Philippa Kaur

Geslin, B. et al. Massively introduced managed species and their consequences for plant–pollinator interactions. Adv. Ecol. Res. 57, 147–199 (2017).
Google Scholar 
Huryn, V. M. B. Ecological impacts of introduced honey bees. Q. R. Biol. 72, 275–297 (1997).
Google Scholar 
Stout, J. C. & Morales, C. L. Ecological impacts of invasive alien species on bees. Apidologie 40, 388–409 (2009).
Google Scholar 
Hung, K.-L.J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. Ser. B 285, 20172140 (2018).
Google Scholar 
Paini, D. R. Impact of the introduced honey bee (Apis mellifera) (Hymenoptera: Apidae) on native bees: A review. Austral Ecol. 29, 399–407 (2004).
Google Scholar 
Moritz, R. F. A., Hartel, S. & Neumann, P. Global invasions of the western honey bee (Apis mellifera) and the consequences for biodiversity. Ecoscience 12, 289–301 (2005).
Google Scholar 
Paini, D. R. & Roberts, J. D. Commercial honey bees (Apis mellifera) reduce the fecundity of an Australian native bee (Hylaeus alcyoneus). Biol. Cons. 123, 103–112 (2005).
Google Scholar 
Munoz, I. & De la Rua, P. Wide genetic diversity in old world honey bees threatened by introgression. Apidologie 52, 200–217 (2021).
Google Scholar 
Williams, I. H. The dependences of crop production within the European Union on pollination by honey bees. Agric. Zool. Rev. 6, 229–257 (1994).
Google Scholar 
Thompson, C. E., Biesmeijer, J. C., Allnutt, T. R., Pietravalle, S. & Budge, G. E. Parasite pressures on feral honey bees (Apis mellifera sp.). PLoS One 9, e105164 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Belsky, J. & Joshi, N. K. Impact of biotic and abiotic stressors on managed and feral bees. Insects 10, 233 (2019).PubMed Central 

Google Scholar 
Medina-Flores, C. A., Guzman-Novoa, E., Hamiduzzaman, M. M., Arechiga-Flores, C. F. & Lopez-Carlos, M. A. Africanized honey bees (Apis mellifera) have low infestation levels of the mite Varroa destructor in different ecological regions in Mexico. Genet. Mol. Res. 13, 7282–7293 (2014).CAS 
PubMed 

Google Scholar 
Portman, Z. M., Tepedino, V. J., Tripodi, A. D., Szalanski, A. L. & Durham, S. L. Local extinction of a rare plant pollinator in Southern Utah (USA) associated with invasion by Africanized honey bees. Biol. Invasions 20, 593–606 (2018).
Google Scholar 
Santos, G. M. D. et al. Invasive Africanized honeybees change the structure of native pollination networks in Brazil. Biol. Invasions 14, 2369–2378 (2012).
Google Scholar 
Chapman, R. E. & Bourke, A. F. G. The influence of sociality on the conservation biology of social insects. Ecol. Lett. 4, 650–662 (2001).
Google Scholar 
Aizen, M. A. et al. When mutualism goes bad: Density-dependent impacts of introduced bees on plant reproduction. New Phytol. 204, 322–324 (2014).
Google Scholar 
Breeze, T. D. et al. Agricultural policies exacerbate honeybee pollination service supply-demand mismatches across Europe. PLoS One 9, e82996 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Baum, K. A. et al. Spatial distribution of Africanized honey bees in an urban landscape. Landsc. Urban Plan. 100, 153–163 (2011).
Google Scholar 
Ratnieks, F. L. W., Piery, M. A. & Cuadriello, I. The natural nest and nest density of the africanized honey-bee (Hymenoptera, Apidae) near Tapachula, Chiapas, Mexico. Can. Entomol. 123, 353–359 (1991).
Google Scholar 
Baum, K. A., Rubink, W. L., Pinto, M. A. & Coulson, R. N. Spatial and temporal distribution and nest site characteristics of feral honey bee (Hymenoptera: Apidae) colonies in a coastal prairie landscape. Environ. Entomol. 33, 727–739 (2004).
Google Scholar 
Rangel, J. et al. Africanization of a feral honey bee (Apis mellifera) population in South Texas: Does a decade make a difference?. Ecol. Evol. 6, 2158–2169 (2016).PubMed 
PubMed Central 

Google Scholar 
Oldroyd, B. P., Thexton, E. G., Lawler, S. H. & Crozier, R. H. Population demography of Australian feral bees (Apis mellifera). Oecologia 111, 381–387 (1997).ADS 
CAS 
PubMed 

Google Scholar 
Arundel, J. et al. Remarkable uniformity in the densities of feral honey bee Apis mellifera Linnaeus, 1758 (Hymenoptera: Apidae) colonies in South Eastern Australia. Austral Entomol. 53, 328–336 (2014).
Google Scholar 
Remm, J. & Lõhmus, A. Tree cavities in forests—The broad distribution pattern of a keystone structure for biodiversity. For. Ecol. Manag. 262, 579–585 (2006).
Google Scholar 
Lindenmayer, D., Crane, M., Blanchard, W., Okada, S. & Montague-Drake, R. Do nest boxes in restored woodlands promote the conservation of hollow-dependent fauna?. Restor. Ecol. 24, 244–251 (2016).
Google Scholar 
New South Wales Department of Planning, Industry and Environment 2003. https://www.environment.nsw.gov.au/topics/animals-and-plants/threatened-species/nsw-threatened-species-scientific-committee/determinations/final-determinations/2000-2003/competition-from-feral-honeybees-key-threatening-process-listing (accessed 22 Feb 2021).Goldingay, R. L., Rohweder, D. & Taylor, B. D. Nest box contentions: Are nest boxes used by the species they target?. Ecol. Manag. Restor. 21, 115–122 (2020).
Google Scholar 
Lindenmayer, D. B. et al. Are nest boxes a viable alternative source of cavities for hollow-dependent animals? Long-term monitoring of nest box occupancy, pest use and attrition. Biol. Cons. 142, 33–42 (2009).
Google Scholar 
Lindenmayer, D. B. et al. The anatomy of a failed offset. Biol. Conserv. 210, 286–292 (2017).
Google Scholar 
Macak, P. V. Nest boxes for wildlife in Victoria: An overview of nest box distribution and use. Vic. Nat. 137, 4–14 (2020).
Google Scholar 
Le Roux, D. S. et al. Effects of entrance size, tree size and landscape context on nest box occupancy: Considerations for management and biodiversity offsets. For. Ecol. Manag. 366, 135–142 (2016).
Google Scholar 
Berris, K. K. & Barth, M. PVC nest boxes are less at risk of occupancy by feral honey bees than timber nest boxes and natural hollows. Ecol. Manag. Restor. 21, 155–157 (2020).
Google Scholar 
Jaffe, R. et al. Estimating the density of honeybee colonies across their natural range to fill the gap in pollinator decline censuses. Conserv. Biol. 24, 583–593 (2010).PubMed 

Google Scholar 
Utaipanon, P., Schaerf, T. M. & Oldroyd, B. P. Assessing the density of honey bee colonies at ecosystem scales. Ecol. Entomol. 44, 291–304 (2019).
Google Scholar 
Utaipanon, P., Holmes, M. J., Chapman, N. C. & Oldroyd, B. P. Estimating the density of honey bee (Apis mellifera) colonies using trapped drones: Area sampled and drone mating flight distance. Apidologie 50, 578–592 (2019).CAS 

Google Scholar 
Williamson, E. M. Reliability of honey bee hive density estimates using drone sampling: does relative hive size or distance affect a colony’s drone contribution? Honours Thesis, The University of Adelaide (2020).Benson, J. S. The effect of 200 years of European settlement on the vegetation and flora of New South Wales. Cunninghamia 2, 343–370 (1991).
Google Scholar 
New South Wales Office of Environment and Heritage 2015. Upgraded NSW woody vegetation extent for 2011. http://data.auscover.org.au/xwiki/bin/view/Product+pages/nsw+5m+woody+extent+and+fpc (accessed 13 May 2020).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). www.R-project.org (accessed 12 January 2021).Burnham, K. P. & Anderson, D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Albert, A. & Anderson, J. A. On the existence of maximum likelihood estimates in logistic regression models. Biometrika 71, 1–10 (1984).MathSciNet 
MATH 

Google Scholar 
Firth, D. Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38 (1993).MathSciNet 
MATH 

Google Scholar 
Kosmidis, I., Pagui, E. C. K. & Sartori, N. Mean and median bias reduction in generalized linear models. Stat. Comput. 30, 43–59 (2020).MathSciNet 
MATH 

Google Scholar 
Anderson, D. R. Model Based Inference in the Life Sciences: A Primer on Evidence (Springer Science & Business Media, 2007).
Google Scholar 
Barton, K. MuMIn: Multi-model inference. R package version 1.43.17 (2016).Hijmans, R. J. Raster: Geographic Data Analysis and Modeling. R package version 3.4-5 (2020).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.3.0 (2018).Kosmidis, I. brglm2: Bias Reduction in Generalized Linear Models. R package version 0.6.2 (2020).Kosmidis, I., Schumacher, D. detectseparation: Detect and Check for Separation and Infinite Maximum Likelihood Estimates. R package version 0.1 (2020).Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).
Google Scholar 
Pateiro-Lopez, B., Rodriguez-Casal, A. Alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane. R package version 2.2 (2019).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 

Google Scholar 
Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
Google Scholar 
Wickham, H. Forcats: Tools for working with categorical variables (factors). R package version 0.5.0 (2018).Wickham, H., François, R., Henry, L., Müller, K. dplyr: A Grammar of Data Manipulation. R package version 1.0.0 (2021).Birtchnell, M. J. & Gibson, M. Long-term flowering patterns of melliferous Eucalyptus (Myrtaceae) species. Aust. J. Bot. 54, 745–754 (2006).
Google Scholar 
Steinhauer, N. et al. Drivers of colony losses. Curr. Opin. Insect Sci. 26, 142–148 (2018).PubMed 

Google Scholar 
Cunningham, S. A., Heard, T. & FitzGibbon, F. The future of pollinators for Australian Agriculture. Aust. J. Agric. Res. 53, 893–900 (2002).
Google Scholar 
Hinson, E. M., Duncan, M., Lim, J., Arundel, J. & Oldroyd, B. P. The density of feral honey bee (Apis mellifera) colonies in South East Australia is greater in undisturbed than in disturbed habitats. Apidologie 46, 403–413 (2015).
Google Scholar 
McIntyre, S. Ecological and anthropomorphic factors permitting low-risk assisted colonization in temperate grassy woodlands. Biol. Conserv. 144, 1781–1789 (2011).
Google Scholar 
Steffan-Dewenter, I. & Kuhn, A. Honeybee foraging in differentially structured landscapes. Proc. R. Soc. B Biol. Sci. 270, 569–575 (2003).
Google Scholar 
Wintle, B. A. et al. Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc. Natl. Acad. Sci. U.S.A. 116, 909–914 (2019).CAS 
PubMed 

Google Scholar 
Arthur, A. D., Li, J., Henry, S. & Cunningham, S. A. Influence of woody vegetation on pollinator densities in oilseed Brassica fields in an Australian temperate landscape. Basic Appl. Ecol. 11, 406–414 (2010).
Google Scholar 
Lindenmayer, D. B. et al. New policies for old trees: Averting a global crisis in a keystone ecological structure. Conserv. Lett. 7, 61–69 (2014).
Google Scholar 
Crane, M. J., Lindenmayer, D. B. & Cunningham, R. B. The value of countryside elements in the conservation of a threatened arboreal marsupial Petaurus norfolcensis in agricultural landscapes of south-eastern Australia—the disproportional value of scattered trees. PLoS One 9, e107178 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gibbons, P., Lindenmayer, D. B., Barry, S. C. & Tanton, M. T. Hollow selection by vertebrate fauna in forests of southeastern Australia and implications for forest management. Biol. Conserv. 103, 1–12 (2002).
Google Scholar 
Seeley, T. D. & Morse, R. A. The nest of the honey bee (Apis mellifera L.). Insectes Soc. 23, 495–512 (1976).
Google Scholar 
Hung, K. L. J., Ascher, J. S., Davids, J. A. & Holway, D. A. Ecological filtering in scrub fragments restructures the taxonomic and functional composition of native bee assemblages. Ecology 100, e02654 (2019).PubMed 

Google Scholar 
Cockle, K. L., Martin, K. & Drever, M. C. Supply of tree-holes limits nest density of cavity-nesting birds in primary and logged subtropical Atlantic forest. Biol. Conserv. 143, 2851–2857 (2010).
Google Scholar 
Heard, T. Stingless bees. In Australian Native Bees: A Practical Hand Book 106–139 (NSW Department of Primary Industries, 2016).Geoscience Australia 2006. GEODATA TOPO 250K. Commonwealth of Australia. http://pid.geoscience.gov.au/dataset/ga/63999 (accessed 11 December 2020). More

Sabine, C. L. et al. The oceanic sink for anthropogenic CO2. Science 305, 367–371 (2004).CAS 
PubMed 

Google Scholar 
Landschützer, P. et al. The reinvigoration of the Southern Ocean carbon sink. Science 349, 1221–1224 (2015).PubMed 

Google Scholar 
Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Global Biogeochem. Cycles 21, https://doi.org/10.1029/2006GB002907 (2007).Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proc. Natl Acad. Sci. USA 117, 9679–9687 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
de Baar, H. J. On iron limitation of the Southern Ocean: experimental observations in the Weddell and Scotia Seas. Mar. Ecol. Prog. Ser. 65, 105–122 (1990).
Google Scholar 
Twining, B. S. & Baines, S. B. The trace metal composition of marine phytoplankton. Annu. Rev. Mar. Sci. 5, 191–215 (2013).
Google Scholar 
Martin, J. H., Fitzwater, S. E. & Gordon, R. M. Iron deficiency limits phytoplankton growth in Antarctic waters. Glob. Biogeochem. Cycles 4, 5–12 (1990).CAS 

Google Scholar 
Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993–2005: synthesis and future directions. science 315, 612–617 (2007).CAS 
PubMed 

Google Scholar 
Sunda, W. Feedback interactions between trace metal nutrients and phytoplankton in the ocean. Front. Microbiol. 3, 204 (2012).PubMed 
PubMed Central 

Google Scholar 
Martin, J. H. Glacial‐interglacial CO2 change: the iron hypothesis. Paleoceanography 5, 1–13 (1990).
Google Scholar 
Martin, J. H., Gordon, R. M. & Fitzwater, S. E. Iron in Antarctic waters. Nature 345, 156 (1990).CAS 

Google Scholar 
Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 

Google Scholar 
Behrenfeld, M. J. & Milligan, A. J. Photophysiological expressions of iron stress in phytoplankton. Annu. Rev. Mar. Sci. 5, 217–246 (2013).
Google Scholar 
Greene, R. M., Geider, R. J., Kolber, Z. & Falkowski, P. G. Iron-induced changes in light harvesting and photochemical energy conversion processes in eukaryotic marine algae. Plant Physiol. 100, 565–575 (1992).CAS 
PubMed 
PubMed Central 

Google Scholar 
Raven, J. A., Evans, M. C. & Korb, R. E. The role of trace metals in photosynthetic electron transport in O2-evolving organisms. Photosynthesis Res. 60, 111–150 (1999).CAS 

Google Scholar 
Raven, J. A. Predictions of Mn and Fe use efficiencies of phototrophic growth as a function of light availability for growth and of C assimilation pathway. N. Phytologist 116, 1–18 (1990).CAS 

Google Scholar 
Wolfe-Simon, F., Grzebyk, D., Schofield, O. & Falkowski, P. G. The role and evolution of superoxide dismutases in algae 1. J. Phycol. 41, 453–465 (2005).CAS 

Google Scholar 
Middag, R. D., De Baar, H. J. W., Laan, P., Cai, P. V. & Van Ooijen, J. C. Dissolved manganese in the Atlantic sector of the Southern Ocean. Deep Sea Res. Part II: Topical Stud. Oceanogr. 58, 2661–2677 (2011).CAS 

Google Scholar 
Buma, A. G., De Baar, H. J., Nolting, R. F. & Van Bennekom, A. J. Metal enrichment experiments in the Weddell‐Scotia Seas: effects of iron and manganese on various plankton communities. Limnol. Oceanogr. 36, 1865–1878 (1991).CAS 

Google Scholar 
Middag, R., de Baar, H. J., Klunder, M. B. & Laan, P. Fluxes of dissolved aluminum and manganese to the Weddell Sea and indications for manganese co‐limitation. Limnol. Oceanogr. 58, 287–300 (2013).CAS 

Google Scholar 
Browning, T. J. et al. Strong responses of Southern Ocean phytoplankton communities to volcanic ash. Geophys. Res. Lett. 41, 2851–2857 (2014).CAS 

Google Scholar 
Wu, M. et al. Manganese and iron deficiency in Southern Ocean Phaeocystis Antarctica populations revealed through taxon-specific protein indicators. Nat. Commun. 10, 1–10 (2019).
Google Scholar 
Browning, T. J., Achterberg, E. P., Engel, A. & Mawji, E. Manganese co-limitation of phytoplankton growth and major nutrient drawdown in the Southern Ocean. Nat. Commun. 12, 884 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Viljoen, J. J. et al. Links between the phytoplankton community composition and trace metal distribution in summer surface waters of the Atlantic southern ocean. Front. Mar. Sci. 6, 295 (2019).
Google Scholar 
Arrigo, K. R. Marine microorganisms and global nutrient cycles. Nature 437, 349–355 (2005).CAS 
PubMed 

Google Scholar 
De Baar, H. J. W. von Liebig’s law of the minimum and plankton ecology (1899–1991). Prog. Oceanogr. 33, 347–386 (1994).
Google Scholar 
Saito, M. A., Goepfert, T. J. & Ritt, J. T. Some thoughts on the concept of colimitation: three definitions and the importance of bioavailability. Limnol. Oceanogr. 53, 276–290 (2008).CAS 

Google Scholar 
Pausch, F., Bischof, K. & Trimborn, S. Iron and manganese co-limit growth of the Southern Ocean diatom Chaetoceros debilis. PLos ONE 14, e0221959 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hopkinson, B. M. et al. Iron limitation across chlorophyll gradients in the southern Drake Passage: phytoplankton responses to iron addition and photosynthetic indicators of iron stress. Limnol. Oceanogr. 52, 2540–2554 (2007).CAS 

Google Scholar 
Trimborn, S., Hoppe, C. J., Taylor, B. B., Bracher, A. & Hassler, C. Physiological characteristics of open ocean and coastal phytoplankton communities of Western Antarctic Peninsula and Drake Passage waters. Deep Sea Res. Part I: Oceanographic Res. Pap. 98, 115–124 (2015).CAS 

Google Scholar 
Rijkenberg, M. J. et al. The distribution of dissolved iron in the West Atlantic Ocean. PLoS ONE 9, e101323 (2014).PubMed 
PubMed Central 

Google Scholar 
Prézelin, B. B., Hofmann, E. E., Mengelt, C. & Klinck, J. M. The linkage between upper circumpolar deep water (UCDW) and phytoplankton assemblages on the west Antarctic Peninsula continental shelf. J. Mar. Res. 58, 165–202 (2000).
Google Scholar 
Varela, M., Fernandez, E. & Serret, P. Size-fractionated phytoplankton biomass and primary production in the Gerlache and south Bransfield Straits (Antarctic Peninsula) in Austral summer 1995–1996. Deep Sea Res. Part II: Topical Stud. Oceanogr. 49, 749–768 (2002).CAS 

Google Scholar 
Hoffmann, L. J., Peeken, I. & Lochte, K. Effects of iron on the elemental stoichiometry during EIFEX and in the diatoms Fragilariopsis kerguelensis and Chaetoceros dichaeta. Biogeosciences 4, 569–579 (2007).CAS 

Google Scholar 
Blanco-Ameijeiras, S. et al. Exopolymeric substances control microbial community structure and function by contributing to both C and Fe nutrition in Fe-limited Southern Ocean provinces. Microorganisms 8, 1980 (2020).CAS 
PubMed Central 

Google Scholar 
Church, M. J., Hutchins, D. A. & Ducklow, H. W. Limitation of bacterial growth by dissolved organic matter and iron in the Southern Ocean. Appl. Environ. Microbiol. 66, 455–466 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Obernosterer, I., Fourquez, M. & Blain, S. Fe and C co-limitation of heterotrophic bacteria in the naturally fertilized region off the Kerguelen Islands. Biogeosciences 12, 1983–1992 (2015).
Google Scholar 
Fourquez, M., Obernosterer, I., Davies, D. M., Trull, T. W. & Blain, S. Microbial iron uptake in the naturally fertilized waters in the vicinity of the Kerguelen Islands: phytoplankton–bacteria interactions. Biogeosciences 12, 1893–1906 (2015).
Google Scholar 
Fourquez, M. et al. Microbial competition in the subpolar southern ocean: an Fe–C Co-limitation experiment. Front. Mar. Sci. 6, 776 (2020).
Google Scholar 
Boyd, P. W. et al. A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization. Nature 407, 695–702 (2000).CAS 
PubMed 

Google Scholar 
De Baar, H. J. et al. Synthesis of iron fertilization experiments: from the iron age in the age of enlightenment. J. Geophys. Res. Oceans 110, https://doi.org/10.1029/2004JC002601 (2005).Smetacek, V. & Naqvi, S. W. A. The next generation of iron fertilization experiments in the Southern Ocean. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 366, 3947–3967 (2008).CAS 

Google Scholar 
Geider, R. J. & La Roche, J. The role of iron in phytoplankton photosynthesis, and the potential for iron-limitation of primary productivity in the sea. Photosynthesis Res. 39, 275–301 (1994).CAS 

Google Scholar 
van Leeuwe, M. A. & Stefels, J. Effects of iron and light stress on the biochemical composition of Antarctic Phaeocystis sp. (Prymnesiophyceae). II. Pigment composition. J. Phycol. 34, 496–503 (1998).
Google Scholar 
Hoffmann, L. J., Peeken, I., Lochte, K., Assmy, P. & Veldhuis, M. Different reactions of Southern Ocean phytoplankton size classes to iron fertilization. Limnol. Oceanogr. 51, 1217–1229 (2006).CAS 

Google Scholar 
Koch, F., Beszteri, S., Harms, L. & Trimborn, S. The impacts of iron limitation and ocean acidification on the cellular stoichiometry, photophysiology, and transcriptome of Phaeocystis antarctica. Limnol. Oceanogr. 64, 357–375 (2019).CAS 

Google Scholar 
Koch, F. & Trimborn, S. Limitation by Fe, Zn, Co, and B12 results in similar physiological responses in two antarctic phytoplankton species. Front. Mar. Sci. 6, 514 (2019).
Google Scholar 
Peers, G. & Price, N. M. A role for manganese in superoxide dismutases and growth of iron‐deficient diatoms. Limnol. Oceanogr. 49, 1774–1783 (2004).CAS 

Google Scholar 
Cefarelli, A. O. et al. Diversity of the diatom genus Fragilariopsis in the Argentine Sea and Antarctic waters: morphology, distribution and abundance. Polar Biol. 33, 1463–1484 (2010).
Google Scholar 
Marchetti, A. & Harrison, P. J. Coupled changes in the cell morphology and elemental (C, N, and Si) composition of the pennate diatom Pseudo-nitzschia due to iron deficiency. Limnol. Oceanogr. 52, 2270–2284 (2007).CAS 

Google Scholar 
Boyd, P. W. et al. Microbial control of diatom bloom dynamics in the open ocean. Geophys. Res. Lett. 39, https://doi.org/10.1029/2012GL053448 (2012).Behrenfeld, M. J. & Kolber, Z. S. Widespread iron limitation of phytoplankton in the South Pacific Ocean. Science 283, 840–843 (1999).CAS 
PubMed 

Google Scholar 
Strzepek, R. F., Hunter, K. A., Frew, R. D., Harrison, P. J. & Boyd, P. W. Iron‐light interactions differ in Southern Ocean phytoplankton. Limnol. Oceanogr. 57, 1182–1200 (2012).CAS 

Google Scholar 
Klunder, M. B. et al. Dissolved Fe across the Weddell Sea and Drake Passage: impact of DFe on nutrient uptake. Biogeosciences 11, 651–669 (2014).
Google Scholar 
Trimborn, S. et al. Iron sources alter the response of Southern Ocean phytoplankton to ocean acidification. Mar. Ecol. Prog. Ser. 578, 35–50 (2017).CAS 

Google Scholar 
Smith, W. O. & Lancelot, C. Bottom-up versus top-down control in phytoplankton of the Southern Ocean. Antarct. Sci. 16, 531–539 (2004).
Google Scholar 
Schoffman, H., Lis, H., Shaked, Y. & Keren, N. Iron–nutrient interactions within phytoplankton. Front. Plant Sci. 7, 1223 (2016).PubMed 
PubMed Central 

Google Scholar 
Meijers, A. J. S. The Southern Ocean in the coupled model intercomparison project phase 5. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 372, 20130296 (2014).CAS 

Google Scholar 
Hauck, J. et al. On the Southern Ocean CO2 uptake and the role of the biological carbon pump in the 21st century. Glob. Biogeochem. Cycles 29, 1451–1470 (2015).CAS 

Google Scholar 
Cabanes, D. J. et al. Using Fe chemistry to predict Fe uptake rates for natural plankton assemblages from the Southern Ocean. Mar. Chem. 225, 103853 (2020).CAS 

Google Scholar 
Cutter, G. A. et al. Sampling and sample-handling protocols for GEOTRACES Cruises, Version 3.0 (2017).Gerringa, L. J. A., De Baar, H. J. W. & Timmermans, K. R. A comparison of iron limitation of phytoplankton in natural oceanic waters and laboratory media conditioned with EDTA. Mar. Chem. 68, 335–346 (2000).CAS 

Google Scholar 
Hoppe, C. J. et al. Iron limitation modulates ocean acidification effects on Southern Ocean phytoplankton communities. PLoS ONE 8, e79890 (2013).PubMed 
PubMed Central 

Google Scholar 
Hathorne, E. C. et al. Online preconcentration ICP-MS analysis of rare earth elements in seawater. Geochemistry, Geophysics, Geosystems 13, https://doi.org/10.1029/2011GC003907 (2012).Rapp, I., Schlosser, C., Rusiecka, D., Gledhill, M. & Achterberg, E. P. Automated preconcentration of Fe, Zn, Cu, Ni, Cd, Pb, Co, and Mn in seawater with analysis using high-resolution sector field inductively-coupled plasma mass spectrometry. Analytica Chim. Acta 976, 1–13 (2017).CAS 

Google Scholar 
Utermöhl, H. Zur vervollkommnung der quantitativen phytoplankton-methodik: Mit 1 Tabelle und 15 abbildungen im Text und auf 1 Tafel. Int. Ver. f.ür. theoretische und Angew. Limnologie: Mitteilungen 9, 1–38 (1958).
Google Scholar 
Edler, L. Recommendations on Methods for Marine Biological Studies in the Baltic Sea. Phytoplankton and Chlorophyll (Publication-Baltic Marine Biologists BMB (Sweden), 1979).Tomas, C. R. & Haste, G. R. Identifying Marine Phytoplankton (Academic Press, 1997).Olson, R. J., Zettler, E. R., Chisholm, S. W. & Dusenberry, J. A. in Particle Analysis in Oceanography 351–399 (Springer, 1991).Koch, F., Sanudo-Wilhelmy, S. A., Fisher, N. S. & Gobler, C. J. Effect of vitamins B1 and B12 on bloom dynamics of the harmful brown tide alga, Aureococcus anophagefferens (Pelagophyceae). Limnol. Oceanogr. 58, 1761–1774 (2013).CAS 

Google Scholar 
Welschmeyer, N. A. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol. Oceanogr. 39, 1985–1992 (1994).CAS 

Google Scholar 
Oxborough, K. et al. Direct estimation of functional PSII reaction center concentration and PSII electron flux on a volume basis: a new approach to the analysis of Fast Repetition Rate fluorometry (FRRf) data. Limnol. Oceanogr.: Methods 10, 142–154 (2012).
Google Scholar 
Schlitzer, R. Ocean Data View. (2015). More

Many women in science, technology, engineering and mathematics (STEM) need to make decisions about marital name change, and have to consider how this might affect their publication record and future career. Mentorship that considers race, ethnicity, culture, religion and parenting, as well as a centralized system to dynamically and retroactively streamline name change, will promote agency and choice for women navigating STEM careers, writes Bala Chaudhary.Women, whether in same-sex or heterosexual relationships, still predominantly make decisions regarding marital name change1. In science, technology, engineering and mathematics (STEM) fields, as the proportion of female researchers rises, more women are considering the potential effects of marital name change on their careers. The stakes are high, as relationship status and name discrimination contribute to gender2 and racial3 inequities in faculty hiring. The shifting demographics of students and a greater proportion of STEM undergraduates engaging in research and publishing has also led to more scientists questioning decisions around name changes. Dual-scientist couples considering sharing a last name may wonder about gendered assessments of their contributions to work. Women occasionally ask for advice on this topic using social-media platforms such as Twitter. Community members chime in with myriad options: keep your name, change your name, hyphenate, add a middle name, couples choose a new name, keep separate personal and legal names, and so on. There is no single correct approach for this personal decision, so online discussions and testimonials4 are invaluable resources for women with few immediate role models. More

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When mange began to kill llama-like animals called vicuñas in the high Andes, their loss reverberated through the food web to affect grasslands and, eventually, condors1.

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doi: https://doi.org/10.1038/d41586-022-00592-8

ReferencesMonk, J. D. et al. Ecol. Lett. https://doi.org/10.1111/ele.13983 (2022).PubMed 
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Ecology

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Esperanza Martínez-Romero is a professor of ecological genomics and was coordinator of the undergraduate programme on genomics at Universidad Nacional Autónoma de México. Her work on plant symbioses, and outreach with local farmers has encouraged uptake of sustainable practices and the use of biofertilizers.It was during my first year as an undergraduate student that I was exposed to genetic engineering, when Dr Francisco Bolívar lectured on his development of vectors for gene cloning. I found these results fascinating, and it was listening to talks from scientists at my institute that made me realize that research was my vocation. Towards the end of my bachelor’s degree, Dr Marc von Montagu from Belgium visited and told us about plant genetic transformations — a new field within genetic engineering. Although I was accepted into his laboratory to do my doctorate, I preferred Mexico. I turned my academic journey around and instead chose to apply to a new research centre in Cuernavaca outside of Mexico City — my next turning point. I suspected that a new research centre would provide more opportunities for the development of novel areas, and would have open positions for researchers. Indeed, I was hired at this new research centre and started my own ecology group. It was there that I started working with nitrogen-fixing bacteria and plants. The effects of nitrogen-fixing bacteria on plants were outstanding. Although the scope of molecular biology was incipient to the characterization of bacterial species and populations, we were nevertheless able to make molecular characterizations of the rhizobial species that formed nitrogen-fixing nodules on beans — the most important legume for human consumption in the world. In 1991, we described a novel species, Rhizobium tropici, which could deliver high levels of nitrogen to legumes. It was then that I realized nitrogen fixation is key to the development of sustainable agriculture and could benefit farmers in Mexico and around the world. Some of the species described by my group are now used as inoculants in agriculture, reducing the use of chemical fertilizers and allowing farmers to make cost savings. To facilitate this, I published a manual on biofertilization for farmers and gave conferences and workshops to them. My group has also undertaken reforestation programmes using nitrogen-fixing legume trees inoculated with the rhizobial species that we described. More

Quantification of causal networksWe first compared the relative strengths of causal links across systems (Supplementary Fig. S3). Phytoplankton species richness was the major controlling factor for phytoplankton biomass (significant in 16 of 19 sites, Fig. 2a) in these diverse aquatic systems, consistent with experimental studies17. However, the averaged linkage strength for this effect was not significantly different from that of NO3 (i.e., BD → EF vs. NO3 → EF; permutation test P = 0.501), highlighting that nitrogen availability was equally important in affecting phytoplankton biomass in natural systems.Fig. 2: Relative strengths of various modules.Standardized linkage strengths of causal variables affecting (a) phytoplankton biomass and (b) species richness (here, BD) and loop weights for various types of (c) pairwise feedbacks and (d) triangular feedbacks. All statistics were calculated from the 19 independent sites (n = 19) and depicted as joint violins and box plots to present the empirical distribution that labels the maxima and minima at the top and bottom of the violins, respectively, and shows 25, 50, and 75% quantiles in the boxes with whiskers presenting at most 1.5 * interquartile range. The two numbers within the parentheses (S; R1) above each violin plot report the number of significant results in CCM (S; labeled blue) and the number of systems in which a particular module had the greatest strength (i.e., rank 1; R1; labeled red). Source data are provided as a Source Data file.Full size imageIn the opposite direction, phytoplankton biomass was a significant driver of phytoplankton species richness in most ecosystems (15 of 19 sites, Fig. 2b). However, NO3 more often had a stronger effect, appearing as the most important driver in 11 of 19 sites compared to phytoplankton biomass (4 of 19 sites) (Fig. 2b). Although the difference in effect strength was not significant (permutation test, P = 0.162), these results implicated nitrogen availability as an essential determinant affecting both phytoplankton diversity and biomass. As a sensitivity test, we also examined the effects of Shannon diversity. The results suggest that the importance of nutrients is robust to the use of other diversity indexes (e.g., Shannon diversity in Supplementary Fig. S4), although the causal effects from phytoplankton biomass became relatively more important compared to biomass effects on species richness (Fig. 2b). Based on these findings, we inferred that processes influencing nutrients (e.g., external loadings and internal cycling38) need to be considered when investigating aquatic biodiversity. Changes in those processes (e.g., climatic39 or anthropogenic40 driven nutrient changes) may indeed substantially impact phytoplankton biodiversity, and subsequent ecosystem functioning.The importance of NO3 uncovered in our analyses might not be a counter-intuitive result, as many systems analyzed in this study were P-rich. For instance, the average phosphate concentration was 57.5 and 41.7 μgP/L for Lake Mendota (Me) and Lake Monona (Mo) (Supplementary Table S1), respectively. In addition, there were also high total phosphorus (TP) concentrations in shallow lake systems, e.g., average TP was 106.1, 112.5, and 126.4 μgP/L in Lake Inba (Ib), Lake Kasumigaura (Ks), and Müggelsee (Mu), respectively. Phosphorus was not always a limiting factor in eutrophic and mesotrophic systems, e.g., Lake Kasumigaura41 and Lake Geneva (Gv)42. In addition, nitrogen was deficient and limited cyanobacteria bloom in Müggelsee (Mu)43. Nonetheless, we cannot exclude the possibility of colimitation44 in N and P and the possibility that P availability also depends on N45, which warrants further investigation.Apart from nutrients and temperature, the causal effects of other important drivers on phytoplankton biomass and diversity were also examined, though not in all 19 systems due to data limitation. The causal effects of physical environmental factors, such as irradiance and water column stability, were presented in Supplementary Fig. S5; the results indicated that the quantified causal strengths on average were not as strong as the effects of diversity and nutrients. Moreover, the effects of consumers (e.g., zooplankton), which have been suggested as important drivers affecting species diversity of phytoplankton communities46, were also examined. Based on our analysis of zooplankton, the causal effects of herbivorous crustaceans on phytoplankton biomass and diversity were significant in most of the analyzed systems. However, these effects were on average not as strong as the effects of phytoplankton diversity and nutrients, respectively (Supplementary Fig. S6). Nonetheless, these findings were not generalized to all 19 systems due to a lack of complete datasets as shown in Supplementary Table S3, and thus warrant more detailed investigation in future studies.In addition to individual causal effects, we investigated feedbacks across systems. Pairwise feedbacks (e.g., BD ↔ EF and NO3 ↔ EF) were common (Fig. 2c). However, the averaged linkage strength was often stronger in one direction when involving BD (Fig. 3). Specifically, the average strength of BD → EF was stronger than for the opposite direction of EF → BD (permutation test P = 0.015); BD → EF was stronger than EF → BD in 14 of the 19 systems (Fig. 3). In addition, biodiversity effects on nutrients (BD → NO3 and BD → PO4) were also stronger than their reversed effects (NO3 → BD and PO4 → BD) in 12 and 13 systems, respectively. In comparison, the interactions between nutrients and productivity were more symmetrical: nutrient effects on biomass (NO3 → EF and PO4 → EF) were stronger than biomass effects on nutrients (EF → NO3 and EF → PO4) in only 9 and 8 of 19 systems, respectively. These results supported the previous findings8 that biodiversity effects more often operate at short-term scales, which makes effects more observable in our monthly-scale analyses than feedback effects on diversity, which are expected to occur on a more prolonged timescale, e.g., through slowly changing nutrient cycling31 or decomposition47. Nevertheless, the timescale dependence of causal interactions in ecosystem networks is a topic that needs further study.Fig. 3: Directional bias in pairwise feedbacks.The difference in standardized linkage strengths between the two directions was computed for each pairwise feedback and depicted as joint violin and box plots. All statistics were calculated from the 19 independent sites (n = 19) and depicted as joint violins and box plots to present the empirical distribution that labels the maxima and minima at the top and bottom of the violins, respectively, and shows 25, 50, and 75% quantiles in the boxes with whiskers presenting at most 1.5 * interquartile range. The number above the plot indicates the number of systems with a positive difference in linkage strength. For example, BD → EF was stronger than its feedback, EF → BD, in 14 of the systems. In general, the strength of diversity effects (BD → EF, BD → NO3, BD → PO4) was usually stronger than feedback effects (EF → BD, NO3 → BD, PO4 → BD). Source data are provided as a Source Data file.Full size imageSubsequently, we quantified the strengths of pairwise feedbacks as the geometric mean of the linkage strengths in each direction, following a previous study9 (see more details in Methods). Among these feedbacks (Fig. 2c and Supplementary Fig. S7), BD ↔ NO3 had the highest median and average strength (0.78 and 0.68, respectively) across systems. However, strengths of BD ↔ NO3 were highly variable among systems (large interquartile range in Fig. 2c), and thus were only significant in 11 of 19 systems, compared to BD ↔ EF (15 of 19 systems). These findings reinforced the importance of nutrients as key determinants for aquatic biodiversity and implied that nutrient effects are context-dependent. In other words, BD ↔ NO3 was less common than BD ↔ EF across systems, despite its stronger average strength. The prevalence of BD ↔ EF indicated a need for more long-term experiments and process-based/theoretical modeling accounting for bidirectional interactions between diversity and biomass16, because bidirectional interactions and feedbacks may challenge our simple predictions for ecosystem dynamics, based on knowledge of unidirectional interactions30.Quantification of the causal network also allowed us to analyze triangular feedbacks. Within the conceptual framework of Fig. 1b, there are four kinds of triangular feedbacks involving biodiversity, ecosystem functioning, and either nitrate or phosphate (Type I: BD → EF → NO3 and BD → EF → PO4; Type II: EF → BD → NO3 and EF → BD → PO4). There was at least one significant triangular feedback in 14 of 19 sites (Fig. 2d). More specifically, NO3-associated feedbacks (Type I-N and Type II-N) were usually stronger than PO4-associated feedbacks (Type I-P and Type II-P) (Fig. 2d), although the difference in strength among the four types of feedbacks was not significant (Fig. 2d; Kruskal–Wallis test, P = 0.59). The dominance of NO3-associated feedbacks in our study was attributed to many of the sites being marine and eutrophic lakes, which are likely to be N-limited due to an imbalance in external loadings48 or strong denitrification49. Among both NO3- and PO4-associated feedbacks, there were no significant differences in strength between Type I and Type II feedbacks (Supplementary Fig. S7), suggesting that biodiversity can directly influence biomass (Type I), as well as through a pathway that involves endogenous nutrient variables (Type II) and eventually feeds back on itself.Causal networks under environmental contextsOur empirical analyses revealed state dependency of the causal links and feedbacks among biodiversity, biomass, and environmental factors in natural systems; that is, their strengths were highly dependent on the state of other variables. Based on a cross-system comparison (Methods), strengths of individual links (e.g., BD → EF), pairwise feedbacks (e.g., BD ↔ EF), and triangular feedbacks (e.g., BD → EF → NO3 → BD) varied systematically, depending on environmental characteristics (Fig. 4 and Supplementary Fig. S8). Ecosystems with higher species diversity (long-term average species richness) and lower average PO4 concentrations had stronger BD → EF links (Fig. 4a; correlation coefficient r = 0.600 and −0.513; P = 0.007 and 0.025 for species diversity and PO4, respectively). These results were further confirmed by stepwise regression, indicating that the ecosystems characterized by higher diversity, lower average temperature, and oligotrophic conditions had stronger BD → EF (best-fit regression model: BD → EF strength = 0.663 + 0.171*BD − 0.139*T − 0.096*PO4; F3, 15 = 9.958 and P  More

Partensky F, Garczarek L. Prochlorococcus: advantages and limits of minimalism. Ann Rev Mar Sci. 2010;2:305–31.PubMed 

Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci. 2013;110:9824–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Follows MJ, Dutkiewicz S, Grant S, Chisholm SW. Emergent biogeography of microbial communities in a model ocean. Science. 2007;315:1843–6.CAS 
PubMed 

Google Scholar 
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol. 2014;13:13–27.PubMed 

Google Scholar 
Farrant GK, Doré H, Cornejo-Castillo FM, Partensky F, Ratin M, Ostrowski M, et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. Proc Natl Acad Sci. 2016;113:E3365–74.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kashtan N, Roggensack SE, Rodrigue S, Thompson JW, Biller SJ, Coe A, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science. 2014;344:416–20.CAS 
PubMed 

Google Scholar 
Tagliabue A, Bowie AR, Boyd PW, Buck KN, Johnson KS, Saito MA. The integral role of iron in ocean biogeochemistry. Nature. 2017;543:51–9.CAS 
PubMed 

Google Scholar 
Gledhill M, van den Berg CMG. Determination of complexation of iron(III) with natural organic complexing ligands in seawater using cathodic stripping voltammetry. Mar Chem. 1994;47:41–54.CAS 

Google Scholar 
Rue EL, Bruland KW. The role of organic complexation on ambient iron chemistry in the equatorial Pacific Ocean and the response of a mesoscale iron addition experiment. Limnol Oceanogr 1997;42:901–10.Hassler CS, van den Berg CMG, Boyd PW. Toward a regional classification to provide a more inclusive examination of the ocean biogeochemistry of iron-binding ligands. Front Mar Sci. 2017;4:1–19.
Google Scholar 
Gledhill M, Buck KN. The organic complexation of iron in the marine environment: a review. Front Microbiol. 2012;3:1–17.
Google Scholar 
Bundy RM, Boiteau RM, McLean C, Turk-Kubo KA, McIlvin MR, Saito MA, et al. Distinct siderophores contribute to Iron cycling in the mesopelagic at station ALOHA. Front. 2018;5:1–15.
Google Scholar 
Boiteau RM, Mende DR, Hawco NJ, McIlvin MR, Fitzsimmons JN, Saito MA, et al. Siderophore-based microbial adaptations to iron scarcity across the eastern Pacific Ocean. Proc Natl Acad Sci. 2016;113:14237–42.Shaked Y, Lis H. Disassembling iron availability to phytoplankton. Front Microbiol. 2012;3:123.PubMed 
PubMed Central 

Google Scholar 
Morrissey J, Bowler C. Iron utilization in marine cyanobacteria and eukaryotic algae. Front Microbiol. 2012;3:43.PubMed 
PubMed Central 

Google Scholar 
Hogle SL, Cameron Thrash J, Dupont CL, Barbeau KA. Trace metal acquisition by marine heterotrophic bacterioplankton with contrasting trophic strategies. Appl Environ Microbiol. 2016;82:1613–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hopkinson B, Barbeau K. Iron transporters in marine prokaryotic genomes and metagenomes. Environ Microbiol. 2012;14:114–28.CAS 
PubMed 

Google Scholar 
Webb EA, Moffett JW, Waterbury JB. Iron stress in open-ocean Cyanobacteria (Synechococcus, Trichodesmium, and Crocosphaera spp.): Identification of the IdiA protein. Appl Environ Microbiol. 2001;67:5444–52.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hopkinson BM, Morel FMM. The role of siderophores in iron acquisition by photosynthetic marine microorganisms. Biometals. 2009;22:659–69.CAS 
PubMed 

Google Scholar 
Malmstrom RR, Rodrigue S, Huang KH, Kelly L, Kern SE, Thompson A, et al. Ecology of uncultured Prochlorococcus clades revealed through single-cell genomics and biogeographic analysis. ISME J. 2013;7:184–98.CAS 
PubMed 

Google Scholar 
Ustick LJ, Larkin AA, Garcia CA, Garcia NS, Brock ML, Lee JA, et al. Metagenomic analysis reveals global-scale patterns of ocean nutrient limitation. Science. 2021;372:287–91.CAS 
PubMed 

Google Scholar 
Garcia CA, Hagstrom GI, Larkin AA, Ustick LJ, Levin SA, Lomas MW, et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos Trans R Soc Lond B Biol Sci. 2020;375:20190254.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hogle SL, Dupont CL, Hopkinson BM, King AL, Buck KN, Roe KL, et al. Pervasive iron limitation at subsurface chlorophyll maxima of the California Current. Proc Natl Acad Sci. 2018;115:13300–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hawco NJ, Fu F, Yang N, Hutchins DA, John SG. Independent iron and light limitation in a low-light-adapted Prochlorococcus from the deep chlorophyll maximum. ISME J. 2020;15:359–62.PubMed 
PubMed Central 

Google Scholar 
Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5:180176.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pesant S, Not F, Picheral M, Kandels-Lewis S, Le Bescot N, Gorsky G, et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci Data. 2015;2:150023.CAS 
PubMed 
PubMed Central 

Google Scholar 
Berube PM, Biller SJ, Hackl T, Hogle SL, Satinsky BM, Becker JW, et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Sci Data. 2018;5:180154.CAS 
PubMed 
PubMed Central 

Google Scholar 
Biller SJ, Berube PM, Berta-Thompson JW, Kelly L, Roggensack SE, Awad L, et al. Genomes of diverse isolates of the marine cyanobacterium Prochlorococcus. Sci Data. 2014;1:140034.CAS 
PubMed 
PubMed Central 

Google Scholar 
Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C, Raes J, et al. A holistic approach to marine eco-systems biology. PLoS Biol. 2011;9:e1001177.CAS 
PubMed 
PubMed Central 

Google Scholar 
Schlitzer R, Anderson RF, Dodas EM, Lohan M, Geibert W, Tagliabue A, et al. The GEOTRACES intermediate data product 2017. Chem Geol. 2018;493:210–23.CAS 

Google Scholar 
Salt LA, van Heuven SMAC, Claus ME, Jones EM, de Baar HJW. Rapid acidification of mode and intermediate waters in the southwestern Atlantic Ocean. Biogeosciences. 2015;12:1387–401.
Google Scholar 
Rijkenberg MJA, Middag R, Laan P, Gerringa LJA, van Aken HM, Schoemann V, et al. The distribution of dissolved iron in the West Atlantic Ocean. PLoS One. 2014;9:e101323.PubMed 
PubMed Central 

Google Scholar 
Wyatt NJ, Milne A, Woodward EMS, Rees AP, Browning TJ, Bouman HA, et al. Biogeochemical cycling of dissolved zinc along the GEOTRACES South Atlantic transect GA10 at 40°S: Dissolved zinc in the Atlantic at 40o S. Glob Biogeochem Cycles. 2014;28:44–56.CAS 

Google Scholar 
Ashkezari MD, Hagen NR, Denholtz M, Neang A, Burns TC, Morales RL, et al. Simons collaborative marine atlas project (Simons CMAP): an open‐source portal to share, visualize, and analyze ocean data. Limnol Oceanogr Methods. 2021;19:488–96.
Google Scholar 
Acker M, Hogle SL, Berube PM, Hackl T, Stepanauskas R, Chisholm SW, et al. Phosphonate production by marine microbes: exploring new sources and potential function. Proc Natl Acad Sci. 2022;119:e2113386119.Becker JW, Hogle SL, Rosendo K, Chisholm SW. Co-culture and biogeography of Prochlorococcus and SAR11. ISME J. 2019;13:1506–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Malmstrom RR, Coe A, Kettler GC, Martiny AC, Frias-Lopez J, Zinser ER, et al. Temporal dynamics of Prochlorococcus ecotypes in the Atlantic and Pacific oceans. ISME J. 2010;4:1252–64.PubMed 

Google Scholar 
Fitzsimmons JN, Hayes CT, Al-Subiai SN, Zhang R, Morton PL, Weisend RE, et al. Daily to decadal variability of size-fractionated iron and iron-binding ligands at the Hawaii Ocean Time-series Station ALOHA. Geochim Cosmochim Acta. 2015;171:303–24.CAS 

Google Scholar 
Buck KN, Sohst B, Sedwick PN. The organic complexation of dissolved iron along the U.S. GEOTRACES (GA03) North Atlantic Section. Deep Sea Res Part 2. 2015;116:152–65.CAS 

Google Scholar 
Mawji E, Gledhill M, Milton JA, Tarran GA, Ussher S, Thompson A, et al. Hydroxamate siderophores: occurrence and importance in the Atlantic Ocean. Environ Sci Technol. 2008;42:8675–80.CAS 
PubMed 

Google Scholar 
Buck KN, Bruland KW. The physicochemical speciation of dissolved iron in the Bering Sea, Alaska. Limnol Oceanogr. 2007;52:1800–8.CAS 

Google Scholar 
Boiteau RM, Fitzsimmons JN, Repeta DJ, Boyle EA. Detection of iron ligands in seawater and marine cyanobacteria cultures by high-performance liquid chromatography-inductively coupled plasma-mass spectrometry. Anal Chem. 2013;85:4357–62.CAS 
PubMed 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.CAS 
PubMed 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
Blin K, Shaw S, Steinke K, Villebro R, Ziemert N, Lee SY, et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–W87.CAS 
PubMed 
PubMed Central 

Google Scholar 
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:11257.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright M, Ziegler A. ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw, Artic. 2017;77:1–17.
Google Scholar 
Jolliffe IT. Principal Component Analysis. 1986. Springer-Verlag.Kursa M, Rudnicki W. Feature selection with the Boruta package. J Stat Softw, Artic. 2010;36:1–13.
Google Scholar 
Milligan AJ, Harrison PJ. Effects of non-steady-state iron limitation on nitrogen assimilatory enzymes in the marine diatom Thalassiosira weissflogii (Bacillariophyceae). J Phycol. 2000;36:78–86.CAS 

Google Scholar 
Lomas MW, Lipschultz F. Forming the primary nitrite maximum: Nitrifiers or phytoplankton? Limnol Oceanogr. 2006;51:2453–67.CAS 

Google Scholar 
Ahlgren NA, Belisle BS, Lee MD. Genomic mosaicism underlies the adaptation of marine Synechococcus ecotypes to distinct oceanic iron niches. Environ Microbiol. 2019;22:1801–15.PubMed 

Google Scholar 
Martiny AC, Coleman ML, Chisholm SW. Phosphate acquisition genes in Prochlorococcus ecotypes: evidence for genome-wide adaptation. Proc Natl Acad Sci. 2006;103:12552–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Coleman ML, Chisholm SW. Ecosystem-specific selection pressures revealed through comparative population genomics. Proc Natl Acad Sci. 2010;107:18634–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Berube PM, Rasmussen A, Braakman R, Stepanauskas R, Chisholm SW. Emergence of trait variability through the lens of nitrogen assimilation in Prochlorococcus. eLife. 2019;8:e41043.PubMed 
PubMed Central 

Google Scholar 
Olgun N, Duggen S, Croot PL, Delmelle P, Dietze H, Schacht U, et al. Surface ocean iron fertilization: The role of airborne volcanic ash from subduction zone and hot spot volcanoes and related iron fluxes into the Pacific Ocean. Global Biogeochem Cycles. 2011;25:GB4001.Manck LE, Park J, Tully BJ, Poire AM, Bundy RM, Dupont CL, et al. Petrobactin, a siderophore produced by Alteromonas, mediates community iron acquisition in the global ocean. ISME J. 2021;16:358–69.Mackey KRM, Post AF, McIlvin MR, Saito MA. Physiological and proteomic characterization of light adaptations in marine Synechococcus. Environ Microbiol. 2017;19:2348–65.CAS 
PubMed 

Google Scholar 
Raven JA. Predictions of Mn and Fe use efficiencies of phototrophic growth as a function of light availability for growth and of C assimilation pathway. N. Phytol. 1990;116:1–18.CAS 

Google Scholar 
Sunda WG, Huntsman SA. Interrelated influence of iron, light and cell size on marine phytoplankton growth. Nature. 1997;390:389–92.CAS 

Google Scholar 
Hawco NJ, Barone B, Church MJ, Babcock-Adams L, Repeta DJ, Wear EK, et al. Iron depletion in the deep chlorophyll maximum: Mesoscale eddies as natural iron fertilization experiments. Global Biogeochem Cycles. 2021;35:e2021GB007112.Barbeau K, Rue EL, Bruland KW, Butler A. Photochemical cycling of iron in the surface ocean mediated by microbial iron(III)-binding ligands. Nature. 2001;413:409–13.CAS 
PubMed 

Google Scholar 
van den Engh GJ, Doggett JK, Thompson AW, Doblin MA, Gimpel CNG, Karl DM. Dynamics of Prochlorococcus and Synechococcus at Station ALOHA revealed through flow cytometry and high-resolution vertical sampling. Front Mar Sci. 2017;4:1–14.
Google Scholar 
Karl DM, Church MJ. Microbial oceanography and the Hawaii Ocean Time-series programme. Nat Rev Microbiol. 2014;12:699–713.CAS 
PubMed 

Google Scholar 
Neuer S, Davenport R, Freudenthal T, Wefer G, Llinás O, Rueda M-J, et al. Differences in the biological carbon pump at three subtropical ocean sites. Geophys Res Lett 2002;29:32–1.Giovannoni SJ, Vergin KL. Seasonality in ocean microbial communities. Science. 2012;335:671–6.CAS 
PubMed 

Google Scholar 
Jickells T, Moore CM. The importance of atmospheric deposition for ocean productivity. Annu Rev Ecol Evol Syst. 2015;46:481–501.
Google Scholar 
Rii YM, Karl DM, Church MJ. Temporal and vertical variability in picophytoplankton primary productivity in the North Pacific Subtropical Gyre. Mar Ecol Prog Ser. 2016;562:1–18.CAS 

Google Scholar 
Boyle EA, Bergquist BA, Kayser RA, Mahowald N. Iron, manganese, and lead at Hawaii Ocean Time-series station ALOHA: Temporal variability and an intermediate water hydrothermal plume. Geochim Cosmochim Acta. 2005;69:933–52.CAS 

Google Scholar 
Caprara S, Buck KN, Gerringa LJA, Rijkenberg MJA, Monticelli D. A compilation of iron speciation data for open oceanic waters. Front Mar Sci. 2016;3:1–7.
Google Scholar 
Gledhill M, Gerringa LJA. The effect of metal concentration on the parameters derived from complexometric titrations of trace elements in seawater: A model study. Front Mar Sci. 2017;4:1–15.
Google Scholar 
Jickells TD, Baker AR, Chance R. Atmospheric transport of trace elements and nutrients to the oceans. Philos Trans R Soc Lond A. 2016;374:20150286.
Google Scholar 
Sedwick PN, Church TM, Bowie AR, Marsay CM, Ussher SJ, Achilles KM, et al. Iron in the Sargasso Sea (Bermuda Atlantic Time-series Study region) during summer: Eolian imprint, spatiotemporal variability, and ecological implications. Glob Biogeochem Cycles. 2005;19:GB4006.
Google Scholar 
Ohnemus DC, Rauschenberg S, Cutter GA, Fitzsimmons JN, Sherrell RM, Twining BS. Elevated trace metal content of prokaryotic communities associated with marine oxygen deficient zones. Limnol Oceanogr. 2016;62:3–25.Browning TJ, Achterberg EP, Rapp I, Engel A, Bertrand EM, Tagliabue A, et al. Nutrient co-limitation at the boundary of an oceanic gyre. Nature. 2017;551:242–6.Louropoulou E, Gledhill M, Achterberg EP, Browning TJ, Honey DJ, Schmitz RA, et al. Heme b distributions through the Atlantic Ocean: evidence for ‘anemic’ phytoplankton populations. Sci Rep. 2020;10:4551.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mark Moore C. Diagnosing oceanic nutrient deficiency. Philos Trans R Soc Lond A. 2016;374:20150290.
Google Scholar 
Moore CM, Mills MM, Arrigo KR, Berman-Frank I, Bopp L, Boyd PW, et al. Processes and patterns of oceanic nutrient limitation. Nat Geosci. 2013;6:701–10.CAS 

Google Scholar 
Moore JK, Doney SC, Lindsay K. Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model. Global Biogeochem Cycles 2004;18.Rafter PA, Sigman DM, Mackey KRM. Recycled iron fuels new production in the eastern equatorial Pacific Ocean. Nat Commun. 2017;8:1100.PubMed 
PubMed Central 

Google Scholar 
Boyd PW, Ellwood MJ, Tagliabue A, Twining BS. Biotic and abiotic retention, recycling and remineralization of metals in the ocean. Nat Geosci. 2017;10:167–73.CAS 

Google Scholar 
Ferreira D, Cessi P, Coxall HK, de Boer A, Dijkstra HA, Drijfhout SS, et al. Atlantic-Pacific asymmetry in deep water formation. Annu Rev Earth Planet Sci. 2018;46:327–52.CAS 

Google Scholar 
Rusch DB, Martiny AC, Dupont CL, Halpern AL, Venter JC. Characterization of Prochlorococcus clades from iron-depleted oceanic regions. Proc Natl Acad Sci. 2010;107:16184–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dutkiewicz S, Ward BA, Monteiro F, Follows MJ. Interconnection of nitrogen fixers and iron in the Pacific Ocean: Theory and numerical simulations. Glob Biogeochem Cycles. 2012;26:GB1012.
Google Scholar 
Resing JA, Sedwick PN, German CR, Jenkins WJ, Moffett JW, Sohst BM, et al. Basin-scale transport of hydrothermal dissolved metals across the South Pacific Ocean. Nature. 2015;523:200–3.CAS 
PubMed 

Google Scholar 
Sela I, Wolf YI, Koonin EV. Theory of prokaryotic genome evolution. Proc Natl Acad Sci. 2016;113:11399–407.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zakem EJ, Al-Haj A, Church MJ, van Dijken GL, Dutkiewicz S, Foster SQ, et al. Ecological control of nitrite in the upper ocean. Nat Commun. 2018;9:1206.PubMed 
PubMed Central 

Google Scholar 
Lauderdale JM, Braakman R, Forget G, Dutkiewicz S, Follows MJ. Microbial feedbacks optimize ocean iron availability. Proc Natl Acad Sci. 2020;117:4842–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hogle SL, Barbeau KA, Gledhill M. Heme in the marine environment: from cells to the iron cycle. Metallomics. 2014;6:1107–20.CAS 
PubMed 

Google Scholar 
Hogle SL, Brahamsha B, Barbeau KA. Direct heme uptake by phytoplankton-associated Roseobacter bacteria. mSystems. 2017;2:e00124–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
Coale KH, Fitzwater SE, Michael Gordon R, Johnson KS, Barber RT. Control of community growth and export production by upwelled iron in the equatorial Pacific Ocean. Nature. 1996;379:621–4.CAS 

Google Scholar 
Bressac M, Guieu C, Ellwood MJ, Tagliabue A, Wagener T, Laurenceau EC, et al. Resupply of mesopelagic dissolved iron controlled by particulate iron composition. Nat Geosci. 2019;12:995–1000.CAS 

Google Scholar 
Basu S, Gledhill M, de Beer D, Prabhu Matondkar SG, Shaked Y. Colonies of marine cyanobacteria Trichodesmium interact with associated bacteria to acquire iron from dust. Commun Biol. 2019;2:284.PubMed 
PubMed Central 

Google Scholar  More

Study site and contextAmbatovy is a very large nickel, cobalt and ammonium sulphate mine in central-eastern Madagascar owned by a consortium of international mining companies50. It represents the largest ever foreign investment in the country24 (US$8 billion by 201650) and a substantial source of fiscal income49. In 2018, the company contributed ~US$50 million in taxes, tariffs, royalties and other payments49 and employed >9,000 people (93% of whom were Malagasy)51. Commercial production began in January 201424 (Supplementary Fig. 1). As key components in batteries, supplies of nickel and cobalt are critical to the green energy transition and demand for these metals is predicted to increase notably in future52.The mining concession covers an area of 7,700 ha located in the eastern rainforests of Madagascar (Fig. 1) which have very high levels of biodiversity and endemism53,54. After avoidance and minimization measures were applied (Supplementary Methods) the mine was predicted to clear or substantially degrade 2,064 ha of high-quality natural forest at the mine footprint and upper pipeline24. Any impacts on plantations or secondary habitat are not included in this estimate. Losses at the impact site were not discounted in relation to a background rate of decline, meaning that the company took responsibility for the full area of forest lost25. Independent verification by our team (by measuring the size of the mine footprint on Google Earth) confirms the extent of forest loss at the mine footprint (Supplementary Fig. 2). Clearance of the footprint accounts for most of the forest loss associated with the mine as losses associated with the pipeline are small54.Ambatovy aims to generate biodiversity gains to offset the mine-induced losses by slowing deforestation driven by shifting agriculture elsewhere26. To this end the company designated four sites, totalling 28,740 ha, to be protected as biodiversity offsets; Ankerana, Corridor Forestier Analamay-Mantadia (CFAM), the Conservation Zone and Torotorofotsy54 (Fig. 1). The offsets are considered like-for-like30 and were selected on the basis of similarity to the impact site in terms of forest structure and type, geology, climate and altitude24. The large combined area of the offsets relative to the impacted area was designed to allow flexibility, account for uncertainty and incorporate as many of the affected biodiversity components as possible24. Ankerana is the flagship offset, selected on the basis of its size, connectivity to the Corridor Ankeniheny-Zahamena (CAZ) forest corridor and the presence of ultramafic outcrops thought to support the same rare type of azonal forest lost at the mine site54. Extensive surveys conducted within Ankerana to establish biological similarity concluded the offset to be of higher conservation significance than the forests of the mine site due to the presence of rare lowland tropical forest24.The Conservation Zone is directly managed by the company, given its location within the concession area, while the other offsets are managed in partnership with local and international NGOs24,25. Ambatovy funds the management of Ankerana by Conservation International and local NGO partners (although before 2015 Ankerana was directly managed by Ambatovy via a Memorandum of Understanding with Conservation International24), supports BirdLife partner Asity with the management of Torotorofotsy and a number of local NGOs including Voary Voakajy25 are involved in CFAM26. The company is also working to secure formal, legal protection for CFAM26 as part of a proposed Torotorofotsy–CFAM complex new protected area (although progress on this has stalled).Overview of methodsTo estimate the impact of the offsets on deforestation and determine whether this has prevented enough deforestation to offset forest loss at the mine site, we combined several complementary methods for robust impact evaluation. First, we used statistical matching to match a sample of pixels from each biodiversity offset to pixels from the wider forested landscape with similar exposure to drivers of deforestation. Then we used a site-based difference-in-differences regression for each matched offset–control sample and a fixed-effects panel regression on the pooled data, to estimate the effect of protection. We systematically explored how arbitrary modelling choices (including the statistical distance measure used in matching, caliper size, ratio of control to treated units, matching with or without replacement and which, if any, additional covariates were included) affected our inference, exploring the robustness of our results to 116 alternative model specifications.MatchingThe former province of Toamasina was selected as the geographic area from which control pixels were sampled as it encompasses forests of the same type as the concession area with varying degrees of intactness and accessibility. The four biodiversity offsets are located within this province (Fig. 1).The unit of analysis is a 30 × 30 m2 pixel that was forested in the baseline year 200045,55. It is important that the scale of analysis aligns with the scale at which the drivers of deforestation (in this case, small-scale shifting agriculture) operate56. The median agricultural plot size (from 564 measured plots) in the study region is ~36 × 36 m2 (ref. 57). We took a subsample of pixels to reduce computational effort while maintaining the capacity for robust statistical inference58,59. We used a grid-based sampling strategy ensuring a minimum distance between sample units to reduce spatial autocorrelation60 and equal coverage of the study area58. A 150 × 150 m2 resolution grid, aligned to the other 30-m resolution data layers (Fig. 1c), was overlaid on the province and the 30 × 30 m2 pixel at the centre of each grid square was extracted to produce a subsample of pixels that are 120 m away from their nearest neighbour. The 120 m is larger than the minimum distance between units used in another matching study in Madagascar (68 m; ref. 59) but smaller than that used in other studies (200 m; ref. 61) and so strikes an appropriate balance between the avoidance of spatial autocorrelation and maximizing the possible sample cells.Protected areas in the study area managed by Madagascar National Parks were excluded from our control sample as they are actively managed and therefore do not represent counterfactual outcomes for the biodiversity offsets in the absence of protection (Fig. 1). However, control pixels were sampled from within the CAZ new protected area as legal protection was only granted in 2015 and resources for management are limited and thinly spread62. Additionally, Ankerana and parts of CFAM overlap with the CAZ and would have experienced the same management, and likely trajectory, as the rest of the CAZ, had they not been designated biodiversity offsets. Areas within 10 km of an offset boundary were excluded from the control sample to reduce the chance of leakage (where pressures are displaced rather than avoided) biasing results17,29. The 10 km was selected as it is a commonly used buffer zone within the literature17,58.To test for leakage effects, we used Veronoi polygons to partition the buffer area for CFAM, the Conservation Zone and Torotorofotsy (which overlap) into three individual buffer areas according to the nearest offset centroid and took a subsample of pixels from each (Fig. 1). Areas that overlapped with the established protected areas of Mantadia National Park and Analamazotra Special Reserve were excluded from the buffer zones.The outcome variable is the annual deforestation rate sourced from the Global Forest Change (GFC) dataset34. Following Vieilledent et al.45 these data were restricted to only include pixels classed as forest in a forest cover map of Madagascar for the year 200045,55, reducing the probability of false positives (whereby tree loss is identified in pixels that were not forested). The resulting tree loss raster was snapped to the forest cover 2000 layer to align cells, resulting in a maximum spatial error of 15 m. The GFC product34 has been shown to perform reasonably well at detecting deforestation in humid tropical forests63. In the north-eastern rainforests of Madagascar, Burivalova et al.39 found that GFC data performed comparably to a local classification of very high resolution satellite imagery at detecting forest clearance for shifting agriculture (although it was not effective at detecting forest degradation from selective logging). As clearance for shifting agriculture is considered the principal agent of deforestation in the study area22 and the forests of the study area are tropical humid ( >75% canopy cover), the GFC data are an appropriate tool for quantifying forest loss. Although recent evidence suggests that GFC data may have temporal biases64, this phenomenon affects our control and treated samples equally and so is unlikely to impact our results.The choice of covariates is extremely important in matching analyses. They must include, or proxy, all important factors influencing selection to treatment and the outcome of interest so that the matched control sample is sufficiently similar to the treated sample in these characteristics to constitute a plausible counterfactual, otherwise the resulting estimates may not be valid33. On the basis of the literature and a local theory of change we selected five covariates that we believe capture or proxy for the aspects of accessibility, demand and agricultural suitability that drive deforestation in the study area22,59,65,66. These are slope, elevation, distance to main road, distance to forest edge and distance to deforestation (Supplementary Methods). These five essential covariates comprise the main matching specification and form the core set used in all alternative specifications that we tested in the robustness checks. We also defined five additional variables (annual precipitation, distance to river, distance to cart track, distance to settlement and population density) and tested the effect of including these in the robustness checks. The additional covariates were so defined because they were of poorer data quality (population density and distance to settlement), correlated with an essential variable (annual precipitation and population density) or simply considered less influential (distance to river and distance to cart track; Supplementary Methods).Statistical matching was conducted in R statistics using the MatchIt package v.4.1 (ref. 67). To improve efficiency and produce closer matches we cleaned the data before matching to remove control units with values outside the calipers of the treated sample in any of the essential covariates (see Supplementary Methods for caliper definition). Following the recommendations of Schleicher et al.68 we tested several matching specifications and selected the one that maximized the trade-off between the number of treated units matched and the closeness of matches as the main specification (Supplementary Table 7). This was 1:1 nearest-neighbour matching without replacement, using Mahalanobis distance and a caliper of 1 s.d. This specification produced acceptable matches (within 1 s.d. of the Mahalanobis distance) for all treated units within all offsets. The maximum postmatching standardized difference in mean covariate values between treated and control samples was 0.05, well below the threshold of 0.25 considered to constitute an acceptable match69. This indicates that, on average, treated and control units were very well matched across all covariates.Matching was run separately for each offset. The resulting matched datasets were aggregated by treated status (offset or control) and year to produce a matrix of the count of pixels that were deforested each year (2001–2019) in the offset and the matched control sample. Converting the outcome variable to a continuous measure of deforestation avoids the problem of attrition associated with binary measures of deforestation and is better suited to the framework of the subsequent regressions70.Robustness checksStatistical matching requires various choices to be made68, many of which are essentially arbitrary. There therefore exists a range of possible alternative specifications that are all a priori valid (although some may be better suited to the data and study objectives69) but which could influence the results20,28. We tested the robustness of our results to 116 different matching model specifications (Fig. 4). First, we tested the robustness of the estimates to the use of three alternative matching distance measures (Mahalanobis, standard propensity score matching using generalized linear model regressions with a logit distribution and propensity score matching using RandomForest), three different calipers (0.25, 0.5 and 1 s.d.), different ratios of control to treated units (one, five and ten nearest neighbours) and matching with/without replacement. Holding the choice of covariates constant (using only the essential covariates), the combination of these led to the estimation of 54 different models. Second, we tested the robustness of results to the inclusion of the five additional covariates. Holding the choice of distance measure and model parameters constant, we constructed 31 models comprising all possible combinations of additional covariates with the core set of essential covariates. Finally, we explore the robustness of results for 31 randomly selected combinations of distance measure, model parameters and additional covariates. All 116 specifications are a priori valid, assuming that the covariates capture or proxy for all important factors influencing outcomes, but may fail to satisfy the parallel trends condition or produce matches for insufficient numbers of treated observations ( More