angiosperm during drought. Plant Cell Environ. 40, 3122–3134 (2017).CAS 
Article 

Google Scholar 
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).CAS 
Article 

Google Scholar 
Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).CAS 
Article 

Google Scholar 
Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).CAS 
Article 

Google Scholar 
Kannenberg, S. A., Driscoll, A. W., Szejner, P., Anderegg, W. R. L. & Ehleringer, J. R. Rapid increases in shrubland and forest intrinsic water-use efficiency during an ongoing megadrought. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2118052118 (2021).Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. https://doi.org/10.1038/s41467-017-02690-y (2018).Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).CAS 
Article 

Google Scholar 
Samaniego, L. et al. Anthropogenic warming exacerbates European soil moisture droughts. Nat. Clim. Change 8, 421–426 (2018).Article 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).Article 

Google Scholar 
Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 189, 71–80 (2014).Article 

Google Scholar 
Zhang, X. Y. Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data. Remote Sens. Environ. 156, 457–472 (2015).Article 

Google Scholar 
Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).Article 

Google Scholar 
White, M. A. et al. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob. Change Biol. 15, 2335–2359 (2009).Article 

Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).Article 

Google Scholar 
Gonsamo, A., Chen, J. M., Price, D. T., Kurz, W. A. & Wu, C. Y. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J. Geophys. Res. 117, G03032 (2012).
Google Scholar 
Muñoz, S. ERA5-Land Monthly Averaged Data from 1981 to Present (C3S CDS, date accessed:10-8-2021); https://doi.org/10.24381/cds.68d2bb30Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).Article 

Google Scholar 
Müller, W. A. et al. A Higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR). J. Adv. Model. Earth Syst. 10, 1383–1413 (2018).Article 

Google Scholar 
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).CAS 
Article 

Google Scholar 
Allen, R. G., Smith, M., Pereira, L. S. & Perrier, A. An update for the calculation of reference evapotranspiration. ICID Bull. 43, 64–92 (1994).
Google Scholar 
Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01112-8 (2021).Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012).CAS 
Article 

Google Scholar 
Peng, J., Wu, C. Y., Zhang, X. Y., Wang, X. Y. & Gonsamo, A. Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere. Glob. Change Biol. 25, 2174–2188 (2019).Article 

Google Scholar 
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Beaudoing, H., Rodell, M. & NASA/GSFC/HSL. GLDAS Noah Land Surface Model L4 3 Hourly 0.25 × 0.25 Degree Version 2.0 (GES DISC, 2015); https://doi.org/10.5067/342OHQM9AK6QBeaudoing, H., Rodell, M. & NASA/GSFC/HSL. GLDAS Noah Land Surface Model L4 3 Hourly 0.25 ×0.25 Degree Version 2.1 (GES DISC, 2016); https://doi.org/10.5067/E7TYRXPJKWOQZheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).Article 

Google Scholar 
Zhang, K. et al. Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Sci. Rep. https://doi.org/10.1038/srep15956 (2015).Li, Y. et al. Estimating global ecosystem isohydry/anisohydry using active and passive microwave satellite data. J. Geophys. Res. 122, 3306–3321 (2017).Article 

Google Scholar 
Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009).Article 

Google Scholar 
Botta, A., Viovy, N., Ciais, P., Friedlingstein, P. & Monfray, P. A global prognostic scheme of leaf onset using satellite data. Glob. Change Biol. 6, 709–725 (2000).Article 

Google Scholar  More

Ray DK, Mueller ND, West PC, Foley JA. Yield trends are insufficient to double global crop production by 2050. PLoS ONE. 2013;8:1–8.
Google Scholar 
United Nations Department of Economic and Social Affairs. World population prospects: the 2017 revision. 2017. https://www.un.org/development/desa/publications/world-population-prospects-the-2017-revision.html.Pe’er G, Dicks LV, Visconti P, Arlettaz R, Báldi A, Benton TG, et al. EU agricultural reform fails on biodiversity. Science. 2014;344:1090–2.PubMed 

Google Scholar 
Jack CN, Petipas RH, Cheeke TE, Rowland JL, Friesen ML. Microbial inoculants: silver bullet or microbial Jurassic Park? Trends Microbiol. 2020;29:299–308.PubMed 

Google Scholar 
Saad M, Eida A, Hirt H. Tailoring plant-associated microbial inoculants in agriculture: a roadmap for successful application. J Exp Bot. 2020;71:3878–901.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, le Roux X, Salles JF. The legacy of microbial inoculants in agroecosystems and potential for tackling climate change challenges. iScience. 2022;25:103821.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bounaffaa M, Florio A, le Roux X, Jayet PA. Economic and environmental analysis of maize inoculation by plant growth promoting rhizobacteria in the French Rhône-Alpes region. Ecol Econ. 2018;146:334–46.
Google Scholar 
Bashan Y, de-Bashan LE, Prabhu SR, Hernandez JP. Advances in plant growth-promoting bacterial inoculant technology: formulations and practical perspectives (1998-2013). Plant Soil. 2014;378:1–33.CAS 

Google Scholar 
Mallon C, van Elsas J, Salles J. Microbial invasions: the process, patterns, and mechanisms. Trends Microbiol. 2015;23:719–29.CAS 
PubMed 

Google Scholar 
Mawarda PC, le Roux X, van Elsas JD, Salles JF. Deliberate introduction of invisible invaders: a critical appraisal of the impact of microbial inoculants on soil microbial communities. Soil Biol Biochem.2020;148:1–13.
Google Scholar 
Mallon C, Poly F, le Roux X, Marring I, van Elsas J, Salles J. Resource pulses can alleviate the biodiversity-invasion relationship in soil microbial communities. Ecology. 2015;96:915–26.PubMed 

Google Scholar 
Xing J, Jia X, Wang H, Ma B, Salles JF, Xu J. The legacy of bacterial invasions on soil native communities. Environ Microbiol. 2020;23:1–13.
Google Scholar 
Eisenhauer N, Schulz W, Scheu S, Jousset A. Niche dimensionality links biodiversity and invasibility of microbial communities. Funct Ecol. 2013;27:282–8.
Google Scholar 
Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, et al. Soil protists: a fertile frontier in soil biology research. FEMS Microbiol Rev. 2018;43:293–323.
Google Scholar 
Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
PubMed 

Google Scholar 
Sherr BF, Sherr EB, Berman T. Grazing, growth, and ammonium excretion rates of a heterotrophic microflagellate fed with four species of bacteria. Appl Environ Microbiol. 1983;45:1196–201.CAS 
PubMed 
PubMed Central 

Google Scholar 
Koller R, Rodriguez A, Robin C, Scheu S, Bonkowski M. Protozoa enhance foraging efficiency of arbuscular mycorrhizal fungi for mineral nitrogen from organic matter in soil to the benefit of host plants. New Phytol. 2013;199:203–11.CAS 
PubMed 

Google Scholar 
Geisen S, Koller R, Hünninghaus M, Dumack K, Urich T, Bonkowski M. The soil food web revisited: diverse and widespread mycophagous soil protists. Soil Biol Biochem. 2016;94:10–18.CAS 

Google Scholar 
Long JJ, Jahn CE, Sánchez-Hidalgo A, Wheat W, Jackson M, Gonzalez-Juarrero M, et al. Interactions of free-living amoebae with rice bacterial pathogens Xanthomonas oryzae pathovars oryzae and oryzicola. PLoS ONE. 2018;13:e0202941.PubMed 
PubMed Central 

Google Scholar 
Iavicoli A, Boutet E, Buchala A, Métraux JP. Induced systemic resistance in Arabidopsis thaliana in response to root inoculation with Pseudomonas fluorescens CHA0. Mol Plant Microbe Interact. 2003;16:851–8.CAS 
PubMed 

Google Scholar 
Jousset A, Rochat L, Scheu S, Bonkowski M, Keel C. Predator-prey chemical warfare determines the expression of biocontrol genes by rhizosphere-associated pseudomonas fluorescens. Appl Environ Microbiol. 2010;76:5263–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Berney C, Romac S, Mahé F, Santini S, Siano R, Bass D. Vampires in the oceans: predatory cercozoan amoebae in marine habitats. ISME J. 2013;7:2387–99.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jousset A, Scheu S, Bonkowski M. Secondary metabolite production facilitates establishment of rhizobacteria by reducing both protozoan predation and the competitive effects of indigenous bacteria. Funct Ecol. 2008;22:714–9.
Google Scholar 
Jousset A, Lara E, Wall LG, Valverde C. Secondary metabolites help biocontrol strain Pseudomonas fluorescens CHA0 to escape protozoan grazing. Appl Environ Microbiol. 2006;72:7083–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mallon CA, le Roux X, van Doorn GS, Dini-Andreote F, Poly F, Salles JF. The impact of failure: unsuccessful bacterial invasions steer the soil microbial community away from the invader’s niche. ISME J. 2018;12:728–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mawarda PC, Lakke SL, Dirk van Elsas J, Salles JF. Temporal dynamics of the soil bacterial community following Bacillus invasion. iScience. 2022;25:1–17.
Google Scholar 
Yi Y, de Jong A, Spoelder J, Theo J, van Elsas JD, Kuipers OP. Draft genome sequence of Bacillus mycoides M2E15, a strain isolated from the endosphere of potato. Genome Announc. 2016;4:e00031.PubMed 
PubMed Central 

Google Scholar 
Loznik B, Oosterkamp PJ. Fertilizer comprising protozoa and bacteria. World Intelectual Property Organization; 2017. https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2017105238.Guo S, Xiong W, Hang X, Gao Z, Jiao Z, Liu H, et al. Protists as main indicators and determinants of plant performance. Microbiome. 2021;9:1–11.
Google Scholar 
Bargabus RL, Zidack NK, Sherwood JE, Jacobsen BJ. Characterisation of systemic resistance in sugar beet elicited by a non-pathogenic, phyllosphere-colonizing Bacillus mycoides, biological control agent. Physiol Mol Plant Pathol. 2002;61:289–98.CAS 

Google Scholar 
Neher OT, Johnston MR, Zidack NK, Jacobsen BJ. Evaluation of Bacillus mycoides isolate BmJ and B. mojavensis isolate 203-7 for the control of anthracnose of cucurbits caused by Glomerella cingulata var. orbiculare. Biol Control. 2009;48:140–6.
Google Scholar 
Gao Z. Soil protists: from traits to ecological functions. University of Utrecht; 2020. https://dspace.library.uu.nl/handle/1874/400054.Amacker N, Gao Z, Hu J, Jousset ALC, Kowalchuk GA, Geisen S. Protist feeding patterns and growth rate are related to their predatory impacts on soil bacterial communities. FEMS Microbiol Ecol. 2022;98:1–11.
Google Scholar 
Wright DA, Killham K, Glover LA, Prosser JI. Role of pore size location in determining bacterial activity during predation by protozoa in soil. Appl Environ Microbiol. 1995;61:3537–43.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright D, Killham K, Glover L, Biota JP-SS. The effect of location in soil on protozoal grazing of a genetically modified bacterial inoculum. In: Brussaard L, Kooistra MJ, editors. Soil structure/soil biota interrelationships. Amsterdam: Elsevier; 1993.p.633–40.
Google Scholar 
Thewes S, Soldati T, Eichinger L. Editorial: amoebae as host models to study the interaction with pathogens. Front Cell Infect Microbiol. 2019;9:47.PubMed 
PubMed Central 

Google Scholar 
Kuppardt A, Fester T, Härtig C, Chatzinotas A. Rhizosphere protists change metabolite profiles in Zea mays. Front Microbiol. 2018;9:857.PubMed 
PubMed Central 

Google Scholar 
Gohl DM, Vangay P, Garbe J, MacLean A, Hauge A, Becker A, et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat Biotechnol. 2016;34:942–9.CAS 
PubMed 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ritz K. The plate debate: cultivable communities have no utility in contemporary environmental microbial ecology. FEMS Microbiol Ecol. 2007;60:358–62.CAS 
PubMed 

Google Scholar 
Amacker N, Gao Z, Agaras BC, Latz E, Kowalchuk GA, Valverde CF, et al. Biocontrol traits correlate with resistance to predation by protists in soil pseudomonads. Front Microbiol. 2020;11:3164.
Google Scholar 
Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.PubMed 

Google Scholar 
Saleem M, Fetzer I, Dormann CF, Harms H, Chatzinotas A. Predator richness increases the effect of prey diversity on prey yield. Nat Commun. 2012;3:1–7.
Google Scholar 
Hünninghaus M, Koller R, Kramer S, Marhan S, Kandeler E, Bonkowski M. Changes in bacterial community composition and soil respiration indicate rapid successions of protist grazers during mineralization of maize crop residues. Pedobiologia. 2017;62:1–8.
Google Scholar 
van Elsas J, Chiurazzi M, Mallon C, Elhottova D, Krištůfek V, Salles J. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci USA 2012;109:1159–64.PubMed 
PubMed Central 

Google Scholar 
Horňák K, Corno G. Every coin has a back side: invasion by limnohabitans planktonicus promotes the maintenance of species diversity in bacterial communities. PLoS ONE. 2012;7:e51576.PubMed 
PubMed Central 

Google Scholar 
Gómez P, Paterson S, de Meester L, Liu X, Lenzi L, Sharma MD, et al. Local adaptation of a bacterium is as important as its presence in structuring a natural microbial community. Nat Commun. 2016;7:1–8.
Google Scholar 
Heilbronner S, Krismer B, Brötz-Oesterhelt H, Peschel A. The microbiome-shaping roles of bacteriocins. Nat Rev Microbiol. 2021;19:726–39.CAS 
PubMed 

Google Scholar 
Xiong W, Li R, Guo S, Karlsson I, Jiao Z, Xun W, et al. Microbial amendments alter protist communities within the soil microbiome. Soil Biol Biochem. 2019;135:379–82.CAS 

Google Scholar 
Schneider FD, Scheu S, Brose U. Body mass constraints on feeding rates determine the consequences of predator loss. Ecol Lett. 2012;15:436–43.PubMed 

Google Scholar 
Brose U, Archambault P, Barnes AD, Bersier L-F, Boy T, Canning-Clode J, et al. Predator traits determine food-web architecture across ecosystems. Nat Ecol Evol. 2019;3:919–27.PubMed 

Google Scholar 
van Elsas JD, Trevors JT, Jansson JK, Nannipieri P, editors. Modern soil microbiology. 3rd ed. Boca Raton: CRC Press; 2019.Berga M, Székely AJ, Langenheder S. Effects of disturbance intensity and frequency on bacterial community composition and function. PLoS ONE. 2012;7:e365969.
Google Scholar 
Wang Z, Chen Z, Kowalchuk GA, Xu Z, Fu X, Kuramae EE. Succession of the resident soil microbial community in response to periodic inoculations. Appl Environ Microbiol. 2021;87:e00046.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Gregory, R. D. & van Strien, A. Wild bird indicators: Using composite population trends of birds as measures of environmental health. Ornithol. Sci. 9, 3–22 (2010).Article 

Google Scholar 
Cox, D. T. C. & Gaston, K. J. Urban bird feeding: Connecting people with nature. PLoS ONE 11, e0158717 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Anderson, R. M. & May, R. M. Population biology of infectious diseases: Part I. Nature 280, 361–367 (1979).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Keesing, F. et al. Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature 468, 647–652 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, K. F., Acevedo-Whitehouse, K. & Pedersen, A. B. The role of infectious diseases in biological conservation. Anim. Conserv. 12, 1–12 (2009).Article 

Google Scholar 
Han, B. A., Kramer, A. M. & Drake, J. M. Global patterns of zoonotic disease in mammals. Trends Parasitol. 32, 565–577 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Estrada-Peña, A., Ostfeld, R. S., Peterson, A. T., Poulin, R. & de la Fuente, J. Effects of environmental change on zoonotic disease risk: An ecological primer. Trends Parasitol. 30, 205–214 (2014).PubMed 
Article 

Google Scholar 
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife–threats to biodiversity and human health. Science 287(5452), 443–449 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pedersen, A. B., Jones, K. E., Nunn, C. L. & Altizer, S. Infectious diseases and extinction risk in wild mammals. Conserv. Biol. 21, 1269–1279 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Atkinson, C. T. & Samuel, M. D. Avian malaria Plasmodium relictum in native Hawaiian forest birds: Epizootiology and demographic impacts on àapapane Himatione sanguinea. J. Avian Biol. 41, 357–366 (2010).Article 

Google Scholar 
George, T. L. et al. Persistent impacts of West Nile virus on North American bird populations. Proc. Natl. Acad. Sci. USA. 112, 14290–14294 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dhondt, A. A., Tessaglia, D. L. & Slothower, R. L. Epidemic mycoplasmal conjunctivitis in house finches from Eastern North America. J. Wildl. Dis. 34, 265–280 (1998).CAS 
PubMed 
Article 

Google Scholar 
Monterroso, P. et al. Disease-mediated bottom-up regulation: An emergent virus affects a keystone prey, and alters the dynamics of trophic webs. Sci. Rep. 6, 36072 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cheng, T. L. et al. The scope and severity of white-nose syndrome on hibernating bats in North America. Conserv. Biol. 35, 1586–1597 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Rushton, S. P. et al. Disease threats posed by alien species: The role of a poxvirus in the decline of the native red squirrel in Britain. Epidemiol. Infect. 134, 521–533 (2006).CAS 
PubMed 
Article 

Google Scholar 
Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363(6434), 1459–1463 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).PubMed 
Article 

Google Scholar 
Murray, M. H. et al. City sicker? A meta-analysis of wildlife health and urbanization. Front. Ecol. Environ. 17, 575–583 (2019).Article 

Google Scholar 
Giraudeau, M., Mousel, M., Earl, S. & McGraw, K. Parasites in the city: Degree of urbanization predicts poxvirus and coccidian infections in house finches (Haemorhous mexicanus). PLoS ONE 9, e86747 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shutt, J. D. & Lees, A. C. Killing with kindness: Does widespread generalised provisioning of wildlife help or hinder biodiversity conservation efforts? Biol. Conserv. 261, 109295 (2021).Article 

Google Scholar 
Van Doren, B. M. et al. Human activity shapes the wintering ecology of a migratory bird. Glob. Chang. Biol. 27, 2715–2727 (2021).PubMed 
Article 
CAS 

Google Scholar 
Plummer, K. E., Risely, K., Toms, M. P. & Siriwardena, G. M. The composition of British bird communities is associated with long-term garden bird feeding. Nat. Commun. 10, 2088 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lawson, B. et al. Health hazards to wild birds and risk factors associated with anthropogenic food provisioning. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170091 (2018).Galbraith, J. A., Stanley, M. C., Jones, D. N. & Beggs, J. R. Experimental feeding regime influences urban bird disease dynamics. J. Avian Biol. 48, 700–713 (2017).Article 

Google Scholar 
Siriwardena, G. M. et al. The effect of supplementary winter seed food on breeding populations of farmland birds: Evidence from two large-scale experiments. J. Appl. Ecol. 44, 920–932 (2007).Article 

Google Scholar 
Kubasiewicz, L. M., Bunnefeld, N., Tulloch, A. I. T., Quine, C. P. & Park, K. J. Diversionary feeding: An effective management strategy for conservation conflict? Biodivers. Conserv. 25, 1–22 (2016).Article 

Google Scholar 
Lawson, B. et al. A clonal strain of Trichomonas gallinae is the aetiologic agent of an emerging avian epidemic disease. Infect. Genet. Evol. 11, 1638–1645 (2011).PubMed 
Article 

Google Scholar 
Robinson, R. A. et al. Emerging infectious disease leads to rapid population declines of common British birds. PLoS ONE 5, e12215 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Forrester, D. J. & Foster, G. W. Trichomonosis. In: Parasitic Diseases of Wild Birds 120–153 (Wiley-Blackwell, 2008).Lawson, B. et al. Evidence of spread of the emerging infectious disease, finch trichomonosis, by migrating birds. EcoHealth 8, 143–153 (2011).PubMed 
Article 

Google Scholar 
Lawson, B. et al. The emergence and spread of finch trichomonosis in the British Isles. Philos. Trans. R. Soc. B Biol. Sci. 367, 2852–2863 (2012).Article 

Google Scholar 
Woodward, I. D. et al. BirdTrends 2020: Trends in numbers, breeding success and survival for UK breeding birds. Research Report 732. BTO, Thetford. (2020).Enoksson, B. Age- and sex-related differences in dominance and foraging behaviour of nuthatches Sitta europaea. Anim. Behav. 36, 231–238 (1988).Article 

Google Scholar 
Tarvin, K. A. & Woolfenden, G. E. Patterns of dominance and aggressive behavior in blue jays at a feeder. Condor 99, 434–444 (1997).Article 

Google Scholar 
Brittingham, M. C. & Temple, S. A. Use of winter feeders by black-capped chickadees. Wildl. Soc. 56, 103–110 (1992).
Google Scholar 
Woodward, I. et al. Population estimates of birds in Great Britain and the United Kingdom. Br. Birds 113, 69–104 (2020).
Google Scholar 
Musgrove, A. J. et al. Population estimates of birds in Great Britain and the United Kingdom. Br. Birds 106, 64–100 (2013).
Google Scholar 
Wernham, C. et al. The Migration Atlas: Movements of the Birds of Britain and Ireland. (T & AD Poyser, 2002).Main, I. G. The partial migration of Fennoscandian Greenfinches Carduelis chloris. Ringing Migr. 20, 167–180 (2000).Article 

Google Scholar 
Lack, P. C. The Atlas of Wintering Birds in Britain and Ireland. (T. & A.D. Poyser, 1986).Robinson, R. A. BirdFacts: profiles of birds occurring in Britain & Ireland. BTO, Thetford (2005). Available at: http://www.bto.org/birdfacts. Accessed: 15 May 2022.Tratalos, J. et al. Bird densities are associated with household densities. Glob. Chang. Biol. 13, 1685–1695 (2007).ADS 
Article 

Google Scholar 
Gregory, R. D. Broad-scale habitat use of sparrows, finches and buntings in Britain. Die Vogelwelt 120, 47–57 (1999).
Google Scholar 
Newton, I. Finches. New Naturalist Series, Volume: 55. (HarperCollins, 1972).Robinson, R. A., Baillie, S. R. & Crick, H. Q. P. Weather-dependent survival: Implications of climate change for passerine population processes. Ibis. 149, 357–364 (2007).Article 

Google Scholar 
Crick, H. Q. P. A bird-habitat coding system for use in Britain and Ireland incorporating aspects of land-management and human activity. Bird Study 39, 1–12 (1992).Article 

Google Scholar 
Davies, Z. G. et al. A national scale inventory of resource provision for biodiversity within domestic gardens. Biol. Conserv. 142, 761–771 (2009).Article 

Google Scholar 
Balmer, D. E. et al. Bird Atlas 2007–11: The breeding and wintering birds of Britain and Ireland. (BTO Books, 2013).Lawson, B. et al. Epidemiology of salmonellosis in garden birds in England and Wales, 1993 to 2003. EcoHealth 7, 294–306 (2010).CAS 
PubMed 
Article 

Google Scholar 
Svensson, L. Identification guide to European passerines, 4th edition. (BTO, 1992).Jenni, L. & Winkler, R. Moult and ageing of European passerines, 2nd edition. (Helm, 2020).Baillie, S. R. The contribution of ringing to the conservation and management of bird populations: A review. Ardea 89, 167–184 (2001).
Google Scholar 
Kéry, M. & Schaub, M. Bayesian Population Analysis using WinBUGS: A hierarchical perspective (Academic Press, 2012).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2020).Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. in Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003) (eds. Hornik, K., Leisch, F. & Zeileis, A.) (2003).Su, Y.-S. & Yajima, M. R2jags: Using R to Run ‘JAGS’. R package version 0.6–1. (2020).Robinson, R. A., Morrison, C. A. & Baillie, S. R. Integrating demographic data: Towards a framework for monitoring wildlife populations at large spatial scales. Methods Ecol. Evol. 5, 1361–1372 (2014).Article 

Google Scholar 
Newson, S. E., Evans, K. L., Noble, D. G., Greenwood, J. J. D. & Gaston, K. J. Use of distance sampling to improve estimates of national population sizes for common and widespread breeding birds in the UK. J. Appl. Ecol. 45, 1330–1338 (2008).Article 

Google Scholar 
Newson, S. E., Massimino, D., Johnston, A., Baillie, S. R. & Pearce-Higgins, J. W. Should we account for detectability in population trends? Bird Study 60, 384–390 (2013).Article 

Google Scholar 
Crick, H. Q. P., Baillie, S. R. & Leech, D. I. The UK Nest Record Scheme: its value for science and conservation. Bird Study 50, 254–270 (2003).Article 

Google Scholar 
Abadi, F., Gimenez, O., Arlettaz, R. & Schaub, M. An assessment of integrated population models: Bias, accuracy, and violation of the assumption of independence. Ecology 91, 7–14 (2010).PubMed 
Article 

Google Scholar 
Plard, F., Turek, D., Grüebler, M. U. & Schaub, M. IPM2: Toward better understanding and forecasting of population dynamics. Ecol. Monogr. 89, e01364 (2019).Article 

Google Scholar 
Weegman, M. D., Arnold, T. W., Clark, R. G. & Schaub, M. Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models. Ecol. Appl. 31, e02258 (2021).Article 

Google Scholar 
Koons, D. N., Iles, D. T., Schaub, M. & Caswell, H. A life-history perspective on the demographic drivers of structured population dynamics in changing environments. Ecol. Lett. 19, 1023–1031 (2016).PubMed 
Article 

Google Scholar 
Koons, D. N., Arnold, T. W. & Schaub, M. Understanding the demographic drivers of realized population growth rates. Ecol Appl. 27, 2102–2115 (2017).PubMed 
Article 

Google Scholar 
Caswell, H. Matrix population models: Construction, analysis and interpretation. (Sinauer Associates, 2001).Stubben, C. & Milligan, B. Estimating and analyzing demographic models using the popbio package in R. J. Stat. Softw. 22, 1–23 (2007).Article 

Google Scholar 
Stanbury, A. et al. The status of our bird populations: The fifth Birds of Conservation Concern in the United Kingdom, Channel Islands and Isle of Man and second IUCN Red List assessment of extinction risk for Great Britain. Br. Birds 114, 723–747 (2021).
Google Scholar 
Lehikoinen, A., Lehikoinen, E., Valkama, J., Väisänen, R. A. & Isomursu, M. Impacts of trichomonosis epidemics on greenfinch Chloris chloris and chaffinch Fringilla coelebs populations in Finland. Ibis 155, 357–366 (2013).Article 

Google Scholar 
PECBMS. EBCC/BirdLife/RSPB/CSO’ Pan-European Common Bird Monitoring Scheme. (2021). Available at: https://pecbms.info/. (Accessed: 14th July 2022)Keller, V. et al. European Breeding Bird Atlas 2: Distribution, Abundance and Change. (European Bird Census Council and Lynx Edicions, 2020).Rijks, J. M. et al. Trichomonosis in greenfinches (Chloris chloris) in the Netherlands 2009–2017: A concealed threat. Front. Vet. Sci. 6, 425 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Boele, A. et al. Broedvogels in Nederland in 2020. Sovonrapport 2022/05. (Sovon Vogelonderzoek Nederland, Nijmegen., 2022).Jones, D. The Birds at My Table: Why We Feed Wild Birds and Why It Matters. (Cornell University Press, 2018).Pennycott, T. W. et al. Causes of death of wild birds of the family fringillidae in Britain. Vet. Rec. 143, 155–158 (1998).CAS 
PubMed 
Article 

Google Scholar 
Bouwman, K. M. & Hawley, D. M. Sickness behaviour acting as an evolutionary trap? Male house finches preferentially feed near diseased conspecifics. Biol. Lett. 6, 462–465 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Lawson, B. et al. Acute necrotising pneumonitis associated with Suttonella ornithocola infection in tits (Paridae). Vet. J. 188, 96–100 (2011).PubMed 
Article 

Google Scholar 
Clewley, G. D., Robinson, R. A. & Clark, J. A. Estimating mortality rates among passerines caught for ringing with mist nets using data from previously ringed birds. Ecol. Evol. 8, 5164–5172 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Francis, M. L. et al. Effects of supplementary feeding on interspecific dominance hierarchies in garden birds. PLoS ONE 13, e0202152 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wojczulanis-Jakubas, K., Kulpińska, M. & Minias, P. Who bullies whom at a garden feeder? Interspecific agonistic interactions of small passerines during a cold winter. J. Ethol. 33, 159–163 (2015).Article 

Google Scholar 
Cramp, S. Handbook of the Birds of Europe, the Middle East and North Africa. Volume VIII: Crows to Finches. (Oxford University Press, 1994).Brook, B. W. & Bradshaw, C. J. A. Strength of evidence for density dependence in abundance time series of 1198 species. Ecology 87, 1445–1451 (2006).PubMed 
Article 

Google Scholar 
Hochachka, W. M. & Dhondt, A. A. Density-dependent decline of host abundance resulting from a new infectious disease. Proc. Natl. Acad. Sci. USA. 97, 5303–5306 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hochachka, W. M., Dobson, A. P., Hawley, D. M. & Dhondt, A. A. Host population dynamics in the face of an evolving pathogen. J. Anim. Ecol. 90, 1480–1491 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Chi, J. F. et al. The finch epidemic strain of Trichomonas gallinae is predominant in British non-passerines. Parasitology 140, 1234–1245 (2013).PubMed 
Article 

Google Scholar 
Orros, M. E. & Fellowes, M. D. E. Wild bird feeding in an urban area: Intensity, economics and numbers of individuals supported. Acta Ornithol. 50, 43–58 (2015).Article 

Google Scholar 
Dirren, S., Borel, S., Wolfrum, N. & Korner-Nievergelt, F. Trichomonas gallinae infections in the naïve host Montifringilla nivalis subsp nivalis. J. Ornithol. 163, 333–337 (2022).Article 

Google Scholar 
Tulloch, A. I. T., Possingham, H. P., Joseph, L. N., Szabo, J. & Martin, T. G. Realising the full potential of citizen science monitoring programs. Biol. Conserv. 165, 128–138 (2013).Article 

Google Scholar 
Silvertown, J., Buesching, C., Jacobson, S. & Rebelo, T. Citizen science and nature conservation. in Key Topics in Conservation Biology 2 (eds. Macdonald, D. W. & Willis, K. J.) 127–142 (John Wiley & Sons, 2013).Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

Google Scholar 
Baillie, S. R., Wernham, C. V. & Clark, J. A. Development of the British and Irish ringing scheme and its role in conservation biology. Ringing Migr. 19, S5–S19 (1999).Article 

Google Scholar 
Greenwood, J. J. D. Citizens, science and bird conservation. J. Ornithol. 148, S77–S124 (2007).Article 

Google Scholar 
Horns, J. J., Adler, F. R. & Şekercioğlu, Ç. H. Using opportunistic citizen science data to estimate avian population trends. Biol. Conserv. 221, 151–159 (2018).Article 

Google Scholar 
Ryan, R. L., Kaplan, R. & Grese, R. E. Predicting volunteer commitment in environmental stewardship programmes. J. Environ. Plan. Manag. 44, 629–648 (2001).Article 

Google Scholar 
Maund, P. R. et al. What motivates the masses: Understanding why people contribute to conservation citizen science projects. Biol. Conserv. 246, 108587 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, V. Y. & Greig, E. I. Young adults’ motivations to feed wild birds and influences on their potential participation in citizen science: An exploratory study. Biol. Conserv. 235, 295–307 (2019).Article 

Google Scholar 
Cox, D. T. C. & Gaston, K. J. Human–nature interactions and the consequences and drivers of provisioning wildlife. Philos.Trans. R. Soc. B Biol. Sci. 373, 20170092 (2018).Article 

Google Scholar 
Murray, M. H., Becker, D. J., Hall, R. J. & Hernandez, S. M. Wildlife health and supplemental feeding: A review and management recommendations. Biol. Conserv. 204, 163–174 (2016).Article 

Google Scholar 
Rocha, G. & Quillfeldt, P. Effect of supplementary food on age ratios of European turtle doves (Streptopelia turtur L.). Anim. Biodivers. Conserv. 38, 11–21 (2015).Article 

Google Scholar  More