Philippa Kaur

Battin, T. J. et al. The boundless carbon cycle. Nat. Geosci. 2, 598–600 (2009).Article 
CAS 

Google Scholar 
Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).Article 
CAS 

Google Scholar 
Hotchkiss, E. R. et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nat. Geosci. 8, 696–699 (2015). Important study conceptualizing (on the basis of a data synthesis) how the sources and magnitude of CO2 evasion flux change along a stream–river continuum.Ciais, P. et al. 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.) Ch. 6 (Cambridge Univ. Press, 2013).Friedlingstein, P. et al. Global carbon budget 2021. Earth Syst. Sci. Data 14, 1917–2005 (2022).Article 

Google Scholar 
Cole, J. J. et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 172–185 (2007). A pioneering study showing the role of inland waters for large-scale carbon fluxes and highlighting them as ‘reactors’ rather than ‘passive pipes’.Article 

Google Scholar 
Drake, T. W., Raymond, P. A. & Spencer, R. G. M. Terrestrial carbon inputs to inland waters: a current synthesis of estimates and uncertainty. Limnol. Oceanogr. Lett. 3, 132–142 (2018).Article 
CAS 

Google Scholar 
Odum, H. T. Primary production in flowing waters. Limnol. Oceanogr. 1, 102–117 (1956).Article 

Google Scholar 
Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, 99–118 (2018). A synthesis of the predominant drivers and constraints on metabolic regimes of stream and river ecosystems.Article 

Google Scholar 
Barnes, A. D. et al. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 33, 186–197 (2018).Article 

Google Scholar 
Costanza, R. & Mageau, M. What is a healthy ecosystem? Aquat. Ecol. 33, 105–115 (1999).Article 

Google Scholar 
Blöschl, G. et al. Changing climate both increases and decreases European river floods. Nature 573, 108–111 (2019).Article 

Google Scholar 
Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–1162 (2021).Article 
CAS 

Google Scholar 
Yang, X., Pavelsky, T. M. & Allen, G. H. The past and future of global river ice. Nature 577, 69–73 (2020).Article 
CAS 

Google Scholar 
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).Article 
CAS 

Google Scholar 
Belletti, B. et al. More than one million barriers fragment Europe’s rivers. Nature 588, 436–441 (2020).Article 
CAS 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).Article 
CAS 

Google Scholar 
Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).Article 
CAS 

Google Scholar 
Jaramillo, F. & Destouni, G. Local flow regulation and irrigation raise global human water consumption and footprint. Science 350, 1248–1251 (2015).Article 
CAS 

Google Scholar 
Quinton, J. N., Govers, G., Oost, K. V. & Bardgett, R. D. The impact of agricultural soil erosion on biogeochemical cycling. Nat. Geosci. 3, 311–314 (2010).Article 
CAS 

Google Scholar 
Mekonnen, M. M. & Hoekstra, A. Y. Global anthropogenic phosphorus loads to freshwater and associated grey water footprints and water pollution levels: a high‐resolution global study. Water Resour. Res. 54, 345–358 (2018).Article 
CAS 

Google Scholar 
Regnier, P. et al. Anthropogenic perturbation of the carbon fluxes from land to ocean. Nat. Geosci. 6, 597–607 (2013). The first study showing the extent to which human activities have altered the magnitude of contemporary lateral carbon fluxes from land to ocean.Article 
CAS 

Google Scholar 
Rüegg, J. et al. Thinking like a consumer: linking aquatic basal metabolism and consumer dynamics. Limnol. Oceanogr. Lett. 6, 1–17 (2021).Article 

Google Scholar 
Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. Change 9, 73–79 (2019).Article 

Google Scholar 
Behrenfeld, M. J. et al. Climate-driven trends in contemporary ocean productivity. Nature 444, 752–755 (2006).Article 
CAS 

Google Scholar 
Phillips, J. S. Time‐varying responses of lake metabolism to light and temperature. Limnol. Oceanogr. 65, 652–666 (2020).Article 
CAS 

Google Scholar 
Uehlinger, U. Annual cycle and inter‐annual variability of gross primary production and ecosystem respiration in a floodprone river during a 15‐year period. Freshw. Biol. 51, 938–950 (2006).Article 
CAS 

Google Scholar 
Uehlinger, U. & Naegeli, M. W. Ecosystem metabolism, disturbance, and stability in a prealpine gravel bed river. J. North Am. Benthol. Soc. 17, 165–178 (1998).Article 

Google Scholar 
Mulholland, P. J. et al. Inter-biome comparison of factors controlling stream metabolism. Freshw. Biol. 46, 1503–1517 (2001).Article 
CAS 

Google Scholar 
Roberts, B. J., Mulholland, P. J. & Hill, W. R. Multiple scales of temporal variability in ecosystem metabolism rates: results from 2 years of continuous monitoring in a forested headwater stream. Ecosystems 10, 588–606 (2007).Article 
CAS 

Google Scholar 
Appling, A. P., Hall, R. O., Yackulic, C. B. & Arroita, M. Overcoming equifinality: leveraging long time series for stream metabolism estimation. J. Geophys. Res. Biogeosci. 123, 624–645 (2018).Article 
CAS 

Google Scholar 
Appling, A. P. et al. The metabolic regimes of 356 rivers in the United States. Sci. Data 5, 180292 (2018).Article 
CAS 

Google Scholar 
Canadell, M. B. et al. Regimes of primary production and their drivers in Alpine streams. Freshw. Biol. 66, 1449–1463 (2021).Article 

Google Scholar 
Myrstener, M., Gómez‐Gener, L., Rocher‐Ros, G., Giesler, R. & Sponseller, R. A. Nutrients influence seasonal metabolic patterns and total productivity of Arctic streams. Limnol. Oceanogr. 66, S182–S196 (2021).Article 
CAS 

Google Scholar 
Savoy, P. et al. Metabolic rhythms in flowing waters: an approach for classifying river productivity regimes. Limnol. Oceanogr. 64, 1835–1851 (2019).Article 

Google Scholar 
Kirk, L., Hensley, R. T., Savoy, P., Heffernan, J. B. & Cohen, M. J. Estimating benthic light regimes improves predictions of primary production and constrains light-use efficiency in streams and rivers. Ecosystems 24, 825–839 (2021).Article 

Google Scholar 
Bernhardt, E. S. et al. Light and flow regimes regulate the metabolism of rivers. Proc. Natl Acad. Sci. USA 119, e2121976119 (2022).Article 
CAS 

Google Scholar 
Savoy, P. & Harvey, J. W. Predicting light regime controls on primary productivity across CONUS river networks. Geophys. Res. Lett. 48, e2020GL092149 (2021).Article 

Google Scholar 
Julian, J. P., Stanley, E. H. & Doyle, M. W. Basin-scale consequences of agricultural land use on benthic light availability and primary production along a sixth-order temperate river. Ecosystems 11, 1091–1105 (2008).Article 

Google Scholar 
Hall, R. O. et al. Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon. Limnol. Oceanogr. 60, 512–526 (2015).Article 

Google Scholar 
Hosen, J. D. et al. Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams. Limnol. Oceanogr. 64, 1458–1472 (2019).Article 

Google Scholar 
Allen, A. P., Gillooly, J. F. & Brown, J. H. Linking the global carbon cycle to individual metabolism. Funct. Ecol. 19, 202–213 (2005).Article 

Google Scholar 
Demars, B. O. L. et al. Temperature and the metabolic balance of streams. Freshw. Biol. 56, 1106–1121 (2011).Article 

Google Scholar 
Song, C. et al. Continental-scale decrease in net primary productivity in streams due to climate warming. Nat. Geosci. 11, 415–420 (2018).Article 
CAS 

Google Scholar 
Hood, J. M. et al. Increased resource use efficiency amplifies positive response of aquatic primary production to experimental warming. Glob. Change Biol. 24, 1069–1084 (2018).Article 

Google Scholar 
Schindler, D. E., Carpenter, S. R., Cole, J. J., Kitchell, J. F. & Pace, M. L. Influence of food web structure on carbon exchange between lakes and the atmosphere. Science 277, 248–251 (1997).Article 
CAS 

Google Scholar 
Iannucci, F. M., Beneš, J., Medvedeff, A. & Bowden, W. B. Biogeochemical responses over 37 years to manipulation of phosphorus concentrations in an Arctic river: The Upper Kuparuk River Experiment. Hydrol. Process. 35, e14075 (2021).Article 
CAS 

Google Scholar 
Rosemond, A. D. et al. Experimental nutrient additions accelerate terrestrial carbon loss from stream ecosystems. Science 347, 1142–1145 (2015). A key study explaining how nutrient excess can accelerate terrestrial carbon loss from stream ecosystems.Article 
CAS 

Google Scholar 
Arroita, M., Elosegi, A. & Hall, R. O. Jr Twenty years of daily metabolism show riverine recovery following sewage abatement. Limnol. Oceanogr. 64, 77–92 (2019).Article 

Google Scholar 
Battin, T. J. et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nat. Geosci. 1, 95–100 (2008). An important article conceptualizing how physical and biological processes combine to shape metabolic dynamics and carbon fluxes in fluvial networks.Article 
CAS 

Google Scholar 
Hoellein, T. J., Bruesewitz, D. A. & Richardson, D. C. Revisiting Odum (1956): a synthesis of aquatic ecosystem metabolism. Limnol. Oceanogr. 58, 2089–2100 (2013).Article 
CAS 

Google Scholar 
Marzolf, N. S. & Ardón, M. Ecosystem metabolism in tropical streams and rivers: a review and synthesis. Limnol. Oceanogr. 66, 1627–1638 (2021).Article 

Google Scholar 
Gounand, I., Little, C. J., Harvey, E. & Altermatt, F. Cross-ecosystem carbon flows connecting ecosystems worldwide. Nat. Commun. 9, 4825 (2018).Article 

Google Scholar 
Ciais, P. et al. Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration. Natl Sci. Rev. 8, nwaa145 (2020).Article 

Google Scholar 
Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013). Important review on the sources, exchange and fates of carbon in the coastal ocean and how human activities have altered the coastal carbon cycle.Article 
CAS 

Google Scholar 
Reichert, P., Uehlinger, U. & Acuña, V. Estimating stream metabolism from oxygen concentrations: effect of spatial heterogeneity. J. Geophys. Res. Biogeosci. 114, G03016 (2009).Article 

Google Scholar 
Koenig, L. E. et al. Emergent productivity regimes of river networks. Limnol. Oceanogr. Lett. 4, 173–181 (2019).Article 

Google Scholar 
Rodríguez-Castillo, T., Estévez, E., González-Ferreras, A. M. & Barquín, J. Estimating ecosystem metabolism to entire river networks. Ecosystems 22, 892–911 (2019).Article 

Google Scholar 
Segatto, P. L., Battin, T. J. & Bertuzzo, E. The metabolic regimes at the scale of an entire stream network unveiled through sensor data and machine learning. Ecosystems 24, 1792–1809 (2021).Article 
CAS 

Google Scholar 
Loreau, M., Mouquet, N. & Holt, R. D. Meta‐ecosystems: a theoretical framework for a spatial ecosystem ecology. Ecol. Lett. 6, 673–679 (2003).Article 

Google Scholar 
Mastrandrea, M. D. et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties (Intergovernmental Panel on Climate Change (IPCC), 2010).Tank, S. E., Fellman, J. B., Hood, E. & Kritzberg, E. S. Beyond respiration: controls on lateral carbon fluxes across the terrestrial‐aquatic interface. Limnol. Oceanogr. Lett. 3, 76–88 (2018). Important synthesis on the mechanisms and controls of organic and inorganic carbon flows across terrestrial–aquatic interfaces.Article 

Google Scholar 
Aitkenhead, J. A. & McDowell, W. H. Soil C:N ratio as a predictor of annual riverine DOC flux at local and global scales. Global Biogeochem. Cycles 14, 127–138 (2000).Article 
CAS 

Google Scholar 
Regnier, P., Resplandy, L., Najjar, R. G. & Ciais, P. The land-to-ocean loops of the global carbon cycle. Nature 603, 401–410 (2022).Article 
CAS 

Google Scholar 
van Hoek, W. J. et al. Exploring spatially explicit changes in carbon budgets of global river basins during the 20th century. Environ. Sci. Technol. 55, 16757–16769 (2021). A global quantitative assessment of river carbon fluxes in the twentieth century, highlighting the combined influence of environmental and anthropogenic controls on the long-term patterns of global carbon export.Article 

Google Scholar 
Abril, G. & Borges, A. V. Ideas and perspectives: carbon leaks from flooded land: do we need to replumb the inland water active pipe? Biogeosciences 16, 769–784 (2019). Important review emphasizing the role of flooding for inland water carbon cycling at the global scale.Article 
CAS 

Google Scholar 
Lauerwald, R., Regnier, P., Guenet, B., Friedlingstein, P. & Ciais, P. How simulations of the land carbon sink are biased by ignoring fluvial carbon transfers: a case study for the Amazon Basin. One Earth 3, 226–236 (2020).Article 

Google Scholar 
Raymond, P. A., Saiers, J. E. & Sobczak, W. V. Hydrological and biogeochemical controls on watershed dissolved organic matter transport: pulse‐shunt concept. Ecology 97, 5–16 (2016).Article 

Google Scholar 
Catalán, N., Marcé, R., Kothawala, D. N. & Tranvik, L. J. Organic carbon decomposition rates controlled by water retention time across inland waters. Nat. Geosci. 9, 501–504 (2016).Article 

Google Scholar 
Maavara, T., Lauerwald, R., Regnier, P. & Cappellen, P. V. Global perturbation of organic carbon cycling by river damming. Nat. Commun. 8, 15347 (2017).Article 
CAS 

Google Scholar 
Mendonça, R. et al. Organic carbon burial in global lakes and reservoirs. Nat. Commun. 8, 1694–1697 (2017).Article 

Google Scholar 
Downing, J. A. et al. Sediment organic carbon burial in agriculturally eutrophic impoundments over the last century. Global Biogeochem. Cycles 22, GB1018 (2008).Article 

Google Scholar 
Deemer, B. R. et al. Greenhouse gas emissions from reservoir water surfaces: a new global synthesis. Bioscience 66, 949–964 (2016).Article 

Google Scholar 
Abril, G. et al. Amazon River carbon dioxide outgassing fuelled by wetlands. Nature 505, 395–398 (2014).Article 
CAS 

Google Scholar 
Dodds, W. K. et al. Abiotic controls and temporal variability of river metabolism: multiyear analyses of Mississippi and Chattahoochee River data. Freshw. Sci. 32, 1073–1087 (2013).Article 

Google Scholar 
Ros, G. R., Sponseller, R. A., Bergström, A. K., Myrstener, M. & Giesler, R. Stream metabolism controls diel patterns and evasion of CO2 in Arctic streams. Glob. Change Biol. 26, 1400–1413 (2020).Article 

Google Scholar 
Rasilo, T., Hutchins, R. H. S., Ruiz-González, C. & Del Giorgio, P. A. Transport and transformation of soil-derived CO2, CH4 and DOC sustain CO2 supersaturation in small boreal streams. Sci. Total Environ. 579, 902–912 (2017).Article 
CAS 

Google Scholar 
Aho, K. S., Hosen, J. D., Logozzo, L. A., McGillis, W. R. & Raymond, P. A. Highest rates of gross primary productivity maintained despite CO2 depletion in a temperate river network. Limnol. Oceanogr. Lett. 6, 200–206 (2021).Article 
CAS 

Google Scholar 
Wehrli, B. Conduits of the carbon cycle. Nature 503, 346–347 (2013).Article 
CAS 

Google Scholar 
Sarmiento, J. L. & Sundquist, E. T. Revised budget for the oceanic uptake of anthropogenic carbon dioxide. Nature 356, 589–593 (1992).Article 
CAS 

Google Scholar 
Lacroix, F., Ilyina, T., Laruelle, G. G. & Regnier, P. Reconstructing the preindustrial coastal carbon cycle through a global ocean circulation model: was the global continental shelf already both autotrophic and a CO2 sink? Glob. Biogeochem. Cycles 35, e2020GB006603 (2021).Article 
CAS 

Google Scholar 
Jacobson, A. R., Fletcher, S. E. M., Gruber, N., Sarmiento, J. L. & Gloor, M. A joint atmosphere‐ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global‐scale fluxes. Global Biogeochem. Cycles 21, GB1019 (2007).
Google Scholar 
Resplandy, L. et al. Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport. Nat. Geosci. 11, 504–509 (2018).Article 
CAS 

Google Scholar 
Lee, L.-C. et al. Unusual roles of discharge, slope and SOC in DOC transport in small mountainous rivers, Taiwan. Sci. Rep. 9, 1574 (2019).Article 

Google Scholar 
Reddy, S. K. K. et al. Export of particulate organic carbon by the mountainous tropical rivers of Western Ghats, India: variations and controls. Sci. Total Environ. 751, 142115 (2021).Article 
CAS 

Google Scholar 
Zhang, X., Tarpley, D. & Sullivan, J. T. Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett. 34, L19405 (2007).Article 

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

Google Scholar 
Heathcote, A. J., Anderson, N. J., Prairie, Y. T., Engstrom, D. R. & del Giorgio, P. A. Large increases in carbon burial in northern lakes during the Anthropocene. Nat. Commun. 6, 10016 (2015).Article 
CAS 

Google Scholar 
Guillemette, F., Berggren, M., Giorgio, P. Adel. & Lapierre, J.-F. Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems. Nat. Commun. 4, 2972 (2013).Article 

Google Scholar 
Hastie, A., Lauerwald, R., Ciais, P., Papa, F. & Regnier, P. Historical and future contributions of inland waters to the Congo Basin carbon balance. Earth Syst. Dyn. 12, 37–62 (2020).Article 

Google Scholar 
Nakhavali, M. et al. Leaching of dissolved organic carbon from mineral soils plays a significant role in the terrestrial carbon balance. Glob. Change Biol. 27, 1083–1096 (2021).Article 
CAS 

Google Scholar 
Tian, H. et al. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: current status and future directions. Global Biogeochem. Cycles 29, 775–792 (2015).Article 
CAS 

Google Scholar 
Öquist, M. G. et al. The full annual carbon balance of boreal forests is highly sensitive to precipitation. Environ. Sci. Technol. Lett. 1, 315–319 (2014).Article 

Google Scholar 
Jones, J. B.Jr, Stanley, E. H. & Mulholland, P. J. Long‐term decline in carbon dioxide supersaturation in rivers across the contiguous United States. Geophys. Res. Lett. 30, 1495 (2003).Article 

Google Scholar 
Raymond, P. A. & Oh, N.-H. Long term changes of chemical weathering products in rivers heavily impacted from acid mine drainage: insights on the impact of coal mining on regional and global carbon and sulfur budgets. Earth Planet. Sci. Lett. 284, 50–56 (2009).Article 
CAS 

Google Scholar 
Ran, L. et al. Substantial decrease in CO2 emissions from Chinese inland waters due to global change. Nat. Commun. 12, 1730 (2021).Article 
CAS 

Google Scholar 
Zarnetske, J. P., Bouda, M., Geophysical, B. A., Saiers, J. & Raymond, P. Generality of hydrologic transport limitation of watershed organic carbon flux across ecoregions of the United States. Geophys. Res. Lett. 45, 11,702–11,711 (2018).Article 
CAS 

Google Scholar 
Liu, S. et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. Proc. Natl Acad. Sci. USA 119, e2106322119 (2022).Article 
CAS 

Google Scholar 
Nydahl, A. C., Wallin, M. B. & Weyhenmeyer, G. A. No long‐term trends in pCO2 despite increasing organic carbon concentrations in boreal lakes, streams, and rivers. Global Biogeochem. Cycles 31, 985–995 (2017).Article 
CAS 

Google Scholar 
Raymond, P. A. & Hamilton, S. K. Anthropogenic influences on riverine fluxes of dissolved inorganic carbon to the oceans. Limnol. Oceanogr. Lett. 3, 143–155 (2018).Article 
CAS 

Google Scholar 
Ulseth, A. J., Bertuzzo, E., Singer, G. A., Schelker, J. & Battin, T. J. Climate-induced changes in spring snowmelt impact ecosystem metabolism and carbon fluxes in an Alpine stream network. Ecosystems 21, 373–390 (2018).Article 
CAS 

Google Scholar 
Berghuijs, W. R., Woods, R. A. & Hrachowitz, M. A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat. Clim. Change 4, 583–586 (2014).Article 

Google Scholar 
Drake, T. W. et al. Mobilization of aged and biolabile soil carbon by tropical deforestation. Nat. Geosci. 12, 541–546 (2019).Article 
CAS 

Google Scholar 
Wit, F. et al. The impact of disturbed peatlands on river outgassing in Southeast Asia. Nat. Commun. 6, 10155 (2015).Article 
CAS 

Google Scholar 
Moore, S., Gauci, V., Evans, C. D. & Page, S. E. Fluvial organic carbon losses from a Bornean blackwater river. Biogeosciences 8, 901–909 (2011).Article 
CAS 

Google Scholar 
Masese, F. O., Salcedo-Borda, J. S., Gettel, G. M., Irvine, K. & McClain, M. E. Influence of catchment land use and seasonality on dissolved organic matter composition and ecosystem metabolism in headwater streams of a Kenyan river. Biogeochemistry 132, 1–22 (2017).Article 
CAS 

Google Scholar 
Bernot, M. J. et al. Inter‐regional comparison of land‐use effects on stream metabolism. Freshw. Biol. 55, 1874–1890 (2010). Among the first studies showing how land use alters ecosystem metabolism across geographic regions.Article 

Google Scholar 
Griffiths, N. A. et al. Agricultural land use alters the seasonality and magnitude of stream metabolism. Limnol. Oceanogr. 58, 1513–1529 (2013).Article 
CAS 

Google Scholar 
Sweeney, B. W. et al. Riparian deforestation, stream narrowing, and loss of stream ecosystem services. Proc. Natl Acad. Sci. 101, 14132–14137 (2004).Article 
CAS 

Google Scholar 
Roley, S. S., Tank, J. L., Griffiths, N. A., Hall, R. O. Jr & Davis, R. T. The influence of floodplain restoration on whole-stream metabolism in an agricultural stream: insights from a 5-year continuous data set. Freshw. Sci. 33, 1043–1059 (2014).Article 

Google Scholar 
Crawford, J. T., Stanley, E. H., Dornblaser, M. M. & Striegl, R. G. CO2 time series patterns in contrasting headwater streams of North America. Aquat. Sci. 79, 473–486 (2016).Article 

Google Scholar 
Blackburn, S. R. & Stanley, E. H. Floods increase carbon dioxide and methane fluxes in agricultural streams. Freshw. Biol. 66, 62–77 (2021).Article 
CAS 

Google Scholar 
Robertson, G. P., Paul, E. A. & Harwood, R. R. Greenhouse gases in intensive agriculture: contributions of individual gases to the radiative forcing of the atmosphere. Science 289, 1922–1925 (2000).Article 
CAS 

Google Scholar 
Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470, 378–381 (2011).Article 
CAS 

Google Scholar 
Yin, J. et al. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun. 9, 4389 (2018).Article 
CAS 

Google Scholar 
Myhre, G. et al. Sensible heat has significantly affected the global hydrological cycle over the historical period. Nat. Commun. 9, 1922 (2018).Article 
CAS 

Google Scholar 
Messager, M. L. et al. Global prevalence of non-perennial rivers and streams. Nature 594, 391–397 (2021).Article 
CAS 

Google Scholar 
Ward, A. S., Wondzell, S. M., Schmadel, N. M. & Herzog, S. P. Climate change causes river network contraction and disconnection in the H.J. Andrews Experimental Forest, Oregon, USA. Front. Water 2, 7 (2020).Article 

Google Scholar 
Sabater, S., Timoner, X., Borrego, C. & Acuña, V. Stream biofilm responses to flow intermittency: from cells to ecosystems. Front. Environ. Sci. 4, 14 (2016).Article 

Google Scholar 
Gómez-Gener, L., Lupon, A., Laudon, H. & Sponseller, R. A. Drought alters the biogeochemistry of boreal stream networks. Nat. Commun. 11, 1795 (2020).Article 

Google Scholar 
Marcé, R. et al. Emissions from dry inland waters are a blind spot in the global carbon cycle. Earth Sci. Rev. 188, 240–248 (2019).Article 

Google Scholar 
Blaszczak, J. R., Delesantro, J. M., Urban, D. L., Doyle, M. W. & Bernhardt, E. S. Scoured or suffocated: urban stream ecosystems oscillate between hydrologic and dissolved oxygen extremes. Limnol. Oceanogr. 64, 877–894 (2019).Article 
CAS 

Google Scholar 
Reisinger, A. J. et al. Recovery and resilience of urban stream metabolism following Superstorm Sandy and other floods. Ecosphere 8, e01776 (2017).Article 

Google Scholar 
O’Donnell, B. & Hotchkiss, E. R. Coupling concentration‐ and process‐discharge relationships integrates water chemistry and metabolism in streams. Water Resour. Res. 55, 10179–10190 (2019).Article 

Google Scholar 
Thellman, A. et al. The ecology of river ice. J. Geophys. Res. Biogeosci. 126, e2021JG006275 (2021).Article 

Google Scholar 
Maavara, T. et al. River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 1, 103–116 (2020).Article 

Google Scholar 
Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. 14, 225–230 (2021).Article 
CAS 

Google Scholar 
Barros, N. et al. Carbon emission from hydroelectric reservoirs linked to reservoir age and latitude. Nat. Geosci. 4, 593–596 (2011).Article 
CAS 

Google Scholar 
Keller, P. S., Marcé, R., Obrador, B. & Koschorreck, M. Global carbon budget of reservoirs is overturned by the quantification of drawdown areas. Nat. Geosci. 14, 402–408 (2021).Article 
CAS 

Google Scholar 
Calamita, E. et al. Unaccounted CO2 leaks downstream of a large tropical hydroelectric reservoir. Proc. Natl Acad. Sci. USA 118, e2026004118 (2021).Article 
CAS 

Google Scholar 
Park, J.-H. et al. Reviews and syntheses: anthropogenic perturbations to carbon fluxes in Asian river systems – concepts, emerging trends, and research challenges. Biogeosciences 15, 3049–3069 (2018).Article 
CAS 

Google Scholar 
Rosamond, M. S., Thuss, S. J. & Schiff, S. L. Dependence of riverine nitrous oxide emissions on dissolved oxygen levels. Nat. Geosci. 5, 715–718 (2012).Article 
CAS 

Google Scholar 
Stanley, E. H. et al. The ecology of methane in streams and rivers: patterns, controls, and global significance. Ecol. Monogr. 86, 146–171 (2016). Key paper highlighting the role of streams and rivers for methane production and emissions and developing a conceptual framework on the environmental drivers of methane dynamics in fluvial ecosystems.Article 

Google Scholar 
Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).Article 

Google Scholar 
Jane, S. F. et al. Widespread deoxygenation of temperate lakes. Nature 594, 66–70 (2021).Article 
CAS 

Google Scholar 
Triska, F. J., Kennedy, V. C., Avanzino, R. J., Zellweger, G. W. & Bencala, K. E. Retention and transport of nutrients in a third‐order stream in northwestern California: hyporheic processes. Ecology 70, 1893–1905 (1989).Article 

Google Scholar 
Carter, A. M., Blaszczak, J. R., Heffernan, J. B. & Bernhardt, E. S. Hypoxia dynamics and spatial distribution in a low gradient river. Limnol. Oceanogr. 66, 2251–2265 (2021).Article 

Google Scholar 
Kadygrov, N. et al. On the potential of the ICOS atmospheric CO2 measurement network for estimating the biogenic CO2 budget of Europe. Atmos. Chem. Phys. 15, 12765–12787 (2015).Article 
CAS 

Google Scholar 
Hanson, P. C., Weathers, K. C. & Kratz, T. K. Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change. Inland Waters 6, 543–554 (2018).Article 

Google Scholar 
Claustre, H., Johnson, K. S. & Takeshita, Y. Observing the global ocean with biogeochemical-Argo. Annu. Rev. Mar. Sci. 12, 23–48 (2019).Article 

Google Scholar 
Jankowski, K. J., Mejia, F. H., Blaszczak, J. R. & Holtgrieve, G. W. Aquatic ecosystem metabolism as a tool in environmental management. Wiley Interdiscip. Rev. Water 8, e1521 (2021).Article 

Google Scholar 
Mao, F. et al. Moving beyond the technology: a socio-technical roadmap for low-cost water sensor network applications. Environ. Sci. Technol. 54, 9145–9158 (2020).Article 
CAS 

Google Scholar 
Park, J., Kim, K. T. & Lee, W. H. Recent advances in information and communications technology (ICT) and sensor technology for monitoring water quality. Water 12, 510 (2020).Article 
CAS 

Google Scholar 
Yamazaki, D. et al. MERIT Hydro: a high‐resolution global hydrography map based on latest topography dataset. Water Resour. Res. 55, 5053–5073 (2019).Article 

Google Scholar 
Lin, P., Pan, M., Wood, E. F., Yamazaki, D. & Allen, G. H. A new vector-based global river network dataset accounting for variable drainage density. Sci. Data 8, 28 (2021).Article 

Google Scholar 
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–587 (2018).Article 
CAS 
MATH 

Google Scholar 
Durand, M. et al. An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope. Water Resour. Res. 52, 4527–4549 (2016).Article 

Google Scholar 
Frasson, R. P. M. et al. Exploring the factors controlling the error characteristics of the surface water and ocean topography mission discharge estimates. Water Resour. Res. 57, e2020WR028519 (2021).Article 

Google Scholar 
Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Rapid changes to global river suspended sediment flux by humans. Science 376, 1447–1452 (2022).Article 
CAS 

Google Scholar 
Campbell, A. D. et al. A review of carbon monitoring in wet carbon systems using remote sensing. Environ. Res. Lett. 17, 025009 (2022).Article 

Google Scholar 
Allen, G. H. et al. Similarity of stream width distributions across headwater systems. Nat. Commun. 9, 610 (2018).Article 

Google Scholar 
Rodriguez-Iturbe, I. & Rinaldo, A. Fractal River Basins: Chance and Self-organization (Cambridge Univ. Press, 2001). Game-changing oeuvre formalizing the structure and function of river networks.Bertuzzo, E., Helton, A. M., Hall, Robert, O. & Battin, T. J. Scaling of dissolved organic carbon removal in river networks. Adv. Water Resour. 110, 136–146 (2017).Article 
CAS 

Google Scholar 
Marzadri, A., Dee, M. M., Tonina, D., Bellin, A. & Tank, J. L. Role of surface and subsurface processes in scaling N2O emissions along riverine networks. Proc. Natl Acad. Sci. USA 114, 4330–4335 (2017).Article 
CAS 

Google Scholar 
Marzadri, A. et al. Global riverine nitrous oxide emissions: the role of small streams and large rivers. Sci. Total Environ. 776, 145148 (2021).Article 
CAS 

Google Scholar 
Botter, G. & Durighetto, N. The stream length duration curve: a tool for characterizing the time variability of the flowing stream length. Water Resour. Res. 56, e2020WR027282 (2020).Article 
CAS 

Google Scholar 
Wollheim, W. M. et al. River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks. Biogeochemistry 141, 503–521 (2018).Article 
CAS 

Google Scholar 
Durighetto, N., Vingiani, F., Bertassello, L. E., Camporese, M. & Botter, G. Intraseasonal drainage network dynamics in a headwater catchment of the Italian Alps. Water Resour. Res. 56, e2019WR02556 (2020).Article 

Google Scholar 
Montgomery, D. R. & Dietrich, W. E. Source areas, drainage density, and channel initiation. Water Resour. Res. 25, 1907–1918 (1989).Article 

Google Scholar 
Fatichi, S., Ivanov, V. Y. & Caporali, E. A mechanistic ecohydrological model to investigate complex interactions in cold and warm water‐controlled environments: 1. Theoretical framework and plot‐scale analysis. J. Adv. Model. Earth. Syst. 4, M05002 (2012).
Google Scholar 
Ulseth, A. J. et al. Distinct air–water gas exchange regimes in low- and high-energy streams. Nat. Geosci. 12, 259–263 (2019).Article 
CAS 

Google Scholar 
Hall, R. O. in Streams and Ecosystems in a Changing Environment (eds. Jones, J. J. & Stanley, E. H.) 151–180 (Academic, 2016).Butman, D. & Raymond, P. A. Significant efflux of carbon dioxide from streams and rivers in the United States. Nat. Geosci. 4, 839–842 (2011).Article 
CAS 

Google Scholar 
Duvert, C., Butman, D. E., Marx, A., Ribolzi, O. & Hutley, L. B. CO2 evasion along streams driven by groundwater inputs and geomorphic controls. Nat. Geosci. 11, 813–818 (2018).Article 
CAS 

Google Scholar 
Zhang, L. et al. Significant methane ebullition from alpine permafrost rivers on the East Qinghai–Tibet Plateau. Nat. Geosci. 13, 349–354 (2020).Article 

Google Scholar  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

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

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

Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).Article 
ADS 

Google Scholar 
Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).Article 
ADS 
CAS 

Google Scholar 
Harris, R. M. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).Article 
ADS 

Google Scholar 
Till, A., Rypel, A. L., Bray, A. & Fey, S. B. Fish die-offs are concurrent with thermal extremes in north temperate lakes. Nat. Clim. Change 9, 637–641 (2019).Article 
ADS 

Google Scholar 
Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change 9, 306–312 (2019).Article 
ADS 

Google Scholar 
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).Article 

Google Scholar 
Ma, G., Rudolf, V. H. & Ma, C. Extreme temperature events alter demographic rates, relative fitness, and community structure. Glob. Change Biol. 21, 1794–1808 (2015).Article 
ADS 

Google Scholar 
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).Article 

Google Scholar 
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).Article 
CAS 

Google Scholar 
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).Article 
ADS 
CAS 

Google Scholar 
Power, S. B. & Delage, F. P. Setting and smashing extreme temperature records over the coming century. Nat. Clim. Change 9, 529–534 (2019).Article 
ADS 

Google Scholar 
Fischer, E. M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Change 11, 689–695 (2021).Article 
ADS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).Article 
ADS 

Google Scholar 
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).Article 
ADS 
CAS 

Google Scholar 
McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).Article 

Google Scholar 
Maxwell, S. L. et al. Conservation implications of ecological responses to extreme weather and climate events. Divers. Distrib. 25, 613–625 (2019).Article 

Google Scholar 
Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) Ch. 11, 1571–1759 (Cambridge Univ. Press, 2021).Mora, C. et al. Global risk of deadly heat. Nat. Clim. Change 7, 501–506 (2017).Article 
ADS 

Google Scholar 
Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).Article 
CAS 

Google Scholar 
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science 360, 791–795 (2018).Article 
CAS 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).Article 
ADS 
CAS 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).Article 
ADS 
CAS 

Google Scholar 
Ma, G., Hoffmann, A. A. & Ma, C.-S. Daily temperature extremes play an important role in predicting thermal effects. J. Exp. Biol. 218, 2289–2296 (2015).
Google Scholar 
Paaijmans, K. P. et al. Temperature variation makes ectotherms more sensitive to climate change. Glob. Change Biol. 19, 2373–2380 (2013).Article 
ADS 

Google Scholar 
Bütikofer, L. et al. The problem of scale in predicting biological responses to climate. Glob. Change Biol. 26, 6657–6666 (2020).Article 
ADS 

Google Scholar 
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).Article 
ADS 
CAS 

Google Scholar 
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).Article 
ADS 

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

Google Scholar 
Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).Article 
ADS 

Google Scholar 
Tamarin-Brodsky, T., Hodges, K., Hoskins, B. J. & Shepherd, T. G. Changes in Northern Hemisphere temperature variability shaped by regional warming patterns. Nat. Geosci. 13, 414–421 (2020).Article 
ADS 
CAS 

Google Scholar 
Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Nature 427, 332–336 (2004).Article 
ADS 

Google Scholar 
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).Article 
ADS 
CAS 

Google Scholar 
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).Article 
ADS 
CAS 

Google Scholar 
Perkins, S. E. & Alexander, L. V. On the measurement of heat waves. J. Clim. 26, 4500–4517 (2013).Article 
ADS 

Google Scholar 
Sunday, J. et al. Thermal tolerance patterns across latitude and elevation. Philos. Trans. R. Soc. B 374, 20190036 (2019).Article 

Google Scholar 
Hoffmann, A. A. Physiological climatic limits in Drosophila: patterns and implications. J. Exp. Biol. 213, 870–880 (2010).Article 
CAS 

Google Scholar 
Buckley, L. B. & Huey, R. B. How extreme temperatures impact organisms and the evolution of their thermal tolerance. Integr. Comp. Biol. 56, 98–109 (2016).Article 

Google Scholar 
Cohen, J. M., Fink, D. & Zuckerberg, B. Avian responses to extreme weather across functional traits and temporal scales. Glob. Change Biol. 26, 4240–4250 (2020).Article 
ADS 

Google Scholar 
Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).Article 
ADS 
CAS 

Google Scholar 
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).Article 
ADS 
CAS 

Google Scholar 
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).Article 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).Article 
ADS 
CAS 

Google Scholar 
Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. B 275, 419–425 (2008).Article 

Google Scholar 
McKechnie, A. E., Rushworth, I. A., Myburgh, F. & Cunningham, S. J. Mortality among birds and bats during an extreme heat event in eastern South Africa. Austral Ecol. 46, 687–691 (2021).Article 

Google Scholar 
Thompson, R. M., Beardall, J., Beringer, J., Grace, M. & Sardina, P. Means and extremes: building variability into community-level climate change experiments. Ecol. Lett. 16, 799–806 (2013).Article 

Google Scholar 
Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).Article 
ADS 
CAS 

Google Scholar 
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. R. Soc. B 276, 1939–1948 (2009).Article 

Google Scholar 
Kingsolver, J. G., Diamond, S. E. & Buckley, L. B. Heat stress and the fitness consequences of climate change for terrestrial ectotherms. Funct. Ecol. 27, 1415–1423 (2013).Article 

Google Scholar 
R. Kearney, M. Activity restriction and the mechanistic basis for extinctions under climate warming. Ecol. Lett. 16, 1470–1479 (2013).Article 

Google Scholar 
Rezende, E. L., Bozinovic, F., Szilágyi, A. & Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science 369, 1242–1245 (2020).Article 
ADS 
CAS 
MATH 

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

Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).Article 
ADS 

Google Scholar 
Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).Article 

Google Scholar 
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).Article 
ADS 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).Article 
ADS 
CAS 

Google Scholar 
Scheffers, B. R., Edwards, D. P., Diesmos, A., Williams, S. E. & Evans, T. A. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).Article 
ADS 

Google Scholar 
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. B 367, 1665–1679 (2012).Article 

Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer “cold-blooded” animals against climate warming. Proc. Natl Acad. Sci. USA 106, 3835–3840 (2009).Article 
ADS 
CAS 

Google Scholar 
Morley, S. A., Peck, L. S., Sunday, J. M., Heiser, S. & Bates, A. E. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).Article 

Google Scholar 
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).Article 

Google Scholar 
Lewis, F. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 147–1926 (Cambridge Univ. Press, 2021).Thakur, M. P., Bakker, E. S., Veen, G. C. & Harvey, J. A. Climate extremes, rewilding, and the role of microhabitats. One Earth 2, 506–509 (2020).Article 
ADS 

Google Scholar 
Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl Acad. Sci. USA 114, 2283–2288 (2017).Article 
ADS 
CAS 

Google Scholar 
Thrasher, B. et al. NASA Global daily downscaled projections, CMIP6. Sci. Data 9, 262 (2022).Article 

Google Scholar 
Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).Article 
ADS 

Google Scholar 
Jin, Z. et al. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Glob. Change Biol. 22, 3112–3126 (2016).Article 
ADS 

Google Scholar 
Zhang, L., Yang, B., Li, S., Hou, Y. & Huang, D. Potential rice exposure to heat stress along the Yangtze River in China under RCP8.5 scenario. Agric. Forest Meteorol. 248, 185–196 (2018).Article 
ADS 

Google Scholar 
Al-Bakri, J. et al. Assessment of climate changes and their impact on barley yield in Mediterranean environment using NEX-GDDP downscaled GCMs and DSSAT. Earth Syst. Environ. 5, 751–766 (2021).Semakula, H. M. et al. Prediction of future malaria hotspots under climate change in sub-Saharan Africa. Clim. Change 143, 415–428 (2017).Article 
ADS 
CAS 

Google Scholar 
Iwamura, T., Guzman-Holst, A. & Murray, K. A. Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat. Commun. 11, 2130 (2020).Article 
ADS 
CAS 

Google Scholar 
Jones, A. E. et al. Bluetongue risk under future climates. Nat. Clim. Change 9, 153–157 (2019).Article 
ADS 

Google Scholar 
Obradovich, N. & Fowler, J. H. Climate change may alter human physical activity patterns. Nat. Hum. Behav. 1, 0097 (2017).Article 

Google Scholar 
Obradovich, N., Migliorini, R., Mednick, S. C. & Fowler, J. H. Nighttime temperature and human sleep loss in a changing climate. Sci. Adv. 3, e1601555 (2017).Article 
ADS 

Google Scholar 
Meehl, G. A. et al. Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. 6, eaba1981 (2020).Article 
ADS 

Google Scholar 
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W. & Zelinka, M. Climate simulations: recognize the ‘hot model’ problem. Nature 605, 26–29 (2022).O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).Article 
ADS 

Google Scholar 
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).IUCN Red List of Threatened Species Version 2017, 3 (IUCN, 2017).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677 (2017).Article 

Google Scholar 
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).Article 
ADS 
CAS 

Google Scholar 
Maclean, I. M. Predicting future climate at high spatial and temporal resolution. Glob. Change Biol. 26, 1003–1011 (2020).Article 
ADS 

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 
Jiguet, F. et al. Thermal range predicts bird population resilience to extreme high temperatures. Ecol. Lett. 9, 1321–1330 (2006).Article 

Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).Article 
ADS 

Google Scholar 
Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).Article 
ADS 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).Article 
ADS 

Google Scholar 
Oliver, E. C. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).Article 
ADS 

Google Scholar 
Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2012).Woolway, R. I. et al. Lake heatwaves under climate change. Nature 589, 402–407 (2021).Article 
ADS 
CAS 

Google Scholar 
Gruber, N., Boyd, P. W., Frölicher, T. L. & Vogt, M. Biogeochemical extremes and compound events in the ocean. Nature 600, 395–407 (2021).Article 
ADS 
CAS 

Google Scholar 
Cahill, A. E. et al. Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. J. Biogeogr. 41, 429–442 (2014).Article 

Google Scholar 
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).Article 

Google Scholar 
Bennett, J. M. et al. The evolution of critical thermal limits of life on Earth. Nat. Commun. 12, 1198 (2021).Article 
ADS 
CAS 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 
ADS 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
Louthan, A. M., Doak, D. F. & Angert, A. L. Where and when do species interactions set range limits? Trends Ecol. Evol. 30, 780–792 (2015).Article 

Google Scholar 
Barbarossa, V. et al. Threats of global warming to the world’s freshwater fishes. Nat. Commun. 12, 1701 (2021).Article 
ADS 
CAS 

Google Scholar 
Clusella-Trullas, S., Blackburn, T. M. & Chown, S. L. Climatic predictors of temperature performance curve parameters in ectotherms imply complex responses to climate change. Am. Nat. 177, 738–751 (2011).Article 

Google Scholar 
Qu, Y.-F. & Wiens, J. J. Higher temperatures lower rates of physiological and niche evolution. Proc. R. Soc. B 287, 20200823 (2020).Article 

Google Scholar 
Pither, J. Climate tolerance and interspecific variation in geographic range size. Proc. R. Soc. Lond. B 270, 475–481 (2003).Article 

Google Scholar 
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).Article 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); http://www.R-project.org/Chen, H., Sun, J., Lin, W. & Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 65, 1415–1418 (2020).Article 

Google Scholar  More

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

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

Chapman, A. It’s okay to call them drones. J. Unmanned Veh. Syst. 2, iii–v (2014).Article 

Google Scholar 
Chabot, D., Hodgson, A. J., Hodgson, J. C. & Anderson, K. ‘Drone’: Technically correct, popularly accepted, socially acceptable. Drone Syst. Appl. 10, 399–405 (2022).Article 

Google Scholar 
Chabot, D. & Bird, D. M. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?. J. Unmanned Veh. Syst. 3, 137–155 (2015).Article 

Google Scholar 
Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M. & Hanson, L. Unmanned aircraft systems in wildlife research: Current and future applications of a transformative technology. Front. Ecol. Environ. 14, 241–251 (2016).Article 

Google Scholar 
Whitehead, K. & Hugenholtz, C. H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unmanned Veh. Syst. 2, 69–85 (2014).Article 

Google Scholar 
Barnas, A. et al. Evaluating behavioral responses of nesting lesser snow geese to unmanned aircraft surveys. Ecol. Evol. 8, 1328–1338 (2018).Article 

Google Scholar 
Mulero-Pázmány, M. et al. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS ONE 12, e0178448 (2017).Article 

Google Scholar 
Linchant, J., Lisein, J., Semeki, J., Lejeune, P. & Vermeulen, C. Are unmanned aircraft systems (UAS s) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Rev. 45, 239–252 (2015).Article 

Google Scholar 
Whitehead, K. et al. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: Scientific and commercial applications. J. Unmanned Veh. Syst. 2, 86–102 (2014).Article 

Google Scholar 
Barasona, J. A. et al. Unmanned aircraft systems for studying spatial abundance of ungulates: Relevance to spatial epidemiology. PLoS ONE 9, e115608 (2014).Article 
ADS 

Google Scholar 
Chrétien, L. P., Théau, J. & Ménard, P. Wildlife multispecies remote sensing using visible and thermal infrared imagery acquired from an unmanned aerial vehicle (UAV). Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40, 241 (2015).Article 

Google Scholar 
Guo, X. et al. Application of UAV remote sensing for a population census of large wild herbivores—Taking the headwater region of the yellow river as an example. Remote Sens. 10, 1041 (2018).Article 
ADS 

Google Scholar 
Hu, J., Wu, X. & Dai, M. Estimating the population size of migrating Tibetan antelopes Pantholops hodgsonii with unmanned aerial vehicles. Oryx 54, 101–109 (2020).Article 

Google Scholar 
Mulero-Pázmány, M., Stolper, R., Van Essen, L. D., Negro, J. J. & Sassen, T. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS ONE 9, e83873 (2014).Article 
ADS 

Google Scholar 
Rey, N., Volpi, M., Joost, S. & Tuia, D. Detecting animals in African Savanna with UAVs and the crowds. Remote Sens. Environ. 200, 341–351 (2017).Article 
ADS 

Google Scholar 
Schroeder, N. M., Panebianco, A., Gonzalez Musso, R. & Carmanchahi, P. An experimental approach to evaluate the potential of drones in terrestrial mammal research: A gregarious ungulate as a study model. R. Soc. Open Sci. 7, 191482 (2020).Article 
ADS 

Google Scholar 
Su, X. et al. Using an unmanned aerial vehicle (UAV) to study wild yak in the highest desert in the world. Int. J. Remote Sens. 39, 5490–5503 (2018).Article 

Google Scholar 
Vermeulen, C., Lejeune, P., Lisein, J., Sawadogo, P. & Bouché, P. Unmanned aerial survey of elephants. PLoS ONE 8, e54700 (2013).Article 
ADS 
CAS 

Google Scholar 
Mallory, M. L. et al. Financial costs of conducting science in the Arctic: Examples from seabird research. Arct. Sci. 4, 624–633 (2018).Article 

Google Scholar 
Sasse, D. B. Job-related mortality of wildlife workers in the United States, 1937–2000. Wildl. Soc. Bull. 31, 1015–1020 (2003).
Google Scholar 
Loarie, S. R., Joppa, L. N. & Pimm, S. L. Satellites miss environmental priorities. Trends Ecol. Evol. 22, 630–632 (2007).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. IUCN Red List of Threatened Species https://www.iucnredlist.org/en (2021).Mech, L. D. & Barber, S. M. A critique of wildlife radio-tracking and its use in National Parks: a report to the National Park Service. (2002).Patterson, C., Koski, W., Pace, P., McLuckie, B. & Bird, D. M. Evaluation of an unmanned aircraft system for detecting surrogate caribou targets in Labrador. J. Unmanned Veh. Syst. 4, 53–69 (2015).Article 

Google Scholar 
Hodgson, J. C. et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol. Evol. 9, 1160–1167 (2018).Article 

Google Scholar 
Seymour, A. C., Dale, J., Hammill, M., Halpin, P. N. & Johnston, D. W. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
COSEWIC. COSEWIC assessment and status report on the caribou (Rangifer tarandus) eastern migratory population, Torngat mountain population in Canada. (COSEWIC, Committee on the Status of Endangered Wildlife in Canada, 2017).Albawi, S., Mohammed, T. A. & Al-Zawi, S. Understanding of a convolutional neural network. in 2017 international conference on engineering and technology (ICET) 1–6 (IEEE, 2017).Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).Article 
ADS 

Google Scholar 
Teuwen, J. & Moriakov, N. Convolutional neural networks. in Handbook of medical image computing and computer assisted intervention 481–501 (Elsevier, 2020).Corcoran, E., Winsen, M., Sudholz, A. & Hamilton, G. Automated detection of wildlife using drones: Synthesis, opportunities and constraints. Methods Ecol. Evol. 12, 1103–1114 (2021).Article 

Google Scholar 
Corcoran, E., Denman, S., Hanger, J., Wilson, B. & Hamilton, G. Automated detection of koalas using low-level aerial surveillance and machine learning. Sci. Rep. 9, 3208 (2019).Article 
ADS 

Google Scholar 
Gray, P. C. et al. Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry. Methods Ecol. Evol. 10, 1490–1500 (2019).Article 

Google Scholar 
Gray, P. C. et al. A convolutional neural network for detecting sea turtles in drone imagery. Methods Ecol. Evol. 10, 345–355 (2019).Article 

Google Scholar 
Peng, J. et al. Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau. ISPRS J. Photogramm. Remote Sens. 169, 364–376 (2020).Article 
ADS 

Google Scholar 
Borowicz, A. et al. Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot. Sci. Rep. 8, 3926 (2018).Article 
ADS 

Google Scholar 
Francis, R. J., Lyons, M. B., Kingsford, R. T. & Brandis, K. J. Counting mixed breeding aggregations of animal species using drones: Lessons from waterbirds on semi-automation. Remote Sens. 12, 1185 (2020).Article 
ADS 

Google Scholar 
Santangeli, A. et al. Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Bowley, C., Mattingly, M., Barnas, A., Ellis-Felege, S. & Desell, T. An analysis of altitude, citizen science and a convolutional neural network feedback loop on object detection in unmanned aerial systems. J. Comput. Sci. 34, 102–116 (2019).Article 

Google Scholar 
Bowley, C., Mattingly, M., Barnas, A., Ellis-Felege, S. & Desell, T. Detecting wildlife in unmanned aerial systems imagery using convolutional neural networks trained with an automated feedback loop. in International Conference on Computational Science 69–82 (Springer, 2018).Delplanque, A., Foucher, S., Lejeune, P., Linchant, J. & Théau, J. Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks. Remote Sens. Ecol. Conserv. 8, 166–179 (2021).Article 

Google Scholar 
Eikelboom, J. A. J. et al. Improving the precision and accuracy of animal population estimates with aerial image object detection. Methods Ecol. Evol. 10, 1875–1887 (2019).Article 

Google Scholar 
Kellenberger, B., Marcos, D. & Tuia, D. Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning. Remote Sens. Environ. 216, 139–153 (2018).Article 
ADS 

Google Scholar 
Hooge, I. T. C., Niehorster, D. C., Nyström, M., Andersson, R. & Hessels, R. S. Is human classification by experienced untrained observers a gold standard in fixation detection?. Behav. Res. Methods 50, 1864–1881 (2018).Article 

Google Scholar 
Barnas, A. F., Darby, B. J., Vandeberg, G. S., Rockwell, R. F. & Ellis-Felege, S. N. A comparison of drone imagery and ground-based methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pérouse Bay. PLoS ONE 14, e0217049 (2019).Article 
CAS 

Google Scholar 
Brook, R. K. & Kenkel, N. C. A multivariate approach to vegetation mapping of Manitoba’s Hudson Bay Lowlands. Int. J. Remote Sens. 23, 4761–4776 (2002).Article 

Google Scholar 
Shilts, W. W., Aylsworth, J. M., Kaszycki, C. A., Klassen, R. A. & Graf, W. L. Canadian shield. in Geomorphic Systems of North America vol. 2 119–161 (Geological Society of America Boulder, Colorado, 1987).Barnas, A. F., Felege, C. J., Rockwell, R. F. & Ellis-Felege, S. N. A pilot (less) study on the use of an unmanned aircraft system for studying polar bears (Ursus maritimus). Polar Biol. 41, 1055–1062 (2018).Article 

Google Scholar 
Ellis-Felege, S. N. et al. Nesting common eiders (Somateria mollissima) show little behavioral response to fixed-wing drone surveys. J. Unmanned Veh. Syst. 10, 1–4 (2021).
Google Scholar 
Barnas, A. F. et al. A standardized protocol for reporting methods when using drones for wildlife research. J. Unmanned Veh. Syst. 8, 89–98 (2020).Article 

Google Scholar 
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91–99 (2016).
Google Scholar 
Chen, T., Xu, B., Zhang, C. & Guestrin, C. Training Deep Nets with Sublinear Memory Cost. ArXiv160406174 Cs (2016).Pinckaers, H. & Litjens, G. Training convolutional neural networks with megapixel images. ArXiv180405712 Cs (2018).Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems. (2015).Janocha, K. & Czarnecki, W. M. On loss functions for deep neural networks in classification. ArXiv Prepr. ArXiv170205659. (2017).Murata, N., Yoshizawa, S. & Amari, S. Learning curves, model selection and complexity of neural networks. Adv. Neural Inf. Process. Syst. 5, 607–614 (1992).
Google Scholar 
Hänsch, R. & Hellwich, O. The truth about ground truth: Label noise in human-generated reference data. in IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium 5594–5597 (IEEE, 2019).Bowler, E., Fretwell, P. T., French, G. & Mackiewicz, M. Using deep learning to count albatrosses from space: Assessing results in light of ground truth uncertainty. Remote Sens. 12, 2026 (2020).Article 
ADS 

Google Scholar 
Brack, I. V., Kindel, A. & Oliveira, L. F. B. Detection errors in wildlife abundance estimates from Unmanned Aerial Systems (UAS) surveys: Synthesis, solutions, and challenges. Methods Ecol. Evol. 9, 1864–1873 (2018).Article 

Google Scholar 
Jagielski, P. M. et al. The utility of drones for studying polar bear behaviour in the Canadian Arctic: Opportunities and recommendations. Drone Syst. Appl. 10, 97–110 (2022).Article 

Google Scholar 
Williams, P. J., Hooten, M. B., Womble, J. N. & Bower, M. R. Estimating occupancy and abundance using aerial images with imperfect detection. Methods Ecol. Evol. 8, 1679–1689 (2017).Article 

Google Scholar 
Link, W. A., Schofield, M. R., Barker, R. J. & Sauer, J. R. On the robustness of N-mixture models. Ecology 99, 1547–1551 (2018).Article 

Google Scholar 
Horvitz, D. G. & Thompson, D. J. A generalization of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47, 663–685 (1952).Article 
MATH 

Google Scholar 
Corcoran, E., Denman, S. & Hamilton, G. New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys. Ecol. Evol. 10, 8176–8185 (2020).Article 

Google Scholar 
Lunga, D., Arndt, J., Gerrand, J. & Stewart, R. ReSFlow: A remote sensing imagery data-flow for improved model generalization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 10468–10483 (2021).Article 
ADS 

Google Scholar 
Fromm, M., Schubert, M., Castilla, G., Linke, J. & McDermid, G. Automated detection of conifer seedlings in drone imagery using convolutional neural networks. Remote Sens. 11, 2585 (2019).Article 
ADS 

Google Scholar 
Velumani, K. et al. Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: Impact of the spatial resolution. Plant Phenomics 2021, 9824843 (2021).Article 
CAS 

Google Scholar 
Hodgson, A., Peel, D. & Kelly, N. Unmanned aerial vehicles for surveying marine fauna: Assessing detection probability. Ecol. Appl. 27, 1253–1267 (2017).Article 

Google Scholar 
Ferguson, M. C. et al. Performance of manned and unmanned aerial surveys to collect visual data and imagery for estimating arctic cetacean density and associated uncertainty. J. Unmanned Veh. Syst. 6, 128–154 (2018).Article 

Google Scholar 
Zmarz, A. et al. Application of UAV BVLOS remote sensing data for multi-faceted analysis of Antarctic ecosystem. Remote Sens. Environ. 217, 375–388 (2018).Article 
ADS 

Google Scholar 
Lyons, M. B. et al. Monitoring large and complex wildlife aggregations with drones. Methods Ecol. Evol. 10, 1024–1035 (2019).Article 

Google Scholar  More