Philippa Kaur

Waples, R. S. & Gaggiotti, O. INVITED REVIEW: What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol. Ecol. 15, 1419–1439 (2006).Article 
CAS 

Google Scholar 
Mendez, M., Rosenbaum, H. C., Subramaniam, A., Yackulic, C. & Bordino, P. Isolation by environmental distance in mobile marine species: Molecular ecology of franciscana dolphins at their southern range. Mol. Ecol. 19, 2212–2228 (2010).Article 
CAS 

Google Scholar 
De Meeûs, T. et al. Population genetics and molecular epidemiology or how to “débusquer la bête”. Infect. Genet. Evol. 7, 308–332 (2007).Article 

Google Scholar 
Durigan, M. et al. Population genetic analysis of Giardia duodenalis: Genetic diversity and haplotype sharing between clinical and environmental sources. MicrobiologyOpen 6, e00424 (2017).Article 

Google Scholar 
Amaral, A. R. et al. Seascape genetics of a globally distributed, highly mobile marine mammal: The short-beaked common dolphin (genus Delphinus). PLoS ONE 7, e31482 (2012).Article 
ADS 
CAS 

Google Scholar 
Mendez, M. et al. Molecular ecology meets remote sensing: Environmental drivers to population structure of humpback dolphins in the Western Indian Ocean. Heredity 107, 349–361 (2011).Article 
CAS 

Google Scholar 
de los Angeles Bayas-Rea, R., Félix, F. & Montufar, R. Genetic divergence and fine scale population structure of the common bottlenose dolphin (Tursiops truncatus, Montagu) found in the Gulf of Guayaquil. Ecuador. PeerJ 6, e4589 (2018).Article 

Google Scholar 
Natoli, A., Peddemors, V. M. & Rus Hoelzel, A. Population structure and speciation in the genus Tursiops based on microsatellite and mitochondrial DNA analyses. J. Evol. Biol. 17, 363–375 (2004).Article 
CAS 

Google Scholar 
Oliveira, L. R., Loizaga De Castro, R., Cárdenas-Alayza, S. & Bonatto, S. L. Conservation genetics of South American aquatic mammals: An overview of gene diversity, population structure, phylogeography, non-invasive methods and forensics. Mammal Rev. 42, 275–303 (2012).Article 

Google Scholar 
Vollmer, N. L. & Rosel, P. E. Fine-scale population structure of common bottlenose dolphins (Tursiops truncatus) in offshore and coastal waters of the US Gulf of Mexico. Mar. Biol. 164, 1–15 (2017).Article 

Google Scholar 
MacLeod, C. D. Global climate change, range changes and potential implications for the conservation of marine cetaceans: A review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
Hartl, D. L., Clark, A. G. & Clark, A. G. Principles of Population Genetics, Vol. 116 (Sinauer associates Sunderland, 1997).Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145 (2004).Article 
ADS 
CAS 

Google Scholar 
Reeves, R. R., Smith, B. D., Crespo, E. A. & Notarbartolo di Sciara, G. Dolphins, whales and porpoises: 2002–2010 conservation action plan for the world’s cetaceans, Vol. 58 (IUCN, 2003).Crespo, E. A. & Hall, M. A. In Marine Mammals, 463–490 (Springer, 2002).Crespo, E. A. et al. Direct and indirect effects of highseas fisheries on the marine mammal populations in the northern and central Patagonian coast. J. Northwest Atl. Fish. Sci. 22, 189–207 (1997).Article 

Google Scholar 
Harlin-Cognato, A. D., Markowitz, T., Würsig, B. & Honeycutt, R. L. Multi-locus phylogeography of the dusky dolphin (Lagenorhynchus obscurus): Passive dispersal via the west-wind drift or response to prey species and climate change?. BMC Evol. Biol. 7, 1–17 (2007).Article 

Google Scholar 
Hoelzel, A. Evolution of population genetic structure in marine mammal species. In Population genetics for animal conservation, 294–318 (Cambridge University Press, Cambridge, 2009).Fraser, C. I., Nikula, R., Ruzzante, D. E. & Waters, J. M. Poleward bound: Biological impacts of Southern Hemisphere glaciation. Trends Ecol. Evol. 27, 462–471 (2012).Article 

Google Scholar 
Louis, M. et al. Influence of past climate change on phylogeography and demographic history of narwhals, Monodon monoceros. Proc. R. Soc. B 287, 20192964 (2020).Article 
CAS 

Google Scholar 
Skovrind, M. et al. Circumpolar phylogeography and demographic history of beluga whales reflect past climatic fluctuations. Mol. Ecol. 30, 2543–2559 (2021).Article 

Google Scholar 
Foote, A. D. et al. Ancient DNA reveals that bowhead whale lineages survived Late Pleistocene climate change and habitat shifts. Nat. Commun. 4, 1–7 (2013).Article 

Google Scholar 
Crespo, E. A. et al. Status, population trend and genetic structure of South American fur seals, Arctocephalus australis, in southwestern Atlantic waters. Mar. Mamm. Sci. 31, 866–890 (2015).Article 

Google Scholar 
Feijoo, M., Lessa, E. P., De Castro, R. L. & Crespo, E. A. Mitochondrial and microsatellite assessment of population structure of South American sea lion (Otaria flavescens) in the Southwestern Atlantic Ocean. Mar. Biol. 158, 1857–1867 (2011).Article 

Google Scholar 
Túnez, J. I., Cappozzo, H. L., Nardelli, M. & Cassini, M. H. Population genetic structure and historical population dynamics of the South American sea lion, Otaria flavescens, in north-central Patagonia. Genetica 138, 831–841 (2010).Article 

Google Scholar 
Oliveira, L., Ott, P. H., Grazziotin, F. G., White, B. & Bonatto, S. In Paper (SC/S11/RW26) presented to the Southern Right Whale Assessment Workshop (Commission International Whaling).Loizaga de Castro, R., Dans, S. L. & Crespo, E. A. Spatial genetic structure of dusky dolphin, Lagenorhynchus obscurus, along the argentine coast: Preserve what scale?. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 173–183 (2016).Article 

Google Scholar 
Pimper, L. E., Goodall, R. N. P. & Remis, M. I. First mitochondrial DNA analysis of the spectacled porpoise (Phocoena dioptrica) from Tierra del Fuego, Argentina. Mamm. Biol. 77, 459–462 (2012).Article 

Google Scholar 
Pichler, F. B. et al. Origin and radiation of Southern Hemisphere coastal dolphins (genus Cephalorhynchus). Mol. Ecol. 10, 2215–2223 (2001).Article 
CAS 

Google Scholar 
Dawson, S. M. In Encyclopedia of Marine Mammals, 166–172 (Elsevier, 2018).Robineau, D., Goodall, R. N. P., Pichler, F. & Baker, C. S. Description of a new subspecies of Commerson’s dolphin, Cephalorhynchus commersonii (Lacépède, 1804), inhabiting the coastal waters of the Kerguelen Islands. Mammalia 71, 172–180 (2007).Article 

Google Scholar 
Crespo, E. A. et al. Cephalorhynchus commersonii, Commerson’s Dolphin. IUCN; The IUCN Red List of Threatened Species; 10-2017; 1-14 (2017).Goodall, R. Commerson’s dolphin Cephalorhynchus commersonii (Lacépède 1804). Handb. Mar. Mamm. 5, 241–267 (1994).
Google Scholar 
Coscarella, M. A. Ecologıa, comportamiento y evaluación del impacto de embarcaciones sobre manadas de tonina overa Cephalorhynchus commersonii en Bahıa Engano, Chubut (Universidad de Buenos Aires, Buenos Aires, 2005).Dellabianca, N. A. et al. Spatial models of abundance and habitat preferences of commerson’s and peale’s dolphin in southern patagonian waters. PLoS ONE 11, e0163441 (2016).Article 

Google Scholar 
Goodall, R. et al. Studies of Commerson’s dolphins, Cephalorhynchus commersonii, off Tierra del Fuego, 1976–1984. Report of the International Whaling Commission (Special Issue 9), 143–160 (1988).White, R. The Distribution of Seabirds and Marine Mammals in Falkland Islands Waters (Joint Nature Conservation Committee, 2002).Loizaga de Castro, R., Dans, S. L., Coscarella, M. A. & Crespo, E. A. Living in an estuary: Commerson’s dolphin (Cephalorhynchus commersonii (Lacépède, 1804)), habitat use and behavioural pattern at the Santa Cruz River, Patagonia, Argentina. Latin Am. J. Aquat. Res. 41, 985–991 (2013).Article 

Google Scholar 
Pedraza, S. Ecología poblacional de la tonina overa, Cephalorhynchus commersonii, (Lacépède, 1804) en el litoral patagónico. Unpublished PhD thesis, Universidad de Buenos Aires, Buenos Aires, Argentina (2008).Garaffo, G. V. et al. Modeling habitat use for dusky dolphin and Commerson’s dolphin in Patagonia. Mar. Ecol. Prog. Ser. 421, 217–227 (2011).Article 
ADS 

Google Scholar 
Cipriano, F., Hevia, M. & Iñíguez, M. Genetic divergence over small geographic scales and conservation implications for Commerson’s dolphins (Cephalorhynchus commersonii) in southern Argentina. Mar. Mamm. Sci. 27, 701–718 (2011).Article 
CAS 

Google Scholar 
Pimper, L. E., Baker, C. S., Goodall, R. N. P., Olavarría, C. & Remis, M. I. Mitochondrial DNA variation and population structure of Commerson’s dolphins (Cephalorhynchus commersonii) in their southernmost distribution. Conserv. Genet. 11, 2157–2168 (2010).Article 

Google Scholar 
O’Brien, S. J. A role for molecular genetics in biological conservation. Proc. Natl. Acad. Sci. 91, 5748–5755 (1994).Article 
ADS 
CAS 

Google Scholar 
Loizaga de Castro, R., Hoelzel, A. & Crespo, E. Behavioural responses of Argentine coastal dusky dolphins (Lagenorhynchus obscurus) to a biopsy pole system. Anim. Welf. 22, 13–23 (2013).Article 
CAS 

Google Scholar 
Elphinstone, M. S., Hinten, G. N., Anderson, M. J. & Nock, C. J. An inexpensive and high-throughput procedure to extract and purify total genomic DNA for population studies. Mol. Ecol. Notes 3, 317–320 (2003).Article 
CAS 

Google Scholar 
Bérubé, M. & Palsbøll, P. Identification of sex in cetaceans by multiplexing with three ZFX and ZFY specific primers. Mol. Ecol. 5, 283–287 (1996).Article 

Google Scholar 
Hoelzel, A., Hancock, J. & Dover, G. Evolution of the cetacean mitochondrial D-loop region. Mol. Biol. Evol. 8, 475–493 (1991).CAS 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).Article 
CAS 

Google Scholar 
Ruzzante, D. E. et al. Validation of close-kin mark–recapture (CKMR) methods for estimating population abundance. Methods Ecol. Evol. 10, 1445–1453 (2019).Article 

Google Scholar 
Faircloth, B. C., Branstetter, M. G., White, N. D. & Brady, S. G. Target enrichment of ultraconserved elements from arthropods provides a genomic perspective on relationships among H ymenoptera. Mol. Ecol. Resour. 15, 489–501 (2015).Article 
CAS 

Google Scholar 
Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).Article 
CAS 

Google Scholar 
Zhan, L. et al. MEGASAT: Automated inference of microsatellite genotypes from sequence data. Mol. Ecol. Resour. 17, 247–256 (2017).Article 
CAS 

Google Scholar 
Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).Article 
CAS 

Google Scholar 
Schneider, S., Roessli, D. & Excoffier, L. Arlequin: A software for population genetics data analysis, version 2.000. Genetics Biometry Laboratory, Department of Anthropology, University of Geneva, Switzerland (2000).Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).Article 
CAS 

Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).Article 
CAS 

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour 10, 564–567 (2010).Article 

Google Scholar 
Bandelt, H.-J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).Article 
CAS 

Google Scholar 
Fu, Y.-X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).Article 
CAS 

Google Scholar 
Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).CAS 

Google Scholar 
Peakall, R. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).CAS 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).Article 

Google Scholar 
Harlin, A. D., Markowitz, T., Baker, C. S., Würsig, B. & Honeycutt, R. L. Genetic structure, diversity, and historical demography of New Zealand’s dusky dolphin (Lagenorhynchus obscurus). J. Mammal. 84, 702–717 (2003).Article 

Google Scholar 
Rambaut, A., Suchard, M., Xie, D. & Drummond, A. Tracer v1. 6. http://beast.bio.ed.ac.uk/Tracer (2014).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 
Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225 (1989).
Google Scholar 
Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9. 3. http://www2.unil.ch/popgen/softwares/fstat.htm (2001).Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).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 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
CAS 

Google Scholar 
Earl, D. A. 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 
Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275 (1989).
Google Scholar 
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).Article 

Google Scholar 
Milinkovitch, M. C., Leduc, R., Tiedemann, R. & Dizon, A. In Marine Mammals: Biology and Conservation (ed Evans, P. G. H. & Raga, J. A.) 325–359 (Springer, 2002).Pichler, F. Population structure and genetic variation in Hector’s dolphin (Cephalorhynchus hectori), ResearchSpace@ Auckland (2001).Pichler, F. & Baker, C. Loss of genetic diversity in the endemic Hector’s dolphin due to fisheries-related mortality. Proc. R. Soc. Lond. Ser. B Biol. Sci. 267, 97–102 (2000).Article 
CAS 

Google Scholar 
Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).Article 

Google Scholar 
Chilvers, B. L. & Wilkinson, I. S. Philopatry and site fidelity of New Zealand sea lions (Phocarctos hookeri). Wildl. Res. 35, 463–470 (2008).Article 

Google Scholar 
Engelhaupt, D. et al. Female philopatry in coastal basins and male dispersion across the North Atlantic in a highly mobile marine species, the sperm whale (Physeter macrocephalus). Mol. Ecol. 18, 4193–4205 (2009).Article 
CAS 

Google Scholar 
Möller, L. M. & Beheregaray, L. B. Genetic evidence for sex-biased dispersal in resident bottlenose dolphins (Tursiops aduncus). Mol. Ecol. 13, 1607–1612 (2004).Article 

Google Scholar 
Jansen van Vuuren, B., Best, P., Roux, J. P. & Robinson, T. Phylogeographic population structure in the Heaviside’s dolphin (Cephalorhynchus heavisidii): Conservation implications. Anim. Conserv. 5, 303–307 (2002).Article 

Google Scholar 
Pérez-Alvarez, M. J. et al. Microsatellite markers reveal strong genetic structure in the endemic Chilean dolphin. PLoS ONE 10, e0123956 (2015).Article 

Google Scholar 
Hamner, R. M., Pichler, F. B., Heimeier, D., Constantine, R. & Baker, C. S. Genetic differentiation and limited gene flow among fragmented populations of New Zealand endemic Hector’s and Maui’s dolphins. Conserv. Genet. 13, 987–1002 (2012).Article 

Google Scholar 
Pichler, F., Dawson, S., Slooten, E. & Baker, C. Geographic isolation of Hector’s dolphin populations described by mitochondrial DNA sequences. Conserv. Biol. 12, 676–682 (1998).Article 

Google Scholar 
Kraft, S. et al. From settlers to subspecies: Genetic differentiation in commerson’s Dolphins between South America and the Kerguelen Islands. Front. Mar. Sci. 8, 782512 (2021).Article 

Google Scholar 
Grant, W. & Bowen, B. W. Shallow population histories in deep evolutionary lineages of marine fishes: Insights from sardines and anchovies and lessons for conservation. J. Hered. 89, 415–426 (1998).Article 

Google Scholar 
Ponce, J. F., Rabassa, J., Coronato, A. & Borromei, A. M. Palaeogeographical evolution of the Atlantic coast of Pampa and Patagonia from the last glacial maximum to the Middle Holocene. Biol. J. Lin. Soc. 103, 363–379 (2011).Article 

Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114 (1943).Article 
CAS 

Google Scholar 
Meirmans, P. G. Nonconvergence in B ayesian estimation of migration rates. Mol. Ecol. Resour. 14, 726–733 (2014).Article 

Google Scholar  More

Monte Carlo analysisSeaweed production costs and net costs of climate benefits were estimated on the basis of outputs of the biophysical and technoeconomic models described below. The associated uncertainties and sensitivities were quantified by repeatedly sampling from uniform distributions of plausible values for each cost and economic parameter (n = 5,000 for each nutrient scenario from the biophysical model, for a total of n = 10,000 simulations; see Supplementary Figs. 14 and 15)47,48,49,50,51,52. Parameter importance across Monte Carlo simulations (Fig. 3 and Supplementary Fig. 9) was determined using decision trees in LightGBM, a gradient-boosting machine learning framework.Biophysical modelG-MACMODS is a nutrient-constrained, biophysical macroalgal growth model with inputs of temperature, nitrogen, light, flow, wave conditions and amount of seeded biomass30,53, that we used to estimate annual seaweed yield per area (either in tons of carbon or tons of dry weight biomass per km2 per year)33,34. In the model, seaweed takes up nitrogen from seawater, and that nitrogen is held in a stored pool before being converted to structural biomass via growth54. Seaweed biomass is then lost via mortality, which includes breakage from variable ocean wave intensity. The conversion from stored nitrogen to biomass is based on the minimum internal nitrogen requirements of macroalgae, and the conversion from biomass to units of carbon is based on an average carbon content of macroalgal dry weight (~30%)55. The model accounts for farming intensity (sub-grid-scale crowding) and employs a conditional harvest scheme, where harvest is optimized on the basis of growth rate and standing biomass33.The G-MACMODS model is parameterized for four types of macroalgae: temperate brown, temperate red, tropical brown and tropical red. These types employed biophysical parameters from genera that represent over 99.5% of present-day farmed macroalgae (Eucheuma, Gracilaria, Kappahycus, Sargassum, Porphyra, Saccharina, Laminaria, Macrocystis)39. Environmental inputs were derived from satellite-based and climatological model output mapped to 1/12-degree global resolution, which resolves continental shelf regions. Nutrient distributions were derived from a 1/10-degree resolution biogeochemical simulation led by the National Center for Atmospheric Research (NCAR) and run in the Community Earth System Model (CESM) framework35.Two nutrient scenarios were simulated with G-MACMODS and evaluated using the technoeconomic model analyses described below: the ‘ambient nutrient’ scenario where seaweed growth was computed using surface nutrient concentrations without depletion or competition, and ‘limited nutrient’ simulations where seaweed growth was limited by an estimation of the nutrient supply to surface waters (computed as the flux of deep-water nitrate through a 100 m depth horizon). For each Monte Carlo simulation in the economic analysis, the technoeconomic model randomly selects either the 5th, 25th, 50th, 75th or 95th percentile G-MACMODS seaweed yield map from a normal distribution to use as the yield map for that simulation. Figures and numbers reported in the main text are based on the ambient-nutrient scenario; results based on the limited-nutrient scenario are shown in Supplementary Figures.Technoeconomic modelAn interactive web tool of the technoeconomic model is available at https://carbonplan.org/research/seaweed-farming.We estimated the net cost of seaweed-related climate benefits by first estimating all costs and emissions related to seaweed farming, up to and including the point of harvest at the farm location, then estimating costs and emissions related to the transportation and processing of harvested seaweed, and finally estimating the market value of seaweed products and either carbon sequestered or GHG emissions avoided.Production costs and emissionsSpatially explicit costs of seaweed production ($ tDW−1) and production-related emissions (tCO2 tDW−1) were calculated on the basis of ranges of capital costs ($ km−2), operating costs (including labour, $ km−2), harvest costs ($ km−2) and transport emissions per distance travelled (tCO2 km−1) in the literature (Table 1, Supplementary Tables 1 and 2); annual seaweed biomass (tDW km−2, for the preferred seaweed type in each grid cell), line spacing and number of harvests (species-dependent) from the biophysical model; as well as datasets of distances to the nearest port (km), ocean depth (m) and significant wave height (m).Capital costs were calculated as:$$c_{cap} = c_{capbase} + left( {c_{capbase} times left( {k_d + k_w} right)} right) + c_{sl}$$
(1)
where ccap is the total annualized capital costs per km2, ccapbase is the annualized capital cost per km2 (for example, cost of buoys, anchors, boats, structural rope) before applying depth and wave impacts, kd and kw are the impacts of depth and waviness on capital cost, respectively, each expressed as a multiplier between 0 and 1 modelled using our Monte Carlo method and applied only to grid cells with depth >500 m and/or significant wave height >3 m, respectively, and csl is the total annual cost of seeded line calculated as:$$c_{sl} = c_{slbase} times p_{sline}$$
(2)
where cslbase is the cost per metre of seeded line, and psline is the total length of line per km2, based on the optimal seaweed type grown in each grid cell.Operating and maintenance costs were calculated as:$$c_{op} = c_{ins} + c_{lic} + c_{lab} + c_{opbase}$$
(3)
where cop is the total annualized operating and maintenance costs per km2, cins is the annual insurance cost per km2, clic is the annual cost of a seaweed aquaculture license per km2, clab is the annual cost of labour excluding harvest labour, and copbase is all other operating and maintenance costs.Harvest costs were calculated as:$$c_{harv} = c_{harvbase} times n_{harv}$$
(4)
where charv is the total annual costs associated with harvesting seaweed per km2, charvbase is the cost per harvest per km2 (including harvest labour but excluding harvest transport), and nharv is the total number of harvests per year.Costs associated with transporting equipment to the farming location were calculated as:$$c_{eqtrans} = c_{transbase} times m_{eq} times d_{port}$$
(5)
where ceqtrans is total annualized cost of transporting equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.The total production cost of growing and harvesting seaweed was therefore calculated as:$$c_{prod} = frac{{left( {c_{cap}} right) + left( {c_{op}} right) + left( {c_{harv}} right) + (c_{eqtrans})}}{{s_{dw}}}$$
(6)
where cprod is total annual cost of seaweed production (growth + harvesting), ccap is as calculated in equation (1), cop is as calculated in equation (3), charv is as calculated in equation (4), ceqtrans is as calculated in equation (5) and sdw is the DW of seaweed harvested annually per km2.Emissions associated with transporting equipment to the farming location were calculated as:$$e_{eqtrans} = e_{transbase} times m_{eq} times d_{port}$$
(7)
where eeqtrans is the total annualized CO2 emissions in tons from transporting equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.Emissions from maintenance trips to/from the seaweed farm were calculated as:$$e_{mnt} = left( {left( {2 times d_{port}} right) times e_{mntbase} times left( {frac{{n_{mnt}}}{{a_{mnt}}}} right)} right) + (e_{mntbase} times d_{mnt})$$
(8)
where emnt is total annual CO2 emissions from farm maintenance, dport is the ocean distance to the nearest port in km, nmnt is the number of maintenance trips per km2 per year, amnt is the area tended to per trip, dmnt is the distance travelled around each km2 for maintenance and emntbase is the CO2 emissions from travelling 1 km on a typical fishing maintenance vessel (for example, a 14 m Marinnor vessel with 2 × 310 hp engines) at an average speed of 9 knots (16.67 km h−1), resulting in maintenance vessel fuel consumption of 0.88 l km−1 (refs. 28,56).Total emissions from growing and harvesting seaweed were therefore calculated as:$$e_{prod} = frac{{(e_{eqtrans}) + (e_{mnt})}}{{s_{dw}}}$$
(9)
where eprod is total annual emissions from seaweed production (growth + harvesting), eeqtrans is as calculated in equation (7), emnt is as calculated in equation (8) and sdw is the DW of seaweed harvested annually per km2.Market value and climate benefits of seaweedFurther transportation and processing costs, economic value and net emissions of either sinking seaweed in the deep ocean for carbon sequestration or converting seaweed into usable products (biofuel, animal feed, pulses, vegetables, fruits, oil crops and cereals) were calculated on the basis of ranges of transport costs ($ tDW−1 km−1), transport emissions (tCO2-eq t−1 km−1), conversion cost ($ tDW−1), conversion emissions (tCO2-eq tDW−1), market value of product ($ tDW−1) and the emissions avoided by product (tCO2-eq tDW−1) in the literature (Table 1). Market value was treated as globally homogeneous and does not vary by region. Emissions avoided by products were determined by comparing estimated emissions related to seaweed production to emissions from non-seaweed products that could potentially be replaced by seaweed (including non-CO2 greenhouse gas emissions from land use)24. Other parameters used are distance to nearest port (km), water depth (m), spatially explicit sequestration fraction (%)57 and distance to optimal sinking location (km; cost-optimized for maximum emissions benefit considering transport emissions combined with spatially explicit sequestration fraction; see ‘Distance to sinking point calculation’ below). Each Monte Carlo simulation calculated the cost of both CDR via sinking seaweed and GHG emissions mitigation via seaweed products.For seaweed CDR, after the seaweed is harvested, it can either be sunk in the same location that it was grown, or be transported to a more economically favourable sinking location where more of the seaweed carbon would remain sequestered for 100 yr (see ‘Distance to optimal sinking point’ below). Immediately post-harvest, the seaweed still contains a large amount of water, requiring a conversion from dry mass to wet mass for subsequent calculations33:$$s_{ww} = frac{{s_{dw}}}{{0.1}}$$
(10)
where sww is the annual wet weight of seaweed harvested per km2 and sdw is the annual DW of seaweed harvested per km2.The cost to transport harvested seaweed to the optimal sinking location was calculated as:$$c_{swtsink} = c_{transbase} times d_{sink} times s_{ww}$$
(11)
where cswtsink is the total annual cost to transport harvested seaweed to the optimal sinking location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10).The costs associated with transporting replacement equipment (for example, lines, buoys,anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type) in the sinking CDR pathway were calculated as:$$c_{eqtsink} = left( {c_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (c_{transbase} times d_{port} times m_{eq})$$
(12)
where ceqtsink is the total annualized cost to transport both used and replacement equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port (see Supplementary Fig. 16). These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).The total value of seaweed that is sunk for CDR was therefore calculated as:$$v_{sink} = frac{{left( {v_{cprice} – left( {c_{swtsink} + c_{eqtsink}} right)} right)}}{{s_{dw}}}$$
(13)
where vsink is the total value (cost, if negative) of seaweed farmed for CDR in $ tDW−1, vcprice is a theoretical carbon price, cswtsink is as calculated in equation (11), ceqtsink is as calculated in equation (12) and sdw is the annually harvested seaweed DW in t km−2. We did not assume any carbon price in our Monte Carlo simulations (vcprice is equal to zero), making vsink negative and thus representing a net cost.To calculate net carbon impacts, our model included uncertainty in the efficiency of using the growth and subsequent deep-sea deposition of seaweed as a CDR method. The uncertainty is expected to include the effects of reduced phytoplankton growth from nutrient competition, the relationship between air–sea gas exchange and overturning circulation (hereafter collectively referred to as the ‘atmospheric removal fraction’) and the fraction of deposited seaweed carbon that remains sequestered for at least 100 yr. The total amount of atmospheric CO2 removed by sinking seaweed was calculated as:$$e_{seqsink} = k_{atm} times k_{fseq} times frac{{tC}}{{tDW}} times frac{{tCO_2}}{{tC}}$$
(14)
where eseqsink is net atmospheric CO2 sequestered annually in t km−2, katm is the atmospheric removal fraction and kfseq is the spatially explicit fraction of sunk seaweed carbon that remains sequestered for at least 100 yr (see ref. 57).The emissions from transporting harvested seaweed to the optimal sinking location were calculated as:$$e_{swtsink} = e_{transbase} times d_{sink} times s_{ww}$$
(15)
where eswtsink is the total annual CO2 emissions from transporting harvested seaweed to the optimal sinking location in tCO2 km−2, etransbase is the CO2 emissions (tons) from transporting 1 ton of material 1 km on a barge (tCO2 per t-km), dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10). Since the unit for etransbase is tCO2 per t-km, the emissions from transporting seaweed to the optimal sinking location are equal to (e_{mathrm{transbase}} times d_{mathrm{sink}} times s_{mathrm{ww}}), and the emissions from transporting seaweed from the optimal sinking location back to the farm are equal to 0 (since the seaweed has already been deposited, the seaweed mass to transport is now 0). Note that this does not yet include transport emissions from transport of equipment post-seaweed-deposition (see equation 16 below and Supplementary Fig. 16).The emissions associated with transporting replacement equipment (for example, lines, buoys, anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type)28,41 in the sinking CDR pathway were calculated as:$$e_{eqtsink} = left( {e_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (e_{transbase} times d_{port} times m_{eq})$$
(16)
where eeqtsink is the total annualized CO2 emissions in tons from transporting both used and replacement equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port. These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).Net CO2 emissions removed from the atmosphere by sinking seaweed were thus calculated as:$$e_{remsink} = frac{{left( {e_{seqsink} – left( {e_{swtsink} + e_{eqtsink}} right)} right)}}{{s_{dw}}}$$
(17)
where eremsink is the net atmospheric CO2 removed per ton of seaweed DW, eseqsink is as calculated in equation (14), eswtsink is as calculated in equation (15), eeqtsink is as calculated in equation (16) and sdw is the annually harvested seaweed DW in t km−2.Net cost of climate benefitsSinkingTo calculate the total net cost and emissions from the production, harvesting and transport of seaweed for CDR, we combined the cost and emissions from the sinking-pathway cost and value modules. The total net cost of seaweed CDR per DW ton of seaweed was calculated as:$$c_{sinknet} = c_{prod} – v_{sink}$$
(18)
where csinknet is the total net cost of seaweed for CDR per DW ton harvested, cprod is the net production cost per DW ton as calculated in equation (6) and vsink is the net value (or cost, if negative) per ton seaweed DW as calculated in equation (13).The total net CO2 emissions removed per DW ton of seaweed were calculated as:$$e_{sinknet} = e_{remsink} – e_{prod}$$
(19)
where esinknet is the total net atmospheric CO2 removed per DW ton of seaweed harvested annually (tCO2 tDW−1 yr−1), eremsink is the net atmospheric CO2 removed via seaweed sinking annually as calculated in equation (17) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where esinknet is negative (that is, net emissions rather than net removal) were not included in subsequent calculations since they would not be contributing to CDR in that location under the given scenario. Note that these net emissions cases only occur in areas far from port in specific high-emissions scenarios. Even in such cases, most areas still contribute to CO2 removal (negative emissions), hence costs from locations with net removal were included.Total net cost was then divided by total net emissions to get a final value for cost per ton of atmospheric CO2 removed:$$c_{pertonsink} = frac{{c_{sinknet}}}{{e_{sinknet}}}$$
(20)
where cpertonsink is the total net cost per ton of atmospheric CO2 removed via seaweed sinking ($ per tCO2 removed), csinknet is total net cost per ton seaweed DW harvested as calculated in equation (18) ($ tDW−1) and esinknet is the total net atmospheric CO2 removed per ton seaweed DW harvested as calculated in equation (19) (tCO2 tDW−1).GHG emissions mitigationInstead of sinking seaweed for CDR, seaweed can be used to make products (including but not limited to food, animal feed and biofuels). Replacing convention products with seaweed-based products can result in ‘avoided emissions’ if the emissions from growing, harvesting, transporting and converting seaweed into products are less than the total greenhouse gas emissions (including non-CO2 GHGs) embodied in conventional products that seaweed-based products replace.When seaweed is used to make products, we assumed it is transported back to the nearest port immediately after being harvested. The annualized cost to transport the harvested seaweed and replacement equipment (for example, lines, buoys, anchors) was calculated as:$$c_{transprod} = frac{{left( {c_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
(21)
where ctransprod is the annualized cost per ton seaweed DW to transport seaweed and equipment back to port from the farm location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.The total value of seaweed that is used for seaweed-based products was calculated as:$$v_{product} = v_{mkt} – left( {c_{transprod} + c_{conv}} right)$$
(22)
where vproduct is the total value (cost, if negative) of seaweed used for products ($ tDW−1), vmkt is how much each ton of seaweed would sell for, given the current market price of conventional products that seaweed-based products replace ($ tDW−1), ctransprod is as calculated in equation (21) and cconv is the cost to convert each ton of seaweed to a usable product ($ tDW−1).The annualized CO2 emissions from transporting harvested seaweed and equipment back to port were calculated as:$$e_{transprod} = frac{{left( {e_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
(23)
where etransprod is the annualized CO2 emissions per ton seaweed DW to transport seaweed and equipment back to port from the farm location, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.Total emissions avoided by each ton of harvested seaweed DW were calculated as:$$e_{avprod} = e_{subprod} – left( {e_{transprod} + e_{conv}} right)$$
(24)
where eavprod is total CO2-eq emissions avoided per ton of seaweed DW per year (including non-CO2 GHGs using a GWP time period of 100 yr), esubprod is the annual CO2-eq emissions avoided per ton seaweed DW by replacing a conventional product with a seaweed-based product, etransprod is as calculated in equation (23) and econv is the annual CO2 emissions per ton seaweed DW from converting seaweed into usable products. esubprod was calculated by converting seaweed DW to caloric content58 for food/feed and comparing emissions intensity per kcal to agricultural products24, or by converting seaweed DW into equivalent biofuel content with a yield of 0.25 tons biofuel per ton DW59 and dividing the CO2 emissions per ton fossil fuel by the seaweed biofuel yield.To calculate the total net cost and emissions from the production, harvesting, transport and conversion of seaweed for products, we combined the cost and emissions from the product-pathway cost and value modules. The total net cost of seaweed for products per ton DW was calculated as:$$c_{prodnet} = c_{prod} – v_{product}$$
(25)
where cprodnet is the total net cost per ton DW of seaweed harvested for use in products, cprod is the net production cost per ton DW as calculated in equation (6) and vproduct is the net value (or cost, if negative) per ton DW as calculated in equation (22).The total net CO2-eq emissions avoided per ton DW of seaweed used in products were calculated as:$$e_{prodnet} = e_{avprod} – e_{prod}$$
(26)
where eprodnet is the total net CO2-eq emissions avoided per ton DW of seaweed harvested annually (tCO2 tDW−1 yr−1), eavprod is the net CO2-eq emissions avoided by seaweed products annually as calculated in equation (24) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where eprodnet is negative (that is, net emissions rather than net emissions avoided) were not included in subsequent calculations since they would not be avoiding any emissions in that scenario.Total net cost was then divided by total net emissions avoided to get a final value for cost per ton of CO2-eq emissions avoided:$$c_{pertonprod} = frac{{c_{prodnet}}}{{e_{prodnet}}}$$
(27)
where cpertonprod is the total net cost per ton of CO2-eq emissions avoided by seaweed products ($ per tCO2-eq avoided), cprodnet is total net cost per ton seaweed DW harvested for products as calculated in equation (25) ($ tDW−1) and eprodnet is total net CO2-eq emissions avoided per ton seaweed DW harvested for products as calculated in equation (26) (tCO2 tDW−1).Parameter ranges for Monte Carlo simulationsFor technoeconomic parameters with two or more literature values (see Supplementary Table 1), we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions. For parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. Values at each end of parameter ranges were then rounded before Monte Carlo simulations as follows: capital costs, operating costs and harvest costs to the nearest $10,000 km−2, labour costs and insurance costs to the nearest $1,000 km−2, line costs to the nearest $0.05 m−1, transport costs to the nearest $0.05 t−1 km−1, transport emissions to the nearest 0.000005 tCO2 t−1 km−1, maintenance transport emissions to the nearest 0.0005 tCO2 km−1, product-avoided emissions to the nearest 0.1 tCO2-eq tDW−1, conversion cost down to the nearest $10 tDW−1 on the low end of the range and up to the nearest $10 tDW−1 on the high end of the range, and conversion emissions to the nearest 0.01 tCO2 tDW−1.We extended the minimum range values of capital costs to $10,000 km−2 and transport emissions to 0 to reflect potential future innovations, such as autonomous floating farm setups that would lower capital costs and net-zero emissions boats that would result in 0 transport emissions. To calculate the minimum value of $10,000 km−2 for a potential autonomous floating farm, we assumed that the bulk of capital costs for such a system would be from structural lines and flotation devices, and we therefore used the annualized structural line (system rope) and buoy costs from ref. 41 rounded down to the nearest $5,000 km−2. The full ranges used for our Monte Carlo simulations and associated literature values are shown in Supplementary Table 1.Distance to optimal sinking pointDistance to the optimal sinking point was calculated using a weighted distance transform (path-finding algorithm, modified from code in ref. 60) that finds the shortest ocean distance from each seaweed growth pixel to the location at which the net CO2 removed is maximized (including impacts of both increased sequestration fraction and transport emissions for different potential sinking locations) and the net cost is minimized. This is not necessarily the location in which the seaweed was grown, since the fraction of sunk carbon that remains sequestered for 100 yr is spatially heterogeneous (see ref. 57). For each ocean grid cell, we determined the cost-optimal sinking point by iteratively calculating equations (11–20) and assigning dsink as the distance calculated by weighted distance transform to each potential sequestration fraction (0.01–1.00) in increments of 0.01. Except for transport emissions, the economic parameter values used for these calculations were the averages of unrounded literature value ranges; we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions, or for parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. For transport and maintenance transport emissions, we extended the minimum values of the literature ranges to zero to reflect potential net-zero emissions transport options and used the mean values of the resulting ranges. The dsink that resulted in minimum net cost per ton CO2 for each ocean grid cell was saved as the final dsink map, and the associated sequestration fraction value that the seaweed is transported to via dsink was assigned to the original cell where the seaweed was farmed and harvested (Supplementary Fig. 19). If the cost-optimal location to sink using this method was the same cell where the seaweed was harvested, then dsink was 0 km and the sequestration fraction was not modified from its original value (Supplementary Fig. 18).Comparison of gigaton-scale sequestration area to previous estimatesPrevious related work estimating the ocean area suitable for macroalgae cultivation13 and/or the area that might be required to reach gigaton-scale carbon removal via macroalgae cultivation13,19,36 has yielded a wide range of results, primarily due to differences in modelling methods. For example, Gao et al. (2022)36 estimate that 1.15 million km2 would be required to sequester 1 GtCO2 annually when considering carbon lost from seaweed biomass/sequestered as particulate organic carbon (POC) and refractory dissolved organic carbon (rDOC), and assume that the harvested seaweed is sold as food such that the carbon in the harvested seaweed is not sequestered. The area (0.31 million km2) required to sequester 1 GtCO2 in our study assumes that all harvested seaweed is sunk to the deep ocean to sequester carbon.Additionally, Wu et al.19 estimates that roughly 12 GtCO2 could be sequestered annually via macroalgae cultivation in approximately 20% of the world ocean area (that is, 1.67% ocean area per GtCO2), which is a much larger area per GtCO2 than our estimate of 0.085% ocean area. This notable difference arises for several reasons (including differences in yields, which in Wu et al. are around 500 tDW yr−1 in the highest-yield areas, whereas yields in our cheapest sequestration areas from G-MACMODS average 3,400 tDW km−2 yr−1) that arise from differences in model methodology. First, Wu et al. model temperate brown seaweeds, while our study considers different seaweed types, many of which have higher growth rates, and uses the most productive seaweed type for each ocean grid cell. The G-MACMODS seaweed growth model we use also has a highly optimized harvest schedule, includes luxury nutrient uptake (a key feature of macroalgal nutrient physiology) and does not directly model competition with phytoplankton during seaweed growth. Finally, tropical red seaweeds (the seaweed type in our cheapest sequestration areas) grow year-round, while others, such as the temperate brown seaweeds modelled by Wu et al., only grow seasonally. These differences all contribute to higher productivity in our model, leading to a smaller area required for gigaton-scale CO2 sequestration compared with Wu et al.Conversely, the ocean areas we model for seaweed-based CO2 sequestration or GHG emissions avoided are much larger than the 48 million km2 that Froehlich et al.13 estimate to be suitable for macroalgae farming globally. Although our maps show productivity and costs everywhere, the purpose of our modelling was to evaluate where different types of seaweed grow best and how production costs and product values vary over space, to highlight the lowest-cost areas (which are often the highest-producing areas) under various technoeconomic assumptions.Comparison of seaweed production costs to previous estimatesAlthough there are not many estimates of seaweed production costs in the scientific literature, our estimates for the lowest-cost 1% area of the ocean ($190–$2,790 tDW−1) are broadly consistent with previously published results: seaweed production costs reported in the literature range from $120 to $1,710 tDW−1 (refs. 40,41,61,62), but are highly dependent on assumed seaweed yields. For example, Camus et al.41 calculate a cost of $870 tDW−1 assuming a minimum yield of 12.4 kgDW m−1 of cultivation line (equivalent to 8.3 kgDW m−2 using 1.5 m spacing between lines). Using the economic values from Camus et al. but with our estimates of average yield for the cheapest 1% production cost areas (2.6 kgDW m−2) gives a much higher average cost of $2,730 tDW−1. Contrarily, van den Burg et al.40 calculate a cost of $1,710 tDW−1 using a yield of 20 tDW ha−1 (that is, 2.0 kg m−2). Instead assuming the average yield to be that from our lowest-cost areas (that is, 2.6 kgDW m−2 or 26 tDW ha−1) would decrease the cost estimated by van den Burg et al. (2016) to $1,290 tDW−1. Most recently, Capron et al.62 calculate an optimistic scenario cost of $120 tDW−1 on the basis of an estimated yield of 120 tDW ha−1 (12 kg m−2; over 4.5 times higher than the average yield in our lowest-cost areas). Again, instead assuming the average yield to be that in our lowest-cost areas would raise Capron et al.’s production cost to $540 tDW−1 (between the $190–$880 tDW−1 minimum to median production costs in the cheapest 1% areas from our model; Fig. 1a,b).Data sourcesSeaweed biomass harvestedWe used spatially explicit data for seaweed harvested globally under both ambient and limited-nutrient scenarios from the G-MACMODS seaweed growth model33.Fraction of deposited carbon sequestered for 100 yrWe used data from ref. 57 interpolated to our 1/12-degree grid resolution.Distance to the nearest portWe used the Distance from Port V1 dataset from Global Fishing Watch (https://globalfishingwatch.org/data-download/datasets/public-distance-from-port-v1) interpolated to our 1/12-degree grid resolution.Significant wave heightWe used data for annually averaged significant wave height from the European Center for Medium-range Weather Forecasts (ECMWF) interpolated to our 1/12-degree grid resolution.Ocean depthWe used data from the General Bathymetric Chart of the Oceans (GEBCO).Shipping lanesWe used data of Automatic Identification System (AIS) signal count per ocean grid cell, interpolated to our 1/12-degree grid resolution. We defined a major shipping lane grid cell as any cell with >2.25 × 108 AIS signals, a threshold that encompasses most major trans-Pacific and trans-Atlantic shipping lanes as well as major shipping lanes in the Indian Ocean, the North Sea, and coastal routes worldwide.Marine protected areas (MPAs)We used data from the World Database on Protected Areas (WDPA) and defined an MPA as any ocean MPA >20 km2.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).Article 
CAS 

Google Scholar 
Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. 52, 273–288 (2005).Article 

Google Scholar 
Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invadsional meltdown? Biol. Invasions 1, 21–32 (1999).Article 

Google Scholar 
Montgomery, W. I., Lundy, M. G. & Reid, N. ‘Invasional meltdown’: evidence for unexpected conseuences and cumulative impacts of multispecies invasions. Biol. Invasions 14, 1111–1115 (2012).Article 

Google Scholar 
Jackson, M. C. Interactions among multiple invasive animals. Ecology 96, 2035–2041 (2015).Article 
CAS 

Google Scholar 
Braga, R. R. et al. Invasional meltdown: an experimental test and a framework to distinguish synergistic, additive, and antagonistic effects. Hydrobiologia 847, 1603–1618 (2020).Article 

Google Scholar 
Crooks, K. R. & Soulé, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566 (1999).Article 
CAS 

Google Scholar 
Klemmer, A. J., Wissinger, S. A., Greig, H. S. & Ostrofsky, M. L. Nonlinear effects of consumer density on multiple ecosystem processes. J. Anim. Ecol. 81, 779–780 (2012).Article 

Google Scholar 
De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).Article 

Google Scholar 
Vitousek, P. M., D’Antonio, C. M., Loope, L. L. & Westbrooks, R. Biological invasions as global environmental change. Am. Sci. 84, 468–478 (1996).
Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).Article 
CAS 

Google Scholar 
Liebig, J. et al. Bythotrephes longimanus: U.S. Geological Survey, Nonindigenous Aquatic Species Database (2021). Available at: https://nas.er.usgs.gov/queries/factsheet.aspx?SpeciesID=162.Benson, A.J. et al. Dreissena polymorpha (Pallas, 1771): U.S. Geological Survey, Nonindigenous Aquatic Species Database (2021). Available at: https://nas.er.usgs.gov/queries/FactSheet.aspx?speciesID=5Stewart, T. J., Johannsson, O. E., Holeck, K., Sprules, W. G. & O’Gorman, R. The Lake Ontario zooplankton community before (1987-1991) and after (2001-2005) invasion-induced ecosystem change. J. Gt. Lakes Res. 36, 596–605 (2010).Article 

Google Scholar 
Strecker, A. L. et al. Direct and indirect effects of an invasive planktonic predator on pelagic food webs. Limnol. Oceanogr. 56, 179–192 (2011).Article 

Google Scholar 
Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. Zebra versus quagga mussels: a review of their spread, population dynamics, and ecosystem impacts. Hydrobiologia 746, 97–112 (2015).Article 
CAS 

Google Scholar 
Kerfoot, W. C. et al. A plague of waterfleas (Bythotrephes): impacts on microcrustacean community structure, seasonal biomass, and secondary production in a large inland-lake complex. Biol. Invasions 18, 1121–1145 (2016).Article 

Google Scholar 
Strayer, D. et al. Long-term variability and density dependence in Hudson River Dreissena populations. Freshw. Biol. 65, 474–489 (2019).Article 

Google Scholar 
Fang, X., Stefan, H. G., Jiang, L., Jacobson, P. C. & Pereira D. L. Projected impacts of climatic changes on cisco oxythermal habitat in Minnesota lakes and management strategies in Handbook of Climate Change mitigation and Adaptation (eds. Chen, W.-Y., Suzuki, T. & Lackner, M.) 657-722 (Springer, 2015).Stefan, H. G., Hondzo, M., Fang, X., Eaton, J. G. & McCormick, J. H. Simulated long-term temperature and dissolved oxygen characteristics of lakes in the north-central United States and associated fish habitat limits. Limnol. Oceanogr. 41, 1124–1135 (1996).Article 

Google Scholar 
Jacobson, P. C., Jones, T. S., Rivers, P. & Pereira, D. L. Field estimation of a lethal oxythermal niche boundary for adult ciscoes in Minnesota lakes. T. Am. Fish. Soc. 137, 1464–1474 (2008).Article 

Google Scholar 
Hecky, R. E. et al. The nearshore phosphorus shunt: a consequence of ecosystem engineering by dreissenids in the Laurentian Great Lakes. Can. J. Fish. Aquat. Sci. 61, 1285–1293 (2004).Article 
CAS 

Google Scholar 
Sousa, R., Gutiérrez, J. L. & Aldridge, D. C. Non-indigenous invasive bivalves as ecosystem engineers. Biol. Invasions 11, 2367–2385 (2009).Article 

Google Scholar 
Higgins, S. N. & Vander Zanden, M. J. What a difference a species makes: a meta-analysis of dreissenid mussel impacts on freshwater ecosystems. Ecol. Monogr. 80, 179–196 (2010).Article 

Google Scholar 
Mayer, C. M. et al. Benthification of Freshwater Lakes: Exotic Mussels Turning Ecosystems Upside Down in Quagga and Zebra Mussels: Biology, Impacts, and Control, 2nd ed. (eds. Nalepa, T. F. & Schloesser D. W.) 575-586 (CRC Press, 2014).Lehman, J. T. & Cárcres, C. E. Food-web responses to species invasion by a predatory invertebrate: Bythotrephes in Lake Michigan. Limnol. Oceanogr. 38, 879–891 (1993).Article 

Google Scholar 
Bunnell, D. B., Keeler, K. M., Puchala, E. A., Davis, B. M. & Pothoven, S. A. Comparing seasonal dynamics of the Lake Huron zooplankton community between 1983-1984 and 2007 and revisiting the impact of Bythotrephes planktivory. J. Gt. Lakes Res. 38, 451–462 (2012).Article 

Google Scholar 
Pawlowski, M. B., Branstrator, D. K., Hrabik, T. R. & Sterner, R. W. Changes in the cladoceran community of Lake Superior and thee role of Bythotrephes longimanus. J. Gt. Lakes Res. 43, 1101–1110 (2017).Article 

Google Scholar 
Hoffman, J. C., Smith, M. E. & Lehman, J. T. Perch or plankton: top-down control of Daphnia by yellow perch (Perca flavescens) or Bythotrephes cederstroemi in an inland lake? Freshw. Biol. 46, 759–775 (2001).Article 

Google Scholar 
Bunnell, D. B., Davis, B. M., Warner, D. M., Chriscinske, M. A. & Roseman, E. F. Planktivory in the changing Lake Huron zooplankton community: Bythotrephes consumption exceeds that of Mysis and fish. Freshw. Biol. 56, 1281–1296 (2011).Article 

Google Scholar 
Merkle, C. & De Stasio, B. Bythotrephes longimanus in shallow, nearshore waters: interactions with Leptodora kindtii, impacts on zooplankton, and implications for secondary dispersal from southern Green Bay, Lake Michigan. J. Gt. Lakes Res. 44, 934–942 (2018).Article 

Google Scholar 
Walsh, J. R., Carpenter, S. R. & Vander Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl. Acad. Sci. USA 113, 4081–4085 (2016).Article 
CAS 

Google Scholar 
Lehman, J. T. Algal biomass unaltered by food-web changes in Lake Michigan. Nature 332, 537–538 (1988).Article 

Google Scholar 
Wahlström, E. & Westman, E. Planktivory by the predacious cladoceran Bythotrephes longimanus: effects on zooplankton size structure and density. Can. J. Fish. Aquat. Sci. 56, 1865–1872 (1999).Article 

Google Scholar 
Strecker, A. L. & Arnott, S. E. Invasive predator, Bythotrephes, has varied effects on ecosystem function in freshwater lakes. Ecosystems 11, 490–503 (2008).Article 

Google Scholar 
Benke, A. C. Concepts and patterns of invertebrate production in running waters. Verh. Int. Theor. Angew. Limnol. 25, 15–38 (1993).
Google Scholar 
Jones, T. & Montz, G. Population increase and associated effects of zebra mussels Dreissena polymorpha in Lake Mille Lacs, Minnesota, U.S.A. Bioinvasion Rec. 9, 772–792 (2020).Article 

Google Scholar 
Strayer, D. L. & Malcom, H. M. Long-term demography of a zebra mussel (Dreissena polymorpha) population. Freshw. Biol. 51, 117–130 (2006).Article 

Google Scholar 
Geisler, M. E., Rennie, M. D., Gillis, D. M. & Higgins, S. N. A predictive model for water clarity following dreissenid invasion. Biol. Invasions 18, 1989–2006 (2016).Article 

Google Scholar 
Barbiero, R. P. & Tuchman, M. L. Long-term dreissenid impacts on water clarity in Lake Erie. J. Gt. Lakes Res. 30, 557–565 (2004).Article 

Google Scholar 
Fishman, D. B., Adlerstein, S. A., Vanderploeg, H. A., Fahnenstiel, G. L. & Scavia, D. Causes of phytoplankton changes in Saginaw Bay, Lake Huron, during the zebra mussel invasion. J. Gt. Lakes Res. 35, 482–495 (2009).Article 

Google Scholar 
Zhang, H., Culver, D. A. & Boegman, L. Dreissenids in Lake Erie: an algal filter or a fertilizer? Aquat. Invasions 6, 175–194 (2011).Article 

Google Scholar 
Higgins, S. N., Vander Zanden, M. J., Joppa, L. N. & Vadeboncoeur, Y. The effect of dreissenid invasions on chlorophyll and the chlorophyll: total phosphorus ration in north-temperate lakes. Can. J. Fish. Aquat. Sci. 68, 319–329 (2011).Article 
CAS 

Google Scholar 
Lehman, J. T. & Branstrator, D. K. A model for growth, development, and diet selection by the invertebrate predator Bythotrephes cederstroemi. J. Gt. Lakes Res. 21, 610–619 (1995).Article 

Google Scholar 
Azan, S. S. E., Arnott, S. E. & Yan, N. D. A review of the effects of Bythotrephes longimanus and calcium decline on zooplankton communities – can interactive effects be predicted? Environ. Rev. 23, 395–413 (2015).Article 
CAS 

Google Scholar 
Pangle, K. L., Peacor, S. D. & Johannsson, O. E. Large nonlethal effects of an invasive invertebrate predator on zooplankton population growth rate. Ecology 88, 402–412 (2007).Article 

Google Scholar 
Cross, T. K. & Jacobson, P. C. Landscape factors influencing lake phosphorus concentrations across Minnesota. Lake Reserv Manag 29, 1–12 (2013).Article 
CAS 

Google Scholar 
McQueen, D. J., Johannes, M. R. S., Post, J. R., Stewart, T. J. & Lean, D. R. S. Bottom-up and top-down impacts on freshwater pelagic community structure. Ecol. Monogr. 59, 289–309 (1989).Article 

Google Scholar 
Mills, E. L. et al. Lake Ontario: food web dynamics in a changing ecosystem (1970-2000). Can. J. Fish. Aquat. Sci. 60, 471–490 (2003).Article 

Google Scholar 
Lehman, J. T. Causes and consequences of cladoceran dynamics in Lake Michigan: implications of species invasion by. Bythotrephes. J. Gt. Lakes Res. 17, 437–445 (1991).Article 

Google Scholar 
Yan, N. D. et al. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interactive effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877 (2008).Article 
CAS 

Google Scholar 
Brooks, J. L. & Dodson, S. I. Predation, body size, and composition of plankton. Science 150, 28–35 (1965).Article 
CAS 

Google Scholar 
Rennie, M. D., Evans, D. O. & Young, J. D. Increased dependence on nearshore benthic resources in the Lake Simcoe ecosystem after dreissenid invasion. Inland Waters 3, 297–310 (2013).Article 

Google Scholar 
Goto, D., Dunlop, E. S., Young, J. D. & Jackson, D. A. Shifting trophic control of fishery-ecosystem dynamics following biological invasions. Ecol. Appl. 30, e02190 (2020).Article 

Google Scholar 
Hansen, G. J. A. et al. Walleye growth declines following zebra mussel and Bythotrephes invasion. Biol. Invasions 22, 1481–1495 (2020).Article 

Google Scholar 
Yan, N. & Pawson, T. Changes in the crustacean zooplankton community of Harp Lake, Canada, following invasion by. Bythotrephes cederstrœmi. Freshw. Biol. 37, 409–425 (1997).Article 

Google Scholar 
Bourdeau, P. E., Bach, M. T. & Peacor, S. D. Predator presence dramatically reduces copepod abundance through condition-mediated non-consumptive effects. Freshw. Biol. 61, 1020–1031 (2016).Article 
CAS 

Google Scholar 
Lehman, J. R. Ecological principles affecting community structure and secondary production by zooplankton in marine and freshwater environments. Limnol. Oceanogr. 33, 931–945 (1988).
Google Scholar 
Walsh, J. R., Lathrop, R. C., & Vander Zanden, M.J.Invasive invertebrate predator, Bythotrephes longimanus, reverses trophic cascade in a north-temperate lake. Limnol. Oceanogr. 62, 2498–2509 (2017).Article 

Google Scholar 
Underwood, A. J. On beyond BACI: sampling designs that might reliably detect environmental disturbances. Ecol. Appl. 4, 3–15 (1994).Article 

Google Scholar 
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).Article 
CAS 

Google Scholar 
Strayer, D. L., Eviner, V. T., Jeschke, J. M. & Pace, M. L. Understanding the long-term effects of species invasions. Trends Ecol. Evol. 21, 645–651 (2006).Article 

Google Scholar 
Magnuson, J. J. Long-term ecological research and the invisible present. BioScience 40, 495–501 (1990).Article 

Google Scholar 
Doak, D. F. et al. Understanding and predicting ecological dynamics: are major surprises inevitable? Ecology 89, 952–961 (2008).Article 

Google Scholar 
Hansen, G. J. A., Gaeta, J. W., Hansen, J. F. & Carpenter, S. R. Learning to manage and managing to learn: sustaining freshwater recreational fisheries in a changing environment. Fisheries 40, 56–64 (2015).Article 

Google Scholar 
Dumont, H. J., Van De Velde, I. & Dumont, S. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19, 75–97 (1975).Article 

Google Scholar 
Culver, D. A., Boucherle, M. M., Bean, D. J. & Fletcher, J. W. Biomass of freshwater crustacean zooplankton from length-weight regressions. Can. J. Fish. Aquat. Sci. 42, 1380–1390 (1985).Article 

Google Scholar 
Manly, B. F. J. Randomization, bootstrap and Monet Carlo methods in biology, 3rd ed. (Chapman and Hall/CRC, 2007).Arar, E. J. Method 446.0. In vitro determination of chlorophylls a, b, c1 + c2 and pheopigments in marine and freshwater algae by visible spectrophotometry, revision 1.2. (U.S. Environmental Protection Agency, 1997).O’Dell, J. W. Method 365.1 Determination of phosphorus by semi-automated colorimetry, revision 2.0. (U.S. Environmental Agency, 1993).Helsel, D. R. & Hirsch, R. M. Statistical methods in water resources (U. S. Geological Survey, 2002).Minnesota Pollution Control Agency (MPCA). Surface water data. https://webapp.pca.state.mn.us/wqd/surface-water (MPCA, 2021).Read, J. S. et al. Data release: Process-based predictions of lake water temperature in the Midwest US: U.S. Geological Survey data release, https://doi.org/10.5066/P9CA6XP8 (USGS, 2021).Hothorn, T., Hornik, K., van de Wiel, M. A. & Zeileis, A. Lego system for conditional inference. Am. Stat. 60, 257–263 (2006).Article 

Google Scholar 
Dewitz, J. National Land Cover Database (NLCD) 2016 Products: U.S. Geological Survey data release, https://doi.org/10.5066/P96HHBIE (USGS, 2019).Use of Fishes in Research Committee. Guidelines for the use of fishes in research. (American Fisheries Society, 2014)Pedersen, E. J., Miller, D. L., Simpson, G. L. & Ross, N. Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ 7, e6876 (2019).Article 

Google Scholar 
Minnesota Geospatial Commons. DNR Hydrology Dataset. (2022). Available at: https://gisdata.mn.gov/dataset/water-dnr-hydrography.Minnesota Geospatial Commons. Lake Bathymetric Outlines, Contours, and DEM. (2021). Available at: https://gisdata.mn.gov/dataset/water-lake-bathymetry.ESRI ArcGIS Desktop: Release 10.6. Redlands, CA: Environmental Systems Research Institute (2018).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. 73, 3–36 (2011).Article 

Google Scholar 
Guerrero, F. & Rodríguez, V. Secondary production of a congeneric species assemblage of Acartia (Copepoda: Calanoida): a calculation based on the size-frequency distribution. Sci. Mar. 58, 161–167 (1994).
Google Scholar 
Cross, W. F. et al. Ecosystem ecology meets adaptive management: food web response to a controlled flood on the Colorado River, Glen Canyon. Ecol. Appl. 21, 2016–2033 (2011).Article 

Google Scholar 
Gillooly, J. F. Effect of body size and temperature on generation time in zooplankton. J. Plankton Res. 22, 241–251 (2000).Article 

Google Scholar 
Benke, A. C. & Huryn, A. D. Secondary production and quantitative food webs in Methods in Stream Ecology, Volume 2: ecosystem function (eds. Lamberti, G.A. & Hauer, F. R.) 235-254 (Academic Press, 2017).Wu, L. & Culver, D. A. Zooplankton grazing and phytoplankton abundance: an assessment before and after invasion of Dreissena polymorpha. J. Gt. Lakes Res. 17, 425–436 (1991).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (Vienna, Austria, 2020). Accessible at: https://www.R-project.org/Minnesota Geospatial Commons. State Boundary. (2013). Available at: https://gisdata.mn.gov/dataset/bdry-state-of-minnesota.United States Geological Survey. North America Political Boundaries. (2006). Available at: https://www.sciencebase.gov/catalog/item/4fb555ebe4b04cb937751db9. More

Correction to: Communications Biology https://doi.org/10.1038/s42003-020-1022-1, published online 11 June 2020.In the original version of the Perspective, a unit conversion error affected calculations for cereal rye, triticale, barley, and oats. Further, berseem clover yield estimates were mistranscribed from the original source. These mistakes led to errors in Supplementary Data 1, Figure 2 and in the presentation of the data in the text.Supplementary Data 1 has now been replaced with a file containing the correct numbers.Figure 2 has been corrected:Original figure 2New figure 2The Abstract stated: “In this Perspective, we estimate land use requirements to supply the United States maize production area with cover crop seed, finding that across 18 cover crops, on average 3.8% (median 2.0%) of current production area would be required, with the popular cover crops rye and hairy vetch requiring as much as 4.5% and 11.9%, respectively”.The text should read: “In this Perspective, we estimate land use requirements to supply the United States maize production area with cover crop seed, finding that across 18 cover crops, on average 2.4% (median 2.1%) of current production area would be required, with the popular cover crops rye and hairy vetch requiring as much as 4.8% and 11.9%, respectively”.In the 1st paragraph of the right hand column on page 2, the text said: “(…), we find that the land requirements for production of cover crop seed would be on average 1.4 million hectares (median 746,000 ha), which is equivalent to 3.8% (median 2.0%) of the U.S. maize farmland. Rye (Secale cereale L.) – a midrange seed yielding cover crop and one of the most commonly used in the corn belt, would require as much as 1,661,000 hectares (4.5% of maize farmland), (…)”The text should read: “(…) we find that the land requirements for production of cover crop seed would be on average 892,526 hectares (median 774,417 ha), which is equivalent to 2.4% (median 2.1%) of the U.S. maize farmland. Rye (Secale cereale L.) – a midrange seed yielding cover crop and one of the most commonly used in the corn belt, would require as much as 1,779,770 hectares (4.8% of maize farmland), (…)”On page 3, second paragraph the text said: “Cover cropping the entire U.S. maize area would require the equivalent of as much as 18% (rye) to 49% (hairy vetch) (…)”The text should read: “Cover cropping the entire U.S. maize area would require the equivalent of as much as 19% (rye) to 49% (hairy vetch) (…)”This errors have now been corrected in the Perspective Article. More

Jordan, D. S. A classification of fishes including families and genera as far as know. Stanford University Publications. Bio. Sci. 3, 79–243. https://doi.org/10.5962/bhl.title.161386 (1923).Article 

Google Scholar 
Akihito, et al. Evolutionary aspects of gobioid fishes based on an analysis of mitochondrial cytochrome b genes. Gene 259, 5–15 (2000).Article 
CAS 

Google Scholar 
Wang, H.-Y., Tsai, M.-P., Dean, J. & Lee, S.-C. Molecular phylogeny of gobioid Wshes (Perciformes: Gobioidei) based on mitochondrial 12S rRNA sequences. Mol. Phylogenet. Evol. 20, 390–408. https://doi.org/10.1016/j.ympev.2005.05.004 (2001).Article 
CAS 

Google Scholar 
Nelson, J. S., Grande, T. C. & Wilson, M. V. Fishes of the World (Wiley, 2016).Book 

Google Scholar 
Fricke, R., Eschmeyer, W. N. & Van der Laan, R. Eschmeyer’s Catalog of fishes: Genera, Species, references. (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp) (Accessed 15 June 2022).Guimarães-Costa, A. et al. Molecular evidence of two new species of Eleotris (Gobiiformes: Eleotridae) in the western Atlantic. Mol. Phylogenet. Evol. 98, 52–56. https://doi.org/10.1016/j.ympev.2016.01.014 (2016).Article 

Google Scholar 
Thacker, C. E. & Hardman, M. A. Molecular phylogeny of basal gobioid fishes: Rhyacichthyidae, Odontobutidae, Xenisthmidae, Eleotridae (Teleostei: Perciformes: Gobioidei). Mol. Phylogenet. Evol. 37, 858–887. https://doi.org/10.1016/j.ympev.2005.05.004 (2005).Article 
CAS 

Google Scholar 
Nordlie, F. G. Life-history characteristics of eleotrid fishes of the western hemisphere, and perils of life in a vanishing environment. Rev. Fish Biol. Fisher. 22(1), 189–224. https://doi.org/10.1007/s11160-011-9229-3 (2012).Article 

Google Scholar 
Berra, T. M. Freshwater Fish Distribution (Academic Press, 2001).
Google Scholar 
Graham, J. B. Air-Breathing Fishes: Evolution, Diversity, and Adaptation (Academic Press, 1997).Book 

Google Scholar 
Thacker, C. E. Phylogeny of Gobioidea and its placement within Acanthomorpha, with a new classification and investigation of diversification and character evolution. Copeia 1, 93–104. https://doi.org/10.1643/CI-08-004 (2009).Article 

Google Scholar 
Chakrabarty, P., Davis, M. P. & Sparks, J. S. The first record of a trans-oceanic sister-group relationship between obligate vertebrate troglobites. PLoS One 7, e44083. https://doi.org/10.1371/journal.pone.0044083 (2012).Article 
ADS 
CAS 

Google Scholar 
Agorreta, A. et al. Molecular phylogenetics of Gobioidei and phylogenetic placement of European gobies. Mol. Phylogenet. Evol. 69, 619–633. https://doi.org/10.1016/j.ympev.2013.07.017 (2013).Article 

Google Scholar 
McCraney, W. T., Thacker, C. E. & Alfaro, M. E. Supermatrix phylogeny resolves goby lineages and reveals unstable root of Gobiaria. Mol. Phylogenet. Evol. 151, 106862. https://doi.org/10.1016/j.ympev.2020.106862 (2020).Article 

Google Scholar 
Karl, S. A. & Avise, J. C. Balancing selection at allozyme loci in oysters: Implications from nuclear RFLPs. Science 256, 100. https://doi.org/10.1126/science.1348870 (1992).Article 
ADS 
CAS 

Google Scholar 
Hey, J. & Machado, C. A. The study of structured populations—New hope for a difficult and divided science. Nat. Rev. Genet. 4, 535–543. https://doi.org/10.1038/nrg1112 (2003).Article 
CAS 

Google Scholar 
Castroviejo-Fisher, S., Guayasamin, J. M., Gonzalez-Voyer, A. & Vilà, C. Neotropical diversification seen through glassfrogs. J. Biogeogr. 41, 66–80. https://doi.org/10.1111/jbi.12208 (2014).Article 

Google Scholar 
Dornburg, A., Townsend, J. P., Friedman, M. & Near, T. J. Phylogenetic informativeness reconciles ray-finned fish molecular divergence times. BMC Evol. Biol. 14, 169. https://doi.org/10.1186/s12862-014-0169-0 (2014).Article 

Google Scholar 
Hundt, P. J., Iglésias, S. P., Hoey, A. S. & Simons, A. M. A multilocus molecular phylogeny of combtooth blennies (Percomorpha: Blennioidei: Blenniidae): Multiple invasions of intertidal habitats. Mol. Phylogenet. Evol. 70, 47–56. https://doi.org/10.1016/j.ympev.2013.09.001 (2014).Article 

Google Scholar 
Olave, M., Avila, L. J., Sites, J. W. & Morando, M. Multilocus phylogeny of the widely distributed South American lizard clade Eulaemus (Liolaemini, Liolaemus). Zool. Scr. 43, 323–337. https://doi.org/10.1111/zsc.12053 (2014).Article 

Google Scholar 
Meyer, B. S., Matschiner, M. & Salzburger, W. A tribal level phylogeny of Lake Tanganyika cichlid fishes based on a genomic multi-marker approach. Mol. Phylogenet. Evol. 83, 56–71. https://doi.org/10.1016/j.ympev.2014.10.009 (2015).Article 

Google Scholar 
Jønsson, K. A. et al. A supermatrix phylogeny of corvoid passerine birds (Aves: Corvides). Mol. Phylogenet. Evol. 94, 87–94. https://doi.org/10.1016/j.ympev.2015.08.020 (2016).Article 

Google Scholar 
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475(7357), 493–496. https://doi.org/10.1038/nature10231 (2011).Article 
CAS 

Google Scholar 
Frantz, R. S. X-efficiency: Theory, Evidence and Applications Vol. 2 (Springer Science & Business Media, 2013).
Google Scholar 
Bessa-Silva, A. et al. The roles of vicariance and dispersal in the differentiation of two species of the Rhinella marina species complex. Mol. Phylogenet. Evol. 145, 106723. https://doi.org/10.1016/j.ympev.2019.106723 (2020).Article 

Google Scholar 
Leutenegger, W. Maternal-fetal weight relationships in primates. Folia Primatol. 20(4), 280–293. https://doi.org/10.1159/000155580 (1973).Article 
CAS 

Google Scholar 
Yeh, J. The effect of miniaturized body size on skeletal morphology in frogs. Evolution 56(3), 628–641. https://doi.org/10.1111/j.0014-3820.2002.tb01372.x (2002).Article 

Google Scholar 
Daza, J. D. et al. An enigmatic miniaturized and attenuate whole lizard from the Mid-Cretaceous amber of Myanmar. Breviora 563(1), 1–18. https://doi.org/10.3099/MCZ49.1 (2018).Article 

Google Scholar 
Hanken, J. & Wake, D. B. Miniaturization of body size: Organismal consequences and evolutionary significance. Annu. Rev. Ecol. Evol. Syst. 24(1), 501–519. https://doi.org/10.1146/annurev.es.24.110193.002441 (1993).Article 

Google Scholar 
Britz, R. & Conway, K. W. Osteology of Paedocypris, a miniature and highly developmentally truncated fish (Teleostei: Ostariophysi: Cyprinidae). J. Morphol. 270(4), 389–412. https://doi.org/10.1002/jmor.10698 (2009).Article 
CAS 

Google Scholar 
Britz, R., Conway, K. W. & Ruber, L. Spectacular morphological novelty in a miniature cyprinid fish, Danionella dracula n. sp.. Proc. R. Soc. Lond. 276(1665), 2179–2186. https://doi.org/10.1098/rspb.2009.0141 (2009).Article 

Google Scholar 
Weitzman, S. H. & Vari, R. P. Miniaturization in South American freshwater fishes; an overview and discussion. Proc. Biol. Soc. Wash. 101(2), 444–465 (1988).
Google Scholar 
Toledo-Piza, M., Mattox, G. M. & Britz, R. Priocharax nanus, a new miniature characid from the rio Negro, Amazon basin (Ostariophysi: Characiformes), with an updated list of miniature Neotropical freshwater fishes. Neotrop. Ichthyol. 12(2), 229–246. https://doi.org/10.1590/1982-0224-20130171 (2014).Article 

Google Scholar 
Caires, R. A. & Figueiredo, J. L. Review of the genus Microphilypnus Myers, 1927 (Teleostei: Gobioidei: Eleotridae) from the lower Amazon basin, with description of one new species. Zootaxa 3036, 39–57. https://doi.org/10.11646/zootaxa.3036.1.3 (2011).Article 

Google Scholar 
Caires, R. A. Microphilypnus tapajosensis, a new species of eleotridid from the Tapajós basin, Brazil (Gobioidei: Eleotrididae). Ichthyol. Explor. Freshw. 23, 155–160 (2013).
Google Scholar 
Caires, R. A. & Guimarães-Costa, A. Family Eleotridae. In Field Guide to Amazonian Fishes (eds van Sleen, P. & Albert, J.) 388–391 (Princeton University Press, 2017).
Google Scholar 
Caires, R. A. & Toledo-Piza, M. A New species of miniature fish of the genus Microphilypnus (Gobioidei: Eleotridae) from the upper Rio Negro Basin, Amazonas Brazil. Copeia 106(1), 49–55. https://doi.org/10.1643/CI-17-634 (2018).Article 

Google Scholar 
Roberts, T.R. Leptophilypnion, a new genus with two new species of tiny central Amazonian gobioid fishes (Teleostei, Eleotridae). Aqua (2013).Gould, R. E. & Delevoryas, T. The biology of Glossopteris: Evidence from petrified seed-bearing and pollen-bearing organs. Alcheringa 1(4), 387–399 (1977).Article 

Google Scholar 
Rüber, L., Kottelat, M., Tan, H. H., Ng, P. K. & Britz, R. Evolution of miniaturization and the phylogenetic position of Paedocypris, comprising the world’s smallest vertebrate. BMC Evol. Biol. 7(1), 1–10. https://doi.org/10.1186/1471-2148-7-38 (2007).Article 
CAS 

Google Scholar 
Britz, R., Conway, K. W. & Rüber, L. Miniatures, morphology and molecules: Paedocypris and its phylogenetic position (Teleostei, Cypriniformes). Zool. J. Linn. Soc. 172(3), 556–615. https://doi.org/10.1111/zoj.12184 (2014).Article 

Google Scholar 
Bloom, D. D., Kolmann, M., Foster, K. & Watrous, H. Mode of miniaturisation influences body shape evolution in New World anchovies (Engraulidae). J. Fish Biol. 96(1), 194–201 (2019).Article 

Google Scholar 
Thacker, C. E. Molecular phylogeny of the gobioid fishes (Teleostei: Perciformes: Gobioidei). Mol. Phylogenet. Evol. 26, 354–368. https://doi.org/10.1016/S1055-7903(02)00361-5 (2003).Article 
CAS 

Google Scholar 
Birdsong, R. S., Murdy, E. O. & Pezold, F. L. A study of the vertebral column and median fin osteology in gobioid fishes with comments on gobioid relationships. Bull. Mar. Sci. 42(2), 174–214 (1988).
Google Scholar 
Thacker, C. E. Patterns of divergence in fish species separated by the Isthmus of Panama. BMC Evol. Biol. 17(1), 1–14. https://doi.org/10.1186/s12862-017-0957-4 (2017).Article 

Google Scholar 
Galván-Quesada, S. et al. Molecular phylogeny and biogeography of the amphidromous fish genus Dormitator Gill 1861 (Teleostei: Eleotridae). PLoS One 11(4), e0153538. https://doi.org/10.1371/journal.pone.0153538 (2016).Article 
CAS 

Google Scholar 
Lessios, H. A. The great American schism: Divergence of marine organisms after therise of the central American isthmus. Annu. Rev. Ecol. Evol. Syst. 2008(39), 63–92. https://doi.org/10.1146/annurev.ecolsys.38.091206.095815 (2008).Article 

Google Scholar 
Lovejoy, N. R., Albert, J. S. & Crampton, W. G. Miocene marine incursions and marine/freshwater transitions: Evidence from Neotropical fishes. J. S. Am. Earth Sci. 21, 5–13. https://doi.org/10.1016/j.jsames.2005.07.009 (2006).Article 

Google Scholar 
Cooke, G. M., Chao, N. L. & Beheregaray, L. B. Marine incursions, cryptic species and ecological diversification in Amazonia: The biogeographic history of the croaker genus Plagioscion (Sciaenidae). J. Biogeogr. 39, 724–738. https://doi.org/10.1111/j.1365-2699.2011.02635.x (2012).Article 

Google Scholar 
Bloom, D. D. & Lovejoy, N. R. On the origins of marine-derived freshwater fishes in South America. J. Biogeogr. 44(9), 1927–1938. https://doi.org/10.1111/jbi.12954 (2017).Article 

Google Scholar 
Monsch, K. A. Miocene fish faunas from the northwestern Amazonia basin (Colombia, Peru, Brazil) with evidence of marine incursions. Palaeogeogr. Palaeoclimatol. Palaeoecol. 143, 31–50. https://doi.org/10.1016/S0031-0182(98)00064-9 (1998).Article 

Google Scholar 
Hoorn, C. Marine incursions and the influence of Andean tectonics on the Miocene depositional history of northwestern Amazonia: Results of a palynostratigraphic study. Palaeogeogr. Palaeoclimatol. Palaeoecol. 105, 267–309. https://doi.org/10.1016/0031-0182(93)90087-Y (1993).Article 

Google Scholar 
Hoorn, C., Guerrero, J., Sarmiento, G. A. & Lorente, M. A. Andean tectonics as a cause for changing drainage patterns in Miocene northern South America. Geology 23, 237–240. https://doi.org/10.1130/0091-7613(1995)023%3C0237:ATAACF%3E2.3.CO;2 (1995).Article 
ADS 

Google Scholar 
Gingras, M. K., Rasanen, M. E., Pemberton, S. G. & Romero, L. P. Ichnology and sedimentology reveal depositional characteristics of bay-margin parasequences in the Miocene Amazonian foreland basin. J. Sediment. Res. 72, 871–883. https://doi.org/10.1306/052002720871 (2002).Article 
ADS 

Google Scholar 
Wesselingh, F. P. et al. Lake Pebas: A palaeoecological reconstruction of a Miocene, long-lived lake complex in western Amazonia. Cainoz. Res. 1, 35–81 (2002).
Google Scholar 
Bloom, D. D. & Lovejoy, N. R. Molecular phylogenetics reveals a pattern of biome conservatism in New World anchovies (family Engraulidae). J. Evol. Biol. 25(4), 701–715 (2012).Article 

Google Scholar 
Ward, A. B. & Azizi, E. Convergent evolution of the head retraction escape response in elongate fishes and amphibians. Zoology 107(3), 205–217. https://doi.org/10.1016/j.zool.2004.04.003 (2004).Article 

Google Scholar 
Palumbi, S. R. & Benzie, J. Large mitochondrial DNA differences between morphologically similar penaeid shrimp. Mol. Mar. Biol. Biotechnol. 1, 27–34 (1991).CAS 

Google Scholar 
Chen, W. J., Bonillo, C. & Lecointre, G. Repeatability of clades as criterion of reliability: A case study for molecular phylogeny of Acanthomorpha (Teleostei) with larger number of taxa. Mol. Phylogenet. Evol. 26, 262–288. https://doi.org/10.1016/j.gene.2008.07.016 (2003).Article 
CAS 

Google Scholar 
Chen, W. J., Miya, M., Saitoh, K. & Mayden, R. L. Phylogenetic utility of two existing and four novel nuclear gene loci in reconstructing Tree of Life of ray-finned fishes: The order Cypriniformes (Ostariophysi) as a case study. Gene 423, 125–134. https://doi.org/10.1016/j.gene.2008.07.016 (2008).Article 
CAS 

Google Scholar 
Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. PNAS 74(12), 5463–5467. https://doi.org/10.1073/pnas.74.12.5463 (1977).Article 
ADS 
CAS 

Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).Article 
CAS 

Google Scholar 
Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: Concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180 (2011).Article 

Google Scholar 
Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T. & Calcott, B. PartitionFinder 2: New methods for selecting partitioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msw260 (2016).Article 

Google Scholar 
Heled, J. & Drummond, A. J. Bayesian inference of population size history from multiple loci. BMC Evol. Biol. 8(1), 1–15. https://doi.org/10.1186/1471-2148-8-289 (2008).Article 
CAS 

Google Scholar 
Bouckaert, R. et al. BEAST 2: A software platform for bayesian evolutionary analysis. PLoS Comput. Biol. 10(4), e1003537. https://doi.org/10.1371/journal.pcbi.1003537 (2014).Article 
CAS 

Google Scholar 
Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4(5), e88. https://doi.org/10.1371/journal.pbio.0040088 (2006).Article 
CAS 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67(5), 901. https://doi.org/10.1093/sysbio/syy032 (2018).Article 
CAS 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214. https://doi.org/10.1186/1471-2148-7-214 (2007).Article 
CAS 

Google Scholar 
Rambaut, A. FigTree, a graphical viewer of phylogenetic trees (Version 1.4.3) (2017).Betancur-R, R. et al. Phylogenetic classification of bony fishes. BMC Evol. Biol. 17(1), 1–40. https://doi.org/10.1186/s12862-017-0958-3 (2017).Article 

Google Scholar 
Jones, G. Algorithmic improvements to species delimitation and phylogeny estimation under the multispecies coalescent. J. Math. Biol. 74, 447–467 (2017).Article 
MathSciNet 
MATH 

Google Scholar  More

Johannesson, K., Smolarz, K., Grahn, M. & André, C. The future of baltic sea populations: Local extinction or evolutionary rescue?. Ambio 40, 179–190 (2011).Article 
CAS 

Google Scholar 
Reusch, T. B. H. et al. The Baltic Sea as a time machine for the future coastal ocean. Sci. Adv. 4, eaar8195 (2018).Article 
ADS 

Google Scholar 
Kahru, M. & Elmgren, R. Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea. Biogeosciences 11, 3619–3633 (2014).Article 
ADS 

Google Scholar 
Kahru, M., Elmgren, R. & Savchuk, O. P. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009–1018 (2016).Article 
ADS 

Google Scholar 
Hjerne, O., Hajdu, S., Larsson, U., Downing, A. S. & Winder, M. Climate driven changes in timing, composition and magnitude of the Baltic Sea phytoplankton spring bloom. Front. Mar. Sci. 6, 482 (2019).Article 

Google Scholar 
Bianchi, T. S. et al. Cyanobacterial blooms in the Baltic Sea: Natural or human-induced?. Limnol. Oceanogr. 45, 716–726 (2000).Article 
ADS 
CAS 

Google Scholar 
Poutanen, E.-L. & Nikkilä, K. Carotenoid pigments as tracers of cyanobacterial blooms in recent and post-glacial sediments of the Baltic Sea. Ambio 30, 179–183 (2001).Article 
CAS 

Google Scholar 
Andersson, A., Höglander, H., Karlsson, C. & Huseby, S. Key role of phosphorus and nitrogen in regulating cyanobacterial community composition in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 164, 161–171 (2015).Article 
CAS 

Google Scholar 
Olofsson, M., Suikkanen, S., Kobos, J., Wasmund, N. & Karlson, B. Basin-specific changes in filamentous cyanobacteria community composition across four decades in the Baltic Sea. Harmful Algae 91, 101685 (2020).Article 
CAS 

Google Scholar 
Rolff, C. & Elfwing, T. Increasing nitrogen limitation in the Bothnian Sea, potentially caused by inflow of phosphate-rich water from the Baltic Proper. Ambio 44, 601–611 (2015).Article 
CAS 

Google Scholar 
Eriksson Wiklund, A.-K., Dahlgren, K., Sundelin, B. & Andersson, A. Effects of warming and shifts of pelagic food web structure on benthic productivity in a coastal marine system. Mar. Ecol. Prog. Ser. 396, 13–25 (2009).Article 
ADS 

Google Scholar 
Wikner, J. & Andersson, A. Increased freshwater discharge shifts the trophic balance in the coastal zone of the northern Baltic Sea. Glob. Change Biol. 18, 2509–2519 (2012).Article 
ADS 

Google Scholar 
Gulati, R. D. & Demott, W. R. The role of food quality for zooplankton: remarks on the state-of-the-art, perspectives and priorities. Freshw. Biol. 38, 16 (1997).Article 

Google Scholar 
Martin-Creuzburg, D., von Elert, E. & Hoffmann, K. H. Nutritional constraints at the cyanobacteria- Daphnia magna interface: The role of sterols. Limnol. Oceanogr. 53, 456–468 (2008).Article 
ADS 

Google Scholar 
Hedberg, P., Albert, S., Nascimento, F. J. A. & Winder, M. Effects of changing phytoplankton species composition on carbon and nitrogen uptake in benthic invertebrates. Limnol. Oceanogr. 66, 469–480 (2021).Article 
ADS 
CAS 

Google Scholar 
Gorokhova, E. Toxic cyanobacteria Nodularia spumigena in the diet of Baltic mysids: Evidence from molecular diet analysis. Harmful Algae 8, 264–272 (2009).Article 
CAS 

Google Scholar 
Karlson, A. M. L., Gorokhova, E. & Elmgren, R. Nitrogen fixed by cyanobacteria is utilized by deposit-feeders. PLoS ONE 9, e104460 (2014).Article 
ADS 

Google Scholar 
Karlson, A. M. L. et al. Nitrogen fixation by cyanobacteria stimulates production in Baltic food webs. Ambio 44, 413–426 (2015).Article 
CAS 

Google Scholar 
Lesutienė, J., Bukaveckas, P. A., Gasiūnaitė, Z. R., Pilkaitytė, R. & Razinkovas-Baziukas, A. Tracing the isotopic signal of a cyanobacteria bloom through the food web of a Baltic Sea coastal lagoon. Estuar. Coast. Shelf Sci. 138, 47–56 (2014).Article 
ADS 

Google Scholar 
Rolff, C. Seasonal variation in d13C and d15N of size-fractionated plankton at a coastal station in the northern Baltic proper. Mar. Ecol. Prog. Ser. 203, 47–65 (2000).Article 
ADS 
CAS 

Google Scholar 
Koski, M., Engström, J. & Viitasalo, M. Reproduction and survival of the calanoid copepod Eurytemora affinis fed with toxic and non-toxic cyanobacteria. Mar. Ecol. Prog. Ser. 186, 187–197 (1999).Article 
ADS 

Google Scholar 
Koski, M. et al. Calanoid copepods feed and produce eggs in the presence of toxic cyanobacteria Nodularia spumigena. Limnol. Oceanogr. 47, 878–885 (2002).Article 
ADS 

Google Scholar 
Schmidt, K. & Jónasdóttir, S. Nutritional quality of two cyanobacteria: How rich is ‘poor’ food?. Mar. Ecol. Prog. Ser. 151, 1–10 (1997).Article 
ADS 

Google Scholar 
Kankaanpää, H., Vuorinen, P. J., Sipiä, V. & Keinänen, M. Acute effects and bioaccumulation of nodularin in sea trout (Salmo trutta m. trutta L.) exposed orally to Nodularia spumigena under laboratory conditions. Aquat. Toxicol. 61, 155–168 (2002).Article 

Google Scholar 
Persson, K.-J., Bergström, K., Mazur-Marzec, H. & Legrand, C. Differential tolerance to cyanobacterial exposure between geographically distinct populations of Perca fluviatilis. Toxicon 76, 178–186 (2013).Article 
CAS 

Google Scholar 
Monserrat, J. M., Yunes, J. O. S. & Bianchini, A. Effects of Anabaena Spiroides (cyanobacteria) aqueous extracts on the acetylcholinesteraseactivity of aquatic species. Environ. Toxicol. Chem. 20, 1228–1235 (2001).Article 
CAS 

Google Scholar 
Lehtonen, K. K. et al. Accumulation of nodularin-like compounds from the cyanobacterium Nodularia spumigena and changes in acetylcholinesterase activity in the clam Macoma balthica during short-term laboratory exposure. Aquat. Toxicol. 64, 461–476 (2003).Article 
CAS 

Google Scholar 
Fulton, M. H. & Key, P. B. Acetylcholinesterase inhibition in esturai fish and invertebrates as an indicator of organophoshorus insecticide exposure and effects. Environ. Toxicol. Chem. 20, 37–45 (2001).Article 
CAS 

Google Scholar 
DeMott, W. R., Zhang, Q.-X. & Carmichael, W. W. Effects of toxic cyanobacteria and purified toxins on the survival and feeding of a copepod and three species of Daphnia. Limnol. Oceanogr. 36, 1346–1357 (1991).Article 
ADS 
CAS 

Google Scholar 
Hogfors, H. et al. Bloom-forming cyanobacteria support copepod reproduction and development in the Baltic Sea. PLoS ONE 9, e112692 (2014).Article 
ADS 

Google Scholar 
Motwani, N. H., Duberg, J., Svedén, J. B. & Gorokhova, E. Grazing on cyanobacteria and transfer of diazotrophic nitrogen to zooplankton in the Baltic Sea: Cyanobacteria blooms support zooplankton growth. Limnol. Oceanogr. 63, 672–686 (2018).Article 
ADS 

Google Scholar 
Gorokhova, E., El-Shehawy, R., Lehtiniemi, M. & Garbaras, A. How copepods can eat toxins without getting sick: Gut bacteria help zooplankton to feed in cyanobacteria blooms. Front. Microbiol. 11, 589816 (2021).Article 

Google Scholar 
Elmgren, R. Structure and dynamics of Baltic benthos communities, with particular reference to the relationship between macro- and meiofauna. Kieler Meeresforsch. Sonderh. 4, 1–22 (1978).
Google Scholar 
Laine, A. O. Distribution of soft-bottom macrofauna in the deep open Baltic Sea in relation to environmental variability. Estuar. Coast. Shelf Sci. 57, 87–97 (2003).Article 
ADS 
CAS 

Google Scholar 
Hill, C., Quigley, M. A., Cavaletto, J. F. & Gordon, W. Seasonal changes in lipid content and composition in the benthic amphipods Monoporeia afinis and Pontoporeia femorata. Limnol. Oceanogr. 37, 1280–1289 (1992).Article 
ADS 
CAS 

Google Scholar 
Lehtonen, K. K. Ecophysiology of the benthic amphipod Monoporeia affinis in an open-sea area of the northern Baltic Sea: Seasonal variations in body composition, with bioenergetic considerations. Mar. Ecol. Prog. Ser. 143, 87–98 (1996).Article 
ADS 

Google Scholar 
Karlson, A. M. L., Nascimento, F. J. A. & Elmgren, R. Incorporation and burial of carbon from settling cyanobacterial blooms by deposit-feeding macrofauna. Limnol. Oceanogr. 53, 2754–2758 (2008).Article 
ADS 

Google Scholar 
Karlson, A. M. L. & Mozūraitis, R. Deposit-feeders accumulate the cyanobacterial toxin nodularin. Harmful Algae 12, 77–81 (2011).Article 
CAS 

Google Scholar 
Savage, C. Tracing the influence of sewage nitrogen in a coastal ecosystem using stable nitrogen isotopes. Ambio 34, 145–150 (2005).Article 

Google Scholar 
Newsome, S. D., Del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).Article 

Google Scholar 
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratio provide for community-wide mesures of trophic structure?. Ecology 88, 42–48 (2007).Article 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable isotope Bayesian ellipses in R: Bayesian isotopic niche metrics. J. Anim. Ecol. 80, 595–602 (2011).Article 

Google Scholar 
Blomqvist, S. & Lundgren, L. A benthic sled for sampling soft bottoms. Helgol. Meeresunters. 50, 453–456 (1996).Article 

Google Scholar 
Karlson, A. M. L., Nascimento, F. J. A., Näslund, J. & Elmgren, R. Higher diversity of deposit-feeding macrofauna enhances phytodetritus processing. Ecology 91, 1414–1423 (2010).Article 

Google Scholar 
Mazur-Marzec, H., Tymińska, A., Szafranek, J. & Pliński, M. Accumulation of nodularin in sediments, mussels, and fish from the Gulf of Gdańsk, southern Baltic Sea. Environ. Toxicol. 22, 101–111 (2007).Article 
ADS 
CAS 

Google Scholar 
van de Bund, W., Ólafsson, E., Modig, H. & Elmgren, R. Effects of the coexisting Baltic amphipods Monoporeia affinis and Pontoporeia femorata on the fate of a simulated spring diatom bloom. Mar. Ecol. Prog. Ser. 212, 107–115 (2001).Article 
ADS 

Google Scholar 
Larsson, U., Hobro, R. & Wulff, F. Dynamics of a Phytoplankton Spring Bloom in a Coastal Area of the Northern Baltic Proper (University of Stockholm, 1986).
Google Scholar 
Heiskanen, A.-S. Factors Governing Sedimentation and Pelagic Nutrient Cycles in the Northern Baltic Sea: = Sedimentaatioon ja Ravinteiden Kiertoon Vaikuttavat Tekijät Pohjoisen Ltämeren Ulapaekosysteemissä (Finnish Environment Institute, 1998).
Google Scholar 
Nadon, M.-O. & Himmelman, J. H. Stable isotopes in subtidal food webs: Have enriched carbon ratios in benthic consumers been misinterpreted?. Limnol. Oceanogr. 51, 2828–2836 (2006).Article 
ADS 
CAS 

Google Scholar 
Gorokhova, E. Shifts in rotifer life history in response to stable isotope enrichment: Testing theories of isotope effects on organismal growth. Methods Ecol. Evol. 9, 269–277 (2017).Article 

Google Scholar 
Karlson, A. M. L., Reutgard, M., Garbaras, A. & Gorokhova, E. Isotopic niche reflects stress-induced variability in physiological status. R. Soc. Open Sci. 5, 171398 (2018).Article 
ADS 

Google Scholar 
del Rio, C. M., Wolf, N., Carleton, S. A. & Gannes, L. Z. Isotopic ecology 10 years after a call for more laboratory experiments. Biol. Rev. 84, 91–111 (2009).Article 

Google Scholar 
Ledesma, M., Gorokhova, E., Holmstrand, H., Garbaras, A. & Karlson, A. M. L. Nitrogen isotope composition of amino acids reveals trophic partitioning in two sympatric amphipods. Ecol. Evol. 10, 10773–10784 (2020).Article 

Google Scholar 
Bocquené, G. & Galgani, F. Biological Effects of Contaminants: Cholinesterase Inhibitation by Organophosphate and Carbamate Compounds (ICES Techniques in Marine Environmental Science (TIMES). Report., 1998). https://doi.org/10.17895/ices.pub.5048.
Book 

Google Scholar 
Ellman, G. L., Courtney, K. D., Andres, V. & Featherstone, R. M. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 7, 88–95 (1961).Article 
CAS 

Google Scholar 
Jarek, S. mvnormtest: Normality test for multivariate variables. (2012). R package version 0.1-9. https://CRAN.R-project.org/package=mvnormtestR Core Team. R: A Language and Environment for Statistical Computing. (2021).Nascimento, F. J. A., Karlson, A. M. L., Näslund, J. & Gorokhova, E. Settling cyanobacterial blooms do not improve growth conditions for soft bottom meiofauna. J. Exp. Mar. Biol. Ecol. 368, 138–146 (2009).Article 

Google Scholar 
Roche-Mayzaud, O., Mayzaud, P. & Biggs, D. Medium-term acclimation of feeding and of digestive and metabolic enzyme activity in the neritic copepod Acartia clause. I. Evidence from laboratory experiments. Mar. Ecol. Prog. Ser. 69, 25–40 (1991).Article 
ADS 
CAS 

Google Scholar 
Stuart, V., Head, E. J. H. & Mann, K. H. Seasonal changes in the digestive enzyme levels of the amphipod Corophium volutator (Pallas) in relation to diet. J. Exp. Mar. Biol. Ecol. 88, 243–256 (1985).Article 
CAS 

Google Scholar 
Schwarzenberger, A., Ilić, M. & Von Elert, E. Daphnia populations are similar but not identical in tolerance to different protease inhibitors. Harmful Algae 106, 102062 (2021).Article 
CAS 

Google Scholar 
Schwarzenberger, A. & Fink, P. Gene expression and activity of digestive enzymes of Daphnia pulex in response to food quality differences. Comp. Biochem. Physiol. B 218, 23–29 (2018).Article 
CAS 

Google Scholar 
Sipiä, V. O. et al. Bioaccumulation and detoxication of nodularin in tissues of flounder (Platichthys flesus), mussels (Mytilus edulis, Dreissena polymorpha), and clams (Macoma balthica) from the Northern Baltic Sea. Ecotoxicol. Environ. Saf. 53, 305–311 (2002).Article 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).Article 
MathSciNet 

Google Scholar 
MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100, 603–609 (1966).Article 

Google Scholar 
Wiklund, A.-K.E., Sundelin, B. & Rosa, R. Population decline of amphipod Monoporeia affinis in Northern Europe: Consequence of food shortage and competition?. J. Exp. Mar. Biol. Ecol. 367, 81–90 (2008).Article 

Google Scholar 
Leonardsson, K., Sörlin, T., Samberg, H. & Sorlin, T. Does Pontoporeia affinis (Amphipoda) optimize age at reproduction in the Gulf of Bothnia?. Oikos 52, 328 (1988).Article 

Google Scholar 
Eriksson Wiklund, A.-K. & Andersson, A. Benthic competition and population dynamics of Monoporeia affinis and Marenzelleria sp. in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 144, 46–53 (2014).Article 
ADS 

Google Scholar 
Karlson, A. M. L. et al. Linking consumer physiological status to food-web structure and prey food value in the Baltic Sea. Ambio 49, 391–406 (2020).Article 
CAS 

Google Scholar 
Olofsson, M. Nitrogen fixation estimates for the Baltic Sea indicate high rates for the previously overlooked Bothnian Sea. Ambio https://doi.org/10.1007/s13280-020-01331-x (2021).Article 

Google Scholar  More

La Sorte, F. A., Johnston, A. & Ault, T. R. Global trends in the frequency and duration of temperature extremes. Clim. Change 166, 1–2 (2021).Article 
ADS 

Google Scholar 
Hansen, J., Sato, M., Ruedy, R., Lo, K. & Medina-Elizade, M. Global temperature change. Proc. Natl. Acad. Sci. U.S.A. 103(39), 14288–14293 (2006).Article 
ADS 
CAS 

Google Scholar 
Borchert, R., Robertson, K., Schwartz, M. D. & Williams-Linera, G. Phenology of temperate trees in tropical climates. Int. J. Biometeorol. 50, 57–65 (2005).Article 
ADS 

Google Scholar 
Misra, G., Sarah, A. & Menzel, A. Ground and satellite phenology in alpine forests are becoming more heterogeneous across higher elevations with warming. Agric. For. Meteorol. 303, 108383 (2021).Article 
ADS 

Google Scholar 
Zuo, Z., Xiao, D. & Qiong, H. Role of the warming trend in global land surface air temperature variations. Sci. China Earth Sci. 6, 866–871 (2021).Article 
ADS 

Google Scholar 
Ling, Y. et al. Assessing the accuracy of forest phenological extraction from sentinel-1 C-band backscatter measurements in deciduous and coniferous forests. Remote Sens. 14(3), 674 (2022).Article 
ADS 

Google Scholar 
Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120(8), 1658–1665 (2015).Article 

Google Scholar 
Cho, J. G. et al. Apple phenology occurs earlier across South Korea with higher temperatures and increased precipitation. Int. J. Biometeorol. 65, 265–276 (2020).Article 

Google Scholar 
Li, C. et al. Response of vegetation phenology to the interaction of temperature and precipitation changes in Qilian mountains. Remote Sens. 14(5), 1248 (2022).Article 
ADS 

Google Scholar 
Berra, E. F. & Gaulton, R. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics. For. Ecol. Manage. 480, 118663 (2021).Article 

Google Scholar 
Zhang, Y. & Li, M. A new method for monitoring start of season (SOS) of forest based on multisource remote sensing. Int. J. Appl. Earth Obs. Geoinf. 104, 102556 (2021).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84(3), 471–475 (2003).Article 
ADS 

Google Scholar 
Thapa, S., Garcia Millan, V. E. & Eklundh, L. Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) remote sensing. Remote Sens. 13, 1597 (2021).Article 
ADS 

Google Scholar 
Bórnez, K., Descals, A., Verger, A. & Peñuelas, J. Land surface phenology from VEGETATION and PROBA-V data: Assessment over deciduous forests. Int. J. Appl. Earth Observ. Geoinf. 84, 101974 (2020).
Google Scholar 
Yu, L., Yan, Z. & Zhang, S. Forest phenology shifts in response to climate change over China–Mongolia–Russia international economic corridor. Forests 11, 757 (2020).Article 

Google Scholar 
Lara, C. et al. Climatic regulation of vegetation phenology in protected areas along Western South America. Remote Sens. 13, 2590 (2021).Article 
ADS 

Google Scholar 
Silveira, E. M. O. et al. Forest phenoclusters for Argentina based on vegetation phenology and climate. Ecol. Appl. 32, 2526 (2022).Article 

Google Scholar 
Tatalovich, Z., Wilson, J. P. & Cockburn, M. A comparison of thiessen polygon, kriging, and spline models of potential UV exposure. Cartogr. Geogr. Inf. Sci. 33, 217–231 (2006).Article 

Google Scholar 
Choubin, B. et al. Spatiotemporal dynamics assessment of snow cover to infer snowline elevation mobility in the mountainous regions. Cold Reg. Sci. Technol. 167, 102870 (2019).Article 

Google Scholar 
Rojas, R., Flexas, J. & Coopman, R. E. Particularities of the highest elevation treeline in the world: Polylepis tarapacana Phil. as a model to study ecophysiological adaptations to extreme environments. Flora 292, 152076 (2022).Article 

Google Scholar 
Du, J. et al. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agric. For. Meteorol. 269–270, 71–77 (2019).Article 
ADS 

Google Scholar 
Du, J. et al. Daily minimum temperature and precipitation control on spring phenology in arid-mountain ecosystems in China. Int. J. Climatol. 40, 2568–2579 (2020).Article 

Google Scholar 
He, Z. et al. Impacts of recent climate extremes on spring phenology in arid-mountain ecosystems in China. Agric. For. Meteorol. 260–261, 31–40 (2018).Article 
ADS 

Google Scholar 
He, Z. et al. Assessing temperature sensitivity of subalpine shrub phenology in semi-arid mountain regions of China. Agric. For. Meteorol. 213, 42–52 (2015).Article 
ADS 

Google Scholar 
Mu, C., Lu, H., Wang, B., Bao, X. & Cui, W. Short-term effects of harvesting on carbon storage of boreal Larix gmelinii–Carex schmidtii forested wetlands in Daxing’anling, northeast China. For. Ecol. Manage. 293, 140–148 (2013).Article 

Google Scholar 
Hu, T. et al. Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: Implications for forest ecosystem carbon cycling. Geoderma 402, 115273 (2021).Article 
ADS 
CAS 

Google Scholar 
Nyikadzino, B., Chitakira, M. & Muchuru, S. Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic. Phys. Chem. Earth 117, 102870 (2020).Article 

Google Scholar 
Gocic, M. & Trajkovic, S. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob. Planet. Change 100, 172–182 (2013).Article 
ADS 

Google Scholar 
Fang, Y. et al. Changing contribution rate of heavy rainfall to the rainy season precipitation in Northeast China and its possible causes. Atmos. Res. 197, 437–445 (2017).Article 

Google Scholar 
Piao, S. et al. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob. Change Biol. 17, 3228–3239 (2011).Article 
ADS 

Google Scholar 
Ahas, R., Aasa, A., Menzel, A., Fedotova, V. G. & Scheifinger, H. Changes in European spring phenology. Int. J. Climatol. 22, 1727–1738 (2002).Article 

Google Scholar 
Liang, L., Henebry, G. M., Liu, L., Zhang, X. & Hsu, L. C. Trends in land surface phenology across the conterminous United States (1982–2016) analyzed by NEON domains. Ecol. Appl. 31, e02323 (2021).Article 

Google Scholar 
Fu, Y. H. et al. Decreasing control of precipitation on grassland spring phenology in temperate China. Glob. Ecol. Biogeogr. 30, 490–499 (2020).Article 

Google Scholar 
Aze, T. Unraveling ecological signals from a global warming event of the past. Proc. Natl. Acad. Sci. U.S.A. 119, e2201495119 (2022).Article 

Google Scholar 
Menzel, A., Estrella, N. & Testka, A. Temperature response rates from long-term phenological records. Climate Res. 30, 21–28 (2005).Article 
ADS 

Google Scholar 
Wang, H., Liu, D., Lin, H., Montenegro, A. & Zhu, X. NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. Int. J. Climatol. 35, 687–698 (2015).Article 

Google Scholar 
Lesica, P. & Kittelson, P. M. Precipitation and temperature are associated with advanced flowering phenology in a semi-arid grassland. J. Arid Environ. 74, 1013–1017 (2010).Article 
ADS 

Google Scholar 
Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).Article 
ADS 

Google Scholar 
Li, Z. et al. Spatio-temporal responses of cropland phenophases to climate change in Northeast China. J. Geog. Sci. 22, 29–45 (2012).Article 
CAS 

Google Scholar 
Badeck, F. W. et al. Responses of spring phenolgy to climate change. New Phytol. 162, 295–309 (2004).Article 

Google Scholar 
Peng, H., Xia, H., Chen, H., Zhi, P. & Xu, Z. Spatial variation characteristics of vegetation phenology and its influencing factors in the subtropical monsoon climate region of southern China. PLoS ONE 16, e0250825 (2021).Article 
CAS 

Google Scholar 
Zhang, J. et al. NIRv and SIF better estimate phenology than NDVI and EVI: Effects of spring and autumn phenology on ecosystem production of planted forests. Agric. For. Meteorol. 315, 108819 (2022).Article 
ADS 

Google Scholar 
Yu, X., Zhuang, D., Hou, X. & Chen, H. Forest phenological patterns of Northeast China inferred from MODIS data. J. Geog. Sci. 15, 239–246 (2005).Article 

Google Scholar 
Chen, X. & Xu, L. Phenological responses of Ulmus pumila (Siberian Elm) to climate change in the temperate zone of China. Int. J. Biometeorol. 56, 695–706 (2012).Article 
ADS 

Google Scholar 
Ma, X., Bai, H., He, Y. & Li, S. The vegetation RSP of Qinling Mountains based on the NDVI and the response of temperature to it. Appl. Mech. Mater. 700, 394–399 (2014).Article 

Google Scholar  More

All methods were performed in accordance with the relevant guidelines and regulations.Study siteThe laboratory experiments on regeneration and wash resistance were conducted at the KCMUCo-PAMVERC Insecticide Testing Facility; while experimental hut study was carried out at Harusini, the facility’s field site located at Mabogini village (S03˚22.764’ E03˚720.793’), adjacent to Lower Moshi rice irrigation scheme in north-eastern Tanzania. The dominant vector at this site is An. arabiensis with moderate level of resistance to pyrethroids conferred by both oxidase and esterase activities32. In this study, pyrethroid-resistant laboratory reared An. gambiae Muleba-Kis mosquitoes were released into the huts for the release-recapture experiment.Test systemsNon-blood fed, 2–5 day old females of susceptible An. gambiae s.s. Kisumu strain and pyrethroid resistant An. gambiae s.s Muleba-Kis strain were used for the evaluation of efficacy in the laboratory (phase I). The Muleba-Kis strain has been colonized for more than 8 years and it is resistant to permethrin with fixed L1014S kdr frequency and metabolic resistance through increased oxidase activity has also been reported21. Only An. gambiae s.s Muleba-Kis were used in release-recapture experiments. The Kisumu strain is fully susceptible to insecticides and free of any detectable insecticide resistance mechanisms. The strain originated from Kisumu, Kenya and has been colonized for many years in laboratory. At the KCMUCo-PAMVERC Moshi insectary, the adult Kisumu strain mosquitoes are reared at a temperature of 24–27 °C, 75 ± 10% relative humidity (RH) and maintained under a dark:light regime of 12:12 h. The Muleba-Kis mosquitoes used for the release-recapture experiments were reared in the field insectary under ambient temperature and relative humidity and treated as previously explained21. The susceptibility status of these colonies is checked every three months using WHO susceptibility test33 and, CDC bottle bioassay test34. The colonies are regularly genotyped for kdr mutations using TaqMan assays35. To maintain the resistance of Muleba-Kis, larvae are frequently selected with alpha-cypermethrin.Regeneration timeTo determine the regeneration time of the insecticide-treated blankets, blankets were cut into 25 × 25 cm pieces and tested before washing and then washed and dried three times consecutively following WHO recommended procedures for LLINs36. The pieces were then re-tested after one, two, three, six and seven days post-washing using WHO cylinders against susceptible An. gambiae s.s (Kisumu).Graphs for 24-h mortality and 60 min knock down (KD) correlating to insecticide bioavailability, as measured by 3 min exposure in cylinder bioassays, were established before and after washing blanket pieces three times consecutively in a day, and tested within a maximum of seven days post-washing. The time in days required to reach initial mortality or 60 min KD plateau is the period required for full regeneration of insecticide-treated blanket.Wash resistanceWHO cylinder bioassays36 were used to assess the wash resistance for the blanket pieces washed 0, 5, 10, 15 and 20 times at the intervals equivalent to the regeneration time. Four pieces cut from 4 permethrin and 4 untreated blankets were used as positive and negative control respectively, against 4 pieces cut from 4 PBO–permethrin blankets.Bioassay proceduresFive, non-blood fed, 2–5 day old An. gambiae Kisumu or An. gambiae Muleba-Kis mosquitoes were exposed for 3 min or 30 min to blanket pieces in WHO cylinder. Bioassays were carried out at 27 ± 2 °C and 75 ± 10% RH. Knock-down was scored after 60 min post-exposure and mortality after 24 h. Fifty mosquitoes (5 mosquitoes per cylinder) were used on each 25 × 25 cm piece of blanket sample. After exposure, the mosquitoes were held for 24 h with access to 10% glucose solution in the paper cups covered with a net material. Mosquitoes exposed to untreated blanket were referred as a negative control.WHO tunnel test methodBlanket pieces which recorded ≤ 80% mortality in cylinder bioassay were tested in the tunnel assay using WHO guidelines. The tunnel was made of an acrylic square cylinder (25 cm in height, 25 cm in width, and 60 cm in length) divided into two sections using a blanket-covered frame fitted into a slot across the tunnel. During the assays a guinea pig was held in a small wooden cage (as a bait) in one of the sections and 50, non-blood fed, female An. gambiae Kisumu or An. gambiae Muleba-Kis aged 5–8 days were released in the other section at dusk and left overnight (13 h) for experimentation at 27 ± 2 °C and 75 ± 10% RH. The blanket surface was deliberately holed (nine 1-cm holes) to allow mosquitoes to contact the blanket material and penetrate to the baited chamber. Treated blankets were tested concurrently together with an untreated blanket. Scoring for the numbers of mosquitoes found alive or dead, fed or unfed, in each section were done in the morning. Mosquitoes found alive were removed and held in paper cups with labels corresponding to each tunnel sections under controlled conditions (25–27 °C and 75–85% RH) and fed on 10% glucose solution to monitor for delayed mortality post exposurely. Outcomes recorded were: mosquito penetration, blood feeding and mortality.Washing of blankets and whole nets for hut trialBlankets and whole nets were separately washed following WHOPES guidelines. In brief, each blanket/net was washed in Savon de Marseilles soap solution (2 g/L) for 10 min: 3 min stirring, 4 min soaking, then another 3 min stirring. This was followed by 2 rinse cycles of the same duration with water only. The water pH was 6 for all washes. The mean water hardness was within the WHOPES limit of ≤ 89 ppm. All nets used in the experimental hut study were cut with holes (4 cm × 4 cm) to simulate the conditions of a torn net. While nets were washed 20 times as per guidelines, blankets were only washed 10 times. To simulate a situation in emergence situations where washing is less frequent due to water scarcity30,31.Experimental hut trial:experimental hut designExperimental hut study was done in Lower Moshi using typical East African experimental huts design as described in the WHOPES35. Huts were constructed with brick walls and featured with cement plaster on the inside and a ceiling board, a metal iron sheet roof, open eaves with window and veranda traps on each side and window traps. Slight modifications from the original structure were made by installing metal eave baffles on two sides. The baffles allow mosquito entry but prevent exits. The window traps were used to collect mosquitoes that tend to exit the huts.Test item labelling, washing and perforatingBoth blankets and LLINs for the trial were distinctively labelled with fabric labels that withstand washes. For wash resistance, the blankets and nets were separately washed according to a protocol adapted from the standard WHO washing procedure36 at the interval equivalent to the regeneration time established in the laboratory for blanket and LLIN respectively. Before testing in the experimental huts, all nets were deliberately holed i.e. 30 holes measuring 4 × 4 cm were made in each net, 9 holes in each of the long side panels, and 6 holes at each short side (head- and foot-side panels) to enhance blood-feeding on the control arm.Test items packagingEach blanket and net were sealed in a plastic bag and then packed in the large plastic container. Each container was labelled for a single treatment to avoid cross contamination between test items.Experimental hut decontaminationA cone assay with 10 susceptible mosquitoes was performed on one wall per hut to rule out any contamination of the wall surface. Only huts with 24 h mortality of susceptible mosquitoes  More