Philippa Kaur

1.McPhaden, M. J. & Busalacchi, A. J. The tropical ocean-global atmosphere observing system: A Decade of progress research. Oceans. https://doi.org/10.1029/97JC02906 (1998).2.Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87(2), 341–359 (2017).Article 

Google Scholar 
3.Adame, M. F. et al. Mangroves in arid regions: Ecology, threats, and opportunities. Estuar. Coast. Shelf Sci. 1, 106796 (2020).
Google Scholar 
4.Asbridge, E. F. et al. Assessing the distribution and drivers of mangrove dieback in Kakadu National Park, Northern Australia. Estuar. Coast. Shelf Sci. 228, 106353 (2019).Article 

Google Scholar 
5.Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15(5), 257–265 (2017).Article 

Google Scholar 
6.Spalding, M. D. et al. The role of ecosystems in coastal protection: adapting to climate change and coastal hazards. Ocean. Coast. Manag. 90, 50–57 (2014).7.Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Mangrove mortality in a changing climate: An overview. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).ADS 
Article 

Google Scholar 
8.Mafi-Gholami, D., Zenner, E. K., & Jaafari, A. Mangrove regional feedback to sea level rise and drought intensity at the end of the 21st century. Ecol. Indic. 110, 105972 (2020).Article 

Google Scholar 
9.Jump, A. S., & Penuelas J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8(9), 1010–1020 (2005).Article 

Google Scholar 
10.Jimenez, J. A., Lugo, A. E. & Cintron, G. Tree mortality in mangrove forests. Biotropica 17, 177–185 (1985).Article 

Google Scholar 
11.Xie, S.-P. et al. Indo-western pacific ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci. 33(4), 411–432 (2016).Article 

Google Scholar 
12.Hamlington, B. D. et al. An ongoing shift in Pacific Ocean sea level. J. Geophys/ Res. Oceans 121, 5084–5097 (2016).ADS 
Article 

Google Scholar 
13.Merrifield, M. A., Thompson, P. R. & Lander, M. Multidecadal sea level anomalies and trends in the western tropical Pacific. Geophys. Res. Lett. 39, 2–6 (2012).Article 

Google Scholar 
14.Godfrey, J. S. & Ridgway, K. R. The large-scale environment of the poleward-flowing Leeuwin Current, Western Australia: Longshore steric height gradients, wind stresses and geostrophic flow. J. Phys. Oceanogr. 15, 481–495 (1985).ADS 
Article 

Google Scholar 
15.Drexler, J. Z. & Ewel, K. C. Wetland complex linked references are available on JSTOR for this article: Effect of the 1997–1998 ENSO-related drought on hydrology and salinity in a Micronesian wetland complex. Estuaries 24, 347–356 (2001).Article 

Google Scholar 
16.Duke, N. C. et al. Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: A severe ecosystem response, coincidental with an unusually extreme weather event. Mar. Freshw. Res. 68(10), 1816–1829 (2017).Article 

Google Scholar 
17.Cai, W. et al. More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature 488, 365–369 (2012).ADS 
CAS 
Article 

Google Scholar 
18.Wilson, S. G., Taylor, J. G., & Pearce, A. F. The Seasonal Aggregation of Whale Sharks at Ningaloo Reef, Western Australia: Currents, Migrations and the El Niño/Southern Oscillation. Environmental Biology of Fishes. https://idp.springer.com/authorize/casa?redirect_uri=https://link.springer.com/article/10.1023/A:1011069914753&casa_token=55v4NHJmcDcAAAAA:owpASeBazqNzQzH7Z9xJI0BOtHzNZMvjTiJHRjLGIFCWzhyiWwMvYJUU8cloH46JDWCSZ7XOhu_CZuzZ0w. (2001).19.Lovelock, C. E., Feller, I. C., Reef, R., Hickey, S. & Ball, M. C. Mangrove dieback during fluctuating sea levels. Sci. Rep. 1, 1–8 (2017).
Google Scholar 
20.Giri, C. Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remot. Sens. 8, 783 (2016).ADS 
Article 

Google Scholar 
21.Fatoyinbo, T. E., Simard, M., Washington-Allen, R. A. & Shugart, H. H. Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove, forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data. J. Geophys. Res. Biogeosci. 113, 1–13 (2008).Article 

Google Scholar 
22.Rodriguez, W., Feller, I. C. & Cavanaugh, K. C. Spatio-temporal changes of a mangrove saltmarsh ecotone in the northeastern coast of Florida, USA. Glob. Ecol. Conserv. 7, 245–261 (2016).Article 

Google Scholar 
23.Bureau of Meteorology. Record-Breaking La Niña Events. Australian Government. http://www.bom.gov.au/climate/enso/history/La-Nina-2010-12.pdf (2012).24.Jensen, J. R. et al. The measurement of mangrove characteristics in southwest Florida using spot multispectral data. Geocarto Int. 6, 13–21 (1991).Article 

Google Scholar 
25.Eslami-Andargoli, L., Dale, P., Sipe, N. & Chaseling, J. Mangrove expansion and rainfall patterns in Moreton Bay, Southeast Queensland, Australia. Estuar. Coast. Shelf Sci. 85, 292–298 (2009).ADS 
Article 

Google Scholar 
26.Hicks, W., Fitzpatrick, R. W., & Bowman, G. (2003) Managing coastal acid sulfate soils: the East Trinity example. in Advances in regolith: Proceedings of the CRC LEME regional regolith symposia. CRC LEME, Bentley 174–177.27.Harris, N. L. et al. Using spatial statistics to identify emerging hot spots of forest loss. Environ. Res. Lett. 12, 024012 (2017).ADS 
Article 

Google Scholar 
28.Bryan-Brown, D. N. et al. Global trends in mangrove forest fragmentation. Sci. Rep. 10(1), 7117 (2020).ADS 
CAS 
Article 

Google Scholar 
29.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the anthropocene. Science 359(6371), 80–83 (2018).30.Wang, H. J., Zhang, R. H., Cole, J. & Chavez, F. El Niño and the related phenomenon southern oscillation (ENSO): The largest signal in interannual climate variation. Proc. Natl. Acad. Sci. USA. 96(20), 11071–11072 (1999).ADS 
CAS 
Article 

Google Scholar 
31.Berg, A. et al. Land-atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Chang. 6, 869–874 (2016).ADS 
Article 

Google Scholar 
32.Perry, S. J., McGregor, S., Gupta, A. S. & England, M. H. Future changes to El Niño-southern oscillation temperature and precipitation teleconnections. Geophys. Res. Lett. 44(20), 10608–10616 (2017).ADS 
Article 

Google Scholar 
33.Osland, M. J. et al. Beyond just sea-level rise: Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change. Glob. Change Biol. 22, 1–11 (2016).ADS 
Article 

Google Scholar 
34.Jentsch, A. & Beierkuhnlein, C. Research frontiers in climate change: Effects of extreme meteorological events on ecosystems. C.R. Geosci. 340, 621–628 (2008).ADS 
Article 

Google Scholar 
35.Chander, G., Markham, B. L. & Helder, D. L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903 (2009).ADS 
Article 

Google Scholar 
36.Landsat 7 (L7) Data Users Handbook. USGS. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1927_L7_Data_Users_Handbook-v2.pdf. (2009).37.Landsat 8 (L8) Data Users Handbook. USGS. https://prd-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf. (2009).38.Story, M. & Congalton, R. G. Accuracy assessment: A user’s perspective. Photogramm. Eng. Remote. Sens. 52, 397–399 (1986).
Google Scholar 
39.Moore, C. et al. Improving essential fish habitat designation to support sustainable ecosystem-based fisheries management. Mar. Policy 69, 32–41 (2016).40.Burnham, K. P., & Anderson, R. A practical information-theoretic approach. in Model Selection and Multimodel Inference 2. http://sutlib2.sut.ac.th/sut_contents/H79182.pdf.41.Burnham, K. P., & Anderson, D. R. Practical use of the information-theoretic approach. in Model Selection and Inference: A Practical Information-Theoretic Approach (eds. Burnham K. P. & Anderson D. R.) 75–117 (New York, NY, Springer, 1998).42.Cornforth, W. A., Fatoyinbo, T. E., Freemantle, T. P. & Pettorelli, N. Advanced land observing satellite phased array type L-Band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remot. Sens. 5, 224–237 (2013).ADS 
Article 

Google Scholar 
43.Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L. L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73, 91–100 (2007).ADS 
Article 

Google Scholar 
44.Long, J., Giri, C., Primavera, J. & Trivedi, M. Damage and recovery assessment of the Philippines ’ mangroves following Super Typhoon Haiyan. MPB 109, 734–743 (2016).CAS 

Google Scholar 
45.Satyanarayana, B., Mohamad, K. A., Idris, I. F., Husain, M.-L. & Dahdouh-Guebas, F. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. Int. J. Remot. Sens. 32, 1635–1650 (2011).Article 

Google Scholar 
46.Almahasheer, H., Aljowair, A., Duarte, C. M. & Irigoien, X. Decadal stability of red sea mangroves. Estuar. Coast. Shelf Sci. 169, 164–172 (2016).ADS 
Article 

Google Scholar 
47.Pettorelli, N. et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 20, 503–510 (2005).Article 

Google Scholar  More

1.Agovino, M., Casaccia, M., Ciommi, M., Ferrara, M. & Marchesano, K. Agriculture, climate change and sustainability: The case of EU-28. Ecol. Ind. 105, 525–543 (2019).Article 

Google Scholar 
2.Vegro, C. L. R. & de Almeida, L. F. in Coffee Consumption and Industry Strategies in Brazil 3–19 (Elsevier, 2020).3.Bunn, C., Läderach, P., Jimenez, J. G. P., Montagnon, C. & Schilling, T. Multiclass classification of agro-ecological zones for Arabica coffee: An improved understanding of the impacts of climate change. PLoS ONE 10, e0140490 (2015).Article 

Google Scholar 
4.Bunn, C., Läderach, P., Rivera, O. O. & Kirschke, D. A bitter cup: climate change profile of global production of Arabica and Robusta coffee. Clim. Change 129, 89–101 (2015).ADS 
Article 

Google Scholar 
5.Pham, Y., Reardon-Smith, K., Mushtaq, S. & Cockfield, G. The impact of climate change and variability on coffee production: A systematic review. Clim. Change 156, 609–630 (2019).ADS 
CAS 
Article 

Google Scholar 
6.Chemura, A., Kutywayo, D., Chidoko, P. & Mahoya, C. Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe. Reg. Environ. Change 16, 473–485 (2016).Article 

Google Scholar 
7.Laderach, P. et al. in The economic, social and political elements of climate change 703–723 (Springer, 2011).8.Baker, P. & Haggar, J. Global warming: Effects on global coffee (SCAA Conference Handout, Long Beach, 2007).9.Craparo, A., Van Asten, P. J., Läderach, P., Jassogne, L. T. & Grab, S. Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric. For. Meteorol. 207, 1–10 (2015).ADS 
Article 

Google Scholar 
10.Alves, M. C., Carvalho, L. G., Pozza, E. A., Sanches, L. & Maia, J. Ecological zoning of soybean rust, coffee rust and banana sigatoka based on Brazilian climate changes. Earth Syst. Sci. Global Change Clim. People 6, 35–46. https://doi.org/10.1016/j.proenv.2011.05.005 (2011).Article 

Google Scholar 
11.Jaramillo, J., Muchugu, E., Vega, F. E., Davis, A. & Borgemesister, C. Some like it hot: The influence and implications of climate change on coffee berry borer (Hypothenemus hampei) and coffee production in East Africa. PLoS ONE 6, e24528. https://doi.org/10.1371/journal.pone.0024528 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Kutywayo, D., Chemura, A., Kusena, W., Chidoko, P. & Mahoya, C. The impact of climate change on the potential distribution of agricultural pests: The case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe. Plos One 8, e73432. https://doi.org/10.1371/journal.pone.0073432 (2013).13.Läderach, P. et al. Climate change adaptation of coffee production in space and time. Clim. Change 141, 47–62 (2017).Article 

Google Scholar 
14.Scholz, M. B. d. S., Kitzberger, C. S. G., Prudencio, S. H. & Silva, R. S. d. S. F. d. The typicity of coffees from different terroirs determined by groups of physico-chemical and sensory variables and multiple factor analysis. Food Res. Int. 114, 72–80. https://doi.org/10.1016/j.foodres.2018.07.058 (2018).15.Bertrand, B. et al. Comparison of the effectiveness of fatty acids, chlorogenic acids, and elements for the chemometric discrimination of coffee (Coffea arabica L.) varieties and growing origins. J. Agric. Food Chem. 56, 2273–2280 (2008).16.Cheng, B., Furtado, A., Smyth, H. E. & Henry, R. J. Influence of genotype and environment on coffee quality. Trends Food Sci. Technol. 57, 20–30 (2016).CAS 
Article 

Google Scholar 
17.Bote, A. D. & Vos, J. Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality. NJAS-Wageningen J. Life Sci. 83, 39–46 (2017).18.de Carvalho, A. M. et al. Relationship between the sensory attributes and the quality of coffee in different environments. Afr. J. Agric. Res. 11, 3607–3614 (2016).Article 

Google Scholar 
19.Sberveglieri, V. et al. in AIP Conference Proceedings. 86–87 (American Institute of Physics).20.Bertrand, B. et al. Climatic factors directly impact the volatile organic compound fingerprint in green Arabica coffee bean as well as coffee beverage quality. Food Chem. 135, 2575–2583 (2012).CAS 
Article 

Google Scholar 
21.International Trade Centre. The Coffee Exporter’s Guide (World Trade Organization and the United Nations, 2011).
Google Scholar 
22.Lambot, C. et al. in The Craft and Science of Coffee (ed Britta Folmer) 17–49 (Academic Press, 2017).23.Ahmed, S. & Stepp, J. R. Beyond yields: Climate effects on specialty crop quality and agroecological management. Element. Sci. Anthropocene 4, 92 (2016).24.Purba, P., Sukartiko, A. & Ainuri, M. in IOP Conference Series: Earth and Environmental Science. 012021 (IOP Publishing).25.Traore, T. M., Wilson, N. L. & Fields, D. What explains specialty coffee quality scores and prices: A case study from the cup of excellence program. J. Agric. Appl. Econ. 50, 349–368 (2018).Article 

Google Scholar 
26.Barjolle, D., Quiñones-Ruiz, X. F., Bagal, M. & Comoé, H. The role of the state for geographical indications of coffee: Case studies from Colombia and Kenya. World Dev. 98, 105–119 (2017).Article 

Google Scholar 
27.Oguamanam, C. & Dagne, T. Geographical indication (GI) options for Ethiopian coffee and Ghanaian cocoa. Innovation and intellectual property: Collaborative dynamics in Africa, 77–108 (2014).28.Boaventura, P. S. M., Abdalla, C. C., Araujo, C. L. & Arakelian, J. S. Value co-creation in the specialty coffee value chain: The third-wave coffee movement. Revista de Administração de Empresas 58, 254–266 (2018).Article 

Google Scholar 
29.Lannigan, J. Making a space for taste: Context and discourse in the specialty coffee scene. Int. J. Inf. Manage. 51, 101987 (2020).Article 

Google Scholar 
30.Masters, G., Baker, P. & Flood, J. Climate change and agricultural commodities. CABI Work. Pap. 2, 1–38 (2010).
Google Scholar 
31.Rahman, S., Gross, M., Battiste, M. & Gacioch, M. Specialty Coffee Farmers’ Climate Change Concern and Perceived Ability to Adapt. (2016).32.Srinivasan, R., Giannikas, V., Kumar, M., Guyot, R. & McFarlane, D. Modelling food sourcing decisions under climate change: A data-driven approach. Comput. Ind. Eng. 128, 911–919 (2019).Article 

Google Scholar 
33.Chemura, A., Schauberger, B. & Gornott, C. Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLoS ONE 15, e0229881 (2020).CAS 
Article 

Google Scholar 
34.FAO. (Food Agriculture Organization of the United Nations, Roma, 2012).35.Hirons, M. et al. Pursuing climate resilient coffee in Ethiopia: A critical review. Geoforum 91, 108–116 (2018).Article 

Google Scholar 
36.Central Statistical Agency (CSA). Agricultural Sample Survey 2018/19. (2019).37.Murken, L. et al. Climate Risk Analysis for Identifying and Weighing Adaptation Strategies in Ethiopia’s Agricultural Sector. (2020).38.Ridley, F. The past and future climatic suitability of arabica coffee (Coffea arabica L.) in East Africa, Durham University, (2011).39.Putri, S. P., Irifune, T. & Fukusaki, E. GC/MS based metabolite profiling of Indonesian specialty coffee from different species and geographical origin. Metabolomics 15, 126 (2019).Article 

Google Scholar 
40.Mengistie, G. in Extending the Protection of Geographical Indications: Case studies of Agricultural Products of Africa Vol. 15 (eds M Blakeney, T Coulet, Getachew Mengistie, & M.T Mahop) 150 (Routledge, 2011).41.Kufa, T., Ayano, A., Yilma, A., Kumela, T. & Tefera, W. The contribution of coffee research for coffee seed development in Ethiopia. J. Agric. Res. Dev. 1, 009–016 (2011).
Google Scholar 
42.Moat, J. et al. Resilience potential of the Ethiopian coffee sector under climate change. Nat. Plants 3, 17081 (2017).Article 

Google Scholar 
43.Moat, J., Gole, T. W. & Davis, A. P. Least Concern to Endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee. Glob. Change Biol. 25, 390–403 (2019).ADS 
Article 

Google Scholar 
44.Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous arabica coffee (Coffea arabica): Predicting future trends and identifying priorities. PLoS ONE 7, e47981. https://doi.org/10.1371/journal.pone.0047981 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.CIAT. Future Climate Scenarios for Tanzania’s Arabica Coffee Growing Areas. 27 (International Center for Tropical Agriculture, Cali, Colombia: , 2012).46.Laderach, P., Jarvis, A. & Ramirez, J. The impact of climate change in coffee-growing regions: The case of 10 municipalities in Nicaragua. 4 (CafeDirect/GTZ, 2006).47.Gomes, L. C. et al. Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agr. Ecosyst. Environ. 294, 106858. https://doi.org/10.1016/j.agee.2020.106858 (2020).Article 

Google Scholar 
48.Brown, N. in Daily Coffee News (Roast Magazine, 2018).49.Labouisse, J.-P., Bellachew, B., Kotecha, S. & Bertrand, B. Current status of coffee (Coffea arabica L.) genetic resources in Ethiopia: implications for conservation. Genet. Resour. Crop Evol. 55, 1079 (2008).50.MFA. Coffee production in Ethiopia. The 4th World Coffee Conference in Addis Ababa, Ministry of Foreign Affairs of Ethiopia, Addis Ababa, Ethiopia (2016).51.Tolessa, K., D’heer, J., Duchateau, L. & Boeckx, P. Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee. J. Sci. Food Agric. 97, 2849–2857 (2017).52.Chemura, A., Mahoya, C., Chidoko, P. & Kutywayo, D. Effect of soil moisture deficit stress on biomass accumulation of four coffee (Coffea arabica) varieties in Zimbabwe. ISRN Agron. 1–10, 2014. https://doi.org/10.1155/2014/767312 (2014).Article 

Google Scholar 
53.Hannah, L. et al. Climate change, wine, and conservation. Proc. Natl. Acad. Sci. 110, 6907–6912 (2013).ADS 
CAS 
Article 

Google Scholar 
54.Impact on variety and origin chemometric determination. Villarreal, D. et al. Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile. J. Agric. Food Chem. 57, 11321–11327 (2009).Article 

Google Scholar 
55.Sisay, B. T. in Sustainable agriculture reviews 33 99–113 (Springer, 2018).56.DaMatta, F. b. M., Avila, R. T., Cardoso, A. A., Martins, S. C. & Ramalho, J. C. Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. J. Agric. Food Chem. 66, 5264–5274 (2018).57.CABI. (2015).58.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
59.Liu, C., Newell, G. & White, M. The effect of sample size on the accuracy of species distribution models: Considering both presences and pseudo-absences or background sites. Ecography 42, 535–548 (2019).Article 

Google Scholar 
60.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).Article 

Google Scholar 
61.R Core Team. R: A language and environment for statistical computing. (2019).62.Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PloS one 9 (2014).63.Nair, K. P. P. The Agronomy and Economy of Important Tree Crops of the Developing World. 368 (Elservier, 2010).64.Coste, J. Coffee: The plant and the product. (Longman, 1992).65.Lin, F.-J. Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Qual. Quant. 42, 417–426 (2008).Article 

Google Scholar 
66.Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
67.Breiman, L. Random forests machine learning. 45: 5–32. View Article PubMed/NCBI Google Scholar (2001).68.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
Article 

Google Scholar 
69.Li, X. & Wang, Y. Applying various algorithms for species distribution modelling. Integr. Zool. 8, 124–135 (2013).Article 

Google Scholar 
70.Gobeyn, S. et al. Evolutionary algorithms for species distribution modelling: A review in the context of machine learning. Ecol. Model. 392, 179–195 (2019).Article 

Google Scholar 
71.Vapnik, V. The nature of statistical learning theory. (Springer science & business media, 2013).72.Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F. & Kløve, B. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Sci. Total Environ. 615, 272–281 (2018).ADS 
CAS 
Article 

Google Scholar 
73.Pourghasemi, H. R., Yousefi, S., Kornejady, A. & Cerdà, A. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609, 764–775 (2017).ADS 
CAS 
Article 

Google Scholar 
74.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
75.Chang, Y. & Bourque, C.P.-A. Relating modelled habitat suitability for Abies balsamea to on-the-ground species structural characteristics in naturally growing forests. Ecol. Ind. 111, 105981 (2020).Article 

Google Scholar 
76.Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

Google Scholar 
77.Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography (2020).78.ArcGIS Desktop v. 10.2 (Environmental Systems Research Institute, Redlands, CA, Redlands, 2012).79.WorldClim. Global climate and weather data. https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html ( 2020).80.Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A. & Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 7, 1–14 (2020).Article 

Google Scholar 
81.van Vuuren, D. P. et al. A new scenario framework for Climate Change Research: scenario matrix architecture. Clim. Change 122, 373–386. https://doi.org/10.1007/s10584-013-0906-1 (2014).Article 

Google Scholar 
82.Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang. 42, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002 (2017).Article 

Google Scholar 
83.O’Neill, B. C. et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Chang. 42, 169–180 (2017).Article 

Google Scholar 
84.Doelman, J. C. et al. Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Chang. 48, 119–135 (2018).Article 

Google Scholar  More

1.Jones, C. G., Lawton, J. H., & Shachak, M. Organisms as ecosystem engineers. In Ecosystem Management 130–147 (Springer, 1994).2.Alvarez-Uria, P. & Körner, C. Low temperature limits of root growth in deciduous and evergreen temperate tree species. Funct. Ecol. 21, 211–218 (2007).Article 

Google Scholar 
3.Rossi, S. et al. Pattern of xylem phenology in conifers of cold ecosystems at the Northern Hemisphere. Glob. Chang. Biol. 22, 3804–3813 (2016).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
4.Körner, C. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004).Article 

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

Google Scholar 
6.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 
7.Albrich, K., Rammer, W. & Seidl, R. Climate change causes critical transitions and irreversible alterations of mountain forests. Glob. Change Biol. 26, 4013–4027 (2020).Article 
ADS 

Google Scholar 
8.De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. PNAS 110, 18561–18565 (2013).PubMed 
Article 
ADS 
CAS 
PubMed Central 

Google Scholar 
9.Maclean, I. M. D. et al. Microclimates buffer the responses of plant communities to climate change. Glob. Ecol. Biogeogr. 24, 1340–1350 (2015).Article 

Google Scholar 
10.Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
11.Weigel, R., Gilles, J., Klisz, M., Manthey, M. & Kreyling, J. Forest understory vegetation is more related to soil than to climate towards the cold distribution margin of European beech. J. Veg Sci. 30, 746–755 (2019).Article 

Google Scholar 
12.Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
13.Dozier, J. & Outcalt, S. I. An approach toward energy balance simulation over rugged terrain. Geogr. Anal. 11, 65–85 (1979).Article 

Google Scholar 
14.Rorison, I. H., Sutton, F. & Hunt, R. Local climate, topography and plant growth in Lathkill Dale NNR. I. A twelve-year summary of solar radiation and temperature. Plant Cell Environ. 9, 49–56 (1986).
Google Scholar 
15.Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).Article 

Google Scholar 
16.Baldocchi, D. D. & Xu, L. What limits evaporation from Mediterranean oak woodlands—The supply of moisture in the soil, physiological control by plants or the demand by the atmosphere?. Adv. Water Resour. 30, 2113–2122 (2007).Article 
ADS 

Google Scholar 
17.Komatsu, H. Forest categorization according to dry-canopy evaporation rates in the growing season: Comparison of the Priestley-Taylor coefficient values from various observation sites. Hydrol. Process. 19, 3873–3896 (2005).Article 
ADS 

Google Scholar 
18.Lenoir, J. et al. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Glob. Chang. Biol. 19, 1470–1481 (2013).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
19.Aussenac, G. Interactions between forest stands and microclimate: Ecophysiological aspects and consequences for silviculture. Ann. For. Sci. 57, 287–301 (2000).Article 

Google Scholar 
20.von Arx, G., Dobbertin, M. & Rebetez, M. Spatio-temporal effects of forest canopy on understory microclimate in a long-term experiment in Switzerland. Agric. For. Meteorol. 166, 144–155 (2012).Article 
ADS 

Google Scholar 
21.Gaudio, N. et al. Impact of tree canopy on thermal and radiative microclimates in a mixed temperate forest: A new statistical method to analyse hourly temporal dynamics. Agric. For. Meteorol. 237, 71–79 (2017).Article 
ADS 

Google Scholar 
22.Niinemets, Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol. Res. 25, 693–714 (2010).Article 

Google Scholar 
23.Breshears, D. D., Myers, O. B. & Barnes, F. J. Horizontal heterogeneity in the frequency of plant-available water with woodland intercanopy-canopy vegetation patch type rivals that occurring vertically by soil depth. Ecohydrology 2, 503–519 (2009).Article 

Google Scholar 
24.Zou, C. B., Barron-Gafford, G. A. & Breshears, D. D. Effects of topography and woody plant canopy cover on near-ground solar radiation: Relevant energy inputs for ecohydrology and hydropedology. Geophys. Res. Lett. 34, L24S21 (2007).Article 

Google Scholar 
25.Renaud, V., Innes, J. L., Dobbertin, M. & Rebetez, M. Comparison between open-site and below-canopy climatic conditions in Switzerland for different types of forests over 10 years (1998–2007). Theor. Appl. Climatol. 105, 119–127 (2011).Article 
ADS 

Google Scholar 
26.De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Harsch, M. A. & Bader, M. Y. Treeline form—A potential key to understanding treeline dynamics. Glob. Ecol. Biogeogr. 20, 582–596 (2011).Article 

Google Scholar 
28.Körner, C. et al. Where, why and how? Explaining the low-temperature range limits of temperate tree species. J. Ecol. 104, 1076–1088 (2016).Article 
CAS 

Google Scholar 
29.Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: Implications for species redistribution. Ecography 40, 253–266 (2017).Article 

Google Scholar 
30.Bonanomi, G. et al. Anthropogenic and environmental factors affect the tree line position of Fagus sylvatica along the Apennines (Italy). J. Biogeogr. 45, 2595–2608 (2018).Article 

Google Scholar 
31.Bonanomi, G. et al. Climatic and anthropogenic factors explain the variability of Fagus sylvatica treeline elevation in fifteen mountain groups across the Apennines. For. Ecosyst. 7, 5 (2020).Article 

Google Scholar 
32.Driessen, P., Deckers, J., Spaargaren, O. & Nachtergaele, F. (Eds.). Lecture notes on the major soils of the world. In World Soil Resources Report; No. 94. (Food and Agricultural Organization of the United Nations, 2001).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
34.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, 2019).35.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).
Google Scholar 
36.Davis, K. T., Dobrowski, S. Z., Holden, Z. A., Higuera, P. E. & Abatzoglou, J. T. Microclimatic buffering in forests of the future: The role of local water balance. Ecography 42, 1–11 (2019).Article 

Google Scholar 
37.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.15. https://CRAN.R-project.org/package=MuMIn (2019).38.Geiger, R., Aron, R. H. & Todhunter, P. The Climate near the Ground (Rowman & Littlefield Publishers, 2003).
Google Scholar 
39.Bader, M., Rietkerk, M. & Bregt, A. Vegetation structure and temperature regimes of tropical alpine treelines. Arct. Antarct. Alp. Res. 39, 353–364 (2007).Article 

Google Scholar 
40.Potter, B. E., Teclaw, R. M. & Zasada, J. C. The impact of forest structure on near-ground temperatures during two years of contrasting temperature extremes. Agric. For. Meteorol. 106, 331–336 (2001).Article 
ADS 

Google Scholar 
41.von Arx, G., Pannatier, E. G., Thimonier, A. & Rebetez, M. Microclimate in forests with varying leaf area index and soil moisture: Potential implications for seedling establishment in a changing climate. J. Ecol. 101, 1201–1213 (2013).Article 

Google Scholar 
42.Frey, B. R. et al. An analysis of sucker regeneration of trembling aspen. Can. J. For. Res. 33, 1169–1179 (2003).Article 

Google Scholar 
43.Lenz, A., Hoch, G. & Vitasse, Y. Fast acclimation of freezing resistance suggests no influence of winter minimum temperature on the range limit of European beech. Tree Physiol. 36, 490–501 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Keitel, C. et al. Carbon and oxygen isotope composition of organic compounds in the phloem sap provides a short-term measure for stomatal conductance of European beech (Fagus sylvatica L.). Plant Cell Environ. 26, 1157–1168 (2003).CAS 
Article 

Google Scholar 
45.van der Maaten, E., Bouriaud, O., van der Maaten-Theunissen, M., Mayer, H. & Spiecker, H. Meteorological forcing of day-to-day stem radius variations of beech is highly synchronic on opposing aspects of a valley. Agric. For. Meteorol. 181, 85–93 (2013).Article 
ADS 

Google Scholar 
46.Smith, D. L. & Johnson, L. Vegetation-mediated changes in microclimate reduce soil respiration as woodlands expand into grasslands. Ecology 85, 3348–3361 (2004).Article 

Google Scholar 
47.Wu, Z., Dijkstra, P., Koch, G. W., Peñuelas, J. & Hungate, B. A. Responses of terrestrial ecosystems to temperature and precipitation change: A meta-analysis of experimental manipulation. Glob. Change Biol. 17, 927–942 (2011).Article 
ADS 

Google Scholar 
48.Gehlhausen, S. M., Schwartz, M. W. & Augspurger, C. K. Vegetation and microclimatic edge effects in two mixed-mesophytic forest fragments. Plant Ecol. 147, 21–35 (2000).Article 

Google Scholar 
49.Hofmeister, J. et al. Microclimate edge effect in small fragments of temperate forests in the context of climate change. For. Ecol. Manag. 448, 48–56 (2019).Article 

Google Scholar 
50.Treml, V. & Banaš, M. The effect of exposure on alpine treeline position: A case study from the High Sudetes, Czech Republic. Arct. Antarct. Alp. Res. 40, 751–760 (2008).Article 

Google Scholar 
51.Zellweger, F. et al. Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe. Glob. Ecol. Biogeogr. 28, 1774–1786 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Frey, S. J. et al. Spatial models reveal the microclimatic buffering capacity of old-growth forests. Sci. Adv. 2, e1501392 (2016).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
53.Ashcroft, M. B. & Gollan, J. R. Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia. Agric. For. Meteorol. 176, 77–89 (2013).Article 
ADS 

Google Scholar 
54.Holden, Z. A., Klene, A. E., Keefe, R. F. & Moisen, G. G. Design and evaluation of an inexpensive radiation shield for monitoring surface air temperatures. Agric. For. Meteorol. 180, 281–286 (2013).Article 
ADS 

Google Scholar 
55.Maher, E. L., Germino, M. J. & Hasselquist, N. J. Interactive effects of tree and herb cover on survivorship, physiology, and microclimate of conifer seedlings at the alpine tree-line ecotone. Can. J. For. Res. 35, 567–574 (2005).Article 

Google Scholar 
56.Maher, E. L. & Germino, M. J. Microsite differentiation among conifer species during seedling establishment at alpine treeline. Ecoscience 13, 334–341 (2006).Article 

Google Scholar 
57.Mayor, J. R. et al. Elevation alters ecosystem properties across temperate treelines globally. Nature 542, 91–95 (2017).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
58.Allevato, E. et al. Canopy damage by spring frost in European beech along the Apennines: Effect of latitude, altitude and aspect. Remote Sens. Environ. 225, 431–440 (2019).Article 
ADS 

Google Scholar 
59.Nolè, A., Rita, A., Ferrara, A. M. S. & Borghetti, M. Effects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI. Ann. For. Sci. 75, 83 (2018).Article 

Google Scholar 
60.Müller, M. et al. Soil temperature and soil moisture patterns in a Himalayan alpine treeline ecotone. Arct. Antarct. Alp. Res. 48, 501–521 (2016).Article 

Google Scholar 
61.Liechty, H. O., Holmes, M. J., Reed, D. D. & Mroz, G. D. Changes in microclimate after stand conversion in two northern hardwood stands. For. Ecol. Manag. 50, 253–264 (1992).Article 

Google Scholar 
62.Peterson, D. W. & Peterson, D. L. Mountain hemlock growth responds to climatic variability at annual and decadal time scales. Ecology 82, 3330–3345 (2001).Article 

Google Scholar 
63.Jarvis, P. et al. Drying and wetting of Mediterranean soils stimulates decomposition and carbon dioxide emission: The “Birch effect”. Tree Physiol. 27, 929–940 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Binkley, D. & Fisher, R. F. Ecology and Management of Forest Soils (Wiley-Blackwell, 2013).
Google Scholar  More

1.Hope, P. R. & Jones, G. Warming up for dinner: Torpor and arousal in hibernating Natterer’s bats (Myotis nattereri) studied by radio telemetry. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 182, 569–578. https://doi.org/10.1007/s00360-011-0631-x (2012).Article 

Google Scholar 
2.Czenze, Z. J., Jonasson, K. A. & Willis, C. K. R. Thrifty females, frisky males: Winter energetics of hibernating bats from a cold climate. Physiol. Biochem. Zool. 90, 502–511. https://doi.org/10.1086/692623 (2017).Article 
PubMed 

Google Scholar 
3.Reynolds, D. S., Shoemaker, K., von Oettingen, S. & Najjar, S. High rates of winter activity and arousals in two New England bat species: Implications for a reduced white-nose syndrome impact?. Northeast. Nat. 24, B188–B208 (2017).Article 

Google Scholar 
4.Kunz, T. H. & Martin, R. A. Plecotus townsendii. Mamm. Species 175, 1–6 (1982).
Google Scholar 
5.Twente, J. W. Aspects of a population study of cavern-dwelling bats. J. Mamm. 36, 379–390 (1955).Article 

Google Scholar 
6.Humphrey, S. R. & Kunz, T. H. Ecology of a Pleistocene relict, the western big-eared bat (Plecotus townsendii), in the southern Great Plains. J. Mamm. 57, 470–494. https://doi.org/10.2307/1379297 (1976).Article 

Google Scholar 
7.Czenze, Z. J., Park, A. D. & Willis, C. K. R. Staying cold through dinner: Cold-climate bats rewarm with conspecifics but not sunset during hibernation. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 183, 859–866. https://doi.org/10.1007/s00360-013-0753-4 (2013).Article 

Google Scholar 
8.Pearson, O. P., Koford, M. R. & Pearson, A. K. Reproduction of the lump-nosed bat (Corynorhinus rafinesquei) in California. J. Mamm. 33, 273–320 (1952).Article 

Google Scholar 
9.Johnson, J. S., Lacki, M. J., Thomas, S. C. & Grider, J. F. Frequent arousals from winter torpor in Rafinesque’s big-eared bat (Corynorhinus rafinesquii). PLoS ONE 7, e49754. https://doi.org/10.1371/journal.pone.0049754 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Lausen, C. L. & Barclay, R. M. R. Winter bat activity in the Canadian prairies. Can. J. Zool.-Rev. Can. Zool. 84, 1079–1086. https://doi.org/10.1139/z06-093 (2006).Article 

Google Scholar 
11.Thomas, D. W. & Cloutier, D. Evaporative water-loss by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 65, 443–456 (1992).Article 

Google Scholar 
12.Ben-Hamo, M., Munoz-Garcia, A., Williams, J. B., Korine, C. & Pinshow, B. Waking to drink: Rates of evaporative water loss determine arousal frequency in hibernating bats. J. Exp. Biol. 216, 573–577. https://doi.org/10.1242/jeb.078790 (2013).Article 
PubMed 

Google Scholar 
13.Czenze, Z. J. & Willis, C. K. R. Warming up and shipping out: Arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). J. Comp. Physiol. B-Biochem. Syst. Environ. Physiol. 185, 575–586. https://doi.org/10.1007/s00360-015-0900-1 (2015).Article 

Google Scholar 
14.Choate, J. R. & Anderson, J. M. Bats of jewel cave national monument, South Dakota. Prairie Nat. 29, 39–47 (1997).
Google Scholar 
15.Klüg-Baerwald, B. J., Gower, L. E., Lausen, C. L. & Brigham, R. M. Environmental correlates and energetics of winter flight by bats in southern Alberta, Canada. Can. J. Zool. 94, 829–836. https://doi.org/10.1139/cjz-2016-0055 (2016).Article 

Google Scholar 
16.Johnson, J. S. et al. Migratory and winter activity of bats in Yellowstone National Park. J. Mamm. 98, 211–221. https://doi.org/10.1093/jmammal/gyw175 (2017).Article 

Google Scholar 
17.Norquay, K. & Willis, C. Hibernation phenology of Myotis lucifugus. J. Zool. 294, 85–92 (2014).Article 

Google Scholar 
18.Barclay, R. M. et al. Variation in the reproductive rate of bats. Can. J. Zool. 82, 688–693 (2004).Article 

Google Scholar 
19.Jonasson, K. A. & Willis, C. K. Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome. PLoS ONE 6, e21061. https://doi.org/10.1371/journal.pone.0021061 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Speakman, J. R., Webb, P. I. & Racey, P. A. Effects of disturbance on the energy expenditure of hibernating bats. J. Appl. Ecol. 28, 1087–1104. https://doi.org/10.2307/2404227 (1991).Article 

Google Scholar 
21.Reeder, D. M., Field, K. A. & Slater, M. H. Balancing the costs of wildlife research with the benefits of understanding a panzootic disease, white-nose syndrome. ILAR J. 56, 275–282. https://doi.org/10.1093/ilar/ilv035 (2015).CAS 
Article 

Google Scholar 
22.Boyles, J. G. Benefits of knowing the costs of disturbance to hibernating bats. Wildl. Soc. Bull. 41, 388–392. https://doi.org/10.1002/wsb.755 (2017).Article 

Google Scholar 
23.Thomas, D. W. Hibernating bats are sensitive to nontactile human disturbance. J. Mamm. 76, 940–946. https://doi.org/10.2307/1382764 (1995).Article 

Google Scholar 
24.Furey, N. M. & Racey, P. A. Bats in the Anthropocene: Conservation of Bats in a Changing World 463–500 (Springer, 2016).
Google Scholar 
25.Sheffield, S. R., Shaw, J. H., Heidt, G. A. & McClenaghan, L. R. Guidelines for the protection of bat roosts. J. Mamm. 73, 707–710 (1992).
Google Scholar 
26.Jones, G., Jacobs, D. S., Kunz, T. H., Willig, M. R. & Racey, P. A. Carpe noctem: The importance of bats as bioindicators. Endang. Species Res. 8, 93–115 (2009).Article 

Google Scholar 
27.Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227. https://doi.org/10.1126/science.1163874 (2009).CAS 
Article 
PubMed 

Google Scholar 
28.Foley, J., Clifford, D., Castle, K., Cryan, P. & Ostfeld, R. S. Investigating and managing the rapid emergence of white-nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231. https://doi.org/10.1111/j.1523-1739.2010.01638.x (2011).Article 
PubMed 

Google Scholar 
29.Ingersoll, T. E., Sewall, B. J. & Amelon, S. K. Effects of white-nose syndrome on regional population patterns of 3 hibernating bat species. Conserv. Biol. 30, 1048–1059. https://doi.org/10.1111/cobi.12690 (2016).Article 
PubMed 

Google Scholar 
30.Minnis, A. M. & Lindner, D. L. Phylogenetic evaluation of Geomyces and allies reveals no close relatives of Pseudogymnoascus destructans, comb. nov., in bat hibernacula of eastern North America. Fungal Biol. 117, 638–649. https://doi.org/10.1016/j.funbio.2013.07.001 (2013).Article 
PubMed 

Google Scholar 
31.Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).ADS 
CAS 
Article 

Google Scholar 
32.Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).Article 

Google Scholar 
33.Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. U.S.A. 109, 6999–7003. https://doi.org/10.1073/pnas.1200374109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Lilley, T. M. et al. White-nose syndrome survivors do not exhibit frequent arousals associated with Pseudogymnoascus destructans infection. Front. Zool. https://doi.org/10.1186/s12983-016-0143-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
35.McGuire, L. P., Mayberry, H. W. & Willis, C. K. R. White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats. Am. J. Physiol.-Regulat. Integr. Compar. Physiol. 313, R680–R686. https://doi.org/10.1152/ajpregu.00058.2017 (2017).CAS 
Article 

Google Scholar 
36.Knudsen, G. R., Dixon, R. D. & Amelon, S. K. Potential spread of white-nose syndrome of bats to the Northwest: Epidemiological considerations. Northwest Sci. 87, 292–306. https://doi.org/10.3955/046.087.0401 (2013).Article 

Google Scholar 
37.Bernard, R. F. & McCracken, G. F. Winter behavior of bats and the progression of white-nose syndrome in the southeastern United States. Ecol. Evol. 7, 1487–1496. https://doi.org/10.1002/ece3.2772 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Cheng, T. L. et al. Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome. J. Anim. Ecol. 88, 591–600 (2019).Article 

Google Scholar 
39.Turner, J. M. et al. Conspecific disturbance contributes to altered hibernation patterns in bats with white-nose syndrome. Physiol. Behav. 140, 71–78 (2015).CAS 
Article 

Google Scholar 
40.Blazek, J. et al. Numerous cold arousals and rare arousal cascades as a hibernation strategy in European Myotis bats. J. Therm. Biol 82, 150–156. https://doi.org/10.1016/j.jtherbio.2019.04.002 (2019).Article 
PubMed 

Google Scholar 
41.Lorch, J. M. et al. First detection of bat white-nose syndrome in Western North America. mSphere 1(4), e00148. https://doi.org/10.1128/mSphere.00148-16 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Weller, T. J. et al. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PLoS ONE https://doi.org/10.1371/journal.pone.0205647 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Whiting, J. C. et al. Bat hibernacula in caves of southern Idaho: Implications for monitoring and management. West. N. Am. Nat. 78, 165–173 (2018).Article 

Google Scholar 
44.Whiting, J. C. et al. Long-term bat abundance in sagebrush steppe. Sci. Rep. 8, 12288 (2018).ADS 
Article 

Google Scholar 
45.Call, R. S. et al. Maternity roosts of Townsend’s big-eared bats in lava tube caves of southern Idaho. Northwest Sci. 92, 158–165 (2018).ADS 
Article 

Google Scholar 
46.Clark, B. S., Clark, B. K. & Leslie, D. M. Seasonal variation in activity patterns of the endangered Ozark big-eared bat (Corynorhinus townsendii ingens). J. Mamm. 83, 590–598. https://doi.org/10.1644/1545-1542(2002)083%3c0590:sviapo%3e2.0.co;2 (2002).Article 

Google Scholar 
47.French, A. R. The patterns of mammalian hibernation. Am. Sci. 76, 568–575 (1988).ADS 

Google Scholar 
48.Reynolds, T. D., Connelly, J. W., Halford, D. K. & Arthur, W. J. Vertebrate fauna of the Idaho National Environmental Research Park. Gt. Basin Nat. 46, 513–527 (1986).
Google Scholar 
49.Genter, D. L. Wintering bats of the upper Snake River Plain: Occurrence in lava-tube caves. Gt. Basin Nat. 46, 241–244 (1986).
Google Scholar 
50.Gillies, K. E., Murphy, P. J. & Matocq, M. D. Hibernacula characteristics of Townsend’s big-eared bats in southeastern Idaho. Nat. Areas J. 34, 24–30 (2014).Article 

Google Scholar 
51.Sikes, R. S. et al. Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J. Mamm. 97(663–688), 2016. https://doi.org/10.1093/jmammal/gyw078 (2016).Article 

Google Scholar 
52.Schwab, N. A. & Mabee, T. J. Winter acoustic activity of bats in Montana. Northwest. Nat. 95, 13–27 (2014).Article 

Google Scholar 
53.Britzke, E. R., Slack, B. A., Armstrong, M. P. & Loeb, S. C. Effects of orientation and weatherproofing on the detection of bat echolocation calls. J. Fish Wildl. Manage. 1, 136–141. https://doi.org/10.3996/072010-jfwm-025 (2010).Article 

Google Scholar 
54.Skalak, S. L., Sherwin, R. E. & Brigham, R. M. Sampling period, size and duration influence measures of bat species richness from acoustic surveys. Methods Ecol. Evol. 3, 490–502. https://doi.org/10.1111/j.2041-210X.2011.00177.x (2012).Article 

Google Scholar 
55.Miller, B. W. A method for determining relative activity of free flying bats using a new activity index for acoustic monitoring. Acta Chiropt. 3, 93–105 (2001).
Google Scholar 
56.Nocera, T., Ford, W. M., Silvis, A. & Dobony, C. A. Patterns of acoustical activity of bats prior to and 10 years after WNS on Fort drum army installation, New York. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00633 (2019).Article 

Google Scholar 
57.Britzke, E. R., Gillam, E. H. & Murray, K. L. Current state of understanding of ultrasonic detectors for the study of bat ecology. Acta Theriol. 58, 109–117. https://doi.org/10.1007/s13364-013-0131-3 (2013).Article 

Google Scholar 
58.O’Farrell, M. J., Miller, B. W. & Gannon, W. L. Qualitative identification of free-flying bats using the Anabat detector. J. Mamm. 80, 11–23. https://doi.org/10.2307/1383203 (1999).Article 

Google Scholar 
59.Whiting, J. C., Doering, B. & Pennock, D. Acoustic surveys for local, free-flying bats in zoos: An engaging approach for bat education and conservation. J. Bat Res. Conserv. 12, 94–99. https://doi.org/10.14709/BarbJ.12.1.2019.12 (2019).Article 

Google Scholar 
60.O’Farrell, M. J. & Gannon, W. L. A comparison of acoustic versus capture techniques for the inventory of bats. J. Mamm. 80, 24–30. https://doi.org/10.2307/1383204 (1999).Article 

Google Scholar 
61.Stahlschmidt, P. & Bruhl, C. A. Bats as bioindicators—The need of a standardized method for acoustic bat activity surveys. Methods Ecol. Evol. 3, 503–508. https://doi.org/10.1111/j.2041-210X.2012.00188.x (2012).Article 

Google Scholar 
62.Avery, M. I. Winter activity of pipistrelle bats. J. Anim. Ecol. 54, 721–738. https://doi.org/10.2307/4374 (1985).Article 

Google Scholar 
63.McCulloch, C. E. & Neuhaus, J. M. Generalized linear mixed models. In Encyclopedia of Biostatistics (eds Armitage, P. & Colton, T.) (Wiley, 2005).
Google Scholar 
64.Nelder, J. A. & Wedderburn, R. W. Generalized linear models. J. R. Stat. Soc. Ser. A (Gen.) 135, 370–384 (1972).Article 

Google Scholar 
65.Hardin, J. W. & Hilbe, J. M. Generalized Linear Models and Extensions (Stata Press, 2007).
Google Scholar 
66.Consul, P. & Famoye, F. Generalized Poisson regression model. Commun. Stat. Theory Methods 21, 89–109 (1992).Article 

Google Scholar 
67.Aho, K. A. Foundational and Applied Statistics for Biologists using R (CRC Press, 2013).
Google Scholar 
68.Akaike, H. Selected Papers of Hirotugu Akaike 199–213 (Springer, 1998).
Google Scholar 
69.Burnham, K. P. & Anderson, D. A. Model Selection and Multimodel Inference: A practical Information-Theoretic Approach 2nd edn. (Springer, 2002).
Google Scholar 
70.RCoreTeam. R: A Language and Environment for Statistical Computing (2020).71.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-PLUS (Springer, 2013).
Google Scholar 
72.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
73.Perkins, J. M., Barss, J. M. & Peterson, J. Winter records of bats in Oregon and Washington. Northwest. Nat. 71, 59–62. https://doi.org/10.2307/3536594 (1990).Article 

Google Scholar 
74.Nagorsen, D. W. et al. Winter bat records for British Columbia. Northwest Nat. 74, 61–66 (1993).Article 

Google Scholar 
75.Hayman, D. T., Cryan, P. M., Fricker, P. D. & Dannemiller, N. G. Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats. Methods Ecol. Evol. 8, 1813–1821 (2017).Article 

Google Scholar 
76.Boyles, J. G., Dunbar, M. B. & Whitaker, J. O. Activity following arousal in winter in North American vespertilionid bats. Mamm. Rev. 36, 267–280. https://doi.org/10.1111/j.1365-2907.2006.00095.x (2006).Article 

Google Scholar 
77.Speakman, J. R. & Racey, P. A. Hibernal ecology of the pipistrelle bat: Energy expenditure, water requirements and mass-loss, implications for survial and the function of winter emergence flights. J. Anim. Ecol. 58, 797–813. https://doi.org/10.2307/5125 (1989).Article 

Google Scholar 
78.Lawrence, B. D. & Simmons, J. A. Measurements of atmospheric attenuation at ultrasonic frequencies and the significance for echolocation by bats. J. Acoust. Soc. Am. 71, 585–590 (1982).ADS 
CAS 
Article 

Google Scholar 
79.Dunbar, M. B. & Tomasi, T. E. Arousal patterns, metabolic rate, and an energy budget of eastern red bats (Lasiurus borealis) in winter. J. Mamm. 87, 1096–1102. https://doi.org/10.1644/05-mamm-a-254r3.1 (2006).Article 

Google Scholar 
80.Ford, W. M., Britzke, E. R., Dobony, C. A., Rodrigue, J. L. & Johnson, J. B. Patterns of acoustical activity of bats prior to and following white-nose syndrome occurrence. J. Fish Wildl. Manage. 2, 125–134. https://doi.org/10.3996/042011-jfwm-027 (2011).Article 

Google Scholar 
81.Bernard, R. F., Foster, J. T., Willcox, E. V., Parise, K. L. & McCracken, G. F. Molecular detection of the causative agent of white-nose syndrome on Rafinesque’s big-eared bats (Corynorhinus rafinesquii) and two species of migratory bats in the southeastern USA. J. Wildl. Dis. 51, 519–522. https://doi.org/10.7589/2014-08-202 (2015).Article 
PubMed 

Google Scholar 
82.Dzal, Y., McGuire, L. P., Veselka, N. & Fenton, M. B. Going, going, gone: the impact of white-nose syndrome on the summer activity of the little brown bat (Myotis lucifugus). Biol. Lett. 7, 392–394 (2010).Article 

Google Scholar 
83.Brooks, R. T. Declines in summer bat activity in central New England 4 years following the initial detection of white-nose syndrome. Biodivers. Conserv. 20, 2537–2541. https://doi.org/10.1007/s10531-011-9996-0 (2011).Article 

Google Scholar 
84.Holloway, G. L. & Barclay, R. M. R. Myotis ciliolabrum. Mamm. Species 670, 1–5. https://doi.org/10.1644/1545-1410(2001)670%3c0001:mc%3e2.0.co;2 (2001).Article 

Google Scholar 
85.Halsall, A. L., Boyles, J. G. & Whitaker, J. O. Jr. Body temperature patterns of big brown bats during winter in a building hibernaculum. J. Mamm. 93, 497–503 (2012).Article 

Google Scholar 
86.Paige, K. N. Bats and barometric pressure: conserving limited energy and tracking insects from the roost. Funct. Ecol. 9, 463–467 (1995).Article 

Google Scholar 
87.Frick, W. F. Acoustic monitoring of bats, considerations of options for long-term monitoring. Therya 4, 69–78 (2013).ADS 
Article 

Google Scholar 
88.Whitaker, J. O. & Rissler, L. J. Winter activity of bats at a mine entrance in Vermillion County, Indiana. Am. Midl. Nat. 127, 52–59. https://doi.org/10.2307/2426321 (1992).Article 

Google Scholar  More

1.Carlson, A. K., Phelps, Q. E. & Graeb, B. D. S. Chemistry to conservation: Using otoliths to advance recreational and commercial fisheries management. J. Fish Biol. 90, 505–527 (2017).CAS 
PubMed 
Article 

Google Scholar 
2.Ward, R. D. Genetics in fisheries management. Hydrobiologia 420, 191–201 (2000).CAS 
Article 

Google Scholar 
3.Tracey, S. R., Lyle, J. M. & Duhamel, G. Application of elliptical Fourier analysis of otolith form as a tool for stock identification. Fish. Res. 77, 138–147 (2006).Article 

Google Scholar 
4.Ferguson, G. J., Ward, T. M. & Gillanders, B. M. Otolith shape and elemental composition: Complementary tools for stock discrimination of mulloway (Argyrosomus japonicus) in southern Australia. Fish. Res. 110, 75–83 (2011).Article 

Google Scholar 
5.Campana, S. E. & Casselman, J. M. Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50(5), 1062-1083 (1993).Article 

Google Scholar 
6.Begg, G. A., Overholtz, W. J. & Munroe, N. J. The use of internal otolith morphometrics for identification of haddock (Melanogrammus aeglefinus) stocks on Georges Bank. Fish. Bull. 99, 1–1 (2001).
Google Scholar 
7.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Ansari, N. G. Truss morphometry and otolith microchemistry reveal stock discrimination in Clarias batrachus (Linnaeus, 1758) inhabiting the Gangetic river system. Fish. Res. 173, 294–302 (2016).Article 

Google Scholar 
8.Nazir, A. & Khan, M. A. Spatial and temporal variation in otolith chemistry and its relationship with water chemistry: Stock discrimination of Sperata aor. Ecol. Freshw. Fish 28, 499–511 (2019).Article 

Google Scholar 
9.Bird, J. L., Eppler, D. T. & Checkley, D. M. Jr. Comparisons of herring otoliths using Fourier series shape analysis. Can. J. Fish. Aquat. Sci. 43(6), 1228-1234 (1986).Article 

Google Scholar 
10.Castonguay, M., Simard, P. & Gagnon, P. Usefulness of Fourier analysis of otolith shape for Atlantic Mackerel (Scomber scombrus) stock discrimination. Can. J. Fish. Aquat. Sci. 48(2), 296-302 (1991).Article 

Google Scholar 
11.Friedland, K. D. & Reddin, D. G. Use of otolith morphology in stock discriminations of Atlantic Salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 51(1), 91-98 (1994).Article 

Google Scholar 
12.Vignon, M. & Morat, F. Environmental and genetic determinant of otolith shape revealed by a non-indigenous tropical fish. Mar. Ecol. Prog. Ser. 411, 231–241 (2010).ADS 
Article 

Google Scholar 
13.Campana, S. E., Chouinard, G. A., Hanson, J. M., Fréchet, A. & Brattey, J. Otolith elemental fingerprints as biological tracers of fish stocks. Fish. Res. 46, 343–357 (2000).Article 

Google Scholar 
14.Elsdon, T. S. & Gillanders, B. M. Reconstructing migratory patterns of fish based on environmental influences on otolith chemistry. Rev. Fish Biol. Fish. 13, 217–235 (2003).Article 

Google Scholar 
15.Stransky, C. Geographic variation of golden redfish (Sebastes marinus) and deep-sea redfish (S. mentella) in the North Atlantic based on otolith shape analysis. ICES J. Mar. Sci. 62, 1691–1698 (2005).Article 

Google Scholar 
16.Grammer, G. L. et al. Coupling biogeochemical tracers with fish growth reveals physiological and environmental controls on otolith chemistry. Ecol. Monogr. 87, 487–507 (2017).Article 

Google Scholar 
17.Izzo, C., Reis-Santos, P. & Gillanders, B. M. Otolith chemistry does not just reflect environmental conditions: A meta-analytic evaluation. Fish Fish. 19, 441–454 (2018).Article 

Google Scholar 
18.Elsdon, T. S. & Gillanders, B. M. Fish otolith chemistry influenced by exposure to multiple environmental variables. J. Exp. Mar. Biol. Ecol. 313, 269–284 (2004).CAS 
Article 

Google Scholar 
19.Khan, M. A., Miyan, K., Khan, S., Patel, D. K. & Ansari, G. Studies on the elemental profile of otoliths and truss network analysis for stock discrimination of the threatened stinging catfish Heteropneustes fossilis (Bloch 1794) from the Ganga river and its tributaries. Zool. Stud. 51, 1195–1206 (2012).
Google Scholar 
20.Miyan, K., Khan, M. A. & Khan, S. Stock structure delineation using variation in otolith chemistry of snakehead, Channa punctata (Bloch, 1793), from three Indian rivers. J. Appl. Ichthyol. 30, 881–886 (2014).CAS 
Article 

Google Scholar 
21.Miyan, K., Khan, M. A., Patel, D. K., Khan, S. & Prasad, S. Otolith fingerprints reveal stock discrimination of Sperata seenghala inhabiting the Gangetic river system. Ichthyol. Res. 63, 294–301 (2016).Article 

Google Scholar 
22.Fowler, A. M., Macreadie, P. I., Bishop, D. P. & Booth, D. J. Using otolith microchemistry and shape to assess the habitat value of oil structures for reef fish. Mar. Environ. Res. 106, 103–113 (2015).CAS 
PubMed 
Article 

Google Scholar 
23.Schilling, H. T. et al. Evaluating estuarine nursery use and life history patterns of Pomatomus saltatrix in eastern Australia. Mar. Ecol. Prog. Ser. 598, 187–199 (2018).ADS 
Article 

Google Scholar 
24.Biolé, F. G. et al. Fish stocks of Urophycis brasiliensis revealed by otolith fingerprint and shape in the Southwestern Atlantic Ocean. Estuar. Coast. Shelf Sci. 229, 106406 (2019).Article 
CAS 

Google Scholar 
25.Maguffee, A. C., Reilly, R., Clark, R. & Jones, M. L. Examining the potential of otolith chemistry to determine natal origins of wild Lake Michigan Chinook salmon. Can. J. Fish. Aquat. Sci. 76(11), 2035-2044 (2019).Article 

Google Scholar 
26.Tanner, S. E., Vasconcelos, R. P., Cabral, H. N. & Thorrold, S. R. Testing an otolith geochemistry approach to determine population structure and movements of European hake in the northeast Atlantic Ocean and Mediterranean Sea. Fish. Res. 125–126, 198–205 (2012).Article 

Google Scholar 
27.Andrade, H. et al. Ontogenetic movements of cod in Arctic fjords and the Barents Sea as revealed by otolith microchemistry. Polar Biol. 43, 409–421 (2020).Article 

Google Scholar 
28.Warton, D. I. Why you cannot transform your way out of trouble for small counts. Biometrics 74, 362–368 (2018).MathSciNet 
PubMed 
MATH 
Article 

Google Scholar 
29.Foster, S. D. & Bravington, M. V. A Poisson-Gamma model for analysis of ecological non-negative continuous data. Environ. Ecol. Stat. 20, 533–552 (2013).MathSciNet 
Article 

Google Scholar 
30.Taylor, L. R. Aggregation, variance and the mean. Nature 189, 732–735 (1961).ADS 
Article 

Google Scholar 
31.Kendal, R. L., Coolen, I. & Laland, K. N. The role of conformity in foraging when personal and social information conflict. Behav. Ecol. 15, 269–277 (2004).Article 

Google Scholar 
32.Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).Article 

Google Scholar 
33.Warton, D. I., Foster, S. D., De’ath, G., Stoklosa, J. & Dunstan, P. K. Model-based thinking for community ecology. Plant Ecol. 216, 669–682 (2015).Article 

Google Scholar 
34.Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund– an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).Article 

Google Scholar 
35.Niku, J., Warton, D. I., Hui, F. K. C. & Taskinen, S. Generalized linear latent variable models for multivariate count and biomass data in ecology. J. Agric. Biol. Environ. Stat. 22, 498–522 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
36.Dunn, P. K. & Smyth, G. K. Randomized quantile residuals. J. Comput. Graph. Stat. 5, 236–244 (1996).
Google Scholar 
37.Dunn, P. K. & Smyth, G. K. Chapter 8: generalized linear models: Diagnostics. In Generalized Linear Models With Examples in R (eds. Dunn, P. K. & Smyth, G. K.) 297–331 (Springer, 2018). https://doi.org/10.1007/978-1-4419-0118-7_8.38.Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D. & Warton, D. I. Model-based approaches to unconstrained ordination. Methods Ecol. Evol. 6, 399–411 (2015).Article 

Google Scholar 
39.Hui, F. K. C. Boral–Bayesian ordination and regression analysis of multivariate abundance Data in r. Methods Ecol. Evol. 7, 744–750 (2016).Article 

Google Scholar 
40.Popovic, G. C., Warton, D. I., Thomson, F. J., Hui, F. K. C. & Moles, A. T. Untangling direct species associations from indirect mediator species effects with graphical models. Methods Ecol. Evol. 10, 1571–1583 (2019).Article 

Google Scholar 
41.Jones, C. M., Palmer, M. & Schaffler, J. J. Beyond Zar: The use and abuse of classification statistics for otolith chemistry. J. Fish Biol. 90, 492–504 (2017).CAS 
PubMed 
Article 

Google Scholar 
42.Rahman, M. A. & Awal, S. Development of captive breeding, seed production and culture techniques of snakehead fish for species conservation and sustainable aquaculture. Int. J. Adv. Agric. Environ. Eng. 3, 117–120 (2016).
Google Scholar 
43.Khan, M. A., Khan, S. & Miyan, K. Stock identification of the Channa striata inhabiting the Gangetic River System using Truss Morphometry. Russ. J. Ecol. 50, 391–396 (2019).Article 

Google Scholar 
44.Phen, C., Thang, T. B., Baran, E. & Vann, L. S. Biological reviews of important Cambodian fish species, based on FishBase 2004. Volume 1: Channa striata; Channa micropeltes; Barbonymus altus; Barbonymus gonionotus; Cyclocheilichthys apogon; Cyclocheilichthys enoplos; Henicorhynchus lineatus; Henicorhynchus siamensis; Pangasius hypophthalmus; Pangasius djambal. (WorldFish Center and Inland Fisheries Research and Development Institute, 2005).45.War, M. & Haniffa, M. A. Growth and survival of larval snakehead Channa striatus (Bloch, 1793) fed different live feed organisms. Turk. J. Fish. Aquat. Sci. 11, 523–528 (2011).
Google Scholar 
46.Cagauan, A. G. Exotic aquatic species introduction in the Philippines for aquaculture—A threat to biodiversity or a boon to the economy?. J. Environ. Sci. Manag. 10, 48–62 (2007).
Google Scholar 
47.Jayaram, K. C. The Freshwater Fishes of the Indian Region (Narendra Publishing House, 1999).
Google Scholar 
48.Talwar, P. K. & Jhingran, A. G. Inland fishes of India and adjacent countries Vol. 2 (CRC Press, 1991).
Google Scholar 
49.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).50.Libungan, L. A. & Pálsson, S. ShapeR: An R package to study otolith shape variation among fish populations. PLoS ONE 10, e0121102 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
51.Graps, A. An introduction to wavelets. IEEE Comput. Sci. Eng. 2, 50–61 (1995).Article 

Google Scholar 
52.Turan, C. The use of otolith shape and chemistry to determine stock structure of Mediterranean horse mackerel Trachurus mediterraneus (Steindachner). J. Fish Biol. 69, 165–180 (2006).CAS 
Article 

Google Scholar 
53.Oksanen, J. vegan: Community Ecology Package. (2019).54.Venables, W. N. & Ripley, B. D. Modern applied statistics with S-PLUS (Springer Science & Business Media, 2013).
Google Scholar 
55.Warton, D. I. Raw data graphing: An informative but under-utilized tool for the analysis of multivariate abundances. Austral. Ecol. 33, 290–300 (2008).Article 

Google Scholar 
56.Begg, G. A., Friedland, K. D. & Pearce, J. B. Stock identification and its role in stock assessment and fisheries management: An overview. Fish. Res. 43, 1–8 (1999).Article 

Google Scholar 
57.Sengupta, B. Water Quality Status of Yamuna River (1999-2005), Assessment and Development of River Basin Series: ADSORBS/41/2006-07. Cent. Pollut. Control Board Delhi (2006).58.Bhardwaj, R., Gupta, A. & Garg, J. K. Evaluation of heavy metal contamination using environmetrics and indexing approach for River Yamuna, Delhi stretch, India. Water Sci. 31, 52–66 (2017).Article 

Google Scholar  More

1.MacArthur, R. H., Wilson, E. O. The theory of island biogeography. in Monographs in Population Biology (Princeton University Press, Princeton, NJ, 1967)2.Hubbell, S. P. The unified neutral theory of biodiversity and biogeography. in Monographs in Population Biology, Vol. 32 (Princeton University Press, Princeton, NJ, 2001).3.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 
4.Ryther, J. Photosynthesis and fish production in the sea. Science 166, 72–76 (1969).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Cushing, D. A difference in structure between ecosystems in strongly stratified waters and in those that are only weakly stratified. J. Plankton Res. 11, 1–13 (1989).Article 

Google Scholar 
6.Barber, R. T. & Hiscock, M. R. A rising tide lifts all phytoplankton: growth response of other phytoplankton taxa in diatom‐dominated blooms. Glob. Biogeoch. Cycl. 20, GB4S03 (2006).
Google Scholar 
7.Siegel, D. A. et al. Global assessment of ocean carbon export by combining satellite observations and food-web models. Global Biogeochem. Cycl. 28, 181–196 (2014).CAS 
Article 

Google Scholar 
8.Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proc. Natl Acad. Sci. USA 117, 9679–9687 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Irwin, A. J., Finkel, Z. V., Schofield, O. M. & Falkowski, P. G. Scaling-up from nutrient physiology to the size-structure of phytoplankton communities. J. Plankt. Res. 28, 459–471 (2006).Article 

Google Scholar 
10.Litchman, E., Klausmeier, C. A. & Yoshiyama, K. Contrasting size evolution in marine and freshwater diatoms. Proc. Natl Acad. Sci. USA 106, 2665–2670 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Tozzi, S., Schofield, O. & Falkowski, P. Historical climate change and ocean turbulence as selective agents for two key phytoplankton functional groups. Mar. Ecol. Prog. Ser. 274, 123–132 (2004).Article 

Google Scholar 
12.Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Gregg, W. W., Casey, N. W. & Rousseaux, C. S. Global surface ocean carbon estimates in a model forced by MERRA NASA Technical Report Series on Global Modeling and Data Assimilation. NASA TM-2013-104606, Vol. 31, 39 (2013).14.Hulburt, E. M. Competition for nutrients by marine phytoplankton in oceanic, coastal, and estuarine regions. Ecology 51, 475–484 (1970).Article 

Google Scholar 
15.Siegel, D. A. Resource competition in a discrete environment: why are plankton distributions paradoxical? Limnol. Oceanogr. 43, 1133–1146 (1998).Article 

Google Scholar 
16.Cyr, H., Peters, R. H. & Downing, J. A. Population density and community size structure: comparison of aquatic and terrestrial systems. Oikos 80, 139–149 (1997).Article 

Google Scholar 
17.White, E. P., Ernest, S. M., Kerkhoff, A. J. & Enquist, B. J. Relationships between body size and abundance in ecology. Trends Ecol. Evol. 22, 323–330 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
18.McCauley, D. J. et al. On the prevalence and dynamics of inverted trophic pyramids and otherwise top-heavy communities. Ecol. Lett. 21, 439–454 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
19.West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.West, G. B., Brown, J. H. & Enquist, B. J. The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 284, 1677–1679 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Sheldon, R. W., Prakash, A. & Sutcliffe, W. Jr The size distribution of particles in the Ocean 1. Limnol. Oceanogr. 17, 327–340 (1972).Article 

Google Scholar 
22.Jonasz, M. & Fournier, G. Light Scattering by Particles in Water: Theoretical and Experimental Foundations. (Elsevier, 2011).23.Huete-Ortega, M., Cermeno, P., Calvo-Díaz, A. & Maranon, E. Isometric size-scaling of metabolic rate and the size abundance distribution of phytoplankton. Proc. Royal Soc. B 279, 1815–1823 (2012).Article 

Google Scholar 
24.Marañón, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Annu. Rev. Mar. Sci. 7, 241–264 (2015).Article 

Google Scholar 
25.Riley, G. A., Stommel, H. M., Bumpus, D. F. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection 12 (Yale Univ., New Haven, CT, 1949)26.Evans, G. T. & Parslow, J. S. A model of annual plankton cycles. Biol. Oceanogr. 3, 327–347 (1985).
Google Scholar 
27.Margalef, R. Perspectives in Ecological Theory. 111 pp (Univ. Chicago Press, Chicago, Ill, 1968).28.Behrenfeld, M. J. & Boss, E. S. Resurrecting the ecological underpinnings of ocean plankton blooms. Ann. Rev. Mar. Sci. 6, 167–194 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Behrenfeld, M. J. & Boss, E. S. Student’s tutorial on bloom hypotheses in the context of phytoplankton annual cycles. Glob. Change Biol. 24, 55–77 (2018).Article 

Google Scholar 
30.Strom, S. L. & Buskey, E. J. Feeding, growth, and behavior of the thecate heterotrophic dinoflagellate Oblea rotunda. Limnol. Oceanogr. 38, 965–977 (1993).Article 

Google Scholar 
31.Strom, S. L., Macri, E. L. & Olson, M. B. Microzooplankton grazing in the coastal Gulf of Alaska: Variations in top-down control of phytoplankton. Limnol. Oceanogr. 52, 1480–1494 (2007).Article 

Google Scholar 
32.Wirtz, K. W. Who is eating whom? Morphology and feeding type determine the size relation between planktonic predators and their ideal prey. Mar. Ecol. Progr. Ser. 445, 1–12 (2012).Article 

Google Scholar 
33.Kiørboe, T. How zooplankton feed: mechanisms, traits and trade-offs. Biol. Rev. 86, 311–339 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Hansen, B., Bjornsen, P. K. & Hansen, P. J. The size ratio between planktonic predators and their prey. Limnol. Oceanogr. 39, 395–403 (1994).Article 

Google Scholar 
35.Sommer, U. & Sommer, F. Cladocerans versus copepods: the cause of contrasting top–down controls on freshwater and marine phytoplankton. Oecologia 147, 183–194 (2006).PubMed 
Article 

Google Scholar 
36.Hébert, M.-P., Beisner, B. E. & Maranger, R. Linking zooplankton communities to ecosystem functioning: Toward an effect-trait framework. J. Plankton Res. 39, 3–12 (2017).Article 
CAS 

Google Scholar 
37.Fuchs, H. L. & Franks, P. J. Plankton community properties determined by nutrients and size-selective feeding. Mar. Ecol. Progr. Ser. 413, 1–15 (2010).Article 

Google Scholar 
38.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. USA 107, 15129–15134 (2010).CAS 
PubMed 
Article 

Google Scholar 
39.Dadon-Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
40.Antoine, D., Andre, J. M. & Morel, A. Oceanic primary production 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll. Global Biogeochem. Cycl. 10, 57–69 (1996).CAS 
Article 

Google Scholar 
41.Brewin, R. J. W. et al. A three-component model of phytoplankton size class for the Atlantic Ocean. Ecol. Model. 221, 1472–1483 (2010).CAS 
Article 

Google Scholar 
42.Marañón, E., Cermeño, P., Latasa, M. & Tadonléké, R. D. Temperature, resources, and phytoplankton size structure in the ocean. Limnol. Oceanogr. 5, 1266–1278 (2012).Article 

Google Scholar 
43.Kerr, S. R., Dickie, L. M. The Biomass Spectrum: a Predator-prey Theory of Aquatic Production (Columbia University Press, 2001).44.Behrenfeld, M. J., et al. Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nat. Geosci. 2017; https://doi.org/10.1038/NGEO2861.45.Kiorboe, T. Turbulence, phytoplankton cell size, and the structure of pelagic food-webs. Adv. Mar. Biol. 29, 1–72 (1993).Article 

Google Scholar 
46.DeLong, J. P. & Vasseur, D. A. Size-density scaling in protists and the links between consumer–resource interaction parameters. J. Animal Ecol. 81, 1193–1201 (2012).Article 

Google Scholar 
47.Smetacek, V. Diatoms and the ocean carbon cycle. Protist 150, 25–32 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Smetacek, V., Assmy, P. & Henjes, J. The role of grazing in structuring Southern Ocean pelagic ecosystems and biogeochemical cycles. Antarct. Sci. 16, 541–558 (2004).Article 

Google Scholar 
49.Behrenfeld, M. J., Halsey, K. H., Boss, E., Karp-Boss, L., Milligan, A. J. & Peers, G. Thoughts on the evolution and ecological niche of diatoms. Ecol. Monogr. 2021; in press.50.Glibert, P. M. Margalef revisited: a new phytoplankton mandala incorporating twelve dimensions, including nutritional physiology. Harmful Algae 55, 25–30 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Margalef, R. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanolog. Acta 1, 493–509 (1978).
Google Scholar 
52.Cullen, J. J. & MacIntyre, J. G. Behavior, physiology and the niche of depth-regulating phytoplankton. Nato ASI Ser. G Ecol. Sci. 41, 559–580 (1998).53.Kemp, A. E. & Villareal, T. A. The case of the diatoms and the muddled mandalas: Time to recognize diatom adaptations to stratified waters. Prog. Oceanogr. 167, 138–149 (2018).Article 

Google Scholar 
54.Kudela, R. M. Does horizontal mixing explain phytoplankton dynamics? Proc. Natl Acad. Sci. USA 107, 18235–18236 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Wyatt, T. Margalef’s mandala and phytoplankton bloom strategies. Deep Sea Res. II 101, 32–49 (2014).Article 

Google Scholar 
56.Waite, A., Fisher, A., Thompson, P. A. & Harrison, P. J. Sinking rate versus cell volume relationships illuminate sinking rate control mechanisms in marine diatoms. Mar. Ecol. Prog. Ser. 157, 97–108 (1997).Article 

Google Scholar 
57.Moore, J. K. & Villareal, T. A. Size-ascent rate relationships in positively buoyant marine diatoms. Limnol. Oceanogr. 41, 1514–1520 (1996).Article 

Google Scholar 
58.Bienfang, P. & Szyper, J. Effects of temperature and salinity on sinking rates of the centric diatom Ditylum brightwellii. Biol. Oceanogr. 1, 211–223 (1982).
Google Scholar 
59.Bienfang, P., Szyper, J. & Laws, E. Sinking rate and pigment responses to light-limitation of a marine diatom – implications to dynamics of chlorophyll maximum layers. Oceanolog. Acta 6, 55–62 (1983).CAS 

Google Scholar 
60.Villareal, T. A., Pilskaln, C. H., Montoya, J. P. & Dennett, M. Upward nitrate transport by phytoplankton in oceanic waters: balancing nutrient budgets in oligotrophic seas. PeerJ 2, e302 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Irigoien, X., Flynn, K. J. & Harris, R. P. Phytoplankton blooms: a “loophole” in micozooplankton grazing impact? J. Plankton Res. 27, 313–321 (2005).Article 

Google Scholar 
62.Bolaños, L. M., et al. Small phytoplankton dominate western North Atlantic biomass. ISME J: 1–12, https://doi.org/10.1038/s41396-020-0636-0 (2020).63.Guillard, R., Kilham, P. The ecology of marine planktonic diatoms. in The Biology of Diatoms, Vol. 13, 372–469 (Blackwell Oxford, 1977).64.Malviya, S. et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc. Natl Acad. Sci. USA 113, E1516–E1525 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Barton, A. D., Finkel, Z. V., Ward, B. A., Johns, D. G. & Follows, M. J. On the roles of cell size and trophic strategy in North Atlantic diatom and dinoflagellate communities. Limnol. Oceanogr. 58, 254–266 (2013).Article 

Google Scholar 
66.Edwards, K. F. Mixotrophy in nanoflagellates across environmental gradients in the ocean. Proc. Natl Acad. Sci. USA 116, 6211–6220 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Boyd, P. W. Environmental factors controlling phytoplankton processes in the Southern Ocean. J. Phycol. 38, 844–861 (2002).Article 

Google Scholar 
68.Fauchereau, N., Tagliabue, A., Bopp, L. & Monteiro, P. M. The response of phytoplankton biomass to transient mixing events in the Southern Ocean. Geophys. Res. Lett. 38, L17601 (2011).Article 

Google Scholar 
69.Wolfe, G. V., Steinke, M. & Kirst, G. O. Grazing-activated chemical defence in a unicellular marine alga. Nature 387, 894–897 (1997).CAS 
Article 

Google Scholar 
70.Colin, S. P. & Dam, H. G. Effects of the toxic dinoflagellate Alexandrium fundyense on the copepod Acartia hudsonica: a test of the mechanisms that reduce ingestion rates. Mar. Ecol. Prog. Ser. 248, 55–65 (2003).Article 

Google Scholar 
71.Van Donk, E., Ianora, A. & Vos, M. Induced defences in marine and freshwater phytoplankton: a review. Hydrobiol. 668, 3–19 (2011).Article 
CAS 

Google Scholar 
72.Pohnert, G., Steinke, M. & Tollrian, R. Chemical cues, defense metabolites and the shaping of pelagic interspecific interactions. Trends Ecol. Evol. 22, 198–204 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
73.DeMott, W. R. & Moxter, F. Foraging cyanobacteria by copepods: responses to chemical defense and resource abundance. Ecology 72, 1820–1834 (1991).Article 

Google Scholar 
74.Ger, K. A., Naus-Wiezer, S., De Meester, L. & Lürling, M. Zooplankton grazing selectivity regulates herbivory and dominance of toxic phytoplankton over multiple prey generations. Limnol. Oceanogr. 64, 1214–1227 (2019).Article 

Google Scholar 
75.Smayda, T. J. & Reynolds, C. S. Community assembly in marine phytoplankton: application of recent models to harmful dinoflagellate blooms. J. Plankt. Res. 23, 447–461 (2001).Article 

Google Scholar 
76.Acevedo-Trejos, E., Brandt, G., Bruggeman, J. & Merico, A. Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean. Sci. Rep 5, 8918 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Cuesta, J. A., Delius, G. W. & Law, R. Sheldon spectrum and the plankton paradox: two sides of the same coin—a trait-based plankton size-spectrum model. J. Math. Biol. 76, 67–96 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
78.Hutchinson, G. E. Ecological aspects of succession in natural populations. Amer. Nat. 75, 406–418 (1941).Article 

Google Scholar 
79.Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).CAS 
Article 

Google Scholar 
80.Tilman, D., Mattson, M. & Langer, S. Competition and nutrient kinetics along a temperature gradient: An experimental test of a mechanistic approach to niche theory 1. Limnol. Oceanogr. 26, 1020–1033 (1981).Article 

Google Scholar 
81.Sommer, U. Nutrient competition between phytoplankton species in multispecies chemostat experiments. Archiv hydrobiol. 96, 399–416 (1983).
Google Scholar 
82.Sommer, U. Comparison between steady state and non-steady state competition: experiments with natural phytoplankton. Limnol. Oceanogr. 30, 335–346 (1985).CAS 
Article 

Google Scholar 
83.Tilman, D. Resource Competition and Community Structure (Princeton University Press, 1982).84.Sommer, U. The role of competition for resources in phytoplankton succession. in Plankton Ecology. Berlin, Heidelberg: Springer. 1989, pp. 57-106.85.Burd, A. B. & Jackson, G. A. Particle aggregation. Annu. Rev. Mar. Sci. 1, 65–90 (2009).Article 

Google Scholar 
86.Kahl, L. A., Vardi, A. & Schofield, O. Effects of phytoplankton physiology on export flux. Mar. Ecol. Prog. Ser. 354, 3–19 (2008).CAS 
Article 

Google Scholar 
87.Guidi, L. et al. Effects of phytoplankton community on production, size and export of large aggregates: a world-ocean analysis. Limnol. Oceanogr. 54, 1951–1963 (2009).Article 

Google Scholar 
88.Kiørboe, T., Lundsgaard, C., Olesen, M. & Hansen, J. L. S. Aggregation and sedimentation processes during a spring phytoplankton bloom: a field experiment to test coagulation theory. J. Mar. Res. 52, 297–323 (1994).Article 

Google Scholar 
89.Prairie, J. C., Montgomery, Q. W., Proctor, K. W. & Ghiorso, K. S. Effects of phytoplankton growth phase on settling properties of marine aggregates. J. Mar. Sci. Engineer. 7, 265 (2019).Article 

Google Scholar 
90.Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 6237 (2015).Article 
CAS 

Google Scholar 
91.Sañudo-Wilhelmy, S. A., Gómez-Consarnau, L., Suffridge, C. & Webb, E. A. The role of B vitamins in marine biogeochemistry. Ann. Rev. Mar. Sci. 6, 339–367 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Helliwell, K. E. The roles of B vitamins in phytoplankton nutrition: new perspectives and prospects. New Phytol. 216, 62–68 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
93.Chisholm, S. W. et al. A novel free-living prochlorophyte abundant in the oceanic euphotic zone. Nature 334, 340–343 (1988).Article 

Google Scholar 
94.Caputo, A., Nylander, J. A. & Foster, R. A. The genetic diversity and evolution of diatom-diazotroph associations highlights traits favoring symbiont integration. FEMS Microbiol. Lett. 366, fny297 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
95.Decelle, J. et al. An original mode of symbiosis in open ocean plankton. Proc. Natl Acad. Sci. USA 109, 18000–18005 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Decelle, J. et al. Algal remodeling in a ubiquitous planktonic photosymbiosis. Curr. Biol. 29, 968–978 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Behrenfeld, M. J. et al. The North Atlantic aerosol and marine ecosystem study (NAAMES): science motive and mission overview. Front. Mar. Sci. 6, 122 (2019).Article 

Google Scholar 
98.Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).CAS 
Article 

Google Scholar  More

1.Perissinotto, R., Mayzaud, P., Nichols, P. D. & Labat, J. P. Grazing by Pyrosoma atlanticum (Tunicata, Thaliacea) in the south Indian Ocean. Mar. Ecol. Prog. Ser. 330, 1–11 (2007).CAS 
Article 

Google Scholar 
2.Drits, A. V., Arashkevich, E. G. & Semenova, T. N. Pyrosoma atlanticum (Tunicata, Thaliacea): grazing impact on phytoplankton standing stock and role in organic carbon flux. J. Plankton Res. 14, 799–809 (1992).Article 

Google Scholar 
3.Henschke, N. et al. Large vertical migrations of Pyrosoma atlanticum play an important role in active carbon transport. J. Geophys. Res. Biogeosci. 124, 1056–1070 (2019).Article 

Google Scholar 
4.Schram, J. B., Sorensen, H. L., Brodeur, R. D., Galloway, A. W. E. & Sutherland, K. R. Abundance, distribution, and feeding ecology of Pyrosoma atlanticum in the Northern California Current. Mar. Ecol. Prog. Ser. 651, 97–110 (2020).5.O’Loughlin, J. H. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 102424 (2020).6.Hobson, E. S. & Chess, J. Trophic relations of the blue rockfish, Sebastes mystinus, in a coastal upwelling system off northern California. in Fishery Bulletin, Vol. 86, 715–743 (National Marine Fisheries Service, 1988).7.Bulman, C. M., He, X. & Koslow, J. A. Trophic ecology of the mid-slope demersal fish community off Southern Tasmania, Australia. Mar. Freshw. Res. 53, 59–72 (2002).Article 

Google Scholar 
8.Harbison, G. R. The parasites and predators of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).9.James, G. D. & Stahl, J. -C. Diet of southern Buller’s albatross (Diomedea bulleri bulleri) and the importance of fishery discards during chick rearing. N. Z. J. Mar. Freshw. Res. 34, 435–454 (2000).Article 

Google Scholar 
10.Hedd, A. & Gales, R. The diet of shy albatrosses (Thalassarche cauta) at Albatross Island, Tasmania. J. Zool. 253, 69–90 (2001).Article 

Google Scholar 
11.Childerhouse, S., Dix, B. & Gales, N. Diet of New Zealand sea lions (Phocarctos hookeri) at the Auckland Islands. Wildl. Res. 28, 291–298 (2001).Article 

Google Scholar 
12.Lindley, J. A., Hernández, F., Scatllar, J. & Docoito, J. Funchalia sp. (Crustacea: Penaeidae) associated with Pyrosoma atlanticum (Thaliacea: Pyrosomidae) off the Canary Islands. J. Mar. Biol. Assoc. UK 81, 173–174 (2001).Article 

Google Scholar 
13.Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209 (2009).CAS 
Article 

Google Scholar 
14.Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
15.McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. 110, 3229–3236 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Sherr E. & Sherr B. Understanding roles of microbes in marine pelagic food webs: a brief history. in Microbial Ecology of the Oceans 27–44 (John Wiley & Sons Ltd, 2008).17.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Décima, M., Stukel, M. R., López-López, L. & Landry, M. R. The unique ecological role of pyrosomes in the Eastern Tropical Pacific. Limnol. Oceanogr. 64, 728–743 (2019).Article 

Google Scholar 
19.Gauns, M., Mochemadkar, S., Pratihary, A., Roy, R. & Naqvi, S. W. A. Biogeochemistry and ecology of Pyrosoma spinosum from the Central Arabian Sea. Zool. Stud. 54, 3 (2015).Article 
CAS 

Google Scholar 
20.Bowlby, M. R., Widder, E. A. & Case, J. F. Patterns of stimulated bioluminescence in two pyrosomes (Tunicata: Pyrosomatidae). Biol. Bull. 179, 340–350 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Haddock, S. H. D., Moline, M. A. & Case, J. F. Bioluminescence in the sea. Annu. Rev. Mar. Sci. 2, 443–493 (2010).Article 

Google Scholar 
22.Swift, E., Biggley, W. H. & Napora, T. A. The bioluminescence emission spectra of Pyrosoma atlanticum, P. spinosum (Tunicata), Euphausia tenera (Crustacea) and Gonostoma sp. (Pisces). J. Mar. Biol. Assoc. UK 57, 817–823 (1977).23.Martínez‐García, M. et al. Ammonia-oxidizing Crenarchaeota and nitrification inside the tissue of a colonial ascidian. Environ. Microbiol. 10, 2991–3001 (2008).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
24.Donia, M. S. et al. Complex microbiome underlying secondary and primary metabolism in the tunicate-Prochloron symbiosis. Proc. Natl Acad. Sci. 108, E1423–E1432 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Kwan, J. C. et al. Host control of symbiont natural product chemistry in cryptic populations of the tunicate Lissoclinum patella. PLoS ONE 9, e95850 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Purcell, J. E. & Arai, M. N. Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia. 451, 27–44 (2001).Article 

Google Scholar 
27.Delannoy, C. M. J., Houghton, J. D. R., Fleming, N. E. C. & Ferguson, H. W. Mauve stingers (Pelagia noctiluca) as carriers of the bacterial fish pathogen Tenacibaculum maritimum. Aquaculture. 311, 255–257 (2011).Article 

Google Scholar 
28.Lee, M. D., Kling, J. D., Araya, R. & Ceh, J. Jellyfish life stages shape associated microbial communities, while a core microbiome is maintained across all. Front. Microbiol. 9, 1534 (2018).29.Troussellier, M., Escalas, A., Bouvier, T. & Mouillot, D. Sustaining rare marine microorganisms: macroorganisms as repositories and dispersal agents of microbial diversity. Front. Microbiol. 8 (2017).30.Brodeur, R. et al. An unusual gelatinous plankton event in the NE Pacific: the Great Pyrosome Bloom of 2017. PICES Press; Sidney Vol. 26, 22–27 (Winter, 2018).31.Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Miller, R. R. et al. Distribution of pelagic Thaliaceans, Thetys vagina and Pyrosoma Atlanticum, during a period of mass occurrence within the California current. CalCOFI Rep. 60, (2019).33.Guigand, C. M., Cowen, R. K., Llopiz, J. K. & Richardson, D. E. A coupled asymmetrical multiple opening closing net with environmental sampling system. Mar. Technol. Soc. J. 39, 22–24 (2005).34.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.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 
36.Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
Article 

Google Scholar 
37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
39.Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

Google Scholar 
42.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Duperron, S. Microbial Symbioses 168 p. (Elsevier, 2016).45.Schmitt, S. et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 6, 564–576 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8 (2017).47.Urbanczyk, H., Ast, J. C., Higgins, M. J., Carson, J. & Dunlap, P. V. Reclassification of Vibrio fischeri, Vibrio logei, Vibrio salmonicida and Vibrio wodanis as Aliivibrio fischeri gen. nov., comb. nov., Aliivibrio logei comb. nov., Aliivibrio salmonicida comb. nov. and Aliivibrio wodanis comb. nov. Int. J. Syst. Evol. Microbiol. 57, 2823–2829 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Stecher, G., Tamura, K. & Kumar, S. Molecular Evolutionary Genetics Analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Booth, B. C. Marine phytoplankton. A guide to naked flagellates and coccolithophorids (C. R. Tomas [ed.]). Limnol. Oceanogr. 39, 982–983 (1994).Article 

Google Scholar 
51.Halse, G. R. & Syvertsen, E. E. Chapter 2—marine diatoms. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 5–385 (Academic Press, 1996).52.Steidinger, K. A. & Tangen, K. Chapter 3—dinoflagellates. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 387–584 (Academic Press, 1996).53.Daniels, C. & Breitbart, M. Bacterial communities associated with the ctenophores Mnemiopsis leidyi and Beroe ovata. FEMS Microbiol. Ecol. 82, 90–101 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Kramar, M. K., Tinta, T., Lučić, D., Malej, A. & Turk, V. Bacteria associated with moon jellyfish during bloom and post-bloom periods in the Gulf of Trieste (northern Adriatic). PLoS ONE 14, e0198056 (2019).Article 
CAS 

Google Scholar 
55.Hernandez-Agreda, A., Leggat, W., Bongaerts, P., Herrera, C. & Ainsworth, T. D. Rethinking the coral microbiome: simplicity exists within a diverse microbial biosphere. mBio 9, e00812–18 (2018).56.Webster, N. S. & Bourne, D. Bacterial community structure associated with the Antarctic soft coral, Alcyonium antarcticum. FEMS Microbiol. Ecol. 59, 81–94 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Rodrigues, C. F., Hilário, A., Cunha, M. R., Weightman, A. J. & Webster, G. Microbial diversity in Frenulata (Siboglinidae, Polychaeta) species from mud volcanoes in the Gulf of Cadiz (NE Atlantic). Antonie Van Leeuwenhoek 100, 83–98 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
58.McCann, J., Stabb, E. V., Millikan, D. S. & Ruby, E. G. Population dynamics of Vibrio fischeri during Infection of Euprymna scolopes. Appl. Environ. Microbiol. 69, 5928–5934 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Hammann, S., Moss, A. & Zimmer, M. Sterile surfaces of Mnemiopsis leidyi; (Ctenophora) in bacterial suspension—a key to invasion success? Open J. Mar. Sci. 05, 237–246 (2015).Article 

Google Scholar 
60.Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 https://doi.org/10.1093/femsle/fnz117 (2019).61.Nedashkovskaya, O. I., Kukhlevskiy, A. D., Zhukova, N. V. & Kim, S. B. Amylibacter ulvae sp. nov., a new alphaproteobacterium isolated from the Pacific green alga Ulva fenestrata. Arch. Microbiol. 198, 251–256 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Burke, C., Thomas, T., Lewis, M., Steinberg, P. & Kjelleberg, S. Composition, uniqueness and variability of the epiphytic bacterial community of the green alga Ulva australis. ISME J. 5, 590–600 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Catão, E. C. P. et al. Shear stress as a major driver of marine biofilm communities in the NW Mediterranean Sea. Front. Microbiol. 10 (2019).64.Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Bondoso, J. et al. Roseimaritima ulvae gen. nov., sp. nov. and Rubripirellula obstinata gen. nov., sp. nov. two novel planctomycetes isolated from the epiphytic community of macroalgae. Syst. Appl. Microbiol. 38, 8–15 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Zhu, P., Li, Q. & Wang, G. Unique microbial signatures of the Alien Hawaiian marine sponge Suberites zeteki. Microb. Ecol. 55, 406–414 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Pimentel-Elardo, S., Wehrl, M., Friedrich, A. B., Jensen, P. R. & Hentschel, U. Isolation of planctomycetes from Aplysina sponges. Aquat. Microb. Ecol. 33, 239–245 (2003).Article 

Google Scholar 
68.da Silva Oliveira, F. A. et al. Microbial epibionts of the colonial ascidians Didemnum galacteum and Cystodytes sp. Symbiosis 59, 57–63 (2013).Article 

Google Scholar 
69.Yakimov, M. M. et al. Phylogenetic survey of metabolically active microbial communities associated with the deep-sea coral Lophelia pertusa from the Apulian plateau, Central Mediterranean Sea. Deep Sea Res. A Oceanogr. Res. Pap. 53, 62–75 (2006).Article 

Google Scholar 
70.Duque-Alarcón, A., Santiago-Vázquez, L. Z. & Kerr, R. G. A microbial community analysis of the octocoral Eunicea fusca. Electron. J. Biotechnol. 15, 15–15 (2012).
Google Scholar 
71.Wiegand, S., Jogler, M. & Jogler, C. On the maverick Planctomycetes. FEMS Microbiol. Rev. 42, 739–760 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Lage, O. M. & Bondoso, J. Planctomycetes and macroalgae, a striking association. Front. Microbiol. 5 (2014).73.Ward, A. C. & Bora, N. Diversity and biogeography of marine Actinobacteria. Curr. Opin. Microbiol. 9, 279–286 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Hahn, M. W. Description of seven candidate species affiliated with the phylum Actinobacteria, representing planktonic freshwater bacteria. Int. J. Syst. Evol. Microbiol. 59, 112–117 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Gandhimathi, R. et al. Antimicrobial potential of sponge associated marine actinomycetes. J. Mycol. Méd. 18, 16–22 (2008).Article 

Google Scholar 
76.Abdelmohsen, U. R., Bayer, K. & Hentschel, U. Diversity, abundance and natural products of marine sponge-associated actinomycetes. Nat. Prod. Rep. 31, 381–399 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Wu, Z. et al. A new tetrodotoxin-producing actinomycete, Nocardiopsis dassonvillei, isolated from the ovaries of puffer fish Fugu rubripes. Toxicon. 45, 851–859 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Reichenbach, H. The ecology of the myxobacteria. Environ. Microbiol. 1, 15–21 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Marshall, R. C. & Whitworth, D. E. Is “Wolf-Pack” predation by antimicrobial bacteria cooperative? Cell behaviour and predatory mechanisms indicate profound selfishness, even when working alongside Kin. BioEssays 41, 1800247 (2019).Article 

Google Scholar 
80.Welsh, R. M. et al. Bacterial predation in a marine host-associated microbiome. ISME J. 10, 1540–1544 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Wang, Z., Kadouri, D. E. & Wu, M. Genomic insights into an obligate epibiotic bacterial predator: Micavibrio aeruginosavorus ARL-13. BMC Genomics 12, 453 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Garcia, G. D. et al. Metagenomic analysis of healthy and white plague-affected Mussismilia braziliensis corals. Microb. Ecol. 65, 1076–1086 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Rosales, S. M. et al. Microbiome differences in disease-resistant vs. susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Evans, A. G. L. et al. Predatory activity of Myxococcus xanthus outer-membrane vesicles and properties of their hydrolase cargo. Microbiology 158, 2742–2752 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Sudo, S. & Dworkin, M. Bacteriolytic enzymes produced by Myxococcus xanthus. J. Bacteriol. 110, 236–245 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Tessler, M. et al. A putative chordate luciferase from a cosmopolitan tunicate indicates convergent bioluminescence evolution across phyla. Sci. Rep. 10, 17724 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Berger, A. et al. Microscopic and Genetic Characterization of Bacterial Symbionts With Bioluminescent Potential in Pyrosoma Atlanticum. Frontiers in Marine Science. 8 https://doi.org/10.3389/fmars.2021.606818 (2021).88.Leisman, G., Cohn, D. H. & Nealson, K. H. Bacterial origin of luminescence in marine animals. Science 208, 1271–1273 (1980).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Mackie, G. O. & Bone, Q. Luminescence and associated effector activity in Pyrosoma (Tunicata: Pyrosomida). Proc. R. Soc. Lond. B Biol. Sci. 202, 483–495 (1978).Article 

Google Scholar 
90.Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Takemura, A. F., Chien, D. M. & Polz M. F. Associations and dynamics of Vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 5 (2014).92.Barnes, E. M., Carter, E. L. & Lewis, J. D. Predicting microbiome function across space is confounded by strain-level differences and functional redundancy across taxa. Front. Microbiol. 11 (2020).93.Tian, L. et al. Deciphering functional redundancy in the human microbiome. bioRxiv 176313 https://doi.org/10.1101/176313 (2017).94.Kaeding, A. J. et al. Phylogenetic diversity and cosymbiosis in the bioluminescent symbioses of “Photobacterium mandapamensis”. Appl. Environ. Microbiol. 73, 3173–3182 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Baker, L. J. et al. Diverse deep-sea anglerfishes share a genetically reduced luminous symbiont that is acquired from the environment. eLife 8 e47606 (2019).96.Godeaux, J. E. A., Bone, Q. & Braconnot, J. C. Anatomy of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).97.Alldredge, A. L. & Madin, L. P. Pelagic tunicates: unique herbivores in the marine plankton. BioScience. 32, 655–663 (1982).Article 

Google Scholar 
98.Bone, Q., Carre, C. & Ryan, K. P. The endostyle and the feeding filter in salps (Tunicata). J. Mar. Biol. Assoc. UK 80, 523–534 (2000).Article 

Google Scholar 
99.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. 107, 15129–15134 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Dadon-Pilosof, A. et al. Surface properties of SAR11 bacteria facilitate grazing avoidance. Nat. Microbiol. 2, 1608–1615 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
101.Larson, R. J. Daily ration and predation by medusae and ctenophores in Saanich Inlet, B.C., Canada. Neth. J. Sea Res. 21, 35–44 (1987).Article 

Google Scholar 
102.Suchman, C. L., Daly, E. A., Keister, J. E., Peterson, W. T. & Brodeur, R. D. Feeding patterns and predation potential of scyphomedusae in a highly productive upwelling region. Mar. Ecol. Prog. Ser. 358, 161–172 (2008).Article 

Google Scholar 
103.Bennke, C. M. et al. The distribution of phytoplankton in the Baltic Sea assessed by a prokaryotic 16S rRNA gene primer system. J. Plankton Res. 40, 244–254 (2018).CAS 
Article 

Google Scholar 
104.Green, B. R. Chloroplast genomes of photosynthetic eukaryotes. Plant J. 66, 34–44 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: a data-driven modeling study. Glob. Biogeochem. Cycles. 34, e2020GB006704 (2020).106.Dadon‐Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

Google Scholar 
107.Brand, A., Liz, A., Micah, A., Marjorie, H. & Jo, S. Beyond Authorship: Attribution, Contribution, Collaboration, and Credit. Learned Publishing. 28, 151–155 (2015).Article 

Google Scholar  More

1.Stanley, D. & Stout, J. Pollinator sharing between mass-flowering oilseed rape and co-flowering wild plants: implications for wild plant pollination. Plant Ecol. 215, 315–325. https://doi.org/10.1007/s11258-014-0301-7 (2014).Article 

Google Scholar 
2.Kovacs-Hostyanszki, A. et al. Contrasting effects of mass-flowering crops on bee pollination of hedge plants at different spatial and temporal scales. Ecol. Appl. 23, 1938–1946. https://doi.org/10.1890/12-2012.1 (2013).Article 
PubMed 

Google Scholar 
3.Holzschuh, A. et al. Mass-flowering crops dilute pollinator abundance in agricultural landscapes across Europe. Ecol. Lett. 19, 1228–1236. https://doi.org/10.1111/ele.12657 (2016).Article 
PubMed 
PubMed Central 

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

Google Scholar 
5.Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc Natl Acad Sci U S A 99, 16812–16816. https://doi.org/10.1073/pnas.262413599 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
7.Kovacs-Hostyanszki, A. et al. Ecological intensification to mitigate impacts of conventional intensive land use on pollinators and pollination. Ecol. Lett. 20, 673–689. https://doi.org/10.1111/ele.12762 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity: ecosystem service management. Ecol. Lett. 8, 857–874. https://doi.org/10.1111/j.1461-0248.2005.00782.x (2005).Article 

Google Scholar 
9.Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe: a review. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-017-0434-x (2017).Article 

Google Scholar 
10.Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072. https://doi.org/10.1111/j.1461-0248.2011.01669.x (2011).Article 
PubMed 

Google Scholar 
11.Bartomeus, I. et al. Contribution of insect pollinators to crop yield and quality varies with agricultural intensification. PeerJ 2, e328. https://doi.org/10.7717/peerj.328 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Holzschuh, A., Dudenhoffer, J. H. & Tscharntke, T. Landscapes with wild bee habitats enhance pollination, fruit set and yield of sweet cherry. Biol. Conserv. 153, 101–107. https://doi.org/10.1016/j.biocon.2012.04.032 (2012).Article 

Google Scholar 
13.Marini, L. et al. Crop management modifies the benefits of insect pollination in oilseed rape. Agric. Ecosyst. Environ. 207, 61–66. https://doi.org/10.1016/j.agee.2015.03.027 (2015).Article 

Google Scholar 
14.Persson, A. S. & Smith, H. G. Seasonal persistence of bumblebee populations is affected by landscape context. Agric. Ecosyst. Environ. 165, 201–209. https://doi.org/10.1016/j.agee.2012.12.008 (2013).Article 

Google Scholar 
15.Rundlof, M., Persson, A. S., Smith, H. G. & Bommarco, R. Late-season mass-flowering red clover increases bumble bee queen and male densities. Biol. Conserv. 172, 138–145. https://doi.org/10.1016/j.biocon.2014.02.027 (2014).Article 

Google Scholar 
16.Westphal, C., Steffan-Dewenter, I. & Tscharntke, T. Mass flowering oilseed rape improves early colony growth but not sexual reproduction of bumblebees. J. Appl. Ecol. 46, 187–193. https://doi.org/10.1111/j.1365-2664.2008.01580.x (2009).Article 

Google Scholar 
17.Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058. https://doi.org/10.1890/11-1006.1 (2012).Article 
PubMed 

Google Scholar 
18.Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432. https://doi.org/10.2307/3071954 (2002).Article 

Google Scholar 
19.Steffan-Dewenter, I., Münzenberg, U. & Tscharntke, T. Pollination, seed set and seed predation on a landscape scale. Proc. Natl. Acad. Sci. USA 268, 1685–1690. https://doi.org/10.1098/rspb.2001.1737 (2001).CAS 
Article 

Google Scholar 
20.Bartual, A. et al. The potential of different semi-natural habitats to sustain pollinators and natural enemies in European agricultural landscapes. Agric. Ecosyst. Environ. 279, 43–52. https://doi.org/10.1016/j.agee.2019.04.009 (2019).Article 

Google Scholar 
21.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. Camb. Philos. Soc. 81, 117–142. https://doi.org/10.1017/s1464793105006949 (2006).Article 
PubMed 

Google Scholar 
22.Blaauw, B. R. & Isaacs, R. Larger patches of diverse floral resources increase insect pollinator density, diversity, and their pollination of native wild flowers. Basic Appl. Ecol. 15, 701–711. https://doi.org/10.1016/j.baae.2014.10.001 (2014).Article 

Google Scholar 
23.Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094. https://doi.org/10.1111/ele.13265 (2019).Article 
PubMed 

Google Scholar 
24.Bihaly, Á., Dóra, V., Lajos, K. & Sárospataki, M. Effect of semi-natural habitat patches on the pollinator assemblages of sunflower in an intensive agricultural landscape. Tájökológiai Lapok 16, 45–52 (2018).
Google Scholar 
25.Foldesi, R. et al. Relationships between wild bees, hoverflies and pollination success in apple orchards with different landscape contexts. Agric. For. Entomol. 18, 68–75. https://doi.org/10.1111/afe.12135 (2016).Article 

Google Scholar 
26.Sárospataki, M. et al. The role of local and landscape level factors in determining bumblebee abundance and richness. Acta Zool. Acad. Sci. Hung. 62, 387–407. https://doi.org/10.17109/AZH.62.4.387.2016 (2016).Article 

Google Scholar 
27.Schellhorn, N. A., Gagic, V. & Bommarco, R. Time will tell: resource continuity bolsters ecosystem services. Trends Ecol. Evol. 30, 524–530. https://doi.org/10.1016/j.tree.2015.06.007 (2015).Article 
PubMed 

Google Scholar 
28.Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. Camb. Philos. Soc. 87, 661–685. https://doi.org/10.1111/j.1469-185X.2011.00216.x (2012).Article 
PubMed 

Google Scholar 
29.Stephens, A. E. A. & Myers, J. H. Resource concentration by insects and implications for plant populations. J. Ecol. 100, 923–931. https://doi.org/10.1111/j.1365-2745.2012.01971.x (2012).Article 

Google Scholar 
30.Tscheulin, T., Neokosmidis, L., Petanidou, T. & Settele, J. Influence of landscape context on the abundance and diversity of bees in Mediterranean olive groves. Bull. Entomol. Res. 101, 557–564. https://doi.org/10.1017/S0007485311000149 (2011).CAS 
Article 
PubMed 

Google Scholar 
31.Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599. https://doi.org/10.1111/ele.12082 (2013).Article 
PubMed 

Google Scholar 
32.Eurostat. Archive: Main annual crop statistics, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Main_annual_crop_statistics&oldid=389868#Oilseeds (2018).33.KSH. STADAT tables – Agriculture. http://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_omn007b.html. (KSH, 2019).34.Hevia, V. et al. Bee diversity and abundance in a livestock drove road and its impact on pollination and seed set in adjacent sunflower fields. Agric. Ecosyst. Environ. 232, 336–344. https://doi.org/10.1016/j.agee.2016.08.021 (2016).Article 

Google Scholar 
35.Silva, C. et al. Bee pollination highly improves oil quality in sunflower. Sociobiology 65, 583–590. https://doi.org/10.13102/sociobiology.v65i4.3367 (2018).Article 

Google Scholar 
36.Terzić, S., Miklič, V. & Čanak, P. Review of 40 years of research carried out in Serbia on sunflower pollination. OCL 24, D608 (2017).Article 

Google Scholar 
37.Perrot, T. et al. Experimental quantification of insect pollination on sunflower yield, reconciling plant and field scale estimates. Basic Appl. Ecol. 34, 75–84. https://doi.org/10.1016/j.baae.2018.09.005 (2019).Article 

Google Scholar 
38.Martin, C. S. & Farina, W. M. Honeybee floral constancy and pollination efficiency in sunflower (Helianthus annuus) crops for hybrid seed production. Apidologie 47, 161–170 (2016).Article 

Google Scholar 
39.DeGrandi-Hoffman, G. & Watkins, J. C. The foraging activity of honey bees Apis mellifera and non—Apis bees on hybrid sunflowers (Helianthus annuus) and its influence on cross—pollination and seed set. J. Apic. Res. 39, 37–45. https://doi.org/10.1080/00218839.2000.11101019 (2000).Article 

Google Scholar 
40.Cerrutti, N. & Pontet, C. Differential attractiveness of sunflower cultivars to the honeybee Apis mellifera L. OCL 23, D204 (2016).Article 

Google Scholar 
41.Chambó, E. D., Garcia, R. C., Oliveira, N. T. E. D. & Duarte-Júnior, J. B. Honey bee visitation to sunflower: effects on pollination and plant genotype. Sci. Agric. 68, 647–651 (2011).Article 

Google Scholar 
42.Oz, M., Karasu, A., Cakmak, I., Goksoy, A. T. & Turan, Z. M. Effects of honeybee (Apis mellifera) pollination on seed set in hybrid sunflower (Helianthus annuus L.). Afr. J. Biotechnol. 8 (2009).43.Puškadija, Z. et al. Influence of weather conditions on honey bee visits (Apis mellifera carnica) during sunflower (Helianthus annuus L.) blooming period. Poljoprivreda 13, 230–233 (2007).
Google Scholar 
44.Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. Proc. Natl. Acad. Sci. USA 103, 13890–13895 (2006).ADS 
CAS 
Article 

Google Scholar 
45.Nderitu, J., Nyamasyo, G., Kasina, M. & Oronje, M. Diversity of sunflower pollinators and their effect on seed yield in Makueni District, Eastern Kenya. Span. J. Agric. Res. 6, 271–278 (2008).Article 

Google Scholar 
46.Carvalheiro, L. G. et al. Natural and within-farmland biodiversity enhances crop productivity. Ecol. Lett. 14, 251–259. https://doi.org/10.1111/j.1461-0248.2010.01579.x (2011).Article 
PubMed 

Google Scholar 
47.Sardiñas, H. S. & Kremen, C. Pollination services from field-scale agricultural diversification may be context-dependent. Agric. Ecosyst. Environ. 207, 17–25 (2015).Article 

Google Scholar 
48.Riedinger, V., Renner, M., Rundlof, M., Steffan-Dewenter, I. & Holzschuh, A. Early mass-flowering crops mitigate pollinator dilution in late-flowering crops. Landscape Ecol. 29, 425–435. https://doi.org/10.1007/s10980-013-9973-y (2014).Article 

Google Scholar 
49.Bennett, A. B. & Isaacs, R. Landscape composition influences pollinators and pollination services in perennial biofuel plantings. Agric. Ecosyst. Environ. 193, 1–8. https://doi.org/10.1016/j.agee.2014.04.016 (2014).Article 

Google Scholar 
50.Lowenstein, D. M., Huseth, A. S. & Groves, R. L. Response of wild bees (Hymenoptera: Apoidea: Anthophila) to surrounding land cover in Wisconsin pickling cucumber. Environ. Entomol. 41, 532–540. https://doi.org/10.1603/EN11241 (2012).CAS 
Article 
PubMed 

Google Scholar 
51.Pfister, S. C. et al. Dominance of cropland reduces the pollen deposition from bumble bees. Sci. Rep. 8, 13873. https://doi.org/10.1038/s41598-018-31826-3 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764. https://doi.org/10.1046/j.1365-2656.2002.00641.x (2002).Article 

Google Scholar 
53.Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596. https://doi.org/10.1007/s00442-007-0752-9 (2007).ADS 
Article 
PubMed 

Google Scholar 
54.Lihoreau, M., Chittka, L., Le Comber, S. C. & Raine, N. E. Bees do not use nearest-neighbour rules for optimization of multi-location routes. Biol. Lett. 8, 13–16. https://doi.org/10.1098/rsbl.2011.0661 (2012).Article 
PubMed 

Google Scholar 
55.Berger-Tal, O. & Bar-David, S. Recursive movement patterns: review and synthesis across species. Ecosphere 6, 149. https://doi.org/10.1890/es15-00106.1 (2015).Article 

Google Scholar 
56.Wesserling, J. Habitatwahl und Ausbreitungsverhalten von Stechimmen (Hymenoptera: Aculeata) in Sandgebieten unterschiedlicher Sukzessionsstadien, University of Karlsruhe, (1996).57.Hagler, J. R., Mueller, S., Teuber, L. R., Machtley, S. A. & Van Deynze, A. Foraging range of honey bees, Apis mellifera, in alfalfa seed production fields. J. Insect Sci. 11, 144 (2011).PubMed 
PubMed Central 

Google Scholar 
58.Couvillon, M. J. et al. Honey bee foraging distance depends on month and forage type. Apidologie 46, 61–70. https://doi.org/10.1007/s13592-014-0302-5 (2015).Article 

Google Scholar 
59.Beekman, M. & Ratnieks, F. L. W. Long-range foraging by the honey-bee, Apis mellifera L.. Funct. Ecol. 14, 490–496. https://doi.org/10.1046/j.1365-2435.2000.00443.x (2000).Article 

Google Scholar 
60.Gary, N. E., Witherell, P. C. & Lorenzen, K. Effect of age on honey bee foraging distance and pollen collection. Environ. Entomol. 10, 950–952 (1981).Article 

Google Scholar 
61.Walther-Hellwig, K. & Frankl, R. Foraging habitats and foraging distances of bumblebees, Bombus spp. (Hym., Apidae), in an agricultural landscape. J. Appl. Entomol. 124, 299–306. https://doi.org/10.1046/j.1439-0418.2000.00484.x (2000).Article 

Google Scholar 
62.Dramstad, W. E. Do bumblebees (Hymenoptera: Apidae) really forage close to their nests?. J. Insect Behav. 9, 163–182. https://doi.org/10.1007/bf02213863 (1996).Article 

Google Scholar 
63.Knight, M. E. et al. An interspecific comparison of foraging range and nest density of four bumblebee (Bombus) species. Mol. Ecol. 14, 1811–1820 (2005).CAS 
Article 

Google Scholar 
64.Wolf, S. & Moritz, R. F. Foraging distance in Bombus terrestris L. (Hymenoptera: Apidae). Apidologie 39, 419–427 (2008).Article 

Google Scholar 
65.Osborne, J. L. et al. Bumblebee flight distances in relation to the forage landscape. J. Anim. Ecol. 77, 406–415 (2008).Article 

Google Scholar 
66.Zurbuchen, A. et al. Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biol. Conserv. 143, 669–676 (2010).Article 

Google Scholar 
67.Hopfenmuller, S., Steffan-Dewenter, I. & Holzschuh, A. Trait-specific responses of wild bee communities to landscape composition, configuration and local factors. PLoS ONE 9, e104439. https://doi.org/10.1371/journal.pone.0104439 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Hung, K. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc R Soc Biol Sci Ser B 285, 20172140. https://doi.org/10.1098/rspb.2017.2140 (2018).Article 

Google Scholar 
69.Requier, F. et al. Honey bee diet in intensive farmland habitats reveals an unexpectedly high flower richness and a major role of weeds. Ecol. Appl. 25, 881–890. https://doi.org/10.1890/14-1011.1 (2015).Article 
PubMed 

Google Scholar 
70.Bonoan, R. E., Gonzalez, J. & Starks, P. T. The perils of forcing a generalist to be a specialist: lack of dietary essential amino acids impacts honey bee pollen foraging and colony growth. J. Apic. Res. 59, 95–103. https://doi.org/10.1080/00218839.2019.1656702 (2020).Article 

Google Scholar 
71.Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, e72016. https://doi.org/10.1371/journal.pone.0072016 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Di Pasquale, G. et al. Variations in the availability of pollen resources affect honey bee health. PLoS ONE 11, e0162818. https://doi.org/10.1371/journal.pone.0162818 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Alaux, C., Ducloz, F., Crauser, D. & Le Conte, Y. Diet effects on honeybee immunocompetence. Biol. Lett. 6, 562–565. https://doi.org/10.1098/rsbl.2009.0986 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Colwell, M. J., Williams, G. R., Evans, R. C. & Shutler, D. Honey bee-collected pollen in agro-ecosystems reveals diet diversity, diet quality, and pesticide exposure. Ecol. Evol. 7, 7243–7253. https://doi.org/10.1002/ece3.3178 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
75.Zhang, G., St. Clair, A. L., Dolezal, A., Toth, A. L. & O’Neal, M. Honey Bee (Hymenoptera: Apidea) pollen forage in a highly cultivated agroecosystem: limited diet diversity and its relationship to virus resistance. J. Econ. Entomol. 113, 1062–1072 (2020).76.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation. http://qgis.osgeo.org. (2009).77.FÖMI. MePAR, the Hungarian Agricultural Land Parcel Identification System, accessed 22 November 2019 http://www.mepar.hu/ (2016).78.McGarigal, K., Cushman, S. & Ene, E. Spatial Pattern Analysis Program for Categorical and Continuous Maps. available from http://www.umass.edu/landeco/research/fragstats/fragstats.html. (University of Massachusetts, 2012).79.McGarigal, K. FRAGSTATS help. Documentation for FRAGSTATS, 4. (2014).80.McGarigal, K. (2017). Landscape metrics for categorical map patterns. Lecture Notes. Available online: accessed 28 Feb 2021 http://www.umass.edu/landeco/teaching/landscape_ecology/schedule/chapter9_metrics.pdf.81.R Core Team. R: A Language and Environment for Statistical Computing. version 3.6.0. https://www.R-project.org. (R Foundation for Statistical Computing, 2020).82.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528. https://doi.org/10.1093/bioinformatics/bty633 (2019).CAS 
Article 
PubMed 

Google Scholar 
83.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Usinglme4. Journal of Statistical Software 67, https://doi.org/10.18637/jss.v067.i01 (2015).84.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Google Scholar 
85.DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. v. 0.3.3.0. (2020).86.Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019). More