Philippa Kaur

Possingham, H. P., Wilson, K. A., Andelman, S. J. & Vynne, C. H. Protected areas. Goals, limitations, and design. In Principles of Conservation Biology (eds Groom, M. J. et al.) 507–549 (Sinauer Associates Inc, 2006).
Google Scholar 
Marquet, P. A., Lessmann, J. & Shaw, M. R. Protected-area management and climate change. In Biodiversity and Climate Change: Transforming the Biosphere (eds Lovejoy, T. E. & Hannah, L.) 283–293 (Yale University Press, 2019).Chapter 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. PNAS https://doi.org/10.1073/pnas.1908221116 (2019).Article 

Google Scholar 
Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).Article 
ADS 

Google Scholar 
Cazalis, V. et al. Effectiveness of protected areas in conserving tropical forest birds. Nat. Commun. 11, 4461 (2020).Article 
ADS 
CAS 

Google Scholar 
Dudley, N., Mansourian, S., Stolton, S. & Suksuwan, S. Do protected areas contribute to poverty reduction?. Biodiversity 11, 5–7 (2010).Article 

Google Scholar 
Dudley, N. & Stolton, S. Arguments for Protected Areas (Earthscan, 2010).Book 

Google Scholar 
CBD. Strategic Plan for Biodiversity 2011–2020, Including Aichi Biodiversity Targets. http://www.cbd.int/sp/ and http://www.cbd.int/decision/cop/?id=12268 (2010).UNEP-WCMC & IUCN. Protected Planet: The World Database on Protected Areas (WDPA). www.protectedplanet.net. Accessed October 2022 (2022).Watson, J. E. M. et al. Persistent disparities between recent rates of habitat conversion and protection and implications for future global conservation targets. Conserv. Lett. 9, 413–421 (2016).Article 

Google Scholar 
Díaz, S. et al. Summary for Policymakers of the IPBES Global Assessment Report on Biodiversity and Ecosystem Services. (2019).Barnes, M. D., Glew, L., Wyborn, C. & Craigie, I. D. Prevent perverse outcomes from global protected area policy. Nat. Ecol. Evol. 2, 759–762 (2018).Article 

Google Scholar 
Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).Article 
ADS 
CAS 

Google Scholar 
Kukkala, A. S. & Moilanen, A. Core concepts of spatial prioritisation in systematic conservation planning. Biol. Rev. 88, 443–464 (2013).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and Far: Biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 
ADS 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).Article 
ADS 
CAS 

Google Scholar 
CBD. CoP 7 Decision VII/30. Strategic Plan: Future Evaluation of progress. 12 https://www.cbd.int/doc/decisions/cop-07/cop-07-dec-30-en.pdf (2004).Venter, O. et al. Bias in protected-area location and its effects on long-term aspirations of biodiversity conventions. Conserv. Biol. 32, 127–134 (2017).Article 

Google Scholar 
Kuempel, C. D., Chauvenet, A. L. M. & Possingham, H. P. Equitable representation of ecoregions is slowly improving despite strategic planning shortfalls. Conserv. Lett. 9, 422–428 (2016).Article 

Google Scholar 
Barr, L. M., Watson, J. E. M., Possingham, H. P., Iwamura, T. & Fuller, R. A. Progress in improving the protection of species and habitats in Australia. Biol. Conserv. 200, 184–191 (2016).Article 

Google Scholar 
Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 1–10 (2019).Article 

Google Scholar 
Hannah, L. Protected areas and climate change. Ann. N. Y. Acad. Sci. 1134, 201–212 (2008).Article 
ADS 

Google Scholar 
Thomas, C. D. & Gillingham, P. K. The performance of protected areas for biodiversity under climate change. Biol. J. Lin. Soc. 115, 718–730 (2015).Article 

Google Scholar 
Ramirez-Villegas, J. et al. Using species distributions models for designing conservation strategies of Tropical Andean biodiversity under climate change. J. Nat. Conserv. 22, 391–404 (2014).Article 

Google Scholar 
Bax, V. & Francesconi, W. Conservation gaps and priorities in the Tropical Andes biodiversity hotspot: Implications for the expansion of protected areas. J. Environ. Manage. 232, 387–396 (2019).Article 

Google Scholar 
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. PNAS 110, E2602–E2610 (2013).Article 
ADS 
CAS 

Google Scholar 
Rodrigues, A. S. L. et al. Global gap analysis: Priority regions for expanding the global protected-area network. Bioscience 54, 1092–1100 (2004).Article 

Google Scholar 
Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble Platform for Species Distribution Modeling. (2015).Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).Article 
MATH 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography 40, 887–893 (2017).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. (2017).Gotelli, N. J. & Graves, G. R. Null Models in Ecology. (1996).Araújo, M. B. & Pearson, R. G. Equilibrium of species’ distributions with climate. Ecography 28, 693–695 (2005).Article 

Google Scholar 
Watson, J. E. M., Grantham, H. S., Wilson, K. A. & Possingham, H. P. Systematic conservation planning: Past, present and future. In Conservation Biogeography (eds Ladle, R. J. & Whittaker, R. J.) (Wiley, 2011).
Google Scholar 
Bevilacqua, M. Áreas protegidas y conservación de la diversidad biológica. Biodivers. Venezuela 2, 922–943 (2003).
Google Scholar 
Franco, P., Saavedra-Rodríguez, C. A. & Kattan, G. H. Bird species diversity captured by protected areas in the Andes of Colombia: A gap analysis. Oryx 41, 57–63 (2007).Article 

Google Scholar 
Barzetti, V. Parks and Progress: Protected Areas and Economic Development in Latin America and the Caribbean. (1993).Schulman, L. et al. Amazonian biodiversity and protected areas: Do they meet?. Biodivers. Conserv. 16, 3011–3051 (2007).Article 

Google Scholar 
Dourojeanni, M. J. Áreas naturales protegidas e investigación científica en el Perú. Rev. For. Perú 33, 91–101 (2018).
Google Scholar 
Rodriguez, L. & Young, K. Biological diversity of peru: Determining priority areas for conservation. Ambio 29, 329–337 (2000).Article 

Google Scholar 
Ministerio del Ambiente & SERNANP. Plan Director de las Áreas Naturales Protegidas (Estrategia Nacional) (2009).Cuesta-Camacho, F. et al. Identificación de Vacíos y Prioridades de Conservación Para la Biodiversidad Terrestre en el Ecuador Continental. http://protectedareas.info/upload/document/ecuador_terrestrial_gap_analysis.pdf (2006).Naveda, J. A. Evaluación del grado de representatividad ecológica y geográfica del sistema de parques nacionales de Venezuela al norte del Orinoco: Anteproyecto. Rev. Geog. Venez. 38, 193–208 (1997).
Google Scholar 
Araujo, N., Müller, R., Nowicki, C. & Ibisch, P. L. Prioridades de conservación de la biodiversidad de Bolivia (editorial FAN, 2010)Arango, N. et al. Vacíos de Conservación del Sistema de Parques Nacionales Naturales de Colombia desde una Perspectiva Ecorregional. https://wwflac.awsassets.panda.org/downloads/vacios_de_conservacion.pdf (2003).Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature 405, 243–253 (2000).Article 
CAS 

Google Scholar 
Sarkar, S., Sánchez-Cordero, V., Londoño, M. C. & Fuller, T. Systematic conservation assessment for the Mesoamerica, Chocó, and Tropical Andes biodiversity hotspots: A preliminary analysis. Biodivers. Conserv. 18, 1793–1828 (2009).Article 

Google Scholar 
Lessmann, J., Muñoz, J. & Bonaccorso, E. Maximizing species conservation in continental Ecuador: A case of systematic conservation planning for biodiverse regions. Ecol. Evol. 4, 2410–2422 (2014).Article 

Google Scholar 
Young, B. E. et al. Using spatial models to predict areas of endemism and gaps in the protection of Andean slope birds. Auk 126, 554–565 (2009).Article 

Google Scholar 
Fajardo, J., Lessmann, J., Bonaccorso, E., Devenish, C. & Muñoz, J. Combined use of systematic conservation planning, species distribution modelling, and connectivity analysis reveals severe conservation gaps in a megadiverse country (Peru). PLoS ONE 9, 1–23 (2014).Article 

Google Scholar 
Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 6187 (2014).Article 

Google Scholar 
Swenson, J. J. et al. Plant and animal endemism in the eastern Andean slope: Challenges to conservation. BMC Ecol. 12, 1 (2012).Article 

Google Scholar 
Lessmann, J., Fajardo, J., Bonaccorso, E. & Bruner, A. Cost-effective protection of biodiversity in the western Amazon. Biol. Conserv. 235, 250–259 (2019).Article 

Google Scholar 
Rodrigues, A. S. L. & Gaston, K. J. How large do reserve networks need to be?. Ecol. Lett. 4, 602–609 (2001).Article 

Google Scholar 
Reyes-Puig, C. Diversity, threat, and conservation of reptiles from continental Ecuador. Amphib. Reptile Conserv. 11, 8 (2017).
Google Scholar 
Shanee, S. et al. Protected area coverage of threatened vertebrates and ecoregions in Peru: Comparison of communal, private and state reserves. J. Environ. Manage. 202, 12–20 (2017).Article 

Google Scholar 
Kujala, H., Moilanen, A., Araújo, M. B. & Cabeza, M. Conservation planning with uncertain climate change projections. PLoS ONE 8, e53315 (2013).Article 
ADS 
CAS 

Google Scholar 
Hannah, L. et al. 30% land conservation and climate action reduces tropical extinction risk by more than 50%. Ecography 43, 1–11 (2020).Article 

Google Scholar 
Velásquez-Tibatá, J., Salaman, P. & Graham, C. H. Effects of climate change on species distribution, community structure, and conservation of birds in protected areas in Colombia. Reg. Environ. Change 13, 235–248 (2013).Article 

Google Scholar 
del Avalos, V. R. & Hernández, J. Projected distribution shifts and protected area coverage of range-restricted Andean birds under climate change. Glob. Ecol. Conserv. 4, 459–469 (2015).Article 

Google Scholar 
Warren, R. et al. Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nat. Clim. Change 3, 678–682 (2013).Article 
ADS 

Google Scholar 
Golden Kroner, R. et al. COVID-era policies and economic recovery plans: Are governments building back better for protected and conserved areas?. PARKS 27, 135–148 (2021).Article 

Google Scholar 
IPCC Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, 2013).
Google Scholar 
Chevalier, M., Zarzo-Arias, A., Guélat, J., Mateo, R. G. & Guisan, A. Accounting for niche truncation to improve spatial and temporal predictions of species distributions. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2022.944116 (2022).Article 

Google Scholar 
Watson, J. E. M. et al. Bolder science needed now for protected areas. Conserv. Biol. 30, 243–248 (2016).Article 

Google Scholar 
CBD. Kunming-Montreal Global Biodiversity Framework, Draft Decision Submitted by the PRESIDENT. (2022). CBD/COP/15/L.25. https://www.cbd.int/doc/c/e6d3/cd1d/daf663719a03902a9b116c34/cop-15-l-25-en.pdfCBD. Report of the Expert Workshop on the Monitoring Framework for the Post-2020 Global Biodiversity Framework (CBD, 2022).
Google Scholar 
Chaplin-Kramer, R. et al. Mapping the planet’s critical natural assets. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01934-5 (2022).Article 

Google Scholar 
Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).Article 

Google Scholar 
Elbers, J. Las Áreas Protegidas de América Latina: Situación Actual y Perspectivas PARA el Futuro (2011).Miller, D. C. & Nakamura, K. S. Protected areas and the sustainable governance of forest resources. Curr. Opin. Environ. Sustain. 32, 96–103 (2018).Article 

Google Scholar 
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186 (2000).Article 

Google Scholar 
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 (2005).Article 

Google Scholar 
van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).Article 

Google Scholar 
Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).Article 

Google Scholar 
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).Article 

Google Scholar 
Thornhill, A. H. et al. Spatial phylogenetics of the native California flora. BMC Biol 15, 96 (2017).Article 

Google Scholar 
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).Article 

Google Scholar 
Kershaw, F. et al. Informing conservation units: Barriers to dispersal for the yellow anaconda. Divers. Distrib. 19, 1164–1174 (2013).Article 

Google Scholar 
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 

Google Scholar 
Gaston, K. J. The Structure and Dynamics of Geographic Ranges (Oxford University Press, 2003).
Google Scholar 
Yin, L., Fu, R., Shevliakova, E. & Dickinson, R. E. How well can CMIP5 simulate precipitation and its controlling processes over tropical South America?. Clim. Dyn. 41, 3127–3143 (2013).Article 

Google Scholar  More

Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).Article 
ADS 

Google Scholar 
Polyakov, I. V. et al. Borealization of the Arctic Ocean in response to anomalous advection from sub-Arctic seas. Front. Mar. Sci. 7, 983–992 (2020).Article 

Google Scholar 
Lannuzel, D. et al. The future of Arctic sea-ice biogeochemistry and ice-associated ecosystems. Nat. Clim. Change. 10, 983–992 (2020).Article 
ADS 

Google Scholar 
Macias-Fauria, M. & Post, E. Effects of sea ice on Arctic biota: An emerging crisis discipline. Biol. Lett. 14, 20170702 (2018).Article 

Google Scholar 
Kohlbach, D. et al. The importance of ice algae-produced carbon in the central Arctic Ocean ecosystem: Food web relationships revealed by lipid and stable isotope analyses. Limnol. Oceanogr. 61, 2027–2044 (2016).Article 
ADS 

Google Scholar 
Søreide, J. E. et al. Sympagic-pelagic-benthic coupling in Arctic and Atlantic waters around Svalbard revealed by stable isotopic and fatty acid tracers. Mar. Biol. Res. 9, 831–850 (2013).Article 

Google Scholar 
Slagstad, D., Wassmann, P. F. J. & Ellingsen, I. Physical constrains and productivity in the future Arctic Ocean. Front. Mar. Sci. 2015, 2 (2015).
Google Scholar 
FISCAO. Final Report of the Fifth Meeting of Scientific Experts on Fish Stocks in the Central Arctic Ocean. https://apps-afsc.fisheries.noaa.gov/documents/Arctic_fish_stocks_fifth_meeting/508_Documents/508_Final_report_of_the_505th_FiSCAO_meeting.pdf (2018).David, C. et al. Under-ice distribution of polar cod Boreogadus saida in the central Arctic Ocean and their association with sea-ice habitat properties. Polar Biol. 39, 981–994 (2016).Article 

Google Scholar 
Gradinger, R. Vertical fine structure of the biomass and composition of algal communities in Arctic pack ice. Mar. Biol. 133, 745–754 (1999).Article 

Google Scholar 
Kosobokova, K. N., Hopcroft, R. R. & Hirche, H.-J. Patterns of zooplankton diversity through the depths of the Arctic’s central basins. Mar Biodivers. 41, 29–50 (2011).Article 

Google Scholar 
Mumm, N. et al. Breaking the ice: Large-scale distribution of mesozooplankton after a decade of Arctic and transpolar cruises. Polar Biol. 20, 189–197 (1998).Article 

Google Scholar 
Snoeijs-Leijonmalm, P. et al. Unexpected fish and squid in the central Arctic deep scattering layer. Sci. Adv. 8, 7536 (2022).Article 

Google Scholar 
David, C., Lange, B., Rabe, B. & Flores, H. Community structure of under-ice fauna in the Eurasian central Arctic Ocean in relation to environmental properties of sea-ice habitats. Mar. Ecol. Prog. Ser. 522, 15–32 (2015).Article 
ADS 

Google Scholar 
Gosselin, M., Levasseur, M., Wheeler, P. A., Horner, R. A. & Booth, B. C. New measurements of phytoplankton and ice algal production in the Arctic Ocean. Deep-Sea Res. Part II(44), 1623–1644 (1997).Article 
ADS 

Google Scholar 
Ardyna, M. & Arrigo, K. R. Phytoplankton dynamics in a changing Arctic Ocean. Nat. Clim. Change. 10, 892–903 (2020).Article 
ADS 
CAS 

Google Scholar 
Hays, G. C. In Migrations and Dispersal of Marine Organisms. (eds Jones, M. B. et al.) 163–170 (Springer).Irigoien, X. et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5, 3271 (2014).Article 
ADS 

Google Scholar 
Geoffroy, M. et al. Mesopelagic sound scattering layers of the high arctic: Seasonal variations in biomass, species assemblage, and trophic relationships. Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Gjøsæter, H., Wiebe, P. H., Knutsen, T. & Ingvaldsen, R. B. Evidence of Diel vertical migration of mesopelagic sound-scattering organisms in the Arctic. Front. Mar. Sci. 2017, 4 (2017).
Google Scholar 
Knutsen, T., Wiebe, P. H., Gjøsæter, H., Ingvaldsen, R. B. & Lien, G. High latitude epipelagic and mesopelagic scattering layers—a reference for future arctic ecosystem change. Front. Mar. Sci. 2017, 4 (2017).
Google Scholar 
Priou, P. et al. Dense mesopelagic sound scattering layer and vertical segregation of pelagic organisms at the Arctic-Atlantic gateway during the midnight sun. Prog. Oceanogr. 196, 102611 (2021).Article 

Google Scholar 
Snoeijs-Leijonmalm, P. et al. A deep scattering layer under the North Pole pack ice. Prog. Oceanogr. 194, 102560 (2021).Article 

Google Scholar 
St-John, M. A. et al. A dark hole in our understanding of marine ecosystems and their services: Perspectives from the mesopelagic community. Front. Mar. Sci. 2016, 3 (2016).
Google Scholar 
Fransson, A. et al. Joint cruise 2-2 2021: Cruise report. The Nansen Legacy Report Series, 30/2022. https://doi.org/10.7557/nlrs.6413 (2022).Rudels, B. et al. Observations of water masses and circulation with focus on the Eurasian Basin of the Arctic Ocean from the 1990s to the late 2000s. Ocean Sci. 9, 147–169 (2013).Article 
ADS 

Google Scholar 
Krumpen, T. et al. Arctic warming interrupts the Transpolar Drift and affects long-range transport of sea ice and ice-rafted matter. Sci. Rep. 9, 5459 (2019).Article 
ADS 

Google Scholar 
Aagaard, K. A synthesis of the Arctic Ocean circulation. Rapp. P.-V. Rcun. Cons. int. Explor. Mer. 188, 11–22 (1989).
Google Scholar 
Perez-Hernandez, M. D. et al. The Atlantic Water boundary current north of Svalbard in late summer. J. Geophys. Res. 122, 2269–2290 (2017).Article 
ADS 

Google Scholar 
Våge, K. et al. The Atlantic Water boundary current in the Nansen Basin: Transport and mechanisms of lateral exchange. J. Geophys. Res. 121, 6946–6960 (2016).Article 
ADS 

Google Scholar 
Crews, L., Sundfjord, A., Albretsen, J. & Hattermann, T. Mesoscale Eddy Activity and Transport in the Atlantic Water Inflow Region North of Svalbard. J. Geophys. Res. 123, 201–215 (2018).Article 
ADS 

Google Scholar 
Kolås, E. H., Koenig, Z., Fer, I., Nilsen, F. & Marnela, M. Structure and Transport of Atlantic Water North of Svalbard From Observations in Summer and Fall 2018. J. Geophys. Res. 125, 6174 (2020).Article 

Google Scholar 
Basedow, S. L. et al. Seasonal variation in transport of zooplankton into the arctic basin through the atlantic gateway. Fram Strait. Front. Mar. Sci. 2018, 5 (2018).
Google Scholar 
Vernet, M., Carstensen, J., Reigstad, M. & Svensen, C. Editorial: Carbon bridge to the Arctic. Front. Mar. Sci. 2020, 7 (2020).
Google Scholar 
Wassmann, P. et al. The contiguous domains of Arctic Ocean advection: Trails of life and death. Prog. Oceanogr. 139, 42–65 (2015).Article 
ADS 

Google Scholar 
Wassmann, P., Slagstad, D. & Ellingsen, I. Advection of mesozooplankton into the northern svalbard shelf region. Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Auel, H. Egg size and reproductive adaptations among Arctic deep-sea copepods (Calanoida, Paraeuchaeta). Helgol. Mar. Res. 58, 147–153 (2004).Article 
ADS 

Google Scholar 
Gluchowska, M. et al. Zooplankton in Svalbard fjords on the Atlantic-Arctic boundary. Polar Biol. 39, 1785–1802 (2016).Article 

Google Scholar 
Wang, Y.-G., Tseng, L.-C., Lin, M. & Hwang, J.-S. Vertical and geographic distribution of copepod communities at late summer in the Amerasian Basin. Arctic Ocean. Plos One. 14, e0219319 (2019).Article 
CAS 

Google Scholar 
Gislason, A. & Silva, T. Abundance, composition, and development of zooplankton in the Subarctic Iceland Sea in 2006, 2007, and 2008. ICES J. Mar. Sci. 69, 1263–1276 (2012).Article 

Google Scholar 
Zhukova, N. G., Nesterova, V. N., Prokopchuk, I. P. & Rudneva, G. B. Winter distribution of euphausiids (Euphausiacea) in the Barents Sea (2000–2005). Deep-Sea Res. Part II(56), 1959–1967 (2009).Article 
ADS 

Google Scholar 
Dalpadado, P. & Skjoldal, H. R. Abundance, maturity and growth of the krill species Thysanoessa inermis and T. longicaudata in the Barents Sea. Mar. Ecol. Prog. Ser. 144, 175–183 (1996).Article 
ADS 

Google Scholar 
Koszteyn, J., Timofeev, S., Węsławski, J. M. & Malinga, B. Size structure of Themisto abyssorum Boeck and Themisto libellula (Mandt) populations in European Arctic seas. Polar Biol. 15, 85–92 (1995).Article 

Google Scholar 
Dalpadado, P., Borkner, N., Bogstad, B. & Mehl, S. Distribution of Themisto (Amphipoda) spp. in the Barents Sea and predator-prey interactions. ICES J. Mar. Sci. 58, 876–895 (2001).Article 

Google Scholar 
Macnaughton, M. O., Thormar, J. & Berge, J. Sympagic amphipods in the Arctic pack ice: Redescriptions of Eusirus holmii Hansen, 1887 and Pleusymtes karstensi (Barnard, 1959). Polar Biol. 30, 1013–1025 (2007).Article 

Google Scholar 
Kraft, A., Graeve, M., Janssen, D., Greenacre, M. & Falk-Petersen, S. Arctic pelagic amphipods: Lipid dynamics and life strategy. J. Plankton Res. 37, 790–807 (2015).Article 
CAS 

Google Scholar 
Kreibich, T., Hagen, W. & Saborowski, R. Food utilization of two pelagic crustaceans in the Greenland Sea: Meganyctiphanes norvegica (Euphausiacea) and Hymenodora glacialis (Decapoda, Caridea). Mar. Ecol. Prog. Ser. 413, 105–115 (2010).Article 
ADS 
CAS 

Google Scholar 
Geoffroy, M. et al. Increased occurrence of the jellyfish Periphylla periphylla in the European high Arctic. Polar Biol. 41, 2615–2619 (2018).Article 

Google Scholar 
Grigor, J. J., Søreide, J. E. & Varpe, Ø. Seasonal ecology and life-history strategy of the high-latitude predatory zooplankter Parasagitta elegans. Mar. Ecol. Prog. Ser. 499, 77–88 (2014).Article 
ADS 

Google Scholar 
Maclennan, D. N., Fernandes, P. G. & Dalen, J. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369 (2002).Article 

Google Scholar 
Gjøsæter, H. & Ushakov, N. G. Acoustic estimates of the Barents Sea Arctic cod Stock (Boreogadus saida). Forage Fishes in Marine Ecosystems. Alaska Sea Grant Collage Program, University of Alaska Fairbanks 97:01, 485–504 (1997).Raskoff, K. A., Hopcroft, R. R., Kosobokova, K. N., Purcell, J. E. & Youngbluth, M. Jellies under ice: ROV observations from the Arctic 2005 hidden ocean expedition. Deep-Sea Res. Part II(57), 111–126 (2010).Article 
ADS 

Google Scholar 
Bluhm, B. A. et al. The Pan-Arctic continental slope: Sharp gradients of physical processes affect pelagic and benthic ecosystems. Front. Mar. Sci. 2020, 7 (2020).
Google Scholar 
Hop, H. et al. Pelagic ecosystem characteristics across the atlantic water boundary current from Rijpfjorden, Svalbard, to the Arctic Ocean During Summer (2010–2014). Front. Mar. Sci. 2019, 6 (2019).
Google Scholar 
Mumm, N. Composition and distribution of mesozooplankton in the Nansen Basin, Arctic Ocean, during summer. Polar Biol. 13, 451–461 (1993).Article 

Google Scholar 
Ona, E. & Nielsen, J. Acoustic detection of the Greenland shark (Somniosus microcephalus) using multifrequency split beam echosounder in Svalbard waters. Prog. Oceanogr. 206, 102842 (2022).Article 

Google Scholar 
Gjøsæter, H., Ingvaldsen, R. & Christiansen, J. S. Acoustic scattering layers reveal a faunal connection across the Fram Strait. Prog. Oceanogr. 185, 102348 (2020).Article 

Google Scholar 
Ingvaldsen, R. B., Gjosaeter, H., Ona, E. & Michalsen, K. Atlantic cod (Gadus morhua) feeding over deep water in the high Arctic. Polar Biol. 40, 2105–2111 (2017).Article 

Google Scholar 
Chawarski, J., Klevjer, T. A., Coté, D. & Geoffroy, M. Evidence of temperature control on mesopelagic fish and zooplankton communities at high latitudes. Front. Mar. Sci. 2022, 9 (2022).
Google Scholar 
Chernova, N. V. Catching of Greenland halibut Reinhardtius hippoglossoides (Pleuronectidae) on the shelf edge of the Laptev and East Siberian Seas. J. Ichthyol. 57, 219–227 (2017).Article 

Google Scholar 
Benzik, A. N., Budanova, L. K. & Orlov, A. M. Hard life in cold waters: Size distribution and gonads show that Greenland halibut temporarily inhabit the Siberian Arctic. Water Biol. Secur. 1, 100037 (2022).Article 

Google Scholar 
Olsen, L. M. et al. A red tide in the pack ice of the Arctic Ocean. Sci. Rep. 9, 9536 (2019).Article 
ADS 

Google Scholar 
Assmy, P. et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci. Rep. 7, 40850 (2017).Article 
ADS 
CAS 

Google Scholar 
Leu, E., Søreide, J. E., Hessen, D. O., Falk-Petersen, S. & Berge, J. Consequences of changing sea-ice cover for primary and secondary producers in the European Arctic shelf seas: Timing, quantity, and quality. Prog. Oceanogr. 90, 18–32 (2011).Article 
ADS 

Google Scholar 
Drivdal, M. et al. Connections to the deep: Deep vertical migrations, an important part of the life cycle of Apherusa glacialis, an arctic ice-associated amphipod. Front. Mar. Sci. 2021, 8 (2021).
Google Scholar 
Scoulding, B., Chu, D., Ona, E. & Fernandes, P. G. Target strengths of two abundant mesopelagic fish species. J. Acoust. Soc. Am. 137, 989–1000 (2015).Article 
ADS 

Google Scholar 
Popova, E. E., Yool, A., Aksenov, Y. & Coward, A. C. Role of advection in Arctic Ocean lower trophic dynamics: A modeling perspective. J. Geophys. Res. 118, 1571–1586 (2013).Article 
ADS 

Google Scholar 
Saunders, R. A., Ingvarsdóttir, A., Rasmussen, J., Hay, S. J. & Brierley, A. S. Regional variation in distribution pattern, population structure and growth rates of Meganyctiphanes norvegica and Thysanoessa longicaudata in the Irminger Sea, North Atlantic. Prog. Oceanogr. 72, 313–342 (2007).Article 
ADS 

Google Scholar 
Tarling, G. A. et al. Can a key boreal Calanus copepod species now complete its life-cycle in the Arctic? Evidence and implications for Arctic food-webs. Ambio 51, 333–344 (2022).Article 

Google Scholar 
Purcell, J. E., Juhl, A. R., Manko, M. K. & Aumack, C. F. Overwintering of gelatinous zooplankton in the coastal Arctic Ocean. Mar. Ecol. Prog. Ser. 591, 281–286 (2018).Article 
ADS 

Google Scholar 
Purcell, J. E., Hopcroft, R. R., Kosobokova, K. N. & Whitledge, T. E. Distribution, abundance, and predation effects of epipelagic ctenophores and jellyfish in the western Arctic Ocean. Deep-Sea Res. Part II(57), 127–135 (2010).Article 
ADS 

Google Scholar 
Solvang, H. K. et al. Distribution of rorquals and Atlantic cod in relation to their prey in the Norwegian high Arctic. Polar Biol. 44, 761–782 (2021).Article 

Google Scholar 
Ingvaldsen, R. B. et al. Physical manifestations and ecological implications of Arctic Atlantification. Nat. Rev. Earth Environ. 2, 874–889 (2021).Article 
ADS 

Google Scholar 
Flores, H. et al. Macrofauna under sea ice and in the open surface layer of the Lazarev Sea, Southern Ocean. Deep-Sea Res. Part II(58), 1948–1961 (2011).Article 
ADS 

Google Scholar 
Godø, O. R., Valdemarsen, J. W. & Engås, A. Comparison of efficiency of standard and experimental juvenile gadoid sampling trawls. ICES Mar. Sci. Symp. 196, 196–201 (1993).
Google Scholar 
Klevjer, T. et al. Micronekton biomass distribution, improved estimates across four north Atlantic basins. Deep-Sea Res. Part II. 180, 104691 (2020).Article 

Google Scholar 
Krafft, B. A. et al. Distribution and demography of Antarctic krill in the Southeast Atlantic sector of the Southern Ocean during the austral summer 2008. Polar Biol. 33, 957–968 (2010).Article 

Google Scholar 
Foote, K. G. Maintaining precision calibrations with optimal copper spheres. J. Acoust. Soc. Am. 73, 1054–1063 (1983).Article 
ADS 

Google Scholar 
Korneliussen, R. J. et al. Acoustic identification of marine species using a feature library. Methods Oceanogr. 17, 187–205 (2016).Article 

Google Scholar 
Lavergne, T. et al. Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records. Cryosphere. 13, 49–78 (2019).Article 
ADS 

Google Scholar 
Firing, E., Ramada, J. & Caldwell, P. Processing ADCP Data with the CODAS Software System Version 3.1. Joint Institute for Marine and Atmospheric Research University of Hawaii. http://currents.soest.hawaii.edu/docs/adcp_doc/index.html (1995).Padman, L. & Erofeeva, S. A barotropic inverse tidal model for the Arctic Ocean. Geophys. Res. Lett. 31, 256 (2004).Article 

Google Scholar  More

Guillou, L. et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ. Microbiol. 10, 3349–3365 (2008).Article 
CAS 

Google Scholar 
Massana, R. Eukaryotic picoplankton in surface oceans. Annu. Rev. Microbiol. 65, 91–110 (2011).Article 
CAS 

Google Scholar 
Rocke, E., Pachiadaki, M. G., Cobban, A., Kujawinski, E. B. & Edgcomb, V. P. Protist community grazing on prokaryotic prey in deep ocean water masses. PLoS ONE 10, e0124505 (2015).Article 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).Article 

Google Scholar 
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).Article 
CAS 

Google Scholar 
Cordier, T. et al. Patterns of eukaryotic diversity from the surface to the deep-ocean sediment. Sci. Adv. 8, https://doi.org/10.1126/sciadv.abj9309 (2022).Giner, C. R. et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 14, 437–449 (2020).Article 

Google Scholar 
Obiol, A. et al. A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Mol. Ecol. Resour. 20, 718–731 (2020).Article 
CAS 

Google Scholar 
Pernice, M. C. et al. Large variability of bathypelagic microbial eukaryotic communities across the world’s oceans. ISME J. 10, 945–958 (2016).Article 

Google Scholar 
Santoferrara, L. et al. Perspectives from ten years of protist studies by high‐throughput metabarcoding. J. Eukaryot. Microbiol. 67, 612–622 (2020).Article 

Google Scholar 
Schoenle, A. et al. High and specific diversity of protists in the deep-sea basins dominated by diplonemids, kinetoplastids, ciliates and foraminiferans. Commun. Biol. 4, 1–10 (2021).Article 

Google Scholar 
Sommeria-Klein, G. et al. Global drivers of eukaryotic plankton biogeography in the sunlit ocean. Science 374, 594–599 (2021).Article 
CAS 

Google Scholar 
Tremblay, J. É. et al. Global and regional drivers of nutrient supply, primary production and CO2 drawdown in the changing Arctic Ocean. Prog. Oceanogr. 139, 171–196 (2015).Article 

Google Scholar 
Zoccarato, L., Pallavicini, A., Cerino, F., Umani, S. F. & Celussi, M. Water mass dynamics shape Ross Sea protist communities in mesopelagic and bathypelagic layers. Prog. Oceanogr. 149, 16–26 (2016).Article 

Google Scholar 
Biggs, T. E. G., Huisman, J. & Brussaard, C. P. D. Viral lysis modifies seasonal phytoplankton dynamics and carbon flow in the Southern Ocean. ISME J. 15, 3615–3622 (2021).Article 
CAS 

Google Scholar 
Clarke, L. J., Bestley, S., Bissett, A. & Deagle, B. E. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 13, 734–737 (2019).Article 
CAS 

Google Scholar 
Gast, R. J., Fay, S. A. & Sanders, R. W. Mixotrophic activity and diversity of Antarctic marine protists in austral summer. Front. Mar. Sci. 5, 13 (2018).Article 

Google Scholar 
Grattepanche, J. D., Jeffrey, W. H., Gast, R. J. & Sanders, R. W. Diversity of microbial eukaryotes along the West Antarctic Peninsula in austral spring. Front. Microbiol. 13, 844856 (2022).Article 

Google Scholar 
Hamilton, M. et al. Spatiotemporal variations in Antarctic protistan communities highlight phytoplankton diversity and seasonal dominance by a novel cryptophyte lineage. mBio 12, e0297321 (2021).Article 

Google Scholar 
Lin, Y. et al. Decline in plankton diversity and carbon flux with reduced sea ice extent along the Western Antarctic Peninsula. Nat. Commun. 12, 4948 (2021).Article 
CAS 

Google Scholar 
Martin, K. et al. The biogeographic differentiation of algal microbiomes in the upper ocean from pole to pole. Nat. Commun. 12, 5483 (2021).Article 
CAS 

Google Scholar 
Vernet, M. et al. The Weddell Gyre, Southern Ocean: present knowledge and future challenges. Rev. Geophysics 57, 623–708 (2019).Article 

Google Scholar 
Callahan, J. E. The structure and circulation of deep water in the Antarctic. Deep‐Sea Res. 19, 563–575 (1972).
Google Scholar 
Janout, M. A. et al. FRIS revisited in 2018: on the circulation and water masses at the Filchner and Ronne ice shelves in the southern Weddell Sea. J. Geophys. Res.: Oceans 126, e2021JC017269 (2021).Article 

Google Scholar 
Orsi, A. H., Smethie, W. M. & Bullister, J. L. On the total input of Antarctic waters to the deep ocean: a preliminary estimate from chlorofluorocarbon measurements. J. Geophys. Res. 107, 3122 (2002).Article 

Google Scholar 
Hoppema, M., Fahrbach, E. & Schröder, M. On the total carbon dioxide and oxygen signature of the circumpolar deep water in the Weddell Gyre. Oceanol. Acta 20, 783–798 (1997).CAS 

Google Scholar 
Karstensen, J. & Tomczak, M. Age determination of mixed water masses using CFC and oxygen data. J. Geophys. Res. 103, 18599–18609 (1998).Article 
CAS 

Google Scholar 
De Cáceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).Article 

Google Scholar 
De Cáceres, M., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

Google Scholar 
Dufrene, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymetrical approach. Ecol. Monogr. 67, 345–366 (1997).
Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).Article 

Google Scholar 
Celussi, M., Bergamasco, A., Cataletto, B., Umani, S. F. & Del Negro, P. Water masses bacterial community structure and microbial activities in the Ross Sea, Antarctica. Antarct. Sci. 22, 361–370 (2010).Article 

Google Scholar 
Galand, P. E., Potvin, M., Casamayor, E. O. & Lovejoy, C. Hydrography shapes bacterial biogeography of the deep Arctic Ocean. ISME J. 4, 564–576 (2010).Article 

Google Scholar 
Hamdan, L. J. Ocean currents shape the microbiome of Arctic marine sediments. ISME J. 7, 685–696 (2013).Article 
CAS 

Google Scholar 
Wilkins, D., van Sebille, E., Rintoul, S. R., Lauro, F. M. & Cavicchioli, R. Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects. Nat. Commun. 4, 2457 (2013).Article 

Google Scholar 
Flegontova, O. et al. Extreme diversity of diplonemid eukaryotes in the ocean. Curr. Biol. 26, 3060–3065 (2016).Article 
CAS 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).Article 
CAS 

Google Scholar 
Jeong, H. J. et al. Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs. Ocean Sci. 45, 65–91 (2010).Article 
CAS 

Google Scholar 
Stoecker, D. K., Hansen, P. J., Caron, D. A. & Mitra, A. Mixotrophy in the marine Plankton. Ann. Rev. Mar. Sci. 9, 311–335 (2016).Article 

Google Scholar 
Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc. Natl Acad. Sci. USA 116, 11824–11832 (2019).Article 
CAS 

Google Scholar 
Gutierrez-Rodriguez, A. et al. High contribution of Rhizaria (Radiolaria) to vertical export in the California Current Ecosystem revealed by DNA metabarcoding. ISME J. 13, 964–976 (2019).Article 
CAS 

Google Scholar 
Lampitt, R. S., Salter, I. & Johns, D. Radiolaria: major exporters of organic carbon to the deep ocean. Glob. Biogeochem. Cycles 23, GB1010 (2009).Article 

Google Scholar 
Suzuki, N. & Not, F. In Marine Protists: Diversity and Dynamics 179–222 (Springer Japan, 2015).Decelle, J. et al. Diversity, ecology and biogeochemistry of cyst-forming Acantharia (Radiolaria) in the oceans. PLoS ONE 8, e53598 (2013).Article 
CAS 

Google Scholar 
Tashyreva, D. et al. Diplonemids—a review on “new“ flagellates on the oceanic block. Protist 173, 125868 (2022).Article 
CAS 

Google Scholar 
Flegontova, O. et al. Environmental determinants of the distribution of planktonic diplonemids and kinetoplastids in the oceans. Environ. Microbiol 22, 4014–4031 (2020).Article 
CAS 

Google Scholar 
Xu, D. et al. Microbial eukaryote diversity and activity in the water column of the South China sea based on DNA and RNA high throughput sequencing. Front. Microbiol. 8, 1121 (2017).Article 

Google Scholar 
Bråte, J. et al. Radiolaria associated with large diversity of marine alveolates. Protist 163, 767–777 (2012).Article 

Google Scholar 
Strassert, J. F. H. et al. Single cell genomics of uncultured marine alveolates shows paraphyly of basal dinoflagellates. ISME J. 12, 304–308 (2017).Article 

Google Scholar 
Yabuki, A. & Tame, A. Phylogeny and reclassification of Hemistasia phaeocysticola (Scherffel) Elbrächter & Schnepf, 1996. J. Eukaryot. Microbiol. 62, 426–429 (2015).Article 

Google Scholar 
Larsen, J. & Patterson, J. Some flagellates (Protista) from tropical marine sediments. J. Nat. Hist. 24, 801–937 (1990).Article 

Google Scholar 
Prokopchuk, G. et al. Trophic flexibility of marine diplonemids – switching from osmotrophy to bacterivory. ISME J. 16, 1409–1419 (2022).Article 
CAS 

Google Scholar 
Arístegui, J. & Gasol, J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).Article 

Google Scholar 
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE 4, e6372 (2009).Article 

Google Scholar 
Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420 (2015).Article 

Google Scholar 
Kolisko, M. et al. EukRef-excavates: seven curated SSU ribosomal RNA gene databases. Database 2020, baaa080 (2020).
Google Scholar 
Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119 (2019).Article 

Google Scholar 
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083 (2019).Article 
CAS 

Google Scholar  More

Hung, K.-L.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. B 285, 20172140 (2018).Article 

Google Scholar 
Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).Article 

Google Scholar 
Mashilingi, S. K., Zhang, H., Garibaldi, L. A. & An, J. Honeybees are far too insufficient to supply optimum pollination services in agricultural systems worldwide. Agr. Ecosyst. Environ. 335, 108003 (2022).Article 

Google Scholar 
Stein, K. et al. Bee pollination increases yield quantity and quality of cash crops in Burkina Faso West Africa. Sci. Rep. 7, 17691 (2017).Article 
ADS 

Google Scholar 
Neumann, P. & Carreck, N. L. Honey bee colony losses. J. Apic. Res. 49, 1–6 (2010).Article 

Google Scholar 
Pettis, J. S. & Delaplane, K. S. Coordinated responses to honey bee decline in the USA. Apidologie 41, 256–263 (2010).Article 

Google Scholar 
Potts, S. G. et al. Declines of managed honey bees and beekeepers in Europe. J. Apic. Res. 49, 15–22 (2010).Article 

Google Scholar 
Moritz, R. F. A. & Erler, S. Lost colonies found in a data mine: Global honey trade but not pests or pesticides as a major cause of regional honeybee colony declines. Agr. Ecosyst. Environ. 216, 44–50 (2016).Article 

Google Scholar 
Osterman, J. et al. Global trends in the number and diversity of managed pollinator species. Agr. Ecosyst. Environ. 322, 107653 (2021).Article 

Google Scholar 
Requier, F. et al. Trends in beekeeping and honey bee colony losses in Latin America. J. Apic. Res. 57, 657–662 (2018).Article 

Google Scholar 
Vandame, R. & Palacio, M. A. Preserved honey bee health in Latin America: a fragile equilibrium due to low-intensity agriculture and beekeeping?. Apidologie 41, 243–255 (2010).Article 

Google Scholar 
Antúnez, K., Invernizzi, C., Mendoza, Y., vanEngelsdorp, D. & Zunino, P. Honeybee colony losses in Uruguay during 2013–2014. Apidologie 48, 364–370 (2017).Article 

Google Scholar 
Castilhos, D., Bergamo, G. C. & Kastelic, J. P. Honey bee colony losses in Brazil in 2018–2019 / Perdas de colônias de abelhas no Brasil em 2018–2019. Braz. J. Anim. Environ. Res. 4, 5017–5041 (2021).
Google Scholar 
Castilhos, D., Bergamo, G. C., Gramacho, K. P. & Gonçalves, L. S. Bee colony losses in Brazil: a 5-year online survey. Apidologie 50, 263–272 (2019).Article 

Google Scholar 
Maggi, M. et al. Honeybee health in South America. Apidologie 47, 835–854 (2016).Article 

Google Scholar 
SIAP. Sistema de Información Agroalimentaria de Consulta. http://www.agricultura.gob.mx/datos-abiertos/siap (2019).Namdar-Irani, M., Sotomayor, O. & Rodrigues, M. Tendencias estructurales en la agricultura de América Latina: desafíos para las políticas públicas. 45 (2020).Torres-Ruiz, A., Jones, R. W. & Barajas, R. A. Present and Potential use of Bees as Managed Pollinators in Mexico1. Southwestern entomologist (2013).Brodschneider, R. et al. Multi-country loss rates of honey bee colonies during winter 2016/2017 from the COLOSS survey. J. Apic. Res. 57, 452–457 (2018).Article 

Google Scholar 
Gray, A. et al. Loss rates of honey bee colonies during winter 2017/18 in 36 countries participating in the COLOSS survey, including effects of forage sources. J. Apic. Res. 58, 479–485 (2019).Article 

Google Scholar 
Medina-Flores, C. A. et al. Pérdida de colonias de abejas melíferas y factores asociados en el centro-occidente de México en los inviernos del 2016 al 2019. Revista Bio Ciencias 8, 11 (2021).Article 

Google Scholar 
vanEngelsdorp, D. & Meixner, M. D. A historical review of managed honey bee populations in Europe and the United States and the factors that may affect them. J. Invert. Pathol. 103(Supplement), S80–S95 (2010).Hristov, P., Shumkova, R., Palova, N. & Neov, B. Honey bee colony losses: Why are honey bees disappearing?. Sociobiology 68, e5851–e5851 (2021).Article 

Google Scholar 
Shanahan, M. Honey bees and industrial agriculture: What researchers are missing, and why it’s a problem. J. Insect Sci. 22, 14 (2022).Article 

Google Scholar 
Nearman, A. & vanEngelsdorp, D. Water provisioning increases caged worker bee lifespan and caged worker bees are living half as long as observed 50 years ago. Sci. Rep. 12, 18660. https://doi.org/10.1038/s41598-022-21401-2 (2022).Article 
ADS 
CAS 

Google Scholar 
Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and Colony Collapse Disorder in the United States. J. Apic. Res. 49, 134–136 (2010).Article 

Google Scholar 
Guzmán-Novoa, E., Benítez, A. C., Montaño, L. G. E. & Novoa, G. G. Colonization, impact and control of Africanized honey bees in Mexico. Veterinaria México OA 42, (2011).Becerra-Guzmán, F., Guzmán-Novoa, E., Correa-Benítez, A. & Zozaya-Rubio, A. Length of life, age at first foraging and foraging life of Africanized and European honey bee (Apis mellifera) workers, during conditions of resource abundance. J. Apic. Res. 44, 151–156 (2005).Article 

Google Scholar 
Guzman-Novoa, E. & Uribe-Rubio, J. L. Honey production by European, Africanized and hybrid honey bee (Apis mellifera) colonies in Mexico. American bee journal (2004).Guzman-Novoa, E. et al. The Process and Outcome of the Africanization of Honey Bees in Mexico: Lessons and Future Directions. Front. Ecol. Evol. 8, (2020).Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 

Google Scholar 
Otto, C. R. V., Roth, C. L., Carlson, B. L. & Smart, M. D. Land-use change reduces habitat suitability for supporting managed honey bee colonies in the Northern Great Plains. PNAS 113, 10430–10435 (2016).Article 
ADS 
CAS 

Google Scholar 
Outhwaite, C. L., McCann, P. & Newbold, T. Agriculture and climate change are reshaping insect biodiversity worldwide. Nature 605, 97–102 (2022).Article 
ADS 
CAS 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A.-L. & Totland, Ø. How does climate warming affect plant-pollinator interactions?. Ecol. Lett. 12, 184–195 (2018).Article 

Google Scholar 
Cooper, P. D., Schaffer, W. M. & Buchmann, S. L. Temperature Regulation of Honey Bees (Apis Mellifera) Foraging in the Sonoran Desert. J. Exp. Biol. 114, 1–15 (1985).Article 

Google Scholar 
Stalidzans, E. et al. Dynamics of weight change and temperature of Apis mellifera (Hymenoptera: Apidae) Colonies in a wintering building with controlled temperature. J. Econ. Entomol. 110, 13–23 (2017).CAS 

Google Scholar 
Qu, M., Wan, J. & Hao, X. Analysis of diurnal air temperature range change in the continental United States. Weather Clim. Extremes 4, 86–95 (2014).Article 

Google Scholar 
Braganza, K., Karoly, D. J. & Arblaster, J. M. Diurnal temperature range as an index of global climate change during the twentieth century. Geophys. Res. Lett. 31, (2004).Halsch, C. A. et al. Insects and recent climate change. Proc. Natl. Acad. Sci. 118, e2002543117 (2021).Article 
CAS 

Google Scholar 
Abou-Shaara, H. F. The foraging behaviour of honey bees, Apis mellifera: A review. Vet. Med. 59, 1–10 (2014).Article 

Google Scholar 
Joshi, N. & Joshi, P. Foraging Behaviour of Apis Spp. on Apple Flowers in a Subtropical Environment. New York Sci. J. 3, (2010).Gounari, S., Proutsos, N. & Goras, G. How does weather impact on beehive productivity in a Mediterranean island? Ital. J. Agrometeorol. 65–81. https://doi.org/10.36253/ijam-1195 (2022).Delgado, D. L., Pérez, M. E., Galindo-Cardona, A., Giray, T. & Restrepo, C. Forecasting the Influence of Climate Change on Agroecosystem Services: Potential Impacts on Honey Yields in a Small-Island Developing State. Psyche J. Entomol. https://www.hindawi.com/journals/psyche/2012/951215/. https://doi.org/10.1155/2012/951215 (2012).Alves, L. H. S., Cassino, P. C. R. & Prezoto, F. Effects of abiotic factors on the foraging activity of Apis mellifera Linnaeus, 1758 in inflorescences of Vernonia polyanthes Less (Asteraceae). Acta Sci. Anim. Sci. 37, 405–409 (2015).Abou-Shaara, H. Expectations about the potential impacts of climate change on Honey Bee Colonies in Egypt. J. Apicult. 31, 157–164 (2016).Article 

Google Scholar 
Donkersley, P., Rhodes, G., Pickup, R. W., Jones, K. C. & Wilson, K. Honeybee nutrition is linked to landscape composition. Ecol. Evol. 4, 4195–4206 (2014).Article 

Google Scholar 
Michel-Cuello, C. & Aguilar-Rivera, N. Climate change effects on agricultural production systems in México. in Handbook of Climate Change Across the Food Supply Chain (eds. Leal Filho, W., Djekic, I., Smetana, S. & Kovaleva, M.) 335–353 (Springer International Publishing, 2022). https://doi.org/10.1007/978-3-030-87934-1_19.LaFevor, M. C. Spatial and temporal changes in crop species production diversity in Mexico (1980–2020). Agriculture 12, 985 (2022).Article 

Google Scholar 
Smart, M. D., Otto, C. R. V. & Lundgren, J. G. Nutritional status of honey bee (Apis mellifera L.) workers across an agricultural land-use gradient. Sci. Rep. 9, 1–10 (2019).Alaux, C., Ducloz, F., Crauser, D. & Conte, Y. L. Diet effects on honeybee immunocompetence. Biol. Lett. rsbl20090986. https://doi.org/10.1098/rsbl.2009.0986 (2010).Dolezal, A. G., Carrillo-Tripp, J., Miller, W. A., Bonning, B. C. & Toth, A. L. Intensively Cultivated Landscape and Varroa Mite Infestation Are Associated with Reduced Honey Bee Nutritional State. PLoS One 11, (2016).Pasquale, G. D. et al. Variations in the Availability of Pollen Resources Affect Honey Bee Health. PLoS ONE 11, e0162818 (2016).Article 

Google Scholar 
Kaluza, B. F. et al. Social bees are fitter in more biodiverse environments. Sci Rep 8, 1–10 (2018).Article 
CAS 

Google Scholar 
Ricigliano, V. A. et al. Honey bee colony performance and health are enhanced by apiary proximity to US Conservation Reserve Program (CRP) lands. Sci. Rep. 9, 4894 (2019).Article 
ADS 

Google Scholar 
Clermont, A., Eickermann, M., Kraus, F., Hoffmann, L. & Beyer, M. Correlations between land covers and honey bee colony losses in a country with industrialized and rural regions. Sci. Total Environ. 532, 1–13 (2015).Article 
ADS 
CAS 

Google Scholar 
Kuchling, S. et al. Investigating the role of landscape composition on honey bee colony winter mortality: A long-term analysis. Sci. Rep. 8, 1 (2018).Article 
CAS 

Google Scholar 
Dixon, D. J., Zheng, H. & Otto, C. R. V. Land conversion and pesticide use degrade forage areas for honey bees in America’s beekeeping epicenter. PLoS ONE 16, e0251043 (2021).Article 
CAS 

Google Scholar 
Mendoza-Ponce, A., Corona-Núñez, R. O., Galicia, L. & Kraxner, F. Identifying hotspots of land use cover change under socioeconomic and climate change scenarios in Mexico. Ambio 48, 336–349 (2019).Article 

Google Scholar 
Magaña, M. et al. Productividad de la apicultura en México y su impacto sobre la rentabilidad. Revista mexicana de ciencias agrícolas 7, 1103–1115 (2016).Article 

Google Scholar 
Mitchell, E. a. D. et al. A worldwide survey of neonicotinoids in honey. Science 358, 109–111 (2017).Pacheco, A. P. Identificación de residuos tóxicos en miel de diferentes procedencias en la zona centro del Estado de Veracruz / Identification of toxic residues in honey from different sources in the central zone of the State of Veracruz. CIBA Revista Iberoamericana de las Ciencias Biológicas y Agropecuarias 1, 1–42 (2014).Article 

Google Scholar 
Ruiz-Toledo, J. et al. Organochlorine Pesticides in Honey and Pollen Samples from Managed Colonies of the Honey Bee Apis mellifera Linnaeus and the Stingless Bee Scaptotrigona mexicana Guérin from Southern Mexico. Insects 9, 54 (2018).Article 

Google Scholar 
Valdovinos-Flores, C., Alcantar-Rosales, V. M., Gaspar-Ramírez, O., Saldaña-Loza, L. M. & Dorantes-Ugalde, J. A. Agricultural pesticide residues in honey and wax combs from Southeastern, Central and Northeastern Mexico. J. Apic. Res. 56, 667–679 (2017).Article 

Google Scholar 
Gómez-Escobar, E. et al. Effect of GF-120 (Spinosad) aerial sprays on colonies of the stingless Bee Scaptotrigona mexicana (Hymenoptera: Apidae) and the Honey Bee (Hymenoptera: Apidae). J. Econ. Entomol. 111, 1711–1715 (2018).Article 

Google Scholar 
Sánchez, D., Solórzano, E. D. J., Liedo, P. & Vandame, R. Effect of the natural pesticide Spinosad (GF-120 Formulation) on the Foraging behavior of Plebeia moureana (Hymenoptera: Apidae). J. Econ. Entomol. 105, 1234–1237 (2012).Article 

Google Scholar 
Cabrera-Marín, N. V., Liedo, P. & Sánchez, D. The Effect of Application Rate of GF-120 (Spinosad) and Malathion on the Mortality of Apis mellifera (Hymenoptera: Apidae) Foragers. J. Econ. Entomol. 109, 515–519 (2016).Article 

Google Scholar 
Valdovinos-Núñez, G. R. et al. Comparative toxicity of pesticides to stingless bees (Hymenoptera: Apidae: Meliponini). J. Econ. Entomol. 102, 1737–1742 (2009).Article 

Google Scholar 
ANADA. Atlas Nacional de las Abejas y Derivados Apícolas. https://atlas-abejas.agricultura.gob.mx/cap2.html#212_Enfermedades_y_Plagas_de_las_Colmenas (2021).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

Google Scholar 
Daberkow, S., Korb, P. & Hoff, F. Structure of the U.S. beekeeping industry: 1982–2002. J. Econ. Entomol. 102, 868–886 (2009).Saunders, S. P. et al. Unraveling a century of global change impacts on winter bird distributions in the eastern United States. Glob. Change Biol. 28, 2221–2235 (2022).Article 
CAS 

Google Scholar 
CICESE. Base de datos climatológica nacional (Sistema CLICOM). http://clicom-mex.cicese.mx/ (2018).CONEVAL. Metodología para la medición de pobreza en México | CONEVAL. https://www.coneval.org.mx/Medicion/MP/Paginas/Metodologia.aspx.Wood, S. N. Generalized Additive Models: An Introduction with R, Second Edition. (CRC Press, 2017).Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R. (Springer-Verlag, 2009).Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2016).Lichstein, J. W., Simons, T. R., Shriner, S. A. & Franzreb, K. E. Spatial autocorrelation and autoregressive models in ecology. Ecol. Monogr. 72, 445–463 (2002).Article 

Google Scholar 
Döke, M. A., Frazier, M. & Grozinger, C. M. Overwintering honey bees: biology and management. Curr. Opin. Insect Sci. 10, 185–193 (2015).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. J. Open Sour. Softw. 6, 3139 (2021).Article 
ADS 

Google Scholar 
Simpson, G. L. Modelling palaeoecological time series using generalised additive models. Front. Ecol. Evol. 6, 1 (2018).Article 

Google Scholar 
Furrer, R., Nychka, D., Sain, S. & Nychka, M. D. Title Tools for spatial data. (2012). More

Research areaThe YREB covers Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan. It includes the Yangtze River Delta urban agglomerations (YRDUA), Yangtze River midstream urban agglomeration (YRMUA), and Chengdu-Chongqing urban agglomeration (CCUA). With a regional area of 2.05 million km2, the YREB runs through the eastern, central and western regions in China32. In 2019, the total GDP of YREB is 45.8 trillion yuan, accounting for 46.2% of the national GDP. The YREB plays a pivotal strategic support and leading role in the overall situation of stable economic growth in China33. At the same time, the contradiction between the shortage of land resources and economic growth in the YREB is very prominent. Therefore, this paper selects 107 cities in YREB as the research sample. The specific geographic locations are shown in Fig. 2. This article uses ARCGIS 10.2 version to draw the map. The URL link is http://demo.domain.com:6080/arcgis/services.Figure 2The geographic location of the YREB in China.Full size imageResearch methodsGlobal Malmquist–Luenberger indexUGLUE refers to the effective utilization degree of land elements under certain input of other elements. The green utilization of urban land mainly comes from three aspects: first, improve the utilization intensity of the existing actual input land, that is, increase the input intensity of other elements of the unit land area. Second, reduce the input of land in the production process to avoid excessive waste of land. Third, promote the optimal allocation of land elements among production units. Technical efficiency refers to the maximum degree that all factor inputs need to expand or shrink in equal proportion when all production units reach the production frontier. However, for production units with high technical efficiency, the factor allocation structure may not be reasonable. The land factors may still have the problem of under-input or over-input, resulting in the reduction of UGLUE.Pastor and Lovell34 proposed a global index, which uses all the inspection periods of each decision-making unit as a benchmark to construct the production frontier. According to the current benchmark construction period t, the production possibility set reference set is defined as follows:$$P_{C}^{t} (x^{t} ) = left{ {left. {(y^{t} ,b^{t} )} right|x^{t} {kern 1pt} can{kern 1pt} , produce{kern 1pt} , b^{t} ,y^{t} } right}$$
(1)
The global benchmark is defined as: (P_{G} = P_{C}^{1} , cup ,P_{C}^{2} , cup , cdots ,P_{C}^{t}), The subscripts C and G represent the current benchmark and the global benchmark respectively. The ML index of decision-making unit i is calculated based on the current reference benchmark:$$ML^{S} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{C}^{S} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{C}^{S} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$
(2)
Among them, the superscript S indicates two adjacent periods, t period and t + 1 period. The subscript C indicates the current benchmark, which is a simplified directional distance function. (ML^{s} > 1), indicates that the productivity increases. (ML^{s} < 1), indicates that the productivity decreases.According to Hofmann et al.35, the GMLI is defined as follows:$$GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$ (3) Among them, (D_{G}^{T} (x,y,b) = max left{ {alpha |(y - alpha y,b - alpha b) in P_{G} (x)} right}). (GMLI^{t,t + 1} > 1) indicates that the productivity has increased. (GMLI^{t,t + 1} < 1) indicates that the productivity decreases. The GMLI is further broken down as follows:$$begin{aligned} & GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} \ & quad = frac{{1 + D_{G}^{t} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{t + 1} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} times left[ {frac{{(1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} ))/(1 + D_{C}^{T} (x^{t} ,y^{t} ,b^{t} ))}}{{(1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))/(1 + D_{C}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))}}} right] \ & quad = frac{{TE^{t + 1} }}{{TE^{t} }} times left( {frac{{BPG_{t + 1}^{t + 1} }}{{BPG_{t}^{t + 1} }}} right) = EC_{t}^{t + 1} times BPC_{t}^{t + 1} \ end{aligned}$$ (4) Among them, TE is the change of technological progress. EC is the change of technological efficiency. The change of technological progress reflects the change of the highest technical level. The improvement of the highest technical level often requires the introduction and innovation of advanced technology, which often requires a large amount of investment. The change of technical efficiency reflects the gap with the highest technical level. Narrowing the gap with the highest technical level often requires improvements in internal management and governance structures. (BPG_{t}^{t + 1}) is the “best practitioner gap” between the current period and overall technological frontier. (BPC_{t}^{t + 1}) measures the changes in the “best practitioner gap” between two periods (technological changes). (BPC_{t}^{t + 1} , > , 1 ,) indicates technological progress. (BPC_{t}^{t + 1} < 1) indicates technology regress.Econometric techniques of industrial agglomeration on UGLUEIn recent years, many scholars used the traditional SPM for empirical analysis, which is a basic measurement model suitable for panel data. Therefore, this article firstly uses the traditional SPM to analyze the impact of industrial agglomeration on UGLUE. The formula is:$$begin{aligned} ln UGLUE_{it} & = alpha_{0} + alpha_{1} ln RZI_{it} + alpha_{2} ln RZI_{it} *ln RZI_{it} + alpha_{3} ln RDI_{it} + alpha_{4} ln EC_{it} \ & quad + alpha_{5} ln GDP_{it} + alpha_{6} ln TEC_{it} + alpha_{7} ln ROAD_{it} + alpha_{8} ln GOV_{it} + varepsilon_{it} \ end{aligned}$$ (5) Among them, ε is the disturbance term. i represents the city, i in this paper involves 107 cities in YREB. t represents the time, and the range of t in this paper is from 2007 to 2016. UGLUE is the explained variable, which represents the UGLUE. RZI and RDI are explanatory variables, representing industrial specialization agglomeration and industrial diversification agglomeration. EC is the industrial structure. GDP is the level of economic development. TEC is the level of technology. ROAD is the level of infrastructure. GOV is the degree of government intervention. (alpha_{1}) to (alpha_{8}) is the coefficient of each variable.Formula (5) assumes that the UGLUE changes with the changes of various influencing factors in the current period. That is, there is no time lag effect. But in reality, land use often has a time lag effect. The previous level has a non-negligible impact on the current results. Therefore, this paper selects the dynamic panel model for empirical analysis. However, there is often a two-way causal relationship between industrial agglomeration and UGLUE, which may cause endogenous bias. For example, cities with higher UGLUE levels tend to have higher levels of economic development, which promotes industrial agglomeration in this city. Therefore, this paper adopts the method of system GMM for regression analysis of dynamic panel model. Compared with mixed OLS, system GMM can make full use of sample information, select appropriate lag terms as instrumental variables36. It can effectively solve the endogeneity problem between industrial agglomeration and UGLUE. Based on the above analysis, this paper introduces the first-order lag term of UGLUE on the basis of formula (5). The DPM is as follows:$$begin{aligned} ln UGLUE_{it} & = beta_{0} + tau ln UGLUE_{i(t - 1)} + beta_{1} ln RZI_{it} + beta_{2} ln RZI_{it} times ln RZI_{it} + beta_{3} ln RDI_{it} \ & quad + beta_{4} ln EC_{it} + beta_{5} ln GDP_{it} + beta_{6} ln TEC_{it} + beta_{7} ln ROAD_{it} + beta_{8} ln GOV_{it} + varepsilon_{{{text{it}}}} \ end{aligned}$$ (6) Among them, (tau) is the first-order lag coefficient of UGLUE, reflecting the time lag effect of UGLUE.Variable descriptionExplained variableThe GMLI is used to measure the UGLUE of 107 cities in YREB. According to existing research37, the following core evaluation index of UGLUE are selected (see Table 1). Regarding input indicators, we mainly choose land element input M, labor element input L, and capital element input K as input indicators. Regarding output indicators, we choose the added value of the secondary and tertiary industries in the municipal area as the expected output, and use the GDP deflator to convert it into a comparable value. At the same time, pollution indicators such as industrial wastewater emissions, industrial sulfur dioxide emissions, and industrial smoke (dust) emissions are selected as undesired output. Since the GMLI reflects the growth rate of UGLUE, this paper assumes that the GMLI in 2006 is 1, and then multiplies the calculated GMLI year by year to obtain the development level of UGLUE in each city from 2007 to 2016.Table 1 Input and output index.Full size tableExplanatory variablesIndustrial specialization index ZI is usually used to measure the specialization level of urban industries. The specialization index is represented by the share of the employment of the industry in the total employment of the city:$$ZI_{i} = Max_{j} (S_{ij} )$$ (7) Nextly, we use the relative specialization index to make a horizontal comparison of the specialization level between different cities:$$RZI_{i} = Max(S_{ij} /S_{j} )$$ (8) The most common measure of the level of industrial diversification is the Herfindahl–Hirschman Index (HHI). For city i, the HHI is the sum of the square sum of employment shares of all industries in the city. The diversification index is the reciprocal of the HHI:$$DZ_{i} = frac{1}{{sumlimits_{j} {S_{ij}^{2} } }}$$ (9) The expression of relative diversification index is as follows:$$RDI_{i} = {1 mathord{left/ {vphantom {1 {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}} right. kern-0pt} {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}$$ (10) Among them, Sij is the employment proportion of j industry in city i, and Sj is the proportion of the total employment of the national j industry. The greater value of RZI and RDI, the higher level of industrial specialization and diversification.Control variablesRegarding control variables, we choose the following variables as control variables.Industrial structure (EC): The continuous optimization of industrial structure promotes the improvement of UGLUE through three aspects: saving land, increasing land income and promoting the optimal allocation of land resources. This paper selects the added value of the tertiary industry as a percentage of GDP (take the logarithm) to express.Technological level (TEC): The higher the technological innovation level of a city is, the more it promotes the use of input elements and the transformation of innovation results, thereby improving the UGLUE. This paper selects the proportion of science and technology expenditure to fiscal expenditure (take the logarithm) to represent.Economic development level (GDP): The continuous economic development promote the rational allocation of various production factors and increase the level of urban land output, thereby improving the UGLUE. This paper selects GDP per capita (take the logarithm) to express.Road infrastructure level (ROAD): The continuous improvement of infrastructure reduces transportation costs and transaction costs, and promotes communication externalities between producers, consumers, and between producers and consumers. This paper selects the average road area per capita (take the logarithm) to express.Government behavior (GOV): Fiscal expenditure is an important means for the government to carry out macro-control. Appropriate fiscal expenditure makes up for market shortages, improves factor flow and resource allocation efficiency, and realizes positive economic externalities. This paper selects the proportion of fiscal expenditure to GDP (take the logarithm) to express. We can see the meaning of specific variables from Table 2.Table 2 The descriptive statistics of variables.Full size tableData sourceThe object of this thesis is the 107 cities in YREB from 2007 to 2016. The urban construction land area data comes from the "China Urban Construction Statistical Yearbook", and the rest of the index data all come from the "China City Statistical Yearbook". The URL link is https://www.cnki.net/. In order to maintain the integrity of the data, this article uses the average method to fill in the missing values. In addition, because Chaohu City began to be under the jurisdiction of Hefei City in 2011, Bijie City and Tongren City in Guizhou Province only became prefecture-level cities in 2011. The three cities and Pu'er City are taken from the sample to maintain the continuity of data. More

Taranu, Z. E. et al. Acceleration of cyanobacterial dominance in north temperate-subarctic lakes during the Anthropocene. Ecol. Lett. 18, 375–384 (2015).Article 

Google Scholar 
Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).Article 
CAS 

Google Scholar 
Monchamp, M. E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324 (2018).Article 

Google Scholar 
Chorus, I. & Bartram, J. Toxic Cyanobacteria in Water. A Guide to Their Public Health Consequences, Monitoring, and Management. In: World Health Organization (eds. Chorus I. & Bertram J.) (CRC Press, 1999).Rabalais, N. N. et al. Dynamics and distribution of natural and human-caused hypoxia. Biogeosciences 7, 585–619 (2010).Article 
CAS 

Google Scholar 
Carmichael, W. W. Health effects of toxin-producing cyanobacteria: “The CyanoHABs”. Hum. Ecol. Risk Assess. Int. J. 7, 1393–1407 (2001).Article 

Google Scholar 
Whitton, B. A. Ecology of Cyanobacteria II: Their Diversity in Space and Time (Springer, 2012).Smol, J. P., Birks, H. J. B. & Last, W. M. Tracking Environmental Change Using Lake Sediments. Volume 4: Zoological Indicators, Developments in Paleoenvironmental Research. (Springer, 2002).Domaizon, I., Winegardner, A., Capo, E., Gauthier, J. & Gregory-Eaves, I. DNA-based methods in paleolimnology: new opportunities for investigating long-term dynamics of lacustrine biodiversity. J. Paleolimnol. 52, 1–21 (2017).Article 

Google Scholar 
Livingstone, D. & Jaworski, G. H. M. The viability of akinetes of blue-green algae recovered from the sediments of rostherne mere. Br. Phycol. J. 15, 357–364 (1980).Article 

Google Scholar 
van Geel, B., Mur, L. R., Ralska-Jasiewiczowa, M. & Goslar, T. Fossil akinetes of Aphanizomenon and Anabaena as indicators for medieval phosphate-eutrophication of Lake Gosciaz (Central Poland). Rev. Palaeobot. Palynol. 83, 97–105 (1994).Article 

Google Scholar 
Hillbrand, M., van Geel, B., Hasenfratz, A., Hadorn, P. & Haas, J. N. Non-pollen palynomorphs show human- and livestock-induced eutrophication of Lake Nussbaumersee (Thurgau, Switzerland) since Neolithic times (3840 bc). Holocene 24, 559–568 (2014).Article 

Google Scholar 
Gosling, W. D. et al. Human occupation and ecosystem change on Upolu (Samoa) during the Holocene. J. Biogeogr. 47, 600–614 (2020).Article 

Google Scholar 
Hertzberg, S., Liaaen-Jensen, S. & Siegelman, H. W. The carotenoids of blue-green algae. Phytochemistry 10, 3121–3127 (1971).Article 
CAS 

Google Scholar 
Leavitt, P. R. & Findlay, D. L. Comparison of fossil pigments with 20 years of phytoplankton data from eutrophic Lake 227, Experimental Lakes Area, Ontario. Can. J. Fish. Aquat. Sci. 51, 2286–2299 (1994).Article 
CAS 

Google Scholar 
Kaiser, J., Ön, B., Arz, H. & Akçer-Ön, S. Sedimentary lipid biomarkers in the magnesium-rich and highly alkaline Lake Salda (south-western Anatolia). J. Limnol. 75, 581–596 (2016).
Google Scholar 
Bauersachs, T., Talbot, H. M., Sidgwick, F., Sivonen, K. & Schwark, L. Lipid biomarker signatures as tracers for harmful cyanobacterial blooms in the Baltic Sea. PLoS ONE 12, e0186360 (2017).Article 

Google Scholar 
Domaizon, I. et al. DNA from lake sediments reveals the long-term dynamics and diversity of Synechococcus assemblages. Biogeosci. Discuss. 10, 2515–2564 (2013).
Google Scholar 
Britton, G., Liaaen-Jensen, S. & Pfander, H. in Carotenoids (eds. Britton, G., Liaaen-Jensen, S., Pfander, H.). Vol. 4, 1–6 (Birkhäuser Press, 2008).Capo, E. et al. Lake sedimentary dna research on past terrestrial and aquatic biodiversity: overview and recommendations. Quaternary 4, 6 (2021).Article 

Google Scholar 
Monchamp, M. E., Walser, J. C., Pomati, F. & Spaak, P. Sedimentary DNA reveals cyanobacterial community diversity over 200 years in two perialpine lakes. Appl. Environ. Microbiol. 82, 6472–6482 (2016).Article 
CAS 

Google Scholar 
Nwosu, E. C. et al. Evaluating sedimentary DNA for tracing changes in cyanobacteria dynamics from sediments spanning the last 350 years of Lake Tiefer See, NE Germany. J. Paleolimnol. 66, 279–296 (2021).Article 

Google Scholar 
Zhang, J. et al. Pre-industrial cyanobacterial dominance in Lake Moon (NE China) revealed by sedimentary ancient DNA. Quat. Sci. Rev. 261, 106966 (2021).Article 

Google Scholar 
Brauer, A., Schwab, M. J., Brademann, B., Pinkerneil, S. & Theuerkauf, M. Tiefer See–a key site for lake sediment research in NE Germany. DEUQUA Spec. Publ. 2, 89–93 (2019).Article 

Google Scholar 
Dräger, N. et al. Varve microfacies and varve preservation record of climate change and human impact for the last 6000 years at Lake Tiefer See (NE Germany). Holocene 27, 450–464 (2017).Article 

Google Scholar 
Dräger, N. et al. Hypolimnetic oxygen conditions influence varve preservation and δ13C of sediment organic matter in Lake Tiefer See, NE Germany. J. Paleolimnol. 62, 181–194 (2019).Article 

Google Scholar 
Theuerkauf, M., Dräger, N., Kienel, U., Kuparinen, A. & Brauer, A. Effects of changes in land management practices on pollen productivity of open vegetation during the last century derived from varved lake sediments. Holocene 25, 733–744 (2015).Article 

Google Scholar 
Heinrich, I. et al. Interdisciplinary geo-ecological research across time scales in the Northeast German Lowland Observatory (TERENO-NE). Vadose Zone J. 17, 1–25 (2018).Article 

Google Scholar 
Roeser, P. et al. Advances in understanding calcite varve formation: new insights from a dual lake monitoring approach in the southern Baltic lowlands. Boreas 50, 419–440 (2021).Article 

Google Scholar 
Nwosu, E. C. et al. From water into sediment—tracing freshwater Cyanobacteria via DNA analyses. Microorganisms 9, 1778 (2021).Article 
CAS 

Google Scholar 
Schmidt, J. -P. Ein Fremdling im Nordischen Kreis Jungbronzezeitliche Funde aus dem Flachen See bei Sophienhof, Lkr. Mecklenburgische Seenplatte. In: D. Brandherm/B. Nessel (Hrsg.), Phasenübergänge und Umbrüche im bronzezeitlichen Europa. Beiträge zur Sitzung der Arbeitsgemeinschaft Bronzezeit auf der 80. Jahrestagung des Nordwestdeutschen Verbandes für Altertumskunde. Vol. 297, 271–281. (Universitätsforschungen zur Prähistorischen Archäologie, 2017).Raese, H. & Schmidt, J. -P. Zur Besiedlung Mecklenburg-Vorpommernswährend des Spätneolithikums und der frühenBronzezeit (2500–1500 v. Chr.). In: Siedlungsarchäologie des Endneolithikums und der frühen Bronzezeit. 11. Mitteldeutscher Archäologentag (eds. Meller, H., Friedderich, S., Küßner, M., Stäuble, H. & Risch, R.) 621–634 (2019).Kienel, U., Dulski, P., Ott, F., Lorenz, S. & Brauer, A. Recently induced anoxia leading to the preservation of seasonal laminae in two NE-German lakes. J. Paleolimnol. 50, 535–544 (2013).Article 

Google Scholar 
Callieri, C. & Stockner, J. Picocyanobacteria success in oligotrophic lakes: fact or fiction? J. Limnol. 59, 72–76 (2000).Article 

Google Scholar 
Sollai, M. et al. The Holocene sedimentary record of cyanobacterial glycolipids in the Baltic Sea: an evaluation of their application as tracers of past nitrogen fixation. Biogeosciences 14, 5789–5804 (2017).Article 

Google Scholar 
Mur, L. R., Skulberg, O. M. & Utkilen, H. In: Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring, and Management. (eds. Chorus, I. and Bartram, J.) 15–40 (St Edmundsbury Press, 1999).Schmidt, J.-P. Ein bronzenes Hallstattschwert der Periode VI aus dem Flachen See bei Sophienhof, Lkr. Mecklenburgische Seenplatte. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 26, 26–34 (2019).
Google Scholar 
Schmidt, J.-P. “Aller guten Dinge sind drei!”–Ein weiteres bronzezeitliches Schwert aus dem Flachen See bei Lütgendorf, Lkr. Mecklenburgische Seenplatte. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 27, 49–55 (2020).
Google Scholar 
Küster, M., Stöckmann, M., Fülling, A. & Weber, R. Kulturlandschaftselemente, Kolluvien und Flugsande als Archive der spätholozänen Landschaftsentwicklung im Bereich des Messtischblattes Thurow (Müritz-Nationalpark, Mecklenburg). In: Neue Beiträge zum Naturraum und zur Landschaftsgeschichte im Teilgebiet. (Geozon Science Media, 2015).Feeser, I., Dörfler, W., Kneisel, J., Hinz, M. & Dreibrodt, S. Human impact and population dynamics in the Neolithic and Bronze Age: Multi-proxy evidence from north-western Central Europe. Holocene 29, 1596–1606 (2019).Article 

Google Scholar 
Alsleben, A. In How’s Life? Living Conditions in the 2nd and 1st Millennia BCE. Scales of Transformation in Prehistoric and Archaic Societies (eds. Dal Corso, M. et al.) 85–102 (Sidestone Press, 2019).Kneisel, J., Bork, H.-R. & Czebreszuk, J. In Defensive Structures from Central Europe to the Aegean in the 3rd and 2nd Millennia bc (eds. Czebreszuk, J., Kadrow, S. & Müller, J.) 155–170 (Habelt, 2008).Haas, J. N. & Wahlmüller, N. Floren-, Vegetations- und Milieuveränderungen im Zuge der bronzezeitlichen Besiedlung von Bruszczewo (Polen) und der landwirtschaftlichen Nutzung der umliegenden Gebiete. In: Ausgrabungen und Forschungen in einer prähistorischen Siedlungskammer Großpolens. (eds. Müller, J., Czebreszuk, J. & Kneisel, J.) Studien zur Archäologie in Ostmitteleuropa Vol. 6.1, 50–81 (Bonn, 2010).Theuerkauf, M. et al. Holocene lake-level evolution of Lake Tiefer See, NE Germany, caused by climate and land cover changes. Boreas 51, 299–316 (2021).Article 

Google Scholar 
Büntgen, U. et al. 2500 years of European climate variability and human susceptibility. Science 331, 578–582 (2011).Article 

Google Scholar 
Büntgen, U. et al. Cooling and societal change during the Late Antique Little Ice Age from 536 to around 660 AD. Nat. Geosci. 9, 231–236 (2016).Article 

Google Scholar 
Kienel, U. et al. Effects of spring warming and mixing duration on diatom deposition in deep Tiefer See, NE Germany. J. Paleolimnol. 57, 37–49 (2017).Article 

Google Scholar 
Monchamp, M. E., Spaak, P. & Pomati, F. High dispersal levels and lake warming are emergent drivers of cyanobacterial community assembly in peri-Alpine lakes. Sci. Rep. 9, 7366 (2019).Article 

Google Scholar 
Erratt, K. et al. Paleolimnological evidence reveals climate-related preeminence of cyanobacteria in a temperate meromictic lake. Can. J. Fish. Aquat. Sci. 79, 558–565 (2021).Article 

Google Scholar 
Schmidt, J.-P. ders., Kein Ende in Sicht? Neue Untersuchungen auf dem Feuerstellenplatz von Naschendorf, Lkr. Nordwestmecklenburg. Arch.äologische Ber. aus Mecklenbg.-Vorpommern 19, 26–46 (2012).
Google Scholar 
Marcott, S. A., Shakun, J. D., Clark, P. U. & Mix, A. C. A reconstruction of regional and global temperature for the past 11,300 years. Science 339, 1198–1201 (2013).Article 
CAS 

Google Scholar 
Wanner, H. et al. Holocene climate variability and change; a data-based review. J. Geol. Soc. Lond. 172, 254–263 (2015).Article 

Google Scholar 
Rigosi, A., Carey, C. C., Ibelings, B. W. & Brookes, J. D. The interaction between climate warming and eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxa. Limnol. Oceanogr. 59, 99–114 (2014).Article 

Google Scholar 
Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43 (2013).Article 
CAS 

Google Scholar 
Dolman, A. M. et al. Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PLoS ONE 7, e38757 (2012).Article 
CAS 

Google Scholar 
Kurmayer, R., Christiansen, G., Fastner, J. & Börner, T. Abundance of active and inactive microcystin genotypes in populations of the toxic cyanobacterium Planktothrix spp. Environ. Microbiol. 6, 831–841 (2004).Article 
CAS 

Google Scholar 
Liu, A., Zhu, T., Lu, X. & Song, L. Hydrocarbon profiles and phylogenetic analyses of diversified cyanobacterial species. Appl. Energy 11, 383–393 (2013).Article 

Google Scholar 
Coates, R. C. et al. Characterization of cyanobacterial hydrocarbon composition and distribution of biosynthetic pathways. PLoS ONE 9, e85140 (2014).Article 

Google Scholar 
Marciniak, S. et al. Ancient human genomics: the methodology behind reconstructing evolutionary pathways. J. Hum. Evol. 79, 21–34 (2015).Article 

Google Scholar 
Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. MapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. in. Bioinformatics 29, 1682–1684 (2013).Article 

Google Scholar 
Borry, M., Hübner, A., Rohrlach, A. B. & Warinner, C. PyDamage: automated ancient damage identification and estimation for contigs in ancient DNA de novo assembly. PeerJ 9, e11845 (2021).Article 

Google Scholar 
Murchie, T. J. et al. Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set. Quat. Res. (U. S.) 99, 305–328 (2021).Article 
CAS 

Google Scholar 
Armbrecht, L., Hallegraeff, G., Bolch, C. J. S., Woodward, C. & Cooper, A. Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes. Sci. Rep. 11, 3220 (2021).Article 
CAS 

Google Scholar 
Wulf, S. et al. Holocene tephrostratigraphy of varved sediment records from Lakes Tiefer See (NE Germany) and Czechowskie (N Poland). Quat. Sci. Rev. 132, 1–14 (2016).Article 

Google Scholar 
Sugita, S. Theory of quantitative reconstruction of vegetation I: Pollen from large sites REVEALS regional vegetation composition. Holocene 17, 2 (2007).Article 

Google Scholar 
Epp, L. S., Zimmermann, H. H. & Stoof-Leichsenring, K. R. In: Ancient DNA. Methods in Molecular Biology (eds. Shapiro B., Barlow A., Heintzman P., Hofreiter M., Paijmans J., Soares A.) Vol. 1963, 31–44 (Humana Press, 2019).Janse, I., Meima, M., Kardinaal, W. E. A. & Zwart, G. High-resolution differentiation of Cyanobacteria by using rRNA-internal transcribed spacer denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 69, 6634–6643 (2003).Article 
CAS 

Google Scholar 
Nwosu, E. C. et al. Species-level spatio-temporal dynamics of cyanobacteria in a hard-water temperate lake in the Southern Baltics. Front. Microbiol. 12, https://doi.org/10.3389/fmicb.2021.761259 (2021).Savichtcheva, O. et al. Quantitative PCR enumeration of total/toxic Planktothrix rubescens and total cyanobacteria in preserved DNA isolated from lake sediments. Appl. Environ. Microbiol. 77, 8744–8753 (2011).Article 
CAS 

Google Scholar 
Coolen, M. J. L. et al. Ancient DNA derived from alkenone-biosynthesizing haptophytes and other algae in Holocene sediments from the Black Sea. Paleoceanography 21, PA1005 (2006).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina 7 amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Kieser, S., Brown, J., Zdobnov, E. M., Trajkovski, M. & McCue, L. A. ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformat. 21, 257 (2020).Article 

Google Scholar 
Yilmaz, P. et al. The SILVA and ‘all-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, 643–648 (2014).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).Article 
CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformat. 11, 119–119 (2010).Article 

Google Scholar 
Huerta-Cepas, J. et al. EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).Article 
CAS 

Google Scholar 
Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msab293 (2021).Shen, W. & Ren, H. TaxonKit: a practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics. 48, 844–850 (2021).Article 

Google Scholar 
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 29, 471–482 (2001).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R Package Version 2.5-2. Cran R (2019).Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).CAS 

Google Scholar 
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).Article 

Google Scholar  More

A visionary and interdisciplinary scientist who brought a fearless passion to everything she did, inspiring all those around her.
Alma Dal Co tragically passed away on 14 November 2022 at the age of 33, doing what she loved most — spearfishing near the Italian island of Pantelleria. Alma was a visionary scientist at the beginning of what was promising to become a stellar career. As a physicist turned biologist, Alma wanted to unravel how complexity emerges from simplicity. Despite her young age, she had already made an important impact on the field by showing how the activities of microbial communities emerge from interactions between individual cells. Alma was a warm and caring friend, and a committed and inspiring mentor. She pursued science with fearless passion, creativity, vision and dedication.Alma Dal Co in 2016 in Joshua Tree National Park, California. Photograph by Simon van Vliet.Alma had an exceptionally sharp and creative mind, and an insatiable curiosity. She kept exploring new directions, working on everything from gene-regulatory circuits to microbial communities, to developmental processes. She was the embodiment of a true interdisciplinary scientist, combining state-of-the-art experiments with advanced computational approaches. The unifying theme of her work was to understand how interactions between individuals (be it fish, microorganisms or pancreatic cells) give rise to complex behaviour at higher levels of organization. She strived to derive simple, quantitative rules to explain the complexity that we see around us. Alma believed that science is a team effort: she was generous with her time, and always happy to discuss ideas and share resources. No matter where she went, she quickly connected with people, built formal and informal networks, and fostered collaborations and friendships.Alma was born in Turin and grew up in Venice, in Italy. Her true home, however, was Pantelleria, an Italian island in the Mediterranean Sea off the coast of Sicily. Alma spent her summers in the sea from an early age, developing a deep and lasting bond with it. The sea was not only a place to recharge, but also a source of inspiration: Alma became fascinated by the intricate behaviours of octopuses and schools of fish, creating a lasting sense of wonder about the natural world. Alma’s primary education focused on the humanities, but most of all music. In 2002, she was accepted to the conservatorium in Venice to study the piano. However, her love for the natural world remained and in 2007 she started studying physics in Padua. In 2011, she finished her BSc in physics and a year later her education at the conservatorium. Both a career in music and in science were an option, but Alma chose science and moved to Turin to study the physics of complex systems. Music always remained important in her life, and she played the piano whenever she could.Alma’s transition to biology started in Turin in the laboratory of Michelle Caselle, where she used mathematical models to study gene regulatory networks. She discovered how the regulation of gene expression can reduce stochastic fluctuations and provide robustness to the expression of an organism’s phenotype (A. Dal Co et al. Nucleic Acids Res. 45, 1069–1078; 2017). In 2014, she exchanged the blackboard for the wet lab, and moved to Zurich, Switzerland, to start her PhD with Martin Ackermann at ETH and the aquatic research institute Eawag. Despite the struggles of having to learn hands-on biology without formal training, she was not deterred from pursuing a highly challenging project.Alma developed an innovative approach to gain a mechanistic understanding of how metabolic interactions between individual microbial cells determine the dynamics of spatially structured communities. She quantified the growth of single cells in a synthetic microbial community and developed computational tools to infer their interaction network. She showed that cells in these communities live in a small world: they only interact with few neighbours (A. Dal Co et al. Nat. Ecol. Evol. 4, 366–375; 2020). This short interaction range limits the growth of mutually dependent microorganisms, thereby counteracting the evolution of metabolic specialization. Moreover, Alma developed a mathematical framework to quantitatively predict the dynamics of microbial communities from the molecular properties of the underlying intercellular interactions (S. van Vliet et al. PLoS Comput. Biol. 18, e1009877; 2022). Together, these works have made an important contribution to our understanding of how microbial communities function, and they have inspired numerous follow-up projects, both by Alma herself (for example, A. Dal Co et al. Phil. Trans. R. Soc. B 374, 20190080; 2019) and by others in the field (for example, J. van Gestel et al. Nat. Commun. 12, 2324; 2021).Alma finished her PhD in 2019, winning the ETH medal for an outstanding thesis. She then moved to Harvard to study developmental processes, together with Michael Brenner. She quickly developed a large network of collaborators and designed an innovative project to study pancreatic islet formation. However, COVID-19-related laboratory restrictions brought an early end to these plans, and Alma instead developed a novel computational framework that can be applied to both animal tissues and microbial communities to study how local cell–cell interactions can create spatial structure at the scale of multicellular systems.In September 2021, Alma started an assistant professorship at the University of Lausanne. At the age of 32, she was one of youngest professors ever appointed there. Thanks to her leadership, she quickly assembled a highly interdisciplinary, collaborative and cohesive team of talented young scientists. The group’s research was as varied as Alma’s interests. A major theme was to gain a quantitative understanding of how cell–cell interactions affect the function and structure of microbial communities and other multicellular systems. Her group combines state-of-the art experimental tools such as optogenetics, microfluidics and single-cell imaging, with computational approaches and mathematical modelling to study the dynamics of a wide range of model systems.During her very short career as an assistant professor, Alma was a core member of the Swiss National Research Program on microbiome research (https://nccr-microbiomes.ch); was awarded two major grants; established a large network of collaborators; and was invited to present her work at numerous international meetings. Most importantly, Alma fostered a strong sense of community, both in her group and beyond — creating an open, inclusive and interactive space to discuss science and life.Interacting with Alma was never dull: her passion and energy were infectious and her curiosity and openness a source of inspiration. She always kept you on your toes with her constant stream of pointed questions. But most of all, her easy laugh and positive energy made working with her an extraordinarily joyous experience.With Alma the world has lost a visionary scientist. We are deeply saddened that we will never see what other discoveries she would have made. However, it offers some conciliation to see how profoundly Alma has impacted the people around her, leaving a lasting impression even on those she only briefly met. Her vision, spirit and leadership have profoundly changed many around her and will continue to be a source of inspiration for many years to come. More

Dulvy, N. K., Sadovy, Y. & Reynolds, J. D. Extinction vulnerability in marine populations. Fish Fish. 4, 25–64 (2003).Article 

Google Scholar 
Fowler S. L. et al. Sharks, Rays and Chimaeras: The Status of the Chondrichthyan Fishes. IUCN/SSC Shark Specialist Group, Gland, Switzerland and Cambridge, UK (2005).Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, e00590 (2014).Article 

Google Scholar 
Lack M. & Sant G. Illegal, Unreported and Unregulated Shark Catch: A review of current knowledge and action. Department of the Environment, Water, Heritage and the Arts and TRAFFIC, Canberra http://www.traffic.org/fish/ (2008).Rose D.A. An Overview of World Trade in Sharks and Other Cartilaginous Fishes. TRAFFIC International, Cambridge, UK (1996).Lam, V. Y. & Sadovy, M. Y. The sharks of South East Asia–unknown, unmonitored and unmanaged. Fish Fish 12, 51–74 (2011).Article 

Google Scholar 
Kessel S.T. Investigation into the behaviour and population dynamics of the lemon shark (Negaprion brevirostris). Cardiff University (United Kingdom) (2010).Morrissey, J. F. & Gruber, S. H. Habitat selection by juvenile lemon sharks Negaprion brevirostris. Environ. Biol. Fishes 38, 311–319 (1993).Article 

Google Scholar 
Filmalter, J. D., Dagorn, L. & Cowley, P. D. Spatial behaviour and site fidelity of the sicklefin lemon shark Negaprion acutidens in a remote Indian Ocean atoll. Mari. Biol. 160, 2425–2436 (2013).Article 

Google Scholar 
DiBattista, J. D. et al. A genetic assessment of polyandry and breeding site fidelity in lemon sharks. Mol. Ecol. 17, 3337–3351 (2008).Article 

Google Scholar 
Wetherbee, B. M., Gruber, S. H. & Rosa, R. S. Movement patterns of juvenile lemon sharks Negaprion brevirostris within Atol das Rocas, Brazil: A nursery characterized by tidal extremes. Mar. Ecol. Prog. Seri. 343, 283–293 (2007).Article 
ADS 

Google Scholar 
Feldheim, K. A. et al. Two decades of genetic profiling yields first evidence of natal philopatry and long-term fidelity to parturition sites in sharks. Mol. Ecol. 23, 110–117 (2014).Article 

Google Scholar 
Stevens J. D. et al. Diversity, abundance and habitat utilisation of sharks and rays: Final report to West Australian Marine Science Institute. CSIRO, editor. Hobart (2009).Schultz, J. K. et al. Global phylogeography and seascape genetics of the lemon sharks (genus Negaprion). Mol. Ecol. 17, 5336–5348 (2008).Article 
CAS 

Google Scholar 
Mourier, J., Buray, N., Schultz, J. K., Clua, E. & Planes, S. Genetic network and breeding patterns of a sicklefin lemon shark (Negaprion acutidens) population in the Society Islands, French Polynesia. PLoS ONE 8, e73899 (2013).Article 
ADS 
CAS 

Google Scholar 
Speed, C. W. et al. Reef shark movements relative to a coastal marine protected area. Reg. Stud. Mar. Sci. 3, 58–66 (2016).
Google Scholar 
Huang, Z. Marine Species and Their Distribution in China’s Seas (Krieger Publishing Company, 2001).
Google Scholar 
Chang, C. W., Huang, C. S. & Wang, S. I. Species composition and sizes of fish in the lagoon of dongsha island (Pratas Island), Dongsha Atoll of the South China sea. Platax 2012, 25–32 (2012).
Google Scholar 
Pillans, R. D. et al. Long-term acoustic monitoring reveals site fidelity, reproductive migrations, and sex specific differences in habitat use and migratory timing in a large coastal shark (Negaprion acutidens). Front. Mar. Sci. 8, 616633 (2021).Article 

Google Scholar 
Daly-Engel, T. S. et al. Global phylogeography with mixed-marker analysis reveals male-mediated dispersal in the endangered scalloped hammerhead shark (Sphyrna lewini). PLoS ONE 7, e29986 (2012).Article 
ADS 
CAS 

Google Scholar 
Félix-López, D. G. et al. Possible female philopatry of the smooth hammerhead shark Sphyrna zygaena revealed by genetic structure patterns. J. Fish Biol. 94, 671–679 (2019).Article 

Google Scholar 
Nosal, A. P., Caillat, A., Kisfaludy, E. K., Royer, M. A. & Wegner, N. C. Aggregation behavior and seasonal philopatry in male and female leopard sharks Triakis semifasciata along the open coast of southern California, USA. Mar. Ecol. Prog. Ser. 499, 157–175 (2014).Article 
ADS 

Google Scholar 
Jirik, K. E. & Lowe, C. G. An elasmobranch maternity ward: Female round stingrays Urobatis halleri use warm, restored estuarine habitat during gestation. J. Fish. Biol. 80(5), 1227–1245 (2012).Article 
CAS 

Google Scholar 
Jacoby, D. M., Croft, D. P. & Sims, D. W. Social behaviour in sharks and rays: Analysis, patterns and implications for conservation. Fish Fish 13(4), 399–417 (2012).Article 

Google Scholar 
Su, S. H., Liu, S. Y. V., Liu, K. M. & Tsai, W. P. Development and characterization of novel microsatellite loci for an endangered hammerhead shark Sphyrna lewini by using shotgun sequencing. Taiwania 65(2), 261–263 (2020).
Google Scholar 
Dieringer, D. & Schlötterer, C. Microsatellite analyser (MSA): A platform independent analysis tool for large microsatellite data sets. Mol. Ecol. Notes 3, 167–169 (2003).Article 
CAS 

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

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

Google Scholar 
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes 7, 574–578 (2007).Article 
CAS 

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

Google Scholar 
Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14), 1801–1806 (2007).Article 
CAS 

Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in excel population genetic software for teaching and research–an update. Bioinformatics 28, 2537–2539 (2012).Article 
CAS 

Google Scholar 
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. POPPR: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).Article 

Google Scholar 
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: A computer program for maximum likelihood estimation of relatedness and relationship. Mol. Ecol. Resour. 6, 576–579 (2006).Article 
CAS 

Google Scholar 
Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).Article 
CAS 

Google Scholar 
Oh, B. Z. et al. Contrasting patterns of residency and space use of coastal sharks within a communal shark nursery. Mar. Freshw. Res. 68, 1501–1517 (2017).Article 

Google Scholar 
McClelland J. Genetic Assessment of Breeding Patterns and Population Size of the Sicklefin Lemon Shark Negaprion acutidens in a Tropical Marine Protected Area: Implications for Conservation and Management (Doctoral dissertation, University of York) (2020).Compagno L. J .V. FAO species catalogue Sharks of the world: An annotated and illustrated catalogue of shark species known to date. FAO Fish. Synop. No. 125 Rome 4, 1–655 (1984).Stevens, J. D. Life-history and ecology of sharks at aldabra Atoll. Indian Ocean. Proc R Soc. B 222, 79–106 (1984).ADS 

Google Scholar 
Kool, J. T., Moilanen, A. & Treml, E. A. Population connectivity: Recent advances and new perspectives. Landsc. Ecol. 28, 165–185 (2013).Article 

Google Scholar 
Ruzzante, D. E. et al. Effective number of breeders, effective population size and their relationship with census size in an iteroparous species Salvelinus fontinalis. Proc. R Soc. B 283, 20152601 (2016).Article 

Google Scholar 
Van Wyngaarden, M. et al. Identifying patterns of dispersal, connectivity and selection in the sea scallop, Placopecten magellanicus, using RADseq-derived SNPs. Evol. Appl. 10, 102–117 (2017).Article 

Google Scholar 
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red list criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).Article 

Google Scholar 
Pazmiño, D. A., Maes, G. E., Simpfendorfer, C. A., Salinas-de-León, P. & van Herwerden, L. Genome-wide SNPs reveal low effective population size within confined management units of the highly vagile Galapagos shark (Carcharhinus galapagensis). Conserv. Genet. 18, 1151–1163 (2017).Article 

Google Scholar 
Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: A largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).Article 

Google Scholar 
Dudgeon, C. L. & Ovenden, J. R. The relationship between abundance and genetic effective population size in elasmobranchs: An example from the globally threatened zebra shark Stegostoma fasciatum within its protected range. Conserv. Genet. 16, 1443–1454 (2015).Article 

Google Scholar 
Feldheim, K. A., Gruber, S. H. & Ashley, M. V. Population genetic structure of the lemon shark (Negaprion brevirostris) in the western Atlantic: DNA microsatellite variation. Mol. Ecol. 10, 295–303 (2001).Article 
CAS 

Google Scholar 
Feldheim, K. A., Gruber, S. H. & Ashley, M. V. The breeding biology of lemon sharks at a tropical nursery lagoon. Proc. R. Soc. Lond. B 269, 1471–2954 (2002).Article 

Google Scholar 
Portnoy, D., McDowell, J. R., Thompson, K., Musick, J. A. & Graves, J. E. Isolation and characterization of five dinucleotide microsatellite loci in the sandbar shark, Carcharhinus plumbeus. Mol. Ecol. Notes 6, 431–433 (2006).Article 
CAS 

Google Scholar  More